Dr. Houssain Kettani received the Bachelor's degree in Electrical and Electronic Engineering from Eastern Mediterranean University, Cyprus in 1998, and Master’s and Doctorate degrees both in Electrical Engineering from the University of Wisconsin at Madison in 2000 and 2002, respectively. Dr. Kettani served as faculty member at the University of South Alabama (2002-2003), Jackson State University (2003-2007), Polytechnic University of Puerto Rico (2007-2012), Fort Hays State University (2012-2016), Florida Polytechnic University (2016-2018) and Dakota State University since 2018. Dr. Kettani has served as Staff Research Assistant at Los Alamos National Laboratory in summer of 2000, Visiting Research Professor at Oak Ridge National Laboratory in summers of 2005 to 2011, Visiting Research Professor at the Arctic Region Supercomputing Center at the University of Alaska in summer of 2008 and Visiting Professor at the Joint Institute for Computational Sciences at the University of Tennessee at Knoxville in summer of 2010. Dr. Kettani’s research interests include algorithms, cyber security, machine learning and population studies. He presented his research in over sixty refereed conference and journal publications and his work received over 500 citations by researchers all over the world. He chaired over hundred international conferences throughout the world and successfully secured external funding in millions of dollars for research and education from US federal agencies such as NSF, DOE, DOD, and NRC.
Title of Speech: Advances in High Performance Computing and Impact on Cybersecurity
Abstract: In the past thirty years, advances in high performance computing have increased the performance by million times, and decreased the volume of the machine by similar order. Accordingly, the fastest computer in the world increased its performance from one Gigaflop/s in mid-1980s to a projected one Exaflop/s by 2020. In addition, current hand-held devices such as smartphones have performance that rivals those machines of the 1980s. Due to hardware limitations, parallel computing became an integral part of our lives that it is hard to imagine a device that is not using multiprocessor power, including smartphones. What started as a hardware solution to physical limitation, prompted software engineers to adopt to parallelism, which also motivates the theoretical solution to algorithms design and analysis to provide a solution that is parallel oriented rather than a serial oriented one. The increased computing power also means an increase in the efficiency of brute force attack algorithms on encryption standards, which will make the widely adopted Advanced Encryption Standard obsolete by the end of this century.
Professor Heng Chen, ever earned his PH.D in Shanghai Jiaotong University, China in Nov.2004, and pursued his postdoctoral study in University of Alberta, Canada from 2005 to 2007. He has been working in Shanghai Institutes for Biological Sciences as a director of database department, who is mainly responsible for the innovative research and development work such as construction of life science information and intelligence database, knowledge mining and extracting, knowledge organization and resource management and other aspects since July 2008. He was ever selected in the outstanding talent introduced plan in the Chinese Academy of Sciences literature and journal publishing field (subject field 100 talent program) in 2010, and was selected in Shanghai Pujiang talent program in 2009. Dr. Heng Chen is currently a member of the advisory Committee of the magazine Hepatitis Monthly (SCIE) and members of editor board of the International Journal of Biochemistry Research & Review, Journal of Biochemistry International, Current Research Journal of Biological Sciences and International Journal of Biochemistry and Molecular Biology. Hitherto, Dr. Heng Chen has published more than 40 scientific research papers in the capacity of the first author or the corresponding author. He has presided over 7 projects at the national, district and local level as project leaders since 2008. So far, he has applied for 4 invention patents, of which 1 has been authorized, and applied for 3 items of software copyright registration.
Title of Speech: Application of Linked Data in HIV Literature Database and HIV Protein Ontology
Abstract: Based on previous research and planning, this study systematically sorted out and classified knowledge related to HIV (Human Immunodeficiency Virus) protein and HIV life history on the basis of collecting HIV- and HIV-related protein information of book, literature and virology dictionary. The HIV-specific literature knowledge database and the HIV protein ontology were successfully constructed. Furthermore, the HIV protein ontology was successfully transformed into the standard RDF (Resource Description Framework) format of the linked data with publication. Afterwards, with the support of computer programming and linked data, the design and realization of the navigation system of HIV knowledge based on the HIV protein ontology with RDF format was completed. At the same time, the protein knowledge mining was performed in the navigated literatures of the database using the HIV protein control vocabulary (dictionary) with the text mining function. Ultimately, the function of knowledge navigation and mining was obtained in the HIV-specific literature database.
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.
Title of Speech: Swarm Intelligence Algorithms-Fireworks Algorithm and Its 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 are presented in detail.
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.
Title of Speech: State-of-the-art 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.