A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

Rate-smoothed schedule with tolerable data dropping for video coding stream

Most algorithms of smoothing schedule compute the required bit rate of video transmission to satisfy all the transmitted data. In this paper, our proposed tolerable data dropping algorithm can adjust transmitting data to fit available bit rate. MPEG-4 with fine grained scalability (FGS) can support partial data dropping to adapt to available bandwidth network. The algorithm is based on the minimum variance bandwidth allocation (MVBA) algorithm proposed by Salehi et al. to compute the bit rate such that still ensuring that the buffer never underflows and overflows for MPEG-4 FGS streams under the limited bandwidth resource. We prove that our proposed algorithm, named MVBADP, is smoother than the MVBA algorithm. The experimental results show the peak rate, the number of rate changes, and the ratio of total dropping data, and the PSNR for four test sequences with different content characteristics. They are varied by buffer sizes and tolerable dropping ratios. We found that the MVBADP algorithm can reduce the peak rate and the number of changes when the transmitted data are dropped by tolerable dropping ratio, especially on the video sequences with the high motion and complex texture characteristic and larger size change of the consecutive frame.     

3D human modeling from a single depth image dealing with self-occlusion

This paper presents a 2D to 3D conversion scheme to generate a 3D human model using a single depth image with several color images. In building a complete 3D model, no prior knowledge such as a pre-computed scene structure and photometric and geometric calibrations is required since the depth camera can directly acquire the calibrated geometric and color information in real time. The proposed method deals with a self-occlusion problem which often occurs in images captured by a monocular camera. When an image is obtained from a fixed view, it may not have data for a certain part of an object due to occlusion. The proposed method consists of following steps to resolve this problem. First, the noise in a depth image is reduced by using a series of image processing techniques. Second, a 3D mesh surface is constructed using the proposed depth image-based modeling method. Third, the occlusion problem is resolved by removing the unwanted triangles in the occlusion region and filling the corresponding hole. Finally, textures are extracted and mapped to the 3D surface of the model to provide photo-realistic appearance. Comparison results with the related work demonstrate the efficiency of our method in terms of visual quality and computation time. It can be utilized in creating 3D human models in many 3D applications.     

Types and associated type families for hardware simulation and synthesis

In this article we overview the design and implementation of the second generation of Kansas Lava. Driven by the needs and experiences of implementing telemetry decoders and other circuits, we have made a number of improvements to both the external API and the internal representations used. We have retained our dual shallow/deep representation of signals in general, but now have a number of externally visible abstractions for combinatorial and sequential circuits, and enabled signals. We introduce these abstractions, as well as our abstractions for reading and writing memory. Internally, we found the need to represent unknown values inside our circuits, so we made aggressive use of associated type families to lift our values to allow unknowns, in a principled and regular way. We discuss this design decision, how it unfortunately complicates the internals of Kansas Lava, and how we mitigate this complexity. Finally, when connecting Kansas Lava to the real world, the standardized idiom of using named input and output ports is provided by Kansas Lava using a new monad, called Fabric. We present the design of this Fabric monad, and illustrate its use in a small but complete example.     

Automatic stress exaggeration by prosody modification to assist language learners perceive sentence stress

In this paper, we propose a set of automatic stress exaggeration methods that can enlarge the differences between stressed and unstressed syllables. Our stress exaggeration methods can be used in computer-aided language learning systems to assist second language learners perceive stress patterns. The intention of our automatic stress exaggeration methods is to support hyper-pronunciation training which is commonly used in classrooms by teachers. In hyper-pronunciation training, exaggeration is used to help learners increase their awareness of acoustic features and effectively apply these features into their pronunciation. Duration, pitch and intensity have been claimed to be the main acoustic features that are closely related to stress in English language. Thus, four stress exaggeration methods are proposed in this paper: (i) duration-based stress exaggeration, (ii) pitch-based stress exaggeration, (iii) intensity-based stress exaggeration, and (iv) a combined stress exaggeration method that integrates the duration-based, pitch-based and intensity-based exaggeration methods. Our perceptual experimental results show that resynthesised stimuli by our proposed stress exaggerated methods can help learners of English as a Second Language (ESL) better perceive English stress patterns significantly.     

Transaction models for Web accessibility

The Web has evolved into a dominant digital medium for conducting many types of online transactions such as shopping, paying bills, making travel plans, etc. Such transactions typically involve a number of steps spanning several Web pages. For sighted users these steps are relatively straightforward to do with graphical Web browsers. But they pose tremendous challenges for visually impaired individuals. This is because screen readers, the dominant assistive technology used by visually impaired users, function by speaking out the screen’s content serially. Consequently, using them for conducting transactions can cause considerable information overload. But usually one needs to browse only a small fragment of a Web page to do a step of a transaction (e.g., choosing an item from a search results list). Based on this observation this paper presents a model-directed transaction framework to identify, extract and aurally render only the “relevant” page fragments in each step of a transaction. The framework uses a process model to encode the state of the transaction and a concept model to identify the page fragments relevant for the transaction in that state. We also present algorithms to mine such models from click stream data generated by transactions and experimental evidence of the practical effectiveness of our models in improving user experience when conducting online transactions with non-visual modalities.     

A space and time efficient algorithm for SimRank computation

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yields O(n³)O\left(n^3\right) worst-case time per iteration with the space requirement O(n²)O\left(n^2\right), where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min{ n ·m , n^r })O \left(\min \left\{ n \cdot m , n^r \right\}\right) time and O ( n + m )O \left( n + m \right) space, where m is the number of edges in a web-graph model and r ≤ log₂ 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.     

Extension of the DEMATEL method in an uncertain linguistic environment

The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations. Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to illustrate the procedure of the proposed method.     

Mutual learning of multi-consciousness agents,including an ego for an autonomous vehicle

In this paper, we propose a multi-agent learning system for the control of an intelligent robot, based on a model of the human consciousnesses, including the ego. We pay attention to the intelligent learning processes of human beings. We try to give a robot a high learning ability by modeling the roles of the human consciousnesses, including the ego. In most ordinary methods, the instructions for learning are given from outside the system only. In the proposed method, the instructions are given not only from outside, but also from inside (from other agents in the system). Therefore, the robot can learn efficiently because it has more instructions than usual. The learning is also more flexible, since an agent learns by instructions from other agents while the learning agent and one of the instructing agents exchange roles according to changes in the environment. We experimentally verified that the proposed method is efficient by using an actual robot.