1 VOIP: Voice over Internet Protocol Broadband Phone Services

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VOIP: Voice over Internet Protocol

Broadband Phone Services

Introduction Voice over Internet Protocol (VoIP) is a protocol optimized for the

transmission of voice through the Internet or other packet switched networks.

VoIP is often used abstractly to refer to the actual transmission of voice

VoIP is also known as IP Telephony, Internet telephony, Broadband telephony, Broadband Phone and Voice over Broadband.

VoIP is the ability to make telephone calls over IP-based data networks with a suitable quality of service and superior cost-efficiency.

www.about.com2

Introduction VoIP converts analog voice signals into digital data packets and support

s real-time, two-way transmission of conversations using Internet protocol.

VoIP calls can be made on the Internet using a VoIP service provider and standard computer audio systems. Some service providers support VoIP through ordinary telephones that use special adapters to connect to a home computer network.

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Broadband Phone Service

Broadband phone service [3] Enables voice telephone calls to work over your hi

gh-speed Internet connection.

A broadband phone (also known as a VoIP or Internet phone) utilizes the same IP network as your Internet service.

Hardware adapters connect a standard telephone to the high-speed Internet connection to create a broadband phone.

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Broadband Phone Service Hardware and software broadband phones are

available [3]. Hardware broadband phones use an adapter (either as an

add-on to your traditional phone or built in to an all-in-one phone unit).

The hardware is then connected to either the router on your network (via Ethernet) or your PC (via USB).

Software broadband phones use a software program to make broadband calls.

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http://qwest.centurylink.com/residential/products/voip/how_it_works.html

Broadband Phone Service Plans

Service providers offer many different broadband phone subscription plans [3].

As with a cell phone, some service plans for these telephones feature unlimited local calling or large numbers of free minutes.

The cost of broadband phone service is highly variable; international, long distance and other calling charges often still apply.

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Broadband Phone Reliability

Compared to an Internet-based broadband phone network, the standard home voice telephone network is extremely reliable [3]. Calls cannot be made with the broadband phone whenever

your home Internet service is down.

Additional failures within the broadband phone service itself will add to any downtime caused by the Internet connection.

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Broadband Phone Sound Quality

The sound quality supported by broadband phone service was significantly less than with traditional telephone services [3].

It can vary by provider and location, in general the quality of broadband phone audio is very good.

You might notice a small delay ("lag") between when you speak and the other party hears your voice.

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Why choose VoIP

[2]9

Why choose VoIP

[2]

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Why choose VoIP

[2]

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Why choose VoIP

[2]

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Why choose VoIP

[2]

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Why choose VoIP [1] Cost reduction.

There can be a real savings in long distance telephone costs which is extremely important to most companies, particularly those with international markets.

Consolidation. The ability to eliminate points of failure, consolidate accounting systems and

combine operations is obviously more efficient.

Simplification. An integrated voice/data network allows more standardization and reduces t

otal equipment needs.

Bandwidth efficiency: PSTN networks reserve one pipe for every call. But in IP networks many call

s can use the same pipe simultaneously.

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Why choose VoIP [1]

Important problems of fixed line telephony: Limited extension of PBX Imperfect simultaneous calls numb

er No common local (short) numbers

between branches Slow installation of changes in all t

elephony system – maintenance is done of few companies (cabling, PBX changes, system changes);

Limited ability to transfer, forward calls;

Paid calls between branches; Slow and expensive installation wh

en moving to new office; No possibility to integrate telephon

y system with computer systems.

Advantages of fixed line telephony Well tried quality of calls, services Well known technology

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Why choose VoIP [1] Advantages of VoIP

Extra functions (conversations recording, statistics of calls. Faster and cheaper installation of the system Portability Integration with computer systems Free calls between company’s branches Free calls in your network Lower subscription fees You can use your current LAN – no need to change infrastructure; No limit to simultaneous calls Not limited number of users connected to PBX Remote and fast maintenance

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References

[1] www.gmsvoip.com

[2] Peter Ingram, “Voice over Internet Protocol (VoIP) - An Introduction”, Ofcom, 18th January 2005.

[3] www.about.com

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Computer Networks and Applications

E-Commerce: Recommender Systems

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Computer Networks [6]

Types of Networks Each computer or user in a network is referred to

as a node.

The interconnection between the nodes is referred to as the communication link.

In most networks, each node is a personal computer, but in some cases a peripheral device such as a printer can be a node.

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Computer Networks [6]

The number of links L required between N PCs (nodes) is determined by using the formula

L = N(N−1) / 2

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Network Fundamentals [6]

A network of four PCs.

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Network Fundamentals [6]

A star LAN configuration with a server as the controlling computer.

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Network Fundamentals [6]

A ring LAN configuration.

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Network Fundamentals [6]

A bus LAN configuration.

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Internet Applications [6] The Internet is a worldwide interconnection of computers by

means of a complex network of many networks.

Anyone can connect to the Internet for the purpose of communicating and sharing information with almost any other computer on the Internet.

The Internet is a communication system that accomplishes one of three broad uses: Share resources Share files or data Communication.

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Internet Applications [6]

The primary applications of the Internet are: E-mail File transfer The World Wide Web E-commerce Searches Voice over Internet Protocol Video

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Internet Applications [6]

E-mail is the exchange of notes, letters, memos, and other personal communication by way of e-mail software and service companies.

File transfer refers to the ability to transfer files of data or software from one computer to another.

The World Wide Web is a specialized part of the Internet where companies, organizations, the government, or individuals can post information for others to access and use.

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Internet Applications [6]

E-commerce refers to doing business over the Internet and other computer networks, usually buying and selling goods and services by way of the Web.

An Internet search allows a person to look for information on any given topic. Several companies offer the use of free search “engines,” which are specialized software that can look for websites related to the desired search topic.

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Internet Applications [6]

Voice over Internet Protocol (VoIP) is the technique of replacing standard telephone service with a digital voice version with calls taking place over the Internet.

Video over Internet Protocol. Video or TV over the Internet (IPTV) is becoming more common.

The video (and accompanying audio) is digitized, compressed, and sent via the Internet. It is expected to gradually replace some video transmitted over the air and by cable television systems.

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World Wide Web

A system of globally unique identifiers for resources on the Web Uniform Resource Locator (URL):

http://example.org/wiki/Main_Page Domain name is example.org. Resource identified as /wiki/Main_Page

The publishing language HyperText Markup Language (HTML);

The Hypertext Transfer Protocol (HTTP).

www.wikipedia.org

31http://www.w3schools.com/html

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World Wide Web

http://www.w3schools.com/html

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e-Commerce

How to enhance e-Commerce sales? Browsers into buyers Cross-sell

Recommender Systems!!

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What are recommender systems? Recommender systems are systems which provide

recommendations to a user Too much information (information overload) Users have too many choices

Recommend different products for users, suited to their tastes. Assist users in finding information Reduce search and navigation time

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Case Study: Amazon

www.amazon.com

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Personalized Product Recommendation?

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Which Sources of Information? Sources of information for recommendations:

[1] Browsing and searching data Purchase data Feedback provided by the users Textual comments Expert recommendations E-mail Rating

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Type of Recommendations [2] Population-based

The most popular news articles, or searches, or downloads

Frequently add content No user tracking needed.

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Type of Recommendations [2] Item-to-item

Content-based One item is recommended based on the user’s

indication that they like another item. If you like Lord of the Rings, you’ll like Legend.

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Type of Recommendations [2] Challenges with item-to-item:

Getting users to tell you what they like Financial and time reasons

Getting enough data to make “novel” predictions. What users really want are recommendations for things

they’re not aware of.

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Type of Recommendations [2] Item-to-item

Most effective when you have metadata that lets you automatically relate items.

Genre, actors, director, etc. Also best when decoupled from payment

Users should have an incentive to rate items truthfully.

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Type of Recommendations [2] User-based

“Users who bought X like Y.” Each user is represented by a vector indicating

his ratings for each product. Users with a small distance between each other

are similar. Find a similar user and recommend things they

like that you haven’t rated.

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Type of Recommendations [2] User-based

Advantages: Users don’t need to rate much. No info about products needed. Easy to implement

Disadvantages Pushes users “toward the middle” – products with more

ratings carry more weight. How to deal with new products? Many products and few users -> lots of things don’t get

recommended.

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Type of Recommendations: General [1] Content-based Recommender System

Recommend items similar to those users preferred in the past

User profiling is the key Items/content usually denoted by keywords Matching “user preferences” with “item characteristics” …

works for textual information Vector Space Model widely used

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Type of Recommendations: General [1]

Not all content is well represented by keywords, e.g. images

Items represented by same set of features are indistinguishable

Overspecialization: unrated items not shown Users with thousands of purchases is a problem New user: No history available Shouldn’t show items that are too different, or too similar

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Type of Recommendations: General [1]Collaborative Recommender System

• Memory-based collaborative filtering techniques Main problems: scalability and handling of new users

• Model-based collaborative filtering techniques High accuracy of prediction No need for searching the whole user-item rating matrix

(grouping users into models)

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Type of Recommendations: General [1]Collaborative Recommender System

Use other users recommendations (ratings) to judge item’s utility

Key is to find users/user groups whose interests match with the current user

Vector Space model widely used (directions of vectors are user specified ratings)

More users, more ratings: better results Can account for items dissimilar to the ones seen in the

past too Example: Movielens.org

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Type of Recommendations: General [1]

Different users might use different scales. Possible solution: weighted ratings, i.e. deviations from

average rating Finding similar users/user groups isn’t very easy New user: No preferences available New item: No ratings available Demographic filtering is required Multi-criteria ratings is required

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Type of Recommendations: Example[1]Cluster Models

Create clusters or groups Put a customer into a category Classification simplifies the task of user matching More scalability and performance Lesser accuracy than normal collaborative filtering

method

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Type of Recommendations: Example[1]Item to item collaboration (one that Amazon.com uses)

Compute similarity between item pairs Combine the similar items into recommendation list Vector corresponds to an item, and directions correspond

to customers who have purchased them “Similar items” table built offline Example: Amazon.com

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Type of Recommendations: Example[1]Knowledge based RS

Use knowledge of users and items Conversational Interaction used to establish current user

preferences i.e. “more like this”, “less like that”, “none of those” … No user profiles maintained, preferences drawn through

manual interaction Query by example … tweaking the source example to fetch

results

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How RS Work? Similarity Measurement [4]

For two data objects, X = (x1, x2, . . . , xn) and Y =(y1, y2, . . . , yn), the popular Minkowski distance is defined as

where n is the dimension number of the object and xi, yi are the values of the ith dimension of object X and Y respectively, and q is a positive integer. When q = 1, d is Manhattan distance; when q = 2, d is Euclidian distance

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How RS Work? Similarity wu,v between two users u and v, or wi,j between two

items i and j, is measured by computing the Pearson correlation [4]

where the i I summations are over the items that both the ∈users u and v have rated and is the average rating of the co-rated items of the u-th user

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Example

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Prediction and Recommendation Computation

To make a prediction for the active user, a, on a certain item, i, we can take a weighted average of all the ratings on that item according to the following formula [4]

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Example

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Example

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Example

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Challenging: # Users and # Items Clustering Algorithms

[5]

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Complex Networks

Recommender Systems and Social Web

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Complex Networks

Realistic networks are Complex Networks Biological Network: How the brain work

efficiently? Propagation Network: How viruses propagate

through the computer? Competitor network: How rumors spread out the

human society? Communication Network: How information

transmission exchanges on the Internet ?

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Biotech Industry in USAhttp://ecclectic.ss.uci.edu/~drwhite/

Movie

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Complex Networks What is a complex network?

Observes any form of user behavior Web surfing logs E-mails transactions Communication over Blogs Friend lists Purchase history on e-

commerce sites Any other kinds action that

demonstrates user intent It creates large scale graph

from all this behavior data

http://www.deqwas.com/en/technology.html

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Recommender Systems and Social W eb [3]

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Recommender Systems and Social W eb [3]

Facebook only allows a bidirectional connection among users if user A is connected to B then B is also connecte

d to A

Twitter users can follow without being followed user A is linked to B, B is not linked to A.

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Recommender Systems and Social W eb [3]

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Recommender Systems and Social W eb [3]

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Recommender Systems and Social W eb [3]

If a user visited certain exhibits and her/his Facebook page mentions she/he is a "Fan" of certain items, those would be saved for later matching against new visitors profiles.

New visitors would be recommended exhibits that were viewed by people whom they most resemble based on the items they are "Fan".

Find user profiles resembling current visitor's profile, extract tagged photos that are also related to museum's key terms, recommend exhibits relating to those.

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References[1] Aalap Kohojkar, Yang Liu, Zhan Shi, “Recommender Systems”, March 31, 2008.

[2] Maria Fasli, “Agent Technology for e-Commerce”, http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm

[3] Amit Tiroshi, Tsvi Kuflik, Judy Kay and Bob Kummerfeld, “Recommender Systems and the Social Web”, International Workshop at UMAP2011 on Augmenting User Models with Real World Experiences to Enhance Personalization and Adaptation, July 15, 2011.

[4] Xiaoyuan Su, Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Advances in Artificial Intelligence, Vol. 2009, 2009.

[5] Badrul M. Sarwar, George Karypis , Joseph Konstan, and John Riedl, “Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering”, The Fifth International Conference on Computer and Information Technology (ICCIT 2002) , 2002.

[6] Louis E. Frenzel, Jr., “Principles of Electronic Communication Systems”, The third edition, McGraw-Hill, 2008.