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In recent decades, the development of social computing systems has realized the efficient information exchange between large groups of people. Nowadays, social computing systems are rather complex platforms supported by not only traditional sociology theory but also computer science and big data based applications. With the increase of the social computing systems' complexities, serious issues of social digital security and privacy have shown up since, in recent years, more and more social data leakage incidents are happening. This fact is due to reasons on many different aspects since there are many sources threatening the security and privacy of the social data in such a complex social computing system. In this paper, we improve the traditional social data protection schemes by combining the information fragmentation concepts with the distributed system architectures to build a novel social data protection scheme. We use social photo protection as the fundamental scenario and deploy our novel scheme to illustrate the improvement on the protection level with the protection analysis in detail. A security analysis of practically realizing such a scheme is also evaluated in this paper. [ABSTRACT FROM AUTHOR]

Abstract: In this position paper, we are discussing achievements and open issues in the interpretability of evolving fuzzy systems (EFS). In addition to pure on-line complexity reduction approaches, which can be an important direction for increasing the transparency of the evolved fuzzy systems, we examine the state-of-the-art and provide further investigations and concepts regarding the following interpretability aspects: distinguishability, simplicity, consistency, coverage and completeness, feature importance levels, rule importance levels and interpretation of consequents. These are well-known and widely accepted criteria for the interpretability of expert-based and standard data-driven fuzzy systems in batch mode. So far, most have been investigated only rudimentarily in the context of evolving fuzzy systems, trained incrementally from data streams: EFS have focussed mainly on precise modeling, aiming for models of high predictive quality. Only in a few cases, the integration of complexity reduction steps has been handled. This paper thus seeks to close this gap by pointing out new ways of making EFS more transparent and interpretable within the scope of the criteria mentioned above. The role of knowledge expansion, a peculiar concept in EFS, will be also addressed. One key requirement in our investigations is the availability of all concepts for on-line usage, which means they should be incremental or at least allow fast processing. [Copyright &y& Elsevier]

Abstract: As a newly introduced interconnection network for parallel computing, the locally twisted cube possesses many desirable properties. In this paper, mesh embeddings in locally twisted cubes are studied. Let LTQ n (V, E) denote the n-dimensional locally twisted cube. We present three major results in this paper: (1) For any integer n ⩾1, a 2×2 n−1 mesh can be embedded in LTQ n with dilation 1 and expansion 1. (2) For any integer n ⩾4, two node-disjoint 4×2 n−3 meshes can be embedded in LTQ n with dilation 1 and expansion 2. (3) For any integer n ⩾3, a 4 ×(2 n−2 −1) mesh can be embedded in LTQ n with dilation 2. The first two results are optimal in the sense that the dilations of all embeddings are 1. The embedding of the 2×2 n−1 mesh is also optimal in terms of expansion. We also present the analysis of 2p ×2q mesh embedding in locally twisted cubes. [Copyright &y& Elsevier]

Abstract: This paper studies covering-based rough sets by means of characteristic functions of sets. We consider covering-based rough sets from both constructive and axiomatic approaches. The relationship among four types of covering-based rough sets is discussed. We also outline the topologies induced by different covering approximations. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE–GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE–GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE–GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE–GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems. [Copyright &y& Elsevier]

Abstract: In this note, we show that the main results in the two papers [C.C. Wu, W.C. Lee, Single-machine and flowshop scheduling with a general learning effect model, Computers and Industrial Engineering 56 (2009) 1553–1558, W.C. Lee, C.C. Wu, Some single-machine and m-machine flowshop scheduling problems with learning considerations, Information Sciences 179 (2009) 3885–3892] are incorrect. [Copyright &y& Elsevier]

Several years of optimization on the cache-based super-scalar architecture has made it more difficult to port the current version of the 3D particle-in-cell code GTC to the NEC SX-6 vector architecture. This paper explains the initial work that has been done to port this code to the SX-6 computer and to optimize the most time consuming parts. After a few modifications, single-processor results show a performance increase of 5.2 compared to the IBM SP Power3 processor, and 2.7 compared to the Power4. [Copyright &y& Elsevier]

This editorial introduces articles about the application of computational intelligence in economics and finance published in the periodical "Information Sciences,". All of the papers are dealing with three active application areas of computational intelligence. One of the articles is an application of computational intelligence to financial engineering, more specifically financial time series forecasting. The idea of guarded experts is shown to have long been pursued throughout the history of machine learning. Furthermore, some of the featured papers contribute to agent-based artificial markets on different aspects. One of which examines the price-volume relation in stock markets.