CSI2010 - Keynote Address by R Gopalakrishnan, … Gopalakrishnan, Executive Director, Tata Sons Monthly subscription ` 9/-CSI COMMUNICATIONS | DECEMBER 2010 1 Volume No. 34 Issue

CSI2010 - Keynote Address byR Gopalakrishnan, Executive Director, Tata Sons

Monthly subscription ` 9/-

CSI COMMUNICATIONS | DECEMBER 2010 1

Volume No. 34 Issue No. 9 December 2010

President Prof. P [email protected]

Vice-PresidentMr. M D [email protected]

Hon. SecretaryProf. H R [email protected]

Hon. TreasurerMr. Saurabh H [email protected]

Immd. Past PresidentMr. S [email protected]

Regional Vice-Presidents

Mr. M P Goel (Region I)[email protected]

Dr. D P Mukherjee (Region II)[email protected]

Prof. S G Shah (Region III)[email protected]

Mr. Sanjay Mohapatra (Region IV)[email protected]

Dr. D B V Sarma (Region V)[email protected]

Mr. C G Sahasrabuddhe (Region VI)[email protected]

Mr. S Ramanathan (Region VII)[email protected]

Mr. Jayant Krishna (Region VIII)[email protected]

Division ChairpersonsDr. Deepak Shikarpur Division-I (Hardware)[email protected]

Dr. T V Gopal Division-II (Software)[email protected]

Dr. S Subramanian [email protected] (Applications)

Mr. H R Mohan Division-IV [email protected] (Communications)

Prof. Swarnalatha Rao Division-V [email protected] (Edu. & Research)

Nominations CommitteeDr. Shyam Sunder Agrawal

Prof. (Dr.) U K Singh

Dr. Suresh Chandra Bhatia

Publications Committee

ChairmanProf. S. V. [email protected]

Chief EditorDr. T V [email protected]

Director (Education)Wg. Cdr. M Murugesan (Retd.)[email protected]

Resident EditorMrs. Jayshree [email protected]

Executive SecretaryMr. Suchit [email protected]

Published by

Mr. Suchit GogwekarFor Computer Society of India

Executive Committee 2010-11/12 CONTENTS

Theme Section : Nature Inspired Computing

04 Nature Inspired Machine Intelligence Ajith Abraham

08 DPSO -Dynamic Particle Swarm Optmization Debora Maria Rossi de Medeiros & Andre C. P. L. F. de Carvalho

1 2 Harmony Search Algorithm Zong Woo Geem

1 4 Nature Inspired Computing in Digital Watermarking Systems Ashraf Darwish

HR Column

1 7 Think Local, Act Global Aditya Narayan Mishra

Articles

19 Neuro Fuzzy Vertical Handoff Decision Algorithm for overlaid Heterogeneous Network Anita Singhrova & Nupur Prakash

26 Data Mining : A Process to Discover Data Patterns and Relationships for Valid Predictions Jasmine K S

Students Korner

30 Analysis of Techniques for Detection of Deep Web Search Interface Dilip Kumar Sharma & A. K. Sharma

CSI2010 - Special Report

35 Technology and Society: the human touch R Gopalakrishnan

38 CSI Annual Convention 2010 at Mumbai – A Report

44 CSI Honors @ 45th Annual National Convention 2010, Mumbai

Departments

02 Community Talk

03 President’s Desk

34 ExecCom Transacts

CSI Topics CSI Calendar 2010-11 (2nd Cover)

CSI Elections 2011-2012/2013 (Back Cover)

45 From CSI Chapters


COMMUNITY TALK

“The Outline of Science” is a four volume series which has the motto “A Plain Story Simply Told”. J. Arthur Thompson, Regius Professor of Natural History in the University of Aberdeen is the Editor of this series fi rst published in 1922. One Chapter in the fi rst volume is “The Dawn of Mind”. In this chapter, Prof. Arthur Thompson succinctly observes, if we are to form a sound judgment on the intelligence of mammals we must not attend too much to those that have profi ted by man’s training, nor to those whose mental life has been dulled by domestication.

The nature versus nurture debate concerns the relative importance of an individual’s innate qualities (“nature”, i.e. nativism, or innatism) versus personal experiences (“nurture”, i.e. empiricism or behaviorism) in determining or causing individual differences in physical and behavioral traits. In the long-running battle of whether our thoughts and personalities are controlled by genes or environment, scientists are building a convincing body of evidence that it could be either or both.

Nobel laureate David Gross outlined 25 questions in science that he thought physics might help answer. One of the Gross’s questions involved human consciousness. The greatest brainteaser in this fi eld has been to explain how processes in the brain give rise to subjective experiences. A closer look at nature from the point of view of information processing can and will change what we mean by computation.

“Biology and computer science–life and computation–are related. I am confi dent that at their interface great discoveries await those who seek them.”

– Leonard Adleman, Scientifi c American, Aug. 1998Nature Inspired Computing is the fi eld of

research that investigates models and computational techniques inspired by nature and, dually, attempts to understand the world around us in terms of information processing. It is a highly interdisciplinary fi eld that connects the natural sciences with computing science, both at the level of information technology and at the level of fundamental research.

“Computers are rigid, unbending, unyielding, infl exible, and quite unwieldy. Let’s face it, they’ve improved our lives in many a way, but they do tend to be a pain. When interacting with them you have to be very methodical and precise, in a manner quite contrary to human nature. Step outside the computer’s programmed repertoire of behavior, it will simply refuse to cooperate, or--even worse--it will “crash” (a vivid term coined by computing professionals to describe a computer’s breaking down). Computers are notoriously bad at learning new things and at dealing with new situations. It all adds up to one thing: At their most fundamental, computers lack the ability to adapt. Adaptation concerns a system’s ability to undergo modifi cations according

to changing circumstances, thus ensuring its continued functionality.”

- Moshe Sipper, Machine Nature, McGraw-Hill, New York, 2002

The “Adaptive, Bio-inspired systems mooted by Moshe Sipper have the Complexity, which that is more than simply being complicated objects or to the diffi culty of building and comprehending them. Adaptive, Bio-Inspiration and Complexity is soon becoming the new ABC of computing.

Among the oldest examples of nature inspired models of computation are the cellular automata conceived by Ulam and von Neumann in the 1940s. John von Neumann, who was trained in both mathematics and chemistry, investigated cellular automata as a framework for the understanding of the behavior of complex systems. In particular, he believed that self-reproduction was a feature essential to both biological organisms and computers.

Despite being marvels of complexity and human ingenuity, computers are notoriously bad at learning new things and dealing with new situations. Marvin Minsky of MIT suggested in his Society of Minds theory, “To explain the mind, we have to show how minds are built from mindless stuff, from parts that are much smaller and simpler than anything we’d consider smart.” So, if a person wants to formulate a problem-solving strategy based on some observation from nature, how and where should he/she start?

Based on the general principles of “survival of the fi ttest” – whereby poor performers will be eliminated – and the “law of the jungle” – whereby weak performers will be eaten by stronger ones systems have been devised to solve some well-known constraint satisfaction problems.

Autonomy Oriented Computing (AOC) has become a new fi eld of computer science that systematically explores the metaphors and models of autonomy as offered by nature (e.g., physical, biological, and social entities of varying complexity), as well as their role in addressing our practical computing needs. It studies emergent autonomy as the core behavior of a computing system and draws on such principles as multi-entity formulation, local interaction, nonlinear aggregation, adaptation, and self-organization.

On behalf of the CSI Communications team, I thank Dr. Ajith Abraham for providing insightful articles for this issue.

Going to CSI2010 has been one of the most memorable experiences for me. I congratulate the CSI2010 Team.

Dr. Gopal T VHon. Chief [email protected]

From: Sanjay [email protected]: Saturday, November 27, 2010 12:27 AM

Dear Mr. Agrawal,

The CSI event was very nicely organized and content was excellent.

The participation of Government IT, Academicians and Industry IT at this scale jointly is never seen in a single event in any forum.

I have been part of multiple awards as sponsor, jury or audience but the process and transparency with participation for awards from Government and Industry was at the highest level of excellence as spoken by you on dais.

Congratulation on the success of this event and hard work of your team.

I am happy that you I got invited and be part of this event. Good luck.

RegardsSanjay Mehta

The CSIC Team thanks the Chairpersons of all the Committees formed for CSI2010 and all the Session Chairpersons for ensuring that we work seamlessly with all the members of their respective teams, the Dignitaries, Award Winners and the Invited Speakers. A special report on CSI2010 is in this issue.

Thank you!


From : [email protected]

Subject : President’s Desk

Date : 1st December, 2010

Dear Affectionate Members of CSI Family,

Like Armed forces to defend the country on continuous basis, the ICT professionals need to be alert on meeting the following challenges, while continuing research in heterogeneous disciplines: 1. The demand of increasing the productivity in various sectors

of Economy,2. The commitment for improving the Quality of life citizens,

and 3. Learning new technologies for designing & developing cost

effective solutions.Review & SWOT Analysis: It is well accepted that India is the most observed country and has been making great impact on use of ICT in the Global market. Most advanced sectors of ICT are depending upon the brains of Indian youth. As we are entering in the second decade of the 21st century, the challenges of the Indian dreams are increasingly visible. Much of the global economy is depending upon the Indian software developers, yet there is always a possibility of the countries like China to come in the race and to share the ICT market with India. Thus India has to be alert not only from external threats of competition but also from the internal demand of shaping the youth in the country as high quality of Human Resources to keep the lead in the world.After two major recessions in the very last decade (one in 2000 and other in 2008), we have learnt many lessons and have strived hard to survive. We have been successful in overcoming these two recessions, yet we have to exercise a lot of thought process and fi nd the venues and avenues where the Indian youth can be deployed for improving productivity and economy of our country in particular and of the world, in general. ICT in its domain has grown signifi cantly but the application domains are still starving for their growth in terms of their ICT enabled format. The development of new infrastructure, the improvement in human healthcare processes, social networking and many other domains are expanding with the help of new versions of ICT such as Cloud Computing, Semantic Web, Search Engines, etc.Thus, we had concentrated on providing such kind of platforms that enable our CSians (members of CSI) to exchange their success stories, to get the feel of new technologies, to develop competitive spirit through innovative applications and e-Governance applications and to provide opportunities on use of new technologies and above all to network themselves at CSI 2010 annual convention at Mumbai during 24-28 November 2010. iGen and CSI 2010CSI2010 Annual convention at Mumbai came up with many critical issues related to betterment of business, improvement in academics & learning, enhancement of productivity in Industries, more effi ciency in Government and many others in the coming decade. The theme of the convention “iGen: Technologies for the Next Decade” has been very exciting and appealing where by there was a great deal of knowledge exchange to arrive at planning optimized ICT based solutions for the focused problems that are likely to occur in the coming decade. The 3-day convention focused on a combined effort to provide all possible solution paradigms to diversifi ed problems. Covering the whole ICT domain, several interesting Tracks that had been arranged in parallel during the three days, include Architecture, Enterprise, Society, Entrepreneurship, Connectivity, Solutions, Education & Research, eGovernance, Excellence in IT. The sessions were chaired by many learned experts and the talks were delivered by many eminent practitioners. We are thankful to the track chairs for their efforts in organizing the session that include Prof. Manohar Chandwani, Prof. Umesh Bellur, Dr Satish Babu & Dr. Sasi Kumar, Prof. Anirudh Joshi, Mr. Manak Singh, Prof.

Karandikar, Shri. Sunil Mehta,. Maj. Gen. R K Bagga, Mr Mohan Datar, Mr. Anil Srivastava and Mr. Awantkia Varma and the 151 distinguished speakers, who have converted the Annual conference as a festival of Learning and delightful knowledge sharing environment.We are honored on the presence of the Past Presidents of CSI to encourage the family spirit in CSI. Happy that Shri. Deepakbhai Parekh of HDFC and Shri. R. Gopala Krishnan of M/s. Tata Sons, could come to inspire the gathering at the inaugural session. It is amazing to see the audience (on their own) giving standing ovation, to Dr. F. C. Kohli on his coming to the stage to receive the CSI Life time achievement award. National Council of CSI and AGM had a new look with good attendance and it was a great honor to present honors to the winners of the CSI chapter level award winners from all over the country, on their voluntary contributions to CSI.We congratulate all the award winners of the CSI-Nihilent e-Governance award winners, IT Award winners, Fellowship award winners of this year, Chapter level achievement award winners, winner of Vandana Goyal award and Shri. Satish Doshi award and the winners of other awards that are instituted by the philanthropists to the CSI members.It was nice that the Revenue Minister of Goa Mr. Jose Philip D’Souza participating in the technical presentations and inviting the CSI-Nihilent e-Governance team for Goa for conducting the Knowledge Sharing Summit.Salaam Pyara Mumbai CSians.Mumbai Chapter of CSI has made all of us proud in creating an atmosphere of knowledge sharing celebrations. While Dr. Vishnu Kanhere and Shri R C Goyal with their teams, running around for providing Hospitality, Dr. Rajiv Gerela, Shri Ravi Eppaturi, Shri Manish Shah, Shri. Ravikiran Mankikar, Shri M R Datar, Shri V.L.Mehta, Shri Rohinton Dhumasia, Dr. T J Mathews, Dr. Sasi Kumar and managing Committee members of Mumbai Chapter, had contributed for managing the sequence of events for CSI 2010 in professional style. The CSI HQ staff lead by Shri Suchit has been excellent.Convention Ambassador Mr. M D Agarwal, Conference Chair Shri. S Mahalingam and Prof. D B Pathak monitored the logistics and had been a great support to interface the Industry, while PC Chairs Dr. Atanu Rakshit ( who had contributed for the theme of the conference) and Dr. Manohar Chandwani with track chairs had organized the resource persons to provide excellent technical presentations. We are grateful to these stalwarts.Past presidents Dr. F C Kohli, Shri Hemant Sonawala, Prof. PVS Rao, Prof. S Ramani, Dr. M. L. Goyal, Brig. SVS Chowdhry, Dr. Rattan Dutta, Prof. H N Mahabala, and Prof. K K Aggarwal had spared their time to review periodically and encouraged the event. Mumbai CSI Chapter deserves my sincere salute for their loving care in hosting the CSI 2010. Salaam to all those CSI members who made all of us proud in the Country.Alarm bell ringing to improve quality of Research and for controlling Plagiarism:About 80% of the papers that were received (against the call for papers to CSI-2010) were rejected by our referees. There were casual papers with out Quality. More alarming is that several cases of blatant plagiarism were reported. The paper committee and the referees adopted ‘Zero tolerance for plagiarism’. I appeal to all budding professionals to shun the plagiarism. India has may stories on ethics. A crow can not become a pea-cock by putting on the feathers of the Pea-Cock. Indian National bird is our icon and we are to continue as original.

Prof. P ThrimurthyPresident, Computer Society of India

PRESIDENT ’S DESK

4CSI COMMUNICATIONS | DECEMBER 2010

GUEST EDITORIAL

Nature Inspired Machine IntelligenceAjith Abraham

Director - Machine Intelligence Research Labs (MIR Labs), Scientifi c Network for Innovation and Research Excellence (SNIRE), P.O. Box 2259, Auburn, Washington 98071, USAEmail: [email protected], [email protected] • WWW: http://www.softcomputing.net, http://www.mirlabs.org

Nature inspired computation is a general term referring to computing inspired by nature. It is an emerging interdisciplinary area in the information technology field. So far a range of techniques and methods are studied for dealing with large, complex, and dynamic problems. The idea is to mimic (concepts, principles and mechanisms) the complex phenomena occurring in the nature as computational processes in order to enhance the way computation is performed mainly from a problem solving point of view. The general area of machine intelligence is currently undergoing an important transformation by trying to incorporate computational ideas borrowed from the nature all around us. Some of the key paradigms falling under this umbrella are detailed below.

Artifi cial Neural NetworksArtifi cial neural networks have been developed as

generalizations of mathematical models of biological nervous systems. In a simplifi ed mathematical model of the neuron, the effects of the synapses are represented by weights that modulate the effect of the associated input signals, and the nonlinear characteristic exhibited by neurons is represented by a transfer function, which is usually the sigmoid, Gaussian function etc. The neuron impulse is then computed as the weighted sum of the input signals, transformed by the transfer function. The learning capability of an artifi cial neuron is achieved by adjusting the weights in accordance to the chosen learning algorithm [7].

Evolutionary Algorithms Evolutionary algorithms (EA) are adaptive

methods, which may be used to solve search and optimization problems, based on the genetic processes of biological organisms [8]. Over many generations, natural populations evolve according to the principles of natural selection and ‘survival of the fi ttest’. By mimicking this process, evolutionary algorithms are able to ‘evolve’ solutions to real world problems, if they have been suitably encoded. Usually grouped under the term evolutionary algorithms or evolutionary computation, we fi nd the domains of genetic algorithms, evolution strategies, evolutionary programming, genetic programming and learning classifi er systems. They all share a common conceptual base of simulating the evolution of individual structures via processes of selection, mutation, and reproduction.

Cultural Algorithms are computational models of cultural evolution. They consist of two basic components, a population space (using evolutionary algorithms), and a belief space. The two components interact by means of a vote-inherit-promote protocol [14]. Likewise the knowledge acquired by the problem solving activities of the population can be stored in the belief space in the form of production rules etc. Cultural algorithms represent a general framework for producing hybrid evolutionary systems that integrate evolutionary search and domain knowledge.

Swarm IntelligenceSwarm intelligence is aimed at collective

behaviour of intelligent agents in decentralized systems. Most of the basic ideas are derived from the real swarms in the nature, which includes ant colonies, bird fl ocking, honeybees, bacteria and microorganisms etc. Swarm models are population-based and the population is initialised with a population of potential solutions [9]. These individuals are then manipulated (optimised) over many several iterations using several heuristics inspired from the social behaviour of insects in an effort to fi nd the optimal solution. Ant Colony Optimization (ACO) algorithms are inspired by the behavior of natural ant colonies, in the sense that they solve their problems by multi agent cooperation using indirect communication through modifi cations in the environment. Ants release a certain amount of pheromone (hormone) while walking, and each ant prefers (probabilistically) to follow a direction, which is rich of pheromone. This simple behavior explains why ants are able to adjust to changes in the environment, such as optimizing shortest path to a food source or a


nest. In ACO, ants use information collected during past simulations to direct their search and this information is available and modifi ed through the environment.

Bacterial Foraging Optimization algorithm is a swarm intelligence based meta-heuristic mimicking the Escherichia coli (E. coli) bacteria whose behavior to move comes from a set of up to six rigid 100–200 rps spinning fl agella, each driven as a biological motor. Selection behavior of bacteria tends to eliminate animals with poor foraging strategies and favor the propagation of genes of those animals that have successful foraging strategies. After many generations, a foraging animal takes actions to maximize the energy obtained per unit time spent foraging [10]. That is, poor foraging strategies are either eliminated or shaped into good ones.

Harmony SearchHarmony search is inspired from

the music improvisation process where musicians improvise their instruments’ pitches searching for a perfect state of harmony. Although the estimation of a harmony is aesthetic and subjective, on the other hand, there are several theorists who have provided the standard of harmony estimation: Greek philosopher and mathematician Pythagoras (582–497BC) worked out the frequency ratios (or string length ratios with equal tension) and found that they had a particular mathematical relationship, after researching what notes sounded pleasant together. The octave was found to be a 1:2 ratio and what we today call a fi fth to be a 2:3 ratio; French composer Leonin (1135–1201) is the fi rst known signifi cant composer of polyphonic ‘‘organum’’, which involved a simple doubling of the chant at an interval of a fi fth or fourth above or below; and French composer Jean-Philippe Rameau (1683–1764) established the classical harmony theories in the book ‘‘Treatise on Harmony’’, which still form the basis of the modern study of tonal harmony [16].

In engineering optimization, the estimation of a solution is carried out by putting values of decision variables to objective function or fi tness function and evaluating the function value with respect to several aspects such as cost, effi ciency, and/or error. Just like music improvisation seeks a best state (fantastic harmony) determined by an aesthetic standard, optimization process seeks a best state (global optimum) determined by objective function evaluation; the pitch of each musical instrument determines the aesthetic quality, just as the objective function value is determined by the set of values assigned to each decision

variable; aesthetic sound quality can be improved practice after practice, objective function value can be improved iteration by iteration. The HS metaheuristic algorithm was derived based on natural musical performance processes that occur when a musician searches for a perfect state of harmony, such as during jazz improvisation.

Simulated AnnealingSimulated annealing is based on the

manner in which liquids freeze or metals re-crystalize in the process of annealing [3]. In an annealing process, molten metal, initially at high temperature, is slowly cooled so that the system at any time is approximately in thermodynamic equilibrium. If the initial temperature of the system is too low or cooling is done insuffi ciently slowly the system may become brittle or unstable with forming defects. The initial state of a thermodynamic system is set at energy E and temperature T, holding T constant the initial confi guration is perturbed and the change in energy dE is computed. If the change in energy is negative the new confi guration is accepted. If the change in energy is positive it is accepted with a probability given by the Boltzmann factor exp -(dE/T). This processes is then repeated for few iterations to give good sampling statistics for the current temperature, and then the temperature is decremented and the entire process repeated until a frozen state is achieved at T=0

Membrane ComputingMembrane computing (P systems)

is a framework, which abstracts from the way live cells process chemical compounds in their compartmental structure [4]. In a membrane system, multisets of objects are placed in the compartments defi ned by the membrane structure, and the objects evolve by means of reaction rules are also associated within the compartments, and applied in a maximally parallel, nondeterministic manner. The objects can be described by symbols or by strings of symbols. For symbol-objects, a set of numbers are computed, and in the case of string-objects a set of strings are computed, which is more like a language. The objects are able to through membranes and the membranes can dissolve, divide and change their permeability. These features are used in defi ning transitions between confi gurations of the system, and sequences of transitions are used to defi ne computations. A sequence of transitions is a computation.

Artifi cial Immune System (AIS)The artifi cial immune systems like

other biologically inspired techniques, tries to extract ideas from a natural system, in

particular the vertebrate immune system, in order to develop computational tools for solving engineering problems. The basic idea of AIS is to exploit the immune system’s characteristics of learning and memory to solve a problem. AIS can be broadly categorized into three subgroups: those using the clonal selection theory, those using negative selection and those using the immune network theory as their main inspiration [11].

DNA ComputationDNA computing is a form of

computing which uses DNA (Deoxyribo-Nucleic Acid) and molecular biology, instead of the traditional silicon-based

microprocessors. Just like a string of binary data is encoded with ones and zeros, a strand of DNA is encoded with four bases, represented by the letters A, T, C and G (nucleotides) and the data density is very impressive. An important property of DNA is its double stranded nature with every DNA sequence having a natural complement. This complementarity feature makes DNA a unique data structure for computation and can be exploited in many ways. In the cell, DNA is modifi ed biochemically (on the molecular level) by a variety of enzymes, which are tiny protein machines. This mechanism along with some synthetic chemistry is responsible for the various DNA computation operators [12].

Computing with WordsComputing with words is a methodology

in which the objects of computation are words and propositions drawn from a natural language. Computing with words is inspired by the brain’s crucial ability to manipulateperceptions without any measurements or computations. Computing with wordsprovides a foundation for a computational theory of perceptions. A basic difference between perceptions and measurements is that, in general, measurements are crisp, whereas perceptions are fuzzy [13].

Artifi cial LifeArtifi cial life (Alife) attempts at setting

up systems with life like properties, which all biological organisms possess, such as reproduction, homeostasis, adaptability etc. Alife is often described as attempting to understand high-level behavior from low-level rules; for example, how the simple rules of Darwinian evolution lead to high-level structure, or the way in which the simple interactions between ants and their environment lead to complex trail-following behavior [15]. Understanding this relationship in particular systems promises to provide novel solutions to complex real-world problems, such as disease prevention,


stock-market prediction, and data-mining on the Internet.

Quantum ComputationIn conventional silicon computers,

the amount of data is measured by bits; in a quantum computer, it is measured by qubits (quantum-bit). A qubit can be a 1 or a 0, or it can exist in a superposition that is simultaneously both 1 and 0 or somewhere in between. The basic principle of quantum computation is that the quantum properties of particles can be used to represent and structure data, and that devised quantum mechanisms can be used to perform operations with this data [5].

Hybrid ApproachesSeveral adaptive hybrid intelligent

systems have in recent years been developed and many of these approaches use the combination of different knowledge representation schemes, decision making models and learning strategies to solve a computational task [6]. This integration aims at overcoming limitations of individual techniques through hybridization or fusion of various techniques. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time varying environment, are important in tackling practical computing problems. In contrast with conventional artifi cial intelligence techniques, which only deal with precision and certainty the guiding principle of hybrid approaches is to exploit the tolerance for imprecision, uncertainty, robustness and to provide optimal solutions etc.

This special issue is a collection of articles refl ecting some of the current technological innovations in the Nature inspired computation fi eld and its real world applications.

In the fi rst article, de Medeiros and de Carvalho illustrate the performance of Particle Swarm Optimization (PSO) and also introduce two new modifi ed versions of PSO, where two parameters from the original PSO version are adjusted on-the-fl y. Experimental results show that these new versions are able to provide better results than traditional versions of PSO in clustering tasks.

Real-life optimization problems are often NP-hard, and CPU time and/or memory consuming. In the second article, Talbi discusses about the usage of parallel bioinspired algorithms to signifi cantly reduce the computational complexity of the search process.

Liu et al. in the third article illustrated the need for a biologically inspired computational model of language cognition.

Functional Magnetic Resonance Imaging (fMRI) provides a high resolution volumetric mapping of the haemodynamic response of the brain, which can be correlated with neural activity, thereby allowing the spatially localized characteristics of brain activity to be observed. Authors illustrate Chinese character and Arabic numerals cognition during the brain activations.

Computational grids are expected to leverage unprecedented larger computing capacities by virtually joining together geographically distributed resources at large scale. To achieve this objective, the design of effi cient Grid schedulers that map and allocate tasks and applications onto Grid resources is a key issue. Xhafa and Abraham in the fourth article illustrate how various nature inspired heuristic and meta-heuristic methods can be used to design effi cient schedulers in computational grids.

In the fi fth article Geem provides a gentle introduction to Harmony search.

Digital watermarking is used to protect the copyrights for multimedia. A signifi cant merit of digital watermarking over traditional protection methods (cryptography) is to provide a seamless interface so that users are still able to utilize protected multimedia transparently by embedding an invisible digital signature (watermark) into multi-media data (audio, images, video). Darwish in the fi nal article present some nature inspired computing methods that have been proposed to solve digital watermarking problems.

We would like to take this opportunity to thank all the contributors of this special issue. We hope this special issue inspires researchers to extend the current nature inspired technologies to build advanced applications. Finally, we would like to gratefully thank Dr. Gopal T.V. (Honorary Editor-in-Chief, CSIC) for the timely advices, efforts and painstaking editorial work during the preparation of this special issue.

Ajith Abraham

References[1] A . Abraham, “Neuro-Fuzzy Systems:

State-of-the-Art Modeling Techniques”, in Jose Mira and Alberto Prieto, eds., Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Springer Verlag Germany, 2001, pp. 269-276.

[2] W . Banzhaf, P. Nordin, E.R. Keller, and F.D. Francone, “Genetic Programming: An Introduction on The Automatic

Evolution of Computer Programs and its Applications”, Morgan Kaufmann Publishers, Inc., 1998

[3] K irkpatrick, S., C. D. Gelatt Jr., M. P. Vecchi, Optimization by Simulated Annealing, Science, 220, 4598, 671-680, 1983.

[4] G . Paun, Computing with membranes, Journal of Computer and System Sciences, 61 (1), 108-143, 2000.

[5] D eutsch, D., Quantum Theory, the Church-Turing Principle, and the Universal Quantum Computer”. Proc. Roy. Soc. Lond. A400, 97–117, 1985.

[6] A . Abraham, Intelligent Systems: Architectures and Perspectives, Recent Advances in Intelligent Paradigms and Applications, Abraham A., Jain L. and Kacprzyk J. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, ISBN 3790815381, Chapter 1, pp. 1-35, 2002.

[7] B ishop C.M., Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.

[8] F ogel, D. B., Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ, Second edition, 1999.

[9] K ennedy J. and Eberhart R. Swarm intel l igence. Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2001.

[10] P assino, K.M., Biomimicry of Bacterial Foraging for Distributed Optimization and Control, IEEE Control Systems Magazine, pp. 52-67, June 2002.

[11] d e Castro, L. N. and Timmis, J. I., Artificial Immune Systems: A New Computational Intelligence Approach,Springer-Verlag, London, 2002.

[12] A mos M., Theoretical and Experimental DNA Computation. Springer, ISBN: 3-540-65773-8, 2005.

[13] Z adeh L.A. and Kacprzyk J. (Eds.) Computing with Words in Information/Intelligent Systems: Foundations, Studies in Fuzziness and Soft Computing, Springer Verlag, Germany, ISBN 379081217X, 1999.

[14] R e y n o l d s R . G . , M i c h a l e w i c z , Z. Cavaretta M.J., Using Cultural Algorithms for Constraint Handling in GENOCOP. Proceedings of the Fourth Annual Conference on Evolutionary Programming. MIT Press, Cambridge, pp. 289-305, 1995.

[15] C . Adami, Introduction to Artifi cial Life. Springer-Verlag New York, Inc., 1998.

[16] Z.W. Geem, J.H. Kim, and G.V. Loganathan, “A new heur ist ic optimization algorithm: harmony search”, Simulation 76 (2), 60–68, 2001.


Ajith Abraham received the M.S. degree from Nay-ang Technological University, Singapore, and the Ph.D. degree in computer science from Monash University, Melbourne, Australia. He is currently the Director of Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, USA, which has members from more than 75 countries. He has a worldwide academic experience with formal appointments in Monash University; Oklahoma State University, Stillwater, OK; Chung-Ang University, Seoul, Korea; Jinan University, Jinan, China; Rovira i Virgili University, Tarragona, Spain; Dalian Maritime University, Dalian, China; Yonsei University, Seoul, Korea; the Open University of Catalonia, Barcelona, Spain; the National Institute of Applied Sciences (INSA-Lyon), Lyon, France; and the Norwegian University of Science and Technology (NTNU), Trond-heim, Norway. He serves/has served the editorial board of over 50 International journals and has also guest edited 40 special issues on various topics. He has published more than 700 publications, and some of the works have also won best paper awards at international conferences. His research and development experience includes more than 20 years in the industry and academia. He works in a multidisciplinary environment involving machine intelligence, terrorism informatics, network security, sensor networks, e-commerce, Web intelligence, Web services, computational grids, data mining, and their applications to various real-world problems. He has given more than 54 plenary lectures and conference tutorials in these areas.Dr. Abraham is a Senior Member of the IEEE Systems Man and Cybernetics Society, the IEEE Computer Society, the Institution of Engineering and Technology (U.K.), the Institution of Engi-neers Australia (Australia), etc. He is a chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is actively involved in the Hybrid Intelligent Systems (HIS); Intelligent Systems Design and Applications (ISDA); Information Assurance and Security (IAS); and Next Generation Web Services Practices (NWeSP) series of international conferences, in addition to other conferences. More information at: http://www.softcomputing.net

About the Guest Editor

These are the main branches of biology: • Aerobiology – the study of airborne organic particles • Agriculture – the study of producing crops from the land, with an

emphasis on practical applications • Anatomy – the study of form and function, in plants, animals, and other

organisms, or specifi cally in humans • Astrobiology – the study of evolution, distribution, and future of life in the

universe. Also known as exobiology, exopaleontology, and bioastronomy • Biochemistry – the study of the chemical reactions required for life to

exist and function, usually a focus on the cellular level • Bioengineering – the study of biology through the means of engineering

with an emphasis on applied knowledge and especially related to biotechnology

• Bioinformatics – the use of information technology for the study, collection, and storage of genomic and other biological data

• Biomathematics or Mathematical Biology – the quantitative or mathematical study of biological processes, with an emphasis on modeling

• Biomechanics – often considered a branch of medicine, the study of the mechanics of living beings, with an emphasis on applied use through prosthetics or orthotics

• Biomedical research – the study of the human body in health and disease • Biophysics – the study of biological processes through physics, by

applying the theories and methods traditionally used in the physical sciences

• Biotechnology – a new and sometimes controversial branch of biology that studies the manipulation of living matter, including genetic modifi cation and synthetic biology

• Building biology – the study of the indoor living environment • Botany – the study of plants • Cell biology – the study of the cell as a complete unit, and the molecular

and chemical interactions that occur within a living cell • Conservation Biology – the study of the preservation, protection, or

restoration of the natural environment, natural ecosystems, vegetation, and wildlife

• Cryobiology – the study of the effects of lower than normally preferred temperatures on living beings.

• Developmental biology – the study of the processes through which an organism forms, from zygote to full structure

• Ecology – the study of the interactions of living organisms with one another and with the non-living elements of their environment

• Embryology – the study of the development of embryo (from fecundation to birth). See also topobiology.

• Entomology – the study of insects • Environmental Biology – the study of the natural world, as a whole or in a

particular area, especially as affected by human activity • Epidemiology – a major component of public health research, studying

factors affecting the health of populations • Ethology – the study of animal behavior • Evolutionary Biology – the study of the origin and descent of species over

time • Genetics – the study of genes and heredity • Herpetology – the study of reptiles and amphibians • Histology – the study of cells and tissues, a microscopic branch of

anatomy • Ichthyology – the study of fi sh • Integrative biology – the study of whole organisms • Limnology – the study of inland waters • Mammalogy – the study of mammals • Marine Biology – the study of ocean ecosystems, plants, animals, and

other living beings • Microbiology – the study of microscopic organisms (microorganisms)

and their interactions with other living things • Molecular Biology – the study of biology and biological functions at the

molecular level, some cross over with biochemistry • Mycology – the study of fungi • Neurobiology – the study of the nervous system, including anatomy,

physiology and pathology • Oceanography – the study of the ocean, including ocean life, environment,

geography, weather, and other aspects infl uencing the ocean

Branches of Biology[Excerpted from the Wikipedia: http://en.wikipedia.org/wiki/Biology]


THEME ARTICLE

DPSO -Dynamic Particle Swarm Optmization Debora Maria Rossi de Medeiros* & Andre C. P. L. F. de Carvalho**

Instituto de Ciências Matemáticas e de Computação [ICMC], University of São Paulo at São Carlos - USPAv. do Trabalhador Saocarlense, 400 13566-590 - São Carlos, BRAZIL*Email: [email protected]**Email: [email protected]* ICMC/USP, São Carlos, Brazil, Email: [email protected]

Particle Swarm Optimization (PSO) comprises population-based techniques that perform optimization tasks by parallel searches among candidate solutions. These techniques have become popular for their simplicity and ability to avoid local optima. This paper presents two new modified versions of PSO, where two parameters from the original PSO version are adjusted on-the-fly. Experimental results show that these new versions are able to provide better results than traditional versions of PSO in clustering tasks.

1. Introduction Population based techniques are optimization

algorithms that use a population of candidate solutions at each point of the optimization process. These techniques, among them Particle Swarm Optimization (PSO), are based on meta¬heuristics, which make them less likely to get stuck into local optima. Such techniques have been successfully applied to several tasks, including data clustering (Hruschka et al., 2009; Egan et al., 1998; Yi et al., 2006; Chen and Zhao, 2009; Cui et al., 2005)

Differently from Evolutionary Algorithms (Goldberg, 1989), which encode solutions as chromosomes and employ genetic operators and selection mechanisms, in PSO, each candidate solution is a particle that adjusts its position in the search space based on its own experience and the other particles experience.

In this paper, we propose two modified versions of PSO and evaluate them in the clustering context. For such, the paper is organized as follows. Section 2 briefly surveys the PSO techniques we use as benchmark and some examples of the PSO application in clustering tasks. Section 3 describes the proposed approaches. Section 4 contains some experimental results obtained by the proposed approaches.

2. Related work In this section, we briefly explain the main

mechanisms of traditional PSO introduced by Kennedy and Eberhart (1995) and the modified version proposed by Shi and Eberhart (1998). Both PSO versions are widely used and are employed here as references to evaluate the performance of the proposed approaches. We also mention some

research works that employ PSO in clustering tasks. In PSO, each particle, ei

, represents a candidate solution in a M-dimensional search space and is denoted by its coordinates. During the optimization process, each particle adjusts its position in the search space based on its own experience and experience from other particles. For such, each particle keeps track of its best position so far, p

i, and the best position

found so far by any particle in the population, global best, g. These parameters, together with a velocity parameter, f

i, define the new velocity and position

for each particle, according to Equations 1 and 2, respectively. e

i = e

i + f

i (1)

fi = wf

i + c

1r

1(p

i – e

i)+ c

2r

2(g – e

i) (2)

where c1 and c

2 are confidence coefficients, r

1 and r

2

are random values in the range [0, 1] and w controls the balance between local search and global search. The initial particles have their velocity and position randomly defined. Another version of PSO was introduced by Shi and Eberhart (1998), where the parameter w is defined as a decreasing function of time, as shown by Equation 3. This time decreasing weight leads to a finer local search. (3)

PSO has been already investigated in the context of clustering problems. In Yi et al. (2006), the authors proposed a PSO-based image clustering. In this approach, they considered each particle to represent a prototype of the cluster. The fitness function is the objective function used by the Fuzzy c-means


Table 1: Main characteristics of the synthetic datasets.

Dataset #clusters #features #objects per cluster Prototypes

Synthetic-1 3 3 50 v1 = [8:54; 7:53; 4:91]T , v

2 = [2:86; 8:97; 1:14]T,

v3 = [4:37; 4:76; 8:37]T

Synthetic-2 6 2 50 v1 = [2:0; 6:0]T, v

2 = [3:5; 2:0]T, v

3 = [5:0; 5:0]T,

v4 = [5:0; 8:0]T, v

5 = [6:5; 2:0]T, v

6 = [8:0; 6:0]T

algorithm Bezdek (1981), augmented with an additional term to maximize the distance between the prototypes. They also proposed a modification where the particles also encode feature weights that represent the importance of the features. These weights are optimized simultaneously with the prototypes. In Chen and Zhao (2009), the authors proposed fuzzy clustering based on the maximum entropy principle (MEP) and the use of PSO. The particles encode candidate cluster centroids and the membership grades of the partition matrix are constructed with the use of the MEP. Cui et al. (2005) presents a hybrid clustering strategy that works in two phases: first, PSO is used to optimize the cluster prototypes, then, the resulting prototypes are used as initial seeds to the K-means algorithm.

3. DPSO algorithmIn this paper, we propose two modified

versions of PSO algorithm, named Dynamic-PSO 1 and 2 (DPSO-1 and DPSO-2). They are called “dynamic” because the parameters c1

and c2 are updated during the convergence

process. In both algorithms, DPSO-1 and DPSO-

2, there is an initial value for c1 and c

2: c

1

o and

c2

o. These values are updated according to the rate of improvement of the population best fitness value along the interactions. In these algorithms, if the best fitness value remains the same during t interactions, c

1

and c2 decrease by s – 10% their original

values, that is, c1 = co

1 (1–0.1s) and c

2 = c

2

(1–0.1s), where s – 1 is the number of times that c

1 e c

2 were already updated. There are

minimum values for c1 and c

2, defined as c1

O

(1 – 0.1 ) and c2o (1 – 0.1 ), respectively,

where is a positive integer constant. In DPSO-2, besides c

1 and c

2 decreasing

mechanism, there is also an increasing rule: if the best fitness value improves during t interactions, c

1 and c

2 increase by s x 10%

their original values, c1 = c

1

o

(1 + 0.1s) and c2 =

c2

o

(1 + 0.1s). The maximum values for c1 and c2 are defined as c

1

o

(1 + 0.1�) and c2

o

(1 + 0.1�), respectively.

4. ExperimentsDPSO-1 and DPSO-2 had their

performance evaluated in data clustering problems. In these problems, DPSO-1 and DPSO-2 evolve populations of clustering solutions. As seen in Figure 4, the particle

is formed by two vectors, representing cluster centroids and feature weights. The left portion, cluster centers, has C x N real numbers specifying the centroid position of the C clusters. The right part, the feature weights, defi ne the importance of each input feature for the clustering. Thus, both cluster centroids and feature values are optimized simultaneously by the PSO.

Fig. 1 : Candidate solution representation, where N is the number of items in the dataset and C is the number of clusters.

In order to evaluate the candidate solutions by different perspectives, three different fi tness functions were employed:• Xie-Beni (Xie and Beni, 1991):

XB =∑C

i=1∑N

k=1 u2ik‖vi−xk‖2

N(mini,j‖vi−vj‖2)

, where lower XB values are better.

where lower XB values are better.• Dataset Reconstruction (Pedrycz and

Oliveira, 2008) error: (X – ~X)2, where

X is the original dataset and ~X is the

dataset rebuilt from the resulting membership matrix U = [u

ij] and

prototypes vi, according to:

~Xis the dataset rebuilt from the resulting membership matrix∑C

i=1 u2i vi∑C

i=1 u2i

. Lower values of this index are better.Lower values of this index are better.

• Fuzzy Davies-Bouldin (Davies and Bouldin, 1979): DB = 1C

∑Ci=1 maxl�=�=� i

{σi+σl

‖vi−vl‖

}, where where

σi =∑N

k=1 uik‖xk−vi‖N and lower values

are better.Two synthetic datasets, produced in

order to follow the multivariate gaussian distribution with spherical clusters, according to the settings in Table 1, were used. Both datasets are illustrated in Fig. 2.

The performance of DPSO-1 and DPSO-2 was compared with the traditional PSO and the modifi ed PSO introduced by Shi and Eberhart (1998). The parameters employed by each PSO version were:

• Traditional PSO: itermax

= 200, c1

= c2 = 2, w = 0:9 and population

size 100.• PSO with decreasing w: iter

max =

200, c1 = c

2 = 2, w

min = 0:2, w

max =

0:9 and population size 100.• DPSO-1 and DPSO-2: co

1 = co

2 =

2, = 5, t = 8 and population size 100.

(a) Synthetic-1 dataset.

(b) Synthetic-2 dataset.

Figure 2: Plots of the synthetic datasets.

The initial population of particles was initialized as follows: for each particle, C data items were randomly selected from the dataset and used as prototypes to calculate a partition matrix for each particle, according to: uik =

(a) Synthetic-1 dataset. (b) Synthetic-2 dataset.


The initial population of particles was initialized as follows: for each particle, C data items were randomly selectedfrom the dataset and used as prototypes to calculate a partition matrix for each particle, according to: uik = 1

∑Cj=1

d2ik

d2jk

.

Then, the centroids corresponding to each partition matrix are computed and used as the initial population of particles.This initialization scheme was found to speed up the convergence and slightly improve the results.

The number of clusters varied from 3 to 16 clusters. The experimental results are measured according to the differencebetween the proximity matrix obtained from the original structure of classes or clusters associated do the dataset andthe resulting clusters, referred here as Proximity error. This error is calculated as

∑k1N

∑k2N

∣∣pok1k2 − pr

k1k2

∣∣, whereP = [pk1k2 ] is the proximity matrix and pk1k2 =

∑Ci=1 min (uik1 , uik2). An additional restriction was applied to the

convergence criterion of each PSO version: the last population best fitness needs to be better than the first population bestfitness.

Figure 4 presents the results for each dataset, with mean and standard deviation over three runs for the three objectivefunctions (performance indexes), by the four versions of PSO: traditional, with decreasing w, DPSO-1 and DPSO-2 fordatasets Synthetic-1 and Synthetic-2.

(a) Synthetic-1 dataset and Re-construction fitness.

(b) Synthetic-1 dataset and FDB fit-ness.

(c) Synthetic-1 dataset and Xie-Benifitness.

(d) Synthetic-2 dataset and Re-construction fitness.

(e) Synthetic-2 dataset and FDB fit-ness.

(f) Synthetic-2 dataset and Xie-Benifitness.

Figure 3: Plot of Proximity error for the Synthetic-1 and Synthetic-2 datasets, as a function of the number of clusters for traditionalPSO, PSO with decreasing w, DPSO-1 and DPSO-2 with 3 fitness functions.

In general, the four PSO versions tested performed similarly for the Synthetic-1 dataset. DPSO-1 and DPSO-2provided better results than the other PSO versions in several situations, mainly when using Xie-Beni function as fitnesscriterion. The results obtained with Synthetic-2 dataset highlight the improvement provided by DPSO-1 and DPSO-2.

To summarize the comparison of the four versions of PSO tested, Table 2 contains the number of wins, ties and lossesof the techniques listed on the first column, comparing to the techniques listed on the first row. The different PSO versionswere tested on 36 situations: 2 datasets × 6 number of clusters × 3 fitness functions. For instance, the entry 26/0/10on the third arrow and second column means that DPSO-1 won 26 times and lost 10 against PSO with decreasing w.It is also interesting to highlight that DPSO-1 provided better results than traditional PSO in 22 cases and lost in only14. Another interesting result regards the comparison between DPSO-2 and PSO with decreasing w, where the first won23 times and lost 13 times. These results suggest that the proposed techniques, DPSO-1 and DPSO-2, can improve the

3

Then, the centroids corresponding to each partition matrix are computed and used as the initial population of particles. This initialization scheme was found to speed up the convergence and slightly improve the results.

The number of clusters varied from 3 to 16 clusters. The experimental results are measured according to the difference between the proximity matrix obtained from the original structure of classes or clusters associated do the dataset and the resulting clusters, referred here as Proximity error. This error is calculated

a s




∑Cj=1

d2ik

d2jk

.



∑k1N

∑k2N

∣∣pok1k2 − pr

k1k2














3

w h e r e P = [p

k1k2] is the proximity matrix and p

k1k2

=




∑Cj=1

d2ik

d2jk

.



∑k1N

∑k2N

∣∣pok1k2 − pr

k1k2














3

An additional restriction was applied to the convergence criterion of each PSO version: the last


population best fi tness needs to be better than the fi rst population best fi tness.

Figure 4 presents the results for each dataset, with mean and standard deviation over three runs for the three objective functions (performance indexes), by the four versions of PSO: traditional, with decreasing w, DPSO-1 and DPSO-2 for datasets Synthetic-1 and Synthetic-2.

In general, the four PSO versions tested performed similarly for the Synthetic-1 dataset. DPSO-1 and DPSO-2 provided better results than the other PSO versions in several situations, mainly when using Xie-Beni function as fi tness criterion. The results obtained with Synthetic-2 dataset highlight the improvement provided by DPSO-1 and DPSO-2.

To summarize the comparison of the four versions of PSO tested, Table 2 contains the number of wins, ties and losses of the techniques listed on the fi rst column, comparing to the techniques listed on the fi rst row. The different PSO versions were tested on 36 situations: 2 datasets X 6 number of clusters X 3 fi tness functions.

For instance, the entry 26/0/10 on the third arrow and second column means that DPSO-1 won 26 times and lost 10 against PSO with decreasing w. It is also interesting to highlight that DPSO-1 provided better results than traditional PSO in 22 cases and lost in only 14. Another interesting result regards the comparison between DPSO-2 and PSO with decreasing w, where the fi rst won 23 times and lost 13 times. These results suggest that the proposed techniques, DPSO-1 and DPSO-2, can improve the performance of PSO in data clustering.

5. ConclusionIn this paper, we proposed two

modifi ed versions of the optimization technique PSO. Both approaches are based on adjusting two PSO parameters during the algorithm convergence, allowing a fi ner local search and reducing the chances of local minima. The techniques were evaluated in clustering problems and the experimental results showed that the proposed PSO versions were able to reach better results than traditional versions of PSO in several

situations.There are still some future

investigations toward the validation of the proposed approaches. To mention a few, it would be interesting to evaluate their performance when applied to real datasets and, also, continue to explore different ways of automatically adjusting the parameters. The proposed approaches could also be compared to other bioinspired approaches for data clustering, such as Genetic Algorithms (Goldberg, 1989).

6. AcknowledgmentsThe authors would like to thanks

CAPES, CNPq and FAPESP for the fi nancial support.

References• Bezdek, J. C. (1981). Pattern recognition

with fuzzy objective function algorithms. Ed. Prenum.

• Chen, D. and Zhao, C. (2009). Data-driven fuzzy clustering based on maximum entropy principle and pso. Expert Systems with Applications, 36(1):625 – 633.

(a) Synthetic-1 dataset and Reconstruction fi tness. (b) Synthetic-1 dataset and FDB fi tness. (c) Synthetic-1 dataset and Xie-Beni fi tness.

(d) Synthetic-2 dataset and Reconstruction fi tness. (e) Synthetic-2 dataset and FDB fi tness. (f) Synthetic-2 dataset and Xie-Beni fi tness.

Fig. 3: Plot of Proximity error for the Synthetic-1 and Synthetic-2 datasets, as a function of the number of clusters for traditional PSO, PSO with decreasing w, DPSO-1 and DPSO-2 with 3 fi tness functions.

Table 2 : Win/tie/loss table of PSO versions

Traditional PSO PSO with decreasing w DPSO-1 DPSO-2

Traditional PSO — 22/0/14 14/0/22 20/0/16

PSO with decreasing w 14/0/22 — 10/0/26 13/0/23

DPSO-1 22/0/14 26/0/10 — 20/0/16

DPSO-2 16/0/20 23/0/13 16/0/20 —


• Cui, X., Potok, T., and Palathingal, P. (2005). Document clustering using particle swarm optimization. In Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE, pages 185 – 191.

• Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1(2):224–227.

• Egan, M., Krishnamoorthy, M., and Rajan, K. (1998). Comparative study of a genetic fuzzy c-means algorithm and a validity guided fuzzy c-means algorithm for locating clusters in noisy data. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pages 440–445.

• Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and

Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition.

• Hruschka, E., Campello, R., Freitas, A., and de Carvalho, A. (2009). A survey of evolutionary algorithms for clustering. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 39(2):133–155.

• Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942 –1948 vol.4.

• Pedrycz, W. and Oliveira, J. V. (2008). A development of fuzzy encoding and decoding through fuzzy clustering. IEEE Transactions on Instrumentation and Measurement, 57(4):829–837.

• Shi, Y. and Eberhart, R. (1998). A modified particle swarm optimizer. In

Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pages 69 –73.

• Xie, X. and Beni, G. (1991). A validity measure for fuzzy clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 13(8):841 –847.

• Yi, W., Yao, M., and Jiang, Z. (2006). Fuzzy particle swarm optimization clustering and its application to image clustering. In Zhuang, Y., Yang, S., Rui, Y., and He, Q., editors, Advances in Multimedia Information Processing-PCM 2006, volume 4261 of Lecture Notes in Computer Science, pages 459–467. Springer Berlin / Heidelberg. 10.1007/11922162_53.

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A N N O U N C E M E N T


Harmony search (HS) is a music-inspired algorithm (Geem et al., 2001) and has been applied to various optimization problems including music composition, Sudoku puzzle, magic square, timetabling, tour planning, logistics, web page clustering, text summarization, Internet routing, visual tracking, robotics, energy system dispatch, power system design, cell phone networking, structural design, water network design, dam scheduling, fl ood model calibration, groundwater management, soil stability analysis, ecological conservation, vehicle routing, heat exchanger design, satellite heat pipe design, offshore structure mooring, RNA structure prediction, medical imaging, medical physics, etc (Geem, 2009; 2010a). Recently, HS was also applied to astronomical data analysis, which was published in Nature (Deeg et al., 2010).

Each musician in music performance plays a musical note at a time, and those musical notes together make a harmony. Likewise, each variable in optimization has a value at a time, and those values together make a solution vector. Just like the music group improves their harmonies practice by practice, the algorithm improves its solution vectors iteration by iteration.

The HS algorithm basically has three operations, such as memory consideration, pitch adjustment, and random selection. Using memory consideration operation, HS chooses a value from harmony memory (HM); using pitch adjustment operation, HS chooses a value which is slightly modifi ed from HM; and using random selection operation, HS chooses a value randomly from entire value range. These basic operations constitute a novel stochastic derivative (Geem, 2008), instead of traditional calculus-based derivative, in order to search for the right direction to the optimal solution.

For more advanced issues in HS, researchers have researched exploratory power (Das et al., 2010), multi-modal solution space (Gao et al., 2009), multi-objective optimization (Geem, 2010b), distributed memory (Pan et al., 2010), hybridization (Fesanghary et al., 2008), and adaptive theory (Geem and Sim,

2010). In addition, HS has a unique derivative which considers the relationship among variables (Geem, 2011).

ReferencesDas, S., Mukhopadhyay, A., Roy, A., Abraham,

A., & Panigrahi, B. K. (2010) Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, http://dx.doi.org/10.1109/TSMCB.2010.2046035

Deeg, H. J., Moutou, C., & Erikson A. et al. A transiting giant planet with a temperature between 250 K and 430 K. Nature, 464, 384-387.

Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., Alizadeh, Y. (2008). Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197(33-40), 3080-3091.

Gao, X. Z., Wang, X., & Ovaska S. J. (2009) Uni-modal and Multi-modal Optimization Using Modifi ed Harmony Search Methods. International Journal of Innovative Computing, Information and Control, 5(10A), 2985-2996.

Geem, Z. W. (2008). Novel Derivative of Harmony Search Algorithm for Discrete Design Variables. Applied Mathematics and Computation, 199(1), 223-230.

Geem, Z. W. (2009). Music-Inspired Harmony Search Algorithms: Theory and Applications. Berlin: Springer.

Geem, Z. W. (2010a). Recent Advances in Harmony Search Algorithm. Berlin: Springer.

Geem, Z. W. (2010b). Multiobjective Optimization of Time-Cost Trade-Off Using Harmony Search. ASCE Journal of Construction Engineering and Management, 136(6), 711-716.

Geem, Z. W. (2011). Stochastic Co-Derivative of Harmony Search Algorithm. International Journal of Mathematical Modelling and Numerical Optimisation, 2(1), 1-12.

THEME ARTICLE

Harmony Search AlgorithmZong Woo Geem

Environmental Planning and Management Program, Johns Hopkins University729 Fallsgrove Drive #6133, Rockville, Maryland 20850, USA. Email: [email protected]://sites.google.com/a/hydroteq.com/www/


Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60-68.

Geem, Z.W., Sim, K.-B. (2010).

Parameter-Setting-Free Harmony Search Algorithm. Applied Mathematics and Computation, http://dx.doi.org/10.1016/j.amc.2010.09.049

Pan, Q.-K., Suganthan, P.N., Liang, J.

J., Tasgetiren, M.F. (2010). A local-best harmony search algorithm with dynamic subpopulations. Engineering Optimization, 42(2), 101 - 117.

ooo

Harmony search[Excerpted from http://en.wikipedia.org/wiki/Harmony_search]

In computer science and operations research, harmony search (HS) is a phenomenon-mimicking algorithm (also known as metaheuristic algorithm, soft computing algorithm or evolutionary algorithm) inspired by the improvisation process of musicians. In the HS algorithm, each musician (= decision variable) plays (= generates) a note (= a value) for fi nding a best harmony (= global optimum) all together. The Harmony Search algorithm has the following merits:

• HS does not require differential gradients, thus it can consider discontinuous functions as well as continuous functions.

• HS can handle discrete variables as well as continuous variables.

• HS does not require initial value setting for the variables.

• HS is free from divergence.

• HS may escape local optima.

• HS may overcome the drawback of GA’s building block theory which works well only if the relationship among variables in a chromosome is carefully considered. If neighbor variables in a chromosome have weaker relationship than remote variables, building block theory may not work well because of crossover operation. However, HS explicitly considers the relationship using ensemble operation.

• HS has a novel stochastic derivative applied to discrete variables, which uses musician’s experiences as a searching direction.

• Certain HS variants do not require algorithm parameters such as HMCR and PAR, thus novice users can easily use the algorithm.

There is abundant evidence of a widened and deepened interest in modern science. How could it be otherwise when we think of the magnitude and the eventfulness of recent advances?

But the interest of the general public would be even greater than it is if the makers of new knowledge were more willing to expound their discoveries in ways that could be “understanded of the people.” No one objects very much to technicalities in a game or on board a yacht, and they are clearly necessary for terse and precise scientifi c description.

It is certain, however, that they can be reduced to a minimum without sacrifi cing accuracy, when the object in view is to explain “the gist of the matter.” So this OUTLINE OF SCIENCE is meant for the general reader, who lacks both time and opportunity for special study, and yet would take an intelligent interest in the progress of science which is making the world always new.

The story of the triumphs of modern science is one of which Man may well be proud. Science reads the secret of the distant star and anatomises the atom; foretells the date of the comet’s return and predicts the kinds of chickens that will hatch from a dozen eggs; discovers the laws of the wind that bloweth where it listeth and

reduces to order the disorder of disease. Science is always setting forth on Columbus voyages, discovering new worlds and conquering them by understanding.

For Knowledge means Foresight and Foresight means Power.

The idea of Evolution has infl uenced all the sciences, forcing us to think of _everything_ as with a history behind it, for we have travelled far since Darwin’s day. The solar system, the earth, the mountain ranges, and the great deeps, the rocks and crystals, the plants and animals, man himself and his social institutions--all must be seen as the outcome of a long process of Becoming. There are some eighty-odd chemical elements on the earth to-day, and it is now much more than a suggestion that these are the outcome of an inorganic evolution, element giving rise to element, going back and back to some primeval stuff, from which they were all originally derived, infi nitely long ago. No idea has been so powerful a tool in the fashioning of New Knowledge as this simple but profound idea of Evolution, that the present is the child of the past and the parent of the future. And with the picture of a continuity of evolution from nebula to social systems comes a promise of an increasing control--a promise that Man will become not only a more accurate student, but a more complete master of his world.

The Outline of Science “A Plain Story Simply Told” [Four Volume Series]J. Arthur ThompsonIntroduction – Volume 1


1. IntroductionThe aim of this article are two folds: first aims

to present a comprehensive survey on research contributions that investigate the utilization of NIC methods in building digital watermarking models; the second aim is to define existing research challenges, and to highlight promising new research directions. The scope of the survey is the core methods of NIC, which encompass artificial neural networks, genetic algorithms, swarm intelligence and some hybrid approaches.

2. Digital Watermarking AlgorithmsRecently, a very important and popular technique,

digital watermarking, was effectively applied to protect the copyrights for multimedia and gained prominent results [6]. A signifi cant merit of digital watermarking over traditional protection methods (cryptography) is to provide a seamless interface so that users are still able to utilize protected multimedia transparently by embedding an invisible digital signature (watermark) into multi-media data (audio, images, video). We present the core NIC approaches that have been proposed to solve digital watermarking problems.

2.1 Neural Networks in Digital WatermarkingAn artifi cial neural network (ANN) consists of a

collection of processing units called neurons that are highly interconnected in a given topology. ANNs have the ability of learning-by-example and generalizing

from limited, noisy, and incomplete data; they have, hence, been successfully employed in a broad spectrum of data intensive applications.

Watermarking techniques integrates both color image processing and cryptography, to achieve content protection and authentication for color images. These watermarking techniques are mainly based on neural networks to further improve the performance of Kutter’s technique for color images [7]. Due to neural networks possessing the learning capability from given learning (training) patterns, the used method can memorize the relations between a watermark and the corresponding watermarked image. This approach can pave the way for developing the watermarking techniques for multimedia data since color images are ubiquitous in the contemporaneous multimedia systems and also are the primary components of MPEG video.

2.2 Genetic Algorithms in Digital WatermarkingIn recent decades with the rapid development of

biomedical engineering, digital medical images have been becoming increasingly important in hospitals and clinical environment. Concomitantly, traversing medical images between hospitals exists complicated network protocol, image compression and security problems. Many techniques have been developed to resolve these problems. For example, HIS (hospital information system) and PACS (picture arching and

THEME ARTICLE

Nature Inspired Computing in Digital Watermarking SystemsAshraf Darwish

Computer Science Department, Helwan University, Cairo, EgyptE-mail: [email protected], [email protected]

Abstract—Digital Watermarking using Nature Inspired Computing (NIC) methodologies is currently attracting considerable interest from the research community. Characteristics of nature inspired computational frameworks are adaptation, fault tolerance, high computational speed and error resilience in noisy information environment and all these fit the requirements of building a good watermarking model. This article provides a quick overview of the research progress in applying nature inspired methods to the problem of digital watermarking (DW). The scope of this review will encompass core methods including artificial neural networks, evolutionary computation, swarm intelligence, and the hybrid of these systems. The findings of this review should provide useful insights into the current digital watermarking literature and be a good source for anyone who is interested in the application of nature inspired approaches to DW systems or related fields.


communication system) are currently the two primary data communication systems used in hospitals. Although HIS may be slightly different between hospitals, data can be exchanged based on the standard—HL7 (health level seven). Similarly, PACS transmits medical images using the standard—DICOM (digital imaging and communications in medicine). Furthermore, IEEE 1073 was published in order to set a standard for measured data and signals from different medical instruments.

In GA, variables of a problem are represented as genes in a chromosome, and the chromosomes are evaluated according to their fitness using some measures of profit or utility that we want to optimize. Recombination typically involves two genetic operators: crossover and mutation. The genetic operators alter the composition of genes to create new chromosomes called offspring. The selection operator is an artificial version of natural selection, a Darwinian survival of the fittest among populations, to create populations from generation to generation, and chromosomes with better fitness have higher probabilities of being selected in the next generation [8]. In recent works, two confl icting requirements for typical watermarking systems are selected, namely, the watermarked image quality and the robustness of the watermarking algorithm. Simulation results also show both the robustness under attacks and the improvement in watermarked image quality by using a genetic algorithm.

2.3 Swarm Intelligence in Digital WatermarkingSwarm intelligence approaches

intend to solve complicated problems by multiple simple agents without centralized control or the provision of a global model. Ant Colony Optimization has been proposed and tested for document image watermarking system. Generally speaking, SI models are population-based. Individuals in the population are potential solutions. These individuals collaboratively search for the optimum through iterative steps. Individuals change their positions in the search space, however, via direct or indirect communications, rather than the crossover or mutation operators in evolutionary computation. There are two popular swarm inspired methods in computational intelligence areas: Ant colony optimization (ACO) [9] and particle swarm optimization (PSO) [10]. ACO simulates the behavior of ants, and has been successfully applied to discrete optimization problems; PSO simulates a simplified social system of a flock of birds or a school of fish, and is

suitable for solving nonlinear optimization problems with constraints.

All security systems based on encryption and watermarking are bound to be broken in time given suffi cient resources. Hence, a number of important factors need to be taken into consideration in designing systems for protecting content in consumer electronics devices. These include robustness, renew ability and cost. Robustness: Refers to how strong the system is against conceivable attacks. Every successful design should produce a security system that is suffi ciently robust for the application it is used for. Renewability: When a protection system is hacked, there must be a way to replace it with a new, more robust system. This general concept can be implemented in two fundamental ways. (1) Replacement of renewable security device: all the security functionality is assigned to a renewable device such a smartcard. When its secrets are disclosed, it is simply replaced by a new card. (2) Revocation of consumer electronics device: the secrets are embedded in the CE device, and cannot be removed. If the device is understood to be a pirate device, it is not allowed to receive copy-protected content. Cost: The consumer electronics industry is in a constant effort to minimize the cost of manufacturing so that the end product is affordable for the consumer. Any additional cost needs to be justifi ed from the consumer›s viewpoint.

3. Hybrids Approaches in Digital WatermarkingWe review some of the hybrid

approaches for digital watermarking, for example, ANN-Fuzzy systems, and Genetic-swarm systems.

A watermarking technique is to prevent digital images that belong to rightful owners from being illegally commercialized or used, and it can verify the intellectual property right. The embedded watermark should be robust and transparent, but the ways of pursuing transparency and robustness are conflict. For instance, if we would like to concentrate on the transparency issue, it is natural to embed the smallest modulation into images whenever possible. However, due to such small values in the embedded watermark, attacks can easily destroy the watermark. Thus, it is an important issue to find a fair balance between transparency and robustness. The watermark is doubly embedded in two opposite directions to resist various attacks. However, the major drawback of cocktail watermarking is that it requires a suitable heuristic-tuning weight for various images. Cocktail watermarking has extended to as a blind multipurpose watermarking system with the capability

of detecting malicious modification if the watermark is known. The trade-off is that the blind watermark algorithms are usually less robust and have relatively higher false alarm than those algorithms requiring original images.

The fusion of neural networks and fuzzy logic benefits both sides: neural networks perfectly facilitate the process of automatically developing a fuzzy system by their learning and adaptation ability. This combination is called neuro-fuzzy systems; fuzzy systems make ANNs robust and adaptive by translating a crisp output to a fuzzy one. This combination is called fuzzy neural networks (FNN). For example, Zhang et al. [11] employed FNNs to detect anomalous system call sequences to decide whether a sequence is ‘‘normal’’ or ‘‘abnormal’’.

The watermark is doubly embedded in two opposite directions to resist various attacks. However, the major drawback of cocktail watermarking is that it requires a suitable heuristic-tuning weight for various images. Cocktail watermarking has extended to as a blind multipurpose watermarking system with the capability of detecting malicious modification if the watermark is known. The trade-off is that the blind watermark algorithms are usually less robust and have relatively higher false alarm than those algorithms requiring original images. Recently, intelligent algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown good performances in optimization problems. Intelligent algorithms based watermark techniques can simultaneously improve security, robustness, and image quality of the watermarked images.

In [12], a hybrid watermarking technique based on GA and PSO is proposed. In the proposed technique, the parameters of PLR obtained from JND values and wavelet coefficients are first derived. Thereafter, GA and PSO are simultaneously performed to search the optimal values of PLR. The proposed hybrid watermarking technique uses GA and PSO to search the optimal values for the derived parameters of PLR. The proposed hybrid watermarking technique in [12] is based on GA and PSO. The general idea of the proposed technique is to combine the advantages of PSO and GA, the ability to cooperatively explore the search space and to avoid premature convergence.

Genetic algorithm (GA) has been successfully applied to solve many combinatorial optimization problems. The application of GA to the evolution of fuzzy rules can be found in [13, 14] for intrusion detection. In [14], a simple GA is applied to


generate and evolve the fuzzy classifiers that use complete expression tree and triangular membership function for the formulation of chromosome. To evaluate the fitness of individual solutions, the weighted sum of fitness values of multiple objective functions is proposed in [14] where the proposed weights are user-defined and cannot be optimized dynamically for different cases.

4. Conclusion Over the past decade intrusion

detection based upon NIC approaches has been a widely studied topic, being able to satisfy the growing demand of reliable and intelligent digital watermarking systems.

In our view, these approaches contribute to digital watermarking in different ways. Digital watermarking based upon NIC is currently attracting considerable interest from the research community. In this article, we attempted to present the challenges faced by digital watermarking technology. While much progress in recent years has been done in this direction especially by robustness of embedded watermarking but attackers can still encounter many challenges in this area.

REFRENCES[1] D. Poole, A. Mackworth, R. Goebel,

Computational Intelligence—A Logical Approach, Oxford University Press, Oxford, UK, 1998, ISBN-10:195102703.

[2] J.C. Bezdek, What is Computational

I n t e l l i g e n c e ? C o m p u t a t i o n a l Intelligence Imitating Life, IEEE Press, New York, 1994, pp. 1–12.

[3] B. Craenen, A. Eiben, Computational intelligence. Encyclopedia of Life Support Sciences, in: EOLSS, EOLSS Co. Ltd., 2002.

[4] W. Duch, What is computational intelligence and where is it going? in:W. Duch, J. Man ´ dziuk (Eds.), Cha l lenges for Computat iona l Intelligence, volume 63 of Studies in Computational Intelligence, Springer, Berlin/Heidelberg, 2007, pp. 1–13.

[5] Pao-Ta Yu, Hung-Hsu Tsai,and Jyh-Shyan Lin, Digital watermarking based on neural networks for color images, Elsevier, Signal Processing 81 663-671, (2001).

[6] Ahmet M. Eskicioglu andEdward J. Delp, An overview of multimedia content protection in consumer electronics devices, Signal Processing: Image Communication 16 681}699, Elsevier, (2001).

[7] Pao-Ta Yu, Hung-Hsu Tsai,and Jyh-Shyan Lin, Digital watermarking based on neural networks for color images, Elsevier, Signal Processing 81 663-671, (2001).

[8] J.H. Holland, Adaptation in Natural and Arti7cial Systems, The University of Michigan Press, Ann Arbor, MI, 1975.

[9] S. Olariu, A.Y. Zomaya (Eds.),

Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, 2006, ISBN-10: 1584884754.

[10] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, vol. 4, November/December, IEEE Press, 1995, pp. 1942–1948.

[11] B. Zhang, Internet intrusion detection by autoassociative neural network, in: Proceedings of International Symposium on In format ion & Communicat ions Technologies , Malaysia, December 2005, 2005.

[12] Zne-Jung Lee, Shih-Wei Lin , Shun-Feng Su , and Chun-Yen Lin, A hybrid watermarking technique applied to digital images, Elsevier, Applied Soft Computing 8 798–808, (2008).

[13] J.E. Dickerson, J. Juslin, O. Koukousoula, J .A. Dickerson, Fuzzy intrusion detection, in: Proceedings of IFSA World Congress and 20th North American Fuzzy Information Processing Society Conference, NAFIPS 2001, Vancouver, British Columbia, July 2001, pp. 1506–1510.

[14] J. Gomez, D. Dasgupta, Evolving fuzzy classifiers for intrusion detection, in: Proceedings of IEEE Workshop on Information Assurance, United States Military Academy, West Point, New York, June 2001, pp. 68–75.

The Human Use of Human Beings is a book by Norbert Wiener. It was fi rst published in 1950 and revised in 1954.

Wiener was the founding thinker of cybernetics theory and an infl uential advocate of automation. Human Use argues for the benefi ts of automation to society. It analyzes the meaning of productive communication and discusses ways for humans and machines to cooperate, with the potential to amplify human power and release people from the repetitive drudgery of manual labor, in favor of more creative pursuits in knowledge work and the arts. He explores how such changes might harm society through dehumanization or subordination of our species, and offers suggestions on how to avoid such risks.

The word cybernetics refers to the theory of message transmission among people and machines. The book’s thesis:

“It is the thesis of this book that society can only be understood through a study of the messages and the communication facilities which belong to it; and that in the future development of these messages and communication facilities, messages between man and machines, between machines and man, and between machine and machine, are destined to play an ever-increasing part.”

Increasingly better sensory mechanics will allow machines to react to changes in stimuli, and adapt more effi ciently to their surroundings. This type of machine will be most useful in factory assembly lines, giving humans the freedom to supervise and use their creative abilities constructively. Medicine can benefi t from robotic advances in the design of prostheses for the handicapped. Wiener mentions the Vocorder, a device from Bell Telephone Company that creates visual speech. He discusses the possibility of creating an automated prosthesis that inputs speech directly into the brain for processing, effectively giving deaf individuals the ability to “hear” speech again.

Machines, in Wiener’s opinion, are meant to interact harmoniously with humanity and provide respite from the industrial trap we have made for ourselves. Wiener describes the automaton as inherently necessary to humanity’s societal evolution. People could be free to expand their minds, pursue artistic careers, while automatons take over assembly line production to create necessary commodities. These machines must be “used for the benefi t of man, for increasing his leisure and enriching his spiritual life, rather than merely for profi ts and the worship of the machine as a new brazen calf”

The Human Use of Human Beings[Excerpted from Wikipedia: http://en.wikipedia.org/wiki/The_Human_Use_of_Human_Beings]


HR COLUMN

Think Local, Act GlobalAditya Narayan Mishra

Director – Marketing, Ma Foi Randstad, No.49, Cathedral Road, Chennai - 600 086, India.E-mail: [email protected]

Through globalization people are becoming increasingly interconnected in all aspects; cultural, economic, political, technological, and environmental. Flow of information, fi nance and goods through multinational corporations is certainly one of the major contributors to globalization. The wisdom of these corporations is contained in the practice of customizing products and services for consumption in accordance with the respective currency, culture, regulatory policies and even to the extent of local language in product manuals. The resultant customized IT solutions and operational procedures further encouraged companies to preserve their own corporate culture while prevailing as players in the global market.

It is fairly easy to retain the local culture and environment of an organization if the business merely entails trading around the globe or even operating through overseas offi ces. This does not defi ne the organization as a global unit. True globalization lies in aligning business functions and management policies & practices of offi ces in various countries to make the entire unit one global organization.

So how do we sustain the uniqueness of our local culture while working in a global environment? The global offi ces are consistently sensitive to the local culture and the legal implications while managing the 24 hour global marketplace. The little pockets of local presence, while retaining their individualistic culture, exist seamlessly in the massive canvas of global enterprise. Globalization impacts all types of businesses; a small product developer might not serve the global market, but can fi nd alternate products in other countries. Larger corporates, of course, benefi t completely from spreading their respective businesses around the globe. Access to resources is no longer limited. Capital, raw material and information fl ow across continents and technology is available for an affordable price to those who cannot develop it. Digital highways, more than airways, have bridged the boundaries between countries.

Initially globalization was perceived to be a challenge with regard to technology and logistics. However, the biggest issue, if not impediment to this phenomenon, is culture. Money, machines and materials that build an organization can be managed even better with overseas resources

available at competitive rates. It is the people, who run organizations and they need to be motivated to effectively perform at global standards. With advanced systems and resources provided to match international standards, management of human resources becomes critical. It is futile to transform organizational practices and processes to emerge as an international player, if the company does not take serious efforts to change the mindset of its people. While the industry boasts of entering the international marketplace, the path to success will remain untraveled if HR practices are not correspondingly enhanced. Organizations must seek benchmarks in international practices of human resource management and quickly catch up with them to become a truly international organization.

The international players have been endeavouring to instil the `Think Global, Act Local’ approach in the business intent as well as corporate culture. Reasonable independence to operate within its territory is given to overseas units. Therefore though business objectives are aligned to the parent organization, the heads of the branch and subsidiary offi ces are treated as `domain’ experts and endowed with the freedom to take decisions regarding their respective markets. The country heads, well aware and accustomed to the local laws and customs enjoy the empowerment of operating their units in a manner they deem fi t to the nature of their regions. These operating functions include business aspects, logistics, purchase and vendor selection and management. There is also a certain privacy with regard to IT policies and deployment that is given to the overseas offi ces.

This movement of global perception in a local marketplace is itself a major change for managers. The current method of allowing control over local operations is therefore vital. It helps in creating the comfort and confi dence that is so essential to work performance. Hence transforming an international organization to a wholly global model would be a drastic change which would well be met with immense resistance. Also, working in a global market and yet retaining the local control works well for the business. The regional experts are important to help business grow in the respective locations and this would take priority over creating a one culture organization. The culture would be one with respect to performance standards and work ethics. Beyond that,


About the Author

HR policies, salary structure with taxation, legal policies, work timings and so on would most essentially pertain to the respective country’s practice and custom.

Having said this, the fact remains that an international organization needs uniform and synchronized business practices and processes across offi ces to function in harmony. The dilemma here lies in evolving or reengineering policies, practices and processes globally relevant as well as effi cient but locally practical and accountable

A multinational company (MNC) has to do an indepth study of the related countries’ governing bodies in addition to understanding and respecting their business philosophy, culture and customs. Long term successful and profi table relationships are built based on mutual respect and awareness. However simple a practice is, it is vital to paid heed to it. Once instance comes to my mind in this context: A business partner from the Western world

was visiting and during an outstation trip with the team, enquired of a woman colleague. `Is it okay if I offer to carry your baggage?’ He did not want to demonstrate his chivalrous trait without checking if it would be misconstrued as an intrusion of privacy or even hint that the colleague is not capable of handling her baggage.

The shared services function caters to regional need for fl exibility and adaptability in terms of legal compliance. Cross cultural training sessions and country specifi c orientation programs are part of corporate induction in multi national organizations. When globalization set in, organizations were only focussing on logistic and communication issues; little or no importance was given to cultural challenges. Since English is the universal language, non English speaking countries are now focussing on language learning as part of vocational skill development. Many an organization insists on communication skills with English language profi ciency as a

must have competency for hiring. Countries like China have structured programs to build language skills across levels and functions.

Finetuning communications, aligning business functions and, management, in particular HR practices so that they are consistent throughout the organization is critical for the success of a multi national business. All this is sustained on the one hand, while on the other, the regional offi ces preserve and adhere to cultural and compliance issues as per the locational requirements. The balance of global function in local setup is the crux of thinking local and acting global.

Driving this two fold approach is not simple. Operational effi ciencies and standardization of several critical processes, professional management and more important, sensitivity to diversity ensures that an organization thrives in the global market.

ADITYA NARAYAN Mishra heads the Marketing function in addition to the operational excellence division which handles business planning and change management. He has been with Ma Foi since 1999. In his current role he is responsible marketing, communication for Ma Foi Randstad, in addition to handling knowledge management, learning and development activities..

Mishra holds a Bachelor’s degree in Engineering (Electronics and Telecommunication) from Sambalpur University, and a Masters in Business Administration from Jadavpur University. He is a Certified Six Sigma Green Belt and a Certified Assessor on Business Excellence on EFQM model.

HumorKnow Your Customers:A disappointed salesman of Coca Cola returns from his Middle East assignment.

A friend asked, “Why weren’t you successful with the Arabs?”

The salesman explained:

“When I got posted in the Middle East , I was very confi dent that I would make a good sales pitch as Cola is virtually unknown there. But, I had a problem I didn’t know to speak Arabic. So, I planned to convey the message through three posters...

First poster: A man lying in the hot desert sand...totally exhausted and fainting.

Second poster: The man is drinking our Cola.

Third poster: Our man is now totally refreshed.

And Then these posters were pasted all over the place

“Then that should have worked!” said the friend.

“The hell it should had!? said the salesman. didn’t realize that Arabs read from right to left”

Software Rules:When software bugs are reported, the standard operating procedure is:

Generate detailed reports showing customers are happy.

Prove bugs are user errors.

Lable bugs as requests for enhancements.

Keep asking for more information until the customer gives up.

Pass a bug around until it goes away.

Prove that the customer does not need a bug fi xed.

Have customers prioritize a list of bugs. With luck, customers will make the mistake of marking some of the bugs as anything but critical.

When all else fails, attempt to fi x a bug within 2-3 revs.


1. Introduction The phenomenal growth in wireless

communication services such as wireless web browsing, real time mobile multimedia streaming and interactive applications, motivate the rapid development of the next generation mobile wireless networks. In India, the mobile user base is expected to increase to 737 million by 2012 as against present strength of 500 million mobile subscribers [1]. Keeping in view the fast growing mobile market in India, Telecom Regulatory Authority of India (TRAI), intends to leap frog to 4G directly, so as to provide comprehensive and secure all-IP based solutions, high speed and low cost of data transfer.

The objective of the next generation of wireless architecture is based on the integration of heterogeneous wireless networks and inter-networking. Table I describes the complexities of heterogeneous networks over and above the homogeneous environment [2].

Table I : Homogeneous vs. Heterogeneous

Homogeneous Networks Heterogeneous NetworksDetection of access points belonging to same system.

Detection of access points belonging to multiple systems.

Mobile Host needs to decide among access points of the same technology.

Mobile host needs to decide among access points of multiple technologies.

Handoff initiation triggered mainly by signal strength fading.

Handoff initiation triggered by multiple events.

The execution method can be applied in every situation.

The execution methods depend on context and not all methods can be applied in every scenario.

Adaptation process is not as important because the mobile host roams between similar conditions (same technology).

Adaptation is essential, the mobile host roams between disparate technologies and conditions change drastically.

ARTICLE

Neuro Fuzzy Vertical Handoff Decision Algorithm for overlaid Heterogeneous NetworkAnita Singhrova* & Nupur Prakash**

* Computer Science and Engineering Department, DCR University of Sc & Tech., Murthal, Sonepat, India Email: [email protected]** University School of IT and Principal, Indira Gandhi Institute of Tech.,GGS IPU, Delhi, India Email: [email protected]

Due to extremely high demand of cell phones, laptops etc. among people, who have become extra mobile over the years, the demand of seamless mobility is on the rise. Withthe increasing availability of different wireless (mobile) devices, it has become essential that the mobile terminal should be able to move across disparate wireless networks. Anefficient vertical handoff algorithm needs to be implemented, to envision this kind of mobility in heterogeneous networks.This paper proposes a vertical handoff decision algorithm. The proposed algorithm uses six parameters namely received signal strength, velocity of mobile terminal, number of users, battery level, bandwidth and coverage area for decision-making. Because of uncertainness in the input data and supervised learning technique, the paper proposes to use neuro-fuzzy approach for making vertical handoff decision. Finally, we analyze and compare our algorithm with the classic approach. The results show that the reduced number of vertical handoffs in the proposed algorithm results in reduced ping-pong andincreased throughput. The quality indicator is also directly dependent upon ping-pong effect. Thus, the reduced ping-pong effect results in improved quality of service.

Keywords: component; seamless mobility; neuro-fuzzy; quality indicator; vertical handoff decision; ping-pong effect; throughput..

This paper has been selected as “The Best Paper” at the National Conference on Mobile and Ad Hoc Networks (NCMAN) held at Dr. Mahalingam College of Engineering and Technology, Pollachi between 29 and 30 October, 2010.

CSI Coimbatore Chapter organized the Conference and was supported by CSI Divisions 3,4 and Region 7 & IEEE Computer Society Madras section.


A signifi cant challenge of 3G and 4G wireless networks is to coordinate among the different wireless technologies used in different networks. Thus, there is a signifi cant need for a single unifi ed approach that integrates disparate wireless technologies (mentioned in Table II) enabling MT to seamlessly roam between access networks.

A. HandoffHandoff is an event, when a mobile

terminal (MT) utilized resources are transferred as MT changes its point of attachment and moves from one cell to another [3]. Different handoff strategies are horizontal handoff and vertical handoff.

In case of horizontal handoff as the mobile user crosses the cell boundary, handoff takes place between two network access points (AP) or base stations (BS) that use the same wireless access network technology for example IEEE 802.11b base station to geographically neighboring IEEE 802.11b i.e. homogeneous networks. In case of vertical handoff, handoff is between two network access points or base stations that use the different network access technologies such as IEEE 802.11b base station to an overlaid cellular network or IEEE 802.16 (WiMax) and vice-versa.

In order to support seamless mobility in the current scenario of heterogeneous networks comprising legacy network (2G, 2.5G, 3G), 4G and hot spot areas covered by WLAN etc, a single unifi ed vertical handoff algorithm is required. The vertical handoff decision algorithm (VHDA) is required over and above the horizontal handoff in case of heterogeneous networks.

B. Overlay Structure of Heterogeneous networksOne of the overlaid heterogeneous

systems discussed in this paper is the integration of the IEEE 802.11 WLAN and cellular systems. These systems coexist as many cellular devices support dual Radio Frequency (RF) interfaces for WLAN and cellular access. Moreover,

these are complementary technologies - WLAN covers hotspot areas and provides greater bandwidth but low mobility at low cost, whereas cellular network provides comparatively low bandwidth but high mobility at a higher cost. The overlay network of two different interfaces - WLAN and cellular network shown in Fig. 1, provides a combination of high bandwidth and high mobility. MT terminal remains connected to at least one base station or access point within the service area.

The three main steps involved in vertical handoff process are system discovery, handoff decision, and handoff execution. This paper focuses on the vertical handoff decision based on several parameters.

II. Literature ReviewThe various existing strategies for

vertical handoff decision in next generation network are reviewed in [4]. Paper [5] discuss the vertical handoff between two network access points that use the different access network technologies, IEEE 802.11b and CDMA cellular network. Paper [6] proposes the optimization of policy based handoff decision along with the cost function to select the target network. In paper [7], the performance of four VHDA namely MEW (Multiplicative Exponent Weighting), SAW (Simple Additive Weighting), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and GRA (Grey Relational Analysis) are compared. For next generation networks, paper [8] suggests a network selection mechanism to guarantee mobile users being always best connected (ABC) using Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA). Paper [9] discusses VHDA based on the fuzzy control theory. The handoff algorithm considers the factors of power level, cost and bandwidth. A vertical handoff algorithm in multi tier (overlay) network proposed in [10] uses pattern recognition to estimate user’s position using global positioning system and making handoff decision. Paper [11] describes the concept of accumulated

user mobility for prediction of handoff attempts and analyses the effect of number of users on Quality of Services (QoS). Paper [12] proposes a VHDA, to enable a wireless access network to balance the overall load among all attachment points and to maximize the collective battery lifetime of Mobile Nodes (MN). An implementation of vertical handoff for a mobile node roaming between IPv4 and IPv6 and continuously communicating with correspondent node in IPv6 network in heterogeneous networks is given in paper [13]. The performance of adaptive hysteresis vertical handoff scheme for overlaid WCDMA and WLAN heterogeneous networks is given in paper [14]. A VHDA based on an advanced fi ltering mechanism (in 3G/WLAN) and traffi c types (real time or non-real time services) is presented in paper [15] and [16] respectively. To assist the handoff decision, the MT utilizes the downloaded base station radio propagation parameters and coverage radius information to estimate the distance and terminal velocity from the surrounding base stations [17]. Paper [18] compares the performance of hysteresis based vertical handoff algorithms, where a two state non-homogeneous Markov chain model models mobile terminal movement between two networks. In addition, a mobility management scheme for IP based networks using the transport layer protocol SCTP and application layer protocol SIP is suggested in paper [19].

All the papers discussed above consider one or the other parameter, at the most three for deciding vertical handoff. In view of the above, the proposed algorithm considers neurofuzzy multi parameter based vertical handoff decision. Since there is an element of uncertainty, the fuzzy logic based membership function is used and for adjusting the weight matrix, we propose to use neural network. To reduce the processing time we use neural network that inherently incorporates parallel processing and latter this can be implemented in

Table II : Diversity in existing and emerging wireless technologies [3]

Network Coverage Data Rates Mobility Cost

Satellite World Max. 144 Kbps High High

GSM/GPRS 35 Km 9.6Kbps–144Kbps High High

IEEE 802.16a 30 Km Max 70Mbps Low/Medium Medium

IEEE 802.20 20 Km 1-9 Mbps Very High High

UMTS 20 Km Up to 2 Mbps High High

HIPERLAN 2 70 to 300m 25 Mbps Medium/High Low

IEEE 802.11a 50 to 300m 54 Mbps Medium/High Low

IEEE 802.11b 50 to 300m 11 Mbps Medium/High Low

Bluetooth 10m Max 700 Kbps Very low Low

II.

BS

Cellular Coverage

(1) Vertical handoff from WLAN to cellular coverage (2) Vertical handoff from cellular coverage to WLAN (3) Horizontal handoff from WLAN to WLANFig. 1 : Overlay structure of WLAN and cellular network


hardware, which further reduces time.The rest of the paper is organized as

follows: Section 2 provides the literature review. For making of vertical handoff decision, the classical approach is discussed in Section 3, whereas the neuro-fuzzy approach is proposed in Section 4. The analysis of results is carried out in Section 5 followed by the paper conclusions and future work in Section 6 and 7 respectively.

III. Classical ApproachIn classical approach, VHDA is based

on Received Signal Strength (RSS) of the Access Point (AP) of WLAN. The RSS in a mobile radio channel shows that the average RSS at any point declines as a power law of the distance between a transmitter and receiver [20]. The average received power (Pr) at a distance (d) from the transmitting

antenna is given as P

r = P

0(d/d

0)-n (1)

where n is path loss exponent, Pr is the average received power at a distance d from the transmitting antenna and P0 is the transmitted power or power received at a close-inreference point in the far fi eld region of antenna at a small distance d

0.

Pr = P

0 – [10 * n * log(d/d

0)] (2)

Value of n depends upon specifi c propagation environment [20].

IV. Multi Parameter Baesd Neuro Fuzzy VHDAThe next generation networks require

implementation of an adaptive and proactive approach to vertical handoff decision. The algorithm proposed here uses six input parameters (shown in Table 3) for arriving at a handoff decision.

Since, some of the input parameters, VMT and BL are dependent on MT whereas other input parameters are related to network conditions; a hybrid approach called mobile assisted handoff (MAHO) is used for handoff execution.

Neuro-fuzzy approach has been chosen for evaluation of the proposed vertical handoff decision, mainly because of its ability to handle imprecise and uncertain data. The fuzzy logic also allows the induction of rules in natural language. Based on these rules, input is conveniently mapped to output and by using adaptive neuro fuzzy inference system (ANFIS); the self-learning process is inherently incorporated. The details of fuzzifi cation, inference engine, defuzzifi cation etc shown in Fig. 2 are described in subsections 5.1 to 5.4.

Table II : Diversity in existing and emerging wireless technologies [3]

Parameters Range Remarks

Received Signal Strength (RSS)

-78dBm to -66 dBm

It is the strength of received signal by MT from AP in WLAN. The MT measures RSS continuously from the present and neighboring cells to initiate Handoff.

Velocity of Mobile Terminal (V

MT)

0 to 54m/sec or 0 to 200Km/hrs

It is the velocity, with which the mobile terminal (MT) is moving. For high speed MT, cellular is preferred because of greater coverage area. This range includes the speed of two-wheeler, four wheeler and travel by fast train.

No. of Users(UN) 0 to 15 users This shows the number of users. The QoS of WLAN is UN sensitive. As the number of users

increase, available bandwidth is reduced and number of collisions results in network congestion.

Bandwidth available (B

AV)

0 to 56 Mbps It is the amount of unused bandwidth of the candidate base station (BS) or access point (AP). This parameter takes care of congestion in the network.

Battery life (BL) 0 to 1 Watt This represents the energy level in a mobile host. The energy is used for every packet transmitted

or received by the mobile host. The attachment to the closest AP or BS is known to consume the least power for individual mobile devices at a given instant.

Coverage Area (C) 0 to 300 m It is the area covered by the network. If the speed of the MT is high then cellular network with wider coverage area is preferred over WLAN.

Standalone fuzzy engine

Training of the fuzzy inference engine

Handoff OutputFuzzy

Inference System

(Sugeno)

Input parameters Bav, Vmt, Un, BI, RSS, Coverage Area

Fig. 2. Fuzzy Inference System


A. Fuzzifi cationIn the fuzzifi cation stage, a data point is assigned membership in each set. A membership function is a curve that defi nes how each point in the input space is mapped to a membership value between 0 and 1. Fuzzy Logic Toolbox includes 11 built in membership function types [22]. For simplicity the piecewise linear membership function namely, Triangular membership function are chosen. For each of the input parameters mentioned above the membership function is drawn and based on that membership function, the membership value is obtained.

1

0.8

0.6

0.4

0.2

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1RSSdBm)

Below Threshold Above Threshold

De

gre

e o

f m

em

be

rsh

ip

Fig. 3(a). Membership curves for normalized RSS

1

0.8

0.6

0.4

0.2

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1INPUT

(b)

Slow HighMedium

De

gre

e o

f m

em

be

rsh

ip

Fig. 3(b) : Membership curve for normalized input parameters (V

MT, U

N, B

AV, B

L, C)

The value of all input variables RSS, V

MT, U

N, B

AV, B

L and C are normalized in a

scale of 0 to 1 by using equation (3).Normalized_Value = (Current_value–

Lowest_Range)/(Highest_Range–Lowest_ Range) (3)where Current_value is generated in a given range (Highest – Lowest) using random number generators and Normalized_Value is the value obtained in the range 0 to 1.

The membership function for normalized RSS is shown in fi g. 3(a) and for V

MT, U

N, B

AV, B

L and C is shown in fi g. 3(b).

B. Fuzzy inference engineFuzzy inference engine Fuzzy inference

engine depicts if-then-else rules. Vertical handoff between WLAN and cellular network is not reversible i.e. the motive to handoff between WLAN to cellular network and vice versa is quite different. The time taken to handoff from WLAN to cellular network is critical as the delay leads to disconnection, whereas handoff from cellular network to WLAN can wait little longer due to overlay structure, (shown in Fig.1) as base station signal of cellular network continues even in WLAN.

If the antecedent of a given rule has more than one part, the fuzzy operator is applied to obtain one number that represents the result of the antecedent of that rule. Every rule has a weight, which is applied to a number given by antecedent, thereafter applying implication method. The fuzzy Inference Process used is Sugeno Type fuzzy Inference method, because it is compact and computationally effi cient representation than the other fuzzy inference method namely, Mamdami System. The Sugeno system lends itself to the use of adaptive techniques for constructing models [22].The adaptive neuro fuzzy inference system (ANFIS) editor of MATLAB is used for implementation of adaptive fuzzy inference engine (FIS).

C. Defuzzifi cationThe input to a defuzzifi cation process is

fuzzy and the output is a single crisp value. The most popular defuzzifi cation method is the centroid calculation, which returns the centre of curve. Different rules cannot share the same output membership function i.e. no rule sharing. The output variable of fuzzy inference handoff decision is given as {Fit, Not Fit} = {F, NF}

D. Training of neural networkThe Neural network is used for training

of weight matrix due to its inherent parallel processing and learning capabilities. Three steps involved are initialization, iteration and termination. The toolbox function ANFIS constructs a fuzzy inference system (FIS) by establishing a relationship between an input and an output data set as shown in Fig.4. In each pass, the function training proceeds through the evaluation of membership function for specifi ed inputs, calculating the output by using centroid method based on fuzzy rules, evaluation of error by using least square method and the network weight adjustment for each input vector by using back-propagation algorithm.

The above-mentioned training steps are repeated until network output (actual output) becomes equal to target vector (desired output) and the error reduces to zero or at least approaches to zero. Error is defi ned as the sum of squared difference between the actual (network output) and the desired output (target vector). This adjustment allows fuzzy systems to learn from the data they are modeling. Once trained, the weights are frozen and now the neural network recognizes even the unseen input.

V. Results and AnalysisThe vertical handoff decision of the

proposed algorithm and the classical approach is passed to the simulator. The simulator evaluates the number of vertical handoffs for both the algorithms. The graph in Fig. 5 shows the reduced number of vertical handoffs in case of proposed handoff than classical approach.

The reduced number of handoffs also affects the pingpong effect and throughput. The parameters are described below:1. Ping-pong Effect: The rate of ping-

pong handoff (PpingpongHO

) is defined as the number of ping-pong handoff (N

pingpongHO) per total handoff executions

(NHO

). P

ping-pongHO = N

ping-pongHO / NHO (4)

The time tag is used to indicate the time-period that has passed from the last successful handoff. As time tag increases, the ping-pong effect reduces.

2. Throughput: It is a positive indicator

Input (P) =

1 0 0 0

0 1 0 0

0 1 0 0

0 0 1 0

0 0 0 1

1 0 0 0

0 1 0 0

Neural networkInitial wts IW =[0 0 0 0 0 0 0]

Initial bias Ib=[0]; n = W*P + bias

n= [0 0 0 0 ]hardlim(n) = {0 if n<0}

{1 otherwise}

Rule base Fuzzy Inference Engine

Trained weights W’=W, b’=b

Compare error e where e =T-a

e = [ 0 -1 -1 -1]

eeeee ===== 00000,,,,, ΔW = 0, Δb = 0

Target Vector T= [1 0 0 0 ]

e = 1, then ΔW =ePTr = PTr e = -1,then ΔW = ePTr=-PTrΔW=[0 -1 -1 -1 -1 0 -1]Δb= (t-a) = [-3]

Adjust weights Wnew = Wold + ΔW

Wnew=[ 0 -1 -1 -1 -1 0 -1]bnew = bold + Δb =

[-3]

a=hardlim(W*P+bias)

Fig. 4. Procedure showing vertical handoff decision


and is dependent upon the effective data rate and delay caused due to handoffs. Lesser number of handoffs and higher effective data rate increases throughput. To reduce the number of handoffs the ping-pong effect shall be reduced. Throughput is given as:

Throughput=Rcellular_nw

(Tcellular-nw

– T

N/2) + R

WLAN (T

WLAN – T

N/2) (5)

where TN = N ∆ / T.

Rcellular_nw

and RWLAN

are the effective data rate available in cellular network & WLAN.

Tcellular-nw

and TWLAN are the total contiguous stretch of time when power is less than threshold or above threshold in cellular and WLAN respectively.

T is total time-period, which is sum of T

cellular-nw and T

WLAN

N is the number of handoffs.∆ is the handoff completion time.Both these analysis parameters are

based on number of correct handoffs.3. Computational complexity: The

number of operations defines the computational complexity of an algorithm.In classic approach, the evaluation of

handoff decision is based on RSS only. The approach is very basic and simple with minimum complexity. However, the limited input to decide upon handoff causes high ping-pong effect and low throughput with minimum complexity.

In the proposed approach, the handoff depends upon multiple parameters. The correct handoff results in low pingpong effect and high throughput.

The reduced ping-pong effect results in high Quality-Indicator (QIh) which is directly dependent upon ping-pong handoff rate.Q

Ih [N

HO - N

ping-pongHO] / N

HO (6)

QIh = K [N

HO - N

ping-pongHO] / N

HO or (7)

QIh =K[1- P

ping-pongHO] (8)

where k is constant of proportionality which is dependent upon other factors like number of calls blocked and number of calls dropped.

The reduced ping-pong means lesser number of handoffs and this in turn makes the negative part of the equation (8) negligibly small, thus increasing throughput.

There is a tradeoff between computational complexity and low unnecessary handoffs. Computational complexity is high in the proposed approach, and to take care of this aspect, the proposed multi parameter based vertical handoff algorithm is implemented using ANFIS. This neural network using ANFIS approach not only invokes selfl earning process but also provides blue prints for implementation at hardware level.

VI. ConclusionWhile traditional handoff is based on

RSS comparisons, the proposed VHDA evaluates on additional factors such as monetary cost, offered services, network conditions and user preferences.

For vertical handoff decision between WLAN and cellular networks, multiple parameters: available bandwidth, speed of mobile terminal, number of users, received signal strength, battery level and coverage area is used. The multi parameter based proposed adaptive vertical handoff decision helps determine which network it should handoff to as incorrect handoff decision will not only result in poor QoS but at times may even break off current communication resulting in increased call dropping.

The results show that the number of vertical handoffs in the proposed algorithm is lesser than the classical approach. These reduced number of vertical handoffs results in reduced ping-pong effect and increased throughput. The quality indicator is also directly dependent upon ping-pong effect.

Thus, the reduced ping-pong effect results in improved quality of service.

VII. Future WorkThe future work involves the

comparison of performance analysis of this proposed multi parameter based VHDA with a conventional VHDA on parameters like Complexity, Dropped Packets, Retransmitted packets comparison and WLAN delay.

References1] http://www.trai.gov.in/NGN.asp2] Pablo Vidales, Javier Baliosian, Joan

Serrat, “Autonomic system for mobility support in 4G networks”, IEEE Journal on selected areas in communications, vol. 23, no.12, Dec 2005, pp. 2288-2303.

3] Pablo Vidales, Glenford Mapp, Frank Stanjano, Jon Crowcroft, A Practical approach for 4G systems: Deployment of overlay networks, Computer Laboratory, University of Cambridge, UK.

4] Anita Singhrova and Nupur Prakash, “Review of various strategies for vertical handoff decision in next generation networks”, in Asia Pacifi c Advanced Network Research Workshop (APAN ’08), Queenstown, New Zealand, 4th -8 th August 2008.

5] Hyosoon Park, Sunghoon Yoon, Taehyoun Kim, Jungshin Park, Misun Do, and Jaiyong Lee, “Vertical handoff procedure and algorithm between IEEE802.11 WLAN and CDMA cellular network”, Lecture Notes in Computer Science (LNCS) Springer Berlin/

Heidelberg, Mobile Communications, vol. 2524, pp. 103-112, January 2003.

6] Fang Zhu and Janise McNair , “Optimizations for vertical handoff decision algorithms”, in IEEE Wireless Communications and Networking Conference (WCNC ’04), vol. 2, no. 21-25, pp. 867-872, Atlanta, Ga, USA, March 2004.

7] Enrique Stevens Navarro and Vincent W.S Wong, “Comparison between vertical handoff decision algorithms for heterogeneous wireless networks” , in the 63rd IEEE Vehicular Technology Conference (VTC ’06), vol.2, pp. 947-951, Melbourne, Vic., Australia, May 2006.

8] Qingyang Song and Abbas Jamalipour, “A network selection mechanism for next generation networks”, in IEEE International Conference on Communications (ICC ’05), vol. 2, pp. 1418-1422,Seoul, Korea, May 2005.

9] Hongwei Liao, Ling Tie and ZhaoDu,, “A vertical handover decision algorithm based on fuzzy control theory”, in the 1st International Multi-Symposiums on Computer and Computational Sciences (IMSCCS ’06), IEEE Computer Society, vol. 2, pp. 309-313, Hangzhou, China, June 2006.

10] A. Mehbodniya, J. Chitizadeh, “An intelligent vertical handoff algorithm for next generation wireless networks”, in the 2nd IEEE/IFIP International Conference on Wireless and Optical Communications Network (WOCN ’05), pp. 244-249, Dubai, UAE, March 2005.

11] Liang Tee Lee and Chen Feng Wu, “An Adaptive Handoff Algorithm with Accumulated attempts of user mobility for supporting QoS in wireless cellular networks”, IJCNS Intl. journal of computer Science and network security, Vol. 6, No. 9B, September 2006, pp. 56-62.

12] Su Kyoung Lee, Kotikalapudi Sriram, et al., “Vertical handoff decision algorithm providing optimized performance in heterogeneous wireless networks”, in IEEE Global Telecommunications Conference (GLOBECOM ’07), pp.5164-5169, Washington DC, USA, November 2007.

13] Qinxue Sun , Bo Hu , Shanzh i Chen and J ingj ing Zhang, “An Implementation of Vertical Handoff in Heterogeneous Networks”, in the 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom ‘09), pp.1-4, Beijing, China, September 2009.

14] Yung-Fa Huang, Chin-Wei Hsu, Fu-


Bin Gao and Hsing-Chung Chen, “Performance of adaptive vertical handoff in heterogeneous networks of WLAN and WCDMA systems”, in the 5th International Joint Conference on INC, IMS and IDC (NCM ‘09), pp. 2103-2107, Seoul, Korea , August 2009.

15] R. Khan, S. Aissa and C. Despins, “Seamless vertical handoff algorithm for heterogeneous wireless networks-an advanced filtering approach”, in IEEE Symposium on Computers and Communications (ISCC 2009), pp.255 – 260, Sousse, Tunisia, July 2009.

16] Ying-Hong Wang, Chih-Peng Hsu, Kuo-Feng Huang and Wei-Chia Huang, “Handoff decision scheme with guaranteed QoS in heterogeneous network”, in the 1st IEEE International Conference on Ubi-Media Computing, pp. 138-143, Lanzhou, China, July-

August 2008.17] Feng He, Furong Wang and Duan Hu,

“Distance and velocity assisted handoff decision algorithm in heterogeneous networks”, in the 2nd International Conference on Future Generation Communication and Networking (FGCN ‘08), vol.1, pp. 349–353, Hainan Island, China, December 2008.

18] A. H. Zahran and Ben L iang, “Performance evaluation framework for vertical handoff algorithms in h e t e r o g e n e o u s n e t w o r k s ” , i n IEEE International Conference on Communications (ICC ‘05), vol.1, pp.173 – 178, Seoul, Korea, May 2005.

19] Yaw Nkansah-Gyekye, and Johnson I Agbinya, “Vertical handoff between WWAN and WLAN”, in International C o n f e r e n c e o n N e t w o r k i n g , International Conference on Systems

and International Conference on Mobile Communications and Learning Technologies (ICN/ICONS/MCL ‘06), pp. 132 – 137, Mauritius, April 2006.

20] Theodore S. Rappaport, Wireless Communications: Principles and Practice, 2nd Edition, Prentice Hall of India, New Delhi, 2007.

21] George J. Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall of India, New Delhi, 1995.

22] A n i t a S i n g h r o v a a n d N u p u r Prakash,“Multi parameter based vertical handoff decision in next generation networks,” Communications of the Systemics and Informatics World Network (SIWN ’08), vol. 4, pp. 68-71, 2008.

February 2011

ITC 2011: First M. P. State IT Convention on Challenges Before Indian IT Industry in Current Scenario

Date : 26 - 27 February, 2011Organised By : Computer Society of India Bhopal Chapter Hosted By : MANIT Bhopal

For Detail Contact:Dr. (Mrs) Poonam Sinha [email protected] Prof. H B Khurasia [email protected]. Rajeev Shrivastava [email protected]

Challenges Before Indian IT Industry in Current ScenarioOrganised By Computer Society of India Bhopal Chapter at MANIT Bhopal

February 26 - 27,2011

Topic includes:Cloud Computing, Green Computing, Mobile Computing, Wireless Networking, Pervasive Computing, Parallel Computing in High-end & Embedded Application, 3G, Device Convergence, Indian language Computing, ERP and any other relevant topics .

Important Dates: Paper Submission : 24 January 2011Acceptance Noticifi cation : 31 January 2011 Early Bird Registration : 05 February 2011Final Paper Submission : 12 February 2011

Registration Fees:CSI Members : Rs. Rs.1,500/- CSI Non-Members Rs Rs.2000/-CSI Student Members : Rs. 100/- CSI Student Non-Members : Rs.200/-

Contact Details: Address for Correspondance: Dr. (Mrs) Poonam Sinha Dr. (Mrs) Poonam Sinha Hon. Secretary Bhopal Chapter Head IT & MCA Deptt.

[email protected] BUIT, Prof. H.B. Khurasia (Vice-Chairman Bhopal Chapter) Barkatullah University Bhopal

[email protected] Bhopal (M.P.) 462026 Dr. Rajeev Shrivastava

[email protected]


CALL FOR PARTICIPATIONCALL FOR PARTICIPATIONCALL FOR PROPOSALS

As India’s largest and one of the world’s earliest IT professional organizations, the Computer Society of India has always aimed at promoting education and research activities, especially in advanced technological domains and emerging research areas. It is also committed to take the benefi ts of technological progress to the masses across India in particular to unrepresented territories. In order to promote research and innovation meeting the grass-root level ICT needs and emphasize the importance of joint research by faculty-students, the CSI has been providing R&D funding for last several years.

The CSI Student Branches are requested to motivate the young faculty members and students (including undergraduate and postgraduate) to benefi t from this scheme. Proposals for 2010-11 meeting the following aim/objectives, expected outcome, indicative thrust areas for research funding may be submitted to: The Director (Education), Computer Society of India, Education Directorate, CIT Campus, IV Cross Road, Taramani, Chennai 600113.Last date for Receipt of Proposals: 31st January 2011

Aim and Objectives ¬ To provide fi nancial support for research by faculty members,

especially for developing innovative techniques and systems to improve teaching-learning and learning management processes.

¬ To provide fi nancial support to students for developing new systems catering to the needs of socially relevant sectors and/or involving proof of concepts related to emerging technologies

¬ To facilitate interaction/collaboration among academicians, practitioners and students

¬ To develop confi dence and core competence among faculty/students through research projects

¬ To foster an ambience of ‘Learning by Doing’ and explore opportunities of industry funding and mentoring for inculcating professionalism and best practices among students and faculty

To recognize innovation and present excellence awards for path-breaking projects through CSI YITP awards and industry associations, Govt. Agencies and professional societies.

Expected Outcome ¬ Identifi cation of thrust areas, capability assessment, gap

analysis, recommendations and future education and research directions

¬ Integration of research methodologies into the university teaching-learning process and evolving a quality control mechanism for academic programmes and curricula

¬ Strengthening of industry-institutes interaction through commercialization of technologies and products developed by students and faculty

¬ Publication of research studies (ICT penetration, technological innovation, diffusion & adaptation), state-of-the-art reports and case studies of education/ research initiatives

¬ Identifi cation of potential new and innovative projects of young faculty, researchers and students for possible business incubation

Indicative Thrust Areas for Research fundingThe fi nancial assistance up to Rs 50,000/- for hardware projects and up to Rs 30,000/- for software projects would be provided to cover items like equipment, books/journals, fi eld work, questionnaire, computation work and report writing. The indicative thrust areas for funding include (but not limited): Technology- OS, Programming Languages, DBMS, Computer & Communication Networks, Software Engineering, Multimedia & Internet Technologies, Hardware & Embedded SystemsProcess & Tools- Requirements Engineering, Estimation & Project Planning, Prototyping, Architecture & Design, Development, Testing & Debugging, Verifi cation & Validation, Maintenance & Enhancement, Change Management, Confi guration Management, Project Management, Software Quality Assurance & Process Improvement, Vertical Applications - Scientifi c Applications, Enterprise Systems, Governance, Judiciary & Law Enforcement, Manufacturing, Healthcare, Education, Infrastructure, Transport, Energy, Defence, Aerospace, Automotive, Telecom, Agriculture & Forest Management, Inter-disciplinary Applications - CAD/CAM/CAE, ERP/SCM, EDA, Geo-informatics, Bioinformatics, Industrial Automation, CTI and Convergence.

Last date for Receipt of Proposals: 31st January 2011For further details, please visit CSI knowledge porta at www.csi-india.org. The application form can also be downloaded from the portal.

For more details, please visit www.siet.in or contact

Mr. Shrikant KarodeCSI National Student Coordinator

Email: [email protected]

Prof. Swarnalatha RaoCSI Division V ChairpersonEmail: [email protected]

Wg Cdr M Murugesan (Retd.)Director (Education), CSI Education Directorate,

CIT Campus, IV Cross Road, Taramani, Chennai-600113. E-mail: [email protected]

Computer Society of India

NATIONAL HEADQUARTERSEducation Directorate, Chennai

Invites Project Proposals from Faculty Members and StudentsUnder the Scheme of R&D Funding for the year 2010-2011


Data Mining : A Process to Discover Data Patterns and Relationships for Valid PredictionsJasmine K S

Assistant Professor, Department of MCA, R V College of Engineering, Mysore Road, R V Vidyaniketan Post, Bangalore-560059, India. Email: [email protected]

“Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets.”

[Two Crows Corporation, 1999]

“Knowledge discovery in databases is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.”

[Fayyad, Piatetsky-Shapiro and Smyth, 1996]

IntroductionData mining is a powerful new technology with

great potential to help researchers/organizations focus on the most important information in their large databases. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve.

Most of the organizations, who have massive quantities of data, started implementing data mining techniques rapidly on their existing software and hardware platforms to enhance the value of existing information resources and also to know how integration is possible with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to predict the promising clients.

1. What is Data Mining? Data mining consists of more than collecting and

managing data; it also includes analysis and prediction. Simply stated, data mining refers to extracting or “mining” knowledge from large amounts of data [1]. Data mining can be performed on data represented in quantitative, textual, or multimedia forms. Data mining applications can use a variety of parameters to examine the data. They include association, sequence or path analysis, classifi cation, clustering and forecasting [2].

Some observers consider data mining to be just one step in a larger process known as knowledge

discovery in databases (KDD). Other steps in the KDD process, in progressive order, include data cleaning, data integration, data selection, data transformation, (data mining), pattern evaluation, and knowledge presentation.

Fig.1: Steps in Data mining [19]

2. History of data miningData mining is a fairly new concept which was

emerged in the late 1980s. But it soon attracted huge interests for research works and fl ourished with many new and remarkable techniques being discovered throughout the 1990s. Data mining, in many ways, is fundamentally the adaptation of machine learning techniques to business applications. Data mining is best described as the union of historical and recent developments in statistics, AI, and machine learning. These techniques are then used together to study data and fi nd previously-hidden trends or patterns within.

The evolution of Database technology is as follows [17]:

1950s: First computers, use of computers for census

1960s: Data collection, database creation

ARTICLE


(hierarchical and network models) 1970s: Relational data model 1980s: Ubiquitous RDBMS, advanced

data models and application oriented DBMS

1990s: Data mining and data warehousing, massive media digitization, multimedia databases and web technology

3. Scope of Data miningGiven databases of suffi cient size

and quality, data mining technology can generate new business opportunities by providing these capabilities: Automated prediction of trends and

behaviors. Data mining automates the process of fi nding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly.

Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. Data mining techniques can yield

the benefi ts of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. i.e, the users can automatically experiment with more models to understand complex data and makes it practical for users to analyze huge quantities of data for improved predictions. 4. Limitations of Data Mining

While data mining products can be very powerful tools, they are not self-suffi cient applications. To be successful, data mining requires skilled technical and analytical specialists who can structure the analysis and interpret the output that is created. Consequently, the limitations of data mining are primarily data or personnel related, rather than technology-related.

Although data mining can help reveal patterns and relationships, it does not tell the user the value or signifi cance of these patterns. These types of determinations must be made by the user. Similarly, the validity of the patterns discovered is dependent on how they compare to “real world” circumstances. For example, to assess the validity of a data mining application designed to identify potential variables from a large pool of identifi ed variables, the user may test the model using data that includes information about known

information/data. However, while possibly re-affi rming a particular profi le, it does not necessarily mean that the application will identify a suspected variable whose behavior signifi cantly deviates from the original model.

Another limitation of data mining is that while it can identify connections between behaviors and/or variables, it does not necessarily identify a causal relationship.

5. Data mining and data warehousingData warehousing is a process of

organizing the storage of large, multivariate data sets in a way that facilitates the retrieval of information for analytic purposes. Frequently, the data to be mined is fi rst extracted from an enterprise data warehouse into a data mining database or data mart (Figure 2). There is some real benefi t if the data is already part of a data warehouse. The problems of cleansing data for a data warehouse and for data mining are very similar. If the data has already been cleansed for a data warehouse, then it most likely will not need further cleaning in order to be mined. Furthermore, many of the problems of data consolidation must have addressed and put in place maintenance procedures. The data mining database may be a logical rather than a physical subset of data warehouse, provided that the data warehouse DBMS can support the additional resource demands of data mining.

Data Source Data Warehouse

Geographic Data Mart

Analysis Data Mart

Data Mining Data Mart

Fig 2: Data mining data mart extracted from a data warehouse [3].

A data warehouse is not a requirement for data mining. Setting up a large data warehouse that consolidates data from multiple sources, resolves data integrity problems, and loads the data into a query database can be an enormous task, sometimes taking years and costing millions of dollars. However, one can mine data from one or more operational or transactional databases by simply extracting it into a read-only database (Figure 3). This new database functions as a type of data mart.

Data Source Data Mining Data Mart

Fig 3: Data mining data mart extracted from operational databases

Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and

analyzing the data [8]. Furthermore, when new insights require operational implementation, integration with the warehouse simplifi es the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization.

6. Data mining and OLAPData mining and OLAP (On-Line

Analytical Processing) are very different tools that can complement each other. OLAP software allows for the real-time analysis of data stored in a database [1]. The OLAP server is normally a separate component that contains specialized algorithms and indexing tools to effi ciently process data mining tasks with minimal impact on database performance.

OLAP is part of the spectrum of decision support tools [10]. Traditional query and report tools describe what is in a database. OLAP goes further; it’s used to answer why certain things are true. The user forms a hypothesis about a relationship and verifi es it with a series of queries against the data. In other words, the OLAP analyst generates a series of hypothetical patterns and relationships and uses queries against the database to verify them or disprove them. OLAP analysis is essentially a deductive process. But it becomes much more diffi cult and time-consuming to fi nd a good hypothesis and analyze the database with OLAP to verify or disprove it when the number of variables being analyzed is very large.

Data mining is different from OLAP because rather than verifying hypothetical patterns, it uses the data itself to uncover such patterns. It is essentially an inductive process. For example, suppose the analyst who wanted to identify the failure factors for a student in exam plan to use a data mining tool. The data mining tool might discover those students with less scores were identifi ed as candidates for failure but it might go further and also discover a pattern the analyst did not think to try, such as that irregularity is also a determinant factor of failure. Here is where data mining and OLAP can complement each other. Before acting on the pattern, the analyst needs to know the reasons behind the patterns analysis. The OLAP tool can allow the analyst to answer those kinds of questions. Furthermore, OLAP is also complementary in the early stages of the knowledge discovery process because it can help to explore the data, for instance by focusing attention on important variables identifying exceptions, or fi nding interactions. This is important because the


better one understands the data, the more effective the knowledge discovery process will be.

7. Data mining applicationsData mining is becoming an

increasingly important tool to transform this data into information. It is commonly used in a wide range of applications such as:1. Telecommunication: To handle

transactional data such as phone call, data on mobile phones, house based phones, etc., other customer data such as billing, personal information, etc.and additional data such as network load, faults, etc.

2. Health: To handle different aspects of the health system such as Personal health records, Hospital data and Billing information

3. Astronomy: To process terabytes of image and other data receives from telescopes and satellites

4. Economics and commerce: Analysis and prediction of stock market

5. Bioinformatics: To predict diseases based on genome sequences

6. Governments: For statistics, census and taxation and also to prevent fraud

7. Credit card and insurance companies:(for example segment customers for targeted marketing)

8. Terror, crime and fraud detection: To fi nd and predict unusual events

8. How data mining worksModeling is the basic technique that is

used in data mining to perform its intended task. Modeling is simply the act of building a model in one situation where one knows the answer and then applying it to another situation that one doesn’t. For example, to predict the marketing possibilities of a particular product, one can use the business experience stored in the database to build a model and predict the results much better than random.

9. Models in Data miningIn the data mining literature, various

models are used to serve as blueprints for how to organize the process of gathering data, analyzing data, disseminating results, implementing results, and monitoring improvements.

Data Mining is an analytic process designed to explore data (usually in large amounts) in search of consistent patterns and/or systematic relationships between variables, and then to validate the fi ndings by applying the detected patterns to new subsets of data. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identifi cation with validation/verifi cation, and (3) deployment.

Stage 1 : Exploration. This stage involves cleaning of data, data transformations, selecting subsets of records based on some preliminary feature selection operations to bring the number of variables to a manageable range.

Stage 2: Model building and validation.This stage involves considering various models and choosing the best one based on their predictive performance.

Stage 3: Deployment. This is the fi nal stage which involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

Data mining models can be generally classifi ed as follows:1. Descriptive Models: Models which

describe all the data or a process for generat ing the data. Data randomly generated from a “good” descriptive model will have the same characteristics as the real data.

2. Predictive Models: A model is created or chosen to try to best predict the probability of an outcome. Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. A predictive model is made up of a

number of predictors, which are variable factors that are likely to infl uence future behavior or results. In marketing, for example, a customer’s gender, age, and purchase history might predict the likelihood of a future sale.

I. Data Description for Data Mining

a. Clustering Clustering is a data mining technique

used to place data elements into related groups without advance knowledge of the group defi nitions. Popular clustering techniques include k-means clustering and expectation maximization (EM) clustering [13].

Clustering divides a database into different groups. The goal of clustering is to fi nd groups that are very different from each other, and whose members are very similar to each other. Unlike classifi cation one doesn’t know what the clusters will be when the procedure starts or by which attributes the data will be clustered. Consequently, someone who is knowledgeable in the business must interpret the clusters. Often it is necessary to modify the clustering by excluding variables that have been employed to group instances, because upon examination the user identifi es them as irrelevant or not meaningful. After you have found clusters that reasonably segment your database, these clusters may then be

used to classify new data. Clustering is different from

segmentation. Segmentation refers to the general problem of identifying groups that have common characteristics. Clustering is a way to segment data into groups that are not previously defi ned.

Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientifi c data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and

many others.

b. Link analysis Link analysis is a descriptive approach

to exploring data that can help identify relationships among values in a database. The two most common approaches to link analysis are association discovery and sequence discovery [1]. Association discovery fi nds rules about items that appear together in an event such as a purchase transaction. Sequence discovery is very similar, in that a sequence is an association related over time.

Associations are written as A B, where A is called the antecedent or left-hand side (LHS), and B is called the consequent or right-hand side (RHS). For example, in the association rule “If people buy a hammer then they buy nails,” the antecedent is “buy a hammer” and the consequent is “buy nails.”

Graphical methods may also be very useful in seeing the structure of links. In Figure 4, each of the circles represents a value or an event. The lines connecting them show a link. The thicker lines represent stronger or more frequent linkages, thus emphasizing potentially more important relationships such as associations.

Fig 4: Linkage diagram

II. Predictive Data MiningThe ultimate goal of data mining is

prediction and predictive data mining is


the most common type of data mining and one that has the most direct business applications. The term Predictive Data Mining is usually applied to identify data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest.a. Neural Networks. Neural Networks are

analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specifi c variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data[1].

Fig 5: Neural network representation

The fi rst step is to design a specifi c network architecture that includes a specifi c number of “layers” each consisting of a certain number of “neurons”. The new network is then subjected to the process of “training.” In that phase, neurons apply an iterative process to the number of inputs to adjust the weights of the network in order to optimally predict the sample data on which the “training” is performed. After the phase of learning from an existing data set, the new network is ready and it can then be used to generate predictions.

One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter. An important disadvantage, however, is that the fi nal solution depends on the initial conditions of the network.

b. Classifi cation Classifi cation is a data mining technique

used to predict group membership for data instances [12]. Popular classifi cation techniques include decision trees and neural networks.

Classifi cation problems aim to identify the characteristics that indicate the group to which each case belongs. This pattern can be used both to understand the existing data

and to predict how new instances will behave. Data mining creates classifi cation

models by examining already classifi ed data (cases) and inductively fi nding a predictive pattern. These existing cases may come from an historical database. They may come from an experiment in which a sample of the entire database is tested in the real world and the results used to create a classifi er. Sometimes an expert classifi es a sample of the database, and this classifi cation is then used to create the model which will be applied to the entire database.

c. Regression Regression is a data mining technique

used to fi t an equation to a dataset. The simplest form of regression, linear regression, uses the formula of a straight line (y = mx + b) and determines the appropriate values for m and b to predict the value of y based upon a given value of x. Advanced techniques, such as multiple regression, allow the use of more than one input variable and allow for the fi tting of more complex models, such as a quadratic equation [1].Regression uses existing values to forecast what other values will be [12]. Unfortunately, many real-world problems are not simply linear projections of previous values. In such cases, more complex techniques such as logistic regression, decision trees, or neural nets etc is necessary to forecast future values.

d. Time series Time series forecasting predicts

unknown future values based on a time-varying series of predictors. Like regression, it uses known results to guide its predictions [15]. Models must take into account the distinctive properties of time, especially the hierarchy of periods (including such varied defi nitions as the fi ve- or seven-day work week, the twelveth-“month” year, etc.), seasonality, calendar effects such as holidays, date arithmetic, and special considerations such as how much of the past is relevant.Conclusion

Wide-ranging data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. In this context, a new technological leap is needed to structure and prioritize information for specifi c end-user problems. The data mining tools can make this leap. Quantifi able business benefi ts have been proven through the integration of data mining with current information systems, and new products are on the horizon that will bring this integration to an even wider audience of users[19][20].

References1] Jiawei Han and Micheline Kamber, Data Mining:

Concepts and Techniques (New York: Morgan Kaufmann Publishers, 2001.

2] Pieter Adriaans and Dolf Zantinge, Data Mining (New York: Addison Wesley, 1996), pp. 5-6.

3] Dr. Osmar R. Zaïane, Principles Knowledge Discovery in Databases, University of Alberta, 1999.

4] Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons,2003

5] Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena. PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreateSpace, 2010

6] Alex Guazzelli, Michael Zeller, Wen-Ching Lin, Graham Williams. PMML: An Open Standard for Sharing Models. The R Journal, vol 1/1, May 2009.

7] Y. Peng, G. Kou, Y. Shi, Z. Chen (2008). “A Descriptive Framework for the Field of Data Mining and Knowledge Discovery”. International Journal of Information Technology and Decision Making, Volume 7, Issue 47: 639 – 682.

8] Fayyad, Usama; Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). “From Data Mining to Knowledge Discovery in Databases”. http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf. Retrieved 2008-12-17.

9] Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. OCLC 224465825.

10] Tony Fountain, Thomas Dietterich & Bill Sudyka (2000) Mining IC Test Data to Optimize VLSI Testing, Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. (pp. 18-25). ACM Press.

11] Xingquan Zhu, Ian Davidson, Knowledge Discovery and Data Mining: Challenges and Realities. Hershey, New Your. pp. 18.,2007.

12] B r i e m a n , F r e i d m a n , O l s h e n , a n d Stone(1984), Classification and Regression Trees,Wadsworth.

13] Dorian Pyle(1999), Data Preparation for Data Mining, Morgan Kaufmann.

14] Norén GN, Bate A, Hopstadius J, Star K, Edwards IR. Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records. Proceedings of the Fourteenth International Conference on Knowledge Discovery and Data Mining SIGKDD 2008, pages 963-971. Las Vegas NV, 2008.

15] James Kobielus (1 July 2008) The Forrester Wave™: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research.

16] Dominique Haughton, Joel Deichmann, Abdolreza Eshghi, Selin Sayek, Nicholas Teebagy, & Heikki Topi (2003) A Review of Software Packages for Data Mining, The American Statistician, Vol. 57, No. 4, pp. 290-309.

17] U. Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy(1996), Advances in Knowledge Discovery and Data Mining, MIT Press.

18] http://dataminingwarehousing.blogspot.com19] Gartner Group Advanced Technologies and

Applications Research Note, 2/1/95.20] META Group Application Development

Strategies: “Data Mining for Data Warehouses: Uncovering Hidden Patterns.”, 7/13/95 .


IntroductionThe whole process of extraction of information

from deep web can be broadly categorized into four steps i.e. query interface analysis, values allotment, response analysis & navigation and relevance ranking. Query interface analysis is the fi rst and most important step for deep web information retrieval. In query interface analysis, a request of fetching a web page from a web server is made by a crawler. After completion of the fetching process, an internal representation of the web page is produced after parsing and processing of html forms based on the developed model. Further the query interface analysis can be broken into the some modules that are detection of hidden web search interface, search form schema matching and domain ontology identifi cation. In these module the detection of hidden web search interface is the fi rst and foremost step towards deep web information retrieval. As expected, a human user can easily identify a deep web search interface but to understand a deep web search interface through a automatic technique without human intervention is a challenging task [1][2][3][4][5]. Figures 1 depict the different types of search interfaces.

Fig. 1 : Different types of search interface

STUDENTS KORNER

Analysis of Techniques for Detection of Deep Web Search InterfaceDilip Kumar Sharma1, A. K. Sharma2

1GLA University, Mathura, UP, India. Email: [email protected] University of Science and Technology, Faridabad, Haryana, India

The volume of information on the web is increasing day by day. The information in the web can be broadly categorized into two types i.e. surface web and deep web. The surface web pages can be easily indexed through conventional techniques but the deep web, whose size assumed to be thousand times larger than surface web, cannot be indexed through conventional search technique. The first stage of the extraction of the deep web information is the detection of deep web search interface. A search interface is generally consisting of html forms. The conventional techniques of searching the deep web information is done by filling the html forms on the search interface manually but recently the research is going on automatic accessing and understanding of html forms. Being the first stage of deep web extraction process, the detection of deep web search interface becomes one of the important module of deep web information retrieval. In this paper a technical analysis of some of the important deep web search interface detection techniques is done to find out their relative strengths and limitations with reference to current development in the field of deep web information retrieval technology.

Keywords Deep web, hidden web, search interface detection, crawler, random forest.


Related WorkOne of the prominent works for

detection of deep web search interface is done by Leo Breiman (2001)[6] in form of random forest algorithm. A random forest algorithm detects the deep web search interface by using a model, based on decision trees classifi cation. A random forest model can be defi ned as a collection of decision trees. A decision tree can be generated by bootstrapping processing of the training data. Various classifi cation trees can be generated through random forest algorithm. To classify a new object from its input vector, the sample vector is passed to every tree defi ned in algorithm. A decision for classifi cation is given by every tree. A decision about most voted classifi cation is done by using all of the classifi cation results of the individual trees. The advantages of random forest algorithm are that it exhibits a substantial performance improvement over single tree classifi ers and injecting of the right kind of randomness makes accurate classifi ers and regulators. The disadvantage of this algorithm is that it may select unimportant and noisy features in the training data, as a result a bad classifi cation results because of its random selection feature.

One of the deep web crawler architecture is proposed by Sriram Raghavan and Hector Garcia-Molina (2001) [7]. In this paper, a task-specifi c, human-assisted approach is used for crawl the hidden web. There are two basic problems related to deep web search, fi rstly the volume of the hidden web is very large and secondly there is a need of such type of crawlers which can handle search interfaces effi ciently, which are designed mainly for humans. In this paper a model of task specifi c human assisted web crawler is designed and relized in HiWE (hidden web exposure). The HiWE prototype built at Stanford which crawl the dynamic pages. HiWE is designed to automatically process, analyze, and submit forms, using an internal model of forms and form submissions. HiWE uses a layout-based information extraction (LITE) technique to process and extract useful information. The advantages of HiWE architecture is that its application/task specifi c approach allows the crawler to concentrate on relevant pages only and with the human assisted approach automatic form fi lling can be done. Limitations of this architecture are that it is not precise with response to partially fi lled forms and it is not able to identify and respond to simple dependency between form elements.

A technique for collecting hidden web pages for data extraction is proposed by Juliano Palmieri Lage et al. (2002) [8] . In

this technique the authors have proposed the concept of web wrappers. A web wrapper is programs which extract the unstructured data from web pages. It takes a set of target pages from the web source as an input. These set of target pages are automatically generated by an approach called “Spiders”. Spiders automatically traverse the web for web pages. Hidden web agents assist the wrappers to deal with the data available on the hidden web. The advantage of this technique is that it can access a large number of web sites from diverse domains and limitation of this technique is that it can access only that web site that follow common navigation patterns. Further, modifi cation can be done in this technique to cover navigation patterns based on these mechanisms.

A technique for automated discovery of search interface from a set of html forms is proposed by Jared Cope, Nick Craswell and David Hawking (2003) [9]. This paper defi ned a novel technique to automatically detect search interface from a group of html forms. A decision tree was developed with the C4.5 learning algorithm using automatically generated features from html markup that can give a classifi cation accuracy of about 85% for general web interfaces. Advantage of this technique is that it can automatically discover the search interface. Limitation of this technique is that it is based on single tree classifi cation method and num ber of feature generation is limited due to use of limited data set. As a future work, modifi cation is suggested that a search engine can be develop using existing methods for other stages along with the proposed one with a technique to eliminate false positives.

A technique for understanding web query interfaces through best effort parsing with hidden syntax is proposed by Zhen Zhang et al. (2004)[10]. This paper addresses the problem of understanding web search interfaces by presenting a best-effort parsing framework. The paper presented a form extractor framework based on 2P grammar and the best effort parses in a language parsing framework. It identifi es the search interface by continuously producing fresh instances by applying productions until attaining a fi x-point, when no fresh instance can be produced. Best effort parser technique minimizes wrong interpretation as much as possible in a very fast manner. It also understands the interface to a large extent. Advantage of this technique is that it is a very simple and consistent technique with no priority among preferences and it can handle missing elements in form and limitation of this technique is that

establishment of single global grammar that can be interacted to the machine globally is a critical issue.

A technique named as “siphoning hidden web data through key word based interface” for retrieval of information from hidden web databases through generation of a small set of representative keywords and build queries is proposed by Luciano Barbosa and Juliana Freire (2004) [11]. This technique is designed to enhance coverage of deep web. Advantage of this technique is that it is a simple and completely automated strategy that can be quite effective in practice, leading to very high coverage of deep web. Limitation of this technique is that it is not able to achieve the coverage for collection whose search interface fi xes a number of results. Further the authors have advised that modifi cation can be done in this algorithm to characterize search interfaces techniques in a better way so that different notions and levels of security can be achieved.

An improved version of random forest algorithm is proposed by Deng et al. (2008) [12]. In this improved technique a weighted feature selection algorithm is proposed to generate the decision trees. The advantage of this improved algorithm is that it minimizes the problem of classifi cation of high dimension and sparse search interface using the ensemble of decision trees. Disadvantage of this improved algorithm is that it is highly sensitive towards the changes in training data set.

Further improvement in random forest algorithm is done by Yunming Ye et al. (2009) [13] by using feature weighting random forest algorithm for detection of hidden web search interface. This paper had presented a feature weighting selection process rather than random selection process. Advantage of this technique is that it makes a weighted feature selection process instead of random selection hence reduces the chances of noisy feature selection and limitation of this techniques is that features available only in the search forms were used. Future modifi cation suggested in random forest algorithm to investigate more feature weighting methods for construction of random forests.

An algorithm named as “The naive bayesian web text classifi cation algorithm” is proposed by Ping Bai and Junqing Li (2009) [14] for automatic and effective classifi cation of web pages with reference to given model for machine learning. In the conventional techniques, category abstracts are produced using the inspection by domain experts either through semiautomatic method or artifi cial method. All the items are provided equal important according to


conventional common bayesian classifi er whereas according to improved naive bayesian web text classifi cation algorithm, whole of the items in every title are provided higher importance to others. The strength of this technique is that text classifi cation results are very accurate and further scope in this algorithm is suggested to make the classifi cation process automatic in an effi cient way.

An approach for automatic detection and unifi cation of web search query

interfaces using domain ontology is proposed by Anuradha and A.K.Sharma (2010) [15]. The technique proposed in this paper works by concentrating the crawler on the given topic considering the domain ontology. This technique results in the pages which contains the domain specifi c search form. The strengths of this technique are that results are produced from multiple sources, human effort is reduced and results are very accurate in less execution time. Limitation of this technique is that it is domain specifi c.

Summary of various techniques for detection of deep web search interface

By going through the literature survey of some deep web search interface detection techniques, it is concluded that eachtechniques for detection of deep web search interface have some relative strengths and limitations. A tabular summary is given below in table 1, which summarizes the techniques, strengths and limitations of some of important detection techniques for deep web search interface.

Table 1 : Summary of various techniques for detection of deep web search interface

Authors Technique Strengths Limitations

Leo Breiman(2001)

Forest of regression trees as classifi ers.

A substantial improvement in performance over single tree classifi ers.

May include un-important or noisy features.

Sri Ram Raghavan et al. (2001)

Hidden Web Exposer. An application specifi c approach to hidden web crawling.

Imprecise in fi lling the forms.

Palmieri Lage et al. (2002)

Hidden Web Agents. Wide coverage of distinct domains. Restricted to web sites that follow common navigation patterns.

Jared Cope et al.(2003)

Single tree classifi ers. Automatically discovery of search interface, performed well when rules are generated on the same domain.

Long rules, large size of feature space in training samples, Over fi tting, Classifi cation precision is not very satisfying.

Zhen Zhang et al. (2004)

2P Grammar and Best effort Parser.

Very simple and consistent, No priority among preferences,Handling of missing elements in form.

Critical to establish single global grammar that can be interacted to the machine globally.

Luciano Barbosa et al. (2004)

Automatic query generation based on small set of keywords.

A simple and completely automated strategy that can be quite effective in practice

A large domain of Keywords has to be generated.

Deng, X. B. et al. (2008) weighted feature selection algorithm

Minimizes the problem of classifi cation of high dimension and sparse search interface using the ensemble of decision trees

Highly sensitive towards the changes in training data set.

Ye, Li, Deng et al.(2009)

Feature weighted selection process

Minimizes the chances of selection of noisy features.

No use of contextual information associated with forms.

Ping Bai et al.(2009)

Naïve Bayesian Algorithm

Text classifi cation results are very accurate. Classifi cation algorithm is not automatic.

Anuradha et al. (2010) Based on domain ontology.

Results are produced from multiple sources, reduces the human effort, less execution time, accuracy is high.

It is domain specifi c.

Conclusion Deep web search interface are the

entry point for the searching of the deep web information. A deep web crawler should understand and detect the deep web search interface effi ciently to facilitate the further process of deep web information retrieval. An effi cient detection of deep web search interface may results towards a signifi cant retrieval of deep web information so the fi rst and foremost step of deep web information retrieval is the effi cient understanding and detection of deep web search interface. In this paper a technical analysis of some of the techniques for detection of deep web search interface is done and it is concluded that each of them have some relative strengths and limitations in detecting of deep web search interface. To explore the deep web information effi ciently, an

effi cient technique for detection of deep web search interface should be designed which should have strengths simultaneously and particularly in terms of wide coverage of different domains, automatic procedure, resistant to noisy and unwanted features, ability to consider the features as per their importance, application specifi c approach as per requirement and user friendly approach. Finally the technique for detection of deep web search interface should be compatible with current web technology.

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14. Ye, Y., et al. (2009). Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces. In Computational Linguistics and Chinese Language Processing , Vol. 13, No. 4, PP: 387-404.

15. Bai, P., and Li, J.(2009). The Improved Naive Bayesian WEB Text Classifi cation Algorithm, In International Symposium on Computer Network and Multimedia Technology, IEEE Explorer.

16. Anuradha, and Sharma, A.K. (2010). A Novel Approach For Automatic Detection and Unifi cation of Web Search Query Interfaces Using Domain Ontology. In International Journal of Information Technology and Knowledge Management, July-December, Vol. 2, No. 2,PP: 196-199.

17. Dilip Kumar Sharma is B.Sc, B.E.(CSE), M.Tech.(IT), M.Tech. (CSE) and pursuing Ph.D in Computer Engineering. He is life member of CSI, IETE, ISTE,, ISCA, SSI and member of CSTA, USA. He has attended 21 short term courses/workshops/seminars organized by various esteemed originations. He has published 21 research papers in International Journals /Conferences of repute and participated in 18 International/

National conferences. Presently he is working as Reader in Department of Computer Science, IET at GLA University, Mathura, U.P. since March 2003 and he is also CSI Student branch Coordinator. His research interests are deep web information retrieval, Digital Watermarking and Software Engineering. He has guided various projects and seminars undertaken by the students of undergraduate/postgraduate.

18. Prof. A. K. Sharma received his M.Tech. (CST) with Hons. from University of Roorkee (Presently I.I.T. Roorkee) and Ph.D (Fuzzy Expert Systems) from JMI, New Delhi and he obtained his second Ph.D. in Information Technology form IIITM, Gwalior in 2004. Presently he is working as Dean, Faculty of Engineering and Technology & Chairman, Dept of Computer Engineering at YMCA University of Science and Technology, Faridabad. His research interest includes Fuzzy Systems, OOPS, Knowledge Representation and Internet Technologies. He has guided 9 Ph.D thesis and 8 more are in progress with about 175 research publications in International and National journals and conferences. The author of 7 books, is actively engaged in research related to Fuzzy logic, Knowledge based systems, MANETS, Design of crawlers. Besides being member of many BOS and Academic councils, he has been Visiting Professor at JMI, IIIT&M, and I.I.T. Roorkee.

ANNOUNCEMENT Computer Society of IndiaNational Headquarters, Education Directorate

CIT Campus, 4th cross Road, Taramani, Chennai 600 113

CSI Education Directorate requests Chennai based MBA fresh graduates to apply for the post of Marketing Executive. The incumbent will work out of Chennai. He / She should have very good communication skills and a fl air for marketing.

Responsibilities include:

• Enrolment of faculty / students for CSI Training programs and certifi cations

• Liaise and co-ordinate with resource persons

• Explore and get sponsorship for CSI programs

• Promote CSI Membership and student branches

Prospective candidates may apply along with a Resume and expected remuneration to above address or by e-mail to [email protected] on or before December 30, 2010. For further details, please contact Mr. S Natarjan at Ph: 044-2254 1102

Wg. Cdr. M MurugesanDirector, Education


ExecCom Transacts1. CSI-2010 Convention: The CSI-2010 was successfully hosted

setting a new record of quality programmes and an unprecedented participation by the CSI Chapters, Student Branches and the members at large. The convention witnessed an overwhelming support from the industry in terms of workshop/tutorials and an exceptional fi nancial sponsorship. The meetings of the CSI Presidents’ Council, Think Tank, the National Council and the Annual General body were well attended and highly stimulating.

2. Membership Development: The membership committee deliberated at length on the strategies and mechanisms with regard to the membership services and new membership development. The CSI HQ and Chapters will have to collaborate and cooperate given the vast geographical areas and potential for the membership. While the HQ will primarily be responsible for membership services with the chapters and student branches across India will drive the new membership development programmes. It is very heartening to note about the voluntary services of several our senior and life members supporting the above collaborative and cooperative model. With the proposed substantial increase in the membership fee for all categories of the membership (wef 1st April 2011), the members are requested to renew their membership and enroll new members on a large scale during the grace period till 31st March 2011.

3. Strengthening the SIGs: The SIG Coordination Committee along with special invitees brainstormed on “CSI’s Special Interest Groups (SIGs): Need for Re-alignment with the CSI Ecosystem”. It was decided to make the necessary changes in the CSI membership application forms – facilitating the members to join the CSI SIGs of their choice. The current members will be sent an offi cial communication from the CSI HQ inviting them to join the CSI SIGs. The CSI SIG– announcements and event reports will be published in the CSI Communications and on the CSI Website. The SIG Coordination Committee shall meet at least twice in a year. Further, it t was resolved to constitute a core committee (Chairman: Prof. D.B. Phatak, Convener: Mr. Satish Babu, Member: Dr. R.K. Bagga) to fi nalize a comprehensive proposal and the set of guidelines for the perusal/approval of the ExecCom. It was also resolved that Mr. Satish Babu would be a special invitee to the ExecCom meeting to represent the SIG Coordination Committee.

4. Research Guidance and Supervision: It is a matter of pride that a large section of our members possess Ph.D. or equivalent qualifi cations. On the other hand, we have several of our members aspiring to acquire Ph.D. qualifi cation. As such, there is a proposal to formalize the Research Mentors Network immediately. It is an earnest appeal to the members with the Ph.D. or equivalent qualifi cations to indicate their availability and willingness to join the Research Mentors Network.

5. Operational Guidelines and Standing Committees: There is a need to formulate operational guidelines and to constitute standing committees at the National, Regional and Chapter levels for Conferences, Membership Development, Publications, Business-Industry-Government Liaison, Collaborative Academic-Research-Consultancy, Student Branch Coordination, Entrepreneurship Development, Career Counselling, Inter-society coordination, Website /KM portal, Media & Public Relationship, Exhibitions, Finance & Sponsorships. The members are requested to send their suggestions for the above as well as their consent to volunteer their services.

6. Recognition/Appreciation for OC/PC Chairs: There are suggestions to recognize/ appreciate the efforts of members

across India who have contributed signifi cantly in organizing national/international events of CSI. There is a proposal to honour such members-contributors during the CSI Annual Conventions to begin with CSI-2011. The specifi c guidelines will be formulated through wider consultation.

7. Publication of CSI Research Digests: The proposals for publications of the CSI Research Digests incorporating highlights and important outcomes of the CSI conventions, workshops and SIG events have been received from a few leading publishers. The organizers of various CSI conventions and programmes are requested to send the content (on CDs) to the CSI-HQ for publication purposes. Subsequently, the CSI Website will have provision for uploading the content online. All such publications shall cite (prominently and visibly) “This research study and publication is supported by Computer Society of India”.

8. Recognition for Sponsors of CSI Events and Programmes: There are several suggestions to recognize the support/sponsorship from the organizations and individuals for the CSI events and programmes – such as conventions, workshops and publications. As it is suggested to the CSI Chapters, Student Branches and other host organizations to appropriately acknowledge such sponsorship and support services. This may include – honouring in valedictory session, issuing letters of appreciation/commendation, listing the regular sponsors on the Website, complimentary CSI membership up to one year, invitation to upcoming CSI events etc.

9. Strengthening CSI Education Directorate: There is a proposal to augment the human resources at the CSI Education Directorate to offer continuing education and professional development programmes. The senior members from the academia, R&D and industry are requested to indicate their availability and willingness to offer their voluntary expert services. The services of such volunteer will be suitably rewarded.

10. Call for Technology Appreciation Seminars: The mission of the CSI is to facilitate research, knowledge sharing, learning and career enhancement for its stakeholders, while simultaneously inspiring and nurturing new entrants into the industry and helping them to integrate into the IT community. The CSI is also working closely with other industry associations, government bodies and academia to ensure that the benefi ts of IT advancement ultimately percolate down to every single citizen of India. In pursuant to the above mission and objectives of the Computer Society of India, it is proposed to host a series of Technology Appreciation Seminars for building/strengthening Knowledge-communities (TASK) for the year 2011-2012. The proposals from the Chapters, Student Branches and Institutional Members may please be sent to [email protected] with cc to [email protected].

11. CSI Signs an MoU with JUET: CSI has executed an MoU with Jaypee University of Engineering and Technology, Raghogarh, Guna, MP (JUET) for the setting up of a Digital Forensics Research Centre (DFRC) at JUET. The center will undertake R&D activities in the areas of Cyber Security and Forensics, specially Image Forensics, Video Forensics, e-mail Forensics and Network Forensics. The deliverables would include awareness building programs, workshops, conferences and short term courses, in addition to execution of R&D projects. CSI will play the role of advisor and provide various types of support for the joint activities of the centre.

Prof. H.R. VishwakarmaHon. Secretary, Computer Society of India



CSI2010 - SPECIAL REPORT

Technology and Society: the human touchR Gopalakrishnan

Executive Director, Tata Sons, Bombay House, 24, Homi Mody Street, Mumbai 400 001. IndiaE-mail: [email protected]

It is a great pleasure to address such distinguished members of a young profession, which has played a key role in repositioning India into global prominence. Your profession will continue to play an even more important role going forward. It is my privilege to speak at this inaugural session of the 45th Annual Convention of Computer Society of India (CSI) with the very relevant theme of “Technologies for the Next Decade”.

Over forty years ago, I began my career at Hindustan lever as a computer trainee. Over the next fi ve years after joining, I resisted my seniors’ suggestion to move into the mainstream marketing of consumer goods. I was quite infatuated with the mysteries of mainframes, Cobol and Fortran languages, and the wonders that computers could accomplish for my company, mankind and society. But the slow acceptance of mainframe computers, the national shortage of foreign exchange to import machines and the stiff resistance from labour unions took their toll. I took leave of computers as a profession in 1972 and succumbed to the seduction of FMCG marketing.

Thirty years later, I addressed students at IIT Bombay on the subject of “Leadership and Foresight.” I said all the predictable things about foresight, hopefully in an inspiring way, until one curious student asked, “What you say about foresight is so true, but I have a question. Since you must have been endowed with foresight, did you not see that to leave the computer fi eld would be a mistake?” I had great diffi culty in explaining the difference between talking about foresight and exercising foresight!

Technology has played a central role in human history for centuries: fi rst, by inventing useful things, and later, by diffusing the benefi ts of technology to transform society. Technology will play an even more signifi cant role in the years to come. As a former ‘techie’ myself, I found that when one is close to a subject, one just might miss the larger social consequences of the technology one practices. I wish to quote two examples of social transformation from history as that will enable us to think about the transformational effects of ICT.

First, before Gutenberg invented the printing press, the church was the only interpreter of the Bible.

People did not even possess a copy of the Bible to read it for themselves. As Gutenberg unknowingly gave the world the gift of being able to possess a personal copy of the Bible, people felt empowered and curious. This impacted society intimately over a few hundred years and led to the European Age of Enlightenment and Reason.

Second, our ancestors were all farmers. That is why the GDP of nations was approximately in the proportion of their population. Until 1800, as Angus Madison has demonstrated, China was about 30% of world GDP and India was roughly 20%. China’s economy was always bigger than that of India’s. I am puzzled to note the fl ood of books and papers on whether or not India will catch up with China. How does it matter when it has not been bigger for 5000 years?

The industrial revolution changed the relationship between farming and wealth. I am sure that I need not dwell on explaining it. But a less recognized effect of the industrial revolution was the way it changed a very hierarchical European society: no more did common people have to be beholden to the Lords, Dukes and Barons. Money earned through industrial methods could be earned by anybody, and that ‘anybody’ could sit at the same table with a ‘somebody’.

Globalization, socialization and adaptation are three circles and technology sits in the space that is common among these. What do these words mean in simple terms? Think of the idea of foreigner or pardesi, for example.

Phagun: 1958: ‘Phagun’ is the tale of a forbidden love between a Zamindar’s son Bijan (Bharat Bhushan) and gypsy chieftain’s daughter Banani (Madhubala), who defy all social conventions. Pardesi = Someone from another village

Raja Hindustani: 1996: Aarti (Karishma Kapoor) goes for a vacation to a small hill station named Palankhet. Once there, she meets Raja Hindustani (Aamir Khan), and after a short period of time, both fall in love with each other. Pardesi = Someone from another city

Dev D: 2009 London educated Dev (Abhay Deol) is in love with Chanda (Kalki Koechlin) who is also a part foreigner. Also seen is in the clip is Dev’s


fi rst girlfriend from Punjab. Pardesi = someone from another country.Through the depiction of Pardesi, the

changing perception of globalization can be sensed.

Socialization too has evolved: a new urban Indian has emerged and so have attitudes to native place, marriage, language and food! It is diffi cult to fi nd youngsters today

with clear answer to ‘where are you from?’ They may be native to one place, may move around when they are growing up, may take a job somewhere else and decide to settle down at yet another place. A new Indian has emerged who perhaps denotes a little bit of all the places and people that he has touched along the way.

During my growing years, it was thought to be inappropriate for a middle class person to get married outside his or her caste. Today it has almost become routine to have international son-in-laws and daughter-in-laws!

Children from parents from different linguistic backgrounds speak in a common language which is often, English! So we have a South Indian father and a Punjabi mother communicating with each other and their kids in English! View this against the curious fact that Punjabi is set to become the fourth most widely-spoken language in Canada by 2011, after English, French and Chinese! Strange are the ways of globalization and socialization!

The same is true of food. Tandoori Chicken and Mutter Paneer came to defi ne the Indian cuisine in overseas countries. Nowadays, Indian food in India is infl uenced by overseas cuisines. This slide exemplifi es my point of how globalization has truly arrived if we look at internationalization of various cuisines. People and technology adapt to each

other with consummate ease. Think of the numerous ways in which this fact is visible from the black phone to sleek cell phones, from box cameras to digital cameras and pictures, from face to face meetings to virtual meetings.

Technology Diffusion in the FutureIn general technology has been an

urban and transformational tool to enhance productivity, convenience and performance. Products are now so ingrained that advent of videogames, playlists and movies are seen as threat to declining attention span and the death of reading habit. Last week’s examples:

Madhuri Sule was an IIT Kanpur student, who was found hanging in her room on 17th November this year, because her academic performance had declined. Authorities traced the reason to excessive use of internet and less sleep. The attempt to restrict internet access to students has caused unrest on the Kanpur campus. (Indian Express, 22nd Nov, 2010)

In the USA, researchers state that computers, cell phones and the constant stream of stimuli that they provide pose a profound challenge to focus and learning. Michael Rich, a professor at Harvard Medical School, says, “Their brains are rewarded not for staying on the task but for jumping to the next thing…the worry is that we are raising a generation of kids whose brains are wired differently….downtime is to the brain what sleep is to the body….but the new generation kids are in a constant state of stimulation.” (International Herald Tribune, 22nd Nov, 2010)

There is a raging debate between techno-holics and techno-critics on whether or not attention spans are being shrunk because of MTV, the Internet and the Web.Of the 6.5 billion on this planet, over

4 billion are somewhat less infl uenced by technology developments; they seem to be concerned with three endemic diffi culties:1. Poverty Alleviation (through education,

health and employment) 2. Information Asymmetries (through

inadequate data access) 3. Bad Governance (through corruption

and leakages)For long, Bharat and India have been

divided by education and prosperity. This divide can be, and is about to be, bridged in the next 15 years through technology, much as the Industrial Revolution bridged the social divides in Europe. What cell phones did through mobile communication in the last fi fteen years between 1995 to 2010, wireless broadband is about to do in India in an even more dramatic and inclusive way.

To this audience, I need hardly offer reasons for this assertion. A report by global advisory and consulting fi rm, Ovum, says countries like India and China with a huge population of mobile users will play the most aggressive role in growth of mobile broadband in the world. The fi rm notes that the advent of 3G in markets such as China and India, the sheer number of mobile users and poor fi xed line penetration in these markets means broadband access to a very large number of people will be based on

mobile access, including handsets. However, I enumerate fi ve reasons for the imminent and dramatic impact of broadband:I. For sure, there is a correlation between

adoption rate of a technology and the Gross Domestic Product (GDP). As per this report, a 10 percentage increase in broadband penetration accounted for highest percentage increase in per capita GDP growth in developing economies as you can see from the right-most bar.

II. Low broadband penetration in India as per the red line (under 1% compared with tele-density of 52%) represents a huge scope and opportunity both for the Government and private players to serve people who would be ready to pay for the immense value that broadband can create in their lives.

III. Technologies like 3G/BWA become more accessible because they do not depend on laborious physical infrastructure.

IV. Success of broadband depends on three critical factors: connectivity, content and customer premises equipment (CPE). The cell phone is the most economical CPE and would ride on the wave of 3G to provide value added information and services.

V. Broadband now fi nds itself in the similar sweet spot as telecom. Similar focus both by the Government and private players will ensure that wireless broadband undergoes an explosive growth:Two questions arise with respect to the

oncoming broadband revolution:1. How will broadband manifest itself?2. What does it take to happen?

How will broadband manifest itself?

Modern Agriculture:An energizing future can only

be created if we empower the most important stakeholder in the entire agri-chain, the ‘Indian Farmer’. There is a need to help farmers to reach out; they must feel empowered through a broader understanding of their attitude, mindset, requirements and needs. Famers must earn more and become self-reliant by adopting the latest agronomic practices and technologies.

Various models and initiatives have been rolled out by Government to reach out to farmers, but they have met limited success because of the certain ineffi ciencies: Need to use local language for delivery

of personalized services. These models have not been holistic

and have instead focused on specifi c parts in the value chain.


Most of these initiatives could not scale up and impact millions of other farmers.Broadband technologies can deliver

personalized and integrated services to millions of farmers.

Companies like TCS, have come up with proprietary models of using mobile as a tool to provide critical information to famers. Such tools integrate technologies such as Wireless Sensors, Camera phone and script technology; to deliver advisory services through a mobile phone. It ensures business benefi ts to the stakeholders by enabling to connect them to farmers directly. Recent developments would suggest that connecting millions of farmers to give them personalized and integrated services, is no pipe dream. India has emerged as the outsourcing and off-shoring destination of choice for western companies. Our call centers and software companies have been giving outstanding services to customers outside India. Surely we have the capability and now the technology (through wireless broadband) to repeat the same feat, for our own farmers this time!

Better Governance:Broadband will play an important

role in automating governance processes and reduce any administrative delays. E-Governance is increasingly being viewed as the route for governments to strengthen good governance, for it not only improves effi ciency, accountability and transparency of government processes, but it can also be a tool to empower citizens by enabling them to participate in the decision-making processes of governments

Broadband will provide last mile connectivity for Services provided through the various e- Government initiatives assist governments in reaching the yet ‘unreached’ and thereby contribute to poverty reduction in rural and remote areas by increasing access to critical information and opportunities. At the same time, this process also enables involvement and empowerment of marginalized groups through their participation in the government process.

The government also intends to further strengthen e-governance initiatives through broadband technology. For instance, Government of India has approved the scheme of establishing Common Service Centres (CSCs) across the country. The CSC scheme envisages the establishment of 100,000 broadband Internet - enabled kiosks in rural areas, which would deliver government and private services at the doorstep of the citizens.

We have enough instances of how technology has empowered people and given them greater access to information: Availability of land records online, applying and tracking passport online, getting railway tickets booked from the comfort of your house, fi ling IT returns online without the need to go through an agent, the list is endless. Greater automation of processes and access to broadband will further spread the benefi ts and empower people.

Reduced CorruptionBroadband connectivity will ensure

that anybody sitting even in remote part of the country can raise a query to ensure that his works get done. It will ensure

that fundamental rights of citizens are not compromised and will give voice to all citizens that cannot be ignored. It will actually redistribute power and ensure that it goes to where it should ideally belong, to citizens of India. Let me share with you an instance in my personal knowledge.

What does it take to happen?Technologists can do a lot and so can

government policies help a lot. But we also need some barefoot professionals, who can connect socially with the target audience. This is where my initial techie insights merge with my subsequent marketing background. We need to enlist the help of anthropologists and story tellers, intuitive and experiential professionals, right brained people rather than only left brained people. I was interested to read about a 40 year old British-born, Shanghai based researcher called Jan Chipchase (FORTUNE Asia Pacifi c, 6th Dec 2010).

Chipchase works for Frog Design and travels the world, trying to understand why the planet’s poor people would want to use the technologist’s products and devices. He is a part anthropologist, part designer, part explorer and part entrepreneur, according to reports. Bill Maurer, Professor of anthropology at UC Irvine says that Jan Chipchase was the fi rst to write about the use of airtime as a form of currency. His employers and clients pay to get his insights into how to reach those billions of diffi cult-to-reach customers for technology.

Can CSI and NASSCOM conspire to strengthen this capability? It is worth the effort.

About the AuthorR GOPALAKRISHNAN (called Gopal) worked for his first 31 years in India’s most Indian multinational, Hindustan Unilever. Since then, he has worked for India’s most multinational Indian company – Tata.

Currently, he is the executive director of Tata Sons. He is also the chairman of Tata AutoComp Systems, Rallis India and Advinus Therapeutics, vice chairman of Tata Chemicals, and a director of Tata Power and Tata Technologies.

He also serves as an independent director on the boards of the Indian subsidiaries of Akzo Nobel and BP Castrol.

Gopal studied physics at Calcutta University and engineering at IIT. From 1967, he served Hindustan Unilever for over three decades in various capacities. The appointments held by Gopal from 1990 onwards were: chairman of Unilever Arabia (based in Jeddah), followed by managing director of Brooke Bond Lipton India (based in Bangalore), followed by vice chairman of Hindustan Lever.

He joined Tata Sons in September 1998 as executive director.

Gopal is involved with education through his board memberships of a school and two management institutes. He is a past president of All India Management Association. He has delivered guest lectures in India and abroad. His articles have been published in management journals and financial newspapers.

In 2007, he authored his first book, The Case of the Bonsai Manager, published by Penguin India. In 2010, his second book entitled When the penny drops: Learning what is not taught has been published by Penguin India.

CSI Annual Convention 2010 at Mumbai iGen Technologies for the next decade...Report Prepared by: Jayshree Dhere, Resident Editor, CSI Communications and the CSIC Team


The People Behind CSI2010:Convention Ambassador: M D Agrawal, Vice President, CSIProgramme Committee Chairs: Prof. Atanu Rakshit, Prof. Manohar ChandwaniOrganizing Committee: Rajiv Gerela (Chairman), Vishnu Kanhere, Ravi Eppaturi, Rohinton Dumasia, Sandip Chintawar, Mable Monthero, Vinay Thaly of the CSI Mumbai Chapter.

CSI Annual Convention for the year 2010 was held in Mumbai during 25-27 November 2010. This was 45th national convention, which was very well attended and conluded with professional grace.

At the inaugural function on 25th November, 2010, the key note address on “Technology and Society: the human touch“ was delivered by Mr. R Gopalkrishnan, Executive Director of Tata Sons, who was the chief guest for the convention. The second key note address was delivered by guest of honour, Mr. Deepak Parikh, Chairman, HDFC Ltd.

Mr. R Gopalkrishnan, Prof. P Thrimurthy, President, CSI and Mr. Deepak Parikh lighting the lamp at the inaugural function.

Various technical sessions were organized in a number of parallel tracks during the convention. Brief abstract of these tracks is described below -

Friday 26 November 2010

Track: iArchitectureTrack Chair: Prof. Umesh Bellur, IIT, Bombay

While setting the tone for the track, Prof. Umesh informed that last few years have witnessed a paradigm shift in the way software is deployed and consumed by the enterprises. Economies of scale have driven “cloud computing” to the forefront, where computing and software is served as a utility much like power. The

emergence of this model has seen a spate of new technologies, virtualization being at the forefront. Besides virtualization, Cloud Computing also requires a layer of management software that can make the mechanics of sharing computer and software resources transparent to the user.

This track focused on the notion of Cloud Computing as a whole. It brought different perspectives - that of a public Infrastructure as a Service (IaaS) cloud provider, that of a Cloud software technology provider, that of Software as a Service (SaaS) provider and fi nally that of traditional IT services providers and strategy consultants, who help customers make the tough decisions to move to this new paradigm.

Dr. Harrick Vin, Vice President and the Chief Scientist TRDDC, spoke about “Enterprise Cloud Computing: Opportunities and Challenges” and Mr. Sumit Mukhija, National Sales Manager, CISCO, talked on “Aligning IT to Business: The Competitive Advantage of Cloud Computing”.

While Mr. Vikram Bhatia of Microsoft gave a talk on “Building Interoperable Cloud Applications Using PaaS”, Mr. Phil Barlow from VMware spoke on “Journey to Private Cloud - Building seamless computing environments”. He elaborated the concept of enabling cloud like benefi ts such as encapsulation, pooling etc for existing applications. He also spoke about extended virtualization for private cloud.

Mr. Ravindra Ranade of Redhat gave an interesting perspective on “Excitements in and Barriers to Cloud Computing” and emphasized on the ease with which it is possible to enhance capacity by incorporating several servers in one go using virtualization in Linux OS. Mr. Simone Brunozzi, a technology enthusiast from Amazon.com, spoke about “Enterprise Cloud Computing” and explained the key concepts related to cloud environment such as no capex, pay as you use, elastic capacity, faster time to market and less time for infrastructure management. He spoke about success stories of using Amazon Web Services such as Hungama.com, Netfl ix and SAP.com, who are heavy users of amazon web services.

Mr. Anish Malhotra of SalesForce.com spoke on “Cloud Computing: The 21st Century Business Platform”. He explained building user-friendly business applications using Salesforce.com cloud services and hampered upon the multi-tenancy concept of cloud.

Mr. Kavindra Sharma, VP Consulting at L&T Infotech, deliberated on “Navigating the XaaS World - Making Choices in Cloud Computing”, while explaining the term ‘cloud’ in a lighter vein. He said that concept of ‘Private Cloud’ is actually oxymoron and gave advice on which applications can easily be taken

A REPORT

to cloud. Development, test and QA environments can easily be migrated to cloud and also the pre-production applications such as those used for training etc. Enterprise applications that have elastic needs – both functional (e.g. seasonal websites) and capacity (e.g. batch processing) – can be moved to cloud and also the outer-ring applications such as CRM, HRM, SRM etc. can be taken to cloud. In addition, even messaging and collaboration applications (satellite applications) can also be candidate applications for moving to cloud.

Mr. Sanjay Mehta, CEO and founder of MAIA Intelligence, talked on various aspects of “Strategy for implementing a BI solution in cloud”. He fi rst addressed the concerns, which are usually raised by users before adopting cloud such as privacy and security of data, business continuity and disaster recovery etc. He explained that these are actually better addressed in many cloud environments and should not deter users from moving their BI solutions onto cloud. His company has developed a solution called 1KEY application, which requires data to be converted into metadata before moving it onto cloud.

Thus, the whole track witnessed experts from leading companies, who are in charge of the Cloud computing, speaking about software architectures, challenges and solutions and open problems about all aspects of Cloud Computing. They presented experiences and case studies highlighting what works well and what does not with the hope that others can improve upon these best practices and avoid the pitfalls already discovered.

Plenary session being delivered by Dr. S Seshadri, CEO Boltell Infomedia Pvt. Ltd., Ex. CTO Yahoo India.

Track: iEnterpriseTrack Chair: Dr. Satish Babu, InApp and Dr. Sasi Kumar, C-DAC

While introducing the topic, Mr. Satish Babu, pointed out that Enterprise Computing spans a wide variety of defi nitions, from Enterprise 2.0 defi nition, which was about Web 2.0 collaborative tools such as blogs, wikis, RSS feeds, and online chat, to high-performance computing, which includes grid, cluster and ultimately, cloud computing. New trends in Enterprise computing also included the penetration of Free and Open Source Software (FOSS). Also, a recent concern is about Green Computing.

Dr. Keshav Nori, Honorary Advisor to TCS, spoke on the topic “A Future of Alignment between IT and Business Enterprises”. He explained the results of a recent research study, which examined the value proposition of IT in the Enterprise, and the factors that require to be aligned for deriving signifi cant value from IT.

Mr. Raj Kalady, MD, PMI India, while covering the topic “Enterprise Project Management”, talked about the emerging developments in Enterprise Project Management. The major point that he introduced was the timely completion of software projects as planned is necessary, but not suffi cient. An additional requirement was that the project team also required to understand the business use and value of the software delivered.

Mr. C. Achuthan, Former Presiding Offi cer, The Securities Appellate Tribunal stressed the need of corporate governance, as we move from proprietary organizations to partnerships, to private limited companies, and to public limited companies. Independent Directors and audit committees can oversee governance, provided they have the right attitude and mindset. If it is not there, there could be other disasters similar to the Satyam debacle.

Mr. Prasad Modali from Intel spoke on “Enterprise Computing: Multicore Architecture Landscape”. Prasad spoke about the roadmap of the multicore computing chips from Intel. He pointed out that the multicore architecture side-stepped the limitations of Moore’s law. While the multicore architecture had immense potential for parallelization, one of the challenges was to convert the single-threaded application to a parallel model. In order to render new applications in a parallelized mode, Intel has provided frameworks that run on top of platforms such as Visual Studio.

Ms. Bishakha Dutta, Board Member, Wikimedia Foundation spoke about Wikipedia, the world’s fi fth largest web site (after Google, Yahoo, Facebook and Youtube), and the largest .org website in the world. Although Wikipedia was used by over 400 million users per month, it was run by a staff of just 40. This was because there were over 100,000 volunteer contributors, who created content in their spare time. Wikipedia is now focusing on Indian languages, and hopes to substantially improve its presence in India.

Mr. Dhruv Singal, Senior Director and Head of Solution Consulting, Oracle India, spoke on “Enterprise Integration for Business Agility”. Mr. Dhruv contrasted two models of enterprise application interfacing: the fi rst was point-to-point interfacing and the second, Service-Oriented Architecture, which is a loosely coupled, web-service based strategy. While the former has low start-up costs, it has progressively increasing maintenance costs, whereas the SOA-approach, while requiring more effort in the beginning, proves to be much cheaper down the line, especially during the maintenance phase.

Dr. G. Nagarjuna, Professor at TIFR, Mumbai, spoke on “Free and Open Source Software in Enterprises of the Future”. Dr. Nagarjuna traced the evolution of FOSS and the principle of ‘Copyleft’. He pointed out that FOSS is a robust and cost-effective alternative to proprietary software. He emphasized that the peer-to-peer social networks provided a superior way to construct software compared to the traditional Enterprise mode, as has been repeatedly shown by FOSS projects such as Gnome, Apache, and the Linux Kernel.

Mr. Navin Mehra from Fortinet, spoke about “Emerging Security Threats”. Navin outlined the current safety threats such as spamming, carding, phishing, viruses and trojans. Navin pointed out that frequency of such threats is increasing, and the monetary losses are massive - although under-reported. He shared the fi ndings of a study conducted by Fortinet, which enumerated different threats and their relative risk profi le.

Track: eGovernanceTrack Chair: Mr. Mohan Datar and Dr. R K Bagga

Out of the three sessions of this track, the fi rst was chaired by Mr. Piyush Gupta of NISG and had following presentations:1. State Planning & Policies Implementation Gujarat by Neeta

Shah, Director, e-Gov, Gujarat2. Directorate of Settlement & Land Records by Mihir Vardhan,

IPS, IG Prisons & Director, Goa 3. District SBS Nagar by Shruti Singh, IAS, Dy Commissioner, SBS

Nagar, Nawanshahr, Punjab4. Sales Tax Administration by Ravindra Patil, Joint Commissioner

Sales Tax, Maharashtra


5. Gujarat Pollution Control Board by Hardik Shah, Member Secretary, GPCB, Gujarat

The second session consisted of two panel discussions. The fi rst panel discussion was on the topic of “UID: Challenges and Opportunities”. In the opening remarks the session chair Mr. Mohan Datar said that UID is defi nitely India’s single largest e-governance project and could also be the largest in the world. Hence, there are tremendous challenges in its implementation. But the bigger challenges will be in making use of UID in other e-Governance as well as corporate, BFSI systems so that its purpose will be fulfi lled. Hence this project presents tremendous challenges and opportunities to Governments, Corporates and IT industry alike. He also said that there are many myths, perceptions and misconceptions about UID and the panel would clear at least some of them.

The panel was moderated by Mr. Sumanesh Joshi, Additional Director General, UIDAI, who also delivered the keynote address. He provided complete details of the UID project from start to its current status. He informed that UIDAI has named this identifi cation number as ‘AADHAAR’. The Government has undertaken a brand building exercise for this brand. He also elaborated on its objectives, technologies and future roadmap. He informed that UIDAI aims at issuing Aadhaar ids to 600 million persons within next 4 years. Two novel features of this UID number are that the number is going to be purely random and will not have any intelligence in it. So nobody can infer any personal attributes by looking at the number. Secondly, it will be issued also at birth. However, at that stage it will not capture any biometric data. The biometrics data will be captured later at the age of 5 and again at the age of 12 to safeguard against changes during the growing years.

Mr. Ravi Saxena, Additional Chief Secretary of Gujarat, compared the Aadhaar number to the naming ceremony of a child and advised that one should not expect anything more from this number. All further uses will come from other e-Governance systems. Prof. R. Ramakumar of TISS, Mumbai, expressed concerns about infringement of privacy, rolling out the project before passing of law by the parliament and lack of data on cost benefi t analysis. Mr. Rajiv Agarwal, Food Commissioner, UP, expressed the desire that UID must issue a card and not just a number. Prof. Guruprasad Murthy, Ex Director of JBIMS and renowned management guru, opined that if it is going to one additional card, then it would be counterproductive. If it is able to replace many other cards, then it is welcome. Mr. Venkatesh Hariharan, Corporate Affairs Director (Asia-Pacifi c) for Redhat, expressed that UIDAI should ensure that it follows open standards.

The second panel discussion was on “e-Governance Implementation and Technology Issues”. The panel was moderated by Dr. Ashok Agarwal, Past Chairman, SIGeGOV. He posed three questions before the panel members, viz. a) has e-governance helped in improving the quality of life? b) has it reduced corruption in government and can we live without touts? And c) What are the areas for improvement? Mr. Anurag Jain, Secretary IT, Government of Madhya Pradesh, answered in the affi rmative to all three questions. He, however, said that touts can be eliminated where direct interaction with Government systems is possible. They cannot be fully removed where manual processes/ interventions are necessary. Mr. Niraj Prakash, Director, Public Sector Marketing, Microsoft, said that lack of comprehensiveness, lack of process reforms and lack of replications were three main areas for improvements. He also stressed that in future e-governance systems should ensure digital inclusion and governments should upgrade their quality of adaptations to remain on top of continuous change. Mr. Keshav Dhakad of BSA stressed the need for software

asset management to ensure highest level of security. Mr. Ravi Teja, Vice President of Nihilent mentioned that today’s solutions become tomorrow’s problems if they are not designed comprehensively. Mr. Sanjay Bhatia, Commissioner of Sales Tax, Maharashtra, bemoaned the current constraints of procurement mechanisms and suggested outsourcing as an option for Government agencies. He also suggested adopting an incremental approach rather than a big-bang approach for ensuring successful implementation.

The third session of the track witnessed the distribution of CSI-Nihilent e-Governance awards to the 16 winners, and the awards were distributed by Dr Vijay Bhatkar Chairman e-governance Committee Government of Maharashtra. While congratulating the winners he informed that India now ranks 4th largest economy in the world in terms of PPP. He felt that India has potential to become number one if it focuses on good education from primary level onwards and has good governance.

Lastly, presentation of CSI Award of Excellence at State Level to Gujarat was done along with the release of the book ‘Enablers of Change: Selected eGovernance Initiatives in India’, published by ICFAI University Press, 5th in the series released by CSI SIGeGOV.

‘Enablers of Change: Selected eGovernance Initiatives in India’ being released by Mr. Philip Jose D’Souza, Hon’ble Revenue Minister of Goa in presence of Piyush Gupta, R K Bagga and Ayaluri Sridevi (Editors) and CSI Offi ce bearers.

Track: iSocietyTrack Chair: Prof. Anirudha Joshi, IIT, Bombay

Dr. Vijay Bhatkar gave keynote address, and set a philosophical tone for the iSociety track. He started from the big bang, and spoke through the evolution of mankind and covered a wide range of topics including development of society and climate change. He contended that we cannot easily predict the outcomes of technological developments and tectonic shifts may be happening today in front of our eyes that we cannot easily perceive.

Mr. Kaushal Sarda gave an interesting talk weaving together two very popular themes - gaming and social networking. He showed many examples ranging from disaster preparedness to motivating healthy lifestyles, where social networks get strengthened because of game play, and games were used for better social development. He identifi ed the elements that make games engaging and social interactions fruitful.

Dr. Amit Nanavati presented three pilot case studies, where he showed how the advantages of the world wide web can be brought to low-literate, less tech-savvy users with the help of the voice-web that his lab has developed. With minimal interventions, users in rural Andhra Pradesh and Gujarat and in the slums of Delhi could discover and use applications such as on-line advertising, greeting cards, matrimony, agricultural expertise, and radio on demand merely with the help of a “dumb phone” and smart technology. If the current challenges are met with, the mobile phone-based voice web can do for the bottom 83% of the world’s population that the computer-based world wide web did for the top 17%.


Saturday 27 November 2010

Plenary session being delivered by Dr. Sorel Reisman, President-Elect, IEEE, Computer Society.

Track: iResearchTrack Chair: Dr. Rattan K. Datta, Hon. Research Director CSI

The track on research was organized with many eminent speakers including key note address by Prof. Sorel Reisman, president elect IEEE (CS). Prof. Reisman presented review process of evaluating the performance a faculty member for promotion etc based on the three factors i) content preparation, ii) delivery system, & iii) Research activities.

Mr. Ashish Sonal & Neha of Orkash Services gave a talk on “An integrated approach to mimic human into collation and create intelligence in an automated manner”. The thrust of the presentation was use of AI techniques & parallel processing.

Dr. Jaya Panvelkar of NVIDIA, Pune presented her lecture on “HPC & cloud computing with GPU: A Paradigm Shift”. She surveyed the progress in high performance computing (HPC) & super computing with their current performance vis-a vis power utilization. She also highlighted the application of GPU as a new paradigm for parallel processing.

Prof. K.S. Rajan of IIIT Hyderabad presented his talk on “Pushing the frontier areas of computer science”. Focus of the talk was use of spatial information for agriculture production using high performance computing.

Dr. Raghuram Krishnapuram of IBM-Research India, gave his talk on “Text Analytics Tools for Customer Insight”. For e-governance & other support to the public there is need to carry out analysis of documents. Various tools developed by IBM were presented by him.

Dr. Hemant Darbari of CDAC presented his talk on “Direction and scope of research in multilingual implementation in software packages and solutions”. It was noted that excellent work done by CDAC in multilingual approach needs projection for universal application in the country.

The interaction & detailed discussion led to the following recommendations by the track group -

A) There is a need to develop a group in parallel computing to promote research on this future look discipline. It was decided to propose to CSI Execom to approve a SIG on Parallel Computing with Dr. Jaya Panvelkar as founder chairperson. She was requested to form a proposal in this regard and send it to CSI.

B) C-DAC may use CSI communication & other platforms of CSI to popularise their work on multilingual applications.

Track: iSolutionTrack Chair: Mr. Sunil Mehta and Prof. Pradeep Pendse

Mr. Pradeep Waychal, Sr. VP – Patni, delivered the keynote address for the track. He shared some of his research related to IT enabled business transformation. He proposed a Maturity framework for IT

enabled business transformation indicating the key process areas at each stage. The highest stage is where there is a continuous innovation led by IT – which he refers as the optimized level on the maturity framework.Mr. Atul Bhandari, VP – SAP – highlighted the fact that many enterprise wide IT projects lack a consciously defi ned business case. He emphasized the need for a proper business case, which is aligned to the company’s business objectives. He also emphasized the fact that enterprise solutions should be viewed as business projects and not IT projects. He also outlined some of the strategies such as On device, On demand etc and the importance of aligning these to overarching business goals. Mr. Shailendra Lande from TIBCO explained the challenge of working with heterogeneous technology environments and the crucial role, which TIBCO plays in ensuring seamless integration across multiple platforms. With the help of schematic diagrams he showed how the architecture for Enterprise level applications using TIBCO, can be transformed. This transformation makes its scalable as well as seamless across platforms to help construct more durable, scalable and reliable infrastructure.Mr. Srikant Palkar, Chief Architect, UST Global, a leading US based software company, spoke on the architecture for software process improvement to a nearly packed room. His talk was interactive, informative and thought provoking. He used the 4 + 1 views framework usually used for Software architecture to discuss the various views of software improvement leading to an appropriate strategy for software process improvement. He used 3 real life case examples to illustrate his views. One of these was a large retail company, which has a USD 95 million budget for process improvement, and another one is a large retail company. He discussed the pros and cons of big bang versus incremental approach to process changes. He shared detailed experiences for each of the views on process improvement. He fi nally proposed a way forward.The fi nal talk during this track was delivered by Dr. Dharam Singh, faculty in Computer Science and the chairman of the CSI SIG on Wireless Networking. He explained his research, which could lead to an optimization of bandwidth and several other benefi ts on wireless networks making delivery of Video of higher quality at lesser bandwidth. His talk was seen as an infrastructure solution for many of the solution ideas discussed in the previous sessions.

Track: iEntrepreneurTrack Chair: Mr. Manak Singh, TIE, Mumbai

Mr. Manak Singh of TiE, Mumbai, put together this track, a day full of inspiration & insights to encourage as many of the delegates to either....

¬ convert their business idea from white paper into a business plan,

¬ scale up their existing enterprise,

¬ look around & collaborate with friends and peers to form teams, Support growing enterprises.

The track provided ‘Thought for Enterprising India’.

The future of Enterprising India is full of such powerful ideas, which can potentially Change the Nation and Lead the World. India is genetically an Enterprising nation with a deep rooted and home grown “Jugaad” culture.

The challenge and opportunity is to shift the Indian Entrepreneurial mindset from “Survival” to “Choice”. The Choice - to Innovate, to collaborate, to scale, to create social Impact, to consciously create jobs, to create and distribute wealth for one and many around .…

With an existing force of millions of SMEs (and fast growing), emerging


generations of new age Entrepreneurs and a large unfolding consumption story, it is time to scale up Indian “Jugaad” to organised growth.

Key thoughts are - Purpose, Ideas, Execution Leveraging the growing domestic support system – Mentoring,

Investors, Networking “Value for Money” enterprises – India’s leadership opportunity

amidst Global TurmoilAnother dimension of entrepreneurship is the infectious art of story telling....so the i-Entrepreneur track primarily featured exciting stories of some very inspiring and enterprising Indians like Mr. VSS Mani of Justdial, Mr. Deepak Ghaisas of Genvocal Strategic Services, Mr. L.C. Singh of Nihilent, Mr. Phanindra Sharma of RedBus.in, Mr. Ajit Nagral of SciFormix, Mr. Mohit Dube of Carwale, Mr. Prashant Bhaskar of Plug HR, and Mr. Rajesh Solanki of EverFocus & Energos.

In India we believe very strongly in the institution of marriage. This mindset tends to be even stronger in IT, given the sheer fantasy of possibilities that technology has to offer. But to sustain this marriage it is imperative that our IT services / products are not just fantasy but are driven by demand, solving real pain points.

It’s this mindset that would help us to build sustainable enterprises, to build solutions and not just a service or product.

There were various panel discussions among the participating entrepreneurs. While fi rst two panels gave the above key message the third panel focused on ‘Teamwork’.

The key message of this panel was – “Building and sustaining growth of your enterprise can be best achieved through collective experience, passion and effort.”

This panel discussed the role of effective collaboration with the ecosystem, building teams, forging partnerships that help propel their enterprises in a high growth trajectory.

The fourth panel concentrated on diving into the minds of Enterprising Indians - “Entrepreneurship is the art of infectious Story-telling!” This panel featured some diverse and maverick examples of enterprising Indians. The stories of Venky (Goli Vada Pav) and Faisal (mouthshut.com <http://mouthshut.com>) stirred and inspired the audience. The foot tapping rhythm of their enterprising journey, gave insights on the true essence of jugaad - the Indian art of the start evolved into sustained value delivery.

The fi fth panel was on “Meet the enablers - Mentors & investors” and panellists were Mr. Chetan Shah of Indian Angel Network, Mr. Anand Lunia of SeedFund and Mr. Manik Arora of IDG India Ventures.

As Entrepreneurs, you can’t just be “Operational’, you have to constantly Plan – validate your plan – replan, Raise Funds, build an organisation – Teams, infrastructure - Selling your product / service - Customers, Partners, etc., strategise for Growth, make corrections in current state.

One has to wear many hats…in short you have to be a Super Human! It is imperative hence for entrepreneurs to surround themselves with the right set of human intelligence that helps them validate plans, challenge assumptions, execute effi ciently, scale fast and smart. This panel discussed the role of mentors, investors playing the acceleration role for entrepreneurs.

The panel also oriented the audience on various forms of equity funding, do & don’ts of approaching investors, as well as did some crystal ball gazing to share emerging trends and opportunity areas.

Track: iConnectTrack Chair: Prof. Abhay Karandikar, IIT, Bombay

iConnect track was chaired by Prof Abhay Karandikar of IIT Bombay. It had 5 interesting talks by industry veterans focussing on mobile broadband and convergence applications.

The fi rst session was a talk by Dr. Vinod Vasudevan, Group CEO of

Flytxt India. While speaking on the topic of “Mobile Broadband – Convergence and C2C”, he presented a different paradigm on mobile broadband applications for telecom operators. According to Dr. Vinod, the major differentiator for an operator will be to deploy innovative applications on mobiles and not treat a mobile device as just yet another broadband device. A typical mobile handset has many contexts including locations, which need to be exploited.

The second talk was delivered by Mr Akhil Bahl of Cisco. Akhil emphasized the need of “Collaboration as a Service”. He presented an architecture of collaboration service and gave an insight into Cisco’s vision.

Mr. C.S. Rao, President, Reliance Communications, spoke on “Next Wave of Mobile Broadband”. He stressed the need of many innovations for next generation architectures and applications. Operators have invested signifi cantly in spectrum and they need innovative models for their return on investment.

Mr. Sreedhar of Avaya gave an interesting presentation on enterprise video communications applications. He also addressed the capabilities of various solutions.

The track was concluded with a talk by Mr. Vishal Gupta, CEO, Salesforce, who spoke on innovative information security architecture. The topic of the talk was “Seclore Collaboration and Security: A confl ict of goals .. Is there a balance?” In this paradigm of innovative information security architecture, meta-data associated with information will ensure security of the data.

All the speakers gave their perspective of innovations required in the next generation converged scenario.

Track: Education & ResearchTrack Chair: Dr. Manohar Chandwani IET DAW, Indore

The track on Education & Research was targeted towards apprising the participants and delegates with the future requirements of ICT education system in India. The emphasis was given on the educational technologies for the next decade, research at UG level, Industry-Institute interfacing, India’s need in 2040 and improving the quality in technical education using ICT.

Prof. Deepak Phatak of IIT, Mumbai, spoke about “Scaling New Heights in Quality Education using ICT”. The Indian education system is now growing at a faster rate than before. The emergence of newer and private sector Institutes has led to broader spectrum of education, but at the same time, the quality has not been taken into consideration carefully by the education system developers. Despite that the fact that ICT has progressed with a great impact on industry, it has not been able to infl uence the education for better quality in a wider perspective. This talk addressed the issues related to scaling up the heights in the fi eld of education for bettering the quality especially in Technical Institutions that are struggling for overcoming the faculty shortage. The talk also addressed how the ICT can be used to impart training to the teachers of engineering colleges with virtual and distance education principles. There can be interactive teaching programs to train teachers at various centres that are remote. A specifi c example of 2-week ISTE sponsored winter/ summer school at engineering colleges from IIT, Mumbai, was illustrated.

Prof. Rajeev Sangal, Director, IIIT, Hyderabad, spoke on the topic of “Research Led Education: A New Model for Research University in India”. In this talk, he introduced a new model of education, which is based on linking its with research. There are two essential elements of this model: (1) Linking UG programmes with research, and (2) Undertaking research that links up with industry and society. The fi rst element implies introducing fl exibility in curriculum, more projects, and most importantly, allowing depth to be pursued without waiting for the breadth to be covered. The second one implies working on research problems that can be linked with industrial applications. Some examples from IIIT, Hyderabad, were given where this model has been successfully developed. It has led to the setting up of strong research


groups in several areas, within a short period.

Prof. Dheeraj Sanghi, IIT, Kanpur, gave a speech on “Improving ICT Education: Role of Various Stakeholders”. It is quite known that the quality of ICT education is very poor in India, except perhaps in 50 odd institutions. This leads to not just poor employability, but also insuffi cient people getting trained to do research and go for academic careers, which implies that even in near future the quality of education will continue to remain poor. To change the situation, all the stakeholders will have to take steps to improve the quality. In this talk, two major stakeholders were considered, namely, Industries and Universities, and it was discussed as to what they can do to improve the quality.

Mr. Anant Krishnan, Chief Technology Offi cer, TCS, Chennai, spoke about “Accelerating Education & Research with Better Quality & Greater Collaboration”. He said that India’s ICT educational infrastructure should take much credit for the country’s growth as a knowledge economy. However, we must swiftly move to the next level to keep our competitive advantage. One area of concern is that we currently produce very low number of Ph.D.s. To do path-breaking research as well as to offer high value R&D services, we need to give this area some focus. As we move ahead in the 21st century, we need to leverage our ICT education to close gaps in several sectors, ranging from education, public health, agriculture, bio-technology, apart from ICT itself. Both academic Institutions and industry are clear that an Institute-Industry interface is vital; It is time to scale this up, by bringing more institutes and industrial outfi ts in a clear, process oriented way. R&D has to be a part of a company’s growth strategy and Indian ICT companies today have the capacity to invest in this and create a virtuous cycle of growth. Collaborative Research – where Academia understand real world challenges, and Industry learns to look at a problem from scientifi c point of view and works on a solution with scientifi c rigor – can bring benefi ts not just to the teams involved, but to customers, the universities and the larger society as well. Orchestrating collaborative research requires transparent processes and the management experience and expertise.

Mr. Subrahmanya S V, Vice President, Education & Research, Infosys, Bangalore, deliberated on “Collaborative Research between IT Industry and Institutes of Higher Learning” in his speech. He recognized that Indian IT industry offers a number of challenges that requires continuous innovation. Indian Universities and Institutes of higher learning such as Indian Institutes of Science and Technology are increasingly focusing on solutions to problems faced by real world including the IT industry. He informed that at Infosys, people are guided by ideals of building

a healthy eco-system of Industry and Institute cooperation. The participation of academia ensures formalism to IT Solutions. The talk presented Infosys’ experience in the Collaboration between the two. The talk also indicated possibilities of pursuing Ph.D. work at Infosys on real problems to provide IT solutions.

Mr. Shankar Iyer of Microsoft India spoke about “21st Century Learning Skills & Technology”. His talk addressed new thinking and developments in the area of learning skills that have emerged lately by means of technology. New learning skills are dependent upon students’ diversity and multi sensory aspects that can be implemented in the form of educational technology. He spoke about a new system called teacher.tv, which is used for easing the learning process by learners ranging from school students to higher-level pupils. 21st century learning is student-centered instead of teacher-centered and uses multimedia technology that causes critical thinking, collaborative work, informed decision making and “pull instead of push” paradigm of learning and information exchange. By way of technology, the issues of global awareness; fi nancial, economic, business and entrepreneurship literacy; civic literacy and health & wellness awareness are tackled from a learner’s perspective. A complete Microsoft road map was discussed covering 21st century learning skills & technologies.

CSI2010 - Awards for Excellence in IT- Anil Srivastava, Convenor

BFSI SectorWinner - HDFC Bank LtdRunners Up - Asset Reconstruction Company (India ) Ltd.

Product Manufacturing SectorWinner - Mahindra GroupRunners Up - Bajaj Electricals Limited

Service Industries SectorWinner - The Tata Power Company LimitedRunners Up - Reliance Infrastructure

Non Profi t Organization SectorCertifi cate of Appreciation- Small & Medium Business Development Chamber of India- Institute of Cybernetics Systems and Information Technology

Quality Assurance Sector– Merged with Product Manufacturing

Life-Time & Fellowship Awards


Dr. F C Kohli receiving Life Time Achieving Award from Prof. P Thrimurthy, President CSI

(L to R) Mr. Anil Srivastava, Mr. V L Mehta and Prof. Kesav Nori receiving Fellowship Awards from Prof. P Thrimurthy, President CSI.


Dr. Faqir Chand KohliA visionary and pioneer by nature, Dr. Kohli is acknowledged as the ‘Father of the Indian Software Industry’.

TCS, under his leadership and propelled by his vision, pioneered India’s IT Revolution and helped the country to build the IT Industry.

Be it the propagation of computerisation in India at a time when no one realized its potential, or bringing the benefi ts of IT to India’s rural masses through computer based Adult Literacy programme, Dr. Kohli saw IT as an instrument of national development. He has been working on advancing engineering education at undergraduate level to world standards to create a large pool of students for undertaking graduate studies and research.

Dr. F. C. Kohli was the President of Computer Society of India in 1974-76. He has given memorable addresses in CSI Conventions. He led a group to draft the Constitution of CSI. He has truly been a mentor to succeeding Presidents of CSI and has been guiding them on the tasks ahead. He has received many awards including the prestigious Dadabhai Naoroji Memorial Award in 2001 and was conferred the Padma Bhusan in the year 2002.In grateful recognition of his immense contribution to the Nation, to the IT Industry and to Computer Society of India, the CSI has decided to confer on him the Lifetime Achievement Award. The Society takes pride and pleasure in presenting him with the citation on the occasion of the Annual Convention of CSI held at Mumbai in November 2010.

Mr. V. L. MehtaMr. V. L. Mehta is a visionary, and he has successfully converted his vision into reality through innovative ideas and methodologies.

During a distinguished career spanning over 39 years, he has signifi cantly contributed towards high-end IT education.

His vision of post-Y2K period compelled him to establish an information security company in 1998. His was a pioneering effort in creating awareness and evangelizing the need for information security in our country during 1999-2001.

Mr. V L Mehta has been a member of the CSI since 1976 and has actively participated in the endeavours of the Society. He has served as an Offi ce Bearer for the last twelve years continuously at the Mumbai Chapter Chairman, Regional VP and Nominations Committee. He revived the activities of Mumbai Chapter while he was the Vice-Chairman and led the Chapter to the level of the Best Chapter Award for two consecutive years. He is fully committed to CSI and has always pursued the interests of the Society.

In grateful recognition of his association and services to the Computer Society of India, and for driving the performance of Mumbai Chapter to its peak during his leadership and his outstanding accomplishments as an IT professional, the CSI has decided to name him FELLOW of the Society. The Society takes pride and pleasure in presenting him with the Citation on the occasion of its 45th Annual Convention held at Mumbai.

Mr. Anil SrivastavaGraduated from IIT, Kanpur and a Post Graduate from IIM,

Bangalore, Anil has worked for 25 years with Government as a member of Indian Administrative Service (IAS). He joined Maxwell School, in Syracuse University during fall 2002, for a Masters in Public Administration and Information Technology Policy Management. Pursuing Ph.D. in Social Science, his area of research is E-governance & Application of Information Technology in Government leading to increase in transparency and issues related with that.

As an IAS offi cer of MP Cadre he served in various capacities including Departments of Revenue, Food Processing Industries, Horticulture as Principal Secretary, Finance Department, Panchayat & Rural Development Department as Secretary, Commissioner Jabalpur division, Commissioner Treasuries & Accounts, Commissioner Industries and Secretary Department of Commerce & Industries, Managing Director M.P. State Electronics Development Corporation and Optel Telecommunications Ltd, Bhopal, Managing Director, M.P. State Small Scale Industries Development Corporation Ltd., Bhopal, Collector and District Magistrate, Hoshangabad and Shajapur. Mr. Srivastava’s present assignment is that of Principal Secretary Department of Revenue Govt. of MP

Anil has been a member of CSI for the last 30 years and has actively participated in the activities of the Society. He has represented CSI in various committees and Forums to pursue the interests of the Society. He has served the society in various elected positions at the Chapter level.

In grateful recognition of his services to the Computer Society of India and his outstanding accomplishments as an IT professional, the CSI has decided to name him FELLOW of the Society. The Society takes pride and pleasure in presenting him with the Citation on the occasion of its 45th Annual Convention held at Mumbai.

Prof. Kesav V. NoriProf. Kesav V. Nori has a BTech (EE) from IIT Bombay (1967) and an MTech (EE) from IIT Kanpur (1970). He was a two-time recipient of UNDP Fellowship, in Europe and USA respectively, during 1974-1975.Kesav Nori’s research contributions and interests include System Design of Computers, Defi nition, Design and Implementation of Programming Languages, Automation in Assembly, Production and Manufacture of Software, Software Process and Product Engineering, Software Quality, A Systems View of Business and its Information Systems, Systems Design Methodology and Systemic Understanding of Diffusion of Innovation in Organizations. He is a Life Member of CSI and a member of IEEE (Computer Society) and ACM. He has readily lectured at various Annual Conventions of CSI since 1971 and was the Program Chairman of its Annual Convention in 1995 in Hyderabad. He was Program Chairman for the Indian FST&TCS Conferences during 1984-1989, and of IEEE Conference on Smart Appliances held in Hyderabad in 2004. In grateful recognition of his services to the Computer Society of India and his outstanding accomplishments as an IT professional, the CSI has decided to name him FELLOW of the Society. The Society takes pride and pleasure in presenting him with the Citation on the occasion of its 45th Annual Convention held at Mumbai.

CSI Honors@ 45th Annual National Convention 2010, Mumbai


Coimbatore

ANITS

Chapter NewsPlease check detailed news at: http://www.csi-india.org/web/csi/chapternews-december2010

SPEAKER(S) TOPIC AND GIST

AURANGABAD

Dr. T. V. Gopal, Manjula Dharmalingam and Ajay Deshkar

Inauguration of the Seminar on “Software 2.0”

9 October 2010: “Software 2.0 - Emerging Competencies for Enterprise Solutions and Knowledge Based Engineering (KBE)”

Software professionals and IT students tasted a bit of advancements in Cybermetics through this seminar. After a long time professionals at Aurangabad could get exposure to the most modern developments in the IT industry.

The key topic, which was discussed during this programme was as follows:

“Software 1.0” has been happening for nearly fi ve decades beginning with the questions “what should it do?” and “what can it do?” The success rate of Software Projects has been alarmingly low considering the number of professionals working in this area over the past 50 years. Several challenges in the areas of Human Resources, Technology, Management and Innovation remain. “Software2.0” is to integrate Cybernetics and Systems Theory with the best practices in “Software1.0”.

BANGALORE

Mr. Kupendra Shivapuram, Sr. Manager, VMware

Editor’s Choice: “From an IT perspective, the way we have reduced our carbon footprint is through virtualization. We instantly saw its power and began to virtualize everything we could. ... Now instead of having 28 servers at 10 percent utilization, we have three machines at 80 percent utilization.”

- Brad Sukut, Midwest Family Mutual

30 October 2010 : “Workshop on Cloud Computing”The VMware team gave an exhaustive coverage on Cloud Computing including introduction on Cloud Computing, its benefi ts, VMware Virtualization, VMware Enterprise Data Centre products, VMware Enterprise Solutions, Cloud Solutions and case studies. In the afternoon, hands-on session on Creating virtual data center, Catalogs, allocating resources to virtual data center/organization, and granting rights to the application were conducted.


BHOPAL

Dr. Sanjay Sharma, Professor at MANIT, Prof. Aasif Hasan, Prof. Patheja, BIST, Prof. Prakash Saxena, Dr. R.P Singh Director, MANIT, Dr. Sanjay Jain Den (P&D), BGI

Workshop of Cloud Computing and Network Technologies

20 October 2010 : “Cloud Computing and Network Technologies”Prof. Sharma explained the objectives of ‘Cloud computing’ to the participants and described how a proper strategy & planning is essential for achieving the scale of excellence in the areas of information technology.Prof. Aasif Hasan emphasized the necessity of soft computing and described the utility of soft computing. Dr. R.P Singh, Director, MANIT, highlighted the great potential of ‘Cloud Computing & Network Technologies’, which can be exploited in the years to come. Dr. Sanjay Jain Den (P&D), BGI, mentioned that ‘Cloud computing’ resources are shared. This helps businesses to save time and money by placing their information all in one location, which is easy for their workers to look up and access.

CHANDIGARH

Dr. Raghav, Institute of Microbiology, Chandigarh

Mr. V P Girdhar greeted speaker Dr. Raghav

16th Oct. 2010 : “Use of Information Technology in Medical Research”The talk covered concepts of Biometrics, molecular nature of human body, Gnomes and use of IT in Medical Research, benefi ts and other critical issues. The talk was highly informative and the session was interactive.

Editor’s Choice:

Patient Records

ICT in Medicine

Research

Expert Systems Equipment

Communications Internet

CHENNAI

Prof. D K Subrahmanian, President FAER, Dr. Anirudha Joshi, IIT, Mumbai, Mr. Kevin Devasia, Apollo Hospitals, Mr. A P Guruswamy, DGM, Indian Bank, Mr. Sanjay Sharma, Polaris, Mr. Deepak from TCS, Mr. Ganesh Kumar, Chief General Manager, IDRBT, Mr. Kiruba Shankar, CEO, Business Blogging Pvt Ltd., Mr. Suman Kuman, ESPN, Mr. Srinivasu Chakravarthula, Yahoo!, India.

Prof. D K Subrahmanian, President, FAER and Event General Chair – UMO2010 delivering the Keynote address on “World Usability Day 2010”

11-13 Nov., 2010 : “Good Design for Better Living”

The deliberations during the two-day seminar focused on integrating the quantitative, qualitative, formal, semi-formal and informal practices into the software development life-cycle to engineer user-centric products and services. In the rapidly dynamic and increasingly self-structured environments, it is clear that traditional user experience research methods will be of limited use, and even then only in the most structured areas of the user experience continuum. As more users move into the more self-structured environments, a new paradigm for user experience research is required to fulfi ll the promise of richer, usable user experiences.

“You can never accurately measure the usability of a software product. When you drag people into a usability lab to watch their behavior, the very act of watching their behavior makes them behave differently.”

The new design paradigms must include research methods that allow organizations to capture the experience whenever the user is interacting a given artifact and the interaction is unmoderated and asynchronous with the researcher’s schedule - even in the middle of the night, and from any time zone and independent of the delivery channel - cell phone, PDA, laptop, kiosk and any operating system.


COIMBATORE

Mr. H R Mohan, Chairman, Div IV and Associate Vice President, The Hindu.

Release of Conference Proceedings at the two-days National Conference NCMAN

29 - 30 October 2010: “Mobile and Ad Hoc Networks”In his inaugural address, Mr. Mohan, traced the developments in the fi eld of communications and how the country has benefi ted by the advancements in this cutting-edge and pervasive technology. He also pointed out that any leading technology like mobile communication is a double-edged sword and had its fair share of problems and misuse in terms of cyber crimes. He added that the recent advancements such as Bluetooth introduced a new type of wireless systems known as mobile ad-hoc networks. These networks operate in the absence of fi xed infrastructure and offer quick and easy network deployment in situations where it is not possible otherwise and typically used in Military scenarios, Sensor networks, Rescue operations, Students networking on campus, Free Internet connection sharing and Conferences. Mobile ad-hoc network is an autonomous system of mobile nodes connected by wireless links; each node operates as an end system and a router for all other nodes in the network. With the advent of 3G, these Ad Hoc networks are bound to become much more popular.

COCHIN

Mr. James Joseph, Director Executive Engagement, Microsoft India

Prof. M.V. Rajesh delivering a talk

14 October 2010 : “Unifi ed Communications – Transcending the national boundaries”The session gave an overview of unifi ed communications in the convergence of email, unifi ed messaging, web conferencing, instant messaging (IM), audio/video collaboration, speech recognition and integrated presence. The session also addressed the challenges, industry trends, and opportunity of unifi ed communications.

Editor’s Choice: Unifi ed communications (UC) is the integration of real-time communication services such as instant messaging (chat), presence information, telephony (including IP telephony), video conferencing, call control and speech recognition with non-real-time communication services such as unifi ed messaging (integrated voicemail, e-mail, SMS and fax). UC is not a single product, but a set of products that provides a consistent unifi ed user interface and user experience across multiple devices and media types.

- Excerpted from http://en.wikipedia.org/wiki/Unifi ed_communications

Prof. M V Rajesh, Head of the Dept., Dept. of Electronics Engineering, College of Engineering, Cherthala

11 November 2010: “Introduction to Fuzzy Logic and its Applications”The speaker explained the concept of fuzzy logic in a simple way using examples.

TIRUCHIRAPALLI

Ms B Smitha Evelin Zoraida, Professor & Head, Dept. of Computer Applications, MAM College Of Engineering, Sirugunoor

Ms. B. Smitha Evelin Zoraida, Professor at MAM College Of Engineering, delivering a lecture on “DNA Computing”

19 October 2010: “DNA Computing”DNA computing is a form of computing which uses DNA strands, biochemistry and molecular biology, instead of the traditional silicon-based computer technologies.It is well established that DNA encodes the genetic information of cellular organisms. Various biological operations can be performed on DNA strands viz., denaturing, annealing, ligation, polymerase chain reaction (PCR), gel electrophoresis and cloning.It is estimated that a mix of 1018 DNA strands could operate 104 times faster than the speed of a today’s advanced supercomputerIt is estimated that the inherent parallelism in DNA computers makes 1020 operations per second realistic for DNA computers. On the other hand, modern supercomputers perform 1012 operations per second. Similarly, the energy consumption is lesser for a DNA computer as compared to a silicon digital computer. Solving Multiple travelling salespersons problem using DNA strands was presented to bring out the employability of DNA strands in problem solving.

TRIVANDRUM

Prof. K C Raveendranathan, Government Engg. College 27 October 2010: “Wireless Home Automation Networks”

Mr. P. Abraham Paul, Ex: Vice President (TS) SPCNL, SIEMENS ICN/GM & SMT TBG BPL Mobile, TES (I) DOT

3 November 2010: “Money through Mobile (MTM) For Financial Inclusion of Lower Strata”

Dr. Venugopal Reddy MD, MRCP, Physician & Life Skills Expert, New York, USA.

10 November 2010: “The Art & Craft of Public Speaking”

Mr. N T Nair, Chief Editor, Executive Knowledge Lines – monthly,

17 November 2010: “Information Technology (IT) - Energy Scenario”


Student BranchesPlease check detailed news at: http://www.csi-india.org/web/csi/chapternews-november2010

SPEAKER(S) TOPIC AND GIST

BDCOE, Sevagram, Vardha

Prof. M. A. Gaikwad, Dean (R & D).

Mr. Gaurav Namjoshi, Director, Endeavor, Nagpur

Mr. Avinash Moharil, Director, Techior Solutions Pvt. Ltd.

Four days workshop on RSA in progress

Mr. Ajay Chaudhari, Sr. Manager (R & D), VMware software Pvt. Ltd., Bangalore

31 August 2010 : “Inauguration of Student Chapter”The inauguration of the student branch was followed by a debate competition.1 September 2010: “Career Avenues after Engineering and Orientation for Aptitude Test” 8 September 2010: “Software Development Skills”

28 - 31 October 2010: “Essentials of Visual Modeling & Rational Software Architecture (RSA)”This was a four-days workshop organized in association with ZenSOFT Services Pvt. Ltd., Pune, under IBM Academic Initiative.

2 November 2010: “Cloud Computing & You”

Editor’s Choice: Visual modeling is the activity of representing objects and systems of interest using graphical languages. As with other modeling languages, visual modeling languages may be classifi ed as: general-purpose/domain-specifi c; executable/non-executable; and open/proprietary. Some examples are - UML, SysML, BPMN, BPEL.

Reference - Visual Modeling Forum.

Chandigarh Engineering College, Landran, Mohali

Dr. G S Singh, Vice Chairman, CSI Chandigarh Chapter, Mr. Amit Deogar, Associate Prof. NITTTR Chandigarh and Mr. Vipin, Incharge Computer Centre

Guests (Right to Left) Prof. (Col.) H S Sarin, Wg. Cdr. D N Mishra (Hony Secy), Dr. G.S.Singh (Vice President), Dr. S. V. Rama Gopal (Scientist & MC Member), Dr. G. D. Bansal (Director-General, CGC Landran, Mohali), Dr. Harmesh Kansal (Prof. at UIET, Chandigarh).

21-22 October 2010 : “Transdisciplinary Computer Applications and Design Challenges”.This was a two-days regional student convention. The chief guest Dr. Singh highlighted the signifi cance of computer applications in various fi elds of our daily life. He empasized upon the new challenges in designing computer programs as per exact requirement of the consumers.On the second day, sessions were conducted on “Emerging Technologies in Open Source Web Platform”. During these sessions, the speakers explained the deployment of Open Source Software (OSS) onto personal business servers and applications arenas.

Editor’s Choice: “Open-source software (OSS) is software available in source code form for which source code and certain other rights normally reserved for copyright holders are provided under a software license that permits users to study, change, and improve the software. A report by Standish Group states that adoption of open-source software models has resulted in savings of about $60 billion per year to consumers.”

- Wikipedia.

Muthayammal Engineering College, Rasipuram

Mr. Jude Xavier, Assistant Vice President HR, Polaris Software Lab Ltd., ChennaiDr. N. Kasthuri, Professor/ECE, Kongu Engineering College, Erode

Dr. N. Kasthuri giving speech on Image Processing

8 October 2010 : “Inauguration of Student Branch”Mr. Xavier inaugurated the function and shared his experiences in his speech.

14 October 2010 : “Research Issues in Image Processing”In her speech, she shared her experience and added more information about the concepts behind image processing. She gave a detailed lecture on the Restoration and Compression concept in image processing.

Editor’s Choice: “Digital has obviously changed things a lot, but not all for the better as far as I’m concerned. Of course it’s much more convenient and you’re getting instant results, but to me it just lacks the fi nesse of a roll of fi lm and it has a slightly superimposed feel.”

- Graeme Le Saux

Mr. K. Vengatesan, Lecturer, CSE, Muthayammal Engineering College

20 October 2010: "Dot Net Programming"

Prof. R. Bhaskaran and S. Karal Marx, CSE, Muthayammal Engineering College

28 October 2010: ”Multi Core Programming”Prof. R. Bhaskaran and S. Karal Marx delivered the special lecture on Multi Core Programming. In their speech, they explained multi core programming, parallel processing, pipeline processing and research issues of multi core programming.

Mr. M. Sayee Kumar and Mr. M. RamKumar, CSE, Muthayammal Engineering College

10 November 2010: "Java Programming" In their speech, the speakers explained OOPs concepts and features of java programming.

Shri S. Baskar, Chief Executive Officer, Linuxpert System, Chennai

12 November 2010: "Open Source System"S. Baskar shared his experience about the open source softwares, freedom on open source softwares and the advantages of using it.

Sinhgad Institute of Technology, Lonavala

Mr. Avinash Nazare, HR Head TCS Pune, Mr. Mayur Tannu, Sr.Executive (Sales) SakaalTimes Pune, Mr. Vishwesh Deshmukh, Manager, Career Forum, Mr. Narayan Maheshwari, AEGIS Technology, Mr. Subhashish Sen Gupta, Regional Manager, IMS learning resources, Pune, Mr. Nitin Kulkarni Director,Router InfoTech, Pune

Technical festival ‘XENOS’ in progress.

28 - 29 September 2010: National level technical festival ‘XENOS’

The event this time had included new activities such as rinkism, rock climbing, obstacle games and trekking as new adventurous tasks. The Tech Fest was initiated by the Microsoft Student’s Club Session and Devcon (Developer’s Conference). The main attractions among the events were Coding, E-Burst, Virtual Recruitment, Gaming and the newest of all The Flying Eagles. Other events such as Technical Paper Presentation, Photoshop, and Treasure Hunt were lived up-to the mark as they usually test the individuals beyond their expectations. This time the Robotics was on its huge excitement as the track was designed specially taking into account the needs of various levels of difficulties and interests to test their inventions (ROBOTS) from all possible ways.

Editor’s Choice: “Computer games don’t affect kids, I mean if Pac Man affected us as kids, we’d all be running around in darkened rooms, munching pills and listening to repetitive music”

- Gareth Owen

Terna Engineering College, Nerul, Navi Mumbai

Dr. Vishnu Kanhere, Chairman of CSI Mumbai Chapter, Mr. K G Chari

Dr. Vishnu Kanhere speaking during inauguration

14 October 2010: “Inauguration of CSI student branch”

Dr. Kanhere delivered an inspirational and motivating speech and made students aware about the current status of computer technology in the world.

Mr. Chari shared his experience in Business Development, Implementation and Training of various software engineering methods. He also guided the students and appealed them to utilize the opportunity of being a CSI member for their benefit.

Editor’s Choice: “So many dreams at first seem impossible. And then they seem improbable. And then, when we summon the will, they soon become inevitable.”

- Christopher Reeve

Published by Suchit Gogwekar for Computer Society of India at 122, TV Indl. Estate, S K Ahire Marg, Worli, Mumbai-400 030 • Tel.: 022-249 34776 and Website : www.csi-india.org • Email : [email protected] and printed by him at GP Offset Pvt. Ltd., Mumbai 400 059.

Licenced to Registered with Registrar of News Papers If undelivered return to : Post Without Prepayment for India - RNI 31668/78 CSI, 122, TV Indl. Estate, MR/TECH/WPP 241/WEST/09-11 Regd. No. MH/MR/WEST-76-2009-11 Mumbai - 400 030

Following is the fi nal slate by the Nominations Committee (2010-2011) for the various offi ces of the Computer Society of India for 2011-2012/2013.

Note : All election related notices and Bio-Data will be published on the CSI website www.csi-india.org

Nominations Committee 2010-2011

Dr. S S Agrawal (Chairman)

Dr. S C Bhatia (Member)

Dr. (Prof.) U K Singh (Member)

CSI Elections 2011-2012/2013

Hon. Treasurer

• Mr. Ajit Kumar Sahoo• Mr. M. P . Goel• Mr. V. L. Mehta

Regional Vice President (Reg. I)

• Mr. Piyush Kumar Goyal

• Mr. R K Vyas

Regional Vice President (Reg. III)

• Mr. Anil Srivastava

• Mr. G F Vohra

Regional Vice President (Reg. V)

• Prof. D B V Sarma• Mr. Iqbal Ahmed

Regional Vice President (Reg. VII)• Mr. S Ramasamy

Divisional Chair Person - Div. I• Dr. C R Chakravarthy• Prof. S G Shah

Divisional Chair Person - Div. III• Mr. Devesh Kumar Dwivedi• Prof. Pradeep Pendse• Mr. S P Soman • Dr. S. Subramanian

Divisional Chair Person - Div. V• Dr. Manohar Chandwani

(April 1, 2011 – March 31, 2013)

(April 1, 2011 – March 31, 2012)

For the Term 2011-2013

For the Term 2011-2012

Nomination Committee

• Prof. (Dr.) A K Nayak

• Mr. P R Rangaswami

• Mr. Sanjay K Mohanty

• Mr. Satish Kumar Khosla

Vice President cum President Elect

• Mr. Satish Babu

• Mr. Satish Kumar Syal

December 2010

Workshop on Java Androids & Web TechnologiesDate : 10-12 December 2010Hosted by: Jaypee University of Engineering & Technology, Guna (MP)Organised by: CSI and Jaypee University of Engineering & Technology, GunaFor details contact: Dr. Shishir Kumar, [email protected]

National Conference on E-Governance & E-Society (NCEGOVS-2010) Date : 11-12 December 2010Hosted by: Allahabad ChapterFor details contact: Mr. D.K. Dwivedi, [email protected]

ICoAC 2010: 2nd International Conference on Advanced ComputingDate: 14-16, Dec. 2010 at Chennai, India Organised by: Dept. of Information Technology, Anna University Chennai, MIT Campus and IEEE Madras Section and Supported by Computer Society of India Div IV & Chennai Chapter, IEEE Computer Society, Madras Chapter, Centre for Development of Advanced Computing (CDAC) and University Grants Commission (UGC)For details contact: Dr. S Thamarai Selvi, Professor, Dept. of Information Technology, MIT Campus, Anna University Chennai, Chromepet, Chennai 600044, India. Phone: 91-44-22516319 / 22516015. Email: [email protected] OR Mr. H R Mohan, Chair Div. IV at [email protected] Website: www.annauniv.edu/icoac2010

ICSIP-2010: International Conference on Signal and Image ProcessingDate : 15-17, Dec. 2010 at Chennai, IndiaOrganized by: RMD College of Engineering and University of Mysore in association with Computer Society of India Div IV & Chennai Chapter and IEEE Computer Society, Madras ChapterFor details contact: Prof. Dr. R. M. Suresh, Chair – Programme Committee at [email protected] or [email protected] OR Mr. H R Mohan, Chair Div IV at [email protected] Website: www.rmd.ac.in/icsip2010/

Role of IT in National Rural Employment Guarantee Act (NREGA)Date : 17-18 December 2010Hosted by: Tata Institute of Social SciencesOrganised by: CSI and Tata Institute of Social SciencesFor details contact: Prof. Bino Paul, [email protected]

Seminar on Knowledge ManagementDate : 18th December 2010Hosted by: Academic Staff College, VIT UniversityOrganised by: CSI SIG-KM and CSI Vellore ChapterFor details contact: [email protected], [email protected]

January 2011ConfER-2011: The 4th National Conference on Education & ResearchDate : 23-24 January, 2011 Hosted by: Shambhunath Institute of Engineering & Technology, AllahabadOrganized by: CSI Division V, Region-I and Allahabad ChapterFor details contact: Prof. J P MishraE-mail: [email protected]), Mr. Zafar Aslam (e-mail: [email protected])

February 2011NCCSE – 2011: Second National Conference on Computational Science and EngineeringDate : 4-5, Feb 2011 at Kochi, IndiaOrganized by: Department of Computer Science & CSI Student Branch Rajagiri College of Social Sciences, Cochin In association with CSI Div. IV on Communications and Cochin ChapterFor details contact: Dr. P. X. Joseph, Conference Convener, Prof. & HOD, Department of Computer Science, Rajagiri College of Social Sciences. Rajagiri P.O, Kalamassery, Cochin - 683104, Kerala, India. Phone: Ph: 0484- 2555564, Email: [email protected] or visit the website at: www.rajagiri.edu

CONSEG-2011 : International Conference on Software EngineeringDate : 17-19 February, 2011Organized by: CSI Div. II (Software) and Bangalore ChapterFor details contact: Dr. Anirban Basu, [email protected]

Second International Conference on Emerging Applications of Information Technology (EAIT 2011) Date : 18-20 February, 2011Host by: Kolkata ChapterFor details contact: Mr. D P Sinha, [email protected]

March 201127th CSI National Student ConventionDate : 9-12, March 2011Hosted by: ITM GwaliorOrganized by: CSI ITM Universe Student Branch and CSI Gwalior ChapterFor details contact: [email protected], [email protected], [email protected]

April 2011NCVESCOM-11: 4th National Conference on VLSI, Embedded Systems, Signal Processing and Communication TechnologiesDate : 8-9, Apr 2011 at Chennai Organized by: Department of Electronics & Comunications Engg., Aarupadai Veedu Institute of Technology, Vinayaka Missions University and supported by CSI Div. IV (Communication), IEEE madras Section, IEEE COMSOC, IEEE CS, IETE, BES(I). For details contact: D Vijendra Babu, Conference Co-Chair, NCVESCOM-11, HOD & Associate Professor/ECE, Aarupadai Veedu Institute of Technology, Paiyanoor-603104. Email: [email protected] Phone: +91 9443538245 or Mr. H.R. Mohan, Chair, Div II at [email protected] Website: www.avit.ac.in

July 2011ACC-2011: International Conference on Advances in Computing and Communications Date : 22-24, Jul 2011 at Kochi, IndiaOrganized by: Rajagiri School of Engineering and Technology (RSET) in association with Computer Society of India (CSI), Div. IV & Cochin Chapter, The Institution of Electronics and Telecommunication Engineers (IETE),The Institution of Engineers (India) and Project Management Institute (PMI),Trivandrum, Kerala Chapter.For details contact: Dr. Sabu M. Thampi, Conference Chair - ACC2011, Professor, Dept. of Computer Science & Engineering, Rajagiri School of Engineering and Technology, Rajagiri Valley, Kakkanad, Kochi 682 039, Kerala, INDIA. Email: [email protected] Website: http://www.acc-rajagiri.org

M D AgrawalVice President & Chair, Conference Committee, CSI

Documents

CSI2010 - Keynote Address by R Gopalakrishnan, … Gopalakrishnan, Executive Director, Tata Sons Monthly subscription ` 9/-CSI COMMUNICATIONS | DECEMBER 2010 1 Volume No. 34 Issue