Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152701
Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou
Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.
{"title":"A Constraint-based Recommender System via RDF Knowledge Graphs","authors":"Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou","doi":"10.1109/CSCWD57460.2023.10152701","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152701","url":null,"abstract":"Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"849-854"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86085578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152826
Yueyao Chen, Beilun Wang, Tianyi Ma, Cheng Chen
Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.
{"title":"Applying Robust Gradient Difference Compression to Federated Learning","authors":"Yueyao Chen, Beilun Wang, Tianyi Ma, Cheng Chen","doi":"10.1109/CSCWD57460.2023.10152826","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152826","url":null,"abstract":"Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"3 1","pages":"1748-1753"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72461426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152733
Qianqian Chang, Lin Xu, L. Zhang
The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.
{"title":"Practical privacy-preserving mixing protocol for Bitcoin","authors":"Qianqian Chang, Lin Xu, L. Zhang","doi":"10.1109/CSCWD57460.2023.10152733","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152733","url":null,"abstract":"The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 6","pages":"17-22"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72470666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152637
Tianshuo Yang, Haibin Zhu
New employee training scheduling is one of the most common events in many enterprises. Solving this problem has its significance and is useful in daily administrations and operations. Group Role Assignment (GRA) model is widely applied in the assignment problem. However, there are still many challenges to applying the GRA model. For example, when we need to assign different jobs for the same person at different times, GRA needs more structures to specify constraints. If we use the strategy that combines the time factor with the agents or roles to formalize new agents or roles, the problem can be converted to a solvable GRA problem with constraints. The focus of this article is to give a practical solution to this kind of problem by using the GRA formulations in expressing constraints. The formalization makes us resolve the problem easily through integer programming (IP) with the PuLP package of Python. Large-scale simulation experiments demonstrate the practicability and robustness of our method.
{"title":"New Employee Training Scheduling Using the E-CARGO Model","authors":"Tianshuo Yang, Haibin Zhu","doi":"10.1109/CSCWD57460.2023.10152637","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152637","url":null,"abstract":"New employee training scheduling is one of the most common events in many enterprises. Solving this problem has its significance and is useful in daily administrations and operations. Group Role Assignment (GRA) model is widely applied in the assignment problem. However, there are still many challenges to applying the GRA model. For example, when we need to assign different jobs for the same person at different times, GRA needs more structures to specify constraints. If we use the strategy that combines the time factor with the agents or roles to formalize new agents or roles, the problem can be converted to a solvable GRA problem with constraints. The focus of this article is to give a practical solution to this kind of problem by using the GRA formulations in expressing constraints. The formalization makes us resolve the problem easily through integer programming (IP) with the PuLP package of Python. Large-scale simulation experiments demonstrate the practicability and robustness of our method.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"691-696"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72864670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152706
Yuejia Wu, Jian-tao Zhou
Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.
{"title":"A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture","authors":"Yuejia Wu, Jian-tao Zhou","doi":"10.1109/CSCWD57460.2023.10152706","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152706","url":null,"abstract":"Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"1520-1525"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75260639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152744
Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang
In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.
{"title":"Computation of Mobile Phone Collaborative Embedded Devices for Object Detection Task","authors":"Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang","doi":"10.1109/CSCWD57460.2023.10152744","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152744","url":null,"abstract":"In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"54 1","pages":"778-783"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77062568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152597
Xiaolong Xu, Yihao Dong, Jian Song
At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence" of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.
{"title":"An Answer Summarization Scheme Based on Multilayer Attention Model","authors":"Xiaolong Xu, Yihao Dong, Jian Song","doi":"10.1109/CSCWD57460.2023.10152597","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152597","url":null,"abstract":"At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve \"long distance dependence\" of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"7 1 1","pages":"143-148"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77619241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152748
Han Zhang, Lubin Lin, Guipeng Zhang, Zhenguo Yang, Wenyin Liu
With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.
{"title":"ATIPM: A Blockchain-Based Anonymous and Traceable Intellectual Property Management Scheme","authors":"Han Zhang, Lubin Lin, Guipeng Zhang, Zhenguo Yang, Wenyin Liu","doi":"10.1109/CSCWD57460.2023.10152748","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152748","url":null,"abstract":"With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1080-1085"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77622604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152617
Y. Lima, C. E. Barbosa, A. Lyra, Herbert Salazar, M. Argôlo, J. Souza
Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.
{"title":"Providing Patients with Actionable Medical Knowledge: mHealth Apps for Laypeople","authors":"Y. Lima, C. E. Barbosa, A. Lyra, Herbert Salazar, M. Argôlo, J. Souza","doi":"10.1109/CSCWD57460.2023.10152617","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152617","url":null,"abstract":"Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"35 1","pages":"654-659"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77235061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capabilities of every supplier. This paper formalized the problem by Group Multirole Assignment (GMRA). Meanwhile, the success of this assignment process depends on the choice of the agent evaluation method. It depends on the industrial chain data, which can acquire feature indexes of parts from previous purchase records. Furthermore, comprehensive evaluation of parts procurement bases on multiple factors. Thus, it is difficult to reflect different quantifications using the multifactorial parameter semantics. Therefore, we propose a new method of Fuzzy Hierarchy Comprehensive Evaluation (FHCE), using membership grades of the fuzzy theory to differentiate the parameter and the weight, which can use objective quantitative analysis to optimize procurement plan. After that, based on GMRA, decision makers are able to maximize the resource utilization ratio to determine optimized solutions when funds or part types are limited. Simulation experiments indicate that the proposed method is efficient and feasible, which is verified practicable.
{"title":"Industrial Chain Data Evaluation in Automobile Parts Procurement via Group Multirole Assignment","authors":"Ziqi Xiong, Haibin Zhu, Dongning Liu, Jianhui Xian","doi":"10.1109/CSCWD57460.2023.10152642","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152642","url":null,"abstract":"In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capabilities of every supplier. This paper formalized the problem by Group Multirole Assignment (GMRA). Meanwhile, the success of this assignment process depends on the choice of the agent evaluation method. It depends on the industrial chain data, which can acquire feature indexes of parts from previous purchase records. Furthermore, comprehensive evaluation of parts procurement bases on multiple factors. Thus, it is difficult to reflect different quantifications using the multifactorial parameter semantics. Therefore, we propose a new method of Fuzzy Hierarchy Comprehensive Evaluation (FHCE), using membership grades of the fuzzy theory to differentiate the parameter and the weight, which can use objective quantitative analysis to optimize procurement plan. After that, based on GMRA, decision makers are able to maximize the resource utilization ratio to determine optimized solutions when funds or part types are limited. Simulation experiments indicate that the proposed method is efficient and feasible, which is verified practicable.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"62 1","pages":"1049-1054"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77912880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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