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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pengju Tang Dawei Dai Kai Zou Xionghui Yang Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor’s clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet.

关键词： Representation learning Visualization Decision making Medical services Feature extraction Graph neural networks Convolutional neural networks Osteoarthritis Diagnostic radiography Diseases

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TactCLNet: Tactile Continual Learning Network Based on Generative Replay for Object Hardness Recognition

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10045-10055页

作者： Liu, Yiwen Yi, Zhengkun Fang, Senlin Zhang, Yupo Wan, Feng Yang, Zhi-Xin Lu, Xu Wu, Xinyu Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China City Univ Macau Fac Data Sci Macau Peoples R China Univ Macau Fac Sci & Technol Ctr Cognit & Brain Sci Ctr Artificial Intelligence & RobotDept Elect & C Macau Peoples R China Univ Macau Inst Collaborat Innovat Taipa Macao Peoples R China Univ Macau Ctr Artificial Intelligence & Robot Dept Electromech Engn State Key Lab Internet Things Smart City SAR Macau Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China Guangdong Polytech Normal Univ Guangdong Prov Key Lab Intellectual Property & Big Guangzhou 510665 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen 518055 Peoples R China

Currently, deep neural networks can be extremely effective in robotic tactile perception. However, a major challenge is to solve the problem of continual learning of robotic tactile perception in an open and dynamic environment. In this paper, we propose a novel continual learning method for the domian incremental learning task in the field of tactile perception. To be specific, we introduce a morphology-specific variational autoencoders which can mitigate catastrophic forgetting by generating pseudo-samples for training in the continual learning process. We integrate the generative model and the discriminative model into one model, which reduces the size of model and improves the continual learning ability. In addition, considering the ordinal information between the hardness levels, we propose to add conditional information to the model and introduce a modified loss function to combine the latent value with the hardness information, which improves the continual learning performance by controlling the distribution and quality of pseudo-sample generation. Following this, we designed a tactile robot experiment, collected hardness data, and tested our model on this object hardness recognition task. We show experimentally that, after training, the model can still maintain the accuracy of more than 94% after learning three tasks in terms. Note to Practitioners-In the field of robotics tactile perception, the issue of continual learning in robots is a crucial problem that urgently requires resolution. We hope robots to effectively engage in continual learning across multiple tasks, ensuring the acquisition of new knowledge while mitigating the risk of forgetting previously acquired knowledge. In this paper, we propose a novel continual learning method for the domian incremental learning task. we introduce a morphology-specific variational autoencoders based on replaying pseudo-samples during continual learning process which reduces the size of model and improves the cont

关键词： Continuing education robots data models Accuracy Training Autoencoders intelligent systems Torque Incremental learning Computational modeling Continual learning robotic tactile perception object hardness recognition ordinal network

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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DASCDC: Dual Attention-based Sparse Common Deep Clustering Method 9

DASCDC: Dual Attention-based Sparse Common Deep Clustering M...

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9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Dong, Yao Shi, Jin Deng, Yahan Dong, Yongfeng Wang, Ziqiu School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China Data Driven Industrial Intelligent Hebei Engineering Research Center Tianjin300401 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator's performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity. © 2022 IEEE.

关键词： Cluster analysis

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Hybrid Pruning Method Based on Convolutional Neural Network Sensitivity and Statistical Threshold 2

Hybrid Pruning Method Based on Convolutional Neural Network ...

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2nd International Conference on Computer, big data and Artificial Intelligence, ICCBDAI 2021

作者： Guo, Changyi Li, Ping School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha410114 China

The hybrid pruning algorithm can not only ensure the precision of the network, but also achieve a good balance between pruning ratio and computation. However, traditional pruning algorithms use coarse-grained or fine-grained pruning networks, which have the tradeoff problem between pruning rate and computation amount. To this end, this paper presents. A hybrid pruning method of sensitivity and statistical threshold. Firstly, coarse-grained pruning is carried out on the network, and a fast sensitivity test is conducted on the convolutional layer of the network to determine the channels that need pruning within the tolerance range of network precision decline. Then, fine-grained pruning is performed on the network. Count the weights of the pruned network, calculate the thresholds of the weights of each layer, and delete the weights less than the thresholds so as to further reduce the size of the network and the amount of calculation. Hybrid pruning performs very well in AlexNet and Resnet networks. In particular, the method proposed in this paper is used in CIFAR-10 dataset, and the compression of FLOPs is 60%, while the compression of parameter number is nearly 80%. Compared with single pruning method, Hybrid pruning is better. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolution

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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