Modelling and analysis of non-cooperative peer-assisted VoD streaming in managed networks

The growing popularity of the Video on Demand service in the Internet Protocol Television environments and the demand for increased quality of the offered videos are becoming a serious threat for the service providers because the high amounts of video traffic are causing congestion in the delivery networks. One of the most acceptable approaches to solve this issue is the peer-assisted streaming, where the peers participate in the streaming process in order to alleviate the load on the streaming servers and in the core of the network. Although the reliability of the Peer-to-Peer service is considerably improved in the managed networks because of the control that the operators have over the clients’ Set-Top Boxes, the failures of the peers still cannot be completely eliminated. The operator can take advantage of the streaming and storage resources of the clients and use them for peer-assisted streaming only while they are watching a video, but not after they finish the streaming session because they may turn off their receiving devices until the next session. In this chapter, we address the issue of the failures of the peers in such environments and their influence on the traffic requested from the servers for providing uninterrupted video experience. For that purpose, we propose a precise mathematical tool for modelling a peer-assisted system for Video on Demand streaming in managed networks with non-cooperative peers, which may decide not to share their resources while they are not active. This tool calculates the performance of the system taking into consideration large variety of system parameters, including the failure probability and the time the peers spend until they decide to turn on the STB and join the network. As the results from the simulations verify the correctness of the mathematical model, we use it to analyse how the failures of the peers are affecting the system’s performance for different system parameters.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

Measuring the role of age in user performance during interaction with computers

The influence of aging on computer interaction has been widely analyzed in human–computer interaction research literature. Despite this, there are no age-based user maps that could support the user-interface customization. Studying the specific needs and constraints of these groups is crucial in order to adapt a user interface to the user’s interaction requirements. This work studies the performance of a sample of participants on three different basic tasks (pointing, dragging and dropping, and text selection) and the influence of age for each of them. It is concluded that this influence differs between specific activities. A group profile map that can support automatic classification in the future has been obtained.     

Special issue on distributed computing and artificial intelligence

4:1! Google’s artificial intelligence (AI) program, AlphaGo, has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9?15, 2016. Seen by many as a landmark moment for AI, the outcome did not come as a surprise, considering the excellent combination of 1920 CPUs with sophisticated AI algorithms, including neural networks and Monte Carlo tree search (Gibney, 2016; Silver et al., 2016). Indeed, research on distributed computing and artificial intelligence (DCAI) has matured during the last decade and many effective applications are now deployed, performing an increasingly important role in modern computer science, including the two most hyped technologies: Internet of Things and Big Data. Indeed, it is fair to say that the application of artificial intelligence in distributed environments is becoming an essential element of high added value and economic potential.     

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.     

AmbiKraf

This paper presents, AmbiKraf, a non-emissive fabric display that subtly animates patterns on common fabrics. We use thermochromic inks and peltier semiconductor elements to achieve this technology. With this technology we have produced numerous prototypes from animated wall paintings to pixilated fabric displays. The ability of this technology to subtly and ubiquitously change the color of the fabric itself has made us able to merge different fields and technologies with AmbiKraf. In addition, with an animated room divider screen, Ambikraf merged its technology with Japanese Byobu art to tighten the gap between traditional arts and contemporary technologies. Through this AmbiKraf Byobu art installation and other installations, we discuss the impact of this technology as a ubiquitous fabric display. With focus to improvements of some limitations of the existing system, we present our future vision that enables us to merge this technology into more applications fields thus making this technology a platform for ubiquitous interactions on our daily peripherals.     

Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks

Recent vehicular routing proposals use real-time road traffic density estimates to dynamically select forwarding paths. Estimating the traffic density in vehicular ad hoc networks requires the transmission of additional dedicated messages increasing the communications load. These proposals are generally based on unicast sender-based forwarding schemes. The greedy nature of sender-based forwarding can result in the selection of forwarders with weak radio links that might compromise the end-to-end performance. To overcome these limitations, this paper presents TOPOCBF, a novel contention-based broadcast forwarding protocol that dynamically selects forwarding paths based on their capability to route packets between anchor points. Such capability is estimated by means of a multi-hop connectivity metric. The obtained results demonstrate that TOPOCBF can provide good packet delivery ratios while reducing the communications load compared to unicast sender-based forwarding schemes using road traffic density estimates.