Processing and Manipulation of Data Collected from the Educational On-Line Game Refraction

A team of students, artists, and researchers at the Center for Game Science at the University of Washington are trying to create video games that can discover optimal pathways for learning. They have focused so far on early mathematics education, including topics such as fractions and algebra, which are some of the main bottlenecks preventing students from pursuing a career in science. As a result, the educational on-line game \Refraction was created, which is aimed at students who start learning fraction computations. When the students are playing the game online, all the data and information, such as mouse movements and mouse clicks, are stored in datasets of dierent formats. In this MS report, we will develop functions in the R software environment that will allow other researchers to easily process and manipulate the data generated from this game for future statistical analyses

Flexible and Adaptive Real-Time Task Scheduling in Cyber-Physical Control Systems

In a Cyber-Physical Control System (CPCS), there is often a hybrid of hard real-time tasks which have stringent timing requirements and soft real-time tasks that are computationally intensive. The task scheduling of such systems is challenging and requires flexible schemes that can meet the timing requirements without being over-conservative. Fixed-priority scheduling (FPS) is a scheduling policy that has been widely used in industry. However, as an open-loop scheduler, FPS has low system dynamics and no feedback from historic operation. As the working conditions of a CPCS will change due to both internal and external factors, an improved scheduling scheme is required which can adapt to changes without a costly system redesign. In recent years, there is a large research interest in the co-design of control and scheduling systems that explicitly considers task scheduling during the design of a controller. Many of these works reveal the possibility of adapting control periods at run-time in order to accommodate varying resource requirements and to optimise CPU utilization. It is also shown that control quality can be traded off for resource usages. In this thesis, an adaptive real-time scheduling framework for CPCS is presented. The adaptive scheduler has a hierarchical structure and it is built on top of a traditional FPS scheduler. The idea of dynamic worst-case execution time is introduced and its cause and methods to identify the existence of a trend are discussed. An adaptation method that uses monitored statistical information to update control task periods is then introduced. Finally, this method is extended by proposing a dual-period model that can switch between multiple operational modes at run-time. The proposed framework can be potentially extended in many aspects and some of these are discussed in the future work. All proposals of this thesis are supported by extensive analysis and evaluations

Period Adaptation of Real-Time Control Tasks with Fixed-Priority Scheduling in Cyber-Physical Systems

Period Adaptation of Real-Time Control Tasks with Fixed Priority Schedulin

An EEG-Based Multi-Modal Emotion Database With Both Posed And Authentic Facial Actions For Emotion Analysis

Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person\u27s emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based emotion analysis has overlooked the role of facial expression changes. There exits little research on the relationship between facial behavior and brain signals due to the lack of dataset measuring both EEG and facial action signals simultaneously. To address this problem, we propose to develop a new database by collecting facial expressions, action units, and EEGs simultaneously. We recorded the EEGs and face videos of both posed facial actions and spontaneous expressions from 29 participants with different ages, genders, ethnic backgrounds. Differing from existing approaches, we designed a protocol to capture the EEG signals by evoking participants\u27 individual action units explicitly. We also investigated the relation between the EEG signals and facial action units. As a baseline, the database has been evaluated through the experiments on both posed and spontaneous emotion recognition with images alone, EEG alone, and EEG fused with images, respectively. The database will be released to the research community to advance the state of the art for automatic emotion recognition

MCS-IOV : Real-time I/o virtualization for mixed-criticality systems

In mixed-criticality systems, timely handling of I/O is a key for the system being successfully implemented and functioning appropriately. The criticality levels of functions and sometimes the whole system are often dependent on the state of the I/O. An I/O system for a MCS must provide simultaneously isolation/separation, performance/efficiency and timing-predictability, as well as being able to manage I/O resource in an adaptive manner to facilitate efficient yet safe resource sharing among components of different criticality levels. Existing approaches cannot achieve all of these requirements simultaneously. This paper presents a MCS I/O management framework, termed MCS-IOV. MCS-IOV is based on hardware assisted virtualisation, which provides temporal and spatial isolation and prohibits fault propagation with small extra overhead in performance. MCS-IOV extends a real-time I/O virtualisation system, by supporting the concept of mixed criticalities and customised interfaces for schedulers, which offers good timing-preditability. MCS-IOV supports I/O driven criticality mode switch (the mode switch can be triggered by detection of unexpected I/O behaviors, e.g., a higher I/O utilization than expected) and timely I/O resource reconfiguration up on that. Finally, We evaluated and demonstrate MCS-IOV in different aspects

Model Based System Assurance Using the Structured Assurance Case Metamodel

Assurance cases are used to demonstrate confidence in system properties of interest (e.g. safety and/or security). A number of system assurance approaches are adopted by industries in the safety-critical domain. However, the task of constructing assurance cases remains a manual, lenghty and informal process. The Structured Assurance Case Metamodel (SACM)is a standard specified by the Object Management Group (OMG). SACM provides a richer set of features than existing system assurance languages/approaches. SACM provides a foundation for model-based system assurance, which bears great application potentials in growing technology domains such as Open Adaptive Systems. However, the intended usage of SACM has not been sufficiently explained. In addition, there has not been support to interoperate between existing assurance case (models)and SACM models. In this article, we explain the intended usage of SACM based on our involvement in the OMG specification process of SACM. In addition, to promote a model-based approach, we provide SACM compliant metamodels for existing system assurance approaches (the Goal Structuring Notation and Claims-Arguments-Evidence), and the transformations from these models to SACM. We also briefly discuss the tool support for model-based system assurance which helps practitioners make the transition from existing system assurance approaches to model-based system assurance using SACM