Prof. Xuebin Chen
University/Department:North China University of Science and Technology
Brief introduction: Chen Xuebin, Ph.D., Professor of North China University of Science and Technology. Director of Tangshan Key Laboratory of Data Science, and Director of Hebei Key Laboratory of Data Science and Application. Senior member of China Computer Federation(CCF)，Council Member of CCF, Secretary General CCF Computer Application Technical Committee, Member of CCF High Performance Computing Technical Committee, Member of CCF Big Data Expert Committee, Standing Director of China Health Big Data Industry Technology Innovation Strategic Alliance , Data Scientist of Key Laboratory of Data Science, Shanghai. Paper Evaluation Expert of "Journal Of Computer Application" and many other academic journals, Served several times as a member of many famous international academic conference program committee, Program Committee Chair of NCCA China 2014 and NCCA China 2016, Member of the academic committee of the International Youth Computer Conference.
Research direction: Network Security, Big Data, Data Security. Presided and participated more than 50 horizontal project of national, provincial and municipal levels. Published more than 60 academic papers, registered more than 30 software copyright. Gained 3 Science and Technology Progress Award.
Prof. Zhenghua Xu
University/Department: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology
Brief introduction: Zhenghua Xu received the B.Eng. degree from Beijing University of Posts and Telecommunications, China, the M.Phil. in Computer Science degree from The University of Melbourne, Australia, in 2012, and the D.Phil in Computer Science degree from University of Oxford, United Kingdom, in 2018. From 2017 to 2018, he worked as a Research Associate at the Department of Computer Science, University of Oxford. Prof. Xu is now a Full Professor at the Hebei University of Technology, China, and also a Distinguished Scholar of “100 Talents Plan” of Hebei Province, China. He has published more than 20 papers in top AI or database conferences, e.g., AAAI, IJCAI, ICDE, EDBT, CIKM, etc. His research focuses on topics within artificial intelligence and data mining, especially deep learning, medical artificial intelligence, health data mining, and reinforcement learning. Prof. Xu’s research is supported by the National Natural Science Foundation of China, the Natural Science Foundation of Tianjin, the “100 Talents Plan” of Hebei Province, and the Yuanguang Scholar Fund of Hebei University of Technology. Dr. Xu continuously serve as the member of Program Committee and Area Chair of several top AI conferences, e.g., AAAI, IJCAI, ECAI, MICCAI, etc. He is also the reviewer of many peer-reviewed journals, e.g., Artificial Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Access, IEEE Intelligent Systems, etc.
Prof. Ahmad T. Al-Taani
University/Department: Department of Computer Sciences, Faculty of Information Technology, Yarmouk University, IRBID, JORDAN
Speach Title: Part-of-Speech Tagging using Swarm Intelligence
Summary : Part of speech (POS) tagging is a process of defining the suitable part of speech for each word in the given
context such as defining if a word is a verb, a noun, or a particle. POS tagging is an important preprocessing
step in many natural language processing (NLP) applications such as question answering, text summarization, and information retrieval. Many approaches have been proposed for the Arabic language, but this area needs more investigations. Many significant studies have been proposed for Arabic POS tagging. These studies used different approaches such as swarm intelligence approaches, evolutionary approaches, rule-based approaches, statistical approaches, and hybrid approaches. The Arabic language presents challenges for POS tagging since it is a complex language and it is a rich morphological language. Also, the derivations of the Arabic language are very complex as the Arabic nouns, verbs, adjectives and adverbs are generally derived from similar roots. This derivational complexity causes many challenges for POS tagging. In this study, I will propose a framework for Arabic POS tagging using swarm intelligence . I will discuss how to find the fitness of particles in a sentence, and then how swarm intelligence will be performed to find suitable tag of each word.
Mr. Nirmalya Thakur
Speech Title：Machine Learning and its Applications for Developing Emotional Intelligence in Smart Homes
Abstract: In this era of humans surrounded with constantly advancing technologies, in the near future, our daily living and functioning environments for instance, Smart Homes and Smart Cities would involve interaction, coordination, collaboration and communication with a myriad of intelligent technology-laden systems including machines, robots and other smart gadgets in multiple ways. Developing ‘Emotional Intelligence’ in such systems in the context of user interactions has the potential to enhance the user experience the user performance in the context of daily routine tasks performed in these environments. Emotional Intelligence in Smart Homes may be summarised as the ability of a Smart Home to study, track, analyse and adapt according to the emotional state of a user in the context of any form of user interaction with the given context parameters.
This talk aims to introduce and discuss the immense potential at the intersection of Machine Learning with Internet of Things, Human-Computer Interaction and their related disciplines towards developing Emotional Intelligence and fostering human-technology partnerships in the future of Internet of Things (IoT)-based Smart Home environments. Several state of art works in these fields will be reviewed and discussed. The recent and ongoing researches in these fields at the University of Cincinnati will also be briefly outlined. The talk will conclude with presenting some of the open challenges in this field to the audience.
A.P. HAN-Teng Liao
Speech Title： Artificial Intelligence Standardization: Implications for Machine Learning Innovation and Application
Abstract: In recent years, Chinese, European and American industry and standard organizations have, based on the pre-existing guidelines and frameworks, have embarked on the tasks of establishing standards for AI. For instance, in July 2020, China has issued the Guidelines for the Establishment of the New Generation of Artificial Intelligence Standards System, by far the most comprehensive framework of China AI standards. U.S. federal efforts in AI standardization also officially started, as part of the 2019 Executive Order on Maintaining American Leadership on Artificial Intelligence. European standardization efforts, including a roadmap for AI standardization expected to be finalized in September 2020, has focused on building trust in human-centric AI, especially for its digital single market. Since standardization efforts generally improve trust, productivity, quality, etc., it is imperative that both technical, ethical and professional experts to understand and participate in the on-going and important process of AI standardization. Based on the latest AI standardization documents and the UN Road Map for Digital Cooperation, this talk will summarize the challenges and opportunities, surrounding machine learning, for intelligent innovations and applications. As the main critical general technology, machine learning development will demand specific technical and ethical attention towards the relationship between the data and the real world, when it is integrated with specific hardware, software and other supporting technologies such as Internet of Things, cloud computing, edge computing, etc. As the fundamental technologies for intelligent products, services, and applications (e.g. intelligent manufacturing and smart city), machine learning innovations require better understanding of the associated risks and values in different use scenarios. Therefore, in addition to open software and open innovation, both competition and cooperation efforts are required to contribute to the open machine learning and artificial intelligence models, along with the open data of both the general terminologies and specific scenarios of use scenarios, as the “digital public goods” of AI and machine learning.