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检索条件"任意字段=19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024"

共 176 条记录，以下是81-90 订阅

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computer supported cooperative work and social computing 1

Computer Supported Cooperative Work and Social Computing

引用

丛书名： Communications in computer and Information Science

1000年

作者： Hailong Sun Hongfei Fan Bowen Du Yongqiang Gao Xiaokang Wang Dongning Liu Tun Lu

ISBN: (数字)9789819623761

ISBN: (纸本)9789819623754

this two part LNCS volumes constitutes the refereed proceedings of the 19th ccf conference on computer supported cooperative work and social computing, chinesecscw 2024, held in Hohhot, China, in July 12- 14, 2024. the 52 full papers and 18 short papers included in this book were carefully reviewed and selected from 200 submissions. they were organized in topical sections as follows : Part-I : Trend; CSCW and social computing, and Human-Machine-things Fusion and Human-AI Collaborative computing. Part-II : Crowd cooperative computing and Evolutionary Computation, and Collaborative Data, Software and Services.

关键词： computers and Society computer Systems Organization and Communication Networks Artificial Intelligence computer Applications

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Local Community Detection Algorithm Based on Core Area Expansion 16th

Local Community Detection Algorithm Based on Core Area Expan...

引用

16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Ji, Pengyun Guo, Kun Yu, Zhiyong Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligence I Fuzhou 350108 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Peoples R China

ISBN: (纸本)9789811945496;9789811945489

Local community detection is an innovative method to mine cluster structure of extensive networks, that can mine the community of seed node without the need for global structure information about the entire network, as distinct from the global community detection algorithm, it is efficient and costs less. However, a key problem with this field is that the location of seed nodes affects the performance of the algorithm to a great extent, and it is easy to add abnormal nodes to the community, the robustness of the algorithm is low. In this study, we proposed a novel algorithm named CAELCD. First, find the high-quality seed node of the community starting from the initial seed node, so as to avoid the seed-dependent problem. Second, generate the community's core area and expand to get the local community, which solves the problem that the expansion from a single seed prefers to add the wrong nodes. Experiments on the parameter, accuracy and visualization of the CAELCD are designed on networks with different characteristics. Experimental results demonstrate that CAELCD has superior performance and high robustness.

关键词： Complex network Local community detection Core node Community expansion

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Dynamic Information Diffusion Model Based onWeighted Information Entropy 16th

Dynamic Information Diffusion Model Based onWeighted Informa...

引用

16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Liu, Zekun Yu, Jianyong Gu, Linlin Han, Xue Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

ISBN: (纸本)9789811945496;9789811945489

Today's social networks present multiplex and heterogeneous attributes, and they are becoming more and more difficult to grasp the unique attributes and structures of the network from the whole network structures. the attribute factors such as Degree Centrality and Closeness Centrality are inadequate in the study of social networks if they are not modified and taken fully into account the real characteristics. Although the evolution of a social network is a macroscopic process, the state of each node at different moments determines the trend of the overall state on the network. In order to better identify the key nodes in the social network and more really simulate information diffusion process, this paper proposes the method using WIE (Weighted Information Entropy) to measure node's influent force and importance degree based on the concept of information entropy in information theory, which carries higher-order local information of each node rather than only contains first-order local information like degree does. Different from the conventional SIR model, this paper realizes the dynamic calculation of the diffusion probability ss in the improved SIR model according to node's local influence and weighted value. Finally, four kinds of real social networks are selected and analyzed. the experiment shows that the WIE measurement and the dynamically calculated diffusion probability can more truly reflect the information transmission in real social networks, and the node sets selected by WIE values have at least 10 percent improvement in diffusion efficiency.

关键词： social networks Local influence Global selection SIR model

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Sentiment Analysis of Chinese Complex Long Sentences Based on Reinforcement Learning 16th

Sentiment Analysis of Chinese Complex Long Sentences Based o...

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16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Zhang, Chaoli Yu, Zhenjie Lin, Dazhen Cao, Donglin Xiamen Univ Dept Artificial Intelligence Xiamen 361005 Peoples R China WuYi Univ Key Lab Cognit Comp & Intelligent Informat Proc F Wuyishan 354300 Peoples R China

ISBN: (纸本)9789811945465;9789811945458

Sentiment Analysis is a hot topic of Natural Language Processing (NLP). there have been relatively good solutions to general sentiment analysis problems, but in the face of the complex long sentences and the change of sentence sentiments such as transition and progression, it is very difficult to effectively learn the sentiment changes of long sentences based on deep learning methods. So we propose an approach named Reinforcement Learning for Long Sentence Classification (RL4LSC), a method based on reinforcement learning, to automatically identify the sentiment transitions of sentences and output the local sentiment tendency of each specific location and give the intensity of the sentiment tendency. Experiment results show that RL4LSC not only outperforms the state-of-the-art methods, BiLSTM and TextCNN, but also outputs the sentiment change of each word in a sentence through Reinforcement Learning.

关键词： Sentiment analysis Reinforcement learning Chinese complex long sentences

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A Survey on Learning Path Recommendation 16th

A Survey on Learning Path Recommendation

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16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Zhu, Fengjun Wen, Yiping Fu, Qi Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Key Lab Knowledge Proc & Networked Mfg Xiangtan Peoples R China

ISBN: (纸本)9789811945465;9789811945458

With the popularization of online learning, a wide range of learning activities have occurred and produced a huge amount of related data. A learning path consists of a set of learning activities that help users achieve particular learning goals. Learning path recommendation is important in smart education applications, which can provide suitable learning resource sequences for large-scale online learners, reduce the impact of information overload on learners, and help learners realize learning goals more quickly. Besides, it is necessary to apply popular technologies such as data mining, machine learning, optimization, knowledge graph and user profile in the domain of learning path recommendation to effectively handle related personalizing learning path parameter problems. So far, a variety of learning path recommendation methods have been proposed, which can be conducted in two ways: 1) single learner-oriented recommendation and 2) grouped learners-oriented recommendation. this paper presents an overview of these methods and analyzes future research directions of learning path recommendation.

关键词： Learning path Learning resource Recommendation system

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Attributed Network Embedding Based on Attributed-Subgraph-Based Random Walk for Community Detection 16th

Attributed Network Embedding Based on Attributed-Subgraph-Ba...

引用

16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Wang, Qinze Guo, Kun Wu, Ling Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350108 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Peoples R China

ISBN: (纸本)9789811945496;9789811945489

the random-walk-based attribute network embedding methods aim to learn a low-dimensional embedding vector for each node considering the network structure and node attributes, facilitating various downstream inference tasks. However, most existing attribute network embedding methods base on random walk usually sample many redundant samples and suffer from inconsistency between node structure and attributes. In this paper, we propose a novel attributed network embedding method for community detection, which can generate node sequences based on attributed-subgraph-based random walk and filter redundant samples before model training. In addition, an improved network embedding enhancement strategy is applied to integrate high-order attributed and structure information of nodes into embedding vectors. Experimental results of community detection on synthetic network and real-world network show that our algorithm is effective and efficient compared with other algorithms.

关键词： Attribute network embedding Attributed-subgraph-based random walk Community detection

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Marine Predators Algorithm with Stage-Based Repairment for the Green Supply Network Design 16th

Marine Predators Algorithm with Stage-Based Repairment for t...

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16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Ma, Zhaobin Ding, Bowen Zhang, Xin Qian, Pengjiang Zhan, Zhihui Jiangnan Univ Wuxi 214000 Jiangsu Peoples R China South China Univ Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9789811945465;9789811945458

Green supply chain network design (GSCND) has become a hot topic in many manufacturing industries today, since it is an environmentally friendly manufacturing mode and is significantly related to the sustainable development of enterprises. theGSCND problem always has multiple complex constraints, which makes it difficult for traditional methods to search for feasible solutions within the limited time. therefore, this paper designs a four-echelon green supply chain network model, and proposes aMarine Predators Algorithm with the Stage-based Repairment (MPA-SR) to solve the complex constrained problem. For infeasible solutions that violate the constraints, MPA-SR repairs its logistics routes firstly, and then allocates the freight volume to the routes. Besides, an adaptive repair probability variable is designed to control the probability of the repair operator. In order to prove the efficacy of MPA-SR, the experiment is carried out on nine instances with three different scales. the experimental results show that MPA-SR performs better than other compared algorithms in terms of both the solving speed and the solution quality.

关键词： Green supply chain network Marine predators algorithm Constrained optimization

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CATS: A Cache Time-to-Live Setting Auto Adjustment Strategy for an Air Ticket Query Service 16th

CATS: A Cache Time-to-Live Setting Auto Adjustment Strategy ...

引用

16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Liu, Chunhou Cao, Jian Tan, Yudong Qian, Shiyou Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Ctrip Com Shanghai Peoples R China

ISBN: (纸本)9789811945465;9789811945458

A cache is an efficient way to speed up the response of services, which can also be applied in air ticket query services. However, the time-to-live (TTL) setting is a challenging task for the caching mechanism especially when the validity periods of the records change with time, which is the case for the air tickets. In air ticket query services, the validity of air ticket information is verified when a user places the order. It provides a chance to adjust the initial TTLs. In this paper, we provide a CAche Time-to-live setting auto adjustment Strategy (CATS) for air ticket query service based on the verification results during order placement. In addition, by considering the associations between cached records, CATS can also adjust the TTL settings for related cached records. Experiments are conducted to test and compare different implementations of CATS and the results show that CATS can maintain both the hit rates and verification success rates at a high level.

关键词： Cache Time-to-live setting Air ticket query service Deep reinforcement learning

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Locally Linear Embedding Discriminant Feature Learning Model 16th

Locally Linear Embedding Discriminant Feature Learning Model

引用

16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Wang, Chensu Wang, Luqing Wang, Hongjun Peng, Bo Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China

ISBN: (纸本)9789811945496;9789811945489

Feature learning is one of the important research trends among researchers in machine learning and other fields, which can select compact representations as feature information from high-dimensional data as well as multi-label data. Discriminative feature learning strengthens discrimination between sample features. therefore, the feature information of samples can be better discriminated against in algorithms. In this paper, we propose a new unsupervised discriminative feature learning model called UD-LLE (Unsupervised Discriminative Locally Linear Embedding) by the improvement on standard Locally Linear Embedding, which not only maintains the manifold structure of mapping from high-dimensional space to low-dimensional space but also increases the discriminative of features. Specifically, we propose the restructure cost function as an objective function by adding constraint conditions about discrimination to standard function, which is solved by using stochastic gradient descent and momentum gradient descent algorithms combined with standard LLE.

关键词： Locally Linear Embedding Feature learning Manifold learning Unsupervised learning Discriminative learning

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HPEMed: Heterogeneous Network Pair Embedding for Medical Diagnosis 16th

HPEMed: Heterogeneous Network Pair Embedding for Medical Dia...

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16th ccf conference on computer supported cooperative work and social computing (chinesecscw)

作者： Li, Mengxi Zhang, Jing Chen, Lixia Fu, Yu Zhou, Cangqi Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiaolingwei St Nanjing 210094 Peoples R China Nanjing Univ Chinese Med Affiliated Hosp 2 23 Nanhu Rd Nanjing 210017 Peoples R China

ISBN: (纸本)9789811945496;9789811945489

Capturing a high-quality representation of clinical events in Electronic Health Record (EHR) is critical for upgrading public medical applications such as intelligent auxiliary diagnosis systems. However, the relationships among clinical events have different semantics and different contributions to disease diagnosis. this paper proposes a novel heterogeneous information network (HIN) based model named HPEMed for disease diagnosis tasks. HPEMed takes advantage of high-dimensional EHR data to model the nodes (with features) and edges of a graph. It exploits meta paths to higher-level semantic relations among EHR data and employs a pair-node embedding scheme that considers patient nodes with rich features and diagnosis nodes together, which achieves a more reasonable clinical event representation. the experimental results show that the performance of HPEMed in diagnosis tasks is better than that of some advanced baseline methods.

关键词： Network embedding Heterogeneous Information Networks Representation learning Electronic Health Record

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