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With the rapid development of computer science, technologies such as virtual reality and semantic feature fusion have been greatly developed in recent years. [n order to construct a digital media intelligent art creation model, this paper first designs a digital media intelligent creation framework based on virtual reality and maintains the original pattern and color distribution of natural images as accurately as possible. This method first performs automatic color segmentation and clustering on the image to extract key lines; then, re-colors the different segmented regions; and finally completes the detail enhancement. For the optimization of color matching, the algorithm integrates the contrast of the color block, and uses the differential evolution strategy to optimize the color configuration scheme. In addition, this paper also proposes a generative adversarial network-based on semantic feature fusion for digital media intelligent art creation. The network introduces multi-scale feature fusion by embedding the residual block feature pyramid structure and directly generates the final fine image by adaptively fusing these features. Only one discriminator can generate a realistic image of 256px x 256px. The experimental results have shown that this paper makes full ilse of virtual reality and seniantic feature fusion technology, and obtains better results and better performance of intelligent art creation results on digital media than existing methods. [ABSTRACT FROM AUTHOR]

The text is a foreword for a special issue of the Journal of Information Science & Engineering on Combinatorial Mathematics and Computational Theory. It explains that these fields have a significant impact on computer science, mathematics, and science in general. Combinatorial Mathematics involves the study of discrete objects and has applications in various areas, while Computational Theory focuses on studying algorithms and their efficiency. The text also mentions a symposium that was held to promote academic exchanges and advance research in these fields. The special issue includes four papers on topics such as opinion optimization, local diagnosis, enumerating furthest pairs, and determining the 2-tuple total domination number of a graph. The authors express gratitude to the reviewers and authors for their contributions to the special issue. [Extracted from the article]

This paper explores the problem of efficient processing of continuous range queries (CRQs) over a large number of moving objects, each of which continually retrieves the moving objects that are currently within a geographic query region of interest. In order to keep the result of CRQs up-to-date, the moving objects should continually communicate with the server to report their current locations. However, this increases the server workload and entails a huge amount of communication cost when the number of the moving objects and CRQs becomes enormous. In this paper, we adopt the approach of leveraging available memory and computational resources of the moving objects to remedy such problems. To this end, we propose a novel query indexing structure, referred to as the space partitioning query index (SPQI), which enables the server to efficiently cooperate with the moving objects for processing CRQs. SPQI greatly improves the overall system performance in terms of server workload and communication cost. Through a set of comprehensive simulations, we verify the superiority of SPQI. [ABSTRACT FROM AUTHOR]

The k-ary n-cube is one of the most popular interconnection networks for parallel computing. This paper addresses the size of a largest connected component of a faulty k-ary n-cube. We prove that, in a k-ary n-cube (k ≥ 4 and n ≥ 2) with up to 4n -- 2 faulty vertices, all fault-free vertices but at most two constitute a connected component. Moreover, this assertion holds if and only if the set of all faulty vertices is equal to the neighborhood of a pair of fault-free adjacent vertices. [ABSTRACT FROM AUTHOR]

Reconfiguring the parameters of PTZ cameras in a surveillance system is a combinatorial optimization problem. Computing the optimal solution is very time consuming, and existing methods can only provide sub-optimal solutions. In this paper, we propose a non-linear objective function that better utilizes a camera network to track multiple targets. We also show that, by expanding the unknown parameters and imposing new constraints, the non-linear objective function can be converted into a linear production game (LPG) problem. Since an LPG yields an optimal solution that can be evaluated in polynomial time, the proposed method is efficient and accurate. The results of simulations and a real-world experiment demonstrate the proposed method's potential. [ABSTRACT FROM AUTHOR]

Mesh simplification has become a key ingredient for real-time graphics applications. However, practitioners have found that automatic simplification methods usually fail to produce satisfactory result when models of very low polygon count are desired. This is due to the fact that existing methods take no semantic or functional metric into account, and moreover, each error metric proposed previously has its own strength and weakness. In this paper, we propose a user-controllable mesh simplification framework that allows users to achieve a predictable resolution improvement in selected regions of a simplified mesh derived by using any error metric. The framework consists of two stages. The first stage employs weighting schemes that allow users to refine unsatisfactory regions to a user-expected resolution. The second stage is a local refinement aiming to provide a user-guided fine-tune to recover local sharp features. Two weighting schemes, namely uniform weighting and nonuniform weighting, are proposed. In uniform weighting scheme, a weight value is applied to all original mesh vertices in a selected region, resulting in a uniform improvement on vertex resolution in the region, while in the nonuniform weighting scheme varying weights are applied to vertices in a selected region and obtain a nonuniform resolution improvement in the region. Different from the previous user-assisted simplification methods that reorder the collapsing sequence indirectly by weighting the collapsing cost of edges, the two proposed weighting schemes directly reorder the edge collapsing sequence, ensuring a predictable resolution improvement in the selected regions. Moreover, the proposed weighting schemes are independent on error metric used for simplification and ensure the same resolution improvement when a weighting value is applied to simplified meshes with different resolutions. [ABSTRACT FROM AUTHOR]

While flash-memory has been widely adopted for various embedded systems, the performance of address translation has become a critical issue for the design of flash translation layers. The aim of this paper is to improve the performance of existing designs by proposing a caching mechanism for efficient address translation. A replacement strategy with low-time complexity and low-memory requirements is proposed to cache the most recently used logical addresses. According to the experiments, the proposed method has shown its efficiency in the reducing of the address translation time. [ABSTRACT FROM AUTHOR]

The design of interconnection networks is very popular in computer science. In this paper we introduce a novel problem, the design of oriented networks. The underlying structure of such networks is a directed graph with weights on the arcs, which are the number of paths of the routing that traverse the arc. The cost of a network with a routing is defined as a sum of arc costs that are computed with concave increasing cost function depending on weights over directed arcs. The objective is to find the routing that is optimal in terms of costs. The corresponding minimization problem is approached with a local search type heuristics. New local search neighborhood is defined and analyzed. [ABSTRACT FROM AUTHOR]

Software complexity metrics are used to predict critical information about reliability and maintainability of software systems. Object oriented software development requires a different approach to software complexity metrics. In this paper, we propose a metric to compute the structural and cognitive complexity of class by associating a weight to the class, called as Weighted Class Complexity (WCC). On the contrary, of the other metrics used for object oriented systems, proposed metric calculates the complexity of a class due to methods and attributes in terms of cognitive weight. The proposed metric has been demonstrated with OO examples. The theoretical and practical evaluations based on the information theory have shown that the proposed metric is on ratio scale and satisfies most of the parameters required by the measurement theory. [ABSTRACT FROM AUTHOR]

The temporal aggregation in temporal databases is an extension of the conventional aggregation including the time as a range condition of the aggregation. In this paper, we propose a new tree based structure for the temporal aggregation, called the CTA-tree. In the CTA-tree, we transform the time interval of a temporal data record into a value, called the T-value, using the Corner transformation and column-scan transformation. Then, we index the data records according to their T-values. Through analyses and experiments, we evaluate and compare the performance of the proposed method with the conventional method, and show the efficiency of the proposed method for the update-intensive applications. [ABSTRACT FROM AUTHOR]