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Magpie: Efficient Big Data Query System Parameter Optimization based on Pre-selection and Search Pruning Approach 31

Magpie: Efficient Big Data Query System Parameter Optimizati...

引用

31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Gu, Rong Wei, Jiajia Wang, Xu Wang, Jiahui Dai, Haipeng Zheng, Jiaqi Mao, Baolong Huang, Yihua Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Tencent Inc. Beijing China

ISBN: (纸本)9798350399738

With the rapid development of big data technology, big data systems are more and more used to process SQL query jobs. However, intricate big data system parameters have big effects on the quality of service for big data query performance. Moreover, the types of query jobs and data sizes of data sets are diversified, which have different optimal parameters for best running performance. There exist high complexity and difficulties in tuning the big data system parameters for efficient query job execution. To address this issue, we propose Magpie, an efficient big data query tuning system based on the genetic algorithm with customized pruning strategies. The experimental results show that the proposed approaches can reduce big data query job execution time by 35.5% on average compared with the cuttingedge big data system parameter tuning methods. © 2023 IEEE.

关键词： Big data

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Pose-guided feature region-based fusion network for occluded person re-identification

Pose-guided feature region-based fusion network for occluded...

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作者： Xie, Gengsheng Wen, Xianbin Yuan, Liming Wang, Jianchen Guo, Changlun Jia, Yansong Li, Minghao School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

Learning distinguishing features from training datasets while filtering features of occlusions is critical to person retrieval scenarios. Most of the current person re-identification (Re-ID) methods based on classification or deep metric representation learning tend to overlook occlusion issues on the training set. Such representations from obstacles are easily over-fitted and misleading due to being considered as a part of the human body. To alleviate the occlusion problem, we propose a pose-guided feature region-based fusion network (PFRFN), to utilize pose landmarks as guidance to guide local learning for a good property of local feature, and the representation learning risk is evaluated on each part loss separately. Compared with only using global classification loss, concurrently considering local loss and the results of robust pose estimation enable the deep network to learn the representations of the body parts that prominently displayed in the image and gain the discriminative faculties on occluded scenes. Experimental results on multiple datasets, i.e., Market-1501, DukeMTMC, CUHK03, demonstrate the effectiveness of our method in a variety of scenarios. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Classification (of information)

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Privacy-preserving and efficient user matching based on attribute encryption in mobile social networks

Privacy-preserving and efficient user matching based on attr...

引用

作者： Wu, Lei Zhao, Shengnan Wei, Xia Meng, Lingzhen School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute-based encryption (CP-ABE), and the match server can perform friend recommendation by completing bi-directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead. © 2021 John Wiley & Sons, Ltd.

关键词： Cost reduction

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DVTest: Deep Neural Network Visualization Testing Framework 10

DVTest: Deep Neural Network Visualization Testing Framework

引用

10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Gu, Siqi Zong, Zhaowei Tian, Fangyuan Chen, Zhenyu Shenzhen Research Institute of Nanjing University China Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350304770

Deep neural networks have shown their powerful potential in various fields, especially vision classification problems, even though it still has black-box property. Visualization methods for deep neural networks, as a common technique to improve the interpretability of neural networks, can help researchers better understand the various properties of neural networks. However, current neural network visualization methods provide a limited explanation of the network's testing process and simultaneously rely on the diversity of test data *** paper proposes DVTest, a Deep Neural Network Visualization Testing Framework to evaluate and improve the testing efficiency, interpretability, and reliability of deep neural networks. By (1) constructing various test samples and (2) utilizing the mutation testing method in the visualization process, our framework can (3) locate the problematic neurons and improve the precision of the model. (4) Rich experiments demonstrate that our method can not only visualize the prediction defects of neural networks but also increase the prediction accuracy of models by neuron freezing method and retraining technique. We also build a local website usage platform based on DVTest, and the source code can be downloaded from https://***/tianfy1258/aim and https://***/tianfy1258/aim-front, representing back end and front end of the system, respectively. © 2023 IEEE.

关键词： Deep neural networks

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Research on Prediction of Medical & IT Sectors in A-shares Based on Random Forest Algorithm 5

Research on Prediction of Medical & IT Sectors in A-shares B...

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5th International Conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Chen, Kunrui Yang, Lei Du, Shipei Zhang, Longqing Chen, Yungui Tian, Liwei School of Computer Science Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory Industrial Software for Intelligent Manufacturing Dongguan China Guangdong University of Science and Technology Dongguan China School of Computer Science Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China

ISBN: (纸本)9798331527662

This research is set against the backdrop of China's Shanghai Stock Exchange A-share market, selecting three representative stocks from each of two distinct sectors as the research objects. Leveraging the "orange" software, we employed the Random Forest model to predict the closing prices of stocks belonging to these two sectors. The findings indicate that, with respect to these two diverse sectors, the Random Forest model yielded no discernible difference in prediction outcomes. © 2024 IEEE.

关键词： Prediction models

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LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities 23

LLM for Test Script Generation and Migration: Challenges, Ca...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Yu, Shengcheng Fang, Chunrong Ling, Yuchen Wu, Chentian Chen, Zhenyu Nanjing University State Key Laboratory for Novel Software Technology China Shenzhen Research Institute of Nanjing University China

ISBN: (纸本)9798350319583

This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. However, existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. These challenges arise due to differences in screen sizes, input modalities, platform behaviors, API inconsistencies, and application architectures. Overcoming these limitations is crucial for achieving robust and comprehensive test *** leveraging the capabilities of LLMs, we aim to address these challenges and explore its potential as a versatile tool for test automation. We investigate how well LLMs can adapt to diverse devices and systems while accurately capturing and generating test scripts. Additionally, we evaluate its cross-platform generation capabilities by assessing its ability to handle operating system variations and platform-specific behaviors. Furthermore, we explore the application of LLMs in cross-app migration, where it generates test scripts across different applications and software environments based on existing *** the investigation, we analyze its adaptability to various user interfaces, app architectures, and interaction patterns, ensuring accurate script generation and compatibility. The findings of this research contribute to the understanding of LLMs' capabilities in test automation. Ultimately, this research aims to enhance software testing practices, empowering app developers to achieve higher levels of software quality and development efficiency. © 2023 IEEE.

关键词： Application programs

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Comprehensive Semantic Repair of Obsolete GUI Test Scripts for Mobile Applications 24

Comprehensive Semantic Repair of Obsolete GUI Test Scripts f...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Cao, Shaoheng Pan, Minxue Pei, Yu Yang, Wenhua Zhang, Tian Wang, Linzhang Li, Xuandong Nanjing University State Key Lab for Novel Software Technology Nanjing China The Hong Kong Polytechnic University Department of Computing Hong Kong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798400702174

Graphical User Interface (GUI) testing is one of the primary approaches for testing mobile apps. Test scripts serve as the main carrier of GUI testing, yet they are prone to obsolescence when the GUIs change with the apps' evolution. Existing repair approaches based on GUI layouts or images prove effective when the GUI changes between the base and updated versions are minor, however, they may struggle with substantial changes. In this paper, a novel approach named COSER is introduced as a solution to re-pairing broken scripts, which is capable of addressing larger GUI changes compared to existing methods. COSER incorporates both external semantic information from the GUI elements and internal semantic information from the source code to provide a unique and comprehensive solution. The efficacy of COSER was demonstrated through experiments conducted on 20 Android apps, resulting in superior performance when compared to the state-of-the-art tools METER and GUIDER. In addition, a tool that implements the COSER approach is available for practical use and future research. © 2024 ACM.

关键词： Graphical user interfaces

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Non-stationary Online Convex Optimization with Arbitrary Delays 41

Non-stationary Online Convex Optimization with Arbitrary Del...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Wan, Yuanyu Yao, Chang Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Online convex optimization (OCO) with arbitrary delays, in which gradients or other information of functions could be arbitrarily delayed, has received increasing attention recently. Different from previous studies that focus on stationary environments, this paper investigates the delayed OCO in non-stationary environments, and aims to minimize the dynamic regret with respect to any sequence of comparators. To this end, we first propose a simple algorithm, namely DOGD, which performs a gradient descent step for each delayed gradient according to their arrival order. Despite its simplicity, our novel analysis shows that the dynamic regret of DOGD can be automatically bounded by (equation presented) under mild assumptions, and (equation presented) in the worst case, where d¯ and d denote the average and maximum delay respectively, T is the time horizon, and PT is the path-length of comparators. Furthermore, we develop an improved algorithm, which reduces those dynamic regret bounds achieved by DOGD to (equation presented), respectively. The key idea is to run multiple DOGD with different learning rates, and utilize a meta-algorithm to track the best one based on their delayed performance. Finally, we demonstrate that our improved algorithm is optimal in the worst case by deriving a matching lower bound. Copyright 2024 by the author(s)

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Peng, Qian Cao, Buqing Xie, Xiang Liu, Shanpeng Kang, Guosheng Liu, Jianxun Hunan University of Science and Technology Xiangtan School of Computer Science and Engineering Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan China

ISBN: (纸本)9798350304855

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method. © 2023 IEEE.

关键词： Semantics

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