Visibly Pushdown Modular Games

Games on recursive game graphs can be used to reason about the control flow of sequential programs with recursion. In games over recursive game graphs, the most natural notion of strategy is the modular strategy, i.e., a strategy that is local to a module and is oblivious to previous module invocations, and thus does not depend on the context of invocation. In this work, we study for the first time modular strategies with respect to winning conditions that can be expressed by a pushdown automaton. We show that such games are undecidable in general, and become decidable for visibly pushdown automata specifications. Our solution relies on a reduction to modular games with finite-state automata winning conditions, which are known in the literature. We carefully characterize the computational complexity of the considered decision problem. In particular, we show that modular games with a universal Buchi or co Buchi visibly pushdown winning condition are EXPTIME-complete, and when the winning condition is given by a CARET or NWTL temporal logic formula the problem is 2EXPTIME-complete, and it remains 2EXPTIME-hard even for simple fragments of these logics. As a further contribution, we present a different solution for modular games with finite-state automata winning condition that runs faster than known solutions for large specifications and many exits.Comment: In Proceedings GandALF 2014, arXiv:1408.556

User data distributed on the social web: how to identify users on different social systems and collecting data about them

This paper presents an approach to uniquely identify users and to retrieve their data distributed in profiles stored in different systems. The objective is exploiting the public user data available in the Web and especially in social networks. The approach does not require the implementation of specific protocols and the provision of authentication data. The evaluation provides good results that encourage us in carrying on the extension of the project. The extension we are working on is aimed at aggregating, using heuristic techniques, the data stored in the retrieved profiles and at inferring new data about the user

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation

The impact of voice on trust attributions

Trust and speech are both essential aspects of human interaction. On the one hand, trust is necessary for vocal communication to be meaningful. On the other hand, humans have developed a way to infer someone’s trustworthiness from their voice, as well as to signal their own. Yet, research on trustworthiness attributions to speakers is scarce and contradictory, and very often uses explicit data, which do not predict actual trusting behaviour. However, measuring behaviour is very important to have an actual representation of trust. This thesis contains 5 experiments aimed at examining the influence of various voice characteristics — including accent, prosody, emotional expression and naturalness — on trusting behaviours towards virtual players and robots. The experiments have the "investment game"—a method derived from game theory, which allows to measure implicit trustworthiness attributions over time — as their main methodology. Results show that standard accents, high pitch, slow articulation rate and smiling voice generally increase trusting behaviours towards a virtual agent, and a synthetic voice generally elicits higher trustworthiness judgments towards a robot. The findings also suggest that different voice characteristics influence trusting behaviours with different temporal dynamics. Furthermore, the actual behaviour of the various speaking agents was modified to be more or less trustworthy, and results show that people’s trusting behaviours develop over time accordingly. Also, people reinforce their trust towards speakers that they deem particularly trustworthy when these speakers are indeed trustworthy, but punish them when they are not. This suggests that people’s trusting behaviours might also be influenced by the congruency of their first impressions with the actual experience of the speaker’s trustworthiness — a "congruency effect". This has important implications in the context of Human–Machine Interaction, for example for assessing users’ reactions to speaking machines which might not always function properly. Taken together, the results suggest that voice influences trusting behaviour, and that first impressions of a speaker’s trustworthiness based on vocal cues might not be indicative of future trusting behaviours, and that trust should be measured dynamically

Predicting Parkinson's disease evolution using deep learning

Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. The disease is manifested by a drop in dopamine production, symptoms are cognitive and behavioural and include a wide range of personality changes, depressive disorders, memory problems, and emotional dysregulation, which can occur as the disease progresses. Early diagnosis and accurate staging of the disease are essential to apply the appropriate therapeutic approaches to slow cognitive and motor decline. Currently, there is not a single blood test or biomarker available to diagnose Parkinson's disease. Magnetic resonance imaging has been used for the past three decades to diagnose and distinguish between PD and other neurological conditions. However, in recent years new possibilities have arisen: several AI algorithms have been developed to increase the precision and accuracy of differential diagnosis of PD at an early stage. To our knowledge, no AI tools have been designed to identify the stage of progression. This paper aims to fill this gap. Using the "Parkinson's Progression Markers Initiative" dataset, which reports the patient's MRI and an indication of the disease stage, we developed a model to identify the level of progression. The images and the associated scores were used for training and assessing different deep-learning models. Our analysis distinguished four distinct disease progression levels based on a standard scale (Hoehn and Yah scale). The final architecture consists of the cascading of a 3DCNN network, adopted to reduce and extract the spatial characteristics of the RMI for efficient training of the successive LSTM layers, aiming at modelling the temporal dependencies among the data. Our results show that the proposed 3DCNN + LSTM model achieves state-of-the-art results by classifying the elements with 91.90\% as macro averaged OVR AUC on four classesComment: 27 pages, 11 figure

Toward a user-adapted question/answering educational approach

This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system

Factors influencing response to ingenol mebutate therapy for actinic keratosis of face and scalp

AIM To determine factors independently influencing response to ingenol mebutate therapy and assess efficacy on clinical setting of non-hypertrophic non-hyperkeratotic actinic keratosis (AK). METHODS Consecutive patients affected by non-hypertrophic non-hyperkeratotic AKs of the face or scalp were enrolled to receive ingenol mebutate 0.015% gel on a selected skin area of 25 cm2 for 3 consecutive days. Local skin reactions were calculated at each follow up visit using a validated composite score. Efficacy was evaluated by the comparison of clinical and dermoscopic pictures before the treatment and at day 57, and classified as complete, partial and poor response. RESULTS A number of 130 patients were enrolled, of which 101 (77.7%) were treated on the face, while 29 (22.3%) on the scalp. The great majority of our study population (n = 119, 91.5%) reached at least a 75% clearance of AKs and, in particular, 58 patients (44.6%) achieved a complete response while 61 (46.9%) a partial one. Logistic backward multivariate analysis showed that facial localization, level of local skin reaction (LSR) at day 2, the highest LSR values and level of crusts at day 8 were factors independently associated with the achievement of a complete response. CONCLUSION Ingenol mebutate 0.015% gel, when properly applied, is more effective on the face than on the scalp and efficacy is directly associated to LSR score

Using explainability to help children understand Gender Bias in AI

The final publication is available at ACM via http://dx.doi.org/10.1145/3459990.3460719Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. We propose an educational platform tailored to raise the awareness of gender bias in supervised learning, with the novelty of using Grad-CAM as an explainability technique that enables the classifier to visually explain its own predictions. Our study demonstrates that preadolescents (N=78, age 10-14) significantly improve their understanding of the concept of bias in terms of gender discrimination, increasing their ability to recognize biased predictions when they interact with the interpretable model, highlighting its suitability for educational programs.Peer ReviewedObjectius de Desenvolupament Sostenible::4 - Educació de Qualitat::4.4 - Per a 2030, augmentar substancialment el nombre de joves i persones adultes que tenen les competències necessàries, en particular tècniques i professionals, per a accedir a l’ocupació, el treball digne i l’emprenedoriaObjectius de Desenvolupament Sostenible::4 - Educació de QualitatPostprint (author's final draft

Differences between computed tomoghaphy and surgical findings in acute complicated diverticulitis

Summary Background/Objective: A preoperative reliable classification system between clinical and computed tomography (CT) findings to better plan surgery in acute complicated diverticulitis (ACD) is lacking. We studied the inter-observer agreement of CT scan data and their concordance with the preoperative clinical findings and the adherence with the intraoperative status using a new classification of diverticular disease (CDD). Methods: 152 patients operated on for acute complicated diverticulitis (ACD) were retrospectively enrolled. All patients were studied with CT scan within 24 h before surgery and CT images were blinded reanalyzed by 2 couples of radiologists (A/B). Kappa value evaluated the inter-observer agreement between radiologists and the concordance between CDD, preoperative clinical findings and findings at operation. Univariate and multivariate analysis were used to evaluate the predicting values of CT classification and CDD stage at surgery on postoperative outcomes. Results: Overall inter-observer agreement for the CDD was high, with a kappa value of 0.905 (95% CI Z 0.850e0.960) for observers A and B, while the concordance between radiologica