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Robust Subspace clustering With Low-Rank Structure Constraint

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1404-1415页

作者： Nie, Feiping Chang, Wei Hu, Zhanxuan Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning Xian 710072 Peoples R China

In this paper, a novel low-rank structural model is proposed for segmenting data drawn from a high-dimensional space. Our method is based on the fact that all groups clustered from a high-dimensional dataset are distributed in multiple low-rank subspaces. In general, it's a very difficult task to find the low-rank structures hidden in data. Different from the classical sparse subspace clustering (SSC) and low-rank representation (LRR) which all take two steps including building the affinity matrix and spectral clustering, we introduce a new rank constraint into our model. This constraint allows our model to learn a subspace indicator which can capture different clusters directly from the data without any postprocessing. To further approximate the rank constraint, a piecewise function is utilized as the relaxing item for the proposed model. Besides, under the subspace indicator constraints, the integer programming problem is avoided, which makes our algorithm more efficient and scalable. In addition, we prove the convergence of the proposed algorithm in theory and further discuss the general case in which subspaces don't pass through the origin. Experiment results on both synthetic and real-world datasets demonstrate that our algorithm significantly outperforms the state-of-the-art methods.

关键词： Robustness clustering algorithms Data models Task analysis Linear programming Minimization Sparse matrices Subspace clustering low-rank structure singular value decomposition (SVD) piecewise function

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ClusterPoison: Poisoning Attacks on Recommender Systems with Limited Fake Users

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IEEE COMMUNICATIONS MAGAZINE 2024年第11期62卷 136-142页

作者： Wang, Yanling Liu, Yuchen Wang, Qian Wang, Cong Wuhan Univ Sch Cyber Sci & Engn Minist Educ Key Lab Aerosp Informat Secur & Trusted Comp Wuhan Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Wuhan Univ Wuhan Peoples R China City Univ Hong Kong Hong Kong Peoples R China

Numerous prior studies on poisoning recommender systems have demonstrated that with access to a user's historical data, an attacker can significantly influence the decision-making process of the recommendation model. However, these studies often assume that the attacker can control fake users amounting to 1% of the real users on the recommendation platform. When dealing with real-world large-scale recommendation platforms hosting millions of users, manipulating such a vast number of fake users without detection by the platform becomes exceedingly challenging. This limitation implies that large-scale recommendation platforms can ignore the impact of poisoning attacks. Motivated by this observation, our article aims to explore the potential impact of an attacker when the number of fake users is extremely limited. Specifically, assuming the attacker controls only one fake user, we design a clustering-based scheme for generating fake users. Our scheme serves as a flexible widget that can be integrated into various poisoning attacks against deep learning-based recommender systems, enhancing their effectiveness when dealing with limited fake users. We demonstrate that combining our approach with two different poisoning attacks results in improved performance under the constraint of minimal fake users. Through experimentation on the Amazon Beauty dataset (22,363 users) and Amazon Sports dataset (35,598 users), we highlight the poisoning impact of this seemingly negligible single fake user. Our findings emphasize the threat of poisoning attacks with a very small set of fake users and call for their stronger defense in real-world recommender systems. Our code is publicly available at https://***/yanling02/ClusterPoison.

关键词： Recommender systems Data models Training data clustering algorithms Closed box Adaptation models Computational modeling

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Abnormal Electricity Detection of Users Based on Improved Canopy-Kmeans and Isolation Forest algorithms

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IEEE ACCESS 2024年 12卷 99110-99121页

作者： Wang, Jianyuan Li, Xiaoyao Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

Aiming at the existing user abnormal electricity consumption detection methods that have the problem of difficult classification of user similar electricity consumption patterns, this paper proposes an unsupervised isolation forest abnormal electricity consumption detection model based on the Canopy-Kmeans algorithm with weighted density improvement. To start, we propose a composite parameter analysis method for user electricity consumption patterns, volatility, trends, and correlations using Irish smart meter data. This method involves joint data cleaning, interpolation, and feature construction. Additionally, principal component analysis is introduced to fuse features across layers and reduce dimensionality in user electricity consumption. Subsequently, we introduce the weighted density improvement Canopy-Kmeans clustering algorithm. This algorithm determines the K value and clustering centers using the maximum weight product method, based on definitions of sample density, average intra-class sample distance, and inter-class distance in the multilayer fusion feature data. Finally, we propose a fusion mechanism of weighted density improvement Canopy-Kmeans and isolation forest algorithms to jointly construct a model for detecting abnormal power usage based on multilayer fusion feature data analysis. The results demonstrate that multilayer fusion feature parameters vary in size and discretization among different user types, enabling classification of users with diverse electricity consumption patterns. Moreover, the anomaly detection model based on multilayer fusion feature data analysis improves accuracy rates, recall rates, and F1 scores compared to other algorithms.

关键词： Electricity clustering algorithms Classification algorithms Anomaly detection Forestry Prediction algorithms Feature extraction Detection algorithms Energy consumption Unsupervised learning Abnormal detection of electricity consumption by users Canopy-Kmeans algorithm isolation forest algorithm unsupervised learning weighted density

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APF-IBRRT*: A Global Path Planning Algorithm for Obstacle Avoidance Robots With Improved Iterative Search Efficiency

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IEEE ACCESS 2024年 12卷 124740-124750页

作者： Gao, Jiuyang Zheng, Xiang Liu, Pan Yang, Peiyan Yu, Jiuyang Dai, Yaonan Wuhan Inst Technol Hubei Prov Engn Technol Res Ctr Green Chem Equipme Sch Mech & Elect Engn Wuhan 430205 Peoples R China Wuhan Broadcasting & TV Stn Wuhan 430022 Peoples R China

The Rapidly-exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in global path planning for robots due to its collision-free and asymptotically optimal solution capabilities. However, in narrow and dynamic indoor environments, RRT and its optimized algorithms suffer from drawbacks such as multiple iterations and long planning times. While the Artificial Potential Field (APF) method can reduce the number of iterations and make the planned path smoother, it still faces issues such as local optima or inability to reach the target. To address these issues, this paper proposes the APF-IBRRT* algorithm, which combines the advantages of APF and B-RRT*. The algorithm first overcomes the local optimum problem of APF through the optimization of potential field factors and the introduction of virtual obstacles. Then, it designs a target threshold and proposes an adaptive step size search iteration strategy to address the issue of ineffective tree searches. Finally, the APF-IBRRT* algorithm is compared with B-RRT*, VPF-RRT*, and the classic A* algorithm. Simulation and actual experimental results demonstrate that the APF-IBRRT* algorithm not only ensures optimization of path length but also achieves a performance improvement of over 20% in planning time and iteration efficiency compared to the benchmark algorithms.

关键词： Heuristic algorithms Path planning Collision avoidance clustering algorithms Active filters Costs B-RRT VPF-RRT APF-IBRRT planning time

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Green Laser-Powered UAV Far-Field Wireless Charging and Data Backhauling for a Large-Scale Sensor Network

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31932-31946页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Adv Networking Lab Newark NJ 07102 USA

Sixth-generation (6G) wireless communications greatly emphasizes the integration of sensing, communicating, and computing. Unmanned aerial vehicles (UAVs), by leveraging their feasibility and mobility, can naturally facilitate flexible far-field wireless charging and data backhauling for widely implemented wireless rechargeable sensor networks (WRSNs) across diverse domains, such as intelligent agriculture, smart cities, and modern factories. However, the energy constraints inherent to UAVs, coupled with the absence of joint optimization in clustering and trajectory design, present formidable challenges in efficiently leveraging UAVs for large-scale WRSN wireless charging and data backhauling. Therefore, in this work, we empower the green energy-powered base station (GBS) to power a UAV by laser charger to prolong the UAV's uptime. This enables the UAV to effectively perform wireless charging and data backhauling for a WRSN. By considering the GBS's green energy budget, we formulate an optimization problem focused on determining the optimal 3-D hovering points for UAV to maximize the number of sensor nodes (SNs) capable of receiving sufficient energy and uploading data. Given the NP-hard nature of this problem, we propose a two-step solution featuring corresponding heuristic algorithms designed to efficiently address it. Extensive simulations have been conducted to validate the efficacy of our proposed algorithms.

关键词： Autonomous aerial vehicles Trajectory Inductive charging Wireless communication Wireless sensor networks clustering algorithms Backhaul networks Data backhauling green energy far-field wireless charging sixth-generation (6G) wireless communications unmanned aerial vehicle (UAV) wireless rechargeable sensor networks (WRSNs)

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An Evolutionary Algorithm-Based Vehicular clustering Technique for VANETs

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IEEE ACCESS 2022年 10卷 14368-14385页

作者： Shah, Yaser Ali Aadil, Farhan Khalil, Amaad Assam, Muhammad Abunadi, Ibrahim Alluhaidan, Ala Saleh Al-Wesabi, Fahd N. COMSATS Univ Islamabad Dept Comp Sci Attock 43600 Pakistan Univ Engn & Technol Dept Comp Syst Engn Peshawar 25000 Pakistan Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Prince Sultan Univ Dept Informat Syst Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Dept Comp Sci Abha 62529 Saudi Arabia

Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network's overall performance and consequently reducing the routing cost of the vehicular network.

关键词： Optimization clustering algorithms Routing Particle swarm optimization Costs Roads Reliability clustering evolutionary algorithms intelligent transportation system (ITS) moth-flame optimization (MFO) swarm-based algorithm vehicular ad hoc networks (VANETs)

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An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13809-13820页

作者： Kumar, Jay Din, Salah Ud Yang, Qinli Kumar, Rajesh Shao, Junming Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China COMSATS Univ Islamabad Dept Comp Islamabad 45550 Pakistan

Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a ``term ambiguity'' problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the ``term ambiguity'' problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.

关键词： Social networking (online) Semantics Task analysis clustering algorithms Mixture models Graphical models Blogs clustering graphical model microclusters short text stream topic evolution topic modeling

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ALEPH: Accelerating Distributed Training With eBPF-Based Hierarchical Gradient Aggregation

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第5期32卷 4128-4143页

作者： Yang, Peng Xu, Hongli Zhao, Gongming Zhang, Qianyu Liu, Jiawei Qiao, Chunming Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou 215123 Jiangsu Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

Distributed training includes two important operations: gradient transmission and gradient aggregation, which will consume massive bandwidth and computing resources. To achieve efficient distributed training, one must overcome two critical challenges: heterogeneity of bandwidth resources and limitation of computing resources among compute nodes. Existing architectures based on Parameter Server (PS) and All-Reduce (AR) fail to cope with these challenges because the PS will aggregate gradients from all workers and suffers from bandwidth bottlenecks, while AR intends to alleviate bandwidth bottlenecks at the PS, but the workers need to process many gradient packets thus can be overloaded. To address these shortcomings, we design a new distributed training system called ALEPH. In the control plane, ALEPH uses an efficient algorithm to group workers into clusters with different sizes so as to fully utilize heterogeneous bandwidth. We show that the proposed algorithm can achieve a good approximation performance. In the data plane, ALEPH leverages, for the first time, extended Berkeley Packet Filter (eBPF) programs to aggregate and forward gradient packets to reduce computation overhead. We show how to overcome several hurdles in using eBPF for distributed training. We implement ALEPH and evaluate its performance on a small-scale testbed and large-scale simulations. Experimental results show that ALEPH reduces training time by 20%-31% and increases bandwidth utilization by 88% compared with state-of-the-art frameworks.

关键词： Training Bandwidth Computer architecture Aggregates clustering algorithms Servers Kernel Distributed training hierarchical aggregation eBPF XDP datacenter

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Teaching Design Model of Media Courses Based on Artificial Intelligence

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IEEE ACCESS 2024年 12卷 121242-121250页

作者： Liu, Xing Jinan Univ Pearl River Film Acad Sch Art Guangzhou 510632 Peoples R China

Under the background of information technology, in order to promote the smooth development of teaching design of media courses, we must adhere to the basic principle of taking students as the center and reasonably create teaching situations. Insert high-flying wings for the production and dissemination of intelligent media content in the AI era, making content production faster and more efficient, realizing scene and experience of content terminals, and making information dissemination more intelligent and personalized. This topic launches the teaching design of media courses based on AI (artificial intelligence). The teaching scheduling model of media courses and the teaching recommendation algorithm of media courses based on AI are established. By introducing LSTM (Long Short-Term Memory) to embed the multi-feature information of users and courses into the model, users' interests and preferences can be fully understood, and satisfactory recommendation results can be given. The results show that the accuracy of CF(collaborative filtering) recommendation based on clustering is higher than that of this recommendation algorithm. The accuracy and recall of this recommendation algorithm can reach 91.4619% and 87.7128% respectively. On the one hand, the cold start problem of CF algorithm is improved, on the other hand, compared with the content-based recommendation algorithm, the effect is significantly improved, and the expected effect is achieved.

关键词： Educational courses Media Artificial intelligence Long short term memory Job shop scheduling clustering algorithms Recommender systems Algorithm design and analysis media courses teaching design recommendation algorithm

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OAM Spatial Field Digital Modulation System for Physical-Level Secure Communication

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18472-18486页

作者： Chen, Yuqi Xiong, Xiaowen Zheng, Shilie Yang, Zhaohui Zhu, Zelin Pan, Bingchen Wu, Bincai Hui, Xiaonan Jin, Xiaofeng Yu, Xianbin Zhang, Xianmin Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China NingboTech Univ Sch Informat Sci & Engn Ningbo 315100 Peoples R China

In this paper, we investigate a novel physical layer secure (PLS) potential technique which is summarized into a category of index modulation (IM) scheme, namely the spatial field digital modulation (SFDM). In an SFDM communication system, bit streams are modulated in the spatial distribution of electromagnetic (EM) field. Orbital angular momentum (OAM) mode groups can be utilized for beamforming and thus realize an OAM based SFDM (OAM-SFDM) system. This kind of IM systems that utilize the distribution of EM field to transmit information can radiate different signals to different directions and possess inherent anti-eavesdropping capability. Since an eavesdropper lacks prior knowledge, it is difficult to construct appropriate and effective estimators for signal demodulation. However, such system is vulnerable when potential eavesdroppers adopt clustering algorithm, a type of non-realtime demodulation. We propose two PLS strategies with different computational complexity, the power hopping (PH) method and the symbol variation (SV) method, to confront single-antenna eavesdroppers and multi-antenna eavesdroppers, respectively. Both approaches are key-free, and do not require any prior channel state information (CSI) about eavesdroppers. The mentioned methods could constantly disrupt the statistical properties of eavesdroppers' channels and even their received signals. Theoretical analysis and numerical simulations have been conducted to validate the feasibility of these two schemes. Furthermore, a prototype of OAM-SFDM based PLS communication system is built in a realistic scenario. The experimental results demonstrate that both PH method and SV method can effectively resist clustering algorithm in their corresponding application scenarios.

关键词： Symbols Receiving antennas Demodulation Wireless communication Antenna arrays Indexes Transmitting antennas clustering algorithms Array signal processing Prototypes Orbital angular momentum spatial field digital modulation physical level security index modulation

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