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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alraddadi, Rawan Alshayeb, Mohammad Mahmood, Sajjad Niazi, Mahmood Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

The rapid development of cognitive cities, driven by IoT and AI technologies, introduces unique cybersecurity challenges aggravated by human-centric vulnerabilities. The objective of this paper is to examine the role of human factors in the cybersecurity landscape of both smart and cognitive cities. Through a literature review, we investigate how human interactions with digital infrastructures impact security and privacy. We highlight the need to address human errors, social engineering attacks, and insider threats while distinguishing how differences in infrastructure, intelligence, and citizen roles between smart and cognitive cities shape the management of human-centric vulnerabilities. Our review of existing research on integrating human behavioral data into AI systems suggests significant threat detection and response improvements. The findings suggest that human-centric AI detection tools not only mitigate risks of human errors and latent failures but also significantly improve protection for vulnerable populations, thereby promoting stronger human-AI collaboration. Ultimately, this paper advocates for advancing these tools to bolster urban cybersecurity frameworks. © 2025 The Author(s).

关键词： Cyber attacks

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CLeAR: continual learning on algorithmic reasoning for human-like intelligence 23

CLeAR: continual learning on algorithmic reasoning for human...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Bong Gyun Kang HyunGi Kim Dahuin Jung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics. Our tasks of abstract logical concepts, in the form of formal language, can be classified into Chomsky hierarchies based on their difficulty. In this study, we conducted extensive experiments consisting of 15 tasks with various levels of Chomsky hierarchy, ranging from in-hierarchy to inter-hierarchy scenarios. CLeAR not only achieved near zero forgetting but also improved accuracy during following tasks, a phenomenon known as backward transfer, while previous CL methods designed for image classification drastically failed.

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Dynamic Sparse Pca: A Dimensional Reduction Method for Sensor Data in Virtual Metrology

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SSRN 2023年

作者： Wang, Tianhui Xie, Yifan Jeong, Young-Seon Jeong, Myong K. Department of Industrial and Systems Engineering Rutgers University Piscataway NJ08854 United States Department of Industrial Engineering Interdisciplinary Program of Arts & Design Technology Chonnam National University Gwangju61186 Korea Republic of

Virtual metrology (VM) is the ideal technique for predicting the quality of every wafer and reducing overall inspection costs in semiconductor manufacturing. The data from numerous monitoring sensors in semiconductor production poses challenges in terms of data dimensionality. Thus, dimensionality reduction techniques are required before building the prediction model in VM. Additionally, to reduce the sensor cost, it is crucial to determine the sensors that contribute significantly to the output of the prediction model. Therefore, in this paper, we propose a dimensionality reduction technique called dynamic sparse principal component analysis to establish global sparsity in the first few principal components. The proposed method seeks extracted features that involve a limited number of sensors in the VM to reduce sensor cost and increase the interpretability of the features while reasonably capturing data variability. The experimental results demonstrate that the proposed method is not sensitive to the scale of the problem and can solve the large-scale high-dimensional problems that are commonly encountered in semiconductor manufacturing. © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

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Projection of climate change effects on flood inundation in the Gin River basin, Sri Lanka 9

Projection of climate change effects on flood inundation in ...

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9th International Conference on Flood Management, ICFM 2023

作者： Jayapadma, Jayapadma Mudalige Miyuru Uthpala Souma, Kazuyoshi Ishidaira, Hiroshi Magome, Jun Wickramaarachchi, Thushara Navodani Special Educational Program on River Basin Environmental Science Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-3-11 Takeda Yamanashi Kofu400-8511 Japan Interdisciplinary Centre for River Basin Environment University of Yamanashi 4-3-11 Takeda Yamanashi Kofu400-8511 Japan Department of Civil and Environmental Engineering Faculty of Engineering University of Ruhuna Galle Hapugala Sri Lanka

Human-induced climate change has altered the frequency and severity of heavy precipitation, droughts, and floods worldwide. Sri Lanka, a developing country in the Indian Ocean, has suffered frequent floods, affecting over 64 % of the population in the past decade. The Gin River, the primary drinking water source of the densely populated Galle district with over 1 million residents, faces recurrent flood disasters. Therefore, this study employed a multi-model ensemble to simultaneously simulate the impact of climate change on both river flow and inundation extent in the frequently flooded Gin River basin located in humid tropical monsoon region. Bias-corrected climate projections of an ensemble of five General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 emission scenario were used to project future river flow and flood inundation in the Gin River basin, Sri Lanka. The validated Rainfall-Runoff-Inundation (RRI) model projected a 5 % increase in high flows (flows exceeding a 5 % probability of exceedance ( Copyright © 2024 Jayapadma Mudalige Miyuru Uthpala Jayapadma et al.

关键词： Floods

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Electronic structure and optical spectra of MSi2N4 (M = Mo, Ta, V) materials with single-atom decoration: a first-principles study

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Journal of Materials Chemistry C 2023年第43期11卷 15097-15105页

作者： Wu, Tzu-Cheng Liao, Yin-Song Kao, Jui-Cheng Chou, Jyh-Pin Department of Physics National Tsing Hua University Hsinchu300044 Taiwan The Affiliated Senior High School of National Taiwan Normal University Taipei10658 Taiwan Tsing Hua Interdisciplinary Program National Tsing Hua University Hsinchu300044 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu300044 Taiwan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Physics National Changhua University of Education Changhua50007 Taiwan

MoSi2N4 has outstanding potential for applications in electronics, spintronics, and semiconducting fabrication. This attractive scenario can be attributed to its remarkable electronic properties. In this study, we investigate the electronic structure and optical spectra of the MSi2N4 (M = Mo, Ta, and V) materials with single atom (H, N, and O) decoration using first-principles calculations. Pristine MoSi2N4 and VSi2N4 are semiconductors, and TaSi2N4 shows the half-metal properties. Decorating MoSi2N4 with single hydrogen and nitrogen atoms induces a magnetic moment. Single oxygen decorated TaSi2N4 does not change its intrinsic electronic properties, which is suitable for spintronic applications. On top of that, the semiconductor VSi2N4 can be turned into a half-metal via single hydrogen atom decoration. The absorption spectra demonstrate an enhancement in optical absorption for VSi2N4 after the decoration with a single nitrogen atom. Single atom decoration is one of the effective methods of tailoring the electronic properties of the two-dimensional MoSi2N4 family. Our findings can accelerate the development of the advanced electronic devices. © 2023 The Royal Society of Chemistry.

关键词： Silicon compounds

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Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN

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IEEE Access 2023年 11卷 127388-127401页

作者： Chen, Shih-Lun Chen, Tsung-Yi Mao, Yi-Cheng Lin, Szu-Yin Huang, Ya-Yun Chen, Chiung-An Lin, Yuan-Jin Chuang, Mian-Heng Abu, Patricia Angela R. Chung Yuan Christian University Department of Electronic Engineering Taoyuan320314 Taiwan Feng Chia University Department of Electronic Engineering Taichung407102 Taiwan Chang Gung Memorial Hospital Department of General Dentistry Taoyuan61363 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing Department of Program on Semiconductor Manufacturing Technology National Cheng Kung University Tainan717005 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei243303 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon1108 Philippines

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model's accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions. © 2013 IEEE.

关键词： Image segmentation

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Starfish Optimization Algorithm for Enhanced Parameter Identification of Proton Exchange Membrane Fuel Cell Models 7

Starfish Optimization Algorithm for Enhanced Parameter Ident...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Menesy, Ahmed S. Sultan, Hamdy M. Kamel, Salah Kassas, Mahmoud Tolba, Mohamed A. Saad, Mohamed Kourany Minia University Faculty of Engineering Electrical Engineering Department Minia 61517 Egypt Aswan University Faculty of Engineering Department of Electrical Engineering Aswan 81542 Egypt King Fahd University of Petroleum & Minerals (KFUPM) Electrical Engineering Department Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) Kfupm Dhahran 31261 Saudi Arabia Nuclear Research Center Egyptian Atomic Energy Authority Reactors Department Cairo 13759 Egypt Nahda University in Beni Suef Faculty of Engineering Electrical and Renewable Energy Department Beni Suef 62764 Egypt

ISBN: (纸本)9798331531836

This study evaluates the performance of the starfish optimization algorithm (SFOA) for parameter identification in Proton Exchange Membrane Fuel Cells (PEMFCs). SFOA, inspired by the decentralized movement and regeneration behavior of starfish, combines global exploration and local exploitation strategies to efficiently estimate unknown PEMFC parameters. Four different PEMFC stacks, namely, 250W, BCS 500W, SR-12 500W, and NetStack-PS6, are analyzed using SFOA, with results benchmarked against state-of-the-art optimization techniques. The optimization is executed 50 times on each PEMFC. Statistical measurements have been conducted, and the statistical results confirm that SFOA yields the minimum total squared deviation (TSD) for all the four PEMFCs studied. The results of convergence characteristics identify that optimum results are achieved within 200 iterations and 30 agents. In addition, the proposed algorithm is capable of capturing the PEMFC performance under varying temperatures and gas pressure and is applicable in real-world situations. The results confirm the better precision, rate of convergence, and efficiency of SFOA and conclude that it is a valuable tool in PEMFC simulation and optimization. © 2025 IEEE.

关键词： Optimization Parameter estimation PEMFC starfish optimization statistical analysis TSD

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： Computerized tomography

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Portfolio Selection and Comparative Portfolio Analysis of Transportation Services, Hotel and Leisure, and Education Subsectors Against Service Sector in the Philippine Market Using Mean-variance Model

Portfolio Selection and Comparative Portfolio Analysis of Tr...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Cruz, E.L. Young, M.N. Chuahay, T.N. Prasetyo, Y. Persada, S. Nadilfatin, R. Contreras, M. School of Industrial Engineering and Engineering Management Mapua University Manila Philippines School of Graduate Studies Mapua University Manila Philippines Department of Finance Chung Yuan Christian University Taoyuan City Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan City Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan City Taiwan Entrepreneurship Business Creation Business School Binus University West Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350386097

This paper presents a framework for portfolio selection in the transportation services, hotel and leisure, and education subsectors of the Philippine market using the mean-variance model. Using the service sector and the broader market as benchmarks, this study spans 7,670 test days from January 1, 1993, to December 31, 2022, identifying optimal risk-return factors (RRFs) of 0.4 for transportation, 0.2 for hotel and leisure, and 0.3 for education. Furthermore, the higher percentage allocations the investors are recommended to invest in are International Container Terminal Services, Inc., Waterfront Philippines, Incorporated, and Far Eastern University, Incorporated. Paired t-test results, with p-values below 0.01 and 0.05, indicate these subsectors can outperform the service sector. The study also conducted a comparative analysis with 30-year historical data to crisis periods like the Global Financial Crisis (GFC) and the COVID-19 pandemic, exhibiting distinct performance trends and emphasizing the need for tailored strategies during economic disruptions. The findings of this research may provide an alternative portfolio selection model for investors seeking to optimize their investment portfolios. © 2024 IEEE.

关键词： Leisure

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A ROBUST OPTIMIZATION APPROACH MODEL FOR A MULTI-VACCINE MULTI-ECHELON SUPPLY CHAIN 50

A ROBUST OPTIMIZATION APPROACH MODEL FOR A MULTI-VACCINE MUL...

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50th International Conference on Computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Bouchenine, A. Almaraj, I. Department of Industrial and Systems Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9781713886952

This research investigates a multi-product, multi-echelon and multi-period vaccine supply chain (SC) network model under uncertainty and quality inspection errors. The objective function seeks optimizing the total cost (economic cost) of the SC. Moreover, the proposed model is formulated as a Mixed Integer Linear programming problem (MILP) under multiple sources of uncertain parameters including demand, inspection errors, vaccine waste generated in healthcare centers, and defective treatment rate of vaccine waste. To provide meaningful solutions which are robust against future fluctuation of parameters, the robust optimization approach is utilized to incorporate the decision maker's risk attitude under different type of uncertainty sets namely, box, polyhedral and combination of interval polyhedral. The performance of the proposed model is demonstrated through an illustrative example. The results show the effect of different types of uncertainties on the overall objective function. Managerial insights and research implications in terms of reverse vaccine SC is advised and future research directions are proposed. © 2023 Computers and Industrial engineering. All rights reserved.

关键词： Vaccines

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