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Meta-Transfer Learning Based Cross-Domain Gesture Recognition Using WiFi Channel State Information

IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Penglin Zhou, Junfei Ma, Jialong Zhang, Hao Wu, Xiao Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Ministry of Education Engineering Research Center of Sustainable Urban Intelligent Transportation China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China

Gesture recognition plays a crucial role in a wide range of consumer electronics applications, including human-computer interaction and virtual reality, by enabling the identification and interpretation of human gestures. In recent times, WiFi-based gesture recognition has garnered significant attention due to its privacy protection and unobtrusive nature. However, this approach heavily depends on neural network-based models and is notably influenced by environmental conditions, such as specific location and orientation. To address these environmental impacts, we propose a network framework that leverages transfer learning and meta-learning. The primary focus is on utilizing transfer learning to train a convolutional neural network (ResNet) for feature extraction, enabling the extraction of domain-independent features specifically related to gestures, rather than being influenced by environmental factors. Additionally, we employ the meta-learning algorithm MAML to train the fully connected network for gesture classification. Following training, a small set of samples can be utilized for rapid adaptation to diverse domains, maintaining high accuracy across different domains to achieve cross-domain gesture recognition. Our performance evaluation is based on the Widar 3.0 dataset, encompassing both in-domain and cross-domain scenarios. The simulation results demonstrate that our proposed algorithm surpasses Widar3.0 and WiGRUNT by approximately 7.9% and 1.12% in gesture recognition accuracy across all scenarios. © 2025 IEEE.

关键词： Wi-Fi

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UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks

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IEEE Transactions on Network science and Engineering 2025年

作者： Hadi, Hassan Jalil Cao, Yue Khan, Muhammad Khurram Ahmad, Naveed Hu, Yulin Fu, Chao Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430073 China King Saud University Center of Excellence in Information Assurance Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh Saudi Arabia Wuhan University School of Electronic Information China Information and Communication Technology Co. LtdChina China

Unmanned Aerial Vehicles (UAVs) are necessary for numerous tasks but are vulnerable to cyber threats due to their widespread use and connectivity. The lack of a comprehensive dataset necessitates the development of effective detection and mitigation solutions. Our work introduces UAV-NIDD, a new dataset that addresses the gaps in understanding and countering both cyber and physical threats in UAV networks. It includes three distinct attack scenarios: compromised UAV initiating a network-wide attack, access point compromised network-wide intrusion, and compromised Ground Control Station (GCS) establishing a network-wide attack. We develop a real-time testbed for creating UAV-NIDD (Unmanned Aerial Vehicles-Network Intrusion Detection Dataset), incorporating UAV devices, data collection tools, and controllers. Our testbed facilitates cyberattack execution and data gathering under normal and attack conditions. Our dataset covers various cyber-attacks like Scanning, Reconnaissance, DoS, DDoS, GPS Jamming & Spoofing, MITM, Replay, Evil Twin, Brute-Force, and Fake Landing packet attacks. Additionally, UAV-NIDD presents a valuable resource for AI and ML solutions, strengthening UAV networks against evolving cyber threats. Moreover, we offer open access and cooperative innovation in terms of long-term updating of dataset. © 2013 IEEE.

关键词： Network intrusion

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A Study on English Front Vowels of Tibetan College Students Based on Information technology

A Study on English Front Vowels of Tibetan College Students ...

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2023 International Conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Zhang, Fu Ma, Naqin Duojie, Gezhi Dao, Jieben School of Foreign Studies of Gansu University of Political Science and Law Gansu Lanzhou730070 China Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Tibetan Information Technology Research Center of Hainan Prefecture Qinghai Hainan813000 China The Computer College of Qinghai Minzu University Qinghai Xining810000 China

ISBN: (纸本)9798400716157

Based on the acoustic speech experiment, this study took English front vowels of Maqu Tibetan college students as the object, and took Maqu Tibetan college participants' and GB native participants' F1, F2 and the duration of English front vowels as the basis for a comparative analysis of bias. The results showed that Maqu Tibetan college students had a good command of [i:];[I] was confused with [i:];the tongue position of [e] was slightly higher than GB native participants;in the acquisition of [æ], there was a big deviation from GB native participants' tongue position, which was too high and forward. At the level of phonetic perception, they perceived English front vowel [I] as [i:] and [æ] as [e]. © 2023 ACM.

关键词： Speech processing

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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence

arXiv

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arXiv 2023年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda The Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Distillation

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The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

arXiv

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arXiv 2024年

作者： Ma, Jiating Zhou, Yipeng Li, Qi Sheng, Quan Z. Cui, Laizhong Liu, Jiangchuan The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Shenzhen518060 China The School of Computing Faculty of Science and Engineering Macquarie University Macquarie ParkNSW2119 Australia The Institute for Network Sciences and Cyberspace Tsinghua University Beijing100190 China The Beijing National Research Center for Information Science and Technology Beijing100190 China The School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients’ privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines. © 2024, CC BY.

关键词： Federated learning

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

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IEEE Transactions on Mobile computing 2025年

作者： Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Merouane Niyato, Dusit Poor, H. Vincent Yuen, Chau Northwestern Polytechnical University School of Computer Science Shaanxi Xi'an710129 China Xidian University School of Cyber Engineering Shaanxi Xi'an710071 China Harbin Engineering University Heilongjiang Harbin150001 China Khalifa University of Science and Technology Center for 6G Technology Abu Dhabi United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Nanyang Technological University School of Electrical and Electronics Engineering Singapore

Optimization is crucial for the efficiency and reliability of multi-access edge computing (MEC) networks. Many optimization problems in this field are NP-hard and do not have effective approximation algorithms. Consequently, there is often a lack of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not leverage the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this point, we focus on the multi-server multi-user computation offloading (MSCO) problem, a common issue in MEC networks that lacks efficient optimal solution methods. In this paper, we introduce the graph diffusion-based solution generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. We develop GDSG, a multi-task diffusion generative model that employs a graph neural network (GNN) to capture the distribution of high-quality solutions. Our approach includes a straightforward and efficient heuristic method to generate a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the GNN architecture to achieve better generalization. Moreover, the proposed GDSG can achieve nearly 100% task orthogonality, which helps prevent negative interference between the discrete and continuous solution generation training objectives. We demonstrate that this orthogonality arises from the diffusion-related training loss in GDSG, rather than from the GNN architecture itself. Finally, our experiments show that the proposed GDSG outperforms other benchmark methods on both optimal and suboptimal training datasets. Regarding the minimization of

关键词： Graph neural networks

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Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning

Temporal Knowledge Graph Reasoning via Time-Distributed Repr...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) reasoning has attracted significant attention. Recent approaches for modeling historical information have led to great advances. However, the problems of time variability and unseen entities have become two major obstacles preventing further development. The time variability problem means that different historical timestamps play different roles in the inference process. Furthermore, in the context of time variability, the unseen entity problem means that a query cannot obtain a predicted entity that is unseen in the scale-varying history rather than in a fixed set, thus turning from static to dynamic. In this paper, we propose a novel method named DHU-NET for addressing the time variability challenge and the dynamic unseen entity challenge derived from it. With regard to the former concern, we propose a time-distributed representation learning method based on a graph convolutional network(GCN) and a self-attention mechanism, which learns the distributed representations of facts at different historical timestamps and comprehensively pays different levels of attention to the different timestamps. With regard to the latter issue, we extract the unseen entities from a global static KG based on a copy mechanism and bring them into consideration during the final prediction step. Experiments on six benchmark datasets demonstrate the substantial improvements achieved by DHUNET in terms of multiple evaluation metrics. Our released codes are available at https://***/CGCL-codes/DHUNET.

关键词： Representation learning Measurement Knowledge engineering Predictive models Turning Cognition Data models

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption computer security Time complexity Standards

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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