Search results for "learning."
showing 10 items of 6527 documents
Dal game-based learning ai serious games: alcune prospettive per l’apprendimento mediato dalla tecnologia digitale
2023
In the last years, serious games have become popular in media education as they are useful tools in engaging, motivating, and helping students learn. However, few studies investigate the long-term effects of game-based learning, and many scholars declare themself skeptical about the learning mediated by digital technology. This paper reflects on the importance of new media and serious games for young people belonging to the Net Generation as defined by Prensky. In particular, I support the idea that game-based learning reflects a broader change in our society, coinciding with the rise of the phenomenon of Gamification and the Homo Ludens paradigm. Apart from the limitations of technology-me…
HEART MOBILE LEARNING
2018
The widespread diffusion of mobile technologies in today’s society and the technological developments of recent years offers new opportunities for learning providing innovative techniques and tools in education. In this paper, we will introduce HeARt, an augmented reality mobile Learning system to support university medical students in their learning activities during an anatomy laboratory. Students usually use, in their daily anatomy laboratory, a physical human heart model to investigate and learn about heart anatomy. Even though these models are perfect education tools to observe details and touch "with hands" all the heart sections, they need a supplementary encyclopaedia to learn all h…
Augmented Reality Gamification for Human Anatomy
2019
This paper focuses on the use of Augmented Reality technologies in relation to the introduction of game design elements to support university medical students in their learning activities during a human anatomy laboratory. In particular, the solution we propose will provide educational contents visually connected to the physical organ, giving also the opportunity to handle a 3D physical model that is a perfect reproduction of a real human organ.
Transforming Experience: The Potential of Augmented Reality and Virtual Reality for Enhancing Personal and Clinical Change
2016
During life, many personal changes occur. These include changing house, school, work, and even friends and partners. However, the daily experience shows clearly that, in some situations, subjects are unable to change even if they want to. The recent advances in psychology and neuroscience are now providing a better view of personal change, the change affecting our assumptive world: (a) the focus of personal change is reducing the distance between self and reality (conflict); (b) this reduction is achieved through (1) an intense focus on the particular experience creating the conflict or (2) an internal or external reorganization of this experience; (c) personal change requires a progression…
Machine Learning VS Transfer Learning - Smart Camera Implementation for Face Authentication
2018
The aim of this paper is to highlight differences between classical machine learning and transfer learning applied to low cost real-time face authentication. Furthermore, in an access control context, the size of biometric data should be minimized so it can be stored on a remote personal media. These constraints have led us to compare only lightest versions of these algorithms. Transfer learning applied on Mobilenet v1 raises to 85% of accuracy, for a 457Ko model, with 3680s and 1.43s for training and prediction tasks. In comparison, the fastest integrated method (Random Forest) shows accuracy up to 90% for a 7,9Ko model, with a fifth of a second to be trained and a hundred of microseconds …
Neuropsychological Profile, Emotional/Behavioral Problems, and Parental Stress in Children with Neurodevelopmental Disorders
2021
Background: The aim of our study was to trace a specific neuropsychological profile, to investigate emotional-behavioral problems and parental stress in children with Autism Spectrum Disorder Level 1/High functioning (ASD-HF), Specific Learning Disorders (SLD) and Attention Deficit/Hyperactivity Disorder (ADHD) disorders and to highlight similarities and differences among the three groups. Methods: We retrospectively collected the data from a total of 62 subjects with ASD-HF (n = 19) ADHD (n = 21), SLD (n = 22) and 20 typical development. All the participants underwent neuropsychological standardized test for the evaluation of cognitive profile (Wechsler Intelligence Scale for Children Four…
HEp-2 intensity classification based on deep fine-tuning
2020
The classification of HEp-2 images, conducted through Indirect ImmunoFluorescence (IIF) gold standard method, in the positive / negative classes, is the first step in the diagnosis of autoimmune diseases. Since the test is often difficult to interpret, the research world has been looking for technological features for this problem. In recent years the methods of deep learning have overcome the other machine learning techniques in their effectiveness and robustness, and now they prevail in artificial intelligence studies. In this context, CNNs have played a significant role especially in the biomedical field. In this work we analysed the capabilities of CNN for fluorescence classification of…
LogDet divergence-based metric learning with triplet constraints and its applications.
2014
How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn…
Multitasking in Driving as Optimal Adaptation Under Uncertainty
2021
Objective The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge. Background Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment. Method We model the driver’s decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The…
Learning what (not) to do: testing rejection and copying of simulated heterospecific behavioural traits
2011
Animals can copy behaviour of heterospecifics (interspecific social learning), but it is not known whether social-learning strategies postulated for conspecific contexts, such as selectively copying individuals (or behaviours) that are more successful or common than the observer, apply here. A recent study found evidence for biased interspecific acquisition of nest site feature preference depending on observed fitness of the demonstrator (clutch size). We experimentally tested whether migratory pied flycatcher, Ficedula hypoleuca, females, given a choice between two novel alternative behavioural traits of nest site feature choice, copy or reject the experimentally induced choice exhibited b…