This paper describes the development and evaluation of the UIs and Scores of musical performance system. The aim of this research is to provide a musical tool for elderly people and caregivers. The UIs are designed on tablet PCs, which look like keyboards. Five UIs are evaluated: plain keyboard, and keyboards with note names, numbers, colors and shapes. A staff notation score was used for the plain keyboard, and four types of scores represented by note names, numbers, colors and shapes were used for other UIs. The result of the experiment indicates that the UIs and scores of note name representation and number representation would be useful to play for people who are not familiar with staff notation and that those of number representation would be useful to play and sing at the same time. The result also indicates that the UI and scores of color representation could be used for some people who have difficulty reading numbers. It is also indicated that people in their 60s and 70s can use the UI and scores of number representation.
In a study, published earlier this month, researchers developed a machine-learning algorithm to detect Alzheimer's in brain scans 86 percent of the time. Even more impressively, it identified changes in the brain that showed mild cognitive impairment (MCI) 84 percent of the time. It might be able to identify these changes even earlier, but the researcher's only tested it on individual's who developed Alzheimer symptoms within nine years.
Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken language. We use three (statistical and neural) approaches to classify audio signals from spoken language into two classes of dementia and control. Result: This work designs a multi-modal feature embedding on the spoken language audio signal using three approaches; N-gram, i-vector, and x-vector. The evaluation of the system is done on the cookie picture description task from Pitt Corpus dementia bank with the accuracy of 83:6
Computational models of the hippocampal-region provide an important method for understanding the functional role of this brain system in learning and memory. The presentations in this workshop focused on how modeling can lead to a unified understanding of the interplay among hippocampal physiology, anatomy, and behavior. One approach can be characterized as "top-down" analyses of the neuropsychology of memory, drawing upon brain-lesion studies in animals and humans. Other models take a "bottom-up" approach, seeking to infer emergent computational and functional properties from detailed analyses of circuit connectivity and physiology (see Gluck & Granger, 1993, for a review). Among the issues discussed were: (1) integration of physiological and behavioral theories of hippocampal function, (2) similarities and differences between animal and human studies, (3) representational vs. temporal properties of hippocampaldependent behaviors,(4) rapid vs. incremental learning, (5) mUltiple vs. unitary memory systems, (5) spatial navigation and memory, and (6) hippocampal interaction with other brain systems.