Vincenzo Piuri has received his Ph.D. in
computer engineering at The Polytechnic
University of Milan, Italy (1989). He is
Full Professor in computer engineering at
the University of Milan, Italy (since 2000).
He has been Associate Professor at The
Polytechnic University of Milan, Italy and
Visiting Professor at the University of
Texas at Austin and at George Mason
His main research interests are: artificial intelligence, computational intelligence, intelligent systems, machine learning, pattern analysis and recognition, signal and image processing, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, cloud computing infrastructures, and internet-of-things. Original results have been published in 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 is President of the IEEE Systems Council (2020-21), and has been 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 has been Editor-in-Chief of the IEEE Systems Journal (2013-19). He is Associate Editor of the IEEE Transactions on Cloud Computing and has been Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Neural Networks, the IEEE Transactions on Instrumentation and Measurement, and IEEE Access.
He received the IEEE Instrumentation and Measurement Society Technical Award (2002) and the IEEE TAB Hall of Honor (2019). He is Honorary Professor at: Obuda University, Hungary; Guangdong University of Petrochemical Technology, China; Northeastern University, China; Muroran Institute of Technology, Japan; and the Amity University, India.
Speech Title: Artificial Intelligence in Cloud Computing and Internet-of-Things
Abstract: Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing and Internet-of-Things environments. However, due to high complexity, Cloud-based and Internet-of-Things infrastructures need advanced components for supporting applications and advanced management techniques for increasing the efficiency.
Adaptivity and autonomous learning abilities become extremely useful to support configuration and dynamic adaptation of these infrastructures to the changing needs of the users as well as to create adaptable applications. This self-adaptation ability is increasingly essential especially for non expert managers as well as for application designers and developers with limited competences in tools for achieving this ability. Artificial intelligence is a set of techniques which greatly can improve both the creation of applications and the management of these infrastructures.
This talk will discuss the use of artificial intelligence in supporting the creation of applications in cloud and IoT infrastructures as well as their use in the various aspects of infrastructure management.
Schahram Dustdar is Full Professor of
Computer Science heading the Research
Division of Distributed Systems at the TU
Wien, Austria. He holds several honorary
positions: University of California (USC)
Los Angeles; Monash University in Melbourne,
Shanghai University, Macquarie University in
Sydney, and University of Groningen (RuG),
The Netherlands (2004-2010). From Dec 2016
until Jan 2017 he was a Visiting Professor
at the University of Sevilla, Spain and from
January until June 2017 he was a Visiting
Professor at UC Berkeley, USA.
From 1999 - 2007 he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by Engineering NetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. Caramba Labs was nominated for several (international and national) awards: World Technology Award in the category of Software (2001); Top-Startup companies in Austria (Cap Gemini Ernst & Young) (2002); MERCUR Innovation award of the Austrian Chamber of Commerece (2002).
He is founding co-Editor-in-Chief of the new ACM Transactions on Internet of Things (ACM TIoT) as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEE Computer. Dustdar is recipient of the ACM Distinguished Scientist award (2009), the IBM Faculty Award (2012), an elected member of the Academia Europaea: The Academy of Europe, where he is chairman of the Informatics Section, as well as an IEEE Fellow (2016).
Speech Title: Edge Intelligence - Edge Computing and Artificial Intelligence
Abstract: With the advent of Edge Computing and the coming of age of Artificial Intelligence, there is a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this talk, we divide Edge Intelligence into AI for Edge (Intelligence-enabled Edge Computing) and AI on Edge (Artificial Intelligence on Edge). We will discuss insights into this new interdisciplinary field from a broader vision and perspective. We discuss the core concepts and the research roadmap, which should provide the necessary background for potential future research programs in Edge Intelligence.
Ling Liu is a Professor in the School of
Computer Science at Georgia Institute of
Technology. She directs the research
programs in the Distributed Data Intensive
Systems Lab (DiSL), examining various
aspects of large scale big data-powered
artificial intelligence (AI) systems, and
machine learning (ML) algorithms and
analytics, including performance,
availability, privacy, security and trust.
Prof. Liu is an elected IEEE Fellow, a
recipient of IEEE Computer Society Technical
Achievement Award (2012), and a recipient of
the best paper award from numerous top
venues, including IEEE ICDCS, WWW, ACM/IEEE
CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu
served on editorial board of over a dozen
international journals, including the editor
in chief of IEEE Transactions on Service
Computing (2013-2016) and currently, the
editor in chief of ACM Transactions on
Internet Computing (TOIT). Prof. Liu is a
frequent keynote speaker in top-tier venues
in Big Data, AI and ML systems and
applications, Cloud Computing, Services
Computing, Privacy, Security and Trust. Her
current research is primarily supported by
USA National Science Foundation under CISE
programs and IBM.
Speech Title: Adversarial Robustness of Real Time Object Detection
Abstract: Deep neural networks (DNN) have fueled the wide deployment of object detection models in a number of mission-critical domains, such as traffic sign detection on autonomous vehicles, and intrusion detection on surveillance systems. Recent studies have revealed that deep object detectors can also be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or wrong objects. However, very few studies how to guarantee the robustness of object detection against adversarial manipulations. This keynote presents an in-depth understanding of vulnerabilities of deep object detection systems by analyzing the adversarial robustness under different DNN detector training algorithms, different attack strategies, different adverse effects and costs. Then I will describe a set of strategies and techniques that are effective for developing a robust object detection system and discuss why it is challenging to develop effective mitigation strategies that can protect a victim detector by guaranteeing high model robustness in the presence of adversarial attacks and at the same time maintain high benign model accuracy in no attack scenarios.
Farid Meziane has over 35 years experience
in higher education. He obtained a PhD in
Computer Science from the University of
Salford, UK on his work on producing formal
specification from Natural Language
requirements. The work was considered at
that time as pioneering in the area and
paved the way for a large interest in
automating the production of software
specifications from informal requirements.
Upon completion of his PhD, he took a
lecturer’s position at the Institute of
Software Technology (IST) at the University
Malaysia Sarawak (UNIMAS) where he spent
three years. He returned in 1998 to the
University of Salford where he spent the
last twenty-two years. He was promoted to a
professor and held a chair in Data and
Knowledge Engineering and leading research
in computer Science as the head of the
Informatics Research Centre. Professor Farid
led the REF2020 UoA11 submission at Salford
until the end of July 2020. For eight years,
Professor Meziane was the Associate Dean for
International Development for the School of
Computing, Science and Engineering and was
behind the development of the
Internationalisation strategy for the school
and setting partnerships and collaborations
with international partners. Professor
Meziane is currently with the University of
Derby holding a chair in Data Science and
the head of the Data Science Research
Centre. He has authored over 100 scientific
papers and participated in many national and
international research projects. He is the
co-chair of the international conference on
application of Natural Language to
information systems; co-chair of the
international conference on Information
Science and Systems. He is serving the
programme committee of over ten
international conferences and in the
editorial board of three international
journals. He was awarded the Highly
Commended Award from the Literati Club, 2001
for his paper on Intelligent Systems in
Manufacturing: Current Development and
Future Prospects. His research expertise
includes Natural Language processing,
semantic computing, data mining and big data
and knowledge Engineering.
Speech Title: Using Fuzzy Logic to Evaluate Trust in E-Commerce
Abstract: Trust is widely recognized as an essential factor for the continual development of business-to-customer (B2C) electronic commerce (EC). Many trust models have been developed; However, most are subjective and do not take into account the vagueness and ambiguity of EC trust and the customers’ intuitions and experience when conducting online transactions. In this chapter, we describe the development and implementation of a model using fuzzy reasoning to evaluate EC trust. This trust model is based on the information customers expect to find on an EC Web site and that is shown from many studies to increase customers trust towards online merchants. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within EC data and like human relationships; it is often expressed by linguistic terms rather than numerical values. The evaluation of the proposed model is illustrated using four case studies and a comparison with two other models is conducted to emphasise the benefits of using fuzzy decision system.