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Multi-Objective Optimization-Based Recommendation for Massive Online Learning Resources

IEEE SENSORS JOURNAL 2021年第22期21卷 25274-25281页

作者： Li, Hui Zhong, Zhaoman Shi, Jun Li, Haining Zhang, Yong Jiangsu Ocean Univ Sch Comp Engn Lianyungang 222000 Peoples R China Jiangsu Prov Res & Dev Inst Marine Resources Lianyungang 222000 Peoples R China Ningxia Med Univ Dept Neurol Gen Hosp Yinchuan 750004 Ningxia Peoples R China

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the "information overload" problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary Algorithm-based online learning Resource Recommendation Model to balance the system's accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm.

关键词： Sensors Optimization Prediction algorithms Evolutionary computation clustering algorithms Indexes Heating systems Multi-objective optimization optimization algorithm online learning resources learning resource recommendation

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Asymmetric Mutual Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

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IEEE ACCESS 2021年 9卷 69971-69984页

作者： Dong, Yachao Liu, Hongzhe Xu, Cheng Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing 100101 Peoples R China

Unsupervised domain adaptive person re-identification aims to solve the problem of poor performance caused by transferring an unlabeled target domain from the labeled source domain in the re-identification task. The clustering pseudo-labels method in unsupervised learning is widely used in unsupervised adaptive person re-identification tasks, and it maintains state-of-the-art performance. However, pseudo-labels obtained through clustering often have much noise, and the use of a single network model structure and a single clustering algorithm can easily cause model learning to stagnate, making the model not generalizable. To solve this problem, this paper proposes an asymmetric mutual mean-teaching method for unsupervised adaptive person re-identification. In terms of feature extraction, two asymmetric network models with different structures are used for mutual mean-teaching on the target domain, making the features extracted by the network more robust. In terms of feature clustering, two clustering methods are used for mutual teaching to dynamically update the centroid of clusters to improve the confidence of clustering pseudo-labels. Finally, the triplet loss is improved based on the updated cluster centroid to improve the clustering effect. The proposed method is used to perform a large number of verification experiments on three public datasets. The experimental results show that the proposed method has better accuracy than other unsupervised person re-identification based on clustering pseudo-labels.

关键词： Feature extraction Adaptation models Training Task analysis Generative adversarial networks clustering algorithms Unsupervised learning Deep learning person re-identification unsupervised domain adaptation mutual mean-teaching

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Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

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IEEE ACCESS 2021年 9卷 88452-88466页

作者： Wang, Xiaoyan Fu, Xiongjun Dong, Jian Jiang, Jiahuan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Army Engn Univ Shijiazhuang Campus Shijiazhuang 050003 Hebei Peoples R China

Radar signal sorting is the core part of electronic support measures, which is responsible for deinterleaving the overlapping pulse sequences received by the receiver from the complex environment, separating different radiation source signals, and providing support for radiation source identification. Particle swarm optimization (PSO) is a population-based global optimization algorithm with great advantages in intelligent sorting of complex signals, which can adapt to the electromagnetic environment with complex and variable radiation source signals and high pulse stream density. However, the PSO-based sorting method is prone to premature convergence and cannot adaptively adjust particle swarm parameters and positions. In this paper, a dynamic modified chaotic PSO algorithm (DMCPSO) is proposed. Chaotic search is used to increase the diversity of particle swarm in the later iteration to avoid premature convergence and falling into local optimum. Adaptive adjustment parameters related to the particle fitness value are adopted to balance the ability of global search and local search. A new fitness function is proposed and the particle position is dynamically corrected by clustering analysis to improve the accuracy of particle position optimization and avoid the influence from the distribution of feature parameters. The simulation results show that the DMCPSO algorithm provides stable and fast performance with excellent sorting indexes in complex, variable, and dense signal environment.

关键词： Sorting clustering algorithms Heuristic algorithms Radar Particle swarm optimization Sociology Optimization Radar signal sorting particle swarm optimization chaotic search adaptive adjustment parameters fitness function dynamic position correction

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Tau Identification With Deep Neural Networks at the CMS Experiment

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2021年第8期68卷 2194-2200页

作者： Choudhury, Somnath Indian Inst Sci Bengaluru 560012 India

The reconstruction and identification of tau leptons decaying to hadrons are crucial for new physics signatures and precision measurements with tau leptons in the final state at the LHC. The recently deployed tau identification algorithm using deep neural network (DNN) at the CMS experiment for the discrimination of hadronic tau decays from quark or gluon induced jets, electrons, or muons is an ideal example for the exploitation of modern deep learning neural network techniques in high energy physics. With this algorithm, significant suppression of tau misidentification rates has been achieved for the same identification efficiency compared to previous algorithms at the LHC, leading to considerable performance gains for physics studies with tau leptons. This new multiclass DNN-based tau identification algorithm at CMS and its performance are presented in this article.

关键词： Mesons Photonics Convolution Neural networks Image reconstruction Computer architecture clustering algorithms Deep neural network (DNN) hadrons machine learning misidentification rate tau identification tau lepton

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Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3947-3962页

作者： Schroth, Christian A. Muma, Michael Tech Univ Darmstadt Signal Proc Grp D-64289 Darmstadt Germany

This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that integrate robustness and skewness into a single unified cluster analysis framework. Non-symmetrically distributed and heavy-tailed data clusters have been reported in a variety of real-world applications. Robustness is essential because a few outlying observations can severely obscure the cluster structure. The RESK distributions are a generalization of the Real Elliptically Symmetric (RES) distributions. To estimate the cluster parameters and memberships, we derive an expectation maximization (EM) algorithm for arbitrary RESK distributions. Special attention is given to a new robust skew-Huber M-estimator, which is also the approximate maximum likelihood estimator (MLE) for the skew-Huber distribution, that belongs to the RESK class. Numerical experiments on simulated and real-world data confirm the usefulness of the proposed methods for skewed and heavy-tailed data sets.

关键词： clustering algorithms Signal processing algorithms Mixture models Robustness Maximum likelihood estimation Symmetric matrices Data models Robust cluster analysis real elliptically skewed distributions EM algorithm heavy-tailed mixture models multivariate RES distributions M-estimator robust data science unsupervised learning skew-Gaussian skew-t skew-Huber

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A Detection Framework Against CPMA Attack Based on Trust Evaluation and Machine Learning in IoT Network

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IEEE INTERNET OF THINGS JOURNAL 2021年第20期8卷 15249-15258页

作者： Liu, Liang Xu, Xiangyu Liu, Yulei Ma, Zuchao Peng, Jianfei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

Internet of Things (IoT) network is vulnerable to various cyberattacks, especially insider attacks. Most existing studies mainly detect nontargeted insider attackers, who manipulate all packets forwarded by them with a probability. Compared with nontargeted attackers, targeted attackers only manipulate specific packets, which makes them more efficient and covert. In this article, we propose a targeted insider attack model called conditional packets manipulation attack (CPMA), in which attackers maliciously manipulate the packets whose attribute values meet specific conditions with a probability. When resisting the CPMA attack, most existing detection algorithms are inefficient to find such malicious behavior. Also, they detect malicious nodes by collecting and analyzing the overall behavior of nodes, which are not appropriate for energy-constrained nodes in the IoT network. To solve these problems, we present CPMAED, a malicious nodes detection framework against CPMA attack. CPMAED maintains some partial trust metrics for each relay node, which indicate the probability of launch attacks when forwarding the packets with different attribute values. Also, our scheme leverages regression and clustering algorithms to evaluate the trust values of nodes and classify them into benign or malicious. In order to obtain higher detection accuracy, we optimize the routing of transmitted packets and inject the packets to collect more information about nodes to enhance detection. The experimental results show that our proposed scheme utilizing support vector machine and K-means can achieve good detection performance and identify malicious nodes' attack modes with high accuracy.

关键词： Internet of Things Routing Relays Machine learning Sensors Measurement clustering algorithms Insider attacks Internet of Things (IoT) network machine learning trust evaluation

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Algorithmic performance-data classification of condensing heat exchangers

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NUMERICAL HEAT TRANSFER PART A-APPLICATIONS 2021年第1期79卷 1-20页

作者： Pacheco-Vega, Arturo Avila, Gabriela Calif State Univ Los Angeles Dept Mech Engn Los Angeles CA 90032 USA Univ Autonoma San Luis Potosi CIEP Fac Ciencias Quim San Luis Potosi San Luis Potosi Mexico

We describe a methodology able to extract the regimes of operation from condensing heat exchanger data. The methodology applies a Gaussian mixtures clustering algorithm to determine the number of groups directly from the data, and a maximum likelihood decision rule to classify such data into these clusters. Published measurements visually classified as dry-surface, dropwise condensation, and film condensation, are used to illustrate the applicability of the classification technique. Since some results from the algorithmic and visual classifications differ, the second part presents an independent evaluation of the allocation process by a procedure based on artificial neural networks (ANNs) and a variant of cross-validation. Results from the ANN approach to the same data show remarkable agreement with the clustering technique confirming that an algorithmic classification of complex physical phenomena is not only possible but also accurate, and represents an excellent alternative to typical visual-based procedures for the analysis of thermal and other systems.

关键词： clustering algorithms

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Protein Complexes Detection Based on Semi-Supervised Network Embedding Model

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第2期18卷 797-803页

作者： Zhu, Jia Zheng, Zetao Yang, Min Fung, Gabriel Pui Cheong Huang, Changqin South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China Chinese Univ Hong Kong Dept SEEM Hong Kong Peoples R China Zhejiang Normal Univ Dept Educ Technol Jinhua 321004 Zhejiang Peoples R China

A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, the subsets of proteins that are tightly coupled, from it. Network embedding is a technique to learn low-dimensional representations of vertices in networks. It has been proved quite useful for community detection in social networks in recent years. However, unlike social networks, PPI network does not contain rich metadata, so that existing network embedding methods cannot fully capture the network structure of PPI to improve the effect of protein complexes detection significantly. We propose a semi-supervised network embedding model by adopting graph convolutional networks to detect densely connected subgraphs effectively. We compare the performance of our model with state-of-the-art approaches on three popular PPI networks with various data sizes and densities. The experimental results show that our approach significantly outperforms other approaches on all three PPI networks.

关键词： Proteins Protein engineering clustering algorithms Biological system modeling Computational modeling Social networking (online) Data models Protein complexes detection network embedding deep learning

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A Cluster-Principal-Component-Analysis-Based Indoor Positioning Algorithm

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IEEE INTERNET OF THINGS JOURNAL 2021年第1期8卷 187-196页

作者： Li, Ang Fu, Jingqi Shen, Huaming Sun, Sizhou Shanghai Univ Dept Mechatron Engn & Automat Shanghai 200444 Peoples R China

Indoor location-based service is emerging as the crucial application of the Internet of Things, which promotes the advance of relevant technology in the indoor scenario. Several positioning algorithms are proposed for different indoor configurations in recent years. The fingerprint-based indoor positioning algorithm has drawn much attention because of the good positioning performance without additional hardware. However, the false fingerprint matching frequently incurs due to the complexity of the indoor positioning environment and affects the positioning accuracy. In this article, a principal component analysis (PCA)-assisted indoor positioning algorithm based on the adaptive hierarchical clustering algorithm (PAHC) is proposed, which can improve the positioning accuracy through aggregating the reference points (RPs) and conducting the cluster-based PCA (C-PCA) features extraction. More specifically, a clustering termination method is proposed to obtain reasonable RPs clusters adaptively according to the preset RPs. A two-stage fingerprint matching algorithm is proposed based on the C-PCA to further increase the difference between similar RPs and thus improving the positioning accuracy. To verify the proposed algorithm, an indoor wireless system is established in the practical indoor scenario. The experimental results indicate that the proposed PAHC algorithm can increase the positioning accuracy by 9.3% compared with the conventional fingerprint algorithm.

关键词： clustering algorithms Principal component analysis Interference Estimation Internet of Things Wireless sensor networks Feature extraction Fingerprint matching algorithm hierarchical clustering indoor positioning algorithm principal component analysis (PCA) sums-of-squared deviations

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BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第4期33卷 1375-1388页

作者： Cai, Hongyun Zhang, Fuzhi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066000 Hebei Peoples R China Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066000 Hebei Peoples R China

Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which does not need to know the attack size or to label the candidate spammers. BS-SC starts from an in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and behavior similarity matrix clustering) to distinguish shilling profiles from genuine ones. The behavior features reflect the behavior difference between genuine and shilling profiles, and the behavior similarity matrix clustering is to cluster shilling profiles based on their highly similar behaviors. Experimental results on the MovieLens and the sampled Amazon review datasets indicate that BS-SC outperforms the baseline unsupervised approaches, even when the prior knowledge is given for them.

关键词： Feature extraction Recommender systems Collaboration Training Correlation clustering algorithms Detection algorithms Collaborative recommender systems shilling attacks detection behavior analysis spectral clustering

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