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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Yang, Jifeng Zhou, Guang-Guan Chen, Yang Southeast University School of Software Nanjing China Southeast University School of Biological Science and Medical Engineering Nanjing China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363173

Benefiting from the development of transformer-based networks in natural language processing and computer vision, researches on 3D point cloud understanding have made great progress in recent years. However, existing works often focus solely on aggregating local features or construct global dependencies through methods that significantly increase memory and computation complexity. Addressing these challenges, we present Point Dual-Key Transformer(PDKT), a novel and straightforward end-to-end network architecture. To expand the receptive fields and preserve the local geometric structural information, we introduce an efficient dual-key cross attention mechanism, which builds upon standard cross attention to facilitate global feature capture across the network without neglecting local detail. Furthermore, we adopt a learnable operator to automatically integrate local and global branches. Extensive experiment results demonstrate the effectiveness and superiority of our method on point cloud understanding tasks. © 2024 IEEE.

关键词： Network architecture

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DualCL: Principled Supervised Contrastive Learning as Mutual Information Maximization for Text Classification 24

DualCL: Principled Supervised Contrastive Learning as Mutual...

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33rd ACM Web Conference, WWW 2024

作者： Junfan, Chen Zhang, Richong Zheng, Yaowei Chen, Qianben Hu, Chunming Mao, Yongyi SKLSDE School of Software Beihang University Beijing China SKLSDE School of Computer Science Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9798400701719

Text classification is a fundamental task in web content mining. Although the existing supervised contrastive learning (SCL) approach combined with pre-trained language models (PLMs) has achieved leading performance in text classification, it lacks fundamental principles. Theoretically motivated by a derived lower bound of mutual information maximization, we propose a dual contrastive learning framework DualCL that satisfies three properties, i.e., parameter-free, augmentation-easy and label-aware. DualCL generates classifier parameters from the PLM and simultaneously uses them for classification and as augmented views of the input text for supervised contrastive learning. Extensive experiments conclusively demonstrate that DualCL excels in learning superior text representations and consistently outperforms baseline models. © 2024 ACM.

关键词： Classification (of information)

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Research on Obstacle Avoidance Algorithm of UAVs Reinforcement Learning based on Vision 5

Research on Obstacle Avoidance Algorithm of UAVs Reinforceme...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Gao, Yuan Shi, Tianwei Xu, Teng Ding, Jianbang Jing, Rongchen University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350309034

In recent years, the combination of deep reinforcement learning and unmanned aerial vehicle (UAV) to achieve autonomous flight has been a hot research field. In this paper, an obstacle avoidance navigation algorithm (PA-SAC) based on priority experience buffer pool (PEBP), attention, and Soft-Actor-Critic (SAC) is proposed to solve the continuous space obstacle avoidance navigation problem of UAVs by using a deep reinforcement learning algorithm. In the two simulation experiments, the success rate of the PA-SAC algorithm was 95.6% in the known environment and 73% in the unknown environment. These results demonstrate that the PA-SAC algorithm can achieve autonomous obstacle avoidance for UAVs with deep image input. © 2023 IEEE.

关键词： Attention component Obstacle Avoidance PEBP UAVs

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A Multi-Metric Ranking with Label Correlations Approach for Library Migration Recommendations 31

A Multi-Metric Ranking with Label Correlations Approach for ...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Zhang, Jiancheng Luo, Qin Wu, Peng School of Computer Science and Software Engineering SouthWest Petroleum University Chengdu China School of Information and Engineering Sichuan Tourism University Chengdu China

ISBN: (纸本)9798350330663

While third-party libraries provide benefit to software systems, they also bring unique challenges. It often happens that developers need to replace some already-used libraries with other functionality-equivalent libraries. However, it is not easy to find a relevant candidate from overwhelming libraries. Despite several approaches have been proposed to mine library migrations from historical data, the study of library recommendation from the perspective of both open-source projects and third-party libraries is lacking. Therefore, conducting such research may assist developer better select suitable third-party libraries. In this paper, we propose a multi-metric ranking with label correlations (MMRLC) algorithm, which can recommend libraries holistically from the both perspectives. Not only does it mine library migrations from existing software data, MMRLC further leverages label correlations of libraries in Maven Central Repository to make recommendations. To demonstrate the usefulness, three popular algorithms were conducted on a benchmark dataset for comparison. The results show that our approach can recommend libraries with precision @ 1 of 0.8454 and recall @20 of 0.9301. Moreover, to demonstrate the generality, we select 366 libraries and resort to TagWiki to generate the related library labels, and the results show that our approach still has comparable performance. © 2024 IEEE.

关键词： Libraries

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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State-of-the-Art Approach: Boosting IoT Sensor Network Security with Visual Secret Sharing 10

State-of-the-Art Approach: Boosting IoT Sensor Network Secur...

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10th IEEE Smart World Congress, SWC 2024

作者： Ali, Sijjad Wang, Jia Leung, Victor Chung Ming Bhatti, Uzair Aslam Bhatti, Mughair Aslam Shenzhen University College of Computer Science and Software Engineering Guangdong China Hainan University School of Information and Communication Engineering China

ISBN: (纸本)9798331520861

This research paper is based on the issue of protecting the sensor networks in the era of IoT because the data is very sensitive and huge, and is collected in resource-constrained environments. We put forward a new approach to the enhancement of the security of IoT sensor networks by applying VSS on visual data. Our approach is based on the implementation of the VSS approach which implies that no single node will contain full information about the visual input which will be divided among the nodes. Furthermore, this approach also increases the security of the data, and at the same time is appropriate for the IoT environments since it does not require a large amount of computational power and network bandwidth. In addition, we explain how this method can be used, discuss its features, and based on the data from the experiments, prove the effectiveness of the suggested approach. The results of the research show that data security and the ability to counteract attacks can be resist if VSS is used in systems of IoT sensor networks. © 2024 IEEE.

关键词： Information hiding

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HSM:Hand Surface Menu Solution for Interaction in VR 10

HSM:Hand Surface Menu Solution for Interaction in VR

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10th International Conference on Virtual Reality, ICVR 2024

作者： Zhang, Hailong Li, Lin Xue, Chang Wei, XiaoCai Zhou, Yin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology School of The Software Engineering Hefei China

ISBN: (纸本)9798350364231

The menu interaction methods in VR, such as floating menus, are still considered unnatural. A solution is proposed in this paper where menus are tightly attached to the user's palm. Firstly, use UV mapping technology to attach the menu to the palm. Secondly, optimal layout parameters for the hand surface menu were determined through user experiments. Finally, a dataset was built to train the interaction error detection model, aiming to improve interaction success rates under various hand poses in VR. Experimental results demonstrate that compared to other VR menu methods, HSM reduces the average interaction error rate by 30.1% and shortens the average completion time by 12.4% under the optimal hand pose. © 2024 IEEE.

关键词： Virtual environments

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