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International Symposium on software Reliability engineering (ISSRE)

作者： Hongjun Wu Zhuo Zhang Shangwen Wang Yan Lei Bo Lin Yihao Qin Haoyu Zhang Xiaoguang Mao National University of Defense Technology Changsha China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Big Data & Software Engineering Chongqing University Chongqing China Defense Innovation Institute Academy of Military Sciences Beijing China

ISBN: (纸本)9781665425889

Smart contracts with natural economic attributes have been widely and rapidly developed in various fields. However, the bugs and vulnerabilities in smart contracts have brought huge economic losses, which has strengthened people's attention to the security issues of smart contracts. The immutability of smart contracts makes people more willing to conduct security checks before deploying smart contracts. Nonetheless, existing smart contract vulnerability detection techniques are far away from enough: static analysis approaches rely heavily on manually crafted heuristics which is difficult to reuse across different types of vulnerabilities while deep learning based approaches also have unique limitations. In this study, we propose a novel approach, Peculiar, which uses Pre-training technique for detection of smart contract vulnerabilities based on crucial data flow graph. Compared against the traditional data flow graph which is already utilized in existing approach, crucial data flow graph is less complex and does not bring an unnecessarily deep hierarchy, which makes the model easy to focus on the critical features. Moreover, we also involve pre-training technique in our model due to the dramatic improvements it has achieved on a variety of NLP tasks. Our empirical results show that Peculiar can achieve 91.80 % precision and 92.40 % recall in detecting reentrancy vulnerability, one of the most severe and common smart contract vulnerabilities, on 40,932 smart contract files, which is significantly better than the state-of-the-art methods (e.g., Smartcheck achieves 79.37% precision and 70.50% recall). Meanwhile, another experiment shows that Peculiar is more discerning to reentrancy vulnerability than existing approaches. The ablation experiment reveals that both crucial data flow graph and pre-trained model contribute significantly to the performances of Peculiar.

关键词： Economics Deep learning Smart contracts Neural networks Static analysis data models software reliability

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Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

arXiv

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Plataniotis, Konstantinos N. Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Edward S Rogers Sr. ECE Department University of Toronto TorontoM5S3G8 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://***/ynanwu/MABN. © 2023, CC BY.

关键词： Time domain analysis

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K-Core Decomposition on Super Large Graphs with Limited Resources

arXiv

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arXiv 2021年

作者： Gao, Shicheng Xu, Jie Li, Xiaosen Fu, Fangcheng Zhang, Wentao Ouyang, Wen Tao, Yangyu Cui, Bin School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Tencent Inc. China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Institute of Computational Social Science Peking University Qingdao China

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Simulation Research on Nonlinear Modeling and Control of Switched Reluctance Motor

Simulation Research on Nonlinear Modeling and Control of Swi...

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第32届中国控制与决策会议

作者： Luan Ru Zou Hongjian Guo Jinru The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

The model of a switched reluctance motor(SRM) directly influences it's performance and control ***,the doubly salient structure,switching power supply and seriously saturated magnetic circuit,make it difficult to accurately *** to solve this problem,this paper presents a new simulation method based on BP neural network to establish the current and torque models of a *** a great deal of current,flux linkage,torque numerical solution data obtained by a finite element software as the sample data,a kind of BP neural network is trained offline,and all the expected data sets,which form a table,are *** looking-up the table the nonlinear models of flux linkage and torque of the SRM are *** the basis of the nonlinear models,the current closed-loop control is formed by using the torque sharing function and the fuzzy switching angles to reduce the torque *** simulation results show that the proposed nonlinear models not only have a fast calculation speed,but also are high precision and can meet the requirements of real-time *** proposed control method can effectively reduce the torque ripple of the SRM.

关键词： Switched reluctance motor nonlinear modeling neural network fuzzy control torque sharing function

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Detail retention and enhancement for camouflaged object detection

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Neurocomputing 2025年

作者： Shangjing Chen Feng Xu Tao Zeng Xin Li Jiale Chen Xin Lyu College of Computer Science and Software Engineering Hohai University Nanjing 211100 China School of Computer Engineering Jiangsu Ocean University Lianyungang 222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing 211100 China

The objective of camouflaged object detection is to segment objects that share similar visual attributes with their backgrounds. Existing methods leverage the consistency and variability of the object to pinpoint its location; however, when presented with samples exhibiting intricate edges, the model tends to overlook critical details. Toward this issue, we analyzed the reasons from the perspective of processing basic features and found that roughly compressing the feature channel could lead to the loss of fine features at the initial stage of the pipeline. Therefore, we introduce a multi-scale feature preprocessing approach named Soft Channel Compression prior to feature fusion, which retains key cues by enhancing the visibility of fine-grained features. We then utilize self-attention to fuse the results of multi-scale feature sampling, synthesizing contextual information between extended details and the main object. Furthermore, we propose the Hierarchical Receptive Field Complementarity module, using a divide-and-conquer strategy to enhance discriminative features with different scales in object details. This facilitates the integration of information across various ranges by employing a hierarchical manner to expand the receptive field, thereby achieving complementary information between local areas. Extensive experimental results on benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

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An Empirical Study of Solidity Language Features

An Empirical Study of Solidity Language Features

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Ziyan Wang Xiangping Chen Xiaocong Zhou Yuan Huang Zibin Zheng Jiajing Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665478373

Solidity, like other programming languages, is evolving with many new features being introduced and deprecated features being removed. However, not every feature benefits developers and sometimes it may do more harm than good. In the hope of knowing the actual use of language features and helping improving them, we summarize 41 common features, discuss their pros and cons, and conduct an empirical study. We design an extensible feature analysis tool to analyze their distribution in 172,645 real-world smart contracts. We further analyze the reason why some features are popular. We invite readers for a case study to understand the usage patterns and how seemingly benign features produce subtle bugs. It turns out that some features like high-level function invocations, visibility, events, and assertions are popular while developers cares little about some other features like function modifier overloading. After summarizing our findings, we give suggestions to different people in Solidity community.

关键词： Computer languages Conferences Smart contracts Computer bugs software quality Reliability engineering software reliability

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Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhiying Xiong Qilin Fan Kai Wang Xiuhua Li Xu Zhang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University Chongqing China School of Computer Science and Technology Harbin Institute of Technology Weihai China Research Institute of Cyberspace Security Harbin Institute of Technology Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China Department of Computer Science University of Exeter Exeter UK

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.

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Animating turbulent fluid with a robust and efficient high-order advection method

Animating turbulent fluid with a robust and efficient high-o...

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作者： Zhu, Jian Li, Silong Cai, Ruichu Hao, Zhifeng Huang, Guoheng Sheng, Bin Wu, Enhua School of Computers Guangdong University of Technology Guangzhou China School of Mathematics and Big Data Foshan University Foshan China Department of Computer Science & Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

The accuracy of advection has a great influence on the visual effect of fluid simulation. Constrained interpolation profile (CIP) method has been an important advection scheme because of its third-order accuracy and the fact that it only needs to be performed over a compact stencil, but extending it to high-dimensional advection equations is not easy, because it involves complex calculations and large memory overheads, and is usually unstable. In this article, we propose a stable and efficient three-dimensional (3D) CIP scheme which can maintain high accuracy but requires low computation and memory cost. We first construct an efficient two-dimensional (2D) CIP scheme based on dimensional splitting and local Taylor expansions, and then propose an effective way to extend it for 3D applications without decreasing the computational accuracy or affecting the stability. The experimental results show the advantages of our method over the state-of-the-art advection schemes. © 2020 John Wiley & Sons, Ltd.

关键词： Advection

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