They’re very sensibly priced and at any time when you’re taking one out, everyone will know what university you’re pleased with. This work is framed in the PIUMA (Personalised Interactive City Maps for Autism)555PIUMA entails a collaboration among the pc Science and Psychology Departments of the University of Torino and the Grownup Autism Heart of the town of Torino. In this paper, now we have explored the tensions that emerge when utilizing pc vision to provide alt text descriptions of people, including identity categories like race, gender, age, disability, etc. We proposed museums as an apt point of comparison, as museums have lengthy navigated these tensions and have developed specific rules and guidelines to assist of their determinations. Costa et al. (2017) develop a activity recommender system that makes use of case-based reasoning to suggest the child’s every day activity to be carried out (related to consuming, maintaining clear, etc.) primarily based on age, gender, and time of day but it does not consider the child’s preferences. Most personalized recommender systems consider the person user’s preferences and contextual circumstances to pick out the Points of Curiosity (PoIs) which are suitable to the person user (Adomavicius and Tuzhilin, 2015). Nevertheless, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these techniques ought to take into account that users have idiosyncratic sensory aversions to noise, brightness, and other options, which affect the way they perceive gadgets, particularly locations (Robertson and Simmons, 2013). Aversions should due to this fact be thought of to counsel PoIs which can be at the identical time interesting and compatible with the target person.

This dataset is a Positive-Unlabeled dataset (PU dataset), for the reason that sentences from HappyDB are all the time the constructive class, but the sentences from eRisk can comprise each completely satisfied moments and impartial (non-blissful) textual content. Utilizing the HappyDB dataset of pleased moments and the Constructive-Unlabeled Studying (PU-studying) framework, we robotically constructed a model to extract pleased moments from the eRisk dataset. In our experiments, we use two datasets comprised of English texts: HappyDB and the eRisk 2018 depression dataset. Based mostly on these necessities, two personas and two situations utilizing storyboards have been created in order to understand users’ goals, motivations, wants, pain factors and more within the context of how they’d use a doable design answer as a way to generate ideas in the next stage. Customers diagnosed with depression use extra verbs related to feelings (e.g., really feel, cry, cuddle), versus customers from the management group, which use extra motion verbs (e.g., construct, lead, run) in the texts of their completely happy moments.

Expression such as ”I have depression” or ”I am depressed” weren’t taken into account in annotating the users, only customers with explicit mentions of depression prognosis had been labeled as having depression. Somewhat than discovering the most often occurring aspects of an item in its evaluations, we purpose at figuring out specific sensory features, presumably reported by few customers, which might reveal points that dramatically influence ASD people. On this preliminary work, we purpose to bridge this gap and develop a computational method for extracting and analyzing glad moments from a large corpus of social media textual content. The pleased moments of control subjects as the background corpus. A score better than 1 indicates that the foreground corpus comprises more phrases from a given class than the background corpus. Moreover, we imagine that our results pave the strategy to a extra in-depth analysis of expressions of happiness, by analyzing points of agency and sociality in blissful moments. The management group, nonetheless, more steadily exhibit joyful moments in everyday situations, in contexts related to leisure, sports and financial plans: ”Knowing I make more money than you does make me happy.”, ”Eating pop corn and seeing movie.”, ”I bought a brand new controller and labored good.”.

We computed dominance scores with every class in the foreground to reveal the dominant LIWC categories in the two corpora (depression and management). Our analysis shows that, for customers from the management group, the main reasons for happiness are related to leisure situations and monetary plans. Users have been annotated as having depression by their point out of diagnosis (e.g., ”I was diagnosed with depression”) of their posts. Moreover, the authors present that highly valuing happiness is a key indicator and a potential threat factor of depression. The results present that the algorithms acquire the best accuracy. Additionally they show that it helps enhancing recommendations to each autistic and neurotypical people. This work additionally compares the efficiency achieved by completely different recommender techniques once they employ crowdsourced information, our TripAdvisor dataset, or each to recommend objects to 2 person teams: ASD people, and people who didn’t beforehand obtain an autism diagnosis (we denote the latter as neurotypical).