Beyond Winning and Losing: Guidelines for Evaluating High Performance Coaches

This paper proposes a three-step method of evaluating high performance coaches involving feedback from the athletes. First, data are collected using an instrument such as the Coaching Behavior Scale for Sport (CBS-S: Côté, Yardley, Hay, Sedgwick, & Baker, 1999). Second, a summary report is prepared with descriptive information regarding the frequency of behaviors demonstrated by the coach that can be compared to previous results or to a criterion measure. The third step involves appropriate personnel reviewing the report and subsequently providing guidance for individual coach development. This three-step appraisal method provides useful evaluative feedback to coaches and has been used in several sport programs in Canada, the United States, and Australia.

Applied Research Sport Experiences, Milestones, and Educational Activities Associated With High-Performance Coaches' Development

What experiences are needed to become a high-performance coach? The present study addressed this question through structured retrospective quantitative interviews with 10 team- and 9 individual-sport coaches at the Canadian interuniversity-sport level. Minimum amounts of certain experiences were deemed necessary but not sufficient to become a high-performance coach (e.g., playing the sport they now coach and interaction with a mentor coach for all coaches, leadership opportunities as athletes for team-sport coaches only). Although coaches reported varying amounts of these necessary experiences, general stages of high-performance coach development were traced. Findings serve to identify and support potential high-performance coaches and increase the effectiveness of formal coaching-education programs.

The Organizational Tasks of High-Performance Gymnastic Coaches

The purpose of this study was to report the knowledge used by expert high performance gymnastic coaches in the organization of training and competition. In-depth interviews were conducted with 9 coaches who worked with male gymnasts and 8 coaches who worked with female gymnasts. Qualitative analyses showed that coaches of males and coaches of females planned training similarly, except that coaches of females appeared to emphasize esthetic and nutritional issues to a greater extent. Coaches of males revealed more concerns about the organization of gymnasts' physical conditioning. Analysis indicated that expert gymnastic coaches of males and females are constantly involved in dynamic social interactions with gymnasts, parents, and assistant coaches. Many areas of coaches' organizational work, such as dealing with the athletes' personal concerns and working with parents, are not part of the structure of coaches' training programs and emerged as crucial tasks of expert gymnastic coaches for developing elite gymnasts.

The Knowledge of High-Performance Gymnastic Coaches : Competition and Training Considerations

The purpose of this study is to report the knowledge used in training and competition by 17 expert high-performance gymnastic coaches. A qualitative research methodology was used to collect and inductively analyze the data. The knowledge elicited for the competition component was categorized as competition site, competition floor, and trial competitions. These categories indicated that the coaches are minimally involved with the gymnasts in competition. The knowledge of the coaches elicited within the training component were categorized as coach involvement in training, intervention style, technical skills, mental skills, and simulation. Properties of these categories that were extensively discussed by the expert coaches, such as teaching progressions, being supportive, and helping athletes to deal with stress,are consistent with the literature on coaching and on sport psychology. Other aspects considered important in the sport psychology literature, such as developing concentration skills, were not discussed as thoroughly by the expert coaches.

A Cloud-based Framework for Security Analysis of Browser Extensions

In today's internet world, web browsers are an integral part of our day-to-day activities. Therefore, web browser security is a serious concern for all of us. Browsers can be breached in different ways. Because of the over privileged access, extensions are responsible for many security issues. Browser vendors try to keep safe extensions in their official extension galleries. However, their security control measures are not always effective and adequate. The distribution of unsafe extensions through different social engineering techniques is also a very common practice. Therefore, before installation, users should thoroughly analyze the security of browser extensions. Extensions are not only available for desktop browsers, but many mobile browsers, for example, Firefox for Android and UC browser for Android, are also furnished with extension features. Mobile devices have various resource constraints in terms of computational capabilities, power, network bandwidth, etc. Hence, conventional extension security analysis techniques cannot be efficiently used by end users to examine mobile browser extension security issues. To overcome the inadequacies of the existing approaches, we propose CLOUBEX, a CLOUd-based security analysis framework for both desktop and mobile Browser EXtensions. This framework uses a client-server architecture model. In this framework, compute-intensive security analysis tasks are generally executed in a high-speed computing server hosted in a cloud environment. CLOUBEX is also enriched with a number of essential features, such as client-side analysis, requirements-driven analysis, high performance, and dynamic decision making. At present, the Firefox extension ecosystem is most susceptible to different security attacks. Hence, the framework is implemented for the security analysis of the Firefox desktop and Firefox for Android mobile browser extensions. A static taint analysis is used to identify malicious information flows in the Firefox extensions. In CLOUBEX, there are three analysis modes. A dynamic decision making algorithm assists us to select the best option based on some important parameters, such as the processing speed of a client device and network connection speed. Using the best analysis mode, performance and power consumption are improved significantly. In the future, this framework can be leveraged for the security analysis of other desktop and mobile browser extensions, too.