Ahmad, MusheerAgarwal, ShafaliAlkhayyat, AhmedAlhudhaif, Adi...
20页查看更多>>摘要：The design and utilization of suitable fractal structures is one of the prominent areas of security for the protection of digital data. This paper proposes a generalized fusion fractal structure by combining two one-dimensional fractals as seed functions from a larger spectrum of fractal functions. A fusion fractal termed as PLFF is formulated by combining traditional Phoenix and Lambda fractals. Improved randomized phase space, self-similar structure on various magnification scales, and fractional dimension are found in the resultant PLFF fractal. The capacity of PLFF to create a pseudo-random number (PRN) sequence in both integer and binary format is validated by its increased complexity and enhanced chaotic range. The generated PRN sequences feature a significant degree of uncorrelation and randomness. A novel image encryption algorithm based on the new PLFF fractal function is proposed which utilizes a generated PRN sequence as secret key. Standard security evaluations such as histogram variance, NPCR and UACI tests for plain-image sensitivity, key sensitivity, information entropy, pixel correlation, and noise and data loss, etc. are used to analyze the performance of the proposed encryption algorithm. The simulation results revealed performance indicators such as entropies > 7.997, NPCR > 96.6, UACI > 33.5, high throughput of similar to 6MBps, and highly uncorrelated neighboring pixels in encrypted images. The findings are also compared with some current image encryption schemes, demonstrating that the proposed digital image encryption algorithm performs well. (C) 2022 Elsevier Inc. All rights reserved.
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NSTL
Elsevier
Ran, XunWang, YongZhang, Leo YuMa, Jun...
15页查看更多>>摘要：Nonnegative matrix factorization (NMF)-based models have been proven to be highly effective and scalable in addressing collaborative filtering (CF) problems in the recommender system (RS). Since RS requires tremendous user data to provide personalized information services, the issue of data privacy has gained prominence. Although the differential privacy (DP) technique has been widely applied to RS, the requirement of nonnegativity makes it difficult to successfully incorporate DP into NMF. In this paper, a differentially private NMF (DPNMF) method is proposed by perturbing the coefficients of the polynomial expression of the objective function, which achieves a good trade-off between privacy protection and recommendation quality. Moreover, to alleviate the influence of the noises added by DP on the items with sparse ratings, an imputation-based DPNMF (IDPNMF) method is proposed. Theoretic analyses and experimental results on several benchmark datasets show that the proposed schemes have good performance and can achieve better recommendation quality on large-scale datasets. Therefore, our schemes have high potential to implement privacy-preserving RS based on big data. (C) 2022 Elsevier Inc. All rights reserved.
原文链接:- NSTL
- Elsevier
Yuan, JiangtaoYang, JingWang, ChenyuJia, Xingxing...
14页查看更多>>摘要：Hierarchical secret sharing is an important key management technique since it is specially customized for hierarchical organizations with different departments allocated with different privileges, such as the government agencies or companies. Hierarchical access structures have been widely adopted in secret sharing schemes, where efficiency is the primary consideration for various applications. How to design an efficient hierarchical secret sharing scheme is an important issue. A famous hierarchical secret sharing (HSS) scheme was proposed by Tassa based on Birkhoff interpolation. Later, based on the same method, many other HSS schemes were proposed. However, these schemes all depend on Polya's condition, which is a necessary condition, not a sufficient condition. It cannot guarantee that Tassa's HSS scheme always exists. Furthermore, this condition needs to check the non-singularity of many matrices. We propose a hierarchical multi-secret sharing scheme based on the linear homogeneous recurrence (LHR) relations and the one-way function. In our scheme, we select m linearly independent homogeneous recurrence relations. The participants in the highly- ranked subsets gamma(1),gamma(2), ..., gamma(j-1) join in the jth subset to construct the jth LHR relation. In addition, the proposed hierarchical multi-secret sharing scheme just requires one share for each participant. Besides, our scheme is both perfect and ideal. Furthermore, our scheme avoids many checks of the non-singularity of many matrices in the presented hierarchical secret sharing schemes. Although we need to publish more public values, our scheme reduces the computational complexity of the hierarchical secret sharing schemes from exponential time to polynomial time, i.e., O(n(km-1 )log n), which is relatively more efficient than schemes in the literature. (C) 2022 Published by Elsevier Inc.
原文链接:- NSTL
- Elsevier
Zhu, QingWu, GuohuaLi, HaifengCao, Jun...
17页查看更多>>摘要：Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with adaptive locality ability, which enables the measurement of the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing the importance of nodes in the topology structure. We believe that the structural importance of neighboring nodes is closely related to their importance in aggregation. In this paper, we introduce discrete graph curvature (the Ricci curvature) to quantify the strength of the structural connection of pairwise nodes. We propose a curvature graph neural network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural properties of graph curvature. To improve the adaptability of curvature on various datasets, we explicitly transform curvature into the weights of neighboring nodes by the necessary negative curvature processing module and curvature normalization module. Then, we conduct numerous experiments on various synthetic and real-world datasets. The experimental results on synthetic datasets show that CGNN effectively exploits the topology structure information and that the performance is significantly improved. CGNN outperforms the baselines on 5 dense node classification benchmark datasets. This study provides a deepened understanding of how to utilize advanced topology information and assign the importance of neighboring nodes from the perspective of graph curvature and encourages bridging the gap between graph theory and neural net- works. The source code is available at https://github.com/GeoX-Lab/CGNN. (C) 2021 Published by Elsevier Inc.
原文链接:- NSTL
- Elsevier
Song, WentingLi, XiaomeiTong, Shaocheng
15页查看更多>>摘要：The finite-time event-triggered H-infinity fuzzy output feedback control problem is investigated in this paper for a class of nonlinear systems. The considered nonlinear system is first modeled by an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy system with the modeling errors. A state observer and an event-triggered mechanism are then designed by using the sampled estimating states and measured output signals. Based on the designed state observer and event-triggered mechanism, an observer-based finite-time event-triggered H-infinity fuzzy controller is developed. The sufficient conditions of guaranteeing the finite-time stability of the addressed system are established in the framework of linear matrix inequalities (LMIs). Furthermore, a computational algorithm of solving observer and controller gain matrices is given in terms of the established sufficient conditions. Finally, a practical simulation example is provided to demonstrate the effectiveness of the obtained theoretical results. (C) 2022 Elsevier Inc. All rights reserved.
原文链接:- NSTL
- Elsevier
Bielak, PiotrKajdanowicz, TomaszChawla, Nitesh, V
15页查看更多>>摘要：Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a variety of downstream learning tasks. The focus of representation learning on graphs has focused mainly on shallow (node-centric) or deep (graph-based) learning approaches. While there have been approaches that work on homogeneous and heterogeneous networks with multi-typed nodes and edges, there is a gap in learning edge representations. This paper proposes a novel unsupervised inductive method called AttrE2Vec, which learns a low-dimensional vector representation for edges in attributed networks. It systematically captures the topological proximity, attributes affinity, and feature similarity of edges. Contrary to current advances in edge embedding research, our proposal extends the body of methods providing representations for edges, capturing graph attributes in an inductive and unsupervised manner. Experimental results show that, compared to contemporary approaches, our method builds more powerful edge vector representations, reflected by higher quality measures (AUC, accuracy) in downstream tasks as edge classification and edge clustering. It is also confirmed by analyzing low-dimensional embedding projections. (C) 2022 Published by Elsevier Inc.
原文链接:- NSTL
- Elsevier
El Hajjar, S.Dornaika, F.Abdallah, F.
15页查看更多>>摘要：Recently, one-step clustering methods have shown good performance. However, very few one-step approaches have addressed the multi-view case, where an instance may have multiple representations. Data can be represented with multiple heterogeneous views. Clustering with multiple views faces the challenge of how to combine all the different views. A general scheme is to represent the views by view-based graphs and/or a consensus graph. Graphs can be well suited for clustering problems since they can capture the local and global structure of the data. In this paper, we present a novel approach to one-step graph-based multi-view clustering. In contrast to existing graph-based one-step clustering methods, our proposed method introduces two key innovations. First, we build an additional graph by using the cluster label correlation to the graphs associated with the data space. Second, a smoothing constraint is exploited to constrain the cluster-label matrix and make it more consistent with the original data graphs as well as with and label graphs. Experimental results on several public datasets show the efficiency of the proposed approach. All cluster evaluation metrics show significant improvement by applying our method to different types and sizes of datasets. The average improvement (across all datasets) is the difference between the indicator obtained by our approach and the indicator obtained by the most competitive method. The average improvement is approximately 4%, 2%, 3%, and 2% for the Accuracy indicator, the Normalized Mutual Information indicator, the Purity indicator, and the Adjusted Rand index, respectively. (C) 2022 The Authors. Published by Elsevier Inc.
原文链接:- NSTL
- Elsevier
Lee, SeungyeonKim, Dohyun
11页查看更多>>摘要：With the recent development of online transactions, recommender systems have increasingly attracted attention in various domains. The recommender system supports the users' decision making by recommending items that are more likely to be preferred. Many studies in the field of deep learning-based recommender systems have attempted to capture the complex interactions between users' and items' features for accurate recommendation. In this paper, we propose a recommender system based on the convolutional neural network using the outer product matrix of features and cross convolutional filters. The proposed method can deal with the various types of features and capture the meaningful higher-order interactions between users and items, giving greater weight to important features. Moreover, it can alleviate the overfitting problem since the proposed method includes the global average or max pooling instead of the fully connected layers in the structure. Experiments showed that the proposed method performs better than the existing methods, by capturing important interactions and alleviating the overfitting issue. (C) 2022 Elsevier Inc. All rights reserved.
原文链接:- NSTL
- Elsevier
Mei, ChunhuiFei, ChenShen, MingxuanFei, Weiyin...
14页查看更多>>摘要：In this article, it is proved that feedback controllers can be designed to stabilize nonlinear neutral stochastic systems with Markovian switching (NSDDEwMS in short) only by using discrete observed state sequences. Due to the superlinear coefficients, the neutral term and the discrete observation data, many routine methods and techniques for the study of stochastic systems are not applicable. A new Lyapunov functional is constructed by using multiple M-matrices to prove that a given unstable NSDDEwMS can be stabilized if the control function can be designed to meet a couple of easy-to-be-verified rules. Finally, an example is given to illustrate the feasibility of the theoretical results. (C) 2022 Elsevier Inc. All rights reserved.
原文链接:- NSTL
- Elsevier
Ru, YaminLi, FangFang, FamingZhang, Guixu...
19页查看更多>>摘要：The nuclear norm-based convex surrogate of the rank function has been widely used in compressive sensing (CS) to exploit the sparsity of nonlocal similar patches in an image. However, this method treats different singular values equally and thus may produce a result far from the optimum one. In order to alleviate the limitations of the nuclear norm, different singular values should be treated differently. The reason is that large singular values can be used to retrieve substantial contents of an image, while small ones may contain noisy information. In this paper, we propose a model via non-convex weighted Smoothly Clipped Absolute Deviation (SCAD) prior. Our motivation is that SCAD shrinkage behaves like a soft shrinkage operator for small enough inputs, whereas for large enough ones, it leaves the input intact and behaves like hard shrinkage. For moderate input values, SCAD makes a good balance between soft shrinkage and hard shrinkage. Numerically, the alternating direction method of multiplier (ADMM) is adopted to split the original problem into several sub-problems with closed-form solutions. We further analyze the convergence of the proposed method under mild conditions. Various experimental results demonstrate that the proposed model outperforms many existing state-of-the-art CS methods. (C) 2022 Elsevier Inc. All rights reserved.
原文链接:- NSTL
- Elsevier
