PDF HTML XML Export Cite reminder GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method DOI: 10.21655/ijsi.1673-7288.00300 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Constraint solving has been applied to many domains of program analysis and is further used in concurrent program analysis. Concurrent programs have been widely used with the rapid development of multi-core processors. However, concurrent bugs threaten the security and reliability of concurrent programs, and thus it is of great importance to detect concurrent bugs. The explosion of thread interleaving caused by the uncertainty of the execution of concurrent program threads brings some challenges to the detection of concurrent bugs. Existing concurrent defect detection algorithms reduce the exploration cost in the state space of concurrent programs by reducing invalid thread interleaving. For example, the maximal causal model algorithm transforms the state space exploration problem of concurrent programs into a constraint solving problem. However, it will produce a large number of redundant and conflicting constraints during constraint construction, which greatly prolongs the time of constraint solving, increases the number of constraint solver calls, and reduces the exploration efficiency of concurrent program state space. Thus, this study proposes a directed graph constraint-guided maximal causality reduction method, called GC-MCR. This method aims to improve the speed of constraint solving and the efficiency of the state space exploration of concurrent programs by filtering and reducing constraints using directed graphs. The experimental results show that the GC-MCR method can effectively optimize the expression of constraints, so as to improve the solving speed of the constraint solver and reduce the number of solver calls. Compared with the existing J-MCR method, GC-MCR can significantly improve the detection efficiency of concurrent program bugs without reducing the detection ability of concurrent bugs, and the test time on 38 groups of concurrent test programs widely used by existing research methods can be reduced by 34.01% on average. Reference Related Cited by
PDF HTML XML导出引用提示GC-MCR:有向图约束引导并发错误检测方法DOI: 10.21655/ijsi.1673-7288.00300作者:隶属关系:Clc编号:基金项目:文章|图|指标|参考|相关|引用|资料|评论摘要:约束求解已应用于程序分析的许多领域，并进一步应用于并发程序分析。随着多核处理器的迅速发展，并发程序得到了广泛的应用。然而，并发bug对并发程序的安全性和可靠性构成威胁，因此对并发bug的检测具有重要意义。由于并发程序线程执行的不确定性，导致了线程交错现象的爆发，这给并发错误的检测带来了挑战。现有的并发缺陷检测算法通过减少无效线程的交错来降低并发程序状态空间的探索成本。例如，最大因果模型算法将并发程序的状态空间探索问题转化为约束求解问题。然而，在约束构造过程中会产生大量的冗余约束和冲突约束，这大大延长了约束求解的时间，增加了约束求解器的调用次数，降低了并发程序状态空间的探索效率。因此，本研究提出了一种有向图约束引导的最大因果约简方法，称为GC-MCR。该方法利用有向图对约束进行过滤和约简，以提高并行程序的约束求解速度和状态空间探索效率。实验结果表明，GC-MCR方法可以有效地优化约束表达式，从而提高约束求解器的求解速度，减少求解器的调用次数。与现有的J-MCR方法相比，GC-MCR可以在不降低并发bug检测能力的前提下显著提高并发程序bug的检测效率，对现有研究方法广泛使用的38组并发测试程序的测试时间平均可减少34.01%。相关参考文献
{"title":"GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method","authors":"Shuochuan Li, Zan Wang, Mingxu Ma, Xiang Chen, Yingquan Zhao, Haichi Wang, Haoyu Wang","doi":"10.21655/ijsi.1673-7288.00300","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00300","url":null,"abstract":"PDF HTML XML Export Cite reminder GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method DOI: 10.21655/ijsi.1673-7288.00300 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Constraint solving has been applied to many domains of program analysis and is further used in concurrent program analysis. Concurrent programs have been widely used with the rapid development of multi-core processors. However, concurrent bugs threaten the security and reliability of concurrent programs, and thus it is of great importance to detect concurrent bugs. The explosion of thread interleaving caused by the uncertainty of the execution of concurrent program threads brings some challenges to the detection of concurrent bugs. Existing concurrent defect detection algorithms reduce the exploration cost in the state space of concurrent programs by reducing invalid thread interleaving. For example, the maximal causal model algorithm transforms the state space exploration problem of concurrent programs into a constraint solving problem. However, it will produce a large number of redundant and conflicting constraints during constraint construction, which greatly prolongs the time of constraint solving, increases the number of constraint solver calls, and reduces the exploration efficiency of concurrent program state space. Thus, this study proposes a directed graph constraint-guided maximal causality reduction method, called GC-MCR. This method aims to improve the speed of constraint solving and the efficiency of the state space exploration of concurrent programs by filtering and reducing constraints using directed graphs. The experimental results show that the GC-MCR method can effectively optimize the expression of constraints, so as to improve the solving speed of the constraint solver and reduce the number of solver calls. Compared with the existing J-MCR method, GC-MCR can significantly improve the detection efficiency of concurrent program bugs without reducing the detection ability of concurrent bugs, and the test time on 38 groups of concurrent test programs widely used by existing research methods can be reduced by 34.01% on average. Reference Related Cited by","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701449","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":"Text-driven Face Image Generation and Manipulation via Multi-level Residual Mapper","authors":"Zonglin Li, Shengping Zhang, Yang Liu, Zhaoxin Zhang, Weigang Zhang, Qingming Huang","doi":"10.21655/ijsi.1673-7288.00313","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00313","url":null,"abstract":"","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135754833","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00299
Shaowei Cai, Zhenbang Chen, Ji Wang, Bohua Zhan, Yongwang Zhao
{"title":"Preface to the Special Issue on Constraint Solving and Theorem Proving","authors":"Shaowei Cai, Zhenbang Chen, Ji Wang, Bohua Zhan, Yongwang Zhao","doi":"10.21655/ijsi.1673-7288.00299","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00299","url":null,"abstract":"","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701459","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00304
Jing Li, Dantong Ouyang, Yuxin Ye
PDF HTML XML Export Cite reminder Consequence-based Axiom Pinpointing for Expressive Description Logic Ontologies DOI: 10.21655/ijsi.1673-7288.00304 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Axiom pinpointing has attracted extensive interest in Description Logics (DLs) due to its effect of exploring explicable defects in the DL ontology and searching for hidden justifications for logical implication. Balancing the expressive power of DLs and the solving efficiency of reasoners has always been the focus of axiom pinpointing research. This study, from both glass-box and black-box perspectives, proposes a consequence-based method for axiom pinpointing. The glass-box method uses modified consequence-based rules (pinpointing rules) to trace the specific process of inference and introduces the concept of pinpointing formula to establish the correspondence between the label of the Boolean formula and all the minimal axiom sets. The black-box method directly calls the reasoner based on the unmodified consequence-based rules and further uses the Hitting Set Tree (HST) to compute all justifications of logical implication. Finally, a reasoning tool is designed based on the two axiom pinpointing algorithms for expressive DL ontologies. Its feasibility is verified theoretically and experimentally, and its solving efficiency is compared with that of existing axiom pinpointing tools. Reference Related Cited by
PDF HTML XML导出引用提醒基于结果的公理表达描述逻辑本体精确定位DOI: 10.21655/ijsi.1673-7288.00304作者:隶属关系:Clc编号:基金项目:摘要:Axiom pinpointing能够探索DL本体中可解释的缺陷，并为逻辑含义寻找隐藏的理由，因此在描述逻辑(Description logic, DL)领域引起了广泛的兴趣。平衡人工智能的表达能力和推理器的求解效率一直是公理定位研究的焦点。本研究从玻璃盒和黑盒两个角度，提出了一种基于结果的公理确定方法。玻璃盒法使用改进的基于结果的规则(精确指向规则)来跟踪推理的具体过程，并引入精确指向公式的概念来建立布尔公式的标签与所有最小公理集之间的对应关系。黑盒方法基于未修改的基于结果的规则直接调用推理器，并进一步使用命中集树(hit Set Tree, HST)计算逻辑蕴涵的所有证明。最后，基于这两种公理定位算法，设计了一个用于表达深度学习本体的推理工具。理论和实验验证了该方法的可行性，并与现有公理定位工具的求解效率进行了比较。相关参考文献
{"title":"Consequence-based Axiom Pinpointing for Expressive Description Logic Ontologies","authors":"Jing Li, Dantong Ouyang, Yuxin Ye","doi":"10.21655/ijsi.1673-7288.00304","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00304","url":null,"abstract":"PDF HTML XML Export Cite reminder Consequence-based Axiom Pinpointing for Expressive Description Logic Ontologies DOI: 10.21655/ijsi.1673-7288.00304 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Axiom pinpointing has attracted extensive interest in Description Logics (DLs) due to its effect of exploring explicable defects in the DL ontology and searching for hidden justifications for logical implication. Balancing the expressive power of DLs and the solving efficiency of reasoners has always been the focus of axiom pinpointing research. This study, from both glass-box and black-box perspectives, proposes a consequence-based method for axiom pinpointing. The glass-box method uses modified consequence-based rules (pinpointing rules) to trace the specific process of inference and introduces the concept of pinpointing formula to establish the correspondence between the label of the Boolean formula and all the minimal axiom sets. The black-box method directly calls the reasoner based on the unmodified consequence-based rules and further uses the Hitting Set Tree (HST) to compute all justifications of logical implication. Finally, a reasoning tool is designed based on the two axiom pinpointing algorithms for expressive DL ontologies. Its feasibility is verified theoretically and experimentally, and its solving efficiency is compared with that of existing axiom pinpointing tools. Reference Related Cited by","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701460","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}
PDF HTML XML Export Cite reminder End-to-end Image Captioning via Visual Region Aggregation and Dual-level Collaboration DOI: 10.21655/ijsi.1673-7288.00316 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:To date, Transformer-based pre-trained models have demonstrated powerful capabilities of modality representation, leading to a shift towards a fully end-to-end paradigm for multimodal downstream tasks such as image captioning, and enabling better performance and faster inference. However, the grid features extracted with the pre-trained model lack regional visual information, which leads to inaccurate descriptions of the object content by the model. Thus, the applicability of using pre-trained models for image captioning remains largely unexplored. Toward this goal, this paper proposes a novel end-to-end image captioning method based on Visual Region Aggregation and Dual-level Collaboration (VRADC). Specifically, to learn regional visual information, this paper designs a visual region aggregation that aggregates grid features with similar semantics to obtain a compact visual region representation. Next, dual-level collaboration uses the cross-attention mechanism to learn more representative semantic information from the two visual features, which in turn generates more fine-grained descriptions. Experimental results on the MSCOCO and Flickr30k datasets show that the proposed method, VRADC, can significantly improve the quality of image captioning, and achieves state-of-the-art performance. Reference Related Cited by
PDF HTML XML导出引用提醒基于视觉区域聚合和双层协作的端到端图像字幕DOI: 10.21655/ijsi.1673-7288.00316作者:隶属单位:Clc编号:基金项目:摘要:迄今为止，基于transformer的预训练模型已经展示了强大的模态表示能力，导致向多模态下游任务(如图像字幕)的完全端到端范式转变，并实现了更好的性能和更快的推理。然而，使用预训练模型提取的网格特征缺乏区域视觉信息，导致模型对目标内容的描述不准确。因此，使用预训练模型进行图像字幕的适用性在很大程度上仍未得到探索。为此，本文提出了一种基于视觉区域聚合和双层协作(VRADC)的端到端图像字幕方法。具体而言，为了学习区域视觉信息，本文设计了一种视觉区域聚合方法，将语义相似的网格特征聚合在一起，得到紧凑的视觉区域表示。接下来，双级协作使用交叉注意机制从两个视觉特征中学习更具代表性的语义信息，进而生成更细粒度的描述。在MSCOCO和Flickr30k数据集上的实验结果表明，所提出的VRADC方法可以显著提高图像字幕的质量，达到最先进的性能。相关参考文献
{"title":"End-to-end Image Captioning via Visual Region Aggregation and Dual-level Collaboration","authors":"Jingkuan Song, Pengpeng Zeng, Jiayang Gu, Jinkuan Zhu, Lianli Gao","doi":"10.21655/ijsi.1673-7288.00316","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00316","url":null,"abstract":"PDF HTML XML Export Cite reminder End-to-end Image Captioning via Visual Region Aggregation and Dual-level Collaboration DOI: 10.21655/ijsi.1673-7288.00316 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:To date, Transformer-based pre-trained models have demonstrated powerful capabilities of modality representation, leading to a shift towards a fully end-to-end paradigm for multimodal downstream tasks such as image captioning, and enabling better performance and faster inference. However, the grid features extracted with the pre-trained model lack regional visual information, which leads to inaccurate descriptions of the object content by the model. Thus, the applicability of using pre-trained models for image captioning remains largely unexplored. Toward this goal, this paper proposes a novel end-to-end image captioning method based on Visual Region Aggregation and Dual-level Collaboration (VRADC). Specifically, to learn regional visual information, this paper designs a visual region aggregation that aggregates grid features with similar semantics to obtain a compact visual region representation. Next, dual-level collaboration uses the cross-attention mechanism to learn more representative semantic information from the two visual features, which in turn generates more fine-grained descriptions. Experimental results on the MSCOCO and Flickr30k datasets show that the proposed method, VRADC, can significantly improve the quality of image captioning, and achieves state-of-the-art performance. Reference Related Cited by","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701471","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00302
Zhongqi Yu, Xiaoyu Zhang, Jianwen Li
PDF HTML XML Export Cite reminder UC-based Approximate Incremental Reachability DOI: 10.21655/ijsi.1673-7288.00302 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In recent years, formal verification technology has received more and more attention, and it plays an important role in ensuring the safety and correctness of systems in safety-critical areas. As a branch of formal verification with a high degree of automation, model checking has a very broad development prospect. This study analyzes and proposes a new model checking technique, which can effectively check transition systems, including bug-finding and safety proof. Different from existing model checking algorithms, the proposed method, Unsatisfiable Core (UC)-based Approximate Incremental Reachability (UAIR), mainly utilizes the UC to solve a series of candidate safety invariants until the final invariant is generated, so as to realize safety proof and bug-finding. In symbolic model checking based on the SAT solver, this study uses the UC obtained by the satisfiability solver to construct the candidate safety invariant, and if the transition system itself is safe, the obtained initial invariant is only an approximation of the safety invariant. Then, while checking the safety, the study incrementally improves the candidate safety invariant until it finds a true invariant that proves the system is safe; if the system is unsafe, the method can finally find a counterexample to prove the system is unsafe. The brand new method exploits UCs for safety model checking and achieves good results. It is known that there is no absolute best method in the field of model checking. Although the proposed method cannot surpass the current mature methods such as IC3 and complement Approximate Reachability (CAR), in terms of the number of solvable benchmarks, the method in this paper can solve three cases that other mature methods are unable to solve. It is believed that the method can be a valuable addition to the model checking toolset. Reference Related Cited by
{"title":"UC-based Approximate Incremental Reachability","authors":"Zhongqi Yu, Xiaoyu Zhang, Jianwen Li","doi":"10.21655/ijsi.1673-7288.00302","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00302","url":null,"abstract":"PDF HTML XML Export Cite reminder UC-based Approximate Incremental Reachability DOI: 10.21655/ijsi.1673-7288.00302 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In recent years, formal verification technology has received more and more attention, and it plays an important role in ensuring the safety and correctness of systems in safety-critical areas. As a branch of formal verification with a high degree of automation, model checking has a very broad development prospect. This study analyzes and proposes a new model checking technique, which can effectively check transition systems, including bug-finding and safety proof. Different from existing model checking algorithms, the proposed method, Unsatisfiable Core (UC)-based Approximate Incremental Reachability (UAIR), mainly utilizes the UC to solve a series of candidate safety invariants until the final invariant is generated, so as to realize safety proof and bug-finding. In symbolic model checking based on the SAT solver, this study uses the UC obtained by the satisfiability solver to construct the candidate safety invariant, and if the transition system itself is safe, the obtained initial invariant is only an approximation of the safety invariant. Then, while checking the safety, the study incrementally improves the candidate safety invariant until it finds a true invariant that proves the system is safe; if the system is unsafe, the method can finally find a counterexample to prove the system is unsafe. The brand new method exploits UCs for safety model checking and achieves good results. It is known that there is no absolute best method in the field of model checking. Although the proposed method cannot surpass the current mature methods such as IC3 and complement Approximate Reachability (CAR), in terms of the number of solvable benchmarks, the method in this paper can solve three cases that other mature methods are unable to solve. It is believed that the method can be a valuable addition to the model checking toolset. Reference Related Cited by","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701210","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00303
Xinyi Wan, Ke Xu, Qinxiang Cao
PDF HTML XML Export Cite reminder Coq Formalization of ZFC Set Theory for Teaching Scenarios DOI: 10.21655/ijsi.1673-7288.00303 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Discrete mathematics is a foundation course for computer-related majors, and propositional logic, first-order logic, and the axiomatic set theory are important parts of this course. Teaching practice shows that beginners find it difficult to accurately understand abstract concepts, such as syntax, semantics, and reasoning system. In recent years, some scholars have begun introducing interactive theorem provers into teaching to help students construct formal proofs so that they can understand logic systems more thoroughly. However, directly employing the existing theorem provers will increase students' learning burden since these tools have a high threshold for getting started with them. To address this problem, we develop a prover for the Zermelo-Fraenkel set theory with the axiom of Choice (ZFC) in Coq for teaching scenarios. Specifically, the first-order logical reasoning system and the axiomatic set theory ZFC are formalized, and several automated proof tactics specific to reasoning rules are then developed. Students can utilize these automated proof tactics to construct formal proofs of theorems in a textbook-style concise proving environment. This tool has been introduced into the teaching of the course of discrete mathematics for freshmen. Students with no prior theorem-proving experience can quickly construct formal proofs of theorems including mathematical induction and Peano arithmetic with this tool, which verifies the practical effectiveness of this tool. Reference Related Cited by
{"title":"Coq Formalization of ZFC Set Theory for Teaching Scenarios","authors":"Xinyi Wan, Ke Xu, Qinxiang Cao","doi":"10.21655/ijsi.1673-7288.00303","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00303","url":null,"abstract":"PDF HTML XML Export Cite reminder Coq Formalization of ZFC Set Theory for Teaching Scenarios DOI: 10.21655/ijsi.1673-7288.00303 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Discrete mathematics is a foundation course for computer-related majors, and propositional logic, first-order logic, and the axiomatic set theory are important parts of this course. Teaching practice shows that beginners find it difficult to accurately understand abstract concepts, such as syntax, semantics, and reasoning system. In recent years, some scholars have begun introducing interactive theorem provers into teaching to help students construct formal proofs so that they can understand logic systems more thoroughly. However, directly employing the existing theorem provers will increase students' learning burden since these tools have a high threshold for getting started with them. To address this problem, we develop a prover for the Zermelo-Fraenkel set theory with the axiom of Choice (ZFC) in Coq for teaching scenarios. Specifically, the first-order logical reasoning system and the axiomatic set theory ZFC are formalized, and several automated proof tactics specific to reasoning rules are then developed. Students can utilize these automated proof tactics to construct formal proofs of theorems in a textbook-style concise proving environment. This tool has been introduced into the teaching of the course of discrete mathematics for freshmen. Students with no prior theorem-proving experience can quickly construct formal proofs of theorems including mathematical induction and Peano arithmetic with this tool, which verifies the practical effectiveness of this tool. Reference Related Cited by","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701461","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00312
Chengji Wang, Jiawei Su, Zhiming Luo, Donglin Cao, Yaojin Lin, Shaozi Li
{"title":"Text-based Person Search via Virtual Attribute Learning","authors":"Chengji Wang, Jiawei Su, Zhiming Luo, Donglin Cao, Yaojin Lin, Shaozi Li","doi":"10.21655/ijsi.1673-7288.00312","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00312","url":null,"abstract":"","PeriodicalId":479632,"journal":{"name":"International Journal of Software and Informatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135754851","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}
Pub Date : 2023-01-01DOI: 10.21655/ijsi.1673-7288.00311
Xuemeng Song, Liqiang Nie, Hengtao Shen, Qi Tian, Hua Huang
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