2023 6th International Conference on Computing and Big Data(ICCBD 2023)
Prof. Mohan Lal Kolhe, University of Agder, Norway
Dr. Prof. Dr. Mohan Lal
Kolhe is a full professor in smart grid and renewable
energy at the Faculty of Engineering and Science of the
University of Agder (Norway). He is a leading renewable
energy technologist with three decades of academic
experience at the international level and previously
held academic positions at the world's prestigious
universities, e.g., University College London (UK /
Australia), University of Dundee (UK); University of
Jyvaskyla (Finland); Hydrogen Research Institute, QC
(Canada); etc. In addition, he was a member of the
Government of South Australia’s first Renewable Energy
Board (2009-2011) and worked on developing renewable
energy policies. Due to his enormous academic
contributions to sustainable energy systems, he has been
offered the posts of Vice-Chancellor of Homi Bhabha
State University Mumbai (Cluster University of
Maharashtra Government, India), full professorship(s) /
chair(s) in 'sustainable engineering
technologies/systems' and 'smart grid' from the Teesside
University (UK) and Norwegian University of Science and
Technology (NTNU) respectively.
Professor Kolhe is an expert evaluator of many prestigious international research councils (e.g., European Commission: Erasmus+ Higher Education – International Capacity Building, Royal Society London (UK), Engineering and Physical Sciences Research Council (EPSRC UK), etc.). In addition, many international organizations have invited him to deliver keynote addresses, expert lectures, workshops, etc. He has also been a member of many academic promotional committees.
Professor Kolhe has successfully won competitive research funding from the prestigious research councils (e.g., Norwegian Research Council, EU, EPSRC, BBSRC, NRP, etc.) for his work on sustainable energy systems. His research works in energy systems have been recognized within the top 2% of scientists globally by Stanford University’s 2020, 2021 matrices. He is an internationally recognized pioneer in his field, whose top 10 published works have an average of over 175 citations each.
Title: Smart Energy System - Role of Big Data in Operation and Management
Abstract:The big data processing and operation of the energy system will require flexible tools to manage the smart energy system, by using Information and Communication Technologies, Distributed Generation and Artificial Intelligence, together. Load forecasting is a required application in Smart-Grid, which provides essential input to other applications such as Demand Response, Topology Optimization and Anomaly Detection, facilitating the integration of intermittent clean energy sources. Machine Learning can provide electrical load demand forecasting, giving information about future loads.
Assoc. Prof. Wei Wang, Xi'an Jiaotong-Liverpool University, China
Dr. Wei Wang is a senior associate professor at the Department of Computing, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, China. He received his PhD in Computer Science from theUniversity of Nottingham in 2009. He then worked as a lecturer at the University of Nottingham (Malaysia Campus) and later a post-doctoral research fellow at the Centre for Communication Systems Research (now known as the Institute for Communication Systems) at the University of Surrey, UK. His research interests lie in the broad area of data and knowledge engineering; in particular, knowledge discovery from textual data, social media data and smart city data, semantic search, and deep learning for data processing. He has published more than 60 papers in reputed journals and conferences in the areas of Internet of Things and knowledge discovery.
External Knowledge for Natural Language Understanding
Abstract: Knowledge resources, e.g. knowledge graphs (KG), which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of linguistic knowledge in many natural language processing (NLP) applications based on deep neural networks. In this talk we provide an overview of the emerging but intriguing topic that exploits quality external knowledge resources in improving the performance of NLP tasks. Existing methods and techniques are summarised in three main categories: 1) static word embeddings, 2) sentence-level deep learning models, and 3) contextualised language representation models, depending on when, how and where external knowledge is integrated into the underlying learning models. We focus on the solutions to mitigate two issues: knowledge fusion and inconsistency between language and knowledge. We also point out some potential future directions in view of the latest trends in natural language understanding research.
Assoc. Prof. Junliang Wang, Donghua University, China
Junliang Wang is an associate professor at Institute of Artificial Intelligence, Donghua University (DHU). He is the deputy director of Textile Industry Production Big Data Research Center of CTES, the deputy secretary general of the Industrial Big Data and Intelligent Systems Chapter of CMES, the committee member of Intelligent Simulation Optimization and Scheduling Chapter of CSF, the committee member of the Digital Twin Chapter of CGS. His research interests mostly lie in the big data and intelligent manufacturing fields, by leveraging cutting-edge information technology and artificial intelligence techniques, to enable industries digital and intelligent transformation. He has continued to conduct in-depth research in the field of industrial big data analytics and has been funded by the Young Elite Scientists Sponsorship Program by CAST, the Natural Science Foundation of China, the Shanghai Sailing Program, the Shanghai Science and Technology Project and the Shanghai Chenguang Program. After receiving basic research fund, he has published more than 40 journal papers, authorized 11 Chinese patents and 11 software copyright. He has won two awards from provincial and ministerial science and technology progress and the Pearl award of the 4th China artificial intelligence and robot developers conference.
Abstract：With the development of the 5G, Internet of Things (IoT), and cloud computing technologies, big data in manufacturing systems has become to be a key resource to enable the intelligent operation of manufacturing systems. To further release the benefits of manufacturing data, the talk gives a comprehensive view of manufacturing big data analytics. First, the characteristic of manufacturing big data, the paradigm of data science, and the application model of big data-driven intelligent manufacturing is proposed. Second, three generations of big data analytics are discussed to reveal the development process. Third, the practical applications of manufacturing big data analytics methods are conducted from production scheduling and quality control. Finally, the challenges faced by big data analytics in manufacturing are discussed, and the future development direction of big data analytics is proposed.
Assoc. Prof. Sook-Ling (Linda) Chua, Multimedia University, Malaysia
Sook-Ling (Linda) Chua received her PhD in computer science from Massey University, New Zealand. She is currently an associate professor in the Faculty of Computing and Informatics at Multimedia University, Malaysia. Her main research interest is in machine learning, particularly to address problems in sensor-based activity recognition. While still working in this area, her other research interests include learning from imbalanced data, application of information-theoretic approaches to feature selection, probabilistic modelling and data analytics. She has secured various grants as principal investigator and published in various reputable journals and conferences.
Title: Predicting Activities of Daily Living with Spatio-Temporal Information
Almost every country in the world is experiencing a
growing and aging population. The smart home is
considered a viable solution to address living problem,
typically the elderly or those with diminished cognitive
capabilities. An important part of the functioning of
smart homes is to monitor user's daily activities and
detect any alarming situations. Most people, when
performing their daily activities, interact with
multiple objects both in space and through time.
The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This talk discusses the importance of spatial and temporal information for reasoning in smart homes and how such information is represented for activity recognition.
Assoc. Prof. Wendy Hui, Lingnan University, China
Wendy Wan Yee HUI holds a B.E. (Hons) in Electrical and Electronics Engineering from University of Canterbury, New Zealand, a Master of Business Administration from Chinese University of Hong Kong, and a PhD in Information Systems from the Hong Kong University of Science and Technology. Since graduation from her PhD, she has worked in various universities in Abu Dhabi, Ningbo, Perth, Hong Kong and Singapore. She is currently an Associate Professor in Infocomm Technology at Singapore Institute of Technology. She is also an Adjunct Associate Professor at Lingnan University, Hong Kong, and Asian Institute of Management, Philippines. Her current research interests include research methods, economics of information security, e-learning, and applied analytics. Her research work has appeared in top Information Systems journals including Journal of Management Information Systems and Decision Support Systems. Wendy has over 15 years' experience teaching various subjects in information systems including information security, business analytics, database design, human-computer interaction, IT ethics, and e-commerce. To promote knowledge transfer, she has led various applied research projects to encourage innovation in the social and business sectors. Some of these projects include algorithmic trading, social media analytics, and the use of AI robots in security, elderly care, and teaching and learning.
Title: Social Innovation with Big Data
Abstract: Social innovation refers to innovation in products and processes that has the primary goal of helping people. Although many successful big data applications have been implemented in the business sector, social innovation with big data is generally considered to be lagging behind. This presentation discusses some of the challenges in using big data to support social innovation and the efforts made to address these challenges. Finally, a couple of social innovation projects are introduced.