Keynote Speakers

Prof. Yungcheol Byun, Jeju National University, South Korea  

Biography: Dr. Yungcheol Byun is a full professor at the Computer Engineering Department (CE) at Jeju National University (http://www.jejunu.ac.kr). His research interests include the areas of Pattern Recognition & Image Processing, Artificial Intelligence & Machine Learning, Pattern-based Security, Home Network and Ubiquitous Computing, u-Healthcare, and RFID & IoT Middleware System. He directs the Machine Laboratory at the CE department. Recently, he studied at University of Florida as a visiting professor from 2012 to 2014. He is currently serving as a director of Information Science Technology Institute, and other academic societies. Outside of his research activities, Dr. Byun has been hosting international conferences including CNSI (Computer, Network, Systems, and Industrial Engineering), ICESI (Electric Vehicle, Smart Grid, and Information Technology), and serving as a conference and workshop chair, program chair, and session chair in various kinds of international conferences and workshops. Dr. Byun was born in Jeju, Korea, and received his Ph.D. and MS from Yonsei University (http://www.yonsei.ac.kr) in 1995 and 2001 respectively, and BS from Jeju National University in 1993. Before joining Jeju National University, he worked as a special lecturer in SAMSUNG Electronics (http://www.samsung.com) in 2000 and 2001. From 2001 to 2003, he was a senior researcher of Electronics and Telecommunications Research Institute (ETRI, https://etri.re.kr/eng/main/main.etri). He was promoted to join Jeju National University as an assistant professor in 2003.

Prof. Dr. Tim Schlippe, IU International University of Applied Sciences, Germany  

Biography: Prof. Dr. Tim Schlippe is a professor of Artificial Intelligence at IU International University of Applied Sciences and CEO of the company Silicon Surfer. He studied computer science at Karlsruhe Institute of Technology and did his master's thesis at Carnegie Mellon University. After successfully completing his PhD at the Karlsruhe Institute of Technology, Prof. Dr. Schlippe worked at Across Systems GmbH as a consultant and project manager for several years before founding the company Silicon Surfer. At Silicon Surfer, he develops AI-powered products and services that have social value, e.g., the WaveFont technology which automatically and intuitively visualizes information and emotion from the voice in subtitles and captions. Prof. Dr. Schlippe has in-depth knowledge in the fields of artificial intelligence, machine learning, natural language processing, multilingual speech recognition/synthesis, machine translation, language modeling, computer-aided translation, and entrepreneurship, which can be seen in his numerous publications at international conferences in these areas.
Prof. Dr. Schlippe’s current research interests are primarily in the fields of AI in Education, Natural Language Processing, and Subtitling/Captioning. As IU International University of Applied Sciences grows rapidly, especially in distance learning, he investigates innovative methods such as automatic short answer grading, conversational AI, and gamification, which are then used in practice at IU to provide optimal support to both students and teaching staff.

Speech Title: Use Cases and Challenges for AI in Education

Access to education is one of people’s most important assets and ensuring inclusive and equitable quality education is goal 4 of United Nation’s Sustainable Development Goals. The research area “AI in Education” addresses the application and evaluation of Artificial Intelligence (AI) methods in the context of education and training. One of the main focuses of this research is to analyze and improve teaching and learning processes with respect to various challenges such automation, multilinguality, accessibility, explainability, recommender systems, question answering, (semi-)automatic grading, sentiment analysis and many more. In his talk, Prof. Dr. Tim Schlippe will present his research activities in these fields as well as his university's efforts to apply AI to various use cases. IU International University of Applied Sciences is the largest university in Germany with students from all over the world, which has set itself the goal as one of the most modern learning institutions in Europe to offer everyone the opportunity for education.

Prof. Phayung Meesad, King Mongkut's University of Technology North Bangkok, Thailand  

Biography: Phayung Meesad was graduated in Master of Science and Doctoral of Philosophy in Electrical Engineering from Oklahoma State University, Stillwater, USA in 1998 and 2002, respectively. He is now an Associate Professor and Director of Central Library, KMUTNB Smart Digital Library, King Mongkut's University of Technology North Bangkok, Thailand. His research of interests are Computational Intelligence, Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics, Data Science, Data Mining, Digital Signal Processing, Image Processing, Business Intelligence, Time Serires Analysis, Natural Language Processing.

Speech Title: Stock Sentiment Analysis with Deep Reinforcement Learning

Stock investors have increasingly turned their attention to leveraging advanced machine learning and data analytics in stock trading. In this keynote speech, we aim to delve deeply into the transformative role that Sentiment Analysis, when integrated with Deep Reinforcement Learning (DRL), plays in shaping contemporary stock trading strategies. We commence by dissecting the theoretical foundations of Natural Language Processing (NLP), emphasizing its indispensable role in extracting public sentiment from diverse sources like news articles, social media platforms, and financial reports. Following this, we transition into an introduction to DRL, focusing specifically on algorithms like Q-learning and Actor-Critic models. These algorithms have been proven to adaptively make buy/sell decisions by processing both historical stock data and sentiment analytics from corporate news. To substantiate our claims, we employ rigorous statistical analyses, leaning on frameworks like the Generalized Method of Moments for parameter estimation, to present compelling case studies. These studies unequivocally validate the effectiveness of incorporating Sentiment Analysis into DRL-based trading systems. We also turn our focus towards addressing inherent challenges that are particularly relevant in this interdisciplinary approach—namely, issues concerning data quality, algorithmic bias, and scalability. Leveraging insights from the field of Fairness, Accountability, and Transparency in Machine Learning, we offer analytical solutions to mitigate these challenges. In conclusion, this speech not only projects future trends at the exciting intersection of Sentiment Analysis and DRL but also underscores the ethical imperative of developing responsible trading algorithms to maintain financial market stability. This keynote aims to provide a comprehensive, data-centric perspective on the evolution and challenges of modern stock trading algorithms.

Assoc. Prof. Koichi Takeuchi, Okayama University, Japan  

Biography: Dr. Koichi Takeuchi is an associate professor of Academic Field of Environmental, Life, Natural Science and Technology, Okayama University. After he received his PhD at Nara Institute of Science and Technology. He worked at National Institute of Informatics as an assistant professor. From 2002 until 2003 he studied at INRIA Lorraine as an invited researcher. His current research interests include automatic essay grading, analysis of predicate-argument structures, text mining, terminology extraction, and the application of language models in the medical field.

Speech Title: Building a Japanese Semantic Role Labeling System for Predicate-Argument Extraction

In this talk, I will present the ongoing development of a language resource for Japanese semantic roles and corresponding automatic annotation system for semantic role labeling. To the best of our knowledge, while established semantic role databases such as FrameNet and PropBank exist for English, a publicly accessible repository for Japanese semantic roles had yet to be developed. Thus, this research project has been constructing a sense repository, referred to as the Predicate-Argument Structure Thesaurus (PT) (https://pth.cl.cs.okayama-u.ac.jp/testp/pth/Vths), alongside a dataset for annotating semantic roles within a syntactically parsed Japanese corpus, referred to as NPTCMJ-PT (https://oncoj.orinst.ox.ac.uk/). Furthermore, we have been developing a pattern matching tool that flexibly extracts the relationship between predicates and their semantic roles based on semantic role labeling system. I will present the current state of development of the semantic role labeling system and explore its applications.