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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Research on sentiment recognition in online shopping reviews using an integrated BERT and BiLSTM approach 4

Research on sentiment recognition in online shopping reviews...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Huang, Jianrong Li, Guohua Li, Guowei Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510681767

This study explores a method integrating BERT and BiLSTM technologies aimed at enhancing the accuracy of sentiment recognition in online shopping reviews. Through detailed experimental validation, the fused model significantly surpasses traditional approaches that solely rely on either BERT or BiLSTM across various key performance metrics. This work not only confirms the potential of deep learning techniques in understanding complex textual emotional expressions but also provides new research directions and practical application possibilities for sentiment analysis of social media data. © 2024 SPIE.

关键词： Deep learning

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Defect-Introducing Defect Prediction Testing 24

Defect-Introducing Defect Prediction Testing

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Xu, Fanjiang Sun, Zeyu Institute of Software Chinese Academy of Sciences National Key Laboratory of Space Integrated Information System China State Key Laboratory of Intelligent Game China

ISBN: (纸本)9798350365658

Machine learning (ML) innovations have significantly advanced the field of defect prediction in software development, offering the potential for automated error detection across vast codebases. These advancements promise to elevate software quality assurance by improving reliability and security. However, despite their benefits, existing defect prediction models are not without limitations, often failing to identify vulnerabilities or inaccurately flagging non-defective code segments as problematic. To overcome these shortcomings, this study introduces DPTester, a novel approach that diverges from traditional label-preserving transformations, aiming to more accurately mimic the complexities of real-world software development. Unlike previous methods, DPTester intentionally injects defects into source code to transform its semantics fundamentally. This methodology tests a defect prediction model's ability to identify and anticipate defects amidst semantic alterations. DPTester employs a two-step framework, consisting of automated test input and oracle generation. The initial phase generates test inputs by modifying conditional statements to induce potential defects. The subsequent phase evaluates the defect prediction models' performance using these inputs. A failure by a model to detect DPTester-introduced defects is considered an issue. Our evaluation of DPTester on two prominent defect prediction models, CodeT5+ and CodeBERT, involved the generation of 222,112 test inputs. This process demonstrated a 99% success rate in creating valid test scenarios. However, it exposed significant weaknesses in both models: CodeT5+ and CodeBERT's accuracy plummeted to 43% and 30%, respectively, when assessed against DPTester's complex test scenarios. Moreover, our analysis uncovered 144,104 and 98,574 prediction inconsistencies in CodeBERT and CodeT5+, respectively, underscoring the urgent need for model improvements to adeptly handle sophisticated testing landscapes. Furthermore

关键词： Prediction models

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Simultaneous Bulk-and Surface-initiated Living Polymerization Studied with a Heterogeneous Stochastic Reaction Model

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Chinese Journal of Polymer Science 2024年第3期42卷 364-372,I0008页

作者： Jia-Shu Ma Zhi-Ning Huang Jia-Hao Li Bang-Ping Jiang Yan-Da Liao Shi-Chen Ji Xing-Can Shen State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources(Ministry of Education of China)School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering&School of Software Guangxi Normal UniversityGuilin 541004China

To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers resemble those of the bulk-initiated polymers. Through a Monte Carlo simulation using a heterogeneous stochastic reaction model, it was discovered that the bulk-initiated polymers exhibit a higher molecular weight and a lower dispersity than the corresponding surface-initiated polymers, which indicates that the equivalent assumption is invalid. Furthermore, the molecular weight distributions of the two types of polymers are also different, suggesting different polymerization mechanisms. The results can be simply explained by the heterogeneous distributions of reactants in the system. This study is helpful to better understand surface-initiated polymerization.

关键词： Surface-initiated polymerization Polymer brush Stochastic reaction model Heterogeneous polymerization Simultaneous polymerization

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 1

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (数字)9783031262845

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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Reliable image classification based on evidence and GCN

Reliable image classification based on evidence and GCN

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： He, Zhonghai Xu, Xiang Lian, Zhuming Luo, Yu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Intelligent Terminal Key Laboratory of Sichuan Province Yibin China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

With good classification performance, digital medical image methods based on deep learning have received more and more attention. However, the problem of these methods is that the black-box characteristic of deep learning makes it difficult to explain the model and prediction results. In order to improve the interpretability and reliability of medical image classification tasks, this paper designs a network structure that introduces eliminating uncertainty in GCN feedback by using evidence theory and subjective logic. The diagnosis experiments of COVID-19 images show that the model can effectively improve the recognition accuracy of COVID-19 patients, and has good reliability. © 2024 SPIE.

关键词： COVID-19

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GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

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BTDS:Blockchain-Enabled Trusted Vehicle Violation Detection by Self-Supervision

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IEEE Internet of Things Journal 2025年第12期12卷 20156-20173页

作者： Zhu, Rui Hu, Shengnan Helal, Sumi Song, Junqiao Wang, Jishu Chen, Yeting Yunnan University School of Software Kunming650091 China Key Laboratory in Software Engineering Yunnan Province Kunming650091 China University of Florida GainesvilleFL32611 United States Yunnan Normal University School of Economics and Management Kunming650500 China

In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this paper, we propose a blockchain-based self-supervision model for vehicle violations that utilizes inter-vehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles. © 2014 IEEE.

关键词： Blockchain

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Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

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Chinese Journal of Electronics 2024年第1期33卷 112-127页

作者： Bowen ZHANG Zhao JIN Hanpin WANG Yongzhi CAO Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University School of Computer and Artificial Intelligence Zhengzhou University School of Computer Science and Cyber Engineering Guangzhou University

Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

关键词： Formal verification Separation logic Cloud block storage Verification tool Coq

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Exploring key Point Analysis with Pairwise Generation and Graph Partitioning

Exploring Key Point Analysis with Pairwise Generation and Gr...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Li, Xiao Jiang, Yong Huang, Shen Xie, Pengjun Cheng, Gong Huang, Fei State Key Laboratory for Novel Software Technology Nanjing University China Institute for Intelligent Computing Alibaba Group China

ISBN: (纸本)9798891761148

key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets. © 2024 Association for Computational Linguistics.

关键词： Graph theory

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