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.

HELPING YOUTH WITH AUTISM SPECTRUM DISORDER DEVELOP SOCIAL COMPETENCE IN COMMUNITY-BASED SETTINGS

The purpose of the current dissertation is to identify the features of effective interventions by exploring the experiences of youth with ASD who participate in such interventions, through two intervention studies (Studies 1 and 2) and one interview study (Study 3). Studies 1 and 2 were designed to support the development of social competence of youth with ASD through Structured Play with LEGO TM (Study 1, 12 youths with ASD, ages 7–12) and Minecraft TM (Study 2, 4 youths with ASD, ages 11–13). Over the course of the sessions, the play of the youth developed from parallel play (children playing alone, without interacting) to co-operative play (playing together with shared objectives). The results of Study 2 showed that rates of initiations and levels of engagement increased from the first session to the final session. In Study 3, 12 youths with ASD (ages 10–14) and at least one of their parents were interviewed to explore what children and their parents want from programs designed to improve social competence, which activities and practices were perceived to promote social competence by the participants, and which factors affected their decisions regarding these programs. The adolescents and parents looked for programs that supported social development and emotional wellbeing, but did not always have access to the programs they would have preferred, with factors such as cost and location reducing their options. Three overarching themes emerged through analysis of the three studies: (a) interests of the youth; (b) structure, both through interactions and instruction; and (c) naturalistic settings. Adolescents generally engage more willingly in interventions that incorporate their interests, such as play with Minecraft TM in Study 2. Additionally, Structured Play and structured instruction were crucial components of providing safe and supportive contexts for the development of social competence. Finally, skills learned in naturalistic settings tend to be applied more successfully in everyday situations. The themes are analysed through the lens of Vygotsky’s (1978) perspectives on learning, play, and development. Implications of the results for practitioners and researchers are discussed.

ISSP Position Stand: To Sample or to Specialize? Seven Postulates About Youth Sport Activities that Lead to Continued Participation and Elite Performance

A comprehensive approach to sport expertise should consider the entire situation that is comprised of the person, the task, the environment, and the complex interplay of these components (Hackfort, 1986). Accordingly, the Developmental Model of Sport Participation (Côté, Baker, & Abernethy, 2007; Côté & Fraser-Thomas, 2007) provides a comprehensive framework for sport expertise that outlines different pathways of involvement in sport. In pathways one and two, early sampling serves as the foundation for both elite and recreational sport participation. Early sampling is based on two main elements of childhood sport participation: 1) involvement in various sports and 2) participation in deliberate play. In contrast, pathway three shows the course to elite performance through early specialization in one sport. Early specialization implies a focused involvement on one sport and a large number of deliberate practice activities with the goal of improving sport skills and performance during childhood. This paper proposes seven postulates regarding the role that sampling and deliberate play, as opposed to specialization and deliberate practice, can have during childhood in promoting continued participation and elite performance in sport.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.