Keynote Speakers | 特邀报告

ICCDE 2020 Keynote Speakers | 特邀报告

Prof. Kay Chen Tan (IEEE Fellow)

City University of Hong Kong, Hong Kong | 香港城市大学

Kay Chen Tan received the B.Eng. (First Class Hons.) degree in electronics and electrical engineering and the Ph.D. degree from the University of Glasgow, U.K., in 1994 and 1997, respectively. He is a Full Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong SAR. He has published over 300 refereed articles and 10 books. Prof. Tan is the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation (IF: 8.058), was the Editor-in-Chief of the IEEE Computational Intelligence Magazine from 2010 to 2013, and currently serves as the Editorial Board Member of over 10 journals. He is currently an elected member of IEEE CIS AdCom, an IEEE DLP Speaker, and a Changjiang Chair Professor in China.

Speech Title: Advances in Artificial Intelligence for Identification of Autism Spectrum Disorder 

Abstract: Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder with clinical syndromes and variable deficits in restrictive interests, repetitive behaviors, social behavior and language. In this seminar, I will present a spatial temporal graph-based classification model for ASD using data-driven AI technologies. A novel graph-based K-nearest neighborhood feature selection method is proposed to select the remarkable connections in ASD. Simulation results based on the Autism Brain Imaging Data Exchange (ABIDE) dataset show that the AI model outperforms other state-of-the art methods in terms of classification accuracy. The interpretability of the AI model also yields data-driven specific findings of correlation patterns in the autistic brain which cannot be easily identified via traditional approaches. 



Prof. Saman K. Halgamuge (IEEE Fellow)

The University of Melbourne, Australia

SAMAN HALGAMUGE, FIEEE is a Professor in the Department of Mechanical Engineering, School of Electrical, Mechanical and Infrastructure Engineering at the University of Melbourne, an honorary Professor of Australian National University (ANU) and an honorary member of ANU Energy Change Institute. He is also a Distinguished Lecturer/Speaker appointed by IEEE Computational Intelligence Society (2019-21). He was previously the Director of the Research School of Engineering at the Australian National University (2016-18), Professor, Associate Dean International, Associate Professor and Reader and Senior Lecturer at University of Melbourne (1997-2016). He graduated with Dipl.-Ing and PhD degrees in Data Engineering from Technical University of Darmstadt, Germany.
His research interests are in Machine Learning including Deep Learning, Big Data Analytics and Optimization and their applications in Energy, Mechatronics, Bioinformatics and Neuro-Engineering. His fundamental research contributions are in Big Data Analytics with Unsupervised and Near Unsupervised type learning as well as in transparent Deep Learning and Bioinspired Optimization. His h-index is 42 (8800 citations) in Google Scholar and he graduated 50 PhD students.

Speech Title: Learning from Continuously Incoming Data 

Abstract: Learning from data is a key area of machine learning or AI that has had significant advancement in recent times. A paradigm shift can be observed in wide-ranging application domains such as entertainment industry, energy management, construction industry, transport modelling, mechatronics and the broader spectrum of design, which are empowered by rapidly advancing technologies supporting IoT that can generate large quantities of “imperfect” data for analysis of processes and compounds.
The recent learning strategies include Generative adversarial nets (GANs) that are widely used to learn the data sampling process. The performance of GANs and their future applications heavily depend on the improvements to learning algorithm. The maximum mean discrepancy (MMD) is used as the loss function in MMD-GAN which discourages the learning of fine details in data. Our recently published research suggests that a repulsive loss function can help to learn better in MMD-GAN.
How well do the existing data analysis tools cope with learning tailored to take the advantage of such continuously incoming data? Our new research shows that only some methods are more capable of analysing such data.  



Prof. Ying Tan

Peking University, China | 北京大学

Ying Tan is a full professor of Peking University, and director of Computational Intelligence Laboratory at Peking University. He worked as a professor of Faculty of Design, Kyushu University, Japan, in 2018, at Columbia University as senior research fellow in 2017, and at Chinese University of Hong Kong in 1999 and 2004-2005 as research fellow, and at University of Science and Technology of China in 1998, 2005-2006 as a professor under the 100-talent program of CAS. He also visited many universities including Columbia University, Auckland University of Technology, Kyushu University, California University, etc. He is the inventor of Fireworks Algorithm (FWA). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), International Journal of Swarm Intelligence Research (IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He also served as an Editor of Springer’s Lecture Notes on Computer Science (LNCS) for 32+ volumes, and Guest Editors of several referred Journals, including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, Natural Computing, Swarm and Evolutionary Optimization, etc. He is a senior member of IEEE. He is the founder general chair of the ICSI International Conference series since 2010 and the DMBD conference series since 2016. He won the 2nd-Class Natural Science Award of China in 2009 and many best paper awards. His research interests include computational intelligence, swarm intelligence, swarm robotics, data mining, machine learning, intelligent information processing for information security and financial prediction, etc. He has published more than 300+ papers in refereed journals and conferences in these areas, and authored/co-authored 11 books, including “Fireworks Algorithm” by Springer-Nature in 2015, and “GPU-based Parallel Implementation of Swarm Intelligence Algorithms” by Morgan Kaufmann (Elsevier) in 2016, and 25 chapters in book, and received 4 invention patents.

Speech Title: Advance in Swarm Intelligence, Fireworks Algorithm and Applications 

Abstract: Inspired from the collective behaviors of many swarm-based creatures in nature or social phenomena, swarm intelligence (SI) has been received attention and studied extensively, gradually becomes a class of efficiently intelligent optimization methods. Inspired by fireworks’ explosion in air, the so-called fireworks algorithm (FWA) was proposed in 2010. Since then, many improvements and beyond were proposed to increase the efficiency of FWA dramatically, furthermore, a variety of successful applications were reported to enrich the studies of FWA considerably. In this talk, the novel swarm intelligence algorithm, i.e., fireworks algorithm, is briefly introduced and reviewed, then several effective improved algorithms are highlighted, individually. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based FWA are also briefly presented, particularly the GPU-based FWA is able to speed up the optimization process extremely. Extensive experiments on benchmark functions demonstrate that the improved algorithms significantly increase the accuracy of found solutions, yet decrease the running time sharply. Finally, several typical applications of FWA, in particular, for big-data application, are presented in detail. 



Prof. Chen-Huei Chou

College of Charleston, SC, USA

Chen-Huei Chou received the B.S. in Information and Computer Engineering from Chung Yuan Christian University, Taiwan, the M.S. in Computer Science and Information Engineering from National Cheng Kung University, Taiwan, the M.B.A. from the University of Illinois at Chicago, Chicago, Illinois, USA, and the Ph.D. in Management Information Systems from the University of Wisconsin-Milwaukee, Wisconsin, USA.
He is an Associate Professor of Information Management and Decision Sciences in the School of Business at the College of Charleston, SC, U.S.A. His research has been published in MIS journals and major conference proceedings, including MIS Quarterly, Journal of Association for Information Systems, Decision Support Systems, IEEE Transactions on Systems, Man, and Cybernetics, Computers in Human Behavior, Internet Research, and Journal of Information Systems and e-Business Management. His areas of interests include web design issues in disaster management, ontology development, Internet abuse in the workplace, text mining, data mining, knowledge management, and behavioral studies related to the use of IT.

Speech Title: Artificial Intelligence for Modern Internet Abuse Detection 

Abstract: As the use of the Internet in organizations continues to grow, so does Internet abuse in the workplace. Internet abuse activities by employees—such as online chatting, gaming, investing, shopping, illegal downloading, pornography, and cybersex—and online crimes are inflicting severe costs to organizations in terms of productivity losses, resource wasting, security risks, and legal liabilities. Organizations have started to fight back via Internet usage policies, management training, and monitoring. Internet filtering software products are finding an increasing number of adoptions in organizations. These products mainly rely on blacklists, whitelists, and keyword/profile matching. In this talk, I would like to share a text mining approach to Internet abuse detection. I have empirically compared a variety of term weighting, feature selection, and classification techniques for Internet abuse detection in the workplace of software programmers. The experimental results are very promising; they demonstrate that the text mining approach would effectively complement the existing Internet filtering techniques. In this speech, I would like to share my knowledge and experience in conducting text mining approach for detecting Internet abuse in the workplace. 



ICCDE 2020 Invited Speaker

Prof. David Billard

University of Applied Sciences in Geneva, Switzerland

David Billard received a PhD in computer science from the University of Montpellier, France, in 1995.
He worked at the University of Geneva, Switzerland, from 1995 to 2000 as a research fellow, then he headed the University software developments until 2008. Since 2008 he is associate professor at the University of Applied Sciences in Western Switzerland in Geneva. He is also a lecturer at the University of Lausanne (criminal school) and University of Stockholm (DSV).
David Billard is a sworn expert to the courts in France and Switzerland (French speaking cantons) and International Criminal Court in the Hague. He participated in more than 150 criminal investigations and 50 civil litigations. He publishes regularly about cyber forensics and privacy.
David is married with three children, he likes to share his free time with his family by sailing on the Mediterranean Sea, off the Camargue.

Speech Title: Digital and Cyber Forensics in the Artificial Intelligence Era 

Abstract: Digital or cyber forensic is the forensic science branch that deals with the collection and investigation of data found in digital devices, in the aim of providing evidence to a justice system. With the tremendous increase of the volume of data collected at a crime scene, the traditional forensic approach shows its limit and some evidence may escape the vigilance of the forensic experts. This talk will present new approaches based on the use of answer set programming (ASP) or Machine Learning (ML) in order to help in solving cases where the sheer volume of data renders next to impossible its processing by humans. This talk in based on preliminary results from the European Union Cost Action "CA17124 - Digital forensics: evidence analysis via intelligent systems and practices". 


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