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arXiv 2024年

作者： Lv, Lu Xu, Dongyang Hu, Rose Qingyang Ye, Yinghui Yang, Long Lei, Xianfu Wang, Xianbin Kim, Dong In Nallanathan, Arumugam The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China The Department of Electrical and Computer Engineering Utah State University LoganUT84322 United States The Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an710121 China The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats, due to the open nature of the air interface, the broadcast characteristic of radio propagation as well as intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security aware design, for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview on the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio waves

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Learning with multiclass AUC: Theory and algorithms

arXiv

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arXiv 2021年

作者： Yang, Zhiyong Xu, Qianqian Bao, Shilong Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen518100 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically;(ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(p1/N) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework.. © 2021, CC BY.

关键词： Machine learning

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Respiratory Sound Classification Based on BiGRU-Attention network with XGBoost

Respiratory Sound Classification Based on BiGRU-Attention Ne...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuesong Zhao Yanbo Shao Juanyun Mai Airu Yin Sihan Xu College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China KLMDASR Tianjin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In recent years, the mortality rate of respiratory diseases ranks high among the major diseases. Early detection of respiratory diseases is a key factor in reducing the mortality rate and curing diseases. In this paper, we propose the BiGRU Attention-XGBoost model to classify respiratory sounds, in order to assist doctors in the early diagnosis of respiratory diseases. Specifically, we first extract two sets of features, i.e., the time domain and spectral features to encode respiratory sounds. Then, we apply the Gradient Boosting Decision Tree algorithm to select important features for classification. Based on the temporal characteristics of respiratory sounds, we design the BiGRU Attention-XGBoost model to classify them. Finally, to enlarge the training dataset and address the problem of data imbalance, we also implement Griffifin-Lim and WORLD Vocoder, two data augmentation methods. Extensive experiments show the superiority of the proposed model compared to seven state-of the-art models in terms of classification accuracy and Fl-score.

关键词： Feature extraction Mathematical model Hidden Markov models Training Mel frequency cepstral coefficient Time-domain analysis Logic gates

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Performance Analysis of Phase Pre-Configured Spatial Modulation Empowered by RIS With Discrete Phase Alphabet

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chaowen Shi, Zhengmin Zheng, Tong-Xing Liu, Boyang Wen, Miaowen Wang, Wenjie Lu, Guangyue Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi'an710068 China Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China South China University of Technology School of Electronics and Information Engineering Guangzhou510641 China

In this paper, we are motivated to introduce and investigate a novel reconfigurable intelligent surface assisted phase preconfigured spatial modulation (RIS-PPSM) scheme, which can be employed to achieve reliability enhanced transmissions. In the RIS-PPSM scheme, pre-configured phases are injected at the transmitter, and the reflection coefficients are designed for the RIS with a practical discrete phase alphabet, such that the resolution of transmit antennas is increased, and hence the overall transmission performance is remarkably elevated. Upon utilizing the moment generating function based unified analysis framework, we deduce out the closed-form expressions for the average bit error probability upper bound and discrete input continuous-output memoryless channel capacity. Finally, simulation results are depicted to demonstrate the accuracy of the theoretical analysis and the superiority of the proposed RIS-PPSM. © 1967-2012 IEEE.

关键词： discrete phase alphabet performance analysis phase pre-configuration Reconfigurable intelligent surface (RIS) spatial modulation

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Cipherchain:A Secure and Efficient Ciphertext Blockchain via mPECK

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Journal of Quantum computing 2020年第1期2卷 57-83页

作者： Hailin Chen Gang Xu Yuling Chen Xiubo Chen Yixian Yang Ruibin Fan Kaixiang Zhang Huizhong Li Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Guizhou University Sate Key Laboratory of Public Big DataGuizhou550025China North China University of Technology School of Computing Science and TechnologyBeijingChina Webank Shenzhen518000China

Most existing blockchain schemes are based on the design concept“openness and transparency”to realize data security,which usually require transaction data to be presented in the form of ***,it inevitably brings the issues with respect to data privacy and operating *** this paper,we proposed a novel blockchain scheme called Cipherchain,which can process and maintain transaction data in the form of ciphertext while the characteristics of immutability and auditability are *** in our scheme,transactions can be encrypted locally based on a searchable encryption scheme called multi-user public key encryption with conjunctive keyword search(mPECK),and can be accessed by multiple specific participants after appended to the globally consistent distributed *** introducing execution-consensus-update paradigm of transaction flow,Cipherchain cannot only make it possible for transaction data to exist in the form of ciphertext,but also guarantee the overall system performance not greatly affected by cryptographic operations and other local execution *** addition,Cipherchain is a promising scheme to realize the technology combination of“blockchain+cloud computing”and“permissioned blockchain+public blockchain”.

关键词： Blockchain Cipherchain cloud computing mPECK

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Minimizing Economic Costs and Task Processing Fails in MEC-Enabled Satellite-Ground Integrated networks With MADDPG and Fuzzy Logic

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IEEE Transactions on Consumer Electronics 2025年

作者： Du, Jianbo Zhang, Jianjun Kong, Ziwen Yu, Zuting Hu, Bintao Jiang, Jing Lu, Guangyue Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi’an710121 China Chinese Academy of Space Technology Beijing100094 China Xi’an Jiaotong-Liverpool University School of Internet of Things Suzhou215123 China

As the Internet of Things (IoT) continues to expand rapidly, traditional ground networks are increasingly unable to meet the growing demands for connectivity and coverage. To address these challenges, integrated satellite-ground networks have emerged as a vital solution, enhancing both coverage and reliability. At the same time, Multi-Access Edge computing (MEC) enables intelligent task execution on IoT devices by deploying resources at the network edge. In this paper, we propose a joint optimization framework for task placement and removal, access control, business instance selection, and bandwidth allocation within MEC-enabled integrated satellite-ground IoT networks. To manage the inherent complexities and uncertainties in dynamic environments, we integrate fuzzy logic into the optimization process. Additionally, we introduce a deep reinforcement learning-based algorithm, specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), to effectively solve this problem. Experimental results demonstrate that the proposed algorithm outperforms benchmark methods in terms of convergence, while achieving minimized economic costs and reduced task processing failures. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Adversity-Based Social Circles Inference via Context-Aware Mobility

Adversity-Based Social Circles Inference via Context-Aware M...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Qiang Gao Fan Zhou Goce Trajcevski Fengli Zhang Xucheng Luo Network and Data Security Key Laboratory of Sichuan Province China University of Electronic Science and Technology of China China Iowa State University Ames USA

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

The ubiquity of mobile devices use has generated huge volumes of location-aware contextual data, providing opportunities enriching various location-based social network (LBSN) applications - e.g., trip recommendation, ride-sharing allocation and taxi demand prediction etc. Trajectory-based social circle inference (TSCI), which aims at inferring the social relationships among users based on the human mobility data, has received great attention in recent years due to its importance in many LBSN applications. However, existing solutions suffer from three key challenges, including (1) lack of modeling contextual feature in user check-ins; (2) cannot capture the structural information in user motion patterns; (3) and fail to consider the underlying mobility distribution. In this paper, we propose a novel framework ASCI-CAM (Adversity-based Social Circles Inference via Context-Aware Mobility) to address the above challenges. ASCI-CAM is a graph-based model taking into account the contextual information associated with check-ins which, combined with an attentive auto-encoder, allows for semantic trajectory representation. We regularize the learned trajectory embedding with an adversarial learning procedure, which allows us to better understand the user mobility patterns and personalized trajectory distribution. Our extensive experiments on real-world mobility datasets demonstrate that our model achieves significant improvement over the state-of-the-art baselines.

关键词： Trajectory Social networking (online) Training Task analysis Semantics Logic gates Training data

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SPECBOX: A label-based transparent speculation scheme against transient execution attacks

arXiv

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arXiv 2021年

作者： Tang, Bowen Wu, Chenggang Wang, Zhe Jia, Lichen Yew, Pen-Chung Cheng, Yueqiang Zhang, Yinqian Wang, Chenxi Xu, Guoqing Harry State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of the Chinese Academy of Sciences China University of Minnesota Twin Cities United States NIO Security Research United States Southern University of Science and Technology China University of California Los Angeles United States

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead;(2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SPECBOX. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SPECBOX also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SPECBOX is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital storage

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衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Cross-modal consensus network for weakly supervised temporal action localization

arXiv

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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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