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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhi Wang Haotian Chi Yonggang Li Yuxuan Zhang Shunrong Jiang Xiaojiang Du Mohsen Guizani School of Computer Science China University of Mining and Technology Xuzhou China School of Automation and Software Engineering Shanxi University Taiyuan China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients’ private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients’ privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients’ privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget.

关键词： Privacy Differential privacy Accuracy Federated learning Inference algorithms Classification algorithms Security Optimization Biomedical imaging Image classification

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

arXiv

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arXiv 2023年

作者： Zhang, Qin Shi, Zelin Zhang, Xiaolin Chen, Xiaojun Fournier-Viger, Philippe Pan, Shirui Big Data Institute College of Computer Science and Software Engineering Shenzhen University China School of Information and Communication Technology Griffith University Queensland Australia

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are often applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on benchmark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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QoE-Based Dynamic Resource Allocation Algorithm of 5G Network Slicing 12th

QoE-Based Dynamic Resource Allocation Algorithm of 5G Networ...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Wang, Wenxiang Li, Kangshun Wang, Hui College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China School of Computer and Information Dongguan City University Guangdong Dongguan523808 China School of Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9789811941085

The slicing technology can meet the diversified and personalized service needs of 5G networks, schedule virtual networks dynamically and improve the operation efficiency of the network. The dynamic resource allocation algorithm among slices is the key for the application of slicing technology, which has also become a research hotspot in recent years. However, the current dynamic resource allocation algorithm of 5G network slicing has many problems such as insufficient fairness, difficulty in ensuring user satisfaction and low resource utilization rate, etc. Under the circumstances of a wide variety of 5G services, it is difficult to meet the needs of user satisfaction under differentiated service conditions only from the perspective of the network. From the perspective of Quality of Experience (QoE), a QoE evaluation system for 5G network is constructed, and a QoE-based dynamic resource allocation algorithm of 5G network slicing on the basis of this system is built in this research. The results of the simulation experiments indicate that the algorithm has significant effect in improving QoE of 5G communities, and it also has good performance in improving the resource utilization rate in the communities. © 2022, Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

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Differential Evolution Algorithm for Medical Image Registration 12th

Differential Evolution Algorithm for Medical Image Registrat...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Li, Kangshun Wang, Wenxiang Wang, Hui School of Computer and Information Dongguan City University Guangdong Dongguan523808 China College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China School of Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9789811941085

Image registration plays an important role in medical image fusion and surgical navigation. Iterative nearest point algorithm (INPA) is a high-precision image registration algorithm, but it also has the problems of huge computation cost and low efficiency. Therefore, we use the differential evolution algorithm to optimize the search process of the iterative nearest point algorithm, so that to improve its registration efficiency. Then a mutation operator selection algorithm based on fitness roughness and a slack selection strategy for differential evolution algorithm were proposed to meet the requirements of high precision and low delay of medical image registration. The registration experiments of chest CT images show that the proposed differential evolution algorithm can not only accelerate the speed of iterative nearest point algorithm, but also improve its registration accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image registration

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software Component Library Management Mechanism for Equipment Parts Service Value-Net 6th

Software Component Library Management Mechanism for Equipmen...

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6th International Conference on Human Centered Computing, HCC 2020

作者： Tian, Zhuo Zhang, Changyou Cai, Xiaofeng Xiao, Jiaojiao Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9783030706258

software reuse realizes the sharing of software resources, and component-based reuse is the main form of software reuse. The classification, storage, retrieval, and release of a large number of component resources require efficient component library management methods. This paper proposes an open software component library management mechanism based on microservices. Taking manufacturing product service life cycle value chain collaboration as a case, it realizes a domain-oriented microservice granularity division mechanism and forms a domain-oriented software component representation, warehousing, discovery, selection, arrangement, filing, and other compatible open software component library management mechanisms;realization of the design of an open software component management system for manufacturing product operation and maintenance reengineering value chain collaboration, The simulation verifies the open ability and management efficiency of the management mechanism. © 2021, Springer Nature Switzerland AG.

关键词： computer software reusability

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Real-Time Pill Identification for the Visually Impaired Using Deep Learning

arXiv

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arXiv 2024年

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun Computer Science San Francisco Bay University FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Computer Science Stevens Institute of Technology HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile computing

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Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

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IEEE International Conference on Big Data

作者： Xiaoyu Li Jiangxuan Long Zhao Song Tianyi Zhou Department of Computer Science Stevens Institute of Technology Hoboken NJ School of Software Engineering South China University of Technology Guangzhou China The Simons Institute for the Theory of Computing University of California Berkeley Berkeley CA Department of Computer Science University of Southern California Los Angeles CA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n 3 ) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ 2 ) respectively, where m = Ω(n 4 ) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ 2 ).

关键词： Training Knowledge engineering Costs Neural networks Machine learning Big Data Computational efficiency Matrix decomposition Optimization Convergence

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WR-Former: Vision Transformer with Weight Reallocation Module for Robust Facial Expression Recognition

WR-Former: Vision Transformer with Weight Reallocation Modul...

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International Conference on computer Supported Cooperative Work in Design

作者： Rui Wang Haitao Wang Peng Zou Xiao Sun Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China College of Software Engineering Southeast University Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Facial expression recognition(FER) is an exceedingly challenging task in the field of computer vision, primarily due to variant head poses, occlusions and illumination conditions. Previous methods focus on extracting more facial features to improve the performance on the FER benchmarks. However, most of them ignore the weights of the key regions. To address the above problem, this paper proposes a vision Transformer with weight reallocation module termed as WR-Former. The weight reallocation module utilizes a hybrid multi-head self-attention and can dynamically iterate during the training process. It guides the model to reallocate tokens based on the importance of different facial regions. Besides, a simple yet effective data augmentation method is proposed to expand training samples and improve the robustness. Experimental results and visualizations demonstrate that our WR-Former outperforms the previous state-of-the-art methods and has the ability to better extract the features conveyed by the key facial regions.

关键词： Training Federated learning Face recognition Lighting Data visualization Transformers Feature extraction

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DAGC: Data-Aware Adaptive Gradient Compression

DAGC: Data-Aware Adaptive Gradient Compression

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Rongwei Lu Jiajun Song Bin Chen Laizhong Cui Zhi Wang Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School School of Software Technology Dalian University of Technology Harbin Institute of Technology Shenzhen College of Computer Science and Software Engineering Shenzhen University Guangdong Laboratory of Artificial Intelligence and Cyber Economics (SZ) Peng Cheng Laboratory

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers; This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios 1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication.

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