Persistent topology for natural data analysis - A survey

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued

The adult literacy evaluator: An intelligent computer-aided training system for diagnosing adult illiterates

An important part of NASA's mission involves the secondary application of its technologies in the public and private sectors. One current application being developed is The Adult Literacy Evaluator, a simulation-based diagnostic tool designed to assess the operant literacy abilities of adults having difficulties in learning to read and write. Using ICAT system technology in addition to speech recognition, closed-captioned television (CCTV), live video and other state-of-the art graphics and storage capabilities, this project attempts to overcome the negative effects of adult literacy assessment by allowing the client to interact with an intelligent computer system which simulates real-life literacy activities and materials and which measures literacy performance in the actual context of its use. The specific objectives of the project are as follows: (1) To develop a simulation-based diagnostic tool to assess adults' prior knowledge about reading and writing processes in actual contexts of application; (2) to provide a profile of readers' strengths and weaknesses; and (3) to suggest instructional strategies and materials which can be used as a beginning point for remediation. In the first and developmental phase of the project, descriptions of literacy events and environments are being written and functional literacy documents analyzed for their components. Examples of literacy events and situations being considered included interactions with environmental print (e.g., billboards, street signs, commercial marquees, storefront logos, etc.), functional literacy materials (e.g., newspapers, magazines, telephone books, bills, receipts, etc.) and employment related communication (i.e., job descriptions, application forms, technical manuals, memorandums, newsletters, etc.). Each of these situations and materials is being analyzed for its literacy requirements in terms of written display (i.e., knowledge of printed forms and conventions), meaning demands (i.e., comprehension and word knowledge) and social situation. From these descriptions, scripts are being generated which define the interaction between the student, an on-screen guide and the simulated literacy environment. The proposed outcome of the Evaluator is a diagnostic profile which will present broad classifications of literacy behaviors across the major areas of metacognitive abilities, word recognition, vocabulary knowledge, comprehension and writing. From these classifications, suggestions for materials and strategies for instruction with which to begin corrective action will be made. The focus of the Literacy Evaluator will be essentially to provide an expert diagnosis and an interpretation of that assessment which then can be used by a human tutor to further design and individualize a remedial program as needed through the use of an authoring system

Text analytics on MOOCs. A comprehensive analysis of emotions

The value of diversity in education is highly emphasized in recent years, particularly in the wake of the COVID-19 pandemic, by many scholars. Massive open online courses (MOOCs) have aided the evolution of online learning by broadening the range of learning opportunities available. They have gained popularity, especially in higher education by providing unlimited access to lectures and rich learning materials by renowned and respected academics in a wide variety of areas, with no restrictions and at very low fees. Furthermore, learners' motivations for enrolling in a MOOC may vary depending on their choices for the course's instructional design as well as their emotions. Knowing this, the development of more effective online courses that address affective concerns would appeal to a wider audience and improve the learning experience. This research aims to uncover the emotional characteristics of MOOCs to better understand why learners choose a specific course among hundreds of options available on MOOC sites. For extracting the learners' emotions from user reviews, the study used Kansei Engineering approach, which is enhanced with text analytics techniques. The research methodology entails gathering reviews from MOOCs and analyzing them using natural language processing (NLP) techniques to discover Kansei words that characterize MOOCs, notably for courses in the discipline of Data Science. The expected output of this study is a Kansei corpus for online courses in this discipline

Fuzzy Navigation Engine: Mitigating the Cognitive Demands of Semi-Natural Locomotion

Many interfaces exist for locomotion in virtual reality, although they are rarely considered fully natural. Past research has found that using such interfaces places cognitive demands on the user, with unnatural actions and concurrent tasks competing for finite cognitive resources. Notably, using semi-natural interfaces leads to poor performance on concurrent tasks requiring spatial working memory. This paper presents an adaptive system designed to track a user\u27s concurrent cognitive task load and adjust interface parameters accordingly, varying the extent to which movement is fully natural. A fuzzy inference system is described and the results of an initial validation study are presented. Users of this adaptive interface demonstrated better performance than users of a baseline interface on several movement metrics, indicating that the adaptive interface helped users manage the demands of concurrent spatial tasks in a virtual environment. However, participants experienced some unexpected difficulties when faced with a concurrent verbal task

A Review of Artificial Intelligence in the Internet of Things

Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields

A two level representation for spatial relations. - Part I

A model to represent spatial relations is presented. It is used for the definition of common sense knowledge of rational agents in a multi-agent-scenario. The main idea is, that it is structured in two levels: the representation of relations may be accomplished in terms of predicate logic at one level or in expressions of Cartesian coordinates at the other. Hence reasoning is possible with common rules of deduction as well as via exact calculations of the positions. Here we give an overview on the whole structure and then investigate in the definition of a set of spatial relations at the "Logical Level". Finally special features like the handling of the context and the problem of multiple views are discussed