ICPCSEE 2025 | The conference speakers are announced

摘要：International Conference of Pioneering Computer Scientists, Engineers and Educators(ICPCSEE) is an annual academic event initiated

第十一届国际计算机前沿大会

International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

Hiroshima University, Japan

September 19-21 2025

International Conference of Pioneering Computer Scientists, Engineers and Educators(ICPCSEE) is an annual academic event initiated by ICPCSEE Academic Committee, and directed by the CCF -Technical Committee on Computer Applications, and is also one of the top events of the Data Science and Application community.

The 11th International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）2025 ( http://2025.icpcsee.org) will be held in Hiroshima shi, Japan, September 19-21 2025, hosted by China Computer Federation (CCF)and ICPCSEE Academic Committee, CCF - Technical Committee on Computer Applications, Hiroshima University, National Academy of Guo Ding Institute of Data Sciences.

The previous ten ICPCSE events were successfully held in Harbin (2015 and 2016), Changsha (2017), Zhengzhou (2018), Guilin (2019), Taiyuan (2020 and 2021, online), Chengdu (2022, online), Harbin (2023) and Macao (2024). Over 200 experts around the world attended each of the ICPCSEE conferences. Following this trend, ICPCSEE 2025 will be held in Japan · Hiroshima shi.

Keynote

Tadashi Dohi

Hiroshima University Professor

Dr. Tadashi Dohi has served as a Full Professor at Hiroshima University, Japan,since 2002. He is currently appointed as Dean of School of Informatics and Data Science and Associate Dean of Graduate School of Advanced Science and engineering, Hiroshima University. He received a Doctor of Engineering degree from Hiroshima University in 1995. His research interests include Software Reliability, Dependable Computing, Performance Evaluation, Operations Research. To date, his research has led to 285 journal papers, 356 peer-reviewed conference papers, 26 book editions, and 49 book chapters in the above research fields. He also gave 68 keynote/invited talks in international/domestic conferences and research institutes/universities all over the world. Dr. Dohi is a Regular Member of IEICE, IPSJ, REAJ, a Fellow Member of ORSJ, and a Senior Member of IEEE (Computer Society and Reliability Society). He was acting President of REAJ in 2018 and 2019. He has served as the General Chair of 15 international conferences, including ISSRE 2011, ATC 2012, DASC 2019, and ICECCS 2022. Of note, he was a founding member of the International Symposium on Advanced Reliability and Maintenance Modeling (APARM) and International Workshop on Software Aging and Rejuvenation (WoSAR). He has been a steering committee member in AIWARM/APARM, ISSRE, DASC, DSA. He has also worked as a program committee member in several premier international conferences such as DSN, ISSRE, COMPSAC, SRDS, QRS, EDCC, PRDC, HASE, SAC, ICPE, among numerous others. He is an Associate Editor/Editorial Board Member of over 20 international journals.

TOPIC：AI-powered lifetime analysis for a mission-critical system

ABSTRACT：This talk aims at ascertaining effectiveness of different statistical techniques in analyzing a turbofan jet engine as the representative mission-critical system. We employ two deep learning models, called DeepSurv and DeepHit, and compare them with the traditional lifetime analysis methods with censoring. To validate the goodness-of-fit and predictive performances of our deep learning methodologies, we analyze the NASA turbofan jet engine dataset consisting of the lifetime data with censoring and the feature data. The results show that the deep learning methods employed here could exhibit greater stability in terms of the predictive performances than the traditional ones, and could show better fitting performances than some of traditional methods such as Weibull accelerated failure time model, Cox proportional hazard models, logit model, randomforest, random survival forest, Ada boosting.

Shin'ichi Satoh

National Institute of Informatics Professor

Shin'ichi Satoh is a professor at National Institute of Informatics(NII), Tokyo. He received PhD degree in 1992 at the University of Tokyo. His research interests include image processing, video content analysis and multimedia database. Currently he is leading the video processing project at NII, addressing video analysis, indexing, retrieval, and mining for broadcasted video archives.

TOPIC：The challenge of automated psychiatric disease diagnosis by multimedia technologies

ABSTRACT：Psychiatric diseases are a group of important diseases that reduce the quality of life of human beings due to their high incidence and long duration. Although it is hoped that they will be overcome as soon as possible, unfortunately, the current situation is far from that. One of the reasons for this is the lack of biomarkers suitable for diagnosing and assessing the severity of mental illness. Diagnosis and severity assessment are performed through conversations between psychiatrists and patients, and are heavily dependent on the doctor's experience. Such methods lack objectivity and quantification, leading to various problems such as inconsistent diagnoses and unclear criteria for starting treatment. In response to these problems, we have been working on the application of multimedia technologies including NLP, computer vision, lifelog, for psychiatric disease detection. We have been conducting multifaceted studies, such as preparing benchmark data for automatic diagnosis of mental illness, examining automatic diagnosis technology using natural language processing and image analysis, analyzing the "mental state of society" through SNS analysis, and analyzing mental and physical health conditions observed from dietary content. The talk will cover our approaches.

Takahiro Hara

The University of Osaka Professor

Takahiro Hara received the BE, ME, and Dr.E degrees in information systems engineering from Osaka University, Osaka, Japan, in 1995, 1997, and 2000, respectively. Currently, he is a full professor in the Graduate School of Information Science and Technology, the University of Osaka. His research interests include database systems, mobile computing, social computing, and AI. He has published more than 600 Journal and international conference papers. He served as a General Chair of IEEE International Symposium on Reliable Distributed Systems (SRDS'14) and International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous'16 and 21). He served and is serving as a Program Chair of a large number of international conferences including IEEE International Conferences on Mobile Data Management (MDM'06, 10 and 18), Mobiquitous'13, and IEEE SRDS'12. He served and is serving as a Program Committee Member of more than 200 international conferences including top-ranked ones such as VLDB, WWW and CIKM. He is a distinguished scientist of ACM and a senior member of IEEE.

TOPIC：Multi-modal and Cross-domain Deep Learning Approaches for User Activity Prediction

ABSTRACT：Nowadays, varieties of services support people's daily life. To provide effective personalized services, multi-modal and cross-domain approaches for predicting user activities are promising, since data obtained from different service domains cover a wide range of user activities. In this talk, we will present our recent work on graph neural network (GNN) models to integrate different types of data and services and predict user activities. We will also talk about some future directions for user activity prediction based on deep learning approaches.

Feida Zhu

Singapore Management University Aptos Move Chair Professor

Prof. Feida Zhu is currently a tenured Aptos Move Chair Professor and Associate Dean at the School of Computing and Information Systems, Singapore Management University. His research interests include AI and collaborative intelligence, blockchain, data asset and AI governance. He was the founding director of both the Pinnacle Lab for Analytics with China Ping An Insurance Group and the DBS-SMU Life Analytics Lab. Prof. ZHU is on the Board of Directors of Blockchain Association of Singapore as the Chairman of EduCert Committee. He is also the Adjunct Senior Principal Scientist of A*Star leading the Deep-Tech Pillar for the Centre for Advanced Technologies in Online Safety (CATOS). Prof. ZHU has over 100 peer-reviewed research publications at top international venues with multiple Best Paper Awards. He won the Early Career Award of PAKDD’19 and is the PC Co-Chair of DASFAA’25, the General Co-Chair of IEEE ICDM’18 and ACM KDD’21.

TOPIC：Collaborative Intelligence & Tokenized Economy: Overview and Frontiers

ABSTRACT：The recent boom of AI brings along with all the wonderful innovations concerns about the governance issues, in particular when the application context is bound to be increasingly collaborative in nature. How to provide both trust and incentive when data, model and computational resources come from different entities? In this talk we will give an overview for the potential integration between AI and Web3 by collaborative intelligence and tokenized economy, with the challenges, core technical components and latest development at the frontier. In particular, we presentnew protocols for data collaboration in decentralized federated learning setting with both privacy-aware data verification and token-based incentive design. As distributed ledger technology is an essential part for trust-by-design data and AI collaboration, safety issues will also be discussed with new algorithms for on-chain data analytics. A few application examples will also be given to illustrate the marriage between the collaborative intelligence and tokenized economy.

Registration