ICISPC 2024 conference 

ICISPC 2024 has been successfully held in Fukuoka, Japan during July 19-21, 2024.

Thank you for your participation and support.

 

 

 

 

Congratulations to the winners of the best paper presentations.

 

Best presentations:
Session 1:
SA1035-A
Chih-Chang Yu
Chung Yuan Christian University, Taiwan
An exploratory study of using slope feature and weakly supervised semantic segmentation for landslide detection in Taiwan

Session 2:
SA1011
Yuan-Kang Lee
National Taiwan University, Taiwan
Efficient Color Image Denoising using DWT-based Noise Estimation and Adaptive Wiener Filter

Session 3:
SA2016-A
Anne Schwerk
IU International University, Germany; Berlin Institute of Health, Berlin, Germany
PHANTOMATRIX: Explainability Dimensions of Affective Computing

Session 4:
SA2008
Adrien Verhulst
Sony Computer Science Laboratories, Inc., Japan
Exploring the Influence of Immersion and Social Characteristics on Social Presence when Conversing with a Conversational Agent

Session 5:
SA1016
Xun Zhang
Harbin Institute of Technology, China
A Measurement Pairing Method Based on MAP Criterion for Dense Targets in Dual-Sensor System

Session 6:
SA2035
In Kyu Park
Inha University, South Korea
Efficient 3D Human Body Reconstruction from Monocular Video with Depth-Guided Learning

Session 7:
SA2047
Chung Kwan LO
The Education University of Hong Kong, Hong Kong, China
An Exploratory Study of Using AI Tools to Analyse Classroom Discourse Data
SA2107
Mesut Alptekin
Paderborn University, Germany
Quantitative and Qualitative Literature Review of Augmented Reality in Teaching and in Technical Laboratories since 2010

Session 8:
SA2029
Dante Silva and Yza Mae A. Cadid
Mapua University, Philippines
Neural Network-Particle Swarm Optimization Approach for Prediction of Deformation and Parallel Bending Strength of Guadua angustifolia Kunth

Session 9:
SA1057
Wanwan Li
University of Tulsa, USA
PM4Bag: A Scriptable Parametric Modeling Interface for Conceptual Bag Design Using PM4VR

Session 10:
SA2011
Enes Yigitbas
Paderborn University, Germany
Effects of Human Avatar Representation in Virtual Reality on Inter-Brain Connections

SA2077
Hanin Hamed Hilal Al Naamani
University of Stirling, Sultanate of Oman
VR Game Development: Team-building Exercises Gamified

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Speakers of ICISPC2024

 

 

 

Prof. Shahram Latifi
IEEE Fellow

University of Nevada, USA
 

 

Biography: Shahram Latifi, received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the co-director of the Center for Information Technology and Algorithms (CITA) at UNLV. He has designed and taught undergraduate and graduate courses in the broad spectrum of Computer Science and Engineering in the past four decades. He has given keynotes and seminars on machine learning/AI and IT-related topics all over the world. He has authored over 300 technical articles in the areas of networking, AI/ML, cybersecurity, image processing, biometrics, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, DoD, Boeing, and Lockheed. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006), an IEEE Distinguished Speaker (1997-2000), Co-founder and Chair of the IEEE Int'l Conf. on Information Technology (2000-2004) and founder and Chair of the International Conf. on Information Technology-New Generations (2005-Present) . Dr. Latifi is the recipient of several research awards, the most recent being the Barrick Distinguished Research Award (2021). Dr. Latifi was recognized to be among the top 2% researchers around the world in December 2020, according to Stanford top 2% list (publication data in Scopus, Mendeley). He is an IEEE Fellow (2002) and a Registered Professional Engineer in the State of Nevada.

 

Speech Title: AI Advancements and Challenges: Navigating the Future of Responsible AI
Over the past two decades, AI technology has advanced at an astonishing pace. Breakthroughs such as Deep Learning, Generative Adversarial Networks, Transfer Learning, and Large Language Models have accelerated this progress, enabling AI to revolutionize various aspects of society. AI has significantly enhanced the performance of systems in fields like education, healthcare, aerospace, manufacturing, security, e-commerce, and art. However, alongside these tremendous benefits come major concerns about the potential threats AI poses to humanity. How can we ensure our training data is unbiased and well-balanced? How can we guarantee that AI systems respect individual privacy? And most importantly, how can we ensure these systems remain controllable and act responsibly?
In this talk, I will provide a brief overview of AI, Machine Learning (ML), and Deep Learning (DL). While there are significant challenges in achieving general-purpose AI (as opposed to Narrow AI), there are even greater issues that must be addressed to ensure AI is safe, fair, and secure. I will also discuss recent efforts in the United States and around the world to build responsible AI.

 

 

 

 

Prof. Dr. Ho-Jin Choi

Korea Advanced Institute of Science & Technology (KAIST), South Korea
 

 

Biography: Prof. Dr. Ho-Jin Choi is a professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. He received a BS in computer engineering from Seoul National University (SNU), Korea, an MSc in computing software and systems design from Newcastle University, UK, and a PhD in artificial intelligence from Imperial College London, UK. During 1980’s he worked for DACOM Corp., Korea, in later 1990’s he joined with Korea Aerospace University, before he moved to KAIST in 2009. In early 2000’s, he visited Carnegie Mellon University (CMU), USA, and served as an adjunct faculty for the Master of Software Engineering (MSE) program operated jointly by CMU and KAIST for 10 years. In 2010’s he participated research in Systems Biomedical Informatics Research Center at the College of Medicine, SNU, worked with Samsung Electronics on big data intelligence solutions, and with UAE’s Khalifa University on intelligent multi-sensor healthcare surveillance. He also participated in a Korean national project called Exobrain for natural language question/answering. Since 2018, he has been the director of Smart Energy Artificial Intelligence Research Center, and since 2020 the director of Center for Artificial Intelligence Research, both at KAIST. His current research interests include natural language processing, machine learning, explainable AI, and smart energy.

 

Speech Title: DialogCC for Creating High-Quality Multi-Modal Dialogue Datasets
For sharing images in instant messaging, active research has been going on learning image-text multi-modal dialogue models. Training a well-generalized multi-modal dialogue model remains challenging due to the low quality and limited diversity of images per dialogue in existing multi-modal dialogue datasets. In this research, we propose an automated pipeline to construct a multi-modal dialogue dataset, ensuring both dialogue quality and image diversity without requiring any human effort. In order to guarantee the coherence between images and dialogue, we prompt GPT-4 to infer potential image-sharing moments, e.g., utterance, speaker, rationale, and image description. Furthermore, we leverage CLIP similarity to maintain consistency between aligned multiple images to the utterance. Using this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal dialogue dataset that surpasses existing approaches in terms of quality and diversity in human evaluation. Our experiments highlight multi-modal dialogue models trained using our dataset, and their generalization performance on unseen dialogue datasets.

 

 

Assoc. Prof. Emi Yuda, Dr.Eng., PhD.

Tohoku University, Japan

 

Speech Title: Future Technology for Non-invasively Estimating Biological Status Using Bio-signal Processing for Human
 

Abstract: In this talk, focus on cutting-edge research in human bio-signal processing and the analysis of bio-medical big data. Especially, I highlight advancements in signal processing techniques for extracting valuable information from bio-signals heart rate variability (HRV) and body acceleration. And discuss innovative methods for enhancing bio-signal data accuracy, human privacy, and security, as well as their applications in healthcare and human-computer interaction.
Additionally, address challenges and future directions in human bio-signal research, emphasizing the importance of interdisciplinary collaboration and ethical considerations in handling bio-medical big data.  

 

Biography: Assoc. Prof. Emi Yuda is one of Japanese information engineer whose areas of expertise are bio-signal processing and bio-medical big data analysis. She is an associate professor at Tohoku University and has contributed to innovative research during her career. D. degrees in both informatics (Ph.D.) and engineering (Dr. Eng.). She applies ECG and acceleration signal analysis techniques from medical devices and wearable sensors to the fields of health science, disease screening, medication adherence and rehabilitation. Her research focuses on human dynamics, starting from pre-symptomatic disease state, disorder to recovered. Her work has helped develop new algorithms and improved the performance of human state estimation techniques. Research projects under her direction have attracted industrial interest and she has collaborated with many companies. She has actively presented her work at international conferences and in academic journals. She is also active as a reviewer of academic papers.

 

 

 

 

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