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arXiv 2022年

作者： Xu, Yuankun Huang, Dong Wang, Chang-Dong Lai, Jian-Huang College of Mathematics and Informatics South China Agricultural University China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs China School of Computer Science and Engineering Sun Yat-sen University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China

Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks. Despite the significant progress, the existing deep clustering works mostly utilize some distribution-based clustering loss, lacking the ability to unify representation learning and multi-scale structure learning. To address this, this paper presents a new deep clustering approach termed Image clustering with contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN), which bridges the gap between convolutional neural network (CNN) and graph convolutional network (GCN) as well as the gap between contrastive learning and multi-scale structure learning for the deep clustering task. Our framework consists of four main modules, namely, the CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster Structure Learning and Instance reconstruction Module (JC-SLIM), and the Multi-scale GCN module (M-GCN). Specifically, the backbone network with two weight-sharing views is utilized to learn the representations for the two augmented samples (from each image). The learned representations are then fed to ISM and JC-SLIM for joint instance-level and cluster-level contrastive learning, respectively, during which an auto-encoder in JC-SLIM is also pretrained to serve as a bridge to the M-GCN module. Further, to enforce multi-scale neighborhood structure learning, two streams of GCNs and the auto-encoder are simultaneously trained via (i) the layer-wise interaction with representation fusion and (ii) the joint self-adaptive learning. Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art. The code is available at https://***/xuyuankun631/IcicleGCN. © 2022, CC BY.

关键词： Convolutional neural networks

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Robot Tactile data Classification Method Using Spiking Neural Network

Robot Tactile Data Classification Method Using Spiking Neura...

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Chinese Automation Congress (CAC)

作者： Jing Yang Xiaoyang Ji Shaobo Li Hao Dong Tingqing Liu Xu Zhou Shuaizhen Yu State Key Laboratory of Public BigData Guizhou University School of Mechanical Engineering Guizhou University Guiyang China School of Computer Science and Technology Guizhou University State Key Laboratory of Public BigData Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China

ISBN: (纸本)9781665426480

The ability of tactile perception is one of the future development directions of robotics, and the research on the classification method of tactile data is to explore the concrete manifestation of its ability. Owing to its event-driven characteristics, the spiking neural network can better process event-based tactile sensing data, but the backpropagation of the network will fail because of the nondifferentiable influence of the propagation function. In this paper, a spiking-graph neural network model for tactile perception is improved, and various differentiable functions are used to approximate the propagation function of the neural network. Finally, the function that achieves the best performance is selected as the backpropagation function through experiments. Our model improves the classification accuracy of tactile data and simultaneously accelerates the convergence speed and reduces the convergence value. The experimental results of the tactile data set collected by the robot show that the performance of the improved model in this paper is better than the original model and classify household objects more accurately.

关键词： Backpropagation Adaptation models Automation Biological system modeling Neural networks Distributed databases Robot sensing systems

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Breast mass classification method based on convolutional neural networks

Breast mass classification method based on convolutional neu...

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3rd Asian Conference on Artificial Intelligence technology, ACAIT 2019

作者： Sun, Lilei Wang, Junqian Xu, Yong Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China Bio-Computing Research Centre Harbin Institute of Technology Shenzhen China

To classify the X-ray mammograms images as benign or malignant is a long-standing unresolved problem, due to the high similarity of different between the mammograms images. In this study, a novel convolutional neural network based X-ray breast mass classification method is proposed. The method receives original breast mass image and its transformed image simultaneously, and extracts more abundant features from the breast mass images. Though the transformed image is a simple inverse of the original image, it allows another side of the thing to be perceived by the network at a very low cost. Experiment results demonstrate that the proposed method significantly outperforms the compared state-of-the-art classification methods for breast mass. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Convolutional neural networks

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Extended Abstract of Graph4Web: A Relation-Aware Graph Attention Network for Web Service Classification

Extended Abstract of Graph4Web: A Relation-Aware Graph Atten...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kunsong Zhao Jin Liu Zhou Xu Xiao Liu Lei Xue Zhiwen Xie Yuxuan Zhou Xin Wang School of Computer Science Wuhan University Wuhan China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) China School of Big Data and Software Engineering Chongqing University Chongqing China School of Information Technology Deakin University Geelong Australia Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Engineering and Computer Science Syracuse University Syracuse USA

Software reuse, as a means to develop new software products with similar functions by virtue of existing software components, has become a popular way during the software development process. In particular, as the service-oriented architecture became popular, web services turned into an indispensable part in modem software development Web services provide a basic composition with high cohesion and loose coupling to support responses among heterogeneous software components, which is the valuable resources for software reuse. The popular web service repositories, such as Programmable Web, contain a mass of web services for beginners and developers to choose from. Nevertheless, the large number of web services also makes it difficult to select the suitable services. Thus, the key to reuse software components lies in how to find appropriate web services from repositories to meet developers requirements in specific application scenarios.

关键词： Couplings Web services Modems Service-oriented architecture Software reusability

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A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz

arXiv

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arXiv 2023年

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China College of Information Science and Engineering ZaoZhuang University Shandong Zaozhuang277160 China School of Cyberspace Security Security Beijing University of Posts Telecommunications Beijing100876 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang550025 China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher Ε-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning. © 2023, CC BY.

关键词： Machine learning

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Quantum adversarial metric learning model based on triplet loss function

arXiv

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arXiv 2023年

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China College of Information Science and Engineering ZaoZhuang University ZaoZhuang Shandong277160 China School of Cyberspace Security Security Beijing University of Posts Telecommunications Beijing100876 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang 550025 China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher ∊-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Improved publicly verifiable auditing protocol for cloud storage

Improved publicly verifiable auditing protocol for cloud sto...

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作者： Zhang, Jindan Lin, Chuan Ogiela, Urszula Nedjah, Nadia Sangaiah, Arun K. Wang, Xuan Department of Electronic Information Xianyang Vocational Technical College Xianyang China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guiyang China Pedagogical University of Krakow Krakow Poland Department of Electronics Engineering and Telecommunications at the Faculty of Engineering State University of Rio de Janeiro Rio de Janeiro Brazil School of Computing Science and Engineering Vellore Institute of Technology Vellore India

Outsourcing data to cloud servers is a popular service for data owners, however, how to check the integrity and freshness of the outsourced data is very challenge. Recently, Jin et al. proposed a cloud auditing protocol with full integrity and freshness support for cloud data, unfortunately in this article, we show their proposal is not secure. Concretely, the cloud servers can forge the authentication tag and thus has the ability to forge proof of data possession, which obviously invalidates their cloud auditing protocol. We also give a new cloud auditing protocol and analysis its security and performance. The results show our protocol is more efficient and secure. © 2020 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Automatically Labeling Aging Scenarios with a Machine Learning Approach 1

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24th International Conference on Human-computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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An improved FOO voting scheme using blockchain

An improved FOO voting scheme using blockchain

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作者： Zhou, Yuanjian Liu, Yining Jiang, Chengshun Wang, Shulan College of Computer Science and Technology Henyang Normal University Henyang421002 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

The traditional FOO e-voting protocols adopt centralized and non-transparent count center, which leads to distrust to the center and doubts the fairness and correctness of the vote. However, blockchain is the most innovative technology in the current era and promises to solve the trust problem in the system with one center. In this paper, an improved FOO e-voting protocol is proposed using blockchain, which tries to address the limitation or weakness in existing systems. The traditional trusted third party is replaced by smart contract;specifically, our scheme is deployed using hyperledger fabric. The implementation is enforced by the consensus mechanism, which ensures the security of the blockchain. Through the analysis, the proposed scheme is proved to satisfy the necessary requirements for an e-voting protocol;meantime the trust assumption is reduced significantly. Therefore, the proposed protocol is more versatile and practical. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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OverlapMamba: Novel Shift state Space Model for LiDAR-based Place Recognition

arXiv

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arXiv 2024年

作者： Xiang, Qiuchi Cheng, Jintao Luo, Jiehao Wu, Jin Fan, Rui Chen, Xieyuanli Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Shanwei516600 China School of Electronic and Information Engineering South China Normal University Guangdong Foshan528225 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and realtime efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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