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NFA:A neural factorization autoencoder based online telephony fraud detection
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作者 Abdul Wahid Mounira Msahli +1 位作者 Albert Bifet Gerard Memmi 《Digital Communications and Networks》 SCIE CSCD 2024年第1期158-167,共10页
The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal ac... The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks. 展开更多
关键词 Telecom industry Streaming anomaly detection Fraud analysis factorization machine Real-time system Security
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Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China
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作者 MA Jie CHU Lijuan +3 位作者 SUN Jing WANG Shenglan GE Miao DENG Li 《Journal of Mountain Science》 SCIE CSCD 2024年第1期100-112,共13页
This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed ... This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors. 展开更多
关键词 Monte Carlo simulation Health risk assessment Trace metal(loid)s Positive matrix factorization Agricultural soils
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Efficient Clustering Network Based on Matrix Factorization
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作者 Jieren Cheng Jimei Li +2 位作者 Faqiang Zeng Zhicong Tao and Yue Yang 《Computers, Materials & Continua》 SCIE EI 2024年第7期281-298,共18页
Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of ... Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms. 展开更多
关键词 Contrastive learning CLUSTERING matrix factorization
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On Factorization of N-Qubit Pure States and Complete Entanglement Analysis of 3-Qubit Pure States Containing Exactly Two Terms and Three Terms
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作者 Dhananjay P.Mehendale Madhav R.Modak 《Journal of Quantum Computing》 2023年第1期15-24,共10页
A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its fac... A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its factors(pure states containing a smaller number of qubits)can be a challenging task,especially for highly entangled states.A new criterion based on the proportionality of the rows of certain associated matrices for the existence of certain factorization and a factorization algorithm that follows from this criterion for systematically extracting all the factors is developed in this paper.3-qubit pure states play a crucial role in quantum computing and quantum information processing.For various applications,the well-known 3-qubit GHZ state which contains two nonzero terms,and the 3-qubit W state which contains three nonzero terms,have been studied extensively.Using the new factorization algorithm developed here we perform a complete analysis vis-à-vis entanglement of 3-qubit states that contain exactly two nonzero terms and exactly three nonzero terms. 展开更多
关键词 Associated matrix proportionality of rows factorization criterion factorization algorithm
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Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding
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作者 卢鹏丽 陈玮 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第1期634-645,共12页
Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat... Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking. 展开更多
关键词 complex networks CENTRALITY joint nonnegative matrix factorization graph embedding smoothness strategy
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Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis
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作者 ZECHAO LU FUCAI TANG +6 位作者 HAOBIN ZHOU ZEGUANG LU WANYAN CAI JIAHAO ZHANG ZHICHENG TANG YONGCHANG LAI ZHAOHUI HE 《BIOCELL》 SCIE 2023年第2期339-350,共12页
Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glyc... Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application. 展开更多
关键词 GLYCOLYSIS Prostate cancer Tumor immune Non-negative matrix factorization Prognostic model
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Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation
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作者 Haonan Huang Guoxu Zhou +2 位作者 Naiyao Liang Qibin Zhao Shengli Xie 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第11期2154-2167,共14页
Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency o... Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches. 展开更多
关键词 Deep matrix factorization(DMF) diversity hypergraph regularization multi-view data representation(MDR)
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DoS Attack Detection Based on Deep Factorization Machine in SDN
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作者 Jing Wang Xiangyu Lei +3 位作者 Qisheng Jiang Osama Alfarraj Amr Tolba Gwang-jun Kim 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1727-1742,共16页
Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,par... Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)attacks.While many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks.To solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in SDN.Firstly,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection feature.Then,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks.After training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is implemented.Simulation results show that our method can effectively detect DoS attacks in SDN.Compared with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values. 展开更多
关键词 Software-defined network denial-of-service attacks deep factorization machine GRMMP
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Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)
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作者 Neetika Bhandari Payal Pahwa 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2043-2055,共13页
Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can... Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index. 展开更多
关键词 Clustering CLARA Davies-Bouldin index Dunn index FCM intelligent systems K-means non-negative matrix factorization(NMF) PAM privacy preserving data mining Silhouette index Xie-Beni index
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Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis 被引量:1
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作者 彭森 许飞云 +1 位作者 贾民平 胡建中 《Journal of Southeast University(English Edition)》 EI CAS 2009年第3期346-350,共5页
Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization (SNTF) is propose... Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization (SNTF) is proposed. First, a non-negative tensor factorization(NTF) algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images' weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra. 展开更多
关键词 non-negative tensor factorization SPARSENESS feature extraction bispectrum gearbox
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Local hierarchical non-negative tensor factorization and its application in machinery fault diagnosis 被引量:1
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作者 王飞 许飞云 王海军 《Journal of Southeast University(English Edition)》 EI CAS 2011年第4期394-399,共6页
Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective ... Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization(NMF) model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor's vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods. 展开更多
关键词 non-negative tensor factorization BISPECTRUM feature extraction air compressor BP neural network
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A Backward Stable Hyperbolic QR Factorization Method for Solving Indefinite Least Squares Problem 被引量:3
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作者 徐洪国 《Journal of Shanghai University(English Edition)》 CAS 2004年第4期391-396,共6页
We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute t... We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed. 展开更多
关键词 indefinite least squares hyperbolic rotation p q-orthogonal matrix hyperbolic QR factorization bidiagonal factorization backward stability.
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[0, ki ]1m -FACTORIZATIONS ORTHOGONAL TO A SUBGRAPH
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作者 马润年 许进 高行山 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2001年第5期593-596,共4页
Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m... Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m}, is a [0, k(i)](1)(m) -factorization of G. If H is a subgraph with m edges in graph G and / E (H) boolean AND E(F-i) / = 1 for all 1 less than or equal to i less than or equal to m, then we can call that (F) over bar is orthogonal to H. It is proved that if G is a [0, k(1) + ... + k(m) - m + 1]-graph, H is a subgraph with m edges in G, then graph G has a [0, k(i)](1)(m)-factorization orthogonal to H. 展开更多
关键词 GRAPH factor factorization orthogonal factorization
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A Novel Multivariate Polynomial Approximation Factorization of Big Data
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作者 Guotao Luo Guang Pei 《国际计算机前沿大会会议论文集》 2015年第1期145-147,共3页
In actual engineering, processing of big data sometimes requires building of mass physical models, while processing of physical model requires relevant math model, thus producing mass multivariate polynomials, the eff... In actual engineering, processing of big data sometimes requires building of mass physical models, while processing of physical model requires relevant math model, thus producing mass multivariate polynomials, the effective reduction of which is a difficult problem at present. A novel algorithm is proposed to achieve the approximation factorization of complex coefficient multivariate polynomial in light of characteristics of multivariate polynomials. At first, the multivariate polynomial is reduced to be the binary polynomial, then the approximation factorization of binary polynomial can produce irreducible duality factor, at last, the irreducible duality factor is restored to the irreducible multiple factor. As a unit root is cyclic, selecting the unit root as the reduced factor can ensure the coefficient does not expand in a reduction process. Chinese remainder theorem is adopted in the corresponding reduction process, which brought down the calculation complexity. The algorithm is based on approximation factorization of binary polynomial and calculation of approximation Greatest Common Divisor, GCD. The algorithm can solve the reduction of multivariate polynomials in massive math models, which can obtain effectively null point of multivariate polynomials, providing a new approach for further analysis and explanation of physical models. The experiment result shows that the irreducible factors from this method get close to the real factors with high efficiency. 展开更多
关键词 Mass physical model MULTIVARIATE POLYNOMIAL APPROXIMATION factorization.Reduction Unit root.Binary APPROXIMATION factorization APPROXIMATION GCD algorithm
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Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization 被引量:25
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作者 GAO Huizhong LIANG Lin +1 位作者 CHEN Xiaoguang XU Guanghua 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第1期96-105,共10页
Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smar... Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space. 展开更多
关键词 time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction
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The Factorization of Adjoint Polynomials of E^G(i)-class Graphs and Chromatically Equivalence Analysis 被引量:15
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作者 ZHANG Bing-ru YANG Ji-ming 《Chinese Quarterly Journal of Mathematics》 CSCD 北大核心 2008年第3期376-383,共8页
Let Sn be the star with n vertices, and let G be any connected graph with p vertices. We denote by Eτp+(r-1)^G(i) the graph obtained from Sr and rG by coinciding the i-th vertex of G with the vertex of degree r ... Let Sn be the star with n vertices, and let G be any connected graph with p vertices. We denote by Eτp+(r-1)^G(i) the graph obtained from Sr and rG by coinciding the i-th vertex of G with the vertex of degree r - 1 of S,, while the i-th vertex of each component of (r - 1)G be adjacented to r - 1 vertices of degree 1 of St, respectively. By applying the properties of adjoint polynomials, We prove that factorization theorem of adjoint polynomials of kinds of graphs Eτp+(r-1)^G(i)∪(r - 1)K1 (1 ≤i≤p). Furthermore, we obtain structure characteristics of chromatically equivalent graphs of their complements. 展开更多
关键词 chromatic polynomial adjoint polynomials factorization chromatically equivalent graph structure characteristics
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Chaotic system and QR factorization based robust digital image watermarking algorithm 被引量:9
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作者 宋伟 侯建军 +1 位作者 李赵红 黄亮 《Journal of Central South University》 SCIE EI CAS 2011年第1期116-124,共9页
In order to protect copyright of digital images,a new robust digital image watermarking algorithm based on chaotic system and QR factorization was proposed.The host images were firstly divided into blocks with same si... In order to protect copyright of digital images,a new robust digital image watermarking algorithm based on chaotic system and QR factorization was proposed.The host images were firstly divided into blocks with same size,then QR factorization was performed on each block.Pseudorandom circular chain(PCC) generated by logistic mapping(LM) was applied to select the embedding blocks for enhancing the security of the scheme.The first column coefficients in Q matrix of chosen blocks were modified to embed watermarks without causing noticeable artifacts.Watermark extraction procedure was performed without the original cover image.The experimental results demonstrate that the watermarked images have good visual quality and this scheme is better than the existing techniques,especially when the image is attacked by cropping,noise pollution and so on.Analysis and discussion on robustness and security issues were also presented. 展开更多
关键词 digital watermarking QR factorization pseudorandom circular chain logistic mapping
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Prime Factorization in the Duality Computer 被引量:8
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作者 WANG Wan-Ying SHANG Bin +1 位作者 WANG Chuan LONG Gui-Lu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2007年第3期471-473,共3页
We give algorithms to factorize large integers in the duality computer. We provide three duality algorithms for factorization based on a naive factorization method, the Shor algorithm in quantum computing, and the Fer... We give algorithms to factorize large integers in the duality computer. We provide three duality algorithms for factorization based on a naive factorization method, the Shor algorithm in quantum computing, and the Fermat's method in classical computing. All these algorithms may be polynomial in the input size. 展开更多
关键词 duality computer prime factorization Fermat's method
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Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation 被引量:10
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作者 Chengcai Leng Hai Zhang +2 位作者 Guorong Cai Irene Cheng Anup Basu 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第2期584-595,共12页
This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information ... This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods. 展开更多
关键词 Data clustering dimensionality reduction GRAPH REGULARIZATION LP SMOOTH non-negative matrix factorization(SNMF)
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Partition-based Collaborative Tensor Factorization for POI Recommendation 被引量:5
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作者 Wenjing Luan Guanjun Liu +1 位作者 Changjun Jiang Liang Qi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第3期437-446,共10页
The rapid development of location-based social networks(LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location.For example,it can he... The rapid development of location-based social networks(LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location.For example,it can help travelers to choose where to go next,or recommend salesmen the most potential places to deliver advertisements or sell products.In this paper,a method for recommending points of interest(POIs)is proposed based on a collaborative tensor factorization(CTF)technique.Firstly,a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices.Secondly,a 3-mode tensor is used to model all users' check-in behaviors,and three feature matrices are extracted to characterize the time distribution,category distribution and POI correlation,respectively.Thirdly,each user's preference to a POI at a specific time can be estimated by using CTF.In order to further improve the recommendation accuracy,PCTF(Partitionbased CTF) is proposed to fill the missing entries of a tensor after clustering its every mode.Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation. 展开更多
关键词 Clustering context feature extraction point of interest(POI) recommendation tensor factorization
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