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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support

EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educat...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Qiaomu Xie, Ying Chakravarty, Sumit Lee, Dabae Kennesaw State University College of Computing and Software Engineering MariettaGA30060 United States Kennesaw State University Department of Instructional Technology MariettaGA30060 United States

ISBN: (纸本)9798350362480

In general, educational support with Large Language Models (LLMs) faces challenges in knowledge organization, expertise integration, and contextual adaptation. So, we present EduMAS, a novel multi-agent framework that coordinates specialized agents with graph-based knowledge navigation. Our framework introduces three key innovations: (1) Specialized Agents that provide expertise in different learning aspects to solve decomposed subtasks professionally;(2) Graph Navigator for graph-based knowledge extraction and selection to improve the quality of responses;(3) The Emotional Awareness mechanism for better contextual adaptation. Through comprehensive experiments on college-level physics education and evaluated by six state-of-the-art LLMs, EduMAS demonstrates significant improvements over the baseline model in complex concept integration, cross-disciplinary understanding, and theory-to-application translation. Ablation studies further validate the contribution of each framework component, Specialized Agents and Graph Navigator play important roles in performance improvement. Our work provides strong support for LLM-powered multi-agent system in AI-assisted education. © 2024 IEEE.

关键词： Multi agent systems

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Advancements in quantum image filtering:Exponential speedup on a subset of images

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 13-13页

作者： Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China

The pursuit of quantum supremacy in computational tasks has driven the exploration of quantum algorithms capable of surpassing classical *** the realm of image processing,a notable advancement towards this objective is highlighted in the study by Cui et al.[1].Their work proposes a quantum image filtering(QImF)algorithm that exhibits exponential acceleration for a specific subset of images,offering a glimpse into the potential of quantum computing in image processing.

关键词： quantum subset filtering

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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A Performance Evaluation of the Clustering-based Labelling Scheme

A Performance Evaluation of the Clustering-based Labelling S...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Klaib, Alhadi A. Venters, Colin C. Elmergib University Department of Software Engineering Khoms Libya Huddersfield University School of Computing and Engineering Huddersfield United Kingdom

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

XML is a common technique for formatting and exchanging data across the Internet world. Updating and retrieving XML data is an active research area. In addition, indexing XML data is a significant task to improve the efficiency of XML queries. Labelling nodes is the used technique for indexing XML data efficiently. The Clustering-based Labelling Scheme (CLS) was proposed to address some limitations and challenges of indexing XML data. This paper aims to evaluate the performance of the clustering-based labelling scheme. Thus, a set of experiments were identified and designed for the evaluation purpose. Moreover, an appropriate dataset for testing the CLS scheme was identified. Subsequently, the experiments were carried out, and then the results were analysed and assessed to gain the findings of the experiments. Consequently, the results of these experiments suggest that the CLS achieved the target results with few expectations. Therefore, this evaluation showed an improvement in the performance and oefficiency of labelling XML documents. © 2022 IEEE.

关键词： XML

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A Zero-Knowledge Set Membership Proof Scheme Based on the SM2 Algorithm 24

A Zero-Knowledge Set Membership Proof Scheme Based on the SM...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhou, Yin Dai, Bingrong Li, Chao School of Computer and Information Engineering Shanghai Polytechnic University My Street Shanghai201209 China Department of Computing Shanghai Development Center of Computer SoftWare Technology Shanghai201102 China

ISBN: (纸本)9798400717147

With the widespread application of blockchain technology, various range proof protocols based on zero-knowledge proofs have been proposed. However, existing range proof protocols suffer from issues such as high communication overhead and computational complexity. Therefore, this paper introduces an efficient and secure Zero-Knowledge Set Membership Proof Protocol (ZSMPP) to address these challenges. Building upon improvements to the proof structure of range proof protocols, the paper integrates the SM2 identity-based digital signature algorithm, effectively avoiding the time-consuming bilinear pairing operations and reducing computational costs. The proposed protocol offers an efficient and secure solution for the given problem. Experimental results demonstrate that, compared to protocols proposed by Bootle, Deng, Mao, and others, the protocol presented in this paper exhibits superior computational efficiency, providing an efficient and secure solution for data security and individual privacy protection in the digital age. © 2024 ACM.

关键词： Authentication

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Crawling and Exploring RESTful Web APIs from RapidAPI 17th

Crawling and Exploring RESTful Web APIs from RapidAPI

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CCF 17th International Conference on Service Science, CCF ICSS 2024

作者： Li, Wen Ren, Hongshuai Nie, Yamei Liu, Zihao Kang, Guosheng Liu, Jianxun Peng, Zhenlian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (纸本)9789819757596

RapidAPI serves as a marketplace and management platform that integrates a wide range of Web APIs, offering developers access to a rich pool of API resources. More and more researchers are delving into ways to maximize the value created by leveraging these resources. Thus, acquiring the Web API data and knowing the details is of significance for researchers and ordinary users. In this paper, we propose the methodology of crawling Web APIs from RapidAPI (https://***/hub) and provide rich visualizations and statistical analysis for the crawled dataset. The crawling code and the crawled dataset are open for public access (https://***/IntelligentServiceLab/RESTful-API-Crawler) so that researchers and users can acquire them with no effort. Moreover, the use cases of RESTful Web APIs are provided as a usage reference for users and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web crawler

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation 22

Multi-view Hypergraph-based Self-supervised Learning Model f...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shen, Jiexun Li, Dongfan Chen, Wanjun Xiao, Yong Kang, Guosheng Liu, Jianxun Peng, Zhenlian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (纸本)9798331509712

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines. © 2024 IEEE.

关键词： Self-supervised learning

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Antisocial Behavior and the Dopamine Loop on Different Technological Platforms and Industries: An Overview 8th

Antisocial Behavior and the Dopamine Loop on Different Te...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Stupavský, Igor Dakić, Pavle Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9789819932351

The aim of this review is to explore the relationship between the architecture of microservices and antisocial behavior. Microservice architecture is a software design approach that involves decomposing a system into small, independent services that can offer benefits such as flexibility and scalability. Expected result is to analyze dataset of statuses, which defines the length of time the status displays on the user’s device. Because people spend time on social networks at every opportunity, even for hours. When combined with the time-valued, we try to gain a deeper understanding of social networks and users behavior. They are forced into this behavior through the feedback loop mechanism of the neurotransmitter Dopamine. Main goal is to understand how to minimize the impact of antisocial behavior on social networks and what exactly makes users spend a disproportionate amount of time on these platforms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： software design

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A Review of Metamorphic Relation Representation Forms 10

A Review of Metamorphic Relation Representation Forms

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Zhao, Yin Li, Meng Liu, Jie University of South China School of Computing Hengyang China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment Hengyang China Cnnc Key Laboratory on High Trusted Computing Hengyang China

ISBN: (纸本)9798350304770

Metamorphic testing can effectively alleviate the problem of testing oracle. The lack of specification descriptions of metamorphic relations affects its generalized application. This text categorizes metamorphic relations into three types based on their representation forms: arithmetic expressions, predicate expressions, and other categories. Among the one hundred and sixty-two relations classified, arithmetic expressions account for sixty-four percent. Within geometry, numerical programs, and algorithms, the main to employ relations on arithmetic expressions, while machine learning and optimization algorithms are mainly inclined toward hiring predicate expression relations. Meanwhile, the research findings about the construction methods of metamorphic relations, the generation of original test cases, and the utilization of testing tools have been systematically organized to guide the application of the above techniques based on representation forms. Moreover, it lays the foundation for studying the fundamental nature of relationships, such as complexity, similarity, and relation composite. © 2023 IEEE.

关键词： Machine learning

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