Fostering Awareness and Personalization of Learning Artificial Intelligence

This paper illustrates the activities of the projects SMAILE and AILEAP, which are devoted to foster the growth of awareness and readyness to learn artificial intelligence in the general population. The first project was mainly oriented to children and young adults, while the second is more oriented to the personalization of the learning experience also in professionals

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Analysing E-BTT data: the E-TAN ANALYST prototype

Here we present the first prototype of the E-TAN ANALYST, an app that is able to analyse spatio-temporal data developed in Python. This app was developed to analyse the Enhanced Baking Tray Task (E-BTT) data, an innovative task that exploits Tangible User Interfaces (TUI) and returns a series of sixteen ordered coordinates as output. The E-TAN ANALYST allows the automatic computation of a series of indexes on these coordinates and the graphical representation of E-BTT performance. This elaboration can then be used by the clinician or by the experimenter to analyse the single subject's performance and their spatial exploration

E-TAN platform and E-baking tray task potentialities: New ways to solve old problems

Spatial abilities allow humans to perceive and act in the world around them. Combining technology with a wide used neuropsychological test, the EBaking Tray Task have proved to be very versatile and useful. Here we examine its properties and potentialities, trying to propose new challenges in visuospatial cognition. Firstly, we address to actual algorithms of data analysis and propose new ones. Then we propose several new variables that could be inspected related to spatial exploration measured with this new device: verticality, stress, emotions, explored areas in peri-personal space and so on

Enhancing neuropsychological testing with gamification and tangible interfaces: The baking tray task

Neuropsychological tests are performance-based tasks to evaluate cognitive functions, but often they are particularly long and boring during their execution; these issues can interfere with performance provided by patients or healthy participants. In this paper, we present our gamified and virtually enhanced version of a specific neuropsychological test: The Baking Tray Task (BTT), aimed to assess unilateral spatial neglect (USN), a visuospatial processing disorder. This enhanced BTT version has been developed through STELT (Smart Technologies to Enhance Learning and Teaching) software, a platform which allows implementation of augmented reality systems based on RFID/NFC technology. These materials permit to link together smart technologies and physical materials, uniting the manipulative approach and digitalized technologies

Using technology and tangible interfaces in a visuospatial cognition task: The case of the Baking Tray Task

The Baking Tray Task (BTT) is a neuropsychological test, aimed to assess unilateral spatial neglect (USN), a visuospatial disorder mainly associated to right parietal lobe damage. Over the years, the BTT has been re-proposed in different forms, other materials to be placed and in both digital and virtual environment preserving the initial settings and the way of administration. In this paper, we present two versions of BTT, the E-BTT and the BTT-SCAN, improved by technology. The aim of these tools is to present a new technological version of the same test in order to preserve a high validity and reliability and to acquire massive and more precise data

Applied behavior analysis (ABA) as a footprint for tutoring systems: A model of ABA approach applied to olfactory learning

Applied Behavior Analysis (ABA) belongs to the analysis of behavior techniques introduced by the theorists of behaviorism in psychological fields. It deals with the application of behaviorism principles to guide the learning process. It can serve as a footprint to build artificial tutoring systems in environments for specific learning processes. In this paper, we delineate the pathway to build an artificial tutoring system following ABA footprints, named the ABA tutor. In implementing the ABA tutor, the techniques of ABA are reproduced. This paper also describes how to build a tutor based on ABA and how to use it to favor olfactory learning. In more detail, the ABA tutor is inserted in SNIFF, a system that combines a software and a hardware side to assess and practice the sense of smell exploiting gamification. A first experiment was run involving 90 participants, and the results indicated that the artificial tutoring system based on ABA principles can effectively promote olfactory learning. The implications of this approach are discussed