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Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

This paper presents a novel event-triggered optimal control (ETOC) method for discrete-time (DT) multi-player non-zero-sum games (NZSGs). First, a novel event-triggered algorithm is developed for DT multi-player NZSGs based on the time-triggered optimal value functions. Therefore, the developed event-triggered algorithm only needs to solve the time-triggered Hamilton–Jacobi-Bellman (HJB) equations. Then, the asymptotic stability of the closed-loop system is proved. Additionally, we show that an upper bound for the sum of the actual performance indices of all the players can be determined in advance. A key step in the implementation of the developed event-triggered algorithm is to obtain the next state of the actual system, which is difficult to implement on the actual system. Thus, a parallel control method is utilized to predict the next state by constructing the parallel system for the actual system and obtain the optimal value functions. The method that combines the developed event-triggered algorithm and parallel control is called the event-triggered optimal parallel control (ETOPC) method. The neural network (NN) technique and the iterative adaptive dynamic programming (IADP) technique are employed in parallel control. Moreover, the control stability is shown further in the consideration of the NN weight approximation errors. Finally, two simulations justify the theoretical conjectures. [ABSTRACT FROM AUTHOR]

[Display omitted] • We propose the TWD-SFNN model to optimize the hidden layer nodes of neural network. • TWD-SFNN adopts three-way decisions to determine the number of hidden layer nodes. • We analyze the space and time complexities of TWD-SFNN. • TWD-SFNN has a more compact network structure than SFNN using empirical formulas. • TWD-SFNN has a better generalization performance than other classification models. Neural networks have a strong self-learning ability and a wide range of applications. The current neural network models mainly determine the number of hidden layer nodes using empirical formulas, which lack theoretical guidance and can easily lead to poor learning performance. To improve the performance of the neural network model, inspired by the three-way decisions method, this paper proposes a model called three-way decisions with a single hidden layer feedforward neural network (TWD-SFNN). TWD-SFNN adopts three-way decisions to find the number of hidden layer nodes for a neural network in a dynamic way. TWD-SFNN has three key issues: discretizing the datasets, adjusting the learning process of the network, and evaluating the learning results of the network. TWD-SFNN adopts the k-means++ algorithm to discretize the datasets, employs the Adam algorithm to adjust the learning process of the network, and uses a confusion matrix to evaluate the learning results of the network. Therefore, the topological structure of the neural network is obtained. The experimental results verify that the network structure of TWD-SFNN is more compact than those of the SFNN models that use empirical formulas to determine the number of hidden layer nodes, and the generalization ability of TWD-SFNN is better than the state-of-the-art classification models. [ABSTRACT FROM AUTHOR]

• We identify the answer sparsity problem and validate its influence. • We propose a triplet network to learn the representations of workers. • We develop PLA algorithm that can predict the answer given by workers. • PLA can improve the performance of the existing algorithms on 8 datasets. Nowadays, crowdsourcing is a widespread and effective method to gather the crowd wisdom. At the same time, label aggregation is used to aggregate the noisy and biased data generated by the crowd. In the real-world crowdsourcing tasks, most workers only answer a small fraction of questions, which makes the collected answer sparse. However, the existing label aggregation approaches often build upon some probabilistic modeling procedures which is sensitive to the data sparsity. In this paper, we exploit the predicted answers to improve the performance of label aggregation and propose PLA (Prediction-based Label Aggregation) to intelligently aggregate the crowd wisdom. With PLA, we firstly learn representations to capture the characteristics of the workers and questions. Then we deploy a neural network model to predict the answer given by different workers. After that we add the most valuable predicted answers to the answer set. Finally, we use the augmented answer set to enhance representative label aggregation algorithms. To validate our proposed PLA, we compare it with other 6 existing methods on 8 real-world datasets. Our results show that PLA can enhance the performance of different aggregation algorithms in crowdsourcing tasks and achieves up to 16% performance improvement. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to solve the portfolio selection problem when security returns contain both randomness and fuzziness. Utilizing a different perspective, this paper gives a new definition of risk for random fuzzy portfolio selection. A new optimal portfolio selection model is proposed based on this new definition of risk. A new hybrid intelligent algorithm is designed for solving the new optimization problem. In the proposed new algorithm, neural networks are employed to calculate the expected value and the chance value. These greatly reduce the computational work and speed up the process of solution as compared with the random fuzzy simulation used in our previous algorithm. A numerical example is also presented to illustrate the new modelling idea and the proposed new algorithm. [Copyright &y& Elsevier]

This paper is concerned with the approximate solution of Hamilton–Jacobi–Isaacs (HJI) equation for constrained-input nonlinear continuous-time systems with unknown dynamics. We develop a novel online adaptive dynamic programming-based algorithm to learn the solution of the HJI equation. The present algorithm is implemented via an identifier-critic architecture, which consists of two neural networks (NNs): an identifier NN is applied to estimate the unknown system dynamics and a critic NN is constructed to obtain the approximate solution of the HJI equation. An advantage of the proposed architecture is that the identifier NN and the critic NN are tuned simultaneously. With introducing two additional terms, namely, the stabilizing term and the robustifying term to update the critic NN, the initial stabilizing control is no longer required. Meanwhile, the developed critic tuning rule not only ensures convergence of the critic to the optimal saddle point but also guarantees stability of the closed-loop system. Moreover, the uniform ultimate boundedness of the weights of the identifier NN and the critic NN are proved by using Lyapunov’s direct method. Finally, to illustrate the effectiveness and applicability of the developed approach, two simulation examples are provided. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances. [Copyright &y& Elsevier]