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Community Deception in Large Networks: Through the Lens of Laplacian Spectrum

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 2057-2069页

作者： Zhang, Chong Fu, Luoyi Ding, Jiaxin Cao, Xinde Long, Fei Wang, Xinbing Zhou, Lei Zhang, Jing Zhou, Chenghu Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Environm Sci & Engn Shanghai 200240 Peoples R China Xinhua News Agcy State Key Lab Media Convergence Prod Technol & Sys Beijing 100077 Peoples R China Shanghai Jiao Tong Univ Sch Oceanog Shanghai 200240 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100864 Peoples R China

Many complex networks in the real world have community structures. Typical examples include online social net-works and ecology networks. While the identification of commu-nities bears numerous practical applications, with the increasing awareness of data security and privacy concerns, the need to protect the community affiliations of individuals from disclosing by attackers emerges. This raises the community deception (CD) problem, that is, the opposite of community detection, which asks for ways to minimally perturb the network structures by rewiring nodes so that the target communities maximally hide themself from community detection algorithms. To this end, we investigate the CD problem through a Laplacian spectrum lens and propose a method named ComDeceptor to hide a flexible target set of communities, which is more universal than most existing methods that either focus on hiding the entire communities or a single community. The key idea of ComDeceptor is to first allocate the resources of perturbations fairly and effectively. By proving that hiding communities through intercommunity edge addition and intracommunity edge deletion correspond to maximizing the second smallest eigenvalue ?(2) and minimizing the largest eigenvalue ?(n) of the graph Laplacian, respectively, ComDeceptor then incorporates efficient heuristics for approx-imately solving the problems, thus selecting the appropriate edge to perturb. Experimental results over nine real-world networks and six community detection algorithms not only demonstrate the efficiency of ComDeceptor, but also the superior performance on obfuscating community structures over the baselines.

关键词： Laplace equations Image edge detection Eigenvalues and eigenfunctions detection algorithms Perturbation methods Social networking (online) Lenses Community deception (CD) community detection Laplacian spectrum large network

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System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform

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IEEE ACCESS 2024年 12卷 32637-32659页

作者： Oliveira-Filho, Adaiton Zemouri, Ryad Pelletier, Francis Tahan, Antoine Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada Res Ctr Hydroquebec Varennes PQ J3X 1S1 Canada Power Factors Brossard PQ J4Z 1A7 Canada

This work introduces a new condition monitoring approach for complex systems based on a standardized latent space representation. Latent variable models such as the variational autoencoders are widely used to analyze systems described by a high-dimensional physical space. The encoding of such space defines a low-dimensional and physically representative latent space. Of note, however, the latent space obtained for complex systems operating under multiple conditions is often difficult to exploit in defining an efficient Health Index, thanks to the non-deterministic and hyperparameter-dependent nature of the latent space. In addition, the distribution of the healthy cluster is not known a priori. The original contribution of this paper is to use the Nataf isoprobabilistic transform to map the latent space into a standardized space. This normalizes the spatial structure of the latent space and relaxes the model's sensitivity to hyperparameters during the learning process. Moreover, the characterization of the healthy condition in the standard Nataf space leads to the definition of two complementary health indices suitable for complex systems. An implementation in two case studies demonstrates the potential of the proposed approach. First, the approach was successfully applied within NASA's Commercial Modular Aero-Propulsion System Simulation dataset. The second case study consisted of analyzing multiple degradation in operating wind turbines. Encouraging results emerge from both case studies, with critical conditions being detected significantly earlier than in competing approaches. The proposed approach can be generalized to complex systems equipped with multiple sensors, and overcomes difficulties related to latent space analysis of multiple condition systems.

关键词： Transforms Condition monitoring Complex systems Training Data models Correlation detection algorithms Extraterrestrial measurements Encoding early detection latent space modeling Nataf transform variational autoencoder

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Rapid detection of Local Communities in Graph Streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第5期34卷 2375-2386页

作者： Liakos, Panagiotis Papakonstantinopoulou, Katia Ntoulas, Alexandros Delis, Alex Univ Athens Dept Informat & Telecommun Athens 15703 Greece Athens Univ Econ & Business Dept Informat Athens 10434 Greece

We examine the problem of uncovering communities in complex real-world networks whose elements and their respective associations manifest as streams of data. Community detection is applied in emerging computational environments and concerns critical applications in diverse areas including social computing, web analysis, IoT and biology. Despite the already expended related research efforts, the task of revealing the community structure of massive and rapidly-evolving networks remains very challenging. More specifically, there is an emerging need for online approaches that ingest graph data as a stream. In this paper, we propose a streaming-graph community-detection algorithm that expands seed-sets of nodes to communities. We consider an online setting and process a stream of edges while aiming to form communities on-the-fly using partial knowledge of the graph structure. We use space-efficient structures to maintain very limited information regarding the nodes of the graph and the sought communities, so as to effectively process large scale networks. In addition to our novel streaming approach, we develop a technique that increases the accuracy of our algorithm considerably and additionally propose a new clustering algorithm that allows for automatically deriving the size of the communities we seek to detect. Using ground-truth communities for a wide range of large real-word and synthetic networks, our experimental evaluation shows that our approach does achieve accuracy comparable, and oftentimes better, to the state-of-the-art non-streaming community detection algorithms. More importantly, we attain significant improvements in both execution time and memory requirements.

关键词： Image edge detection Clustering algorithms Memory management Biology Task analysis detection algorithms Social network services Local community detection graph streams seed-set expansion social networks

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A Crack detection Algorithm for Concrete Pavement Based on Attention Mechanism and Multi-Features Fusion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11710-11719页

作者： Qu, Zhong Chen, Wen Wang, Shi-Yan Yi, Tu-Ming Liu, Ling Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Southwest Comp Co Ltd Inst Informat Technol Chongqing 400060 Peoples R China Chongqing Coll Mobile Commun Chongqing 401520 Peoples R China

Crack detection for concrete pavement is an important and fundamental task to ensure road safety. However, automatic crack detection is a challenging topic due to the complicated concrete pavement background and the diversity of cracks. Inspired by the latest developments of deep learning in computer vision, we propose a novel crack detection algorithm of concrete pavement based on attention mechanism and multi-features fusion, and make it possible to deal with various cracks in different pavement backgrounds. The proposed network is constructed using the encoder-decoder structure. The architecture of the encoder part is consisted of Res2Net modules with attention mechanism to achieve fast focus of cracks. Cascade and parallel mode dilated convolutions are set as the center part to enlarge the receptive field of feature points without reducing the resolution of the feature maps. The decoder integrates multiple side output feature maps for pavement crack detection in the manner of feature pyramid. We use ODS, OIS and AP to evaluate the performance of our network. To demonstrate the validity and accuracy of the proposed method, we compare it with some existing methods. The experimental results in multiple crack datasets reveal that our method is superior to these methods.

关键词： Feature extraction Semantics Decoding Encoding Intelligent transportation systems detection algorithms Task analysis Concrete pavement crack detection dilated convolution attention mechanism multi-features fusion

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PW-YOLO-Pose: A Novel Algorithm for Pose Estimation of Power Workers

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IEEE ACCESS 2024年 12卷 116841-116860页

作者： Su, Qin Zhang, Juling Chen, Mingju Peng, Hongming Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Power Internet Things Key Lab Sichuan Prov Chengdu 610095 Peoples R China

To address the detection challenges of keypoints, such as misdetections and omissions causedby backgrounds, occlusions, small targets, and extreme viewpoints in complex electrical power operationenvironments for power workers. This study proposes a 2D pose estimation algorithm for power workersbased on YOLOv5s6-Pose: PW-YOLO-Pose. In this study, the detection rate of occluded keypoints isimproved by embedding the Swin Transformer encoder in the top layer of the backbone network. Theproposed BiFPN (a weighted bi-directional feature pyramid network) structure with a small target detectionlayer improves the detection rate of small target characters and the precision of their keypoints'*** keypoint regression precision is improved overall by using CA (coordinate attention) in the model neckand improving the bounding box regression loss function to Wise-IoU. The algorithmic model in this studydemonstrates excellent detection and largely meets the real-time requirements on the proposed power workerpose estimation dataset in this study. ThemAP(0.5)(The mean average precision when the threshold for objectkeypoint similarity is set to 0.5.) andmAP(0.5:0.95)are 93.35% and 64.75% respectively, which are 5.22% and1.53% higher than the baseline model. The detection time of a single image is 21.3 ms, respectively. It canserve as a valuable theoretical foundation and reference for behavior recognition and state monitoring ofpower workers in intricate electrical power operation environments.

关键词： Pose estimation Prediction algorithms YOLO Feature extraction Inference algorithms detection algorithms Transformers Bidirectional control Power system reliability Personnel Encoding Electrical power operation pose estimation YOLO-Pose detection of keypoints

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Group Testing With Correlation Under Edge-Faulty Graphs

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第12期70卷 8478-8494页

作者： Nikpey, Hesam Kim, Jungyeol Chen, Xingran Sarkar, Saswati Bidokhti, Shirin Saeedi Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA JP Morgan Chase Jersey City NJ 07310 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

In applications of group testing in networks, e.g. identifying individuals who are infected by a disease spread over a network, exploiting correlation among network nodes provides fundamental opportunities in reducing the number of tests needed. We model and analyze group testing on n correlated nodes whose interactions are specified by a graph G. We model correlation through an edge-faulty random graph formed from G in which each edge is dropped with probability 1- r, and in the newly formed graph, all nodes in the same component have the same state. We consider three classes of graphs: cycles and trees, d-regular graphs and stochastic block models or SBM, and obtain lower and upper bounds on the number of tests needed to identify the defective nodes. Roughly speaking, we use correlation among the states of the nodes to transform the problem into that of a smaller graph with independent node states. This enhancement is quantified through the ratio of the diminished node count to the overall count of nodes, n;thus, a lower ratio signifies superior performance. The lower bounds are derived by illustrating a strong dependence of the number of tests needed on the expected number of components. In this regard, we establish a new approximation for the distribution of component sizes in "d-regular trees" which may be of independent interest and leads to a lower bound on the expected number of components in dregular graphs. The upper bounds are found by forming dense subgraphs in which nodes are more likely to be in the same state. When G is a cycle or tree, we show an improvement by a factor of log(1/r). For grid, a graph with almost 2n edges, the improvement is by a factor of (1 - r) log(1/r), indicating drastic improvement compared to trees. When G has a larger number of edges, as in SBM, the improvement can scale in n.

关键词： Graph theory adaptive algorithms detection algorithms

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New Generation Deep Learning for Video Object detection: A Survey

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 3195-3215页

作者： Jiao, Licheng Zhang, Ruohan Liu, Fang Yang, Shuyuan Hou, Biao Li, Lingling Tang, Xu Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.

关键词： Object detection Feature extraction Deep learning Task analysis detection algorithms Learning systems Convolution Learning neural networks object detection pipeline processing video signal processing

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An Ensemble Approach to Computationally Efficient Radiological Anomaly detection and Isotope Identification

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2022年第10期69卷 2168-2178页

作者： Lee, J. Cooper, R. J. Joshi, T. H. Bilton, K. J. Raji, D. M. Bandstra, M. S. Vetter, K. Univ Calif Berkeley Nucl Engn Dept Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Appl Nucl Phys ANP Program Berkeley CA 94720 USA Univ Tennessee Nucl Engn Dept Knoxville TN 37996 USA

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed;however, their computational complexity and, hence, reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm;a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the non-negative matrix factorization approach-based source identification (NMF-ID) algorithm were combined and implemented on a single-board microcontroller, and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

关键词： Detectors detection algorithms Benchmark testing Estimation Barium Power demand Energy resolution Anomaly detection gamma-ray detection low-power electronics non-negative matrix factorization (NMF) radiation source search

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detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 2978-2990页

作者： Moudoud, Hajar Mlika, Zoubeir Khoukhi, Lyes Cherkaoui, Soumaya Univ Sherbrooke INTERLAB Res Lab Dept Elect & Comp Sci Engn Sherbrooke PQ J1K 2R1 Canada Univ Technol Troyes Auton Networking Environm ERA Charles Delaunay Inst ICD F-10300 Troyes France Natl Grad Sch Engn & Res Ctr GREYC Lab ENSICAEN F-14000 Caen France Polytech Montreal Dept Comp & Software Engn Montreal PQ H3T 1J4 Canada

False data injection (FDI) attacks aim to threaten the security of Internet of Things (IoT) systems by falsifying a device's measurements without being detected. In this paper, we propose a process for detecting and predicting FDI attacks, which aims to predict future attacks before they occur and induce IoT devices to behave reliably. First, we propose a novel artificial intelligence (AI)-based detection and prediction module that uses a hidden Markov model (HMM) to observe the behavior of IoT devices and predict their future actions. Next, we design a distributed trust management module that establishes trust between devices using a set of weighted votes. To defend against FDI attacks in communication channels, we formulate a bandwidth optimization problem to meticulously allocate bandwidth to trusted devices. In addition, we propose an efficient incentive mechanism that uses reputation rewards to encourage trustworthy behavior and uses a punishment mechanism to neutralize malicious behavior. Simulations show that the proposed process outperforms recent benchmark FDI attack detection algorithms in the literature in terms of significantly improving attack detection accuracy and reducing attack detection latency.

关键词： Hidden Markov models Internet of Things Security Reliability Trust management Markov processes detection algorithms Internet of Things (IoT) false data injection attacks hidden Markov model incentive mechanism security

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Gas-Theft Suspect detection Among Boiler Room Users: A Data-Driven Approach

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第12期34卷 5796-5808页

作者： Yi, Xiuwen Yang, Xiaodu Huang, Yanyong Ke, Songyu Zhang, Junbo Li, Tianrui Zheng, Yu JD Intelligent Cities Res Beijing 539038 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu 610074 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Southwest Jiaotong Univ Inst Artificial Intelligence Chengdu 611756 Peoples R China

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. With the rapidly deployed IoT sensors, we can collect real-time gas consumption data to analyze users' behavior patterns, where the gas-theft suspects could be discovered early and accurately. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines. We have developed a real-time system on the cloud, providing daily gas-theft suspects for gas companies.

关键词： Boilers detection algorithms Temperature distribution Data integrity Anomaly detection Feature extraction Companies Gas-theft suspect normality detection anomaly detection urban computing

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