Zuotong Xie, Kai Ouyang, Xiangjin Xie, Hai-Tao Zheng, Wenqiang Liu, Dongxiao Huang, Bei Wu
{"title":"KLAPrompt: Infusing Semantic Knowledge into Pre-trained Language Models by Long-answer Prompt Learning","authors":"Zuotong Xie, Kai Ouyang, Xiangjin Xie, Hai-Tao Zheng, Wenqiang Liu, Dongxiao Huang, Bei Wu","doi":"10.18293/seke2023-200","DOIUrl":"https://doi.org/10.18293/seke2023-200","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"44 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":"130856191","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":"Fortran Code Refactoring Based on MapReduce Programming Model","authors":"Junfeng Zhao, Wenhui Gai, Han Wu","doi":"10.18293/seke2023-072","DOIUrl":"https://doi.org/10.18293/seke2023-072","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"74 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":"126177140","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}
Haifeng Wang, Kun Yang, Xiangnan Zhao, Yuchen Cui, Weiwei Wang
{"title":"Contribution-based Test Case Reduction Strategy for Mutation-based Fault Localization (S)","authors":"Haifeng Wang, Kun Yang, Xiangnan Zhao, Yuchen Cui, Weiwei Wang","doi":"10.18293/seke2023-180","DOIUrl":"https://doi.org/10.18293/seke2023-180","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"8 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":"129513340","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}
Bishshoy Das, M. Mondal, B. Lall, S. Joshi, Sumantra Dutta Roy
{"title":"Altering Backward Pass Gradients to Improve Convergence (S)","authors":"Bishshoy Das, M. Mondal, B. Lall, S. Joshi, Sumantra Dutta Roy","doi":"10.18293/seke2023-177","DOIUrl":"https://doi.org/10.18293/seke2023-177","url":null,"abstract":"","PeriodicalId":291002,"journal":{"name":"International Conference on Software Engineering and Knowledge Engineering","volume":"81 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":"130637244","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}
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