Optimistic total order in wide area networks

Total order multicast greatly simplifies the implementa- tion of fault-tolerant services using the replicated state ma- chine approach. The additional latency of total ordering can be masked by taking advantage of spontaneous order- ing observed in LANs: A tentative delivery allows the ap- plication to proceed in parallel with the ordering protocol. The effectiveness of the technique rests on the optimistic as- sumption that a large share of correctly ordered tentative deliveries offsets the cost of undoing the effect of mistakes. This paper proposes a simple technique which enables the usage of optimistic delivery also in WANs with much larger transmission delays where the optimistic assumption does not normally hold. Our proposal exploits local clocks and the stability of network delays to reduce the mistakes in the ordering of tentative deliveries. An experimental evalu- ation of a modified sequencer-based protocol is presented, illustrating the usefulness of the approach in fault-tolerant database management

Broadband Spectrum Survey Measurements for Cognitive Radio Applications

It is well known that the existing spectrum licensing system results in a gross under-utilization of the frequency spectrum. Spectrum background measurements – spectrum surveys – provide useful data for spectrum regulation, planning or finding frequency niches for spectrum sharing. Dynamic spectrum sharing as a main goal of cognitive radio (CR) is the modern option on how to optimize usage of the frequency spectrum. A spectrum survey measurement system is introduced with results obtained from a variety of markedly different scenarios allowing us, unlike other studies, to focus on wideband and fast spectrum scans. The sensitivity of the receiver is no worse than -113 dBm in the whole band. The utilization of the frequency spectrum is analyzed to prove its under-utilization and to show spectrum sharing opportunities. This was shown to be true in the frequency band higher than 2.5 GHz. A comparison with other spectrum survey campaigns is provided

Improving BitTorrent's Peer Selection For Multimedia Content On-Demand Delivery

The great efficiency achieved by the BitTorrent protocol for the distribution of large amounts of data inspired its adoption to provide multimedia content on-demand delivery over the Internet. As it is not designed for this purpose, some adjustments have been proposed in order to meet the related QoS requirements like low startup delay and smooth playback continuity. Accordingly, this paper introduces a BitTorrent-like proposal named as Quota-Based Peer Selection (QBPS). This proposal is mainly based on the adaptation of the original peer-selection policy of the BitTorrent protocol. Its validation is achieved by means of simulations and competitive analysis. The final results show that QBPS outperforms other recent proposals of the literature. For instance, it achieves a throughput optimization of up to 48.0% in low-provision capacity scenarios where users are very interactive.Comment: International Journal of Computer Networks & Communications(IJCNC) Vol.7, No.6, November 201

Optimal Network Control in Partially-Controllable Networks

The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

GOOGLE+: A BOOST TO E-LEARNING EDUCATION & TRAINING @ COVENANT UNIVERSITY

Just as the advancement in Information Technology (IT) continues to evolve and change rapidly over the past few decades, the art of learning, acquiring and dispersing knowledge and information have also continued to evolve rapidly. These rapid innovations and improvements in IT were designed with the aim of boosting knowledge and education at just the click of the button. Some of the most outstanding innovations and inventions in the (IT) world, which have influenced education and learning in this decade are found in social networks such as Yahoo, Google, Face book, twitter, You Tube, to mention but a few. However, recent studies conducted in this regard revealed that these current innovations and advancements in IT have constituted grave challenges to the knowledge and learning process. The teachers / lecturers of this century, during the process of seeking to transfer knowledge to their student, are often faced with the dilemma of finding how best to gainfully occupy the minds of their students within and outside the class room environment. The students who more often than not, distracted by incoming messages, videos, picture of friends, or simply text messages from friends via iphones, laptops, i-pods, or even i-pads etc. The respondent instantly cannot resist the urge to quickly read and send back a reply via either of the means mentioned here. Other students who log on to either of the networks for a specific task are easily distracted or carried away by say, a fresh advertisement or alternate information which dramatically catches the attention of the students. Consequently, they are drawn and carried away by the new piece of information that drastically - in most cases - changes their trend of thought before they are fully aware of the harm done by the distraction. While applying the simple survey methods of research, this paper shall examine the findings of an ongoing study with two groups of students who offer University Wide Courses (UWC) here at Covenant University. Results from the pilot study conducted gives us reasons to argue that a resent product by Google simply known as “Google+” may be that much desired formula that teachers and lectures all over the world, need to boost the teaching and learning experiences of both teachers, students and researchers within and outside the classroom environment