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In this paper, a method for optimizing the parameters of multi-objective population-based algorithms is proposed. It is based on a meta-optimization, in which an external algorithm optimizes the parameters of the main algorithm. The purpose of proposed optimization is to select the functions, on the basis of which the values of parameters of the main algorithm are calculated for each algorithm iteration. The assumption was made that the functions should be selected in order to achieve the best possible Pareto front in terms of multi-objective performance metrics. The proposed method has been designed to allow the optimization of any algorithms and thus to avoid the time consuming manual selection of their parameters. To compare the presented method with other approaches, a set of well-known modeling benchmarks were used. [ABSTRACT FROM AUTHOR]

Highlights: [•] A knowledge-based numerical mapping for nominal attributes is presented. [•] Using this mapping, semantically-coherent means, variances, covariance, and distance functions are proposed. [•] Using these functions, anonymization methods can be coherently applied to nominal data. [•] As a result, anonymization of patient record data retains their semantics better. [•] Hence, our proposal yields anonymized results with better utility for clinical research. [ABSTRACT FROM AUTHOR]

Abstract: Mobile user similarity is significant for location-based social network services. With the pervasiveness of location-acquisition technologies, research on measuring mobile user similarity based on their trajectories has attracted a lot of attention. However, trajectories imply only short-term mobile regularities, and thus users’ long-term activity similarity is difficult to be captured. In this paper, we address the problem of mining users’ long-term activity similarity based on their trajectories. To solve this problem, we propose a two-stage approach. At the first stage, the notion of routine activity is proposed to capture users’ long-term activity regularities. The routine activities of a user are extracted from his/her daily trajectories. At the second stage, user similarity is calculated hierarchically based on the extracted routine activities. Finally, we evaluated our approach based on both real and artificial datasets. The experimental results show that users with different profiles can be discriminated on the basis of our similarity metric, and thus demonstrate the effectiveness of our approach. [Copyright &y& Elsevier]

Abstract: In this paper, we present a novel keyed hash function based on a dynamic lookup table of functions. More specifically, we first exploit the piecewise linear chaotic map (PWLCM) with secret keys used for producing four 32-bit initial buffers and then elaborate the lookup table of functions used for selecting composite functions associated with messages. Next, we convert the divided message blocks into ASCII code values, check the equivalent indices and then find the associated composite functions in the lookup table of functions. For each message block, the four buffers are reassigned by the corresponding composite function and then the lookup table of functions is dynamically updated. After all the message blocks are processed, the final 128-bit hash value is obtained by cascading the last reassigned four buffers. Finally, we evaluate our hash function and the results demonstrate that the proposed hash algorithm has good statistical properties, strong collision resistance, high efficiency, and better statistical performance compared with existing chaotic hash functions. [Copyright &y& Elsevier]

Abstract: In this paper, a novel steganographic method is proposed employing an immune programming strategy to find a near-optimal solution for the pair-wise least-significant-bit (LSB) matching scheme. The LSB matching method proposed by Mielikaien utilizes a binary function to reduce the number of changed pixel values. However, his method still has room for improvement. A tier-score system is proposed in this paper to assess the performance of different orders for LSB matching. An immune programming approach is adopted to search for a near-optimal solution among all the permutation orders. The proposed method can reduce the distortion of the stego image, improve the visual quality, and decrease the probability of detection. The experimental results show that the proposed method achieves better performance than Mielikainen’s pair-wise LSB matching method in terms of distortion and survival probability against steganalysis. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of how to derive mass functions systematically from data samples. We also consider the ensuing problem of how to combine different mass functions derived in this way. We show that a mass function can be efficiently and systematically derived from multivariate data. We also demonstrate that combining mass functions obtained in this manner can be done easily. The way of deriving and combining mass functions is illustrated with a simple example. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a neural network model for solving the nonlinear complementarity problem (NCP). The neural network is derived from an equivalent unconstrained minimization reformulation of the NCP, which is based on the generalized Fischer–Burmeister function . We establish the existence and the convergence of the trajectory of the neural network, and study its Lyapunov stability, asymptotic stability as well as exponential stability. It was found that a larger p leads to a better convergence rate of the trajectory. Numerical simulations verify the obtained theoretical results. [Copyright &y& Elsevier]

Abstract: The method described in this article evaluates case similarity in the retrieval stage of case-based reasoning (CBR). It thus plays a key role in deciding which case to select, and therefore, in deciding which solution will be eventually applied. In CBR, there are many retrieval techniques. One feature shared by most is that case retrieval is based on attribute similarity and importance. However, there are other crucial factors that should be considered, such as the possible consequences of a given solution, in other words its potential loss and gain. As their name clearly implies, these concepts are defined as functions measuring loss and gain when a given retrieval case solution is applied. Moreover, these functions help the user to choose the best solution so that when a mistake is made the resulting loss is minimal. In this way, the highest benefit is always obtained. [Copyright &y& Elsevier]