Design and evaluation of tile selection algorithms for tiled HTTP adaptive streaming (Best paper award)

The future of digital video is envisioned to have an increase in both resolution and interactivity. New resolutions like 8k UHDTV are up to 16 times as big in number of pixels compared to current HD video. Interactivity includes the possibility to zoom and pan around in video. We examine Tiled HTTP Adaptive Streaming (TAS) as a technique for supporting these trends and allowing them to be implemented on conventional Internet infrastructure. In this article, we propose three tile selection algorithms, for different use cases (e.g., zooming, panning). A performance evaluation of these algorithms on a TAS testbed, shows that they lead to better bandwidth utilization, higher static Region of Interest (ROI) video quality and higher video quality while manipulating the ROI. We show that we can transmit video at resolutions up to four times larger than existing algorithms during bandwidth drops, which results in a higher quality viewing experience. We can also increase the video quality by up to 40 percent in interactive video, during panning or zooming

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Virtual Office Design For The Effectiveness Of Web_based Office Administration Works

Virtual Office is the science of information technology which is a new thing for some people. The word of virtual has a meaning of visible or tangible, it means that in the future an office is intangible because it will be replaced by a system which is specifically designed to replace the role of office by applying web -based computer programming. Nowdays, Offices have been applied are still using a certain place with a wide range of file and requiring a storage cabinet. These issues triggered the researcher tried to design a virtual office with a concept which is able to handle a variety of problems, ranging from archival storage , searching the archives , phone , video conference , sending letters, categorizing archives and saving faculty and staff data. These concepts developed by the researcher offered well organized data that all files or all archives of administrative office tucked neatly away in a database in well organizing so it made easier in searching a spesific archive. The final objective of the research is the improvement of the workers’ performance because it will shorten the time to manage archives or files. Manufacture of incoming mail, outgoing mail, disposition and also numbering letters will be arranged neatly, thus will increase the effectiveness of a worker. (Abstract

Real-Time Data Processing With Lambda Architecture

Data has evolved immensely in recent years, in type, volume and velocity. There are several frameworks to handle the big data applications. The project focuses on the Lambda Architecture proposed by Marz and its application to obtain real-time data processing. The architecture is a solution that unites the benefits of the batch and stream processing techniques. Data can be historically processed with high precision and involved algorithms without loss of short-term information, alerts and insights. Lambda Architecture has an ability to serve a wide range of use cases and workloads that withstands hardware and human mistakes. The layered architecture enhances loose coupling and flexibility in the system. This a huge benefit that allows understanding the trade-offs and application of various tools and technologies across the layers. There has been an advancement in the approach of building the LA due to improvements in the underlying tools. The project demonstrates a simplified architecture for the LA that is maintainable

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm and demonstrate that under mild conditions, it converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.Comment: 6 pages, conference submissio