{"title":"Heterogeneous Multi-Agent Communication Learning via Graph Information Maximization","authors":"Wei Du, Shifei Ding","doi":"10.18293/seke2023-099","DOIUrl":"https://doi.org/10.18293/seke2023-099","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134382008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SLPKT: A Novel Simulated Learning Process Model for Knowledge Tracing","authors":"Jingxia Zeng, Mianfan Chen, Jianing Liu, Yuncheng Jiang","doi":"10.18293/seke2023-049","DOIUrl":"https://doi.org/10.18293/seke2023-049","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129632994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Triplet Network Approach for Chinese Confusing Text Classification","authors":"Rui Xu, Cheng Zeng, Yujin Liu, Peng He, Min Chen","doi":"10.18293/seke2023-037","DOIUrl":"https://doi.org/10.18293/seke2023-037","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123364911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GSAGE2defect: An Improved Approach to Software Defect Prediction based on Inductive Graph Neural Network","authors":"Ju Ma, Yi-Yang Sun, Peng He, Zhang-Fan Zeng","doi":"10.18293/seke2023-068","DOIUrl":"https://doi.org/10.18293/seke2023-068","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129264320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inclusive Gamification: An Exploratory Study in Software Development Enterprises (S)","authors":"Fayrouz Elsalmy, N. Sherief, Walid Abdelmoez","doi":"10.18293/seke2023-096","DOIUrl":"https://doi.org/10.18293/seke2023-096","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
—The judgment of controversial cases has always been an important judicial issue, but it is not easy to discover them in practice. In this paper, based on 1,361,354 legal instruments data collected from China Judgments Online, we adopt a deep learning framework to classify 147 different kinds of crimes. The proposed method has three critical steps: 1) We adopt a deep learning model to predict crime categorization; 2) With the trained model, each case is given a score vector which represents the probability that it belongs to each crime; 3) With the probability score, we develop an entropy-based index to measure the controversy of each case. We find that the larger the entropy, the more inconsistent the result given by the model based on the first instance judgment. To verify the proposed entropy measure, we provide 1) two-sided evidence based on second instance judgments; 2) comparison with some baseline models. Both confirm the practical usefulness of the entropy measure. Our results indicate that the proposed framework has an ability to discover potentially controversial cases. It should be noted that the goal of this study is not to substitute the model result for the judge’s decision, but to provide a guiding reference for the judicial practice of sentencing.
{"title":"Automatic Discovery of Controversial Legal Judgments by an Entropy-Based Measurement (S)","authors":"Jing Zhou, Shan Leng, Fang Wang, Hansheng Wang","doi":"10.18293/seke2023-035","DOIUrl":"https://doi.org/10.18293/seke2023-035","url":null,"abstract":"—The judgment of controversial cases has always been an important judicial issue, but it is not easy to discover them in practice. In this paper, based on 1,361,354 legal instruments data collected from China Judgments Online, we adopt a deep learning framework to classify 147 different kinds of crimes. The proposed method has three critical steps: 1) We adopt a deep learning model to predict crime categorization; 2) With the trained model, each case is given a score vector which represents the probability that it belongs to each crime; 3) With the probability score, we develop an entropy-based index to measure the controversy of each case. We find that the larger the entropy, the more inconsistent the result given by the model based on the first instance judgment. To verify the proposed entropy measure, we provide 1) two-sided evidence based on second instance judgments; 2) comparison with some baseline models. Both confirm the practical usefulness of the entropy measure. Our results indicate that the proposed framework has an ability to discover potentially controversial cases. It should be noted that the goal of this study is not to substitute the model result for the judge’s decision, but to provide a guiding reference for the judicial practice of sentencing.","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128255111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Fiore, Marina Mongiello, Giovanni Tricarico, Francesco Bozzo, C. Montemurro, Alessandro Petrontino, Clemente Giambattista, Giorgio Mercuri
{"title":"Blockchain-based Food Traceability System for Apulian Marketplace: Enhancing Transparency and Accountability in the Food Supply Chain (S)","authors":"Marco Fiore, Marina Mongiello, Giovanni Tricarico, Francesco Bozzo, C. Montemurro, Alessandro Petrontino, Clemente Giambattista, Giorgio Mercuri","doi":"10.18293/seke2023-152","DOIUrl":"https://doi.org/10.18293/seke2023-152","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116120295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shunwen Shen, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang
{"title":"DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition","authors":"Shunwen Shen, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang","doi":"10.18293/seke2023-138","DOIUrl":"https://doi.org/10.18293/seke2023-138","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114945701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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