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2021-02-15
Drakopoulos, G., Giotopoulos, K., Giannoukou, I., Sioutas, S..  2020.  Unsupervised Discovery Of Semantically Aware Communities With Tensor Kruskal Decomposition: A Case Study In Twitter. 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA. :1–8.
Substantial empirical evidence, including the success of synthetic graph generation models as well as of analytical methodologies, suggests that large, real graphs have a recursive community structure. The latter results, in part at least, in other important properties of these graphs such as low diameter, high clustering coefficient values, heavy degree distribution tail, and clustered graph spectrum. Notice that this structure need not be official or moderated like Facebook groups, but it can also take an ad hoc and unofficial form depending on the functionality of the social network under study as for instance the follow relationship on Twitter or the connections between news aggregators on Reddit. Community discovery is paramount in numerous applications such as political campaigns, digital marketing, crowdfunding, and fact checking. Here a tensor representation for Twitter subgraphs is proposed which takes into consideration both the followfollower relationships but also the coherency in hashtags. Community structure discovery then reduces to the computation of Tucker tensor decomposition, a higher order counterpart of the well-known unsupervised learning method of singular value decomposition (SVD). Tucker decomposition clearly outperforms the SVD in terms of finding a more compact community size distribution in experiments done in Julia on a Twitter subgraph. This can be attributed to the facts that the proposed methodology combines both structural and functional Twitter elements and that hashtags carry an increased semantic weight in comparison to ordinary tweets.
Hu, X., Deng, C., Yuan, B..  2020.  Reduced-Complexity Singular Value Decomposition For Tucker Decomposition: Algorithm And Hardware. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1793–1797.
Tensors, as the multidimensional generalization of matrices, are naturally suited for representing and processing high-dimensional data. To date, tensors have been widely adopted in various data-intensive applications, such as machine learning and big data analysis. However, due to the inherent large-size characteristics of tensors, tensor algorithms, as the approaches that synthesize, transform or decompose tensors, are very computation and storage expensive, thereby hindering the potential further adoptions of tensors in many application scenarios, especially on the resource-constrained hardware platforms. In this paper, we propose a reduced-complexity SVD (Singular Vector Decomposition) scheme, which serves as the key operation in Tucker decomposition. By using iterative self-multiplication, the proposed scheme can significantly reduce the storage and computational costs of SVD, thereby reducing the complexity of the overall process. Then, corresponding hardware architecture is developed with 28nm CMOS technology. Our synthesized design can achieve 102GOPS with 1.09 mm2 area and 37.6 mW power consumption, and thereby providing a promising solution for accelerating Tucker decomposition.
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).
2020-12-15
Kaur, S., Jindal, A..  2020.  Singular Value Decomposition (SVD) based Image Tamper Detection Scheme. 2020 International Conference on Inventive Computation Technologies (ICICT). :695—699.
Image authentication techniques are basically used to check whether the received document is accurate or actual as it was transmitted by the source node or not. Image authentication ensures the integrity of the digital images and identify the ownership of the copyright of the digital images. Singular Value Decomposition (SVD) is method based on spatial domain which is used to extract important features from an image. SVD function decomposes an image into three matrices (U, S, V), the S matrix is a diagonal matrix constitutes singular values. These values are important features of that particular image. The quick response code features are utilized to create QR code from the extracted values. The evaluations produced represents that this designed method is better in producing authenticated image as compared to existing schemes.
2020-10-14
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.

2020-09-21
Xia, Huiyun, Han, Shuai, Li, Cheng, Meng, Weixiao.  2019.  Joint PHY/MAC Layer AN-Assisted Security Scheme in SVD-Based MIMO HARQ system. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :328–333.
With the explosive data growth arise from internet of things, how to ensure information security is facing unprecedented challenges. In this paper, a joint PHY/MAC layer security scheme with artificial noise design in singular value decomposition (SVD) based multiple input multiple output hybrid automatic retransmission request (MIMO HARQ) system is proposed to resolve the problem of low data rates in existing cross-layer security design and further adapt to the high data rate requirement of 5G. First, the SVD was applied to simplify MIMO systems into several parallel sub-channels employing HARQ protocol. Then, different from traditional null space based artificial noise design, the artificial noise design, which is dependent on the characteristics of channel states and transmission rounds, is detailed presented. Finally, the analytical and simulation results proved that with the help of the proposed artificial noise, both the information security and data rate performance can be significantly improved compared with that in single input single output (SISO) system.
2018-01-23
Dabas, N., Singh, R. P., Kher, G., Chaudhary, V..  2017.  A novel SVD and online sequential extreme learning machine based watermark method for copyright protection. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–5.

For the increasing use of internet, it is equally important to protect the intellectual property. And for the protection of copyright, a blind digital watermark algorithm with SVD and OSELM in the IWT domain has been proposed. During the embedding process, SVD has been applied to the coefficient blocks to get the singular values in the IWT domain. Singular values are modulated to embed the watermark in the host image. Online sequential extreme learning machine is trained to learn the relationship between the original coefficient and the corresponding watermarked version. During the extraction process, this trained OSELM is used to extract the embedded watermark logo blindly as no original host image is required during this process. The watermarked image is altered using various attacks like blurring, noise, sharpening, rotation and cropping. The experimental results show that the proposed watermarking scheme is robust against various attacks. The extracted watermark has very much similarity with the original watermark and works good to prove the ownership.

2015-05-06
Trabelsi, W., Selmi, M.H..  2014.  Multi-signature robust video watermarking. Advanced Technologies for Signal and Image Processing (ATSIP), 2014 1st International Conference on. :158-163.

Watermarking is a recently developed technique which is currently dominating the world of security and digital processing in order to ensure the protection of digitized trade. The purpose of this work is twofold. It is firstly to establish a state of the art that goes through the existing watermarking methods and their performances. And secondly to design, implement and evaluate a new watermarking solution that aims to optimize the compromise robustness-invisibility-capacity. The proposed approach consists on applying a frequency watermarking based on singular value decomposition (SVD) and exploiting the mosaic made from all video frames as well as inserting a double signature in order to increase watermarking algorithm capacity.