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Network latencies and losses in online interactive multimedia applications may lead to a degraded perception of quality, such as lower interactivity or sluggish responses. We can measure these degradations in perceptual quality by the just-noticeable difference, awareness, or probability of noticeability ($p_{\text{note}}$pnote); the latter measures the likelihood that subjects can notice a change from a reference to a modified reference. In our previous work, we developed an efficient method for finding the perceptual quality for one metric under simplex control. However, integrating the perceptual qualities of several metrics is a heuristic. In this article, we present a formal approach to optimally combine the perceptual quality of multiple metrics into a joint measure that shows their tradeoffs. Our result shows that the optimal balance occurs when the $p_{\text{note}}$pnote of all the component metrics are equal. Furthermore, our approach leads to an algorithm with a linear (instead of combinatorial) complexity of the number of metrics. Finally, we present the application of our method in two case studies, one on VoIP for finding the optimal operating points and the second on fast-action games to hide network delays while maintaining the consistency of action orders.

In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significance and opportunities that big data brings to us. Next, we present representative big data initiatives all over the world. We describe the grand challenges (namely, data complexity, computational complexity, and system complexity), as well as possible solutions to address these challenges. Finally, we conclude the paper by presenting several suggestions on carrying out big data projects.

Deals with the use of Lagrange-multiplier methods in solving nonlinear constrained optimization problems. Description of the Lagrange-multiplier method in continuous space and discrete space; Effect of the weight adjustment of the objective function on the convergence speed and solution quality of Lagrangian methods.

In this paper we investigate systolic processing for problems formulated in dynamic programming. These problems are classified as monadic-serial, polyadic-serial, monadic-nonserial, and polyadic-nonserial. Problems in serial formulations can be implemented easily in systolic arrays; however, nonserial problems may have to be transformed into a serial one before an efficient implementation can be found. monadic-serial dynamic programming problem can be solved as the search of an optimal path in a multistage graph and can be computed as a string of matrix multiplications. Three efficient systolic-array designs are presented. A polyadic-serial dynamic programming problem can be solved by either a divide- and-conquer algorithm or the search of optimal solutions in a serial AND/OR-graph. We have evaluated the asymptotically optimal architecture for divide- and-conquer algorithms and have developed efficient methods of mapping a regular AND/OR-graph into systolic arrays. Cases are studied for transforming a problem in a nonserial formulation into a serial one.

The (m,n) selection problem is defined as the selection of the m smallest numbers in any order from a set of n numbers (m ≤n). In this paper, we have proposed a class of design procedures for selection networks based on partitioning. Conditions are defined so that the optimal design can be found in polynomial time. The resulting selection network has O([log2n] · [log2m]) time complexity and O(n · [log2/2m]) hardware complexity for all values of m. As a comparison, networks previously known can optimize either hardware or delay but not both simultaneously, and perform worse than pure sorting when m approaches n/2.

In this paper, we develop new methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment. Such methods are related to the scheduling of resources for tests performed in genetic algorithms. Assuming that the population size is given, we address two problems related to the design of efficient scheduling algorithms specifically important in noisy environments. First, we study the durution-scheduling problemthat is related to setting dynamically the duration of each generation. Second, we study the sample-allocation problemthat entails the adaptive determination of the number of evaluations taken from each candidate in a generation. In our approach, we model the search process as a statistical selection process and derive equations useful for these problems. Our results show that our adaptive procedures improve the performance of genetic algorithms over that of commonly used static ones.

The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.