In many distributed systems, tokens are fundamental tools to manage resources shared by processes. Thus, monitoring tokens has become a significant problem in developing the distributed programs. This paper formulates the problems of monitoring tokens in terms of detecting the special global predicates, called summative global predicates. In this paper, several algorithms to detect various summative global predicates are developed and their time complexities are discussed.

Deploying and managing content objects efficiently is critical for building a scalable and transparent content delivery system. This paper investigates the advanced incremental deploying problem of which the objects are delivered in a successive manner. Recently, the researchers show that the minimum-cost content deployment can be obtained by reducing the problem to the well-known network flow problem. In this paper, the maximum flow algorithm for a single graph is extended to the incremental growing graph. Based on this extension, an efficient incremental content deployment algorithm is developed in this work.

Most traditional board and card games, such as Chess, Chinese Chess, Go, Chinese Mahjong, Hearts, Bridge, etc., share the same playing model: Players play around tables using physical objects such as cards and may hold objects in their own private areas, e.g., players hold cards in their own hands in Bridge. In this paper, this model is called the play-on-table (POT) game model and these games following the model are called POT games. The research of this paper is summarized as follows. First, formalize the definition of the POT game model. Second, present some game systems to allow players to design and play new POT games. Third, prove that these game systems are general for all POT games. Finally, in order to demonstrate the theory, practically implement one of the general game systems that allows players to design and play new POT games in a what-you-see-is-what-you-get (WYSIWYG) manner.

In this letter, we propose a novel Uniformity-Approximated Histogram Equalization (UAHE) algorithm to enhance the image as well as to preserve the image features. First, the UAHE algorithm generates the image histogram and computes the average value of all bins as the histogram threshold. In order to approximate the uniform histogram, the bins of image histograms greater than the above threshold are clipped, and the subtracted counts are averaged and uniformly assigned to the remaining bins lower than the threshold. The approximated uniform histogram is then applied to generate the intensity transformation function for image contrast enhancement. Experimental results show that our algorithm achieves the maximum entropy as well as the feature similarity values for image contrast enhancement.