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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Ziyu Cui Wensheng Dou Qianwang Dai Jiansen Song Wei Wang Jun Wei Dan Ye State Key Lab of Computer Science Institute of Software Chinese Academy of Science China

ISBN: (纸本)9781450394758

Database Management Systems (DBMSs) utilize transactions to ensure the consistency and integrity of data. Incorrect transaction implementations in DBMSs can lead to severe consequences, e.g., incorrect database states and query results. Therefore, it is critical to ensure the reliability of transaction implementations. In this paper, we propose DT2, an approach for automatically testing transaction implementations in DBMSs. We first randomly generate a database and a group of concurrent transactions operating the database, which can support complex features in DBMSs, e.g., various database schemas and cross-table queries. We then leverage differential testing to compare transaction execution results on multiple DBMSs to find discrepancies. The non-determinism of concurrent transactions can affect the effectiveness of our method. Therefore, we propose a transaction test protocol to ensure the deterministic execution of concurrent transactions. We evaluate DT2 on three widely-used MySQL-compatible DBMSs: MySQL, MariaDB and TiDB. In total, we have detected 10 unique transaction bugs and 88 transaction-related compatibility issues from the observed discrepancies. Our empirical study on these compatibility issues shows that DBMSs suffer from various transaction-related compatibility issues, although they claim that they are compatible. These compatibility issues can also lead to serious consequences, e.g., inconsistent database states among DBMSs.

关键词： compatibility issue Database transaction isolation level differential testing

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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Disentangled Hypergraph Collaborative 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 collaborative 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： Collaborative filtering

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Simulation-based Validation for Autonomous Driving Systems

arXiv

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arXiv 2023年

作者： Li, Changwen Sifakis, Joseph Wang, Qiang Yan, Rongjie Zhang, Jian State Key Laboratory of Computer Science ISCAS Beijing China Univ. Grenoble Alpes CNRS Grenoble INP VERIMAG Grenoble France Academy of Military Sciences Beijing China

Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does indeed cover a large fraction of the relevant real-world situations. In addition, the validation must concern not only incidents, but also the detection of any type of potentially dangerous situation, such as traffic violations. We investigate a rigorous simulation and testing-based validation method for autonomous driving systems that integrates an existing industrial simulator and a formally defined testing environment. The environment includes a scenario generator that drives the simulation process and a monitor that checks at runtime the observed behavior of the system against a set of system properties to be validated. The validation method consists in extracting from the simulator a semantic model of the simulated system including a metric graph, which is a mathematical model of the environment in which the vehicles of the system evolve. The monitor can verify properties formalized in a first-order linear temporal logic and provide diagnostics explaining their non satisfaction. Instead of exploring the system behavior randomly as many simulators do, we propose a method to systematically generate sets of scenarios that cover potentially risky situations, especially for different types of junctions where specific traffic rules must be respected. We show that the systematic exploration of risky situations has uncovered many flaws in the real simulator that would have been very difficult to discover by a random exploration process. Copyright © 2023, The Authors. All rights reserved.

关键词： Formal specification

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Droflysim: A Collective Behavior Analysis and Evaluation Tool 10

Droflysim: A Collective Behavior Analysis and Evaluation Too...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhu, Rui Zeng, Yudian Yang, Chuying Wang, Bangchuan Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

The collective behaviors of various organisms exhibit complex patterns and statistical characteristics, especially evident within large populations. The complexity of these behaviors necessitates in-depth analysis of extensive trajectory data to reveal underlying dynamics and patterns. Current methods fall short in quantitative analysis and multi-angle assessment of collective behaviors, particularly in terms of the limitations of macroscopic indicators and the singularity of evaluation methods. This study introduces Droflysim, an efficient computational platform for analyzing and assessing large-scale trajectory data of collective behavior. Droflysim enhances data processing speed through parallel processing technology and integrates a variety of simulation models and multi-angle evaluation methods, overcoming the limitations of traditional tools. Notably, Droflysim emphasizes the statistical analysis of trajectories under environmental disturbances, providing a new perspective and deeper understanding of the complexity of collective behavior by establishing a trajectory fitting model. The design of Droflysim includes an intuitive interactive interface, modular architecture, and parallel processing capabilities, enhancing the efficiency and scalability of data analysis. By quantitatively analyzing trajectories, Droflysim lays the foundation for precise model calibration and authentic reproduction of behavior, offering a powerful tool for the study of collective behavior. © 2024 IEEE.

关键词： Collective Behavior Analysis Computational Modeling Environmental Disturbances Statistical Characteristics Trajectory Data Visualization

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Formally Verified C Code Generation from Hybrid Communicating Sequential Processes

arXiv

引用

arXiv 2024年

作者： Wang, Shuling Ji, Zekun Zhan, Bohua Xu, Xiong Gao, Qiang Zhan, Naijun State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Hybrid Communicating Sequential Processes (HCSP) is a formal model for hybrid systems, including primitives for evolution along an ordinary differential equation (ODE), communication, and parallel composition. Code generation is needed to convert HCSP models into code that can be executed in practice, and the correctness of this conversion is essential to ensure that the generated code accurately reflects the formal model. In this paper, we propose a code generation algorithm from HCSP to C with POSIX library for concurrency. The main difficulties include how to bridge the gap between the synchronized communication model in HCSP and the use of mutexes for synchronization in C, and how to discretize evolution along ODEs and support interrupt of ODE evolution by communication. To prove the correctness of code generation, we define a formal semantics for POSIX C, and build transition system models for both HCSP and C programs. We then define an approximate bisimulation relation between traces of transition systems, and show that under certain robustness conditions for HCSP, the generated C program is approximately bisimilar to the original model. Finally, we evaluate the code generation algorithm on a detailed model for automatic cruise control, showing its utility on real-world examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

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HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 310-319页

作者： Wang, Xumeng Chen, Wei Xia, Jiazhi Wen, Zhen Zhu, Rongchen Schreck, Tobias TMCC CS Nankai University China State Key Lab of CAD&CG Zhejiang University China Laboratory of Art and Archaeology Image Zhejiang University Ministry of Education China School of Computer Science and Engineering Central South University China Graz University of Technology Austria

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis. © 2022 IEEE.

关键词： Cluster analysis

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LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATISFACTION 11

LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATIS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Zenan Liu, Zehua Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Mathematics The University of Hong Kong Hong Kong Department of Computer Science Birkbeck University of London United Kingdom

Recent studies have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss ***, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the *** this paper, we present a new framework for learning with logical ***, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is *** further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training *** theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Signal encoding

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