7.4 – Dissemination, Communication, Exploitation and ... · D 7.4 – Dissemination, Communication, Exploitation and Standardisation Report Year 2 CogNet Version final Page 7 of

7.4 – Dissemination, Communication, Exploitation and Standardisation Report

Year 2 Domenico Gallico, Matteo Biancani

Document Number D 7.4

Status Final

Work Package WP 7

Deliverable Type Report

Date of Delivery 30/June/2017

Responsible Unit Interoute

Contributors All

Keywords Dissemination, Exploitation, Standardisation

Dissemination level PU

D 7.4 – Dissemination, Communication, Exploitation and Standardisation Report Year 2

CogNet Version final Page 2 of 25

Change History

Version Date Status Author (Unit) Description

0.1 26/05/2017 Draft Domenico Gallico (IRT) T.o.C. definition

0.1 24/05/2017 Draft Olga Uryupina (UniTN) UniTN contribution

0.1 06/06/2017 Draft Teodora Sandra Buda (IBM)

IBM contribution

0.1 09/06/2017 Draft Bruno Ordozgoiti (UPM) UPM contribution

0.2 12/06/2017 Draft Domenico Gallico (IRT) First consolidated version

0.2 14/06/2017 Draft Udi Margolin (Nokia) Exploitation

0.3 20/06/2017 Draft Domenico Gallico Ready for review version

0.4 27/06/2017 Draft Martin Tolan (WIT) First Review

0.4 27/06/2017 Draft Olga Uryupina (UniTN) Second Review

Final 29/06/2017 Final Domenico Gallico (IRT) Final version



Acronyms and Definitions

Acronym Defined as

SDN Software Defined Network

NFV Network Function Virtualisation

M2M Machine to Machine

IOT Internet Of Things

MANO Management and Orchestration

OSM Open Source MANO

NLP Natural Language Processing

IRTF Internet Research Task Force

NetCla Network Traffic Classification Challenge

NFVRG Network Function Virtualization Research Group

NMRG Network Management Research Group

SDNRG Software Defined Network Research Group

NMLRG Network Machine Learning Research Group

SUPA Simplified Use of Policy Abstractions

ETSI European Telecommunications Standards Institute

ISG Industry Specification Group

EVE WG Evolution and Ecosystem Working Group

H2A Human Health Analytics



Executive Summary

This Deliverable provides an update on the WP7 activities carried out

during the second year of the CogNet Project. The document is organized

according to the different tasks that compose WP7, thus it deals with

dissemination, standardization and exploitation. As such, this document

should be considered as an update of D7.3 delivered in M12, and it

maintain the same internal structure to facilitate the readability.

On the dissemination side, we report on all the communication channels

used by partners to promote the CogNet project and on the

dissemination activity in terms of publications, conference and other

events.

In sec. 2 the updated exploitation strategy is reported on a per partner

basis in order to highlight the business opportunities and technology

transfer action envisaged by each partner.

The standardization activities are reported in D7.7 which has the same due

date of this document, thus the consortium decided to provide just a

reference to D7.7 in order to avoid content duplication.



Table of Contents

1. Dissemination and Communication activities ................................................................ 6

1.1. Online presence .............................................................................................................................................. 6

1.1.1. Website..................................................................................................................................................... 6

1.1.2. Twitter ....................................................................................................................................................... 7

1.1.3. Other social media ............................................................................................................................... 8

1.2. Publications in conferences and journals ............................................................................................. 8

1.2.1. Industry oriented events .................................................................................................................... 9

1.2.2. Academic Excellence ......................................................................................................................... 10

1.3. On-going liaisons with other 5G-PPP Projects ................................................................................. 20

2. Updated exploitation strategy ...................................................................................... 22

3. Standardisation activities ............................................................................................... 24

4. Conclusion ....................................................................................................................... 25



1. Dissemination and Communication activities

This section aims at reporting all the actions carried out by the CogNet partners to disseminate the research and development activities and related results achieved during the second year of project. In the first year, the CogNet website as well as social networks accounts have been setup and maintained as the primary portals to advertise the CogNet results (public deliverables, list of events, and so on). Then, the complete list of Y2 publications in conferences, journals and events has been provided, both for industry and research/academia.

1.1. Online presence

1.1.1. Website

The CogNet website (http://www.cognet.5g-ppp.eu/) has been continuously updated since its release in September 2015. It has been described in D7.1 [1] and it contains all the information concerning the consortium partners, the Project’s activities, events’ participation, publications and the full list of deliverables, with the possibility to download the public deliverables once they are finalized and delivered. Moreover, it has a “News” section where a blog is maintained and updated frequently with the latest activities as shown below.

Figure 1-1: CogNet website - News page

http://www.cognet.5g-ppp.eu/



The Project website is provided with a Wordpress feature that collects and shows all the statistics related to the number of website visitors. As Figure 1-2 shows, the webpage attracts an average of approximately 60 unique visitors daily, thus, suggesting a high interest in the CogNet technology.

Figure 1-2: Website’s statistics

The above image highlights a couple of peaks in the number of unique visitors during the last year. These peaks can be brought back to the EUCNC 2016 event on 27-30 June 2016 where CogNet has organized a “Workshop on Network Management, Quality of Service and Security for 5G Networks” and the other to the Orange Exhibition days held at the Orange Gardens in Chatillon (France).

1.1.2. Twitter

In year one, we concentrated on getting the CogNet project name “out there” via Twitter. This was a deliberate approach to build momentum around CogNet since most of our key deliverables will not be due for publication until years two and three of the project. In line with our phased approach to dissemination (starting with awareness-raising, then onto positioning, and finally market), our social media strategy also followed a phased approach. Now that CogNet has moved through the second year of the project, we feel that we can continue with awareness-raising and progress onto positioning the project for the final market phase.

Out of the 19 projects funded as part of 5G-PPP, CogNet ranks fifth on Twitter in terms of the number of Followers. Table 1 contains a snapshot of all 5G-PPP projects on Twitter as of 19-June, 2017.

Project Twitter Account Followers Tweets Following

EURO 5G @5GPPP 2546 1717 139

METIS-II @metis2020 651 162 42

5G-Ensure @5GEnsure 553 1144 279

5G-NORMA @5G_NORMA 330 49 33



CogNet @5GPPPCogNet 294 380 253

5G-Crosshaul @Crosshaul_eu 267 77 12

Sonata @sonataNFV 230 476 98

Selfnet @5GPPP_SELFNET 228 599 299

Speed5G @SPEED_5G 204 24 22

SESAME @Sesame_H2020 197 110 141

CHARISMA @charisma5G 190 138 83

SUPERFLUIDITY @Superfluidity5g 159 64 76

5GEX @5g_ex 136 65 69

5G-Xhaul @5G_Xhaul 122 44 44

Flex5Gware @Flex5Gware 104 37 30

FANTASTIC @FANTASTIC5G 79 34 9

mmMAGIC @mmMAGIC_5GPPP 63 34 27

COHERENT @H2020_COHERENT 59 21 48

VirtuWind @VirtuWind 22 12 14

Table 1 Twitter figures for 5G-PPP Projects as of 19th June, 2017

1.1.3. Other social media To date CogNet has had 4 discussions/announcements on LinkedIn as well as 2 videos on YouTube. The primary social media vehicles in use by CogNet are Twitter and Facebook. Both of these are updated regularly and in year two these were the main social media streams used to publicise the project’s move to the positioning phase of our dissemination. CogNet will continue to maintain a strong presence on Twitter and Facebook but will naturally move towards more nuanced dissemination via the CogNet website as the project work matures and the deliverables are made available. Year three will see an increase in social media activity on Twitter and Youtube to showcase the results of CogNet as it is made available for commercial exploitation.

1.2. Publications in conferences and journals

This section reports all the papers that have been presented or journal articles published in the last year of activity together with the list of events where partners have presented the CogNet Project.



1.2.1. Industry oriented events

1. Title: “CogNet: An NFV/SDN based architecture for Autonomic 5G Network Management using Machine Learning” (poster)

Authors: Domenico Gallico (IRT), Matteo Biancani (IRT), Haytham Assem (IBM), Diego Lopez (TID).

Event: 2nd Global 5G Event – “Enabling the 5G EcoSphere”

Date: November 9-10, 2016

Venue: Rome, Italy.

Description: The event offered participants both lively debates around spectrum, standards and deployment of 5G as well as exciting showcases of the latest 5G developments from industrial players such as Datang, Huawei, Nokia, Qualcomm and ZTE. Moreover, the latest results and demos from 17 5G PPP projects have been presented: 5G-CROSSHAUL, 5GEx, 5G ENSURE, 5G-NORMA, 5G-XHAUL, CHARISMA, COGNET, COHERENT, FANTASTIC-5G, FLEX5GWARE, METIS II, mmMagic, SELFNET, SESAME, SPEED-5G, SUPERFLUIDITY and VIRTUWIND. The exhibition of the demonstrations was open in parallel to the conference for the two days and participants had the possibility to visit the CogNet booth where our architecture and demos were presented.

URL: https://5g-ppp.eu/event/second-global-5g-event-on-9-10-november-2016-in-rome-italy/

2. Event: The 3rd Global 5G event.

Description: The 3rd Global 5G Event was held in Tokyo on 24-25 May 2017 and was organised by the 5G Mobile Communications Promotion Forum (5GMF). CogNet was represented at this event in the form of attendance and provided the consortium with an overview of current developments, accomplishments and future direction within the global 5G community.

Date: May 24-25, 2017

Venue: Tokyo, Japan

URL: http://5gmf.jp/en/news/20170405105240/

3. Title: SLA enforcement

Authors: Imen Grida Ben Yahia and Jaafar Bendriss (Orange).

Event: Orange Exhibition days

Date: December 6-7-8, 2016

Venue: Orange Gardens, Chatillon France.

https://5g-ppp.eu/event/second-global-5g-event-on-9-10-november-2016-in-rome-italy/

https://5g-ppp.eu/event/second-global-5g-event-on-9-10-november-2016-in-rome-italy/

http://5gmf.jp/en/news/20170405105240/



Description: Annual event (not public but upon invitation) to show the innovation in Orange, where affiliates, innovation, research team and several invited VIPs exchange on the basis of demo.

1.2.2. Academic Excellence During this period, the CogNet consortium has exercised continued dedication to the production of valuable research results. This has brought numerous new publications at some of the most reputable venues worldwide, focusing both on the advancement of machine learning methods and on their application to problems related to 5G network management. In total, 25 academic publications have been produced in the form of conference and journal papers, book chapters and whitepaper, out of which 20 are already accepted or published.

In addition to the academic publications produced during the second year, the CogNet partners have also devoted efforts to the organization of events such as workshops and competitions, and have given several talks on the topics being addressed in the project. In particular, the UNITN team hosted the NetCla competition, which brought members of the machine learning community worldwide closer to the challenges arising in 5G networks.

In this section, a list of all the achievements in academic dissemination during the second year is presented.

1.2.2.1 Publications

1. Title: “Cognitive Services Portfolio for 5G Network Management.” (ACCEPTED)

Authors: Bora Caglayan, Teodora Sandra Buda, Haytham Assem, Imen Grida Ben Yahia, Jaafar Bendriss, Angel Martin, Gorka Velez, Udi Margolin, Itai Segall, Antonio Pastor, Diego Lopez, Alberto Mozo, Bruno Ordozgoiti, Marius-Iulian Corici, Mikhail Smirnov, Kateryna Timoshenko, Olga Uryupina, Joe Tynan, Martin Tolan.

Event: 2nd Workshop on Network Management, Quality of Service and Security for 5G Networks colocated with EUCNC 2017.

2. Title: 5G PPP – 5G Architecture White Paper (PUBLISHED)

Authors: 5G PPP Architecture Working Group

URL: https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-5G-Architecture-WP-July-2016.pdf

Date: July 2016

3. Title: “Dynamic Policy Based Actuation for Autonomic Management of Telecoms Networks.” (ACCEPTED)

Authors: Martin Tolan, Joe Tynan, Angel Martin, Felipe Mogollon.

Event: 2nd Workshop on Network Management, Quality of Service and Security for 5G Networks colocated with EUCNC 2017

https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-5G-Architecture-WP-July-2016.pdf

https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-5G-Architecture-WP-July-2016.pdf



4. Title: Book chapter for 5G European Vision (ACCEPTED)

Authors: Haytham Assem, Jaafar Bendriss, Teodora Sandra Buda, Imen Grida Ben Yahia, Diego Lopez, Udi Margolin, Angel Martin, Alberto Mozo, Marouane Mechteri, Kieran Sullivan, Martin Tolan.

5. Title: “A Cognitive Management Architecture for 5G Networks.” (UNDER REVIEW)

Journal: IEEE Transactions on Network and Service Management.

6. Title: “RCMC: Recognizing Crowd Mobility Patterns in Cities based on Location Based Social Networks Data” (ACCEPTED)

Authors: Haytham Assem, Teodora Sandra Buda, Declan O’Sullivan

Journal: ACM Transactions on Intelligent Systems and Technology (TIST),

7. Title: "Discovering New Socio-demographic Regional Patterns in Cities" (PUBLISHED)

Authors: Haytham Assem, Lei Xu, Teodora Sandra Buda, Declan O’Sullivan

Event: LBSN Workshop, SIGSPATIAL 2016, ACM 9thInternational Conference on

8. Title: “Machine Learning as a Service for enabling Internet of Things and People” (PUBLISHED)


Journal: Personal and Ubiquitous Computing (PUC) Journal

9. Title: “RCMC: Recognizing Crowd Mobility Patterns in Cities based on Location Based Social Networks Data” (ACCEPTED)


Journal: ACM Transactions on Intelligent Systems and Technology (TIST),

10. Title: "Spatio-Temporal Clustering Approach for Detecting Functional Regions in Cities" (PUBLISHED)

Authors: Haytham Assem, Lei Xu, Teodora Sandra Buda, Declan O’Sullivan.

Event: IEEE ICTAI (International Conference on Tools for Artificial Intelligence) 2016, San Jose, California.

11. Title: “Cognitive Applications and Their Supporting Architecture for Smart Cities” (PUBLISHED)

Authors: Haytham Assem, Lei Xu, Teodora Sandra Buda, Declan O’Sullivan.

Journal: Big Data Analytics for Sensor-Network Collected Intelligence

12. Title: "ADE: An ensemble approach for early anomaly detection" (PUBLISHED)

Authors: Teodora Sandra Buda, Haytham Assem, Lei Xu.

Event: IFIP/IEEE International Symposium on Integrated Network Management (IM) 2017



13. Title: “Instantaneous Throughput Prediction in Cellular Networks: Which Information Is Needed?”

Description: Downlink data rates can vary significantly in cellular networks, with a potentially non-negligible effect on the user experience. Content providers address this problem by using different representations (e.g., picture resolution, video resolution and rate) of the same content and switch among these based on measurements collected during the connection. If it were possible to know the achievable data rate before the connection establishment, content providers could choose the most appropriate representation from the very beginning. We have conducted a measurement campaign involving 60 users connected to a production network in France, to determine whether it is possible to predict the achievable data rate using measurements collected, before establishing the connection to the content provider, on the operator's network and on the mobile node. We show that it is indeed possible to exploit these measurements to predict, with a reasonable accuracy, the achievable data rate.

Authors: Alassane Samba (Orange Labs, France), Gwendal Simon (Telecom Bretagne, France), Philippe Dooze (Orange Labs, France), Yann Busnel (Crest (Ensai) / Inria Rennes, France), Alberto Blanc (Telecom Bretagne, France).

Event: IFIP/IEEE International Symposium on Integrated Network Management

Date: May 18th-12th, 2017

Location: Lisbon, Portugal.

URL: http://im2017.ieee-im.org/poster-sessions

Event topic: integrated Management in the Cloud and 5G Era

14. Title: “Forward Event-Chain Monte Carlo: fast sampling by randomness control in irreversible Markov chains” (UNDER REVIEW)

Authors: Manon Michel (Orange Labs), Stephane Senecal (Orange Labs)

Event: NIPS (Neural Information Processing Systems) 2017 conference

Description: Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain, have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. However, while applying such schemes to standard statistical models encountered in machine learning, one generally needs to introduce an additional randomization for sake of correctness. We propose here an adaptation of the Event-Chain method that reduces this extra-randomization to a bare minimum. We test this Forward Event-Chain Monte Carlo method on the sampling of high-dimensional ill-conditioned Gaussian distribution and compare its efficiency to standard Event-Chain and Monte Carlo methods. Accelerations up to several magnitudes are exhibited.

Date: December 4th- 9th 2017

Location: Long Beach, CA/USA

http://im2017.ieee-im.org/poster-sessions



Organizers: Neural Information Processing Systems Foundation

URL: https://nips.cc/Conferences/2017

Event topic: Machine Learning

15. Title: “Don't you understand a measure? Learning it: structured prediction for Coreference Resolution using its evaluation measure as a loss function” (ACCEPTED)

Authors: Iryna Haponchyk (UNITN), Alessandro Moschitti (UNITN)

Description: An essential aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use of more complex loss functions. Most noteworthily, we show that such functions can be (i) automatically learned also from controversial but commonly accepted CR measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL--2012 setting shows the benefit of more expressive loss.

Event: ACL (Association for Computational Linguistics) 2017

Event topic: Machine Learning, Computational Linguistics

16. Title: “RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations” (ACCEPTED)

Authors: Kateryna Tymoshenko (UNITN), Alessandro Moschitti (UNITN), Massimo Nicosia (UNITN) and Aliaksei Severyn (Google)

Description (abstract): We present a highly-flexible UIMA-based pipeline for developing structural kernel-based systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For example, the proposed pipeline can represent an input question and answer sentence pairs as syntactic-semantic structures, enriching them with relational information, e.g., links between question class, focus and named entities, and serializes them as training and test files for the tree kernel-based reranking framework. The pipeline generates several dependency and shallow chunk-based representations shown to achieve competitive results in previous work. It also enables easy evaluation of the models thanks to cross-validation facilities.

Event: ACL 2017 (demo)


17. Title: “Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model” (UNDER REVIEW)

Authors: Kateryna Tymoshenko (UNITN), Daniele Bonadiman (UNITN), Alessandro Moschitti (UNITN)

https://nips.cc/Conferences/2017



Description: Combining convolution tree kernels (CTKs) and convolutional neural networks (CNNs) in Support Vector Machines (SVMs) is very appealing as structural syntactic properties captured by CTKs can work in synergy with the representation layers learned by CNNs. However, it is not clear how such layers should be added to the kernels in learning to rank (L2R) models. In this paper, we define a hybrid pairwise-classification L2R model, which can effectively integrate neural embeddings within CTKs. Our experiments on two datasets clearly show a significant improvement of our approach.

Event: EMNLP (Empirical Methods on Natural Language Processing) 2017


18. Title: “Structural Semantic Models for Automatic Analysis of Urban Areas” (UNDER REVIEW)

Authors: Gianni Barlacchi (UNITN), Alberto Rossi (FBK), Bruno Lepri (FBK), Alessandro Moschitti (UNITN)

Description: The growing availability of data from cities (e.g., traffic flow, human mobility and geographical data) open new opportunities for predicting and thus optimizing human activities. For example, the automatic analysis of land use enables the possibility of better administrating a city in terms of resources and provided services. However, such analysis requires specific information, which is often not available for privacy concerns. In this paper, we propose a novel machine learning representation based on the available public information to classify the most predominant land use of an urban area, which is a very common task in urban computing. In particular, in addition to standard feature vectors, we encode geo-social data from Location-Based Social Networks (LBSNs) into a conceptual tree structure that we call Geo-Tree. Then, we used such representations in kernel machines, which can thus perform accurate classification exploiting hierarchical substructure of concepts as features. Our extensive comparative study on the areas of New York and its boroughs shows that tree kernels (TKs) applied to Geo-Trees are very effective improving the state of the art up to 18% in Macro-F1.

Event: ECML (European Conference on Machine Learning) 2017

Event topic: Machine Learning

19. Title: “Self-Crowdsourcing Training for Relation Extraction” (ACCEPTED)

Authors: Azad Abad(UNITN), Moin Nabi (UNITN) and Alessandro Moschitti (UNITN)

Description: In this paper, we introduce a self-training strategy for crowdsourcing. The training examples are automatically selected to train the crowd workers. Our experimental results show an impact of 5% improvement in terms of F1 for relation extraction task, compared to the method based on distant supervision.

Event: ACL (Association for Computational Linguistics) 2017




20. Title: “Annotating a broader range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus” (ACCEPTED)

Authors: Olga Uryupina (UNITN), Ron Artstein, Antonella Bristot, Federica Cavicchio, Francesca Delogu, Kepa J. Rodriguez, Massimo Poesio

Description: This paper presents ARRAU, a multi-genre corpus of anaphora created to provide much needed data for the next generation of coreference and anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of ARRAU include: thorough annotation of mention boundaries (minimal/maximal spans, discontinuous mentions); full annotation of the (non) referentiality and (non) anaphoricity of mentions, distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; the annotation of a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; the mark-up of genericity; the annotation of a wide range of anaphoric relations, including also bridging relations and discourse deixis; and, finally, the annotation of anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC.

Journal: Journal of Natural Language Engineering

Journal publication date: 2018

Event topic: Data Generation, Computational Linguistics

21. Title: “A fast iterative algorithm for improved unsupervised feature selection” (PUBLISHED)

Authors: Bruno Ordozgoiti (UPM), Sandra Gómez Canaval (UPM), Alberto Mozo (UPM)

Event: 2016 IEEE 16th International Conference on Data Mining (ICDM)

Description: Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction is feature selection. Due to the impossibility of labelling many data sets, unsupervised approaches are frequently the only option. The column subset selection problem translates naturally to this purpose, and has received consider able attention over the last few years, as it provides simple linear models for data reconstruction. Existing methods, however, often achieve approximation errors that are far from the optimum. In this paper, we present a novel algorithm for column subset selection that consistently outperforms state-of-the-art methods in approximation error. We present a series of key derivations that allow an efficient implementation, making it comparable in speed and in some cases faster than other algorithms. We also prove results that make it possible to deal with huge matrices, which has strong implications for other algorithms of this type in the big data field. We validate our claims through experiments on a wide variety of well-known data sets.

Date: December 13th–15th, 2017



Location: Barcelona, Spain

Organizers: IEEE, Universitat Pompeu Fabra, Grupo Pacífico, Eurecat

URL: http://icdm2016.eurecat.org/

Event topic: ICDM covers all aspects of data mining, including algorithms, software and systems, and applications.

22. Title: “Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure” (PUBLISHED)

Authors: Bruno Ordozgoiti (UPM), Sandra Gómez Canaval (UPM), Alberto Mozo (UPM), Udi Margolin (Nokia), Elisha Rosensweig (Nokia), Itai Segall (Nokia)

Description: Cloud infrastructure in data centers is expected to be one of the main technologies supporting Internet communications in the coming years. Virtualization is employed to achieve the flexibility and dynamicity required by the wide variety of applications used today. Therefore, optimal allocation of virtual machines is key to ensuring performance and efficiency. Noisy neighbor is a term used to describe virtual machines competing for physical resources and thus disturbing each other, a phenomenon that can dramatically degrade their performance. Detecting noisy neighbors using simple thresholding approaches is ineffective. To exploit the time-series nature of cloud monitoring data, we propose an approach based on deep convolutional networks. We test it on real infrastructure data and show it outperforms well-known classifiers in detecting noisy neighbors.

Event: 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Date: April 26th–28th, 2016

Location: Bruges, Belgium

Organizers: Université catholique de Louvain - ICTEAM-ELEN-Machine Learning Group

URL: https://www.elen.ucl.ac.be/esann/

Event topic: Machine learning, computational intelligence and artificial neural networks topics. Theory and applications.

23. Title: “Probabilistic Leverage Scores for Parallelized Unsupervised Feature Selection” (PUBLISHED)


Description: Dimensionality reduction is often crucial for the application of machine learning and data mining. Feature selection methods can be employed for this purpose, with the advantage of preserving interpretability. There exist unsupervised feature selections methods based on matrix factorization algorithms, which can help choose the most informative features in terms of approximation error. Randomized methods have been proposed recently to provide better theoretical guarantees and better

http://icdm2016.eurecat.org/

https://www.elen.ucl.ac.be/esann/



approximation errors than their deterministic counterparts, but their computational costs can be significant when dealing with big, high dimensional data sets. Some existing randomized and deterministic approaches require the computation of the singular value decomposition in O(mn min(m, n)) time (for m samples and n features) for providing leverage scores. This compromises their applicability to domains of even moderately high dimensionality. In this paper, we propose the use of Probabilistic PCA to compute the leverage scores in O(mnk) time, enabling the applicability of some of these randomized methods to large, high-dimensional data sets. We show that using this approach, we can rapidly provide an approximation of the leverage scores that is works well in this context. In addition, we offer a parallelized version over the emerging Resilient Distributed Datasets paradigm (RDD) on Apache Spark, making it horizontally scalable for enormous numbers of data instances. We validate the performance of our approach on different data sets comprised of real-world and synthetic data.

Event: 14th International Work-Conference on Artificial Neural Networks 2017 (IWANN)

Date: June 14th-16th, 2017

Location: Cádiz, Spain

Organizers: IEEE Computational Intelligence Society, Universitat Politècnica de Catalunya, Universidad de Cádiz, Universidad de Málaga, Universidad de Granada

URL: http://iwann.uma.es/

Event topic: Mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation, emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence, applications.

24. Title: “Iterative column subset selection” (Invited extension of A fast iterative algorithm for improved unsupervised feature selection UNDER REVIEW)


Journal: Knowledge and Information Systems (KAIS)

Description: Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction is feature selection. Due to the impossibility of labelling many data sets, unsupervised approaches are frequently the only option. The column subset selection problem translates naturally to this purpose, and has received considerable attention over the last few years, as it provides simple linear models for low-rank data reconstruction. Recently it was empirically shown that an iterative algorithm, which can be implemented efficiently, provides better subsets than other state-of-the-art methods. In this paper, we describe this algorithm and provide a more in-depth analysis. We carry out numerous experiments to gain insights on its behavior and derive a simple bound for the conditioning of the resulting matrix. To the best of our knowledge, this is the first theoretical result of this kind for this algorithm.

http://iwann.uma.es/



URL: http://kais.bigke.org/

Journal topic: The journal focuses on knowledge systems and advanced information systems, including their theoretical foundations, infrastructure, enabling technologies and emerging applications.

25. Title: “CogNet: Network Management Architecture Featuring Cognitive Capabilities”

Authors: Lei Xu, Haytham Assem, Imen Grida Ben Yahia, Teodora Sandra Buda, Angel Martin, Domenico Gallico, Matteo Biancani, Antonio Pastor, Pedro A. Aranda, Mikhail Smirnov, Danny Raz, Olga Uryupina, Alberto Mozo, Bruno Ordozgoiti, Marius-Iulian Corici, Pat O’Sullivan, Robert Mullins.

Event: EUCNC 2016.

Date: June 27th-30th, 2016

Location: Athens, Greece

26. Title: “Cognitive Reflector-based Radio Access Network in Terahertz band for Connected Vehicles” (SUBMITTED)

Authors: Harbil Arregui, Andoni Mujika, Estíbaliz Loyo, Gorka Vélez, Michael T. Barros, Sasitharan Balasubramaniam, Oihana Otaegui.

Journal: IEEE Transactions on Intelligent Transportation Systems.

URL: https://www.ieee-itss.org/

Journal topic: The journal focuses on advances on the theoretical, experimental, and operational aspects of Electrical Engineering and Information Technologies as applied to intelligent transportation systems (ITS).

27. Title: “LAMB-DASH: A DASH-HEVC adaptive streaming algorithm in a sharing bandwidth environment for heterogeneous contents and dynamic connections in practice” (UNDER REVIEW)

Authors: Angel Martin, Roberto Viola, Josu Gorostegui, Mikel Zorrilla, Jon Montalban.

Journal: Journal of Real-Time Image Processing.

URL: https://link.springer.com/journal/11554

Journal topic: The journal focuses on real-time aspect of image and video processing.

1.2.2.2 Tutorials, invited talks, workshops and competitions

1. Title: "Virtualization and 5G" (tutorial)

Author: Fabrizio Granelli (UNITN)

Event: European Wireless 2017 (Dresden)

2. Title: "Enabling 5G architectures through Software Defined Networking" (tutorial)

http://kais.bigke.org/

https://www.ieee-itss.org/

https://link.springer.com/journal/11554



Author: Fabrizio Granelli (UNITN)

Event: IEEE CAMAD 2017 (Lund)

3. Title: “Deep Learning and Structural Kernels for Semantic Inference on Web Data” (invited talk)

Author: Alessandro Moschitti (UNITN)

Venue: SLTC 2016: The Sixth Swedish Language Technology Conference (SLTC) Umea University, 17-18 November, 2016, Umea, Sweden.

4. Title: “Machine Learning for 5G applications” (invited talk)

Author: Olga Uryupina (UNITN)

Event: NetCla: ECML-PKDD 2016 Network Classification Challenge

5. Title: “NetCla: ECML-PKDD 2016 Network Classification Challenge” (workshop)

Organizers: UNITN

Description: A workshop collocated with ECML-PKDD presenting the results of the NetCla challenge (cf. 6 below)

6. Title: “NetCla: Network Classification Challenge” (competition)

Organizers: UNITN

Description: In the second year of the project, we have organized NetCla—a Network Traffic Classification challenge (http://www.ecmlpkdd2016.org/index.html). The main objective of the NetCla initiative was the assessment of the state of the art in the Network Traffic Classification task: the performance of the existing machine learning solutions, their advantages and shortcomings. The challenge was very successful, with more than 100 teams from all over the world downloading the data (cf. Figure 1) and 25 participants submitting their results on the test set.

http://www.ecmlpkdd2016.org/index.html



Figure 1-3 NetCla participation

Apart from organizing the challenge, CogNet has also achieved a strong participation track: three partners submitted their runs; the top two systems were built within CogNet.

One important outcome of the challenge is the release of the NetCla dataset. Unlike other traffic classification datasets, the NetCla data is publicly available and thus can constitute a valuable source for benchmarking activities in 5G scenarios.

The results of the challenge were presented and discussed at the NetCla workshop, collocated with ECML-PKDD, one of the major European conferences on Machine Learning. This way we raised the interest of the Machine Learning community, opening possibilities for future collaborations between data science and telecom.

1.3. On-going liaisons with other 5G-PPP Projects

This section reports all of the on-going collaborations with other 5G-PPP Projects. Throughout the life of CogNet one of the main focuses was the dissemination to a wider audience. A particular emphasis was on interactions with other 5G-PPP projects which included presentations, networking events, published papers and workshops. Below is a list of these activities carried out in the second year of the project.

Event Activity Steering Board meeting, September 2016, Brussels

Presentation about the progress of CogNet and the Network Management & QoS Working Group.

Steering Board meeting, December 2016, London

Presentation on project updates and the project’s position was conveyed on various issues related to 5G-PPP.



7th FUSECO Forum, November 2016, Berlin Panel on open source approaches to NFV orchestration, where CogNet results were mentioned.

5GMan 2017, May, 2017 Workshop organized with createNet which are part of Coherent and Sesame project.

Table 2 On-going liaisons with other 5Gppp projects.

CogNet is part of the Architecture Working Group, in this context we have contributed in the first release of the 5G Architecture White Paper (issued on June 2016) and we are now working on the second issue of the same white paper.

CogNet is also chairing the Network Management Working Group and hosted a Network Management Workshop at EuCNC 2017 in Oulu, Finland. This was held in conjunction with the SelfNet 5G-PPP project and the title of the workshop was “2nd Network Management, Quality of Service and Security for 5G Networks”. The reason for the workshop is to show case the work of the Network Management, Quality of Service and Security Working Group of the EU 5G-PPP and to present the newly developed whitepaper on these same topics as developed by the projects involved in the Working Group.

The workshop brought together the various contributing projects within the 5G-PPP that are involved in this working group and other interested parties (projects and/or organisations) which have a common interest in the development and progression of the following:

• Network Management

• Security

• Quality of Service

As a result of this workshop CogNet and Selfnet created a joint interest on these topics, and have also established a first contact with the Charisma project that will be further explored over the coming months.



2. Updated exploitation strategy The CogNet project, dealing with deploying machine learning methodologies to 5G management applications offers many opportunities for exploiting the project results in multiple domains.

As CogNet is a research project, some exploitation requires an additional effort by the partners to bring the research results to a level of maturity that can be included in a commercial management application.

All CogNet partners find real opportunities to leverage the CogNet results in their ongoing and/or future activities whether that be commercial, research or academic.

The Exploitation of CogNet can be divided to 2 major parts:

• Commercial Exploitation – Using CogNet results and outcomes in a commercial product. We expect this type of exploitation to be primarily executed by the commercial partners in CogNet.

• Academic & Research Exploitation – Use the knowledge and algorithms developed within CogNet to further enhance academic and research activities. It is expected this will be mainly pursued by the Academic and Research partners.

The following table shows the highlights of CogNet partners intent to exploit the project results in all exploitation domains.

A more detailed drill down to each exploitation opportunity will be presented in deliverable D7.9 (due at the end of the project).

Commercial exploitation

Partner Exploitation Details

Interoute Virtual network resources management to extend the network features of Interoute’s Virtual Data Center (VDC) product.

Telefonica Contact with Cyber Security Telefonica BU, regarding ML technologies applied to the services provided together with usage of synthetic data-sets.

Nokia Nokia is using ML technologies to automatically create Openstack Vitrage templates

Nokia Nokia is exploring ways to add Noisy Neighbour detection capabilities to Cloudband roadmap

Orange The media SLA framework is under adaptation in real Orange service for managing cloud resource prediction.



IBM IBM is in contact with Watson Work, TNF, Predictive Insights and Bluemix for exploiting the ML technologies related to anomaly detection and network demand prediction in urban areas.

Research & Academic Exploitation

Partner Exploitation Details

Fraunhofer Increase the resilience of testbed deployment by using ML insights

Fraunhofer Direct Projects with software network providers for new IPR in resilience and security in NFV

Fraunhofer, Vicomtech

apply virtualization technologies to scalable data-processing SDKs.

Vicomtech contribute to the mobility planning SDK

UPM UPM will use CogNet prototypes as use cases in the course of Big Data Applications in the master of IoT and Big data.

TUB TUB aims at creating a new course for security in NFV environment using CogNet as basis for dynamic statistics based ML, + direct usage in the 5G course

WIT Extend current communication software masters with big data module including CogNet based ML methods

WIT Follow on development and integration of CogNet security methods into the institutes’ own network to facilitate the rapid detection of attacks and provide updated information back into the national educational infrastructure.

UNITN Software portfolio improved (ACL-16, ACL-17 demos). Inclusion of LSE related topics into didactic curriculum + student projects.



3. Standardisation activities

A report on the standardization activities will be provided in D7.7 which is due in M24.



4. Conclusion This deliverable presented the latest updates on all the WP7 related tasks and activities for the second year of the project.

In the framework of WP7 the main objective is creating a wide awareness of our research activities in the different relevant communities, propose contribution to standardization bodies and outline the exploitation strategy and impact of all the Consortium members.

All these activities will be continuously monitored and reported in the following WP7 Deliverables which are due by the end of the Project.

Documents

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