Autonomic Control for Quality Collaborative Video Viewing

We present an autonomic controller for quality collaborative video viewing, which allows groups of geographically dispersed users with different network and computer resources to view a video in synchrony while optimizing the video quality experienced. The autonomic controller is used within a tool for enhancing distance learning with synchronous group review of online multimedia material. The autonomic controller monitors video state at the clients' end, and adapts the quality of the video according to the resources of each client in (soft) real time. Experimental results show that the autonomic controller successfully synchronizes video for small groups of distributed clients and, at the same time, enhances the video quality experienced by users, in conditions of fluctuating bandwidth and variable frame rate

Experiences in Teaching eXtreme Programming in a Distance Learning Program

As university-level distance learning programs become more and more popular, and software engineering courses incorporate eXtreme Programming (XP) into their curricula, certain challenges arise when teaching XP to students who are not physically co-located. In this paper, we present our experiences and observations from managing such an online software engineering course, and describe some of the specific challenges we faced, such as students' aversion to using XP and difficulties in scheduling. We also present some suggestions to other educators who may face similar situations

A Uniform Programming Abstraction for Effecting Autonomic Adaptations onto Software Systems

Most general-purpose work towards autonomic or self-managing systems has emphasized the front end of the feedback control loop, with some also concerned with controlling the back end enactment of runtime adaptations -- but usually employing an effector technology peculiar to one type of target system. While completely generic "one size fits all" effector technologies seem implausible, we propose a general purpose programming model and interaction layer that abstracts away from the peculiarities of target specific effectors,enabling a uniform approach to controlling and coordinating the low-level execution of reconfigurations, repairs,micro-reboots, etc

Optimizing Quality for Collaborative Video Viewing

The increasing popularity of distance learning and online courses has highlighted the lack of collaborative tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources used by the students. We present an architecture and adaptation model called AI2TV (Adaptive Internet Interactive Team Video), a system that allows geographically dispersed participants, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2TV upholds the invariant that each participant will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any of the participants and the results of those actions are seen by all the members. These features allow group members to review a lecture video in tandem to facilitate the learning process. We employ an autonomic (feedback loop) controller that monitors clients' video status and adjusts the quality of the video according to the resources of each client. We show in experimental trials that our system can successfully synchronize video for distributed clients while, at the same time, optimizing the video quality given actual (fluctuating) bandwidth by adaptively adjusting the quality level for each participant

Epidémiologie et prophylaxie de la mélioïdose, zoonose tropicale

Aucun résumé disponible en français

Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant

A Control Theory Foundation for Self-Managing Computing Systems

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate

Self-reported Smoking and Urinary Cotinine Levels among Pregnant Women in Korea and Factors Associated with Smoking during Pregnancy

This study examined urinary cotinine levels and self-reported smoking among pregnant women in Korea and the factors associated with smoking during pregnancy. The subjects were selected from pregnant women who visited 30 randomly sampled obstetric clinics and prenatal care hospitals in Korea in 2006. Smoking status was determined by self-reporting and urinary cotinine measurement. A total of 1,090 self-administered questionnaires and 1,057 urine samples were analyzed. The percentage of smoking revealed by self-reporting was 0.55% (95% confidence interval [CI], 0.11-0.99) and that revealed by urinary cotinine measurement (>100 ng/mL) was 3.03% (95% CI, 1.99-4.06). The kappa coefficient of agreement between self-reported smoking status and urinary cotinine measurement was 0.20 (95% CI, 0.03-0.37). Multiple logistic regression analysis revealed that early gestational period, low educational level, and being married to a smoker were significant risk factors for smoking during pregnancy. Smoking among pregnant women in Korea is not negligible, and those who are concerned to maternal and child health should be aware of this possibility among pregnant women in countries with similar cultural background

Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis

Abstract: Background: Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results: We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into ‘resistant’ and ‘susceptible’, highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions: We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR