Dr. Yangjun Chen is a full professor at Dept. Applied Computer Science, University Of Winnipeg. Yangjun Chen received a BS degree in information system engineering from the Technical Institute of Changsha, China, in 1982, and the Diploma and PhD degrees in computer science from the University of Kaiserslautern, Germany, in 1990 and 1995, respectively. From 1995 to 1997, he worked as a postdoctor at the Technical University of Chemnitz-Zwickau, Germany. After that, he worked as a senior engineer at the German National Research Center of Information Technology (GMD) for more than two years. After a short stay at Alberta University, he joined the Department of Applied Computer Science at the University of Winnipeg, Canada. His research interests include deductive and federated databases, constraint satisfaction problems, graph theory, and combinatorics. He has about 100 publications in these areas.
| Prof. Oscar Eduardo Ruiz Salguero Universidad EAFIT, COLOMBIA
Research Interests:
Computer Aided Geometric Design, Geometric Reasoning and Applied Computational Geometry | Professor Oscar Ruiz was born in Tunja, Colombia. He obtained B.Sc. degrees in Mechanical Eng. (1983) and Computer Science (1987) at Los Andes University, Bogota, Colombia, a M.Sc. degree with emphasis in CAM (1991) and a Ph.D. with emphasis in CAD (1995) from the Mechanical & Industrial Eng. Dept. of University of Illinois at Urbana- Champaign, USA. Dr. Ruiz has held Visiting Researcher positions at Ford Motor Co. (Dearborn, USA. 1993 and 1995), Fraunhofer Inst. Graphische Datenverarbeitung (Darsmstad, Germany 1999 and 2001), University of Vigo (1999 and 2002), Max Planck Institute for Informatik (2004) and Purdue University (2009). In 1996 Dr. Ruiz was appointed as Faculty of the Mechanical Eng. and Computer Science Depts. at EAFIT University, Medellin, Colombia, and has been ever since the Coordinator of the Laboratory for interdisciplinary Research on CAD / CAM / CAE. Dr. Ruiz’ interests are Computer Aided Geometric Design, Geometric Reasoning and Applied Computational Geometry. 
| Prof. Jie Yang Shanghai Jiao Tong University (SJTU), China
Research Interests:
Research direction, image processing, pattern recognition | Jie Yang received a bachelor’s degree in Automatic Control in Shanghai Jiao Tong University (SJTU), where a master’s degree in Pattern Recognition & Intelligent System was achieved three years later. In 1994, he received Ph.D. at Department of Computer Science, University of Hamburg, Germany. Now he is the Professor and Director of Institute of Image Processing and Pattern recognition in Shanghai Jiao Tong University. He is the principal investigator of more than 30 national and ministry scientific research projects in image processing, pattern recognition, data mining, and artificial intelligence. He has published six books，more than five hundreds of articles in national or international academic journals and conferences. Google citation over 19000，H-index 65. Up to now, he has supervised 5 postdoctoral, 46 doctors and 70 masters, awarded six research achievement prizes from ministry of Education, China and Shanghai municipality. He has owned 48 patents. Three Ph.D. dissertation he supervised was evaluated as “National Best Ph.D. Dissertation” in 2009, in 2017, in 2019. He has been chairman and keynote speaker of more than 10 international conferences.
Talk Titel: Recent research work on AI medical Diagnosis Abstract
Part A: Development of a Chromosome Classification Approach Using Deep Convolutional Networks. Bottlenecks of chromosome classification are analyzed. A flowchart of the proposed Varifocal-Net is proposed, including global-scale and local-scale feature learning, and classification based on the fused features. Part B: SEGMENTATION METHODS FOR CHEST CT IMAGE ANALYSIS. A flowchart of the proposed pulmonary nodule segmentation framework is presented. A voxel-connectivity-aware approach for accurate airway segmentation using convolutional neural networks is presented. A flowchart of the proposed method for pulmonary airway and artery-vein segmentation is introduced. More than 10 papers about the above research have been published in top journals (e.g. TMI) and top conferences (e.g. MICCAI). |
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| Prof. JingTao Yao IEEE Senior Member, ACM Member, Department of Computer Science, University of Regina, Canada
Research Interests:
granular computing, rough sets, soft computing, data mining, three-way decisions, forecasting, neural networks, computational finance, electronic commerce, Web intelligence, and Web-based support systems
| Dr. Yao joined U of R in January 2002. Before arriving Canada, he taught in the Department of Information Systems at the Massey University, New Zealand, the Department of Information Systems at the National University of Singapore, Singapore, the Computer Science Program of The Open University, Singapore and the Department of Computer Science and Engineering at Xi'an Jiaotong University, China. JingTao received his PhD degree at the National University of Singapore. He did a B.Eng. degree and an M.Sc. degree at Xi'an Jiaotong University.Dr. Yao's research interests include granular computing, rough sets, soft computing, data mining, three-way decisions, forecasting, neural networks, computational finance, electronic commerce, Web intelligence, and Web-based support systems. His research publications in these areas can be found at Publications or Google Scholar Citations. Dr. Yao is the coordinator of the Rough Set Technology Lab, and the coordinator of the The Web Intelligence Consortium (WIC) Canada Research Centre. He is a Member of ACM, a Senior Member of IEEE, IEEE Computational Intelligence Society, IEEE Computer Society, and IEEE Systems, Man, and Cybernetics Society, the president, a fellow and past chair of the Steering Committee of the International Rough Set Society, and also a member of ACCP.
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| Dr. Iacovos Ioannou Department of Computer Science, University of Cyprus; CYENS Centre of Excellence, Nicosia, Cyprus
Research Interests: 5G mobile communication,cellular radio,telecommunication computing,6G mobile communication,Internet of Things,artificial intelligence,fuzzy logic,fuzzy reasoning,jamming,Long Term Evolution,computer network security,decision trees,distributed processing,fuzzy control,learning (artificial intelligence),logistic regression,operating systems (computers),pattern classification,pattern clustering,power consumption,quality of service,radio equipment,radio spectrum management,resource allocation,routing protocols | Iacovos I. Ioannou received an associate degree in computer science from the Cyprus College, in 2001, a B.Sc. degree in computer science from the University of Cyprus, in 2006, an M.Sc. degree (Hons.) in computer and network security from the Open University of Cyprus, in 2017, and the Ph.D. degree from the University of Cyprus, in 2021, focusing on telecommunications with AI/ML., He is currently a Researcher at the Networks Research Laboratory, the University of Cyprus. He is also a Junior Researcher at the Smart Networked Systems Research Group, RISE Center. He is a highly skilled developer with 20 years of hands-on working experience in the IT industry, ranging in the spectrum of IT systems from analysis, development, installation, and management. He has worked at the Phileleftheros Publishing Group as an IT Administrator and a Programmer for seven years, Cyprus Stock Exchange as an IT and a Programmer for seven years, and PrimeTel as an IT and Software Engineer at the Services Department for six years. He has vast experience with cellular network infrastructure and all modern development platforms and languages. He has a certificate in ICONIX/SCRUM. He is also CCNET certified by CISCO. His research interests include mobile and wireless communications, next-generation networks (5G), and device-to-device (D2D) communications, using artificial intelligence techniques.
Abstract
The rise of
new-generation mobile networks, including 5G and the impending 6G, poses
formidable technical hurdles in attaining the ambitious benchmarks set by the
research and industry communities. These challenges encompass accommodating a
multitude of devices on a single network, ensuring ultra-reliable low-latency
communication, sustaining adaptability and dynamism, and delivering ample
high-quality bandwidth. To tackle these complexities effectively, there is an
escalating demand for a unified approach amalgamating network management and
control, featuring autonomous and adaptable actions.
The presented
Distributed Artificial Intelligence (DAI) framework harnesses Belief Desire
Intention (BDI) agents endowed with machine learning capabilities, denoted as
BDIx agents, because it uses ML under believes. These agents are dispersed
across mobile devices, forming a multi-agent system (MAS) that incorporates
Fuzzy Logic and Back-Propagation Neural Networks for Reinforcement Learning at
the agents' perceptual and cognitive tiers. A prime illustration of the DAI
framework is demonstrated in the context of Device-to-Device (D2D)
communication within 5G and beyond networks. D2D communication's decentralized
nature, coupled with a multitude of user devices (User Equipment or UEs),
presents an ideal platform to showcase the capabilities of the DAI framework.
By integrating BDIx agents into D2D UEs, it becomes possible to circumvent the
conventional Base Station (BS) and establish direct links among neighboring
UEs. This approach promises enhancements in spectral and energy efficiency,
data rates, throughput, latency, interference management, and fairness. Given
the manifold challenges introduced by D2D communication in 5G and 6G networks,
the DAI framework is anticipated to play a pivotal role in surmounting these
obstacles and fostering innovations in Artificial Neural Networks and other
facets of these dynamic mobile networks.
In my
presentation, I will showcase the ADROIT6G EU project, which utilizes the
proposed framework to realize BDIx agents. ADROIT6G's primary goal is to
establish innovative research principles to advance low Technology Readiness
Level technologies in preparation for the future 6G network architectures. This
project seeks to enhance the current service-based structures of 5G mobile
networks by developing and validating a forward-looking, cognitive 6G
architecture. This will be achieved through a fully distributed paradigm driven
by Artificial Intelligence, deploying functional elements as virtual functions
in cloud-native environments spanning the far-edge, edge, and cloud domains,
and involving multiple stakeholders. These advancements aim to deliver improved
performance, greater control, increased transparency in digital service
interactions, support for innovative applications, and societal acceptance,
marking a significant step towards the evolution of next-generation 6G
networks.
| Prof. Zhiquan Liu Professor,
College of Cyber Security, Jinan University, Guangzhou, China
| Zhiquan Liu is a
full professor with the College of Cyber Security, Jinan University. In recent
years, Prof. Liu has published more than 80 SCI/EI papers on authoritative
journals and conferences, such as IEEE JSAC, IEEE TIFS, IEEE TDSC, IEEE TMC,
IEEE TKDE, IEEE TPAMI, IEEE TITS, IEEE IOTJ, IEEE TVT, IEEE TII, IEEE TCC, IEEE
Network, Science China Information Sciences, Information Fusion, Information
Sciences, IEEE ICWS, IEEE WCNC, ACISP, and Chinese Computer Journal (including
more than 40 papers on CCF-A/JCR-1/TOP journals, 4 best papers on international
conferences, and 2 most popular papers on international journals), and has
applied for/been authorized more than 100 invention patents and PCT patents.
Besides, Prof. Liu has served as the Chair, Program Committee Chair,
Publication Chair, Publicity Chair, Finance Chair, Workshop Chair, or Program
Committee Member for more than 20 international conferences. Meanwhile, Prof.
Liu has served as the editor-in-chief of Advances in Transportation and
Logistics, the associate editor of IEEE IOTJ, the academic editor of PLOS ONE,
and the guest editors of two international journals, Security and Communication
Networks and Electronics, and has served as the reviewers of more than 40
authoritative journals and conferences. His homepage is https://www.zqliu.com/. Title: Security, Trust,
and Privacy in Vehicular Networks
Abstract: Vehicular
networks, as an important application of Internet of things in the automotive
industry, and as the core component of intelligent transportation system, can
realize all-round network connection and efficient information interaction
between vehicles and other nearby vehicles, road infrastructures, pedestrians,
and network, etc., so as to provide various information services, improve
driving safety and efficiency, and promote energy saving and emission
reduction. Vehicular networks are regarded as a global innovation hotspot and
an important commanding point of economic development, with huge industrial
development potential and application market space. However, due to the large,
open, highly dynamic, delay sensitive, and other characteristics, the security,
trust, and privacy in vehicular networks face huge challenges. Thus, this talk will
focus on highlighting the recent advances, challenges, and approaches for the
security, trust, and privacy in vehicular networks.
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