Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs

To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs). An explainable AI (XAI) model was applied to interpret black-box model predictions and assess its performance. Seven CNNs were trained on CheXpert. Three transfer learning approaches were thereafter applied to a local dataset. The classification results were ensembled using simple and entropy-weighted averaging. We applied Grad-CAM (an XAI model) to produce a saliency map. Grad-CAM maps were compared to manually extracted regions of interest, and the training time was recorded. The best transfer learning model was that which used image embeddings and random forest with simple averaging, with an average AUC of 0.856. Grad-CAM maps showed that the models focused on specific features of each CXR. CNNs pretrained on a large public dataset of medical images can be exploited as feature extractors for tasks of interest. The extracted image embeddings contain relevant information that can be used to train an additional classifier with satisfactory performance on an independent dataset, demonstrating it to be the optimal transfer learning strategy and overcoming the need for large private datasets, extensive computational resources, and long training times

An Agent-Based Approach for Procedural Puzzle Generation in Graph-Based Maps

We present an algorithm to add puzzles to maps represented as graphs. The algorithm starts from an empty map, represented as a graph, with at least one entry area and one exit area. It runs several specialized agents responsible for adding puzzles (e.g., locked doors, keys, switches). It generates a map with at least one acceptable solution (path) whose difficulty depends on the type of agents used (that is, the variety of puzzles added) and the number of puzzles added by each agent. Most importantly, no sequence of actions can leave the players stuck in a dead-end situation with no way to reach the goal. We include two examples of agents specialized in (i) switch mechanics (e.g., a lever that opens a passage and closes another one, the lighting of a fire that shows an inscription needed to solve another puzzle), and (ii) element collection mechanics (e.g., collecting keys or other puzzle elements to open a passage)

An analysis of Single-Player Monte Carlo Tree Search performance in Sokoban

We apply the extension of Monte Carlo Tree Search for single player games (SP-MCTS) to Sokoban and compare its performance to a solver integrating Iterative Deepening A* (IDA*) with several problem-specific heuristics. We introduce two extensions of MCTS to deal with some of the challenges that Sokoban poses to MCTS methods, namely, the reduced search space that deadlock situations can cause and the large number of cycles. We also evaluate three domain-independent enhancements that have been shown to improve MCTS performance, namely, UCB1-Tuned, Rapid Action Value Estimation (RAVE), and Node Recycling. We perform a series of experiments to determine the best SP-MCTS configuration and then compare its performance to IDA*. We show that SP-MCTS can solve around 85% of the levels with 1000000 iterations, that is the same performance reached by IDA* with only 10000 nodes. Overall, our results suggest that IDA* is still the best solver for Sokoban, also because it can easily integrate much domain knowledge. At the same time, our results also highlight some interesting directions to design better MCTS solvers for this domain

A Framework to Create Collaborative Games for Team Building using Procedural Content Generation

We present a framework to design collaborative games for team building that employs a search-based procedural content generation toolset to help designers creating levels. It comprises a multiplayer game with asymmetric interaction in which a player must reach the exit of a maze within a time limit, while wearing a head mounted display. The maze is complex and the player could not reach the exit on time without a team there to help her using a large number of printed maps describing candidate mazes. The player describes what she sees to the team who use such information to identify which one of the several available maps the player is currently navigating in virtual reality. Each game requires the generation of hundreds of very similar maps with several aliasing (confusing) situations but at the same time need to have a solution to guarantee that if the team members collaborate effectively they can guide the player to the exit. It would be infeasible for a human designer to provide such a massive number of maps for every game played. Accordingly, we developed automatic authoring tools to help designers generate such large sets of maps and also to optimize them based on design principles focusing on fun and pace. Our preliminary results show that the authoring pipeline we created can generate games (set of maps) adherent to such design principles

Discovering interesting information in XML data with association rules

measures, performance measures Data mining algorithms are designed to extract interesting information from large amounts of data. They usually assume that source data are in relational (tabular) form. However, the recent success of XML as a standard to represent semi-structured data and the increasing amount of data available in XML pose new challenges to the data mining community. In this paper we introduce association rules for XML data. To accomplish this, we propose a new operator, based on XPath and inspired by the syntax of XQuery, which allows us to express complex mining tasks, compactly and intuitively. The operator can indifferently (and simultaneously) target both the content and the structure of the data, since the distinction in XML is slight. 1

One day in a Roman Domus: Human Factors and Educational Properties Involved in a Virtual Heritage Application

As stated by Mosaker (2001), Virtual Reality (VR) environments that portray the past can be considered modern-day time machines. A substantial variety of Virtual Heritage (VH) applications have been developed recently, with the mission of using VR technologies for cultural preservation purposes. However, few of these projects focused on properly assessing these applications' educational value and goodness of interaction. In light of these considerations, a VR application reproducing an ancient Roman Domus has been developed to assess Human Factors variables and learning ratio of users. Therefore, 161 participants have been divided into three conditions in a between-subjects design: a Virtual Reality Experience (VRE), a First Person Experience (FPE), and a Multi-media Presentation Experience (MPE). Results showed an overall appreciation of the topic in all conditions, with comparable learning performances. However, we discovered a higher engagement and enjoyment of users with the VRE. Therefore, the Domus Romana application has been proven to be an effective complementary educational tool in explaining Roman houses

Lower Limb Rehabilitation in Juvenile Idiopathic Arthritis using Serious Games

Patients undergoing physical rehabilitation therapy must perform series of exercises regularly over a long period of time to improve, or at least not to worsen, their condition. Rehabilitation can easily become boring because of the tedious repetition of simple exercises, which can also cause mild pain and discomfort. As a consequence, patients often fail to follow their rehabilitation schedule with the required regularity, thus endangering their recovery. In the last decade, video games have become largely popular and the availability of advanced input controllers has made them a viable approach to make physical rehabilitation more entertaining while increasing patients motivation. In this paper, we present a framework integrating serious games for the lower-limb rehabilitation of children suffering from Juvenile Idiopathic Arthritis (JIA). The framework comprises games that implement parts of the therapeutic protocol followed by the young patients and provides modules to tune, control, record, and analyze the therapeutic sessions. We present the result of a preliminary validation we performed with patients at the clinic under therapists supervision. The feedback we received has been overall very positive both from patients, who enjoyed performing their usual therapy using video games, and therapists, who liked how the games could keep the children engaged and motivated while performing the usual therapeutic routine

V-Arcade: design and development of a serious games framework to support the upper limbs rehabilitation

In recent years new technologies have been developed to track user's motions, as the Leap Motion Controller for hands and wrist tracking. We propose a framework for children's hand, wrist and forearm rehabilitation at home and at hospital designed and developed in collaboration with the therapists of the De Marchi Pediatric Clinic in Milan. The framework contains an application with four serious games supporting hands and wrists rehabilitation, an application specific for the therapist to manage children's progresses remotely and a server to store data about the exercises' performed by children. We developed the framework with an iterative process, prototyping, testing and updating it basing on the children and therapists feedback. We performed five testing sessions with three children in the De Marchi Clinic, at the presence of a therapist and the child's parents, each session involved one child per time