ICISPC 2023 conference 

ICISPC 2023 conference was held in Kumamoto, Japan during July 21-23, 2023. 

We acknowledge the invaluable assistance of the keynote speakers, the International advisory committee, the program committee members and session chairs.







  • Congratulations to the winners of the best paper presentations.

    Technical Session 1: Artificial Intelligence and Hybrid Reality (Onsite)
    JY014: Kazuki Kurata, Kyushu Institute of Technology, Japan
    Hardware Development of Sphere Intersection in Ray-casting Using High-Level-Synthesis

    Technical Session 2: Image, Video Analysis and Processing (Onsite)
    JY015: Yuka Otani, Kyushu Institute of Technology, Japan
    Data Path Parallelization to Improve Performance of High-level Synthesized Sprite Drawing Hardware

    Technical Session 3: Virtual Environment and Virtual Reality Technology (Onsite)
    JY2021: Judith Josupeit, Engineering Psychology and Applied Cognitive Research, TU Dresden, Germany
    Inside the Black Box: Modeling a Cybersickness Dose Value Through Built-In Sensors of Head-Mounted Displays

    Technical Session 4: Data Visualization and Interaction System Based on Artificial Intelligence (Online)
    JY2017: Sophie Dewil, Stevens Institute of Technology, USA
    Neural responses to altered visual feedback in computerized interfaces driven by force or motion control

    Technical Session 5: Communication System and Signal Processing (Online)
    JY008: Sophie Adama, Leipzig University, Germany
    Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient

    Technical Session 6: Modern Image Detection and Processing
    JY2047: Ariel Larey, Huawei Research, Israel
    Facial Expression Re-targeting from a Single Character


    Speakers of ICISPC2023




    Prof. Nikola Kasabov
    IEEE Fellow, INNS Fellow and RSNZ Fellow

    Auckland University of Technology, New Zealand
    IICT, Bulgarian Academy of Sciences, Bulgaria
    ISRC, University of Ulster, the UK


    Speech Title: Spatio-Temporal Learning (STL) machines using brain-inspired spiking neural networks and their applications

    Abstract:The majority of data, that are dealt with across information and data sciences, are temporal or spatio/spectro temporal, including: biological and brain signals; audio-visual; environmental; financial and economic; communication. In many cases this data is simplified just as temporal or spatial, due to lack of computational models to model both spatial and temporal components of the data in their dynamic interaction and integration.

    The talk introduces a new type of learning methods and systems, called spatio-temporal learning (STL). These are evolvable and explainable learning systems that are structured according to the spatial or other relationship information of temporal data and are trained to evolve their structure by learning spatio-temporal associations of the data, that are explainable. Inspired by the STL in the human brain, the talk presents a STL machine NeuCube, based on spiking neural networks. It demonstrates its applications for STL of biological and brain signals, audio-visual data, environmental data such as seismic sensory data, financial and economic data such as stock and trades, communication data such as using VR. When compared to traditional machine learning techniques on the same data, including deep neural networks, the STL machines demonstrate significantly better accuracy and a clear interpretability and explainability of the data in their dynamics over time (Reference).

    Reference: Kasabov, N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer Nature (2019) 750p., https://www.springer.com/gp/book/9783662577134


    Biography: Professor Nikola Kasabov is Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. Kasabov is the 2019 President of the Asia Pacific Neural Network Society(APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neuro-systems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 620 publications highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University and CASIA China, Visiting Professor at ETH/University of Zurich and Robert Gordon University UK, Honorary Professor of Teesside University, UK.Prof. Kasabov has received a number of awards, among them:Doctor Honoris Causa from Obuda University, Budapest; INNS AdaLovelace Meritorious Service Award; NN Best Paper Award for2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Awardfor ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of theBulgarian, the Greek and the Scottish Societies for ComputerScience. More information of Prof. Kasabov can be found from:




    Prof. Fabrice Labeau

    McGill University, Canada


    Speech Title: Using deep learning to safely share information: privacy-preserving frameworks


    Abstract: Our use of technology generates vast amounts of digital data from the Sensors, IoT devices and personal devices that we use in our daily lives. In turn, advances in diagnostic, planning and decision aid systems brought about by artificial intelligence rely on the availability of data to train the deep learning algorithms that underpin these new systems.
    Although, on the surface, it sounds like these two trends can nicely feed into each other, there are many issues related to the confidentiality of the data that we produce, and individual data producers may resist sharing data for algorithmic development, for fear of seeing their private or confidential data exploited.
    In this presentation, we will explore deep learning-based systems that allow for the reshaping of data so that it can be released publicly without revealing confidential portions that may have been included in the original data. We will illustrate this approach in particular in the case of sharing smart meter power consumption readings from individual households without revealing data such as, for instance, occupancy patterns.



    Biography: F. Labeau is the Deputy Provost (Student Life and Learning) at McGill University. His research interests are in applications of signal processing. He has (co-)authored more than 200 papers in refereed journals and conference proceedings in these areas.
    At McGill, he has held different administrative positions, including Graduate Program Director, Acting Department Chair, Associate Dean (Faculty Affairs) and Acting Dean. He is currently the Planning Chief for the University’s Emergency Operations Center.
    As Deputy Provost, he leads a team of 600 employees offering all student services at the University. He is the Director of Operations of STARaCom, an interuniversity research center grouping 50 professors and 500 researchers from 10 universities in the province of Quebec, Canada.
    He is Senior Past President of the IEEE Sensors Council, former President (2014-2015) of the IEEE Vehicular Technology Society, and a former chair of the Montreal IEEE Section. He was or will be General co-chair for IEEE ICIP 2021, IEEE ICIP 2025, IEEE GlobalSIP 2019, IEEE SENSORSSENSORS, IEEE EPEC 2019, URSI GASS 2017 and IEEE VTC 2016-Fall. He was a TPC co-chair for IEEE 5G World Forum 2021, IEEE CCECE 2018, IEEE ICIP 2015, IEEE VTC 2012-Fall, IEEE VTC 2006-Fall.
    He was a recipient in 2015 and 2017 of the McGill University Equity and Community Building Award (team category), of the 2008 and 2016 Outstanding Service Award from the IEEE Vehicular Technology Society and of the 2017 W.S. Read Outstanding Service Award form IEEE Canada. He was recognized in 2018 “Ambassadeur Accrédité” for the Montreal Convention Center.