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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A Review of Artificial Intelligence in Serious Game for Public Health

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Abdul Rafiq Tuty Asmawaty Abdul Kadir Siti Normaziah Ihsan Soft Computing and Intelligent System Research Group (SPINT) Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques.

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An Interdisciplinary Survey on Information Flows in Supply Chains

arXiv

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arXiv 2023年

作者： Pennekamp, Jan Matzutt, Roman Klinkmüller, Christopher Bader, Lennart Serror, Martin Wagner, Eric Malik, Sidra Spib, Maria Rahn, Jessica Gürpinar, Tan Vlad, Eduard Leemans, Sander J.J. Kanhere, Salil S. Stich, Volker Wehrle, Klaus RWTH Aachen University Ahornstrasse 55 Aachen North Rhine-Westphalia 52074 Germany BPMotion 10 Gore Ave KirraweeNSW2232 Australia Cyber Analysis & Defense Fraunhofer FKIE Fraunhofer-straße 20 North Rhine-Westphalia Wachtberg53343 Germany Software Systems Research Group Data61 CSIRO Level 5/13 Garden St EveleighNSW2015 Australia Institute for Industrial Management RWTH Aachen University Campus-Boulevard 55 North Rhine-Westphalia Aachen52074 Germany Supply Chain Development and Strategy Fraunhofer IML Joseph-von-Fraunhofer-Straße 2-4 North Rhine-Westphalia Dortmund44227 Germany School of Computer Science and Engineering University of New South Wales Engineering Rd SydneyNSW2052 Australia

Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chain management

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Coordinated control method of virtually coupled train formation based on multi agent system 2nd

Coordinated control method of virtually coupled train format...

引用

2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Liu, Ling Wang, Ping Zhang, Bo Wei, Wei School of Software and Microelectronics Peking University Beijing102600 China Beijing National Railway Research and Design Institute of Signal and Communication Group Co. Ltd. Beijing100070 China Beijing Engineering Technology Research Center of Operation Control Systems for High Speed Railways Beijing100070 China National Engineering Research Center for Software Engineering Peking University Beijing102600 China Key Laboratory of High Confidence Software Technologies Peking University Beijing102600 China

ISBN: (纸本)9783030045814

As China has become the country with the longest operating mileage and largest construction scale of high-speed railway (HSR) in the world, how to improve the operational excellence and customer satisfaction draws increasing attention. In order to further optimize the allocation of transport resource and improve the transportation efficiency of HSR, a coordinated control method using the idea of virtual coupling (VC) based on multi agent system (MAS) is proposed in this paper firstly. At last, the efficiency and feasibility of proposed model and method are demonstrated using a simulation case, which shows that the coordinated control model based on MAS can not only ensure the safety of operation, but also improve the efficiency of a whole train group. © Springer Nature Switzerland AG 2019.

关键词： Multi agent systems

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Energy loss calculation and voltage profile improvement for the rehabilitation of 0.4 kV low voltage distribution network (LVDN)

Measurement: Energy

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Measurement: Energy 2024年 4卷

作者： Hasham Khan Main Farhan Ullah Muhammad Saqib Ali Muhammad Shahzaib Shah Muhammad Adnan Khan Muhammad Waseem Ahmed Mohammed Saleh Government College of Technology Abbottabad Pakistan Elementary & Secondary Education Department Khyber Pakhtunkhwa Pakistan College of Electrical Engineering Zhejiang University 310027 China Department of Electrical Engineering University of Engineering and Technology Taxila Taxila 47050 Pakistan School of Electrical Engineering Chongqing University Chongqing 400044 China International Renewable and Energy Systems Integration Research Group (IRESI) Department of Electronic Engineering Maynooth University Co. Kildare Ireland Lero - Science Foundation Ireland Research Centre for Software School of Business Innovation Value Institute (IVI) Maynooth University Co. Kildare Ireland Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BME V1 Building Egry József u. 18 1111 Budapest Hungary

In recent decades, electrical utilities have made significant advancements in Electrical Power Distribution systems (EPDS). However, energy loss in distribution networks, particularly in 0.4 kV systems, remains a critical challenge. It is increasingly essential for countries to review and update their power loss policies to deliver electrical energy to consumers at the most feasible and economical rates. The performance of Low Voltage Distribution Networks (LVDN) often falls short, resulting in high voltage fluctuations and significant energy losses for end consumers in 0.4 kV systems. This research aims to address these issues by minimizing energy losses and enhancing the voltage profile of the 0.4 kV distribution network. Various rehabilitation techniques have been employed to significantly improve the efficiency of the LVDN. Since most energy losses are directly associated with the LVDN, two 0.4 kV distribution networks in the villages of New Kalyam and Upper Tharjial in the Mandra Sub-Division, under Islamabad Electricity Supply Corporation (IESCO), Pakistan, were selected as case studies. Field surveys were conducted in collaboration with the local utility staff to collect actual data. The analysis was performed using computer-aided tools, including the Computer-Aided Distribution Planning and Design (CADPAD) software and Energy Loss Reduction (ELR) programs. The proposed methodology is straightforward and practical. The case study results demonstrate significant improvements in the voltage profile and reductions in energy losses within the LVDN. These results have been validated and found to be within permissible standard limits, underscoring the effectiveness of the proposed approach.

关键词： CADPAD Distribution networks Distributed generation ELR Energy losses EPDS Financial saving LVDN Power loss Power quality Rehabilitation techniques Technical and non-technical losses Voltage profile improvement

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Indoor localization estimation techniques in wireless sensor network: A review

Indoor localization estimation techniques in wireless sensor...

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2018 IEEE International Conference on Automatic Control and Intelligent systems, I2CACIS 2018

作者： Shahkhir Mozamir, M. Bakar, Rohani Binti Abu Din, Wan Isni Soffiah Wan Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia Soft Computing and Intelligent System Research Group Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia System Network and Security Research Group Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia

ISBN: (纸本)9781538656549

Recently, Wireless Sensor Network (WSN) are an important research area because of its real-time response, accurate, improved node capability, low in cost and simple infrastructure. Because of the huge number of sensor nodes, the main issue of WSN is the estimation of their location. The nodes which are deployed may not be known their own position. In localization, different techniques are used for estimation to gain the coordinate of the node. In this paper, we reviewed on the current techniques in estimation phase for indoor localization from 2013 to 2018. This reviewed will be useful for the researchers to implement the techniques for estimation phase to increase the accuracy. ©2018 IEEE.

关键词： Sensor nodes

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Explainable software for Cyber-Physical systems (ES4CPS) Report from the GI Dagstuhl Seminar 19023 January 06–11 2019, Schloss Dagstuhl

arXiv

引用

arXiv 2019年

作者： Greenyer, Joel Lochau, Malte Vogel, Thomas Software Engineering for Ubiquitous Applications Leibniz Universität Hannover DE Germany Real-time Systems Lab TU Darmstadt DE Germany Software Engineering Research Group Humboldt-Universität zu Berlin DE Germany

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Feature-based Product Line engineering: An Essential Ingredient in Agile Acquisition

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INCOSE International Symposium 2021年第1期31卷 956-971页

作者： Darbin, Rowland Pitz, Randy Taylor, Matthew Teaff, James Young, Bobbi Wilson, Beth Hartley, David Clements, Paul General Dynamics Mission Systems 12001 Research Parkway Suite 500 OrlandoFL32826 United States The Boeing Company 5775 Campus Parkway St. LouisMO63042 United States Burr Ridge IL60527 United States Raytheon Technologies Intelligence and Space 16800 East CentreTech Parkway AuroraCO80011 United States Raytheon Technologies 1151 E Hermans Rd TucsonAZ85756 United States INCOSE Security Systems Engineering Working Group NorthboroughMA United States BigLever Software Inc. 10500 Laurel Hill Cove AustinTX78730 United States

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