They’re very sensibly priced and at any time when you are taking one out, all people will know what university you’re pleased with. This work is framed in the PIUMA (Personalized Interactive Urban Maps for Autism)555PIUMA entails a collaboration among the pc Science and Psychology Departments of the University of Torino and the Adult Autism Middle of the city of Torino. On this paper, we now have explored the tensions that emerge when utilizing pc vision to produce alt text descriptions of people, together with identity classes like race, gender, age, disability, and so forth. We proposed museums as an apt point of comparison, as museums have long navigated these tensions and have developed particular rules and pointers to assist of their determinations. Costa et al. (2017) develop a job recommender system that makes use of case-primarily based reasoning to suggest the child’s each day activity to be carried out (related to eating, holding clean, and so forth.) based mostly on age, gender, and time of day however it does not consider the child’s preferences. Most personalised recommender methods consider the individual user’s preferences and contextual conditions to pick the Factors of Curiosity (PoIs) which can be suitable to the person consumer (Adomavicius and Tuzhilin, 2015). Nevertheless, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these systems should take under consideration that customers have idiosyncratic sensory aversions to noise, brightness, and other features, which influence the way they understand gadgets, especially places (Robertson and Simmons, 2013). Aversions should due to this fact be considered to counsel PoIs that are at the identical time attention-grabbing and appropriate with the goal person.

This dataset is a Positive-Unlabeled dataset (PU dataset), because the sentences from HappyDB are always the constructive class, but the sentences from eRisk can include both blissful moments and neutral (non-completely satisfied) textual content. Using the HappyDB dataset of pleased moments and the Optimistic-Unlabeled Learning (PU-studying) framework, we robotically constructed a mannequin to extract completely satisfied moments from the eRisk dataset. In our experiments, we use two datasets comprised of English texts: HappyDB and the eRisk 2018 depression dataset. Primarily based on these necessities, two personas and two situations using storyboards were created so as to understand users’ goals, motivations, wants, pain factors and extra in the context of how they might use a doable design answer as a way to generate concepts in the following stage. Users diagnosed with depression use more verbs associated to feelings (e.g., feel, cry, cuddle), versus users from the management group, which use extra motion verbs (e.g., construct, lead, run) within the texts of their glad moments.

Expression corresponding to ”I have depression” or ”I am depressed” weren’t taken under consideration in annotating the customers, only customers with express mentions of depression analysis have been labeled as having depression. Rather than finding the most regularly occurring features of an item in its critiques, we goal at figuring out specific sensory options, possibly reported by few users, which could reveal points that dramatically impact ASD people. On this preliminary work, we purpose to bridge this hole and develop a computational methodology for extracting and analyzing happy moments from a big corpus of social media textual content. The completely happy moments of management subjects as the background corpus. A rating better than 1 indicates that the foreground corpus comprises extra words from a given class than the background corpus. Furthermore, we consider that our results pave the technique to a more in-depth analysis of expressions of happiness, by analyzing aspects of company and sociality in blissful moments. The management group, nonetheless, more regularly exhibit joyful moments in on a regular basis situations, in contexts related to entertainment, sports and financial plans: ”Knowing I make more cash than you does make me pleased.”, ”Eating pop corn and seeing movie.”, ”I bought a brand new controller and labored excellent.”.

We computed dominance scores with each class within the foreground to reveal the dominant LIWC classes in the two corpora (depression and management). Our evaluation shows that, for customers from the management group, the primary reasons for happiness are associated to entertainment conditions and monetary plans. Users were annotated as having depression by their mention of diagnosis (e.g., ”I was diagnosed with depression”) in their posts. Moreover, the authors present that extremely valuing happiness is a key indicator and a possible risk issue of depression. The outcomes show that the algorithms acquire the best accuracy. Additionally they present that it helps bettering options to both autistic and neurotypical people. This work also compares the efficiency achieved by totally different recommender techniques when they make use of crowdsourced information, our TripAdvisor dataset, or each to suggest items to two consumer groups: ASD people, and people who didn’t previously obtain an autism analysis (we denote the latter as neurotypical).