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International Symposium on software Reliability engineering (ISSRE)

作者： Jun Liu Jiwei Yan Yuanyuan Xie Jun Yan Jian Zhang Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences (UCAS) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers’ programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%.

关键词： Measurement Codes Large language models Static analysis Production Maintenance engineering Syntactics Programming software software reliability

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Towards More Accurate Tiny Object Tracking: Benchmark and Algorithm

Towards More Accurate Tiny Object Tracking: Benchmark and Al...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jizhe Yu Yu Liu Hongkui Wei Kaiping Xu DUT School of Software Technology & DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Despite the significant progress in tiny object tracking algorithms based on convolutional neural networks, the performance falls short of the ideal level due to dataset challenges like limited scale, limited match backdrop, low match popularity, and low-resolution(720p) images. The lack of specialized large-scale tiny object tracking datasets leads data-hungry tracking algorithms to rely on small and saturated datasets from TrackNet. This paper propels the development of tiny object tracking and contributes the first large-scale dataset, Badminton100K, for tiny ball tracking. We provide over 100K annotations covering over 200 videos from 12 professional match events. Our dataset covers high-resolution(1080p) match videos in broad and diverse contexts. Moreover, considering the fast-moving tiny balls in videos often leads to challenges such as blur, afterimage, overlap, and disappearance. As another contribution, we also propose a novel Multi-stage and Multi-scale Fusion Network (MMFNet), which can extract local high-resolution details in the shallow stages and capture global semantic information in the deep stages, addressing these challenging tasks through multiple stages multi-scale feature fusion, enabling accurate identification and localization of tiny balls. Extensive experiments show that our method achieves unprecedented state-of-the-art tracking performance on Badminton100K. The dataset and the algorithm code are available at https://***/Gi-gigi/MMFNet.

关键词： Location awareness Accuracy Image resolution Semantics Transfer learning Propulsion Feature extraction

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization 13

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Yongfeng Zhang, Yuze Ma, Lijia Ji, Junkai Liu, Dugang Leung, Victor C. M. Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518123 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

ISBN: (纸本)9798350308365

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information engineering, Big Data, and Computer Technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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IFKG: An Intelligent Fault Diagnosis Tool with Knowledge Graph and Generative LLM

IFKG: An Intelligent Fault Diagnosis Tool with Knowledge Gra...

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

作者： Xixuan Yang Tong Jia Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

The development of effective diagnostic methodolo-gies for software system failures is of paramount importance. Traditional methods, which rely on specialized terminology and intricate reasoning, require users to have a technical background, resulting in reduced flexibility and decreased user-friendliness. With the rise of generative large language models, optimizing human-computer interaction has become a critical area of focus. Additionally, the inherent intelligence and extensive knowledge of large language models make them both easy and effective to employ for fault diagnosis assistance. We introduce IFKG, an advanced tool for diagnosing software system failures. IFKG integrates generative large language models with knowledge graphs, employing natural language interactions to implement fault detection and deliver solutions. IFKG enables users to upload descriptive problems, retrieve pertinent information from the knowledge graph, and present diagnostic results in natural language. Our accuracy assessments across diverse software system failures indicate that the IFKG provides targeted and actionable recommendations, effectively assisting users in ad-dressing a range of software system issues. The tool is available on GitHub at https://***/mako-xxlIFKG, and the demo video can be found on YouTube: https://***2vgZm2hk.

关键词： Fault diagnosis Human computer interaction Video on demand Terminology Large language models Natural languages Knowledge graphs software systems Web sites software development management

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

Distributionally Robust Optimization for Computation Offload...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Northwestern Polytechnical University School of Software Xi'an710072 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024 IEEE.

关键词： Computation offloading

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Unusually high corrosion resistance in Mo_(x)CrNiCo medium entropy alloy enhanced by acidity in aqueous solution

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Journal of Materials Science & Technology 2023年第8期139卷 59-68页

作者： S.Shuang G.J.Lyu D.Chung X.Z.Wang X.Gao H.H.Mao W.P.Li Q.F.He B.S.Guo X.Y.Zhong Y.J.Wang Y.Yang Department of Mechanical Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Solidification Processing Center of Advanced Lubrication and Seal MaterialsNorthwestern Polytechnical UniversityXi’an 710072China Unit of Structures Department of Materials Science and EngineeringKTHStockholm SE-10044Sweden Thermo-Calc Software Råsundav.18SolnaSE-16767Sweden Department of Materials Science and Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina Institute of Advanced Wear&Corrosion Resistant and Functional Materials Jinan UniversityGuangzhou 510632China Department of Advanced Design and System Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina

High corrosion resistance of alloys is essential for their structural applications;however,most alloys suffer from degradation of their corrosion resistance with the increasing acidity of their ***,we developed a series of medium-entropy alloys(MEAs)in this work,which ex-hibit high strength,superior fracture toughness and ultra-high corrosion resistance,outperforming the variety of corrosion resistant alloys hitherto *** interestingly,our MEAs exhibit an unusual anti-corrosion behavior and their corrosion resistance increases with acidity in Cl−containing *** extensive thermodynamic calculations,density functional theory(DFT)simulations and experi-ments,we reveal that the unusual anti-corrosion behavior of our MEAs can be attributed to their surface chemical complexity,which facilitates the physio-chemical-absorption of H_(2)O and O_(2)and thus the rapid formation of metastable medium entropy passive films that contain the lowest amount of defects,as compared to the passive films on conventional alloys reported in the literature.

关键词： Corrosion Passivation Alloy design Multi-principal element alloys Medium-entropy alloys

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