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Mining minimal failure-causing schema for software complex configuration space

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International Journal of Performability Engineering 2019年第12期15卷 3253-3261页

作者： Wei, Liangfen Li, Yong Wang, Yong Chen, Xiangyu Xu, Zhaohui Department of Computer Engineering Anhui Sanlian University Hefei23060 China Ministry of Industry and Information Technology Nanjing210000 China Data Security Key Laboratory Xinjiang Normal University Urumqi830054 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210000 China School of Computer and Information Anhui Polytechnic University Wuhu241000 China Co. LTD. Shanghai201100 China

Minimal failure-causing schema (MFS) may be affected by masking effects in software complex configuration space. A method for mining MFS based on combination testing and its testing results is proposed. The method firstly constructs a combination-fault tree (CFF-tree) based on the combined test suite and its results, extracts the frequent parameter-value-combinations from the tree as suspicious MFS and calculates their suspiciousness scores, and finally sorts them according to their suspicious scores. Though an iterative framework, MFS is repeatedly mined and checked by programmers until a certain stopping criterion is satisfied. Simulation experiments are used to validate the effectiveness of our method with and without masking effects. The experimental results show that the proposed method can mine MFS in the two scenarios and effectively reduce the number of additional test cases. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Iterative methods

Distributed quantum algorithm for discrete logarithm problem

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arXiv 2025年

作者： Li, Hao Qiu, Daowen Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China

The quantum algorithm with polynomial time for discrete logarithm problem proposed by Shor is one of the most significant quantum algorithms, but a large number of qubits may be required in the Noisy Intermediate-scale Quantum (NISQ) era. The main contributions of this paper are as follows: (1) A distributed quantum algorithm for discrete logarithm problem is designed, and its space complexity and success probability exhibit certain advantages to some extent;(2) The classical error correction technique proposed by Xiao and Qiu et al. is generalized by extending three bits to more than three bits. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

Feasible Basic Path Generation Based on Genetic Algorithm 6

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Feasible Basic Path Generation Based on Genetic Algorithm

6th International Conference on Dependable Systems and Their Applications, DSA 2019

作者： Xia, ChunYan Wang, XingYa Qiao, Li Zhang, Yan Ma, Baoying Shi, ChenYang School of Computer and Information Technology Mudanjiang Normal University Mudanjiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Health Management Mudanjiang Medical University Mudanjiang China

ISBN: (纸本)9781728160573

In this paper, a feasible basic path generation method based on a genetic algorithm is proposed, which combines a probabilistic statistical method with a genetic algorithm to generate a feasible basic path. First, conditional probability relations and a maximum likelihood estimation method are used to measure the correlation among the conditional statements and determine the mutually exclusive edges in the Decision-to-Decision Graph of the program under test. Then, the feasibility of generating a path is judged by the exclusive edge relation. Finally, a genetic algorithm is used to generate the feasible basic path set of the program under test. The experimental results based on six benchmark programs and three industrial programs show that, compared with the traditional method, this method can effectively improve the coverage rate of feasible basic path generation and reduce the time cost of software testing. © 2020 IEEE.

关键词： software testing

Feasible Basic Path Generation Based on Genetic Algorithm

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Feasible Basic Path Generation Based on Genetic Algorithm

International Conference on Dependable Systems and Their Applications (DSA)

作者： ChunYan Xia XingYa Wang Li Qiao Yan Zhang Baoying Ma ChenYang Shi School of Computer and Information Technology Mudanjiang Normal University Mudanjiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Health Management Mudanjiang Medical University Mudanjiang China

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

关键词： Correlation Genetic algorithms Image edge detection Probability Maximum likelihood estimation software testing

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

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arXiv 2021年

作者： Chen, Mingyang Zhang, Wen Zhu, Yushan Zhou, Hongting Yuan, Zonggang Xu, Changliang Chen, Huajun College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Huawei Technologies Co. Ltd China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China ZJU Hangzhou Global Scientific and Technological Innovation Center Alibaba-Zhejiang University Joint Institute of Frontier Technologies China

Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

A novel eemd-based privacy preserving approach for top-k snps query in genome-wide association studies 6

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A Novel eemd-based privacy preserving approach for top-k snp...

6th IEEE International Conference on Control science and Systems Engineering, ICCSSE 2020

作者： Xiaoyun, He Hao, Guo Hongsong, Chen Global Energy Interconnection Research Institute Co Ltd State Grid Key Laboratory of Information Network Security Beijing China University of Science and Technology Beijing School of Computer Communication Engineering Beijing China

ISBN: (纸本)9781728198460

Genome-wide association studies (GWAS) have been a popular method for querying Top-k most significant sets of singe-nucleotide polymorphism locations (SNPs) to discover the genetic factors of diseases. Doctors have been increasing interest to in querying SNPs that significantly associated to diseases. However, the queries and GWAS data share service bring great privacy breach risk to the patients. In traditional Laplace privacy preserving mechanism, privacy budget \varepsilonis different to be determined, the Laplace noise is added to original data only once, which causes randomness and potential security risk to privacy protection. A novel approach is proposed to implement the differential privacy preserving for Top-k SNPs query in GWAS. Ensemble Empirical Mode Decomposition(EEMD) is a noise-Assisted data analysis method, it is firstly proposed to be used as noise-Adding approach in privacy protection mechanism. In the EEMD approach, random white noise will be added into the SNPs querying results for many times. Moreover, the parameters of added white noise can be precisely controlled to achieve better effect. PriSTRAT software tool was redeveloped to evaluate EEMD-based privacy preserving approach, the dataset was generated by PLINK tools. Experimental results show that EEMD-based privacy preserving approach has achieved higher accuracy than Laplace mechanism under the same noise-Adding strength. The novel approach could be used to realize differential privacy preserving for Top-k SNPs query in GWAS to achieve the balance between privacy preserving and query accuracy. © 2020 IEEE.

关键词： Empirical mode decomposition

Multiple Feature Fusion-based Video Face Tracking for IoT Big Data

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arXiv 2021年

作者： Li, Tianping Liu, Zhifeng Qiao, Jianping Key Laboratory of Medical Physics and Image Processing in Shandong Province Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Physics and Electronics Jinan Shandong China School of Physics and Electronics Shandong Normal University JinanShandong China School of Physics and Electronics Shandong Normal University Jinan China

With the advancement of IoT and artificial intelligence technologies, and the need for rapid application growth in fields such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. In this paper, we propose a multi-feature fusion algorithm based on integral histograms and a real-time update tracking particle filtering module. First, edge and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the particle filtering algorithm to simplify the calculation steps of complex particles. Finally, the tracking window size is adjusted in real time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness. © 2021, CC BY.

关键词： Monte Carlo methods

Corrections to “Brain-Inspired Computing: A Systematic Survey and Future Trends”

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Proceedings of the IEEE 2025年第12期112卷 1850-1850页

作者： Guoqi Li Lei Deng Huajin Tang Gang Pan Yonghong Tian Kaushik Roy Wolfgang Maass Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Department of Precision Instrument Tsinghua University Beijing China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Department of Computer Science Peking University Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Computer Science Graz University of Technology Graz Austria

Presents corrections to the paper, (Corrections to “Brain-Inspired Computing: A Systematic Survey and Future Trends”).

关键词： Artificial intelligence Computational modeling Brain modeling Biological system modeling Surveys computer architecture Benchmark testing Bio-inspired computing Neuromorphic engineering Sensors Neural networks software tools

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

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arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Xu, Bingbing Wang, Chenxing Zeng, Liang School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas;ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network;iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing