查看更多>>摘要：In this paper, we propose a new regression method called the epsilon-kernel-free soft quadratic surface support vector regression (epsilon-SQSSVR). After converting the n-dimensional regression problem into the (n + 1)-dimensional classification problem, the principle of maximizing the sum of relative geometrical margin of each training point is used to construct our optimization problem, where the quadratic surface is restricted to be a hyperparaboloid by setting both the (n + 1)-th row and (n + 1)-th column of the corresponding matrix to be zero. The existence and uniqueness of the optimal solution to both primal and dual problems are also addressed. It should be pointed out that our model is nonlinear and kernel-free, so it does not need to select kernel function and corresponding parameters. At the same time, it is highly interpretable. In addition, our model is still a quadratic convex programming problem similar to the standard SVR. To visualize the effectiveness of our epsilon-SQSSVR, 6 artificial datasets and 15 benchmark datasets are implemented in numerical experiments. The results show that our method is less time-consuming and as good as the nonlinear standard SVR with kernel function in comprehensive performances. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In multi-label learning, each training instance is associated with multiple class labels. It is typical in reality that relevant labels are partially missing and only a part of labels are valid, resulting in the problem of weak multi-label learning with missing labels. It is still an evident challenge to estimate the ground-truth label matrix and to generate a prediction function, especially on the multi-label data with a large number of missing labels. In this paper, we propose a multi-label learning framework within which feature structure and label manifold are both utilized to recover the incomplete label matrix and to train the classification model simultaneously. To mitigate the imbalanced risks brought by the sparse label issue, a self-adaptive penalty factor is imposed on the deviated predictions of different labels. Moreover, instance granular discrimination is incorporated in the framework to characterize the approximate distribution structure of data. Matrix vectorization, cave-convex programming (CCCP), and block coordinate descent techniques are employed to solve the proposed optimization problem. Extensive experiments illustrate the superiority of the proposed method against some well-established methods. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Communication technologies play a key role in various fields. Hybrid Soft Computing approaches have significant potential for design and investigation of complex systems. In this paper, we use combination of fuzzy logic and chaos theory to model uncertainty and complexity of a secure communication system. Fuzzy rule base is used to describe dependence of behavior of a chaotic system on its parameters and initial conditions. The rule base is constructed by applying fuzzy clustering to a large data set. The approach is characterized by its relatively low computational complexity due to the use of fuzzy rule base instead of intensive simulations of a fuzzy chaotic system. Complexity of this hybrid fuzzy-chaotic approach assures an increased level of security. Trade-off between complexity and security may be achieved by generation of transmitted information using fuzzy modeling of chaotic behavior. Computer simulations are used to verify feasibility and effectiveness of the proposed approach. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Machine learning offers automatic and objective approaches for disease detection based on biomedical data. However, 1) the percentage of patients in the real world is smaller than that of healthy people; 2) the annotations by medical experts are costly. Therefore, medical datasets are often imbalanced and partially labeled. To address this problem, we propose balanced evolutionary semi-stacking (BESS) for disease detection using partially labeled imbalanced (PLI) data. BESS aims to detect illnesses by considering the input of the color, texture, and geometry features extracted from tongue images. Specifically, the proposed method first mitigates the class imbalance problem and leverages the unlabeled data through the so-called balanced evolutionary co-training approach. Then BESS exploits both the data and classifier diversity obtained by balanced evolutionary co-training to improve the performance of the stacking ensemble. We quantitatively evaluate the proposed algorithm based on the PLI tongue image database. BESS achieves the best performance in detecting diabetes mellitus, chronic kidney disease, breast cancer, and chronic gastritis, compared to other state-of-the-art methods. The results of the experiments substantiate the superiority and effectiveness of the proposed algorithm. Codes and datasets have been made publicly available at url: https://github.com/CUHKSZ-NING/Balanced-Evolutionary-Semi-Stacking. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Fully homomorphic signature schemes in identity-based settings can provide authenticity, homomorphism, and non-repudiation as do traditional digital signatures, while simplifying the public key infrastructure (PKI) requirements, in which each user in the system can use his or her identity as a public key. As identity-based systems (IBS) have a natural link between unique identity information and the user, allowing user revocation is usually more difficult in IBS than in PKI settings. Although several revocable identity-based fully homomorphic signature (RIBFHS) schemes have been proposed, these schemes are vulnerable to signing key exposure. With this study, we are the first to consider a realistic threat signing key exposure in RIBFHS systems. In addition, we introduce a new security definition of RIBFHS with signing key exposure resistance. Then, we employ Agrawal's left-right lattices and delegation technology in fixed dimensions to construct a new RIBFHS scheme over the lattice with regularly broadcasted update keys, which not only resists signing key exposure, but also meets scalability and context hiding properties. Finally, we prove that our construction is existentially unforgeable against chosen message attacks under the standard short integer solution (SIS) assumption in the random oracle model. (C) 2022 Published by Elsevier Inc.
Ivvan Valdez, S.Hernandez-Aguirre, ArturoBotello-Aceves, Salvador
22页
查看更多>>摘要：In multi-objective optimization, the direction vectors in the objective space that improve the current non-dominated set are named improvement directions. The Improvement Direction Mapping (IDM) methods apply a spatial transformation to map improvement directions in the objective space to search for directions in the variable space. Jacobian based transformations can perform the mapping, however, they require the analytic expressions of the objectives and their derivatives. Hence, they serve as a reference in academic problems but are impractical in real-world applications. Furthermore, they are commonly ill-conditioned and under-determined; consequently, they deviate the search as the current non-dominated set approaches to the Pareto set. This work proposes a solution via an iterative updating based on the Broyden method. The proposal promotes parallelism between the displacement of the non-dominated solutions in the objective space and improvement directions delivered by Chebyshev scalarizing functions. Compared to other transformations reported in the specialized literature, the proposed method demonstrates a better conditioning that provokes to err at a lesser extent in providing successful search directions. This impacts the algorithm performance, mainly in locations close to the Pareto set. These advantages are demonstrated using benchmark functions and metrics. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Traffic demand prediction is significant and practical in the resource scheduling of transportation application systems. Meanwhile, it remains a challenging topic due to the complexities of contextual effects and the highly dynamic nature of demand. Many works based on graph neural network (GNN) have recently been proposed to cope with this task. However, most previous studies treat the spatial dependence as a static graph, and their inference mechanism lacks interpretability. To address the issues, a Dynamical Spatial-Temporal Graph Neural Network model (DSTGNN) is proposed in this paper. DSTGNN has two critical phases: (1) Creating a spatial dependence graph. To capture the dynamical relationship, we propose building a spatial graph based on the stability of node's spatial dependence. (2) Inferring intensity. We model the changing demand process using the inhomogeneous Poisson process, which addresses the interpretability issue, and build a spatial-temporal embedding network to infer the intensity. Specifically, the spatial-temporal embedding network integrates the diffusion convolution neural network (DCNN) and a modified transformer. Extensive experiments are carried out on two real data sets, and the experimental results demonstrate that the performance of DSTGNN outperforms the state-of-the-art models on traffic demand prediction. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In the scenario of attack-defense involved with unmanned combat air vehicles (UCAVs), it is often envisioned that a large group of UCAVs is deployed to complete some complex tasks which could be specifically modeled as large-scale matrix games. Solving such matrix games by the traditional linear programming approaches, however, could be quite time-consuming and thus cannot be implemented in real-time which is, in fact, a key requirement for real air combat. On this account, an algorithm, termed as dimensionality reduction based matrix game solving algorithm (DR-MG), is proposed in this paper to solve large-scale matrix games in a timely manner. Our algorithm builds on the technique of dimensionality reduction which inherently finds the convex hull vertices of a vector set. Through establishing the connection between Nash equilibria of the matrix games before and after dimensionality reduction, the proposed algorithm is capable of finding the solutions while only dealing with the matrix game with reduced dimensions. As a consequence, it is expected the time complexity of the proposed algorithm is significantly decreased, and thus the algorithm could be applicable in real air combat. Finally, numerical results are provided to show the effectiveness of our algorithm. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Trisecting agents, issues, and agent pairs are essential topics of three-way conflict analysis. They have been commonly studied based on either a rating or an auxiliary function. A rating function defines the positive, negative, or neutral ratings of agents on issues. An auxiliary function defines the alliance, conflict, and neutrality relations between agents. These functions measure two opposite aspects in a single function, leading to challenges in interpreting their aggregations over a group of issues or agents. For example, when studying agent relations regarding a set of issues, a standard aggregation takes the average of an auxiliary function concerning single issues. Therefore, a pair of alliance +1 and conflict -1 relations will produce the same result as a pair of neutrality 0 relations, although the attitudes represented by the two pairs are very different. To clarify semantics, we separate the two opposite aspects in an auxiliary function into a pair of alliance and conflict functions. Accordingly, we trisect the agents, issues, and agent pairs and investigate their applications in solving a few crucial questions in conflict analysis. Particularly, we explore the concepts of alliance sets and strategies. A real-world application is given to illustrate the proposed models. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Membrane computing provides computational devices inspired by living cells (called membrane systems) that are proved to be computationally universal. It is a theoretical challenge to find the minimum of resources to get the full power of a Turing machine. The major contribution of this paper is to present such a system with only ONE membrane and without ANY additional ingredient (like cooperation, division, catalysts) in which the synchronization between non-cooperative rules (of length at most three) plays an essential role. Furthermore, such a system is able to solve the SAT NP-complete problem in polyno-mial time. (c) 2022 Elsevier Inc. All rights reserved.
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