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Tutorial
On effective dissemination of research results: essentials of effective publishing
Professor Witold Pedrycz
  • Professor, University of Alberta, Canada
Abstract
The objective of this workshop is to provide the audience with essentials on how to effectively disseminate results of research through publishing papers in peer-reviewed journals and conference proceedings.

A number of important issues will be addressed including such crucial topics as (a) structuralizing of research material, (b) highlighting originality aspects, (c) choosing a proper writing style, (c) using proper referencing, (d) determining effective styles of reporting experimental results, and (e) dealing with plagiarism and ethics aspects. The workshop will include numerous illustrative examples as well as elaborate on commonly encountered pitfalls one may face in the publishing process. Fundamental differences between journal and conference publications will be highlighted and discussed in detail.

The hands-on style of presentation will offer the participants ample opportunities to engage in discussion, share their experience and develop effective and personalized strategies of dissemination of their research results.
Biography
Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.

His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering.

Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.

Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning
Dr.Battista Biggio
  • Assistant Professor, University of Cagliari, Italy
Abstract
Data-driven AI and machine-learning technologies have become pervasive, and even able to outperform humans on specific tasks. However, it has been shown that they suffer from hallucinations known as adversarial examples, i.e., imperceptible, adversarial perturbations to images, text and audio that fool these systems into perceiving things that are not there. This has severely questioned their suitability for mission-critical applications, including self-driving cars and autonomous vehicles. This phenomenon is even more evident in the context of cybersecurity domains with a clearer adversarial nature, like malware and spam detection, in which data is purposely manipulated by cybercriminals to undermine the outcome of automatic analyses. As current data-driven AI and machine-learning methods have not been designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specifc vulnerabilities that attackers can exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the research field of adversarial machine learning, along with the design of more secure and explainable learning algorithms.

In this talk, I review previous work on evasion attacks, where malicious samples are manipulated at test time to evade detection, and poisoning attacks, which can mislead learning by manipulating even only a small fraction of the training data. I discuss some defense mechanisms against both attacks in the context of real-world applications, including computer vision, biometric identity recognition and computer security. Finally, I briefly discuss our ongoing work on attacks against deep-learning algorithms, and sketch some promising future research directions.
Biography
Battista Biggio (MSc ’06, PhD ‘10) is an Assistant Professor at the Department of Electrical and Electronic Engineering at the University of Cagliari, Italy, and a co-founder of Pluribus One, a startup company developing secure AI algorithms for cybersecurity tasks. In 2011, he visited the University of Tuebingen, Germany. His pioneering research on adversarial machine learning involved the development of secure learning algorithms for spam and malware detection, and computer-vision problems, playing a leading role in the establishment and advancement of this research field. On these topics, he has published more than 60 papers, collecting more than 2770 citations (Google Scholar, April 2019). Dr. Biggio regularly serves as a reviewer and program committee member for several international conferences and journals on the aforementioned research topics (including ICML, NeurIPS, IEEE Symp. S&P and ACM CCS), coorganizes three well-established workshops (AISec, DLS, S+SSPR) and he is Associate Editor for three high-impact journals (Pattern Recognition, IEEE TNNLS , and IEEE Comp. Intell. Magazine). He is chair of the TC1 on Statistical Pattern Recognition of the IAPR, a senior member of the IEEE and a member of the IAPR and ACM.
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