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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： M. Kowsalya In-Ho Ra K. S. Naveenraj S. Thruvenkadam Seok-Woo Park School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan South Korea Internet Protocol Division Jio Platform Limited Mumbai India

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches.

关键词： Adaptive systems 5G mobile communication Convolution Network slicing Transforms Throughput software

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Towards Efficient Auction Design with ROI Constraints 24

Towards Efficient Auction Design with ROI Constraints

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xinyu Tang Hongtao Lv Yingjie Gao Fan Wu Lei Liu Lizhen Cui School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University & Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798400704864

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3 over 2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5 over 4; when it falls below 2, the lower bound becomes 3+γ over 2+3γ-γ2.

关键词： incentive compatibility mechanism design return on investment

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Semantic, Technical and Legal Interoperability of European Company Open Data in Practice: The STIRData Approach

Semantic, Technical and Legal Interoperability of European C...

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12th International Conference on Data Science, technology and Applications, DATA 2023

作者： Klımek, Jakub Chortaras, Alexandros Mısek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Charles University Faculty of Mathematics and Physics Department of Software Engineering Czech Republic School of Electrical and Computer Engineering National Technical University of Athens Greece Institute of Law and Technology Masaryk University Czech Republic Norwegian Digitalisation Agency Norway Brønnøysund Register Centre Norway

ISBN: (纸本)9789897586644

As part of the Open Data Directive, the European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data. The list also contains specific data items that must be included in these datasets. However, it does not prescribe any technical means of how the data should be published, severely hindering the interoperability of the datasets once they are published. One of the HVD topics is company data. In this practice report paper, we present results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on technical, semantic, and legal interoperability of open data from business registries, covering the company data HVDs topic. The results include a data architecture and a data specification to make the published data technically and semantically interoperable, and legal interoperability guidelines to ensure legal interoperability of the published data. Moreover, proof-of-concept transformations of data from selected European business registries are shown using open source tools and according to the specification. Finally, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. © 2023 by SCITEPRESS–Science and technology Publications, Lda.

关键词： Data consistency

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Relaxed Rotational Equivariance via G-Biases in Vision 39

Relaxed Rotational Equivariance via G-Biases in Vision

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Zhiqiang Liu, Yingjie Sun, Licheng Yang, Jian Dong, Hanlin Lin, Shing-Ho J. Tang, Xuan Mi, Jinpeng Jin, Bo Wei, Xian Software Engineering Institute East China Normal University China School of Geospatial Information Information Engineering University China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Communication and Electronic Engineering East China Normal University China Institute of Machine Intelligence University of Shanghai for Science and Technology China School of Computer Science and Technology Tongji University China

ISBN: (纸本)157735897X

Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equivariance, commonly referred to as Rotational Symmetry-Breaking (RSB) in the system or dataset, making GConv unable to adapt effectively to this phenomenon. Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called G-Biases under the group order to break strict group constraints and then achieve a Relaxed Rotational Equivariant Convolution (RREConv). To validate the efficiency of RREConv, we conduct extensive ablation experiments on the discrete rotational group Cn. Experiments demonstrate that the proposed RREConv-based methods achieve excellent performance compared to existing GConv-based methods in both classification and 2D object detection tasks on the natural image datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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PerReactor: Offline Personalised Multiple Appropriate Facial Reaction Generation 39

PerReactor: Offline Personalised Multiple Appropriate Facial...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Hengde Kong, Xiangyu Xie, Weicheng Huang, Xin He, Xilin Liu, Lu Shen, Linlin Zhang, Wei Gunes, Hatice Song, Siyang School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom Affect AI Anhui China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China South China Normal University Foshan China School of Software Technology Zhejiang University Hangzhou China Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)157735897X

In dyadic human-human interactions, individuals may express multiple different facial reactions in response to the same/similar behaviours expressed by their conversational partners depending on their personalised behaviour patterns. As a result, frequently-employed reconstruction loss-based strategies lead the training of previous automatic facial reaction generation (FRG) models to not only suffer from the 'one-to-many mapping' problem, but also fail to comprehensively consider the quality of the generated facial reactions. Besides, none of them considered such personalised behaviour patterns in generating facial reactions. In this paper, we propose the first adversarial FRG model training strategy which jointly learns appropriateness and realism discriminators to provide comprehensive task-specific supervision for training the target facial reaction generators, and reformulates the 'one-to-many (facial reactions) mapping' training problem as a 'one-to-one (distribution) mapping' training task, i.e., the FRG model is trained to output a distribution representing multiple appropriate/plausible facial reaction from each input human behaviour. In addition, our approach also serves as the first offline FRG approach that considers personalised behaviour patterns in generating of target individuals' facial reactions. Experiments show that our PerReactor not only largely outperformed all existing offline solutions for generating appropriate, diverse and realistic facial reactions, but also is the first offline approach that can effectively generate personalised appropriate facial reactions. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection

arXiv

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arXiv 2025年

作者： Li, Yuqi Lin, Xingyou Zhang, Kai Yang, Chuanguang Guo, Zhongliang Gou, Jianping Li, Yanli Institute of Computing Technology Chinese Academy of Sciences China College of Information Engineering Sichuan Agricultural University China Shenzhen University of Advanced Technology China School of Computer Science University of St Andrews United Kingdom College of Computer and Information Science College of Software Southwest University China School of Artificial Intelligence and Computer Science Nantong University China The School of Electrical and Computer Engineering The University of Sydney Australia

Federated Learning (FL) provides novel solutions for machine learning (ML)-based lithography hotspot detection (LHD) under distributed privacy-preserving settings. Currently, two research pipelines have been investigated to aggregate local models and achieve global consensus, including parameter/non-parameter based (also known as knowledge distillation, namely KD). While these two kinds of methods show effectiveness in specific scenarios, we note they have not fully utilized and transferred the information learned, leaving the potential of FL-based LDH remains unexplored. Thus, we propose FedKD-hybrid in this study to mitigate the research gap. Specifically, FedKD-hybrid clients agree on several identical layers across all participants and a public dataset for achieving global consensus. During training, the trained local model will be evaluated on the public dataset, and the generated logits will be uploaded along with the identical layer parameters. The aggregated information is consequently used to update local models via the public dataset as a medium. We compare our proposed FedKD-hybrid with several state-of-the-art (SOTA) FL methods under ICCAD-2012 and FAB (real-world collected) datasets with different settings;the experimental results demonstrate the superior performance of the FedKD-hybrid algorithm. Our code is available at https://***/itsnotacie/NN-FedKD-hybrid. © 2025, CC BY-NC-ND.

关键词： Federated learning

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Zone Based Writing Optimization in User Space 23

Zone Based Writing Optimization in User Space

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shen, Runnan Liu, Yuting Zhang, Rui Zhu, Jinbin Liu, Zhonglin Xiao, Limin School of Computer Science and Engineering Beihang University Beijing100191 China North China Institute of Computing Technology Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665494571

Write operation of the latest storage media such as NAND SSDs has certain restrictions. Data can only be written in an additional write mode. When erasing, some valid data still needs to be written again, which will cause write amplification. Researches about write amplification focused on conventional NVMe protocol and did not consider some forward features. In response to this problem, this paper designs a user-mode write operation optimization algorithm based on the dynamic adjustment of zone space. When the block is erased, the impact caused by write amplification can be significantly resolved, and operations of multiple tenants can be effectively isolated. The Zone Based Algorithm will implement a mapping algorithm based on zone space, and select a suitable GC to maximize the performance. We also designed and implemented a variety of mapping algorithms and GC algorithms to improve the processing efficiency of IO requests. Experiments based on FEMU show at least 25% improvement when increasing data of different life span. © 2021 IEEE.

关键词： Protocols Smart cities Heuristic algorithms Clustering algorithms Writing Media Jitter

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Collaborative Multi-Agent Proximal Policy Based Resource Allocation in Clustered IoV Networks

Collaborative Multi-Agent Proximal Policy Based Resource All...

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Smart World Congress (SWC), IEEE

作者： Arif Raza Junfan Xiang Salabat Khan Shivanshu Shrivastava M. Wasim Abbas Ashraf Lu Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Computer and Information Engineering Qilu Institute of Technology Jinan China Department of Electrical and Electronics Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi India School of Mathematics and Computer Science Hanjiang Normal University Shiyan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This paper presents a novel multiagent reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm for optimizing resource allocation in clusters of cells within IoV networks. The framework dynamically allocates transmission power and bandwidth across base stations, each acting as a collaborative agent. Extensive simulations demonstrate that the multi-agent PPO approach outperforms other reinforcement learning algorithms, including Soft Actor-Critic (SAC) and Advantage Actor-Critic (A2C), regarding throughput, power efficiency, dynamic bandwidth allocation, and overall network performance.

关键词： Base stations Heuristic algorithms Clustering algorithms Collaboration Reinforcement learning Bandwidth Resource management Vehicle dynamics Optimization Internet of Vehicles

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