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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xiang, Jiayan Chen, Wanjun Wang, Yang Liang, Bowen Liu, Zihao Kang, Guosheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hnust Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350331684

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collab.rative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalab.lity. This paper proposes a light neural graph collab.rative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalab.lity over the baselines. © 2023 IEEE.

关键词： Collab.rative filtering

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Reach-avoid Controllers Synthesis for Safety Critical Systems

arXiv

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arXiv 2023年

作者： Xue, Bai State Key Lab. of Computer Science Institute of Software Cas Beijing China

In this paper we propose sufficient conditions to synthesizing reach-avoid controllers for deterministic systems modelled by ordinary differential equations and stochastic systems modeled by stochastic differential equations based on the notion of control guidance-barrier functions. We begin with considering deterministic systems. Given an open safe set, a target set and a nominal controller, we attempt to synthesize a reach-avoid controller, which modifies the nominal controller in a minimal way enforcing the reach-avoid objective, i.e., the system will enter the target set eventually while staying inside the safe set before the first target hitting time. Three control guidance-barrier functions are developed and three corresponding conditions for synthesizing reach-avoid controllers are constructed, which get progressively weaker and thus facilitate the optimal controller design. The first and second ones are termed exponential and asymptotic control guidance-barrier functions, which guarantee that every trajectory starting from the safe set will enter the target set respectively at an exponential rate and in an asymptotic way. However, it is observed that these two control guidance-barrier functions have one condition in common of enforcing invariance of its every positive sublevel set until the system enters the target set, which is strict and thus limits the space of admissible controllers. Consequently, a lax control guidance-barrier function is further developed such that only the safe set set is an invariance before the system enters the target set, expanding the space of admissible control inputs. Then, we extend the methodologies for deterministic systems to stochastic systems, which synthesize reach-avoid controllers in the probabilistic setting. Finally, several numerical examples demonstrate the performance of proposed methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Graph-based Multi-view Clustering for Web services 21

Graph-based Multi-view Clustering for Web services

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yang, Wang Zhenzhen, Yuan Guosheng, Kang Buqing, Cao Jianxun, Liu Yong, Xiao Hnust Xiangtan China Hunan University of Science and Technology Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering China

ISBN: (纸本)9798350329223

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process. © 2023 IEEE.

关键词： Web services

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Disentangled Hypergraph Collab.rative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collab.rative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collab.rative filtering

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Detecting Exception Handling Bugs in C++ Programs 23

Detecting Exception Handling Bugs in C++ Programs

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45th IEEE/ACM International Conference on software Engineering, ICSE 2023

作者： Zhang, Hao Luo, Ji Hu, Mengze Yan, Jun Zhang, Jian Qiu, Zongyan Institute of Software Cas China Univ. of Chinese Academy of Sciences State Key Lab. of Computer Science Beijing China Univ. of Chinese Academy of Sciences Tech. Center of Softw. Eng. Beijing China Hangzhou Institute for Advanced Study Univ. of Chinese Academy of Sciences State Key Lab. of Computer Science Beijing China School of Mathematical Sciences Peking University Beijing China

ISBN: (纸本)9781665457019

Exception handling is a mechanism in modern programming languages. Studies have shown that the exception handling code is error-prone. However, there is still limited research on detecting exception handling bugs, especially for C++ programs. To tackle the issue, we try to precisely represent the exception control flow in C++ programs and propose an analysis method that makes use of the control flow to detect such bugs. More specifically, we first extend control flow graph by introducing the concepts of five different kinds of basic blocks, and then modify the classic symbolic execution framework by extending the program state to a quadruple and properly processing try, throw and catch statements. Based on the above techniques, we develop a static analysis tool on the top of Clang Static Analyzer to detect exception handling bugs. We run our tool on projects with high stars from GitHub and find 36 exception handling bugs in 8 projects, with a precision of 84%. We compare our tool with four state-of-the-art static analysis tools (Cppcheck, Clang Static Analyzer, Facebook Infer and IKOS) on projects from GitHub and handmade benchmarks. On the GitHub projects, other tools are not able to detect any exception handling bugs found by our tool. On the handmade benchmarks, our tool has a significant higher recall. © 2023 IEEE.

关键词： Static analysis

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Bingshu Lin, Jia Zhuang, Xiaodong Zhang, Guanghui Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi'an China Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665410205

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

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Frontiers of computer science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial lab.r costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

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A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems

arXiv

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arXiv 2023年

作者： Xue, Bai State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a predefined finite time horizon, and the probability of reaching a set of specified states at a particular time instant. The barrier functions presented in this manuscript are developed either by relaxing a parabolic partial differential equation that characterizes the exact reachability probability or by applying the Grönwall's inequality. In comparison to the prevailing construction method, which relies on Doob's non-negative supermartingale inequality (or Ville's inequality), the proposed barrier functions provide stronger alternatives, complement existing methods, or fill gaps. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Dynamic ocean inverse modeling based on differentiable rendering

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Computational Visual Media 2024年第2期10卷 279-294页

作者： Xueguang Xie Yang Gao Fei Hou Aimin Hao Hong Qin State Key Lab of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Qingdao Research Institute Beihang UniversityQingdao 266100Chinaand Peng Cheng LabShenzhen 518000China SKLCS Institute of SoftwareChinese Academy of SciencesBeijing 100190Chinaand University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stony Brook University(SUNY at Stony Brook)Stony BrookNew York 11794-2424USA

Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and *** bridge the technical gap between virtual and real environments,we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans,taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised ***,we infer hierarchical geometry using two networks,which are optimized via the differentiable *** extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean ***,ocean dynamics can be evolved using the reconstructed wave *** extensive experiments,we verify that our new method yields satisfactory results for both geometry reconstruction and wave ***,the new framework has the inverse modeling potential to facilitate a host of graphics applications,such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.

关键词： inverse modeling surface reconstruction wave modeling ocean waves differentiable rendering(DR)

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