Prof. Vincenzo Piuri
University of Milan, Italy
Title: Computational Intelligence for Dependable and Resilient Cloud Computing
Abstract: Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing environments. However, due to high complexity, Cloud-based services often experience a large number of failures and security breaches, and consequently, impose numerous challenges on the dependability and resilience of users' applications.
Unfortunately, current dependability and resilience solutions focus either on the infrastructure itself or on application analysis, but fail to consider the complex inter-dependencies between system components and application tasks.
This aspect is highly crucial especially when Cloud environments are used, as it is increasingly considered nowadays, in critical applications.
Besides, definition of application requirements, allocations of resources to application tasks, and optimization of global management parameters usually are based either on statistical approaches or on heuristics strategies typical of operating research. Computational intelligence may give additional opportunities and flexibility in specifying the requirements especially when they are defined by non-experts and in optimizing the resource allocation and the global management parameters.
This talk will discuss a user-centric, dependability- and resilience-driven framework that considers deploying and protecting users' applications in the Cloud infrastructure so as to minimize their exposure to the vulnerabilities in the network, as well as offering fault tolerance and resilience as a service to the users who need to deploy their applications in the Cloud.
In this scenario, the talk analyzes the opportunities offered by computational intelligence to specify the characteristics and the requirements of these environments and support their management in the presence of many local optimization minima.
Short Biography: Professor Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000).
His main research interests are: intelligent systems, cloud computing, fault tolerance, signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, embedded systems, and arithmetic architectures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters. He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Past Vice President for Technical Activities (2016), IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19) and Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Cloud Computing and IEEE Access, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.
He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary (since 2014), Guangdong University of Petrochemical Technology, China (since 2014), the Muroran Institute of Technology, Japan (since 2016), and the Amity University, India (since 2017).
Prof. Michalis Vazirgiannis
(DASCIM), LIX, Ecole Polytechnique, Palaiseau, France
Title: "Graph-of-word: boosting text mining with graphs"
Abstract: The Bag-of-words model has been the dominant approach for IR and Text mining for many years assuming the word independence and the frequencies as the main elements for feature selection and query to document similarity computation. Although the long and successful usage, bag-of-words ignores words' order and distance within the document – weakening thus the expressive power of the distance metrics. We propose graph-of-word, an alternative approach that capitalizes on a graph representation of documents and challenges the word independence assumption by taking into account words' order and distance. We applied graph-of-word in various tasks such as ad-hoc Information Retrieval, Single-Document Keyword Extraction, Text Categorization and Sub-event Detection in Textual Streams. In all cases the graph of word approach, assisted by graph degeneracy at times, outperforms the state of the art base lines.
Short Biography: Dr. Vazirgiannis is a Professor at LIX, Ecole Polytechnique in France.
He holds a degree in Physics and a PhD in Informatics from Athens University(Greece) and a Master degree in AI from HerioWatt Univ Edinburgh. He has conducted research in GMD-IPSI, Max Planck MPI (Germany), in INRIA/FUTURS (Paris). He has been a teaching in AUEB (Greece), Ecole Polytechnique, Telecom-Paristech, ENS (France), Tsinghua, Jiaotong Shanghai (China) and in Deusto University (Spain).
His current research interests are on machine learning and combinatorial methods for Graph analysis (including community detection, graph clustering and embeddings, influence maximization), Text mining including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization. He has active cooperation with industrial partners in the area of data analytics and machine learning for large scale data repositories in different application domains. He has supervised fifteen completed PhD theses. He has published three books and more than a 150 papers in international refereed journals and conferences.
He has organized large scale conferences in the area of Data Mining and Machine Learning (such as ECML/PKDD) while he participates in the senior PC of AI and ML conferences – such as AAAI and IJCAI, He has received the ERCIM and the Marie Curie EU fellowships and since 2015 he leads the AXA Data Science chair.
Dr Yiannis Kompatsiaris
Multimedia, Knowledge and Social Media Analytics Lab, Information Technologies Institute, Greece
Title: "Media Analytics for Fake News Detection: Content, Context and Social approaches"
Abstract: The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, media analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. The objective of this talk is to present various approaches towards content verification, starting with semi-supervised misleading multimedia content detection on Twitter in real time. Image splicing detection on the Web and on images disseminated through social media will follow next. Contextual information will be examined for verifying Web videos by analyzing their online context such as comments, description, likes/dislikes and uploader information.
Finally, social features will be presented in a framework that combines community detection with key-player identification to retrieve communities who share specific type of information with a case study in terrorism related content.
Short Biography: Dr. Ioannis (Yiannis) Kompatsiaris is a Senior Researcher (Researcher A') with the Information Technologies Institute / Centre for Research and Technology Hellas and the Head of the Multimedia Knowledge and Social Media Analytics Laboratory. His research interests include semantic multimedia analysis, indexing and retrieval, social media and big data analysis, knowledge structures, reasoning and personalization for multimedia applications, eHealth, security and environmental applications. He received his Ph.D. degree in 3-D model based image sequence coding from the Aristotle University of Thessaloniki in 2001.
He is the co-author of 129 papers in refereed journals, 46 book chapters, 8 patents and more than 420 papers in international conferences. He has been the co-organizer of various international conferences and workshops and has served as a regular reviewer, associate and guest editor for a number of journals and conferences.
He is a Senior Member of IEEE and member of ACM.