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Keynotes

(listed in alphabetical order by last name)

 

Title: Latest Research Progress in Fireworks Algorithm and Its Applications

Speaker: Ying Tan

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’YingTan 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 fireworks algorithm is briefly introduced and reviewed, then several effective improved fireworks algorithms are highlighted, individually. By changing the ways of calculating numbers and amplitudes of sparks in fireworks’ explosion, the improved FWA algorithms become more reasonable and explainable. 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 IEEE-CEC’s benchmark functions demonstrate that the improved fireworks algorithms significantly increase the accuracy of found solutions, yet decrease the running time sharply. Finally, several typical applications of FWA are concisely described while its future research directions are highlighted.

Short Bio: Ying Tan is a full professor and PhD advisor at Peking University, and director of Computational Intelligence Laboratory at Peking University. He was electee of CAS 100 Talents Program in 2005, worked at Chinese University of Hong Kong in 1999 and 2004-2005, and visited many universities including Columbia University, Kyushu University, Auckland University of Technology, 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 Cybern
etics (CYB), IEEE Transactions on Neural Networks and Learning Systems (NNLS), 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 20+ volumes, and Guest Editors of several referred Journals, including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, etc. He is a member of Emergent Technologies Technical Committee (ETTC) of IEEE Computational Intelligence Society since 2010. He is the founder and general chair of the ICSI International Conference series since 2010. He was also the general chair of joint general chair of 1st&2nd BRICS CCI, program committee co-chair of WCCI 2014, etc. He is the general chair of ICMEB’2017 at Seoul. He won the 2nd-Class Natural Science Award of China in 2009. His research interests include computational intelligence, swarm intelligence, swarm robotics, data mining, pattern recognition, intelligent information processing for information security and material engineering, etc. He has published more than 280 papers in refereed journals and conferences in these areas, and authored/co-authored 11 books and 12 chapters in book, and received 4 invention patents.


Title: The Recent Progress of Artificial Intelligence

Speaker: Linqiang Pan

Abstract:Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms LinqiangPanto applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.

Short Bio:Dacheng Tao is Professor of Computer Science and ARC Future Fellow in the School of Information Technologies and the Faculty of Engineering and Information Technologies at The University of Sydney. He was Professor of Computer Science and Director of the Centre for Artificial Intelligence in the University of Technology Sydney. He mainly applies statistics and mathematics to Artificial Intelligence and Data Science. His research interests spread across computer vision, data science, image processing, machine learning, and video surveillance. His research results have expounded in one monograph and 500+ publications at top journals and conferences, such as IEEE T-PAMI, T-NNLS, T-IP, JMLR, IJCV, IJCAI, AAAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM; and ACM SIGKDD, with several best paper awards, such as the best theory/algorithm paper runner up award in IEEE ICDM’07, the best student paper award in IEEE ICDM’13, and the 2014 ICDM 10-year highest-impact paper award. He received the 2015 Australian Scopus-Eureka Prize, the 2015 ACS Gold Disruptor Award and the 2015 UTS Vice-Chancellor’s Medal for Exceptional Research. He is a Fellow of the IEEE, OSA, IAPR and SPIE.


Title:Computational Complexity: a Membrane Computing Point of View

Speaker: Claudio Zandron

Abstract:Membrane systems are a parallel, nondeterministic, synchronous and distributed model of computation inspired by the structure and functioning of living cells. The m7odel consists of a hierarchical structure composed by several membranes, embedded into a main membrane called the skin. Membranes divide the Euclidean space into regions, that contain multisets of objects and evolution rules.One interesting property of this model is the possibility to generate, mimicking the mitosis process, an exponential number of membranes in polynomial time, and use them in parallel to solve computationally hard problems. This possibility raises many interesting theoretical questions concerning the trade-off between time and space needed to solve various classes of computational problems by means of membrane systems.In this talk, I will illustrate the most important results concerning time and space complexity in the area of membrane computing, as well as the relations between classical complexity classes (defined in terms of Turing machines) and complexity classes defined in terms of membrane systems.

Short Bio:Claudio Zandron got the PhD in Computer Science from the University of Milan in 2002. Since 2006 he is Associate Professor at the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy.His research interests concern the areas of formal languages, molecular computing models, DNA computing, Membrane Computing and Computational Complexity. Co-author of about 100 research papers, presented at international conferences or published in international scientific journals, he won three times the best paper award at the International Conference on Membrane Computing.He partecipated in various Italian and European funded ICT projects.He is editor of the scientific journal Open Computer Science, and guest editor for various scientific volumes and journals (Theoretical Computer Science, Natural Computing).He is the chair of the steering committee of the International Conference on Membrane Computing, the conference chair in the executive board of the International Membrane Computing Society, anda member of the International Advisory Committee for “Energy, Materials, Nanotechnology (ENM) Meeting on Membranes”.


Title: The Recent Progress of Artificial Intelligence

Abstract:Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms to applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.

Short Bio:Dacheng Tao is Professor of Computer Science and ARC Future Fellow in the School of Information Technologies and the Faculty of Engineering and Information Technologies at The University of Sydney. He was Professor of Computer Science and Director of the Centre for Artificial Intelligence in the University of Technology Sydney. He mainly applies statistics and mathematics to Artificial Intelligence and Data Science. His research interests spread across computer vision, data science, image processing, machine learning, and video surveillance. His research results have expounded in one monograph and 500+ publications at top journals and conferences, such as IEEE T-PAMI, T-NNLS, T-IP, JMLR, IJCV, IJCAI, AAAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM; and ACM SIGKDD, with several best paper awards, such as the best theory/algorithm paper runner up award in IEEE ICDM’07, the best student paper award in IEEE ICDM’13, and the 2014 ICDM 10-year highest-impact paper award. He received the 2015 Australian Scopus-Eureka Prize, the 2015 ACS Gold Disruptor Award and the 2015 UTS Vice-Chancellor’s Medal for Exceptional Research. He is a Fellow of the IEEE, OSA, IAPR and SPIE.


TBA

Paper Submission deadline

July 30, 2017

Notification of Paper Acceptance

August 15, 2017

Final paper submission

September 15, 2017

Conference date

December 01-03, 2017