2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)最新文献

A Scalable Storage Scheme for On-Chain Big Data using Historical Blockchains 基于历史区块链的链上大数据可扩展存储方案

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00017

Marcos Felipe, Haiping Xu

引用次数: 0

CFIWSE: A Hybrid Preprocessing Approach for Defect Prediction on Imbalance Real-World Datasets CFIWSE:一种用于不平衡真实数据集缺陷预测的混合预处理方法

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00064

Jiaxi Xu, Jingwei Shang, Zhichang Huang

引用次数: 0

A General Characterization of Representing Spatiotemporal Knowledge Graph based on OWL 基于OWL的时空知识图表示的一般表征

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00108

Lin Zhu, Luyi Bai, Xuesong Hao, Hongji Yang

{"title":"A General Characterization of Representing Spatiotemporal Knowledge Graph based on OWL","authors":"Lin Zhu, Luyi Bai, Xuesong Hao, Hongji Yang","doi":"10.1109/QRS-C57518.2022.00108","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00108","url":null,"abstract":"Knowledge graph is used to represent the concepts, entities and relationships existing in the real world, which can be applied to many applications such as creative computing and recommendation system. Structurally, knowledge graph includes data layer and schema layer. Spatiotemporal knowledge graph extends the common knowledge graph to a certain extent, which is mainly reflected in the entity layer (data layer). Spatiotemporal knowledge graph includes temporal feature, spatial feature and spatiotemporal feature. In the pattern layer, spatiotemporal knowledge graph mainly adds concepts and relationships between concepts, which needs to be re-modeled. In this paper, as a spatiotemporal extension of the general description logic based on OWL logic, the spatiotemporal description logic (ST DL) is proposed to describe the spatiotemporal knowledge graph, and ST OWL is extended from three aspects: OWL class description, OWL axiom and OWL data type. Then, the corresponding transformation rules are proposed, and the instance is transformed from spatiotemporal ontology structure to spatiotemporal knowledge graph.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116551324","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}

引用次数: 0

TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews TTAG+R: Google Play Store热门Android游戏和用户评论数据集

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00093

Raheela Chand, Saif Ur Rehman Khan, S. Hussain, Wen Wang

{"title":"TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews","authors":"Raheela Chand, Saif Ur Rehman Khan, S. Hussain, Wen Wang","doi":"10.1109/QRS-C57518.2022.00093","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00093","url":null,"abstract":"Context: Android games are gaining wide attention from users in recent years. However, the existing literature reports alarming statistics about banning popular and top-trending Android apps. The popular gaming apps have been removed from Google Play Store due to various user concerns. Objectives: The goal of this work is twofold: (i) to assist the researchers and practitioners in identifying the state-of-the-art challenges, constraints, and compliments about Android apps for future Android-specific studies, and (ii) to encourage active users' perspectives on the Android development process because usability remains a core deciding factor about the success or failure of Android apps. Method: To accomplish this, we introduce a novel open-source dataset, Top Trending Android apps with their user Reviews (TTAG+R) in GitHub. Results and Contributions: Briefly, TTAG+R presents information about 245 top trending Android Free Games, 97 top trending Android Grossing Games, and 52 top trending Android Paid Games with a total of 8,423 user reviews in 12 different. csv files. The main contributions of this paper are: (i) provides one-place comprehensive data on Android Apps, (ii) describes various features of Android apps and their user reviews, (iii) reports the updated and latest knowledge about Android apps, and (iv) provides the data in an unfiltered form so that researchers may not find difficulty in using this dataset in their data-driven experimentation. From a research implication viewpoint, the dataset supports: (i) understanding the usability characteristics of Android apps, (ii) discovering current trends and pitfalls in Android apps, and (iii) analyzing the Android financial market. Conclusion and Future Work: Thus, TTAG+R is freely available to the research community, and useful for future enhancements in the Android domain. In the future, we plan to keep the data up-to-date with the most recent information for the continued usage of the dataset.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129612253","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}

引用次数: 0

Quantum Random Access Codes with Mutually Unbiased Bases in Three-Dimensional Hilbert Space 三维希尔伯特空间中互无偏基量子随机接入码

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00081

Qi Yao, Yuqian Zhou, Yaqi Dong

引用次数: 0

A Data-Efficient Method of Deep Reinforcement Learning for Chinese Chess 一种数据高效的中国象棋深度强化学习方法

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00109

Chang Xu, Heng Ding, Xuejian Zhang, Cong Wang, Hongji Yang

引用次数: 1

Recommendation Algorithm for Graph Convolutional Networks based on Multi-Ralational Knowledge Graph 基于多关系知识图的图卷积网络推荐算法

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00068

Yunhao Li, Shijie Chen, Jiancheng Zhao

{"title":"Recommendation Algorithm for Graph Convolutional Networks based on Multi-Ralational Knowledge Graph","authors":"Yunhao Li, Shijie Chen, Jiancheng Zhao","doi":"10.1109/QRS-C57518.2022.00068","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00068","url":null,"abstract":"Classic recommendation technologies such as collaborative filtering have some challenging problems such as cold start. Because knowledge graph can enrich data information, in recent years, many scholars have applied it to recommendation systems to solve the above problems. However, Most of the methods only exploit relations and entities involved in the knowledge graph, and do not further explore the correlation information between the entities in the knowledge graph. In order to solve the above problems, recommendation algorithm for graph convolutional networks based on multi-relational knowledge graph (Multi-RKGCN) is proposed, which expands the relations and entities in knowledge graph through reflexive and self-circulating ways. At the time of aggregation, the tail entity and the corresponding relationship are combined and embedded by the knowledge graph embedding technology to enrich the semantics of the entity. Finally, the performance of AUC and F1 is verified on two publicly available datasets. The experimental results show that Multi-RKGCN method is better than KGCN method.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125842937","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}

引用次数: 0

Metamorphic Testing for the Medical Image Classification Model 医学图像分类模型的变形检验

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00057

Yue Ma, Ya Pan, Yong Fan

{"title":"Metamorphic Testing for the Medical Image Classification Model","authors":"Yue Ma, Ya Pan, Yong Fan","doi":"10.1109/QRS-C57518.2022.00057","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00057","url":null,"abstract":"The existing studies have applied metamorphic testing technique to testing the medical image classification models, effectively alleviating the test oracle problem and reducing the testing difficulty. However, existing methods mainly focus on constructing metamorphic relations by using general image transformation methods, without combining the knowledge characteristics of medical imaging domain, resulting in problems such as low validity of metamorphic relations. According to the above problems, this paper based on the premise of conforming to the real scenario of image diagnosis, combining the key information of medical image semantics, and constructing general metamorphic relations in this field from three dimensions: the characteristics of medical images in real environment, the regular changes of lesion stage in images and the motion artifacts produced by patients in the process of filming. The medical images classification models of COVID-19 were also selected for instance validation, and the metamorphic relations were quantitatively analyzed to detect inconsistency in the classification results of different models and to assess the robustness of the model. The experimental results show that the constructed metamorphic relations by the key information of medical image semantics are able to detect inconsistencies in the models with a high detection capability, with the inconsistency percentage reaching up to 38.05%. This method can also be extended to test different types of medical image classification models.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128175515","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}

引用次数: 0

A Stochastic Model for Calculating Well-Founded Probabilities of Vulnerability Exploitation 计算有充分根据的漏洞利用概率的随机模型

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00015

Ryohei Sato, Hidetoshi Kawaguchi, Yuichi Nakatani

{"title":"A Stochastic Model for Calculating Well-Founded Probabilities of Vulnerability Exploitation","authors":"Ryohei Sato, Hidetoshi Kawaguchi, Yuichi Nakatani","doi":"10.1109/QRS-C57518.2022.00015","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00015","url":null,"abstract":"To efficiently manage security risks of network systems, vulnerabilities in the systems need to be assessed to determine their severity or priority. The Bayesian attack graph (BAG) is a risk analysis model that takes into account the probabilities of vulnerability exploitation (exploit probabilities) and their dependencies to calculate the probabilities that specific assets are compromised (compromise probabilities) in a system. In many BAG analysis methods, an exploit probability is obtained assuming that it strongly correlates with base metrics of the Common Vulnerability Scoring System (CVSS) assigned to the corresponding vulnerability. However, the authors found that this assumption does not necessarily hold, and thus the accuracy of compromise probabilities estimated by these methods may be impaired. Therefore, this paper proposes the exploit time probability (ETP)-model to calculate well-founded exploit probabilities on the basis of empirical data on vulnerabilities and exploits. The model uses Weibull distributions to approximate the probability distribution of the time between the publication of a vulnerability to the National Vulnerability Database (NVD) and its exploitation. Finally, by applying the ETP-model to a test network, the model is shown to be able to provide reasonable exploit probabilities and be a fundamental technique to improve the accuracy of existing BAG analysis methods.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836661","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}

引用次数: 0

Anti-Money Laundering Risk Identification of Financial Institutions based on Aspect-Level Graph Neural Networks 基于方面层图神经网络的金融机构反洗钱风险识别

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00086

Yahan Yu, Yixuan Xu, Jian Wang, Zhenxing Li, Bin Cao

引用次数: 0