Rationale and study protocol for the \u27Active Teen Leaders Avoiding Screen-time\u27 (ATLAS) group randomized controlled trial: An obesity prevention intervention for adolescent boys from schools in low-income communities

Introduction The negative consequences of unhealthy weight gain and the high likelihood of pediatric obesity tracking into adulthood highlight the importance of targeting youth who are \u27at risk\u27 of obesity. The aim of this paper is to report the rationale and study protocol for the \u27Active Teen Leaders Avoiding Screen-time\u27 (ATLAS) obesity prevention intervention for adolescent boys living in low-income communities. Methods/design The ATLAS intervention will be evaluated using a cluster randomized controlled trial in 14 secondary schools in the state of New South Wales (NSW), Australia (2012 to 2014). ATLAS is an 8-month multi-component, school-based program informed by self-determination theory and social cognitive theory. The intervention consists of teacher professional development, enhanced school-sport sessions, researcher-led seminars, lunch-time physical activity mentoring sessions, pedometers for self-monitoring, provision of equipment to schools, parental newsletters, and a smartphone application and website. Assessments were conducted at baseline and will be completed again at 9- and 18-months from baseline. Primary outcomes are body mass index (BMI) and waist circumference. Secondary outcomes include BMI z-scores, body fat (bioelectrical impedance analysis), physical activity (accelerometers), muscular fitness (grip strength and push-ups), screen-time, sugar-sweetened beverage consumption, resistance training skill competency, daytime sleepiness, subjective well-being, physical self-perception, pathological video gaming, and aggression. Hypothesized mediators of behavior change will also be explored. Discussion ATLAS is an innovative school-based intervention designed to improve the health behaviors and related outcomes of adolescent males in low-income communities

Cognitive task analysis: current use and practice in the UK Armed Forces and elsewhere

This report is concerned with Cognitive Task Analysis (CTA). It covers the origins, growth, and diversity of CTA as an activity, current practice in the UK Armed Forces and civilian operations, reviews an extensive range of archive material and draws a number of conclusions as to the best practice. It reveals that there is no consistent use of CTA in the Armed Forces. Recommendations are made as to how CTA techniques could be implemented so as to benefit UK MoD with regard to both training and procuremen

Anna Rutherford asked the following writers the question, \u27What did ANZAC mean to you as a child and why did you choose to write about the subject?\u27 What follows is their answers.

Les A. Murray, David Malouf, Geoff Page, Roger McDonald, John Romeril, Philip Salmon, Louis Nowr

Experimental study of router buffer sizing

During the past four years, several papers have proposed rules for sizing buffers in Internet core routers. Appenzeller et al. suggest that a link needs a buffer of size (� �), where � is the capacity of the link, and is the number of flows sharing the link. If correct, buffers could be reduced by 99 % in a typical backbone router today without loss in throughput. Enachecsu et al., and Raina et al. suggest that buffers can be reduced even further to 20-50 packets if we are willing to sacrifice a fraction of link capacities, and if there is a large ratio between the speed of core and access links. If correct, this is a five orders of magnitude reduction in buffer sizes. Each proposal is based on theoretical analysis and validated using simulations. Given the potential benefits (and the risk of getting it wrong!) it is worth asking if these results hold in real operational networks. In this paper, we report buffer-sizing experiments performed on real networks- either laboratory networks with commercial routers as well as customized switching and monitorin

Investigating the Influence of Feature Sources for Malicious Website Detection

Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites in order to trick users into trusting them. These websites, often a primary method for credential collection, pose a severe threat to large enterprises. Credential collection enables malicious actors to infiltrate enterprise systems without triggering the usual alarms. Therefore, there is a vital need to gain deep insights into the statistical features of these websites that enable Machine Learning (ML) models to classify them from their benign counterparts. Our objective in this paper is to provide this necessary investigation, more specifically, our contribution is to observe and evaluate combinations of feature sources that have not been studied in the existing literature—primarily involving embeddings extracted with Transformer-type neural networks. The second contribution is a new dataset for this problem, GAWAIN, constructed in a way that offers other researchers not only access to data, but our whole data acquisition and processing pipeline. The experiments on our new GAWAIN dataset show that the classification problem is much harder than reported in other studies—we are able to obtain around 84% in terms of test accuracy. For individual feature contributions, the most relevant ones are coming from URL embeddings, indicating that this additional step in the processing pipeline is needed in order to improve predictions. A surprising outcome of the investigation is lack of content-related features (HTML, JavaScript) from the top-10 list. When comparing the prediction outcomes between models trained on commonly used features in the literature versus embedding-related features, the gain with embeddings is slightly above 1% in terms of test accuracy. However, we argue that even this somewhat small increase can play a significant role in detecting malicious websites, and thus these types of feature categories are worth investigating further

Investigating the Influence of Feature Sources for Malicious Website Detection

Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites in order to trick users into trusting them. These websites, often a primary method for credential collection, pose a severe threat to large enterprises. Credential collection enables malicious actors to infiltrate enterprise systems without triggering the usual alarms. Therefore, there is a vital need to gain deep insights into the statistical features of these websites that enable Machine Learning (ML) models to classify them from their benign counterparts. Our objective in this paper is to provide this necessary investigation, more specifically, our contribution is to observe and evaluate combinations of feature sources that have not been studied in the existing literature—primarily involving embeddings extracted with Transformer-type neural networks. The second contribution is a new dataset for this problem, GAWAIN, constructed in a way that offers other researchers not only access to data, but our whole data acquisition and processing pipeline. The experiments on our new GAWAIN dataset show that the classification problem is much harder than reported in other studies—we are able to obtain around 84% in terms of test accuracy. For individual feature contributions, the most relevant ones are coming from URL embeddings, indicating that this additional step in the processing pipeline is needed in order to improve predictions. A surprising outcome of the investigation is lack of content-related features (HTML, JavaScript) from the top-10 list. When comparing the prediction outcomes between models trained on commonly used features in the literature versus embedding-related features, the gain with embeddings is slightly above 1% in terms of test accuracy. However, we argue that even this somewhat small increase can play a significant role in detecting malicious websites, and thus these types of feature categories are worth investigating further