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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Jue Guan, Naiyang Huang, Xuhui Luo, Zhigang Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Inst Software Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Techonoly Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479986965

Nonnegative matrix factorization (NMF) is a powerful technique for dimensionality reduction. Conventional NMF algorithms usually keep the matrices W and H nonnegative while iterating. However, to get the NMF of a matrix, it's unnecessary to force the temporary solutions in iterations nonnegative. In this paper, we propose a two-staged approach for NMF. At the relaxation stage, the nonnegative constraint of temporary solutions is relaxed and a real valued matrix factorization is generated. At the constraint stage, the real valued matrix factorization is transformed to a nonnegative matrix factorization by an invertible linear transformation. Based on this approach, we study on exact nonnegative matrix factorization when rank=2. We proved that, given two real valued matrices of rank=2, there exists an invertible linear transformation which can transform the real valued matrices to nonnegative matrices with their product stable. We propose an algorithm to find out the transformation. When rank is higher than 2, this kind of transformation may not exist. In the experiments, it's showed that this approach can reach a nonnegative matrix factorization with lower reconstruction error than conventional methods, and the technique for rank=2 exact NMF works well.

关键词： nonnegative matrix factorization constraint-relaxation exact rank-2 NMF linear transformation

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Symmetric Non-negative Matrix Factorization Based Link Partition Method for Overlapping Community Detection

Symmetric Non-negative Matrix Factorization Based Link Parti...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Zhang, Xiang Guan, Naiyang Zhang, Wenju Huang, Xuhui Wu, Shuyi Luo, Zhigang Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Inst Software Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479986965

Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency. In particular, SNMF-Link represents data in a lower-dimensional space spanned by the node-link incidence matrix. By solving a lighter SNMF problem, SNMF-Link learns the clustering indicators of each links. Since traditional multiplicative update rule (MUR) based optimization algorithm for SNMF suffers from slow convergence, we applied the augmented Lagrangian method (ALM) to efficiently optimize SNMF. Experimental results show that SNMF-Link is much more efficient than the representative clustering algorithms without reducing the OCD performance.

关键词： Symmetric Non-negative Matrix Factorization (SNMF) Link Partition Overlapping Community Detection (OCD) Augmented Lagrangian Method (ALM)

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Plumber: Boosting the Propagation of Vulnerability Fixes in the npm Ecosystem

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IEEE TRANSACTIONS ON software ENGINEERING 2023年第5期49卷 3155-3181页

作者： Wang, Ying Sun, Peng Pei, Lin Yu, Yue Xu, Chang Cheung, Shing-Chi Yu, Hai Zhu, Zhiliang Northeasthern Univ Software Coll Boston MA 02115 USA Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Northeasthern Univ Software Coll Shenyang 110819 Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Hong Kong Univ Sci & Technol Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou 511458 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Key Lab Data Analyt & Optimizat Smart Ind Shenyang 110819 Peoples R China Northeastern Univ Software Coll Shenyang 110819 Peoples R China

Vulnerabilities are known reported security threats that affect a large amount of packages in the npm ecosystem. To mitigate these security threats, the open-source community strongly suggests vulnerable packages to timely publish vulnerability fixes and recommends affected packages to update their dependencies. However, there are still serious lags in the propagation of vulnerability fixes in the ecosystem. In our preliminary study on the latest versions of 356,283 active npm packages, we found that 20.0% of them can still introduce vulnerabilities via direct or transitive dependencies although the involved vulnerable packages have already published fix versions for over a year. Prior study by (Chinthanet et al. 2021) lays the groundwork for research on how to mitigate propagation lags of vulnerability fixes in an ecosystem. They conducted an empirical investigation to identify lags that might occur between the vulnerable package release and its fixing release. They found that factors such as the branch upon which a fix landed and the severity of the vulnerability had a small effect on its propagation trajectory throughout the ecosystem. To ensure quick adoption and propagation of a release that contains the fix, they gave several actionable advice to developers and researchers. However, it is still an open question how to design an effective technique to accelerate the propagation of vulnerability fixes. Motivated by this problem, in this paper, we conducted an empirical study to learn the scale of packages that block the propagation of vulnerability fixes in the ecosystem and investigate their evolution characteristics. Furthermore, we distilled the remediation strategies that have better effects on mitigating the fix propagation lags. Leveraging our empirical findings, we propose an ecosystem-level technique, Plumber, for deriving feasible remediation strategies to boost the propagation of vulnerability fixes. To precisely diagnose the causes of fix propagation

关键词： Empirical study npm ecosystem vulnerable dependencies

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Harmonizing Efficiency and Practicability: Optimizing Resource Utilization in Serverless Computing with JIAGU

Harmonizing Efficiency and Practicability: Optimizing Resour...

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USENIX Annual Technical Conference

作者： Liu, Qingyuan Yang, Yanning Du, Dong Xia, Yubin Zhang, Ping Feng, Jia Larus, James R. Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst SEIEE Shanghai Peoples R China Minist Educ Engn Res Ctr Domain specif Operating Syst Beijing Peoples R China Chinese Acad Sci Key Lab Syst Software Beijing Peoples R China Huawei Cloud Shenzhen Peoples R China Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781939133410

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces JIAGU to harmonize efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, dual-staged scaling achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubemetes) while maintaining QoS, and 81.0%-93.7% lower scheduling costs and a 57.4%-69.3% reduction in cold start latency compared to existing QoS-aware schedulers.

关键词： Efficiency

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Accelerating aerodynamic design optimization based on graph convolutional neural network

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第1期35卷 2450007-2450007页

作者： Li, Tiejun Yan, Junjun Chen, Xinhai Wang, Zhichao Zhang, Qingyang Zhou, Enqiang Gong, Chunye Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Hunan Peoples R China

Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network (GCN), a deep learning method based on artificial neural networks (ANNs). In this paper, we propose a novel GCN-based aerodynamic design optimization acceleration framework, GCN-based aerodynamic design optimization acceleration framework. The framework significantly improves processing efficiency by optimizing data flow and data representation. We also introduce a network model called GCN4CFD that uses the GCF framework to create a compact data representation of the flow field and an encoder-decoder structure to extract features. This approach enables the model to learn underlying physical laws in a space-time efficient manner. We then evaluate the proposed method on an airfoil aerodynamic design optimization task and show that GCN4CFD provides a significant speedup compared to traditional CFD solvers while maintaining accuracy. Our experimental results demonstrate the robustness of the proposed framework and network model, achieving a speedup average of 3.0 x.

关键词： Computational fluid dynamics aerodynamic design optimization deep learning graph convolutional neural network

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Development of a general framework of resonance self-shielding treatment for broad-spectrum reactor lattice physics calculation

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NUCLEAR ENGINEERING AND TECHNOLOGY 2024年第10期56卷 4335-4354页

作者： Zhang, Jinchao Zhang, Qian Zou, Hang Yu, Jialei Cao, Wei Wu, Shifu Qin, Shuai Zhao, Qiang Gilad, Erez Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China Zhejiang Univ Zhejiang Inst Modern Phys Dept Phys Lab Adv Nucl Energy Theory & Applicat Hangzhou 310027 Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Ben Gurion Univ Negev Unit Nucl Engn POB 653 IL-8410501 Beer Sheva Israel

Some core designs integrate high-enriched fuel and moderator materials to enhance neutron utilization. This combination results in a broad spectrum within the system, posing challenges in resonance calculation. This paper introduces a general framework to realize resonance self-shielding treatment in broad-spectrum fuel lattice problems. The framework consists of three components. First, a new energy group structure is devised to support resonance calculation in the entire energy range and capture spectral transition and thermalization effects during eigenvalue calculation. Second, the subgroup method based on narrow approximation is selected as a universal method to perform resonance calculation. Finally, transport equations for each fissionable region are solved for neutron flux to collapse the fission spectrum. The proposed method is verified against fast, intermediate, and thermal spectrum pin cell problems and an assembly problem featuring a fast-thermal coupled spectrum. Numerical results affirm the accuracy of the proposed method in handling these scenarios, with eigenvalue errors below 154 pcm for pin cell problems and 106 pcm for the assembly problem. The verification results revealed that the proposed method enables accurate resonance self-shielding treatment for broad-spectrum problems.

关键词： Broad-spectrum Fission spectrum Group structure Subgroup method

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A data-free Kolmogorov-Arnold Network-based method for structured mesh generation

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PHYSICS OF FLUIDS 2024年第11期36卷

作者： Peng, Jiaming Chen, Xinhai Zhang, Qingyang Deng, Liang Shen, Liming Liu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China China Aerodynam Res & Dev Ctr Mianyang Peoples R China

Mesh generation is a critical but time-consuming process for stable and accurate numerical simulations. Although multi-layer perceptron-based meshing methods can be effective, they suffer from slow training convergence and heavy reliance on prior datasets. To overcome these problems, we propose the Kolmogorov-Arnold Network-based meshing network, an efficient data-free method for structured mesh generation. The proposed method takes the meshing task as an optimization problem and embeds meshing-related differential equations into the loss function of Kolmogorov-Arnold Networks. It employs two parts to generate meshes efficiently. The Kolmogorov-Arnold Network part introduces learnable activation functions on the edges of the network, which enables the network to learn meshing rules between parametric and computational domains. The physics-informed learning part provides meshing-related information to guide the network training. Finally, the proposed method can produce high-quality structured meshes with a user-defined number of quadrilateral or hexahedral cells through feed-forward prediction. Experiments on different geometries show that the proposed method achieves up to three orders of magnitude improvement in meshing efficiency compared to traditional methods. It also outperforms state-of-the-art multi-layer perceptron-based methods, yielding high-quality meshes in both two-dimensional and three-dimensional cases without prepared data.

关键词： Mesh generation

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An optimized design of CAN FD for automotive cyber-physical systems

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 81卷 101-111页

作者： Xie Yong Zeng Gang Kurachi, Ryo Xie Guoqi Dou Yong Zhou Zhili Xiamen Univ Technol Coll Comp & Informat Engn Xiamen Peoples R China Natl Univ Defens Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Nagoya Univ Grad Sch Informat Sci Nagoya Aichi Japan Nagoya Univ Grad Sch Informat Sci Nagoya Aichi Japan Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

CAN with flexible data rate (CAN FD) is considered the next generation in-vehicle network standard for automotive cyber-physical systems. CAN FD supports a data phase bit-rate of up to 10 Mbps and message payload of up to 64 bytes. However, the substantial differences regarding the allowed message payloads and the heterogeneity of the signal periods indicate the need for a systematic design method to fully utilize its large transmission bandwidth. We propose an optimized design method for CAN FD to minimize bandwidth utilization while meeting the signal timing constraint. First, two slack evaluation metrics are defined for the quantitative analysis of the potential packing choices. Based on these metrics, we propose a clustering-based signal packing algorithm, and the schedulability of the signals and the packed messages are both verified. The proposed method is compared with other design methods proposed for both CAN and CAN FD. The experimental results demonstrated that our method is the most bandwidth efficient and can meet the timing constraint simultaneously.

关键词： Automotive cyber-physical systems Bandwidth utilization CAN FD Design method Signal clustering Schedulability

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Contrastive learning of user behavior sequence for context-aware document ranking

arXiv

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arXiv 2021年

作者： Zhu, Yutao Nie, Jian-Yun Dou, Zhicheng Ma, Zhengyi Zhang, Xinyu Du, Pan Zuo, Xiaochen Jiang, Hao Université de Montréal MontréalQC Canada Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Zhejiang Hangzhou China

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking. Copyright © 2021, The Authors. All rights reserved.

关键词： Search engines

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Amelioration of Teaching Strategies by Exploring Code Quality and Submission Behavior

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IEEE ACCESS 2019年 7卷 152744-152754页

作者： Bai, Yu Wang, Tao Wang, Huaimin Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Key Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Changsha Peoples R China

Online learning platforms provide an opportunity to better understand students weaknesses by tracking both their learning behavior and knowledge. This information can then be used to assist teachers in making instructional decisions and to further guide those who are at risk of failure. In this paper, we tracked student learning data from a C programming course over a whole semester of their freshman year via the Trustie platform. A total of 17,854 code submissions were collected. We then used CppCheck, SonarQube and Trustie to capture the quality characteristics and submission characteristics of the code, including lineOfCode, cyclomaticComplexity, codeSmell, syntacticError, averageScore, submission, and logicError, and analyzed the impact of code quality on the assignment work results. Several factors were discovered that we believe can help teachers to develop more effective teaching strategies.

关键词： Programming Computer languages Databases Syntactics Data collection Complexity theory Education Engineering students code quality programming course teaching strategy

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