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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Prasetyo Adi, Puput Dani Pramono, Andi Rahayu, Anita Persada, Satria Fadil Nadlifatin, Reny School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta Indonesia Institut Teknologi Sepuluh Nopember Department of Information System Surabaya Indonesia

ISBN: (纸本)9798350389654

The Internet of Things (IoT) has revolutionized the concept of Smart Cities by enabling the seamless integration of various technologies and devices to enhance urban living. This abstract presents a comprehensive overview of the research trends and advancements in IoT applications within Smart Cities from 2019 to 2023. By conducting a scientometric review and bibliometric analysis using R studio and VOSviewer software, this study aims to identify key focus areas, challenges, and opportunities in the implementation of IoT technologies in urban environments. Data selection from the Scopus database yielded a total of 3849 documents from 1515 sources. The most cited article from 2021 is 166 citations. Average citations per doc is 18.56. China, India, and Korea appear to dominate in terms of the number of published documents. Future research directions include exploring innovative solutions leveraging technologies like blockchain, edge computing, and artificial intelligence to improve the efficiency, sustainability, and resilience of Smart City initiatives. By synthesizing insights from existing literature and exploring emerging trends, this study contributes to the advancement of Smart Cities and the development of a more connected and intelligent urban future. © 2024 IEEE.

关键词： Sustainable city

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Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models 25

Debiasing Classifiers by Amplifying Bias with Latent Diffusi...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Donggeun Ko Dongjun Lee Namjun Park Wonkyeong Shim Jaekwang Kim AiM Future Seoul Republic of Korea Maum AI Gyeonggi-do Republic of Korea Department of Computer Science and Engineering/Convergence Program for Social Innovation Sungkyunkwan University Suwon Republic of Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea School of Convergence / Convergence Program for Social Innovation Sungkyunkwan University Suwon Republic of Korea

ISBN: (纸本)9798400706295

Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance. Previous methods require attribute labels (e.g. background, color) or utilizes Generative Adversarial Networks (GANs) to mitigate biases. We introduce DiffuBias, a novel pipeline for text-to-image generation that generates bias-conflict samples, without any training. By utilizing pretrained diffusion and image captioning models, DiffuBias generates, bias-conflict samples using the top-K losses from a biased classifier (fB) to debias the classifier. This method not only debiases effectively but also boosts classifier generalization capabilities. Our comprehensive experimental evaluations demonstrate that DiffuBias achieves state-of-the-art performance on benchmark datasets.

关键词： debiasing

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Behavior Profiling of Role-Playing Game Players Based on Heuristic Event Log Mining 25th

Behavior Profiling of Role-Playing Game Players Based on Heu...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Jason Tsai, Chia-Wei Yang, Chun-Wei Lafortune, Elie Department of Computer Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University Taichung406040 Taiwan

ISBN: (纸本)9789811995811

In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic filtering rules are applied to eliminate uninteresting events. The proposed model uses sequences and tendencies of event types as the main mining criteria to extract significant player action behaviors. Action sequences reflect the interdependencies of different actions and how a player may use their insights about the game rules to form a (hidden) action priority list (APL) with respect to action types. By mining for significant events from play logs, we aim to produce the APM and APL for different players. In experiments, the fuzzy miner tool was used to analyze a few event logs for the World of Warcraft game to obtain some noticeable discoveries about the structural and timing patterns of effective players. Experimental results show that two players in the same class with similar contributions to encounters will have a similar mined APM and may not have a minimal delay in the timing of actions. Players with a high contribution to an encounter will have an APM like a simulated optimal player along with large thickness edges in the APM to indicate a significant routine of actions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Causal net Fuzzy miner Player behavior

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A Comprehensive Analysis of: A Systematic Review 4th

A Comprehensive Analysis of: A Systematic Review

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4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2023

作者： Lestari, Dian Maulana, Fairuz Iqbal Purnomo, Agung Adi, Puput Dani Prasetyo Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

This systematic review explores the rising use of virtual clinics, including telemedicine technologies like virtual visits and virtual clinics, in maternity care. These technologies enable continuous monitoring of expectant mothers, offering valuable and satisfactory services, especially in the post-COVID-19 pandemic era. The study aims to comprehensively analyze the applications, effectiveness, benefits, challenges, and overall impact of virtual clinics in gynecological and pregnancy care. A systematic review search encompassing the period from 2019 to 2023 was performed on Scopus and PubMed databases. Additional manual searches on reference lists were conducted for comprehensive coverage. Eight articles were included, comprising various study designs, such as experimental, cohort, and cross-sectional studies. Pregnant women, parents, and healthcare practitioners were the target users. Virtual clinics were utilized for virtual visits, consultations, monitoring, follow-up, and home care, resulting in highly satisfactory patient experiences. However, challenges, including poor internet connection, were identified. The reviewed studies reveal promising outcomes in patient and provider satisfaction. Virtual clinics have the potential to revolutionize maternity care by providing accessible, efficient, and personalized healthcare services. Despite technical challenges, the integration of telemedicine solutions, particularly virtual clinics, is expected to play a growingly significant role in obstetric care. These insights are valuable for healthcare providers, policymakers, and researchers, leading to improved maternal care outcomes through the adoption of virtual clinics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Telemedicine

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Feasibility Study of Semi-supervised Intrusion Detection Systems for Industrial Control Systems 10

Feasibility Study of Semi-supervised Intrusion Detection Sys...

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10th International Conference on Applied System innovation, ICASI 2024

作者： Loo, Fu-Nie Chen, Tung-Shou Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering No.129 Sec.3 Sanmin Rd. North Dist. Taichung City404 Taiwan National Chung Hsing University Department of Computer Science and Engineering 145 Xingda Rd. South Dist. Taichung City402202 Taiwan China Medical University Master Program for Digital Health Innovation No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung City406040 Taiwan

ISBN: (纸本)9798350394924

Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often inefficiently detecting with supervised learning. This study employs semi-supervised learning using one-class SVM, isolation forest, and Local Outlier Factor (LOF), to train IDS models. Utilizing dataset collected from a self-build virtual ICS environment, the study demonstrates the feasibility of these models in detecting common attack like Injection, ARP, and Man-in-the-Middle. © 2024 IEEE.

关键词： Zero-day attack

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Big Data as Capital. A Case Study on the innovation Labs Tech Accelerator 23

Big Data as Capital. A Case Study on the Innovation Labs Tec...

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23rd International Conference on Control Systems and computer science Technologies, CSCS 2021

作者： Budeanu, Andrea-Mariana Rosner, Daniel Tech Lounge Association Innovation Labs Program Bucharest Romania University POLITEHNICA of Bucharest Computer Science Department Bucharest Romania

ISBN: (纸本)9781665439398

We are currently living in an economy driven by data as a form of capital and by increasingly diverse, emerging data accumulation methods. Based on a case study of innovation Labs, a tech accelerator implemented in Romania since 2013, we discuss the challenges and opportunities for cultivating innovation and entrepreneurship competences that are sensitive to the potential and limitations of big data. Data capitalism has significant implications not only in our everyday life and business, but also on the instruments that we employ to understand the world and to achieve value. The data-based society needs complex information technology to process information, to observe, to map, to extract, to put together streams of data which eventually are leading to decision making and to value creation, including - but not limited to - financial value. Big data applications gather data from heterogeneous activity fields and seek to create insights through their cross-sectional interpretation. How are young tech-oriented people equipped to navigate the data-storm that this global current of digitalization and automation is creating? We will look at the case of innovation Labs tech accelerator, by analyzing the last four editions of the program in terms of working with (big) data. © 2021 IEEE.

关键词： Decision making

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Feasibility Study of Semi-supervised Intrusion Detection Systems for Industrial Control Systems

Feasibility Study of Semi-supervised Intrusion Detection Sys...

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International Conference on Applied System innovation (ICASI)

作者： Fu-Nie Loo Tung-Shou Chen Chia-Wei Tsai Jason Lin Chun-Wei Yang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Master Program for Digital Health Innovation China Medical University Taichung City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

关键词： Training Support vector machines Technological innovation Supervised learning Intrusion detection Forestry Semisupervised learning

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