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Liang, Y., Bai, L., Shao, J., Cheng, Y..  2020.  Application of Tensor Decomposition Methods In Eddy Current Pulsed Thermography Sequences Processing. 2020 International Conference on Sensing, Measurement Data Analytics in the era of Artificial Intelligence (ICSMD). :401–406.
Eddy Current Pulsed Thermography (ECPT) is widely used in Nondestructive Testing (NDT) of metal defects where the defect information is sometimes affected by coil noise and edge noise, therefore, it is necessary to segment the ECPT image sequences to improve the detection effect, that is, segmenting the defect part from the background. At present, the methods widely used in ECPT are mostly based on matrix decomposition theory. In fact, tensor decomposition is a new hotspot in the field of image segmentation and has been widely used in many image segmentation scenes, but it is not a general method in ECPT. This paper analyzes the feasibility of the usage of tensor decomposition in ECPT and designs several experiments on different samples to verify the effects of two popular tensor decomposition algorithms in ECPT. This paper also compares the matrix decomposition methods and the tensor decomposition methods in terms of treatment effect, time cost, detection success rate, etc. Through the experimental results, this paper points out the advantages and disadvantages of tensor decomposition methods in ECPT and analyzes the suitable engineering application scenarios of tensor decomposition in ECPT.
Wu, Y., Olson, G. F., Peretti, L., Wallmark, O..  2020.  Harmonic Plane Decomposition: An Extension of the Vector-Space Decomposition - Part I. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. :985–990.
In this first paper of a two-part series, the harmonic plane decomposition is introduced, which is an extension of the vector-space decomposition. In multiphase electrical machines with variable phase-pole configurations, the vector-space decomposition leads to a varying numbers of vector spaces when changing the configuration. Consequently, the model and current control become discontinuous. The method in this paper is based on samples of each single slot currents, similarly to a discrete Fourier transformation in the space domain that accounts for the winding configuration. It unifies the Clarke transformation for all possible phase-pole configurations such that a fixed number of orthogonal harmonic planes are created, which facilitates the current control during reconfigurations. The presented method is not only limited to the modeling of multiphase electrical machines but all kinds of existing machines can be modeled. In the second part of this series, the harmonic plane decomposition will be completed for all types of machine configurations.
Hemmati, A., Nasiri, H., Haeri, M. A., Ebadzadeh, M. M..  2020.  A Novel Correlation-Based CUR Matrix Decomposition Method. 2020 6th International Conference on Web Research (ICWR). :172–176.
Web data such as documents, images, and videos are examples of large matrices. To deal with such matrices, one may use matrix decomposition techniques. As such, CUR matrix decomposition is an important approximation technique for high-dimensional data. It approximates a data matrix by selecting a few of its rows and columns. However, a problem faced by most CUR decomposition matrix methods is that they ignore the correlation among columns (rows), which gives them lesser chance to be selected; even though, they might be appropriate candidates for basis vectors. In this paper, a novel CUR matrix decomposition method is proposed, in which calculation of the correlation, boosts the chance of selecting such columns (rows). Experimental results indicate that in comparison with other methods, this one has had higher accuracy in matrix approximation.
Rana, M. M., Mehedie, A. M. Alam, Abdelhadi, A..  2020.  Optimal Image Watermark Technique Using Singular Value Decomposition with PCA. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :342–347.
Image watermarking is very important phenomenon in modern society where intellectual property right of information is necessary. Considering this impending problem, there are many image watermarking methods exist in the literature each of having some key advantages and disadvantages. After summarising state-of-the-art literature survey, an optimum digital watermark technique using singular value decomposition with principle component analysis (PCA) is proposed and verified. Basically, the host image is compressed using PCA which reduces multi-dimensional data to effective low-dimensional information. In this scheme, the watermark is embedded using the discrete wavelet transformation-singular value decomposition approach. Simulation results show that the proposed method improves the system performance compared with the existing method in terms of the watermark embedding, and extraction time. Therefore, this work is valuable for image watermarking in modern life such as tracing copyright infringements and banknote authentication.
Uzhga-Rebrov, O., Kuleshova, G..  2020.  Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set. 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS). :1–4.
The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).
Wang, J., Wang, A..  2020.  An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :310–315.
In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
Li, W., Zhu, H., Zhou, X., Shimizu, S., Xin, M., Jin, Q..  2018.  A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). :418–422.
The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.
Xie, Kun, Li, Xiaocan, Wang, Xin, Xie, Gaogang, Xie, Dongliang, Li, Zhenyu, Wen, Jigang, Diao, Zulong.  2019.  Quick and Accurate False Data Detection in Mobile Crowd Sensing. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2215—2223.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Parvina, Hashem, Moradi, Parham, Esmaeilib, Shahrokh, Jalilic, Mahdi.  2018.  An Efficient Recommender System by Integrating Non-Negative Matrix Factorization With Trust and Distrust Relationships. 2018 IEEE Data Science Workshop (DSW). :135—139.

Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.

Razin, Yosef, Feigh, Karen.  2019.  Toward Interactional Trust for Humans and Automation: Extending Interdependence. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1348–1355.
Trust in human-automation interaction is increasingly imperative as AI and robots become ubiquitous at home, school, and work. Interdependence theory allows for the identification of one-on-one interactions that require trust by analyzing the structure of the potential outcomes. This paper synthesizes multiple, formerly disparate research approaches by extending Interdependence theory to create a unified framework for outcome-based trust in human-automation interaction. This framework quantitatively contextualizes validated empirical results from social psychology on relationship formation, stability, and betrayal. It also contributes insights into trust-related concepts, such as power and commitment, which help further our understanding of trustworthy system design. This new integrated interactional approach reveals how trust and trustworthiness machines from merely reliable tools to trusted teammates working hand-in-actuator toward an automated future.
Li, Tao, Ren, Yongzhen, Ren, Yongjun, Wang, Lina, Wang, Lingyun, Wang, Lei.  2019.  NMF-Based Privacy-Preserving Collaborative Filtering on Cloud Computing. 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :476–481.
The security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This paper proposes a hybrid algorithm based on NMF and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are non-negative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, etc., and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.
Zhang, Yonghong, Zheng, Peijia, Luo, Weiqi.  2019.  Privacy-Preserving Outsourcing Computation of QR Decomposition in the Encrypted Domain. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :389—396.

Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.

Li, Feiyan, Li, Wei, Huo, Hongtao, Ran, Qiong.  2019.  Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. :401—404.

Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

Jia, Guanbo, Miller, Paul, Hong, Xin, Kalutarage, Harsha, Ban, Tao.  2019.  Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :458—465.

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.

Singh, Neha, Joshi, Sandeep, Birla, Shilpi.  2019.  Suitability of Singular Value Decomposition for Image Watermarking. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). :983—986.

Digital images are extensively used and exchanged through internet, which gave rise to the need of establishing authorship of images. Image watermarking has provided a solution to prevent false claims of ownership of the media. Information about the owner, generally in the form of a logo, text or image is imperceptibly hid into the subject. Many transforms have been explored by the researcher community for image watermarking. Many watermarking techniques have been developed based on Singular Value Decomposition (SVD) of images. This paper analyses Singular Value Decomposition to understand its use, ability and limitations to hide additional information into the cover image for Digital Image Watermarking application.

Saha, Arunima, Srinivasan, Chungath.  2019.  White-Box Cryptography Based Data Encryption-Decryption Scheme for IoT Environment. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :637–641.

The economic progress of the Internet of Things (IoT) is phenomenal. Applications range from checking the alignment of some components during a manufacturing process, monitoring of transportation and pedestrian levels to enhance driving and walking path, remotely observing terminally ill patients by means of medical devices such as implanted devices and infusion pumps, and so on. To provide security, encrypting the data becomes an indispensable requirement, and symmetric encryptions algorithms are becoming a crucial implementation in the resource constrained environments. Typical symmetric encryption algorithms like Advanced Encryption Standard (AES) showcases an assumption that end points of communications are secured and that the encryption key being securely stored. However, devices might be physically unprotected, and attackers may have access to the memory while the data is still encrypted. It is essential to reserve the key in such a way that an attacker finds it hard to extract it. At present, techniques like White-Box cryptography has been utilized in these circumstances. But it has been reported that applying White-Box cryptography in IoT devices have resulted in other security issues like the adversary having access to the intermediate values, and the practical implementations leading to Code lifting attacks and differential attacks. In this paper, a solution is presented to overcome these problems by demonstrating the need of White-Box Cryptography to enhance the security by utilizing the cipher block chaining (CBC) mode.

Yang, Chao, Chen, Xinghe, Song, Tingting, Jiang, Bin, Liu, Qin.  2018.  A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1413–1418.
In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent.
Uto, K., Mura, M. D., Chanussot, J..  2018.  Spatial Resolution Enhancement of Optical Images Based on Tensor Decomposition. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. :8058-8061.

There is an inevitable trade-off between spatial and spectral resolutions in optical remote sensing images. A number of data fusion techniques of multimodal images with different spatial and spectral characteristics have been developed to generate optical images with both spatial and spectral high resolution. Although some of the techniques take the spectral and spatial blurring process into account, there is no method that attempts to retrieve an optical image with both spatial and spectral high resolution, a spectral blurring filter and a spectral response simultaneously. In this paper, we propose a new framework of spatial resolution enhancement by a fusion of multiple optical images with different characteristics based on tensor decomposition. An optical image with both spatial and spectral high resolution, together with a spatial blurring filter and a spectral response, is generated via canonical polyadic (CP) decomposition of a set of tensors. Experimental results featured that relatively reasonable results were obtained by regularization based on nonnegativity and coupling.

Vaidya, S. P..  2018.  Multipurpose Color Image Watermarking in Wavelet Domain Using Multiple Decomposition Techniques. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :251-255.

A multipurpose color image watermarking method is presented to provide \textcopyright protection and ownership verification of the multimedia information. For robust color image watermarking, color watermark is utilized to bring universality and immense applicability to the proposed scheme. The cover information is first converted to Red, Green and Blue components image. Each component is transformed in wavelet domain using DWT (Discrete Wavelet Transform) and then decomposition techniques like Singular Value Decomposition (SVD), QR and Schur decomposition are applied. Multiple watermark embedding provides the watermarking scheme free from error (false positive). The watermark is modified by scrambling it using Arnold transform. In the proposed watermarking scheme, robustness and quality is tested with metrics like Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). Further, the proposed scheme is compared with related watermarking schemes.

Ma, C., Yang, X., Wang, H..  2018.  Randomized Online CP Decomposition. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). :414-419.

CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.

Wang, Y., Zhang, L..  2017.  High Security Orthogonal Factorized Channel Scrambling Scheme with Location Information Embedded for MIMO-Based VLC System. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). :1–5.
The broadcast nature of visible light beam has aroused great concerns about the privacy and confidentiality of visible light communication (VLC) systems.In this paper, in order to enhance the physical layer security, we propose a channel scrambling scheme, which realizes orthogonal factorized channel scrambling with location information embedded (OFCS-LIE) for the VLC systems. We firstly embed the location information of the legitimate user, including the transmission angle and the distance, into a location information embedded (LIE) matrix, then the LIE matrix is factorized orthogonally in order that the LIE matrix is approximately uncorrelated to the multiple-input, multiple-output (MIMO) channels by the iterative orthogonal factorization method, where the iteration number is determined based on the orthogonal error. The resultant OFCS-LIE matrix is approximately orthogonal and used to enhance both the reliability and the security of information transmission. Furthermore, we derive the information leakage at the eavesdropper and the secrecy capacity to analyze the system security. Simulations are performed, and the results demonstrate that with the aid of the OFCS-LIE scheme, MIMO-based VLC system has achieved higher security when compared with the counterpart scrambling scheme and the system without scrambling.
Xi, X., Zhang, F., Lian, Z..  2017.  Implicit Trust Relation Extraction Based on Hellinger Distance. 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). :223–227.

Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.

Li, Q., Xu, B., Li, S., Liu, Y., Cui, D..  2017.  Reconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems. 2017 36th Chinese Control Conference (CCC). :7571–7576.

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.

Bampis, C. G., Rusu, C., Hajj, H., Bovik, A. C..  2017.  Robust Matrix Factorization for Collaborative Filtering in Recommender Systems. 2017 51st Asilomar Conference on Signals, Systems, and Computers. :415–419.

Recently, matrix factorization has produced state-of-the-art results in recommender systems. However, given the typical sparsity of ratings, the often large problem scale, and the large number of free parameters that are often implied, developing robust and efficient models remains a challenge. Previous works rely on dense and/or sparse factor matrices to estimate unavailable user ratings. In this work we develop a new formulation for recommender systems that is based on projective non-negative matrix factorization, but relaxes the non-negativity constraint. Driven by a simple yet instructive intuition, the proposed formulation delivers promising and stable results that depend on a minimal number of parameters. Experiments that we conducted on two popular recommender system datasets demonstrate the efficiency and promise of our proposed method. We make available our code and datasets at\_release.

Alom, M. Z., Taha, T. M..  2017.  Network Intrusion Detection for Cyber Security on Neuromorphic Computing System. 2017 International Joint Conference on Neural Networks (IJCNN). :3830–3837.

In the paper, we demonstrate a neuromorphic cognitive computing approach for Network Intrusion Detection System (IDS) for cyber security using Deep Learning (DL). The algorithmic power of DL has been merged with fast and extremely power efficient neuromorphic processors for cyber security. In this implementation, the data has been numerical encoded to train with un-supervised deep learning techniques called Auto Encoder (AE) in the training phase. The generated weights of AE are used as initial weights for the supervised training phase using neural networks. The final weights are converted to discrete values using Discrete Vector Factorization (DVF) for generating crossbar weight, synaptic weights, and thresholds for neurons. Finally, the generated crossbar weights, synaptic weights, threshold, and leak values are mapped to crossbars and neurons. In the testing phase, the encoded test samples are converted to spiking form by using hybrid encoding technique. The model has been deployed and tested on the IBM Neurosynaptic Core Simulator (NSCS) and on actual IBM TrueNorth neurosynaptic chip. The experimental results show around 90.12% accuracy for network intrusion detection for cyber security on the physical neuromorphic chip. Furthermore, we have investigated the proposed system not only for detection of malicious packets but also for classifying specific types of attacks and achieved 81.31% recognition accuracy. The neuromorphic implementation provides incredible detection and classification accuracy for network intrusion detection with extremely low power.