No abstract

MyHeifer is an autism spectrum disorder (ASD) intervention application aimed at better understanding patients' behavioral patterns and informing healthcare decisions, easing caregiver burden, and providing an emotional outlet for patients. Children with ASD often struggle with the complexity of human communication because of the array of verbal and nonverbal communication methods at play. Because of this, technological interventions can be a valuable tool for communicating with children with ASD because of their simplicity. Hence the MyHeifer application seeks to provide an uncomplicated environment for children with ASD to express and explore their emotions. Children perform “actions” or “interactions” which are classified as either positive or negative behaviors. Through these interactions, children learn various ways to react to situations. The choices children make are collected and serve as a basis for future healthcare decisions. Because communication is often difficult for children with ASD, utilizing data from past actions or interactions helps caregivers anticipate and understand the challenges to make better emotional and behavioral connections in individual patients in order to address personalized care needs.

Stanford Research Institute (SRI) has an extensive file of actual computer misuse cases. The National Bureau of Standards asked SRI to use these caaes as a foundation to develop ranked lists of computer safeguards that would have prevented or detected the recorded intentional misuses. This report provides a working definition of intentional computer misuse, a construction of a vulnerability taxonomy of intentional computer misuse, a list of 88 computer safeguards, and a model for classifying the safeguards. In addition, there are lists ranking prevention and detection safeguards, with an explanation of the method of approach used to arrive at the lists.

Abstract-Vascular dementia (VD), the second most common type of dementia, effects approximately 13.9 per cent of people over the age of 71 in the United States alone. 26% of individuals develop VD after being diagnosed with congestive heart failure. Memory and cognition are increasingly affected as dementia progresses. However, these are not the first symptoms to appear in some types of dementia. Alterations in gait and executive functioning have been associated Vascular Cognitive Impairment (VCI). Research findings suggest that gait may be one of the earliest affected systems during onset of VCI, immediately following a vascular episode. The diagnosis tools currently utilized for VD are focused on memory impairment, which is only observed in later stages of VD. Hence we are proposing a framework that isolates gait and executive functioning analysis by applying machine learning to predict VD before cognition is affected, so pharmacological treatments can be used to postpone the onset of cognitive impairment. Over a period of time, we hope to be able to develop prediction algorithms that will not only identify but also predict vascular dementia.