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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Khoshvaght, Parisa Haider, Amir Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Anka, Ferzat Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of AI and Robotics Sejong University Seoul05006 Korea Republic of Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Fatih Sultan Mehmet Vakif University Istanbul Turkey Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms. © 2025 Elsevier B.V.

关键词： Resource allocation

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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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Medical Image Analysis 2022年 78卷 102396-102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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Identifying latent disease factors differently expressed in patient subgroups using group factor analysis

arXiv

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arXiv 2024年

作者： Ferreira, Fabio S. Ashburner, John Bouzigues, Arabella Suksasilp, Chatrin Russell, Lucy L. Foster, Phoebe H. Ferry-Bolder, Eve van Swieten, John C. Jiskoot, Lize C. Seelaar, Harro Sanchez-Valle, Raquel Laforce, Robert Graff, Caroline Galimberti, Daniela Vandenberghe, Rik de Mendonça, Alexandre Tiraboschi, Pietro Santana, Isabel Gerhard, Alexander Levin, Johannes Sorbi, Sandro Otto, Markus Pasquier, Florence Ducharme, Simon Butler, Chris R. Le Ber, Isabelle Finger, Elizabeth Tartaglia, Maria Carmela Masellis, Mario Rowe, James B. Synofzik, Matthis Moreno, Fermin Borroni, Barbara Kaski, Samuel Rohrer, Jonathan D. Mourão-Miranda, Janaina Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Max Planck University College London Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology London United Kingdom Institute of Cognitive Neuroscience University College London London United Kingdom Department of Neurology Erasmus Medical Centre Rotterdam Netherlands Alzheimer's Disease and Other Cognitive Disorders Unit Neurology Service Hospital Clinic Institut d'Investigacions Biomediques August Pi I Sunyer University of Barcelona Barcelona Spain Clinique Interdisciplinaire de Memoire Departement des Sciences Neurologiques CHU de Quebec Faculte de Medecine Universite Laval QC Canada Department of Neurobiology Care Sciences and Society Center for Alzheimer Research Division of Neurogeriatrics Bioclinicum Karolinska Institutet Solna Sweden Fondazione Ca Granda IRCCS Ospedale Policlinico Milan Italy Laboratory for Cognitive Neurology Department of Neurosciences KU Leuven Leuven Belgium Faculty of Medicine University of Lisbon Lisbon Portugal Fondazione IRCCS Istituto Neurologico Carlo Besta Milano Italy Neurology Service Faculty of Medicine University of Coimbra Coimbra Portugal Division of Psychology Communication and Human Neuroscience Wolfson Molecular Imaging Centre University of Manchester Manchester United Kingdom Department of Neurology Ludwig-Maximilians Universitat Munchen Munich Germany Department of Neurofarba University of Florence Italy Department of Neurology University of Ulm Germany Univ Lille France Douglas Mental Health University Institute Department of Psychiatry McGill University Montreal Canada Nuffield Department of Clinical Neurosciences Medical Sciences Division University of

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity, neuropsychological tasks and medical assessments of disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders. © 2024, CC BY-NC-ND.

关键词： Brain mapping

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Precision tomography of a three-qubit donor quantum processor in silicon

arXiv

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arXiv 2021年

作者： Mądzik, Mateusz T. Asaad, Serwan Youssry, Akram Joecker, Benjamin Rudinger, Kenneth M. Nielsen, Erik Young, Kevin C. Proctor, Timothy J. Baczewski, Andrew D. Laucht, Arne Schmitt, Vivien Hudson, Fay E. Itoh, Kohei M. Jakob, Alexander M. Johnson, Brett C. Jamieson, David N. Dzurak, Andrew S. Ferrie, Christopher Blume-Kohout, Robin Morello, Andrea School of Electrical Engineering and Telecommunications UNSW Sydney SydneyNSW2052 Australia Centre for Quantum Software and Information University of Technology Sydney UltimoNSW2007 Australia Department of Electronics and Communication Engineering Faculty of Engineering Ain Shams University Cairo Egypt Quantum Performance Laboratory Sandia National Laboratories Albuquerque NM 87185 LivermoreCA94550 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States School of Fundamental Science and Technology Keio University Kohoku-ku Yokohama Japan School of Physics University of Melbourne MelbourneVIC3010 Australia

Nuclear spins were among the first physical platforms to be considered for quantum information processing[1, 2], because of their exceptional quantum coherence[3] and atomic-scale footprint. However, their full potential for quantum computing has not yet been realized, due to the lack of methods to link nuclear qubits within a scalable device combined with multi-qubit operations with sufficient fidelity to sustain fault-tolerant quantum computation. Here we demonstrate universal quantum logic operations using a pair of ion-implanted 31P donor nuclei in a silicon nanoelectronic device. A nuclear two-qubit controlled-Z gate is obtained by imparting a geometric phase to a shared electron spin[4], and used to prepare entangled Bell states with fidelities up to 94.2(2.7)%. The quantum operations are precisely characterised using gate set tomography (GST)[5], yielding one-qubit average gate fidelities up to 99.95(2)%, two-qubit average gate fidelity of 99.37(11)% and two-qubit preparation/measurement fidelities of 98.95(4)%. These three metrics indicate that nuclear spins in silicon are approaching the performance demanded in fault-tolerant quantum processors [6]. We then demonstrate entanglement between the two nuclei and the shared electron by producing a Greenberger-Horne-Zeilinger three-qubit state with 92.5(1.0)% fidelity. Since electron spin qubits in semiconductors can be further coupled to other electrons[7, 8, 9] or physically shuttled across different locations[10, 11], these results establish a viable route for scalable quantum information processing using donor nuclear and electron spins. Copyright © 2021, The Authors. All rights reserved.

关键词： Spin dynamics

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Reduction theorem for secrecy over linear network code for active attacks

arXiv

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arXiv 2020年

作者： Hayashi, Masahito Owari, Masaki Kato, Go Cai, Ning Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore Department of Computer Science Faculty of Informatics Shizuoka University Japan NTT Communication Science Laboratories NTT Corporation Japan School of Information Science and Technology ShanghaiTech University Middle Huaxia Road no 393 Pudong Shanghai201210 China

We discuss the effect of sequential error injection on information leakage under a network code. We formulate a network code for the single transmission setting and the multiple transmission setting. Under this formulation, we show that the eavesdropper cannot improve the power of eavesdropping by sequential error injection when the operations in the network are linear *** demonstrate the usefulness of this reduction theorem by applying a concrete example of network. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

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Current-induced dissociation in molecular junctions beyond the paradigm of vibrational heating: The role of anti-bonding electronic states

arXiv

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arXiv 2020年

作者： Erpenbeck, André Ke, Yaling Peskin, Uri Thoss, Michael Institute of Physics Albert-Ludwig University Freiburg Hermann-Herder-Strasse 3 Freiburg79104 Germany Schulich Faculty of Chemistry Technion-Israel Institute of Technology Haifa32000 Israel EUCOR Centre for Quantum Science and Quantum Computing Albert-Ludwig University Freiburg Hermann-Herder-Strasse 3 Freiburg79104 Germany

The interaction between electronic and nuclear degrees of freedom in single-molecule junctions is an essential mechanism, which may result in the current-induced rupture of chemical bonds. As such, it is fundamental for the stability of molecular junctions and for the applicability of molecular electronic devices. In this publication, we study current-induced bond rupture in molecular junctions using a numerically exact scheme, which is based on the hierarchical quantum master equation (HQME) method in combination with a discrete variable representation for the nuclear degrees of freedom. Employing generic models for molecular junctions with dissociative nuclear potentials, we identify distinct mechanisms leading to dissociation, namely the electronic population of anti-bonding electronic states and the current-induced heating of vibrational modes. Our results reveal that the latter plays a negligible role whenever the electronic population of anti-bonding states is energetically possible. Consequently, the significance of current-induced heating as a source for dissociation in molecular junctions involving an active anti-bonding state is restricted to the non-resonant transport regime, which reframes the predominant paradigm in the field of molecular electronics. Copyright © 2020, The Authors. All rights reserved.

关键词： Dissociation

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Enabling Students with Severe Disabilities to Communicate with Learning Environments 5

Enabling Students with Severe Disabilities to Communicate wi...

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5th Asia-Pacific World Congress on Computer science and Engineering, APWC on CSE 2018

作者： Assaf, M. Kumar, R. Nambiar, K. Narayan, S. Nath, N. Reddy, Y. Sharma, K. Sharma, B. School of Engineering and Physics Faculty of Science Technology and Environment Suva Fiji School of Computing Info and Maths Faculty of Science Technology and Environment Suva Fiji Centre for Flexible Learning University of the South Pacific Suva Fiji

ISBN: (纸本)9781728113906

This paper presents the design of an assistive technology that allows people with disabilities to communicate with learning environments. We propose an assistive Tongue Drive System (TDS) which enables the end user to use their tongue to communicate by means of an Android Device. In this study, the design of the TDS is discussed. © 2018 IEEE.

关键词： Brain computer interface

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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Effects of Green Solvents and Surfactants on the Characteristics of Few-Layer Graphene Produced by Dual-Frequency Ultrasonic Liquid Phase Exfoliation Technique

SSRN

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SSRN 2022年

作者： Tyurnina, Anastasia V. Morton, Justin A. Kaur, Amanpreet Mi, Jiawei Grobert, Nicole Porfyrakis, Kyriakos Tzanakis, Iakovos Eskin, Dmitry G. Brunel Centre for Advanced Solidification Technology Brunel University London Kingston Lane UB8 3PH United Kingdom School of Engineering Computing and Mathematics Oxford Brookes University College Cl Wheatley OxfordOX33 1HX United Kingdom Department of Engineering University of Hull Cottingham Rd HullHU6 7RX United Kingdom Department of Materials University of Oxford Parks Rd OxfordOX1 3PH United Kingdom Faculty of Engineering and Science University of Greenwich Park Row LondonSE10 9LS United Kingdom Tomsk State University 36 Lenin. Ave. Tomsk634050 Russia

Nowadays, one of the promising methods for scalable graphene production is ultrasound-aided liquid phase exfoliation (ULPE) of graphite. Two current limiting factors of ULPE are the use of harmful solutions (such as NMP or DMF) and a relatively low graphene yield. In this study, we demonstrate a new dual frequency (20 kHz and 1174 kHz) ULPE approach in various eco-friendly media, which enabled us to produce various FLG solutions of high quality. By implementing sophisticated characterisation techniques consisting of Raman spectroscopy, UV-vis spectroscopy and high-resolution electron microscopy, the final graphene flakes structure was confirmed to correlate the properties of each individual solution. The thinner (3 Ls) and larger (~1.5 μm2) flakes were observed while using just water, with the highest yield (11%) of smaller FLG flakes to be achieved in the mixture of water and a surfactant. In order to understand the cavitation mechanism in different solutions, the ULPE process was investigated by acoustic measurements. This study demonstrates the crucial role of ethanol (as a solvent) and surfactants as it regulates the cavitation power and intensity of the ultrasonic field and, thereby, the cavitation effectiveness. It is suggested that the mixture of water, ethanol and a surfactant is the best medium for ULPE process where a high yield of low-defective FLG flakes can be obtained in a solution stable at least for 3 months (around 80%). © 2022, The Authors. All rights reserved.

关键词： Cavitation

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1stParis Van Java International Seminar on Computer, science, Engineering and technology (PVJ ISComSET) 2020

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Journal of Physics: Conference Series 2021年第1期1764卷 011001-011001页

作者： Mujiarto Djohar, As'Ari Vaidyanathan, Sundarapandian Mamat, Mustafa Bin Lien, C.-H. Habib, Md. Mamun Sulaema, Erwin Suswandari Muhtadi Sarjito Komaro, Mumu Hermawan, Sigit Tjahjanti, Prantasi Harmi Kautsar, Irwan Alnarus Saputra, Jumadil Sambas, Aceng Hendrawan, Budi Universitas Muhammadiyah Tasikmalaya Tasikmalaya Indonesia Universitas Pendidikan Indonesia Bandung Indonesia Research and Development Centre Vel Tech University Avadi Chennai India Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia Department of Marine Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Independent University Dhaka Bangladesh International Islamic University Malaysia Kuala Lumpur Malaysia Universitas Muhammadiyah Prof. HAMKA Jakarta Indonesia Universitas Muhammadiyah Surakarta Surakarta Indonesia Universitas Muhammadiyah Sidoarjo Sidoarjo Indonesia Universiti Malaysia Terengganu Kuala Terengganu Malaysia

Mujiarto1*, As’ari Djohar2, Sundarapandian Vaidyanathan3, Mustafa Bin Mamat4,C-H Lien5, Md. Mamun Habib6, Erwin Sulaema7, Suswandari8, Muhtadi9, Sarjito9, Mumu Komaro2, Sigit Hermawan10, Prantasi Harmi Tjahjanti10, Irwan Alnarus Kautsar10, Jumadil Saputra11, Aceng Sambas1, Budi Hendrawan1 1Universitas Muhammadiyah Tasikmalaya, Tasikmalaya Indonesia 2Universitas Pendidikan Indonesia, Bandung, Indonesia 3 Research and Development centre, Vel Tech University, Avadi, Chennai, India 4 faculty of Informatics and computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia 5 Department of Marine Engineering, National Kaohsiung University of science and technology, Kaohsiung, Taiwan 6Independent University, Dhaka, Bangladesh 7International Islamic University Malaysia, Kuala Lumpur, Malaysia 8Universitas Muhammadiyah Prof. HAMKA, Jakarta, Indonesia 9Universitas Muhammadiyah Surakarta, Surakarta, Indonesia 10Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia 11Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia *mujiarto@*** Preface I want to say thank you to he Keynote speakers in The 1st Paris Van Java International Seminar on Computer, science, Engineering and technology (PVJ-ISComSET 2020) conjuction with Business Innovation and Engineering Conference (BIEC 2020), for your willingness to become the speakers in this conference. And also to all the participants of The 1st PVJ-ISComSET 2020. The 1st Paris Van Java International Seminar on Computer, science, Engineering and technology (PVJ-ISComSET), provides an excellent international forum for sharing knowledges and results in theory, methodology, and applications of Computer, science, Engineering and technology in theoretical and practical aspects. The conference aims to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development. It was carried out online through the Zoom application with a capacity of

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