The cultivation of fundamental movement skills (FMS) during the early stages of childhood bears immense importance in shaping an individual’s involvement and achievements in sports throughout their entire life. Therefore, the assessment of motor skills in early childhood (age 3-5 years) significantly influences the advancement of sports within a nation. To this end, early childhood motor skill assessment has been performed manually posing efficiency challenges. The current automation approaches either involve invasiveness or depend on a substantial volume of annotated video training data. In this research, we introduce the Early Childhood Actions Monitoring and Analytics System (ECAMS) toolkit designed to evaluate early childhood motor skills, including activities like sitting up, running, walking, and jumping. This research employs computer vision and machine learning techniques integrated with the TGM2D (Test of Gross Motor Development-2nd Edition) toolkit to accurately detect motor skills from a given video input. This real-time analysis will offer accurate guidance to individuals involved in early childhood development, including educators, trainers, parents, and sports analysts.

With many trigger-action platforms that integrate Internet of Things (IoT) systems and online services, rich functionalities transparently connecting digital and physical worlds become easily accessible for the end users. On the other hand, such facilities incorporate multiple parties whose data control policies may radically differ and even contradict each other, and thus privacy violations may arise throughout the lifecycle (e.g., generation and transmission) of triggers and actions. In this work, we conduct an in-depth study on the privacy issues in multi-party trigger-action integration platforms (TAIPs). We first characterize privacy violations that may arise with the integration of heterogeneous systems and services. Based on this knowledge, we propose Taifu, a dynamic testing approach to identify privacy weaknesses from the TAIP. The key insight of Taifu is that the applets which actually program the trigger-action rules can be used as test cases to explore the behavior of the TAIP. We evaluate the effectiveness of our approach by applying it on the TAIPs that are built around the IFTTT platform. To our great surprise, we find that privacy violations are prevalent among them. Using the automatically generated 407 applets, each from a different TAIP, Taifu detects 194 cases with access policy breaches, 218 access control missing, 90 access revocation missing, 15 unintended flows, and 73 over-privilege access.

A BAR system specifies a cooperation between agents who can be altruistic when they follow the specified behaviours, Byzantine when they randomly deviate from specifications and rational when they deviate to increase their own benefits. We consider whether a rational agent indeed follows the specification of a probabilistic BAR system as verifying whether the system is a Nash-equilibrium in the corresponding stochastic games. In this article, we propose an intuitive specification for probabilistic BAR systems and an algorithm to automatically verify Nash-equilibrium. To validate our implementation of the algorithm, we present two case studies – the three-player Rock-paper-scissors game and a probabilistic secret sharing protocol.