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检索条件"机构=School of Computer Science& Software Engineering"

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Open Source software Supply Chain Recommendation Based on Heterogeneous Information Network 14th

Open Source Software Supply Chain Recommendation Based on H...

引用

14th International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022

作者： Lin, HaiMing Liang, Guanyu Wu, Yanjun Wu, Bin Tian, Chunqi Wang, Wei Nanjing Institute of Software Technology Nanjing211135 China School of Computer and Science Tongji University Shanghai201804 China Institude of Software Chinese Academy of Science Beijing100190 China School of Data Science and Engineering East China Normal University Shanghai200062 China

ISBN: (纸本)9783031311796

In the GitHub open-source collaborative development scenario, each entity type and the link relationship between them have natural heterogeneous attributes. In order to improve the accuracy of project recommendation, it is necessary to effectively integrate this multi-source information. Therefore, for the project recommendation scenario, this paper defines an open source weighted heterogeneous information network to represent the different entity types and link relationships in the GitHub open source collaborative development scenario, and effectively model the complex interaction among developers, projects and other entities. Using the weighted heterogeneous information network embedding method, extract and use the rich structural and semantic information in the weighted heterogeneous open source information network to learn the node representation of developers and projects, and fuse the personalized nonlinear fusion function into the matrix decomposition model for open source project recommendation. Finally, this paper makes a large number of comparative experiments based on the real GitHub open data set, and compares it with other project recommendation methods to verify the effectiveness of our proposed open source project recommendation model. At the same time, it also explores the impact of different metapaths on the effect of project recommendation. The experimental results show that the recommendation method based on heterogeneous information network can effectively improve the recommendation quality. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Open source software

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A Code Completion Approach Combining Pointer Network and Transformer-XL Network 19th

A Code Completion Approach Combining Pointer Network and Tr...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zhang, Xiangping Liu, Jianxun Long, Teng Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411100 China Hunan Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan411100 China

ISBN: (纸本)9783031545207

Code completion is an integral component of modern integrated development environments, as it not only facilitates the software development process but also enhances the quality of software products. By leveraging large-scale codes to learn the probability distribution among code token units, deep learning methods have demonstrated significant improvements in the accuracy of token unit recommendations. However, the effectiveness of code completion with deep learning techniques is hindered by information loss. To alleviate the above problem, we proposed a code language model which combines the pointer network and Transformer-XL network to overcome the limitations of existing approaches in code completion. The proposed model takes as input the original code fragment and its corresponding abstract syntax tree and leverages the Transformer-XL model as the basis model for capturing long-term dependencies. Furthermore, we integrate a pointer network as a local component to predict the out-of-vocabulary words. The proposed method is evaluated on real PY150 and JS150 datasets. The comparative experimental results demonstrate the effectiveness of our model in improving the accuracy of the code completion task at the token unit level. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Probability distributions

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Named Entity Recognition in Emergency Domain based on BERT-BILSTM-CRF 2

Named Entity Recognition in Emergency Domain based on BERT-B...

引用

2nd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Tian, Yu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Chinese named entity recognition is a key problem in natural language processing. The traditional language processing model can not effectively represent the context semantic information in the text, and can not deal with the different contexts of polysemy, which affects the effect of entity recognition. A BERT-BILSTM-CRF entity recognition model for the emergency domain is proposed. BERT pre-training language model generates word vectors which represent the semantic information of context, and the generated vectors are characterized by two-way long-term and short-term memory network. Self-attention mechanism can effectively obtain the long-distance dependence in text sentences. And finally, decoding is carried out through the CRF to generate an entity label sequence. The experimental results show that the model have achieved excellent results in the emergency domain corpus. © 2022 IEEE.

关键词： Semantics

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A Stable Task Allocation Method Based on Insertion Operation in Ridesharing

A Stable Task Allocation Method Based on Insertion Operation...

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2022 Chinese Automation Congress, CAC 2022

作者： Lv, Jingwei Yao, Shuzhen Tan, Huobin Beihang University School of Computer Science and Engineering Beijing China Beihang University School of Software Beijing China

ISBN: (纸本)9781665465335

In recent years, ridesharing based on mobile Internet has played an important role in public transport services. Ride-hailing platforms have been developed rapidly because of their fully resource utilization of vehicles. In this paper, we propose a task allocation method that satisfies stable matching in ridesharing scenarios, and experiments with real data are carried out to verify the effectiveness of the method. We also compare the effectiveness of this method with the FCFS method. © 2022 IEEE.

关键词： ridesharing single-worker multiple-requesters stable matching

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Chang-Long Xu, Xin-Xin Chen, Chun-Hua Hong, Jun He, Zhenan Yu, Dengxiu Kwong, Sam Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665410205

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations. © 2024 IEEE.

关键词： Charging stations

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Stable Task Allocation with Simulated Annealing in Taxi Ride-hailing Services

Stable Task Allocation with Simulated Annealing in Taxi Ri...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Lv, Jingwei Wei, Ziyu Yao, Shuzhen Tan, Huobin School of Computer Science and Engineering Beihang University Beijing100191 China School of Physics Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

Taxis have been becoming an important part of the urban public transportation and the ride-hailing systems offer services that would make life more convenient. One of the most important tasks in a taxi ride-hailing platform is to match the passengers and drivers into pairs. However, the current technologies focus more on overall interests but less on personal preferences of passengers and drivers. We proposed a novel algorithm based on Simulated Annealing to achieve the tradeoff between stable matching and the total pickup distance. Compared to a baseline approach for stable matching, the total distance decreases significantly, while the proportion of unstable pairs can still be controlled within a certain range. Finally, evaluations on experimental data verified the effectiveness of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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Knowledge Graph-Based computer Aided Adaptive Learning 10

Knowledge Graph-Based Computer Aided Adaptive Learning

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Yi, Wenlong Song, Tingting Chen, Jie Zhao, Yingding Yin, Hua Xu, Yilu School of Software Jiangxi Agricultural University Nanchang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer and Information Engineering Jiangxi Agricultural University Nanchang China

ISBN: (纸本)9798350332933

Although the teaching progress is easily controllable in traditional linear classroom teaching, the individual cognitive ability of the students is ignored in undifferentiated teaching, which easily leads to the mismatch between the teaching content and the learners' ability and makes them lose confidence in learning. The study proposes a computer-assisted method of the adaptive learning plan. First, the knowledge graph technology is used to represent the semantics of course knowledge points;second, a recommended algorithm for adaptive learning based on questionnaire scoring is designed;finally, Neo4j, Spring Boot and *** are used for programming. The experimental results showed that this method promoted the learners' self-adaptive learning, met the students' needs for differentiated learning, improved the overall teaching quality, and achieved the student-oriented teaching concept. © 2022 IEEE.

关键词： Knowledge graph

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Unsupervised Monte Carlo Denoising via Learning Contrastive Disentanglement Representation

Unsupervised Monte Carlo Denoising via Learning Contrastive ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiao Liang Siyuan Duan Lijie Zheng Yuqian Zeng School of Computer Science and Software Engineering Southwest Petroleum University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent Monte Carlo denoising methods have achieved impressive progress in generating visually compelling images, which accelerate path-tracing based rendering. However, most of them rely on strong supervision, resulting in a high computational burden for creating paired data and potential overfitting issues. Instead, we present an unsupervised approach based on contrastive disentanglement representation. Specifically, we introduce disentanglement representation combined with cycle-consistency and adversarial loss to factorize noise and content features from a noisy input. Besides, we enforce contrastive learning on abundant samples generated in the backbone to regularize the extracted content distribution, reducing noise features. Moreover, we introduce a multi-scale modulation to refine the representation of auxiliary features, providing better denoising guidance. Experimental results demonstrate that our approach performs favor-ably against existing state-of-the-art unsupervised methods and generates comparable results against supervised models while requiring less inference time.

关键词： Monte Carlo methods Noise reduction Noise Modulation Contrastive learning Rendering (computer graphics) Feature extraction Visual effects Noise measurement

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