They’re very sensibly priced and each time you take one out, everybody will know what university you’re proud of. This work is framed in the PIUMA (Customized Interactive City Maps for Autism)555PIUMA includes a collaboration amongst the computer Science and Psychology Departments of the University of Torino and the Grownup Autism Heart of town of Torino. In this paper, we’ve got explored the tensions that emerge when using laptop imaginative and prescient to produce alt text descriptions of people, including identification 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 specific ideas and pointers to aid of their determinations. Costa et al. (2017) develop a activity recommender system that uses case-based reasoning to recommend the child’s each day activity to be carried out (associated to eating, protecting clear, etc.) based mostly on age, gender, and time of day but it does not consider the child’s preferences. Most personalized recommender systems consider the individual user’s preferences and contextual conditions to pick out the Points of Curiosity (PoIs) which might be appropriate to the individual consumer (Adomavicius and Tuzhilin, 2015). However, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these programs should take under consideration that customers have idiosyncratic sensory aversions to noise, brightness, and different options, which affect the way in which they perceive objects, especially places (Robertson and Simmons, 2013). Aversions ought to therefore be considered to recommend PoIs that are at the identical time attention-grabbing and appropriate with the target person.

This dataset is a Constructive-Unlabeled dataset (PU dataset), for the reason that sentences from HappyDB are all the time the optimistic class, however the sentences from eRisk can include both glad moments and impartial (non-completely satisfied) textual content. Using the HappyDB dataset of happy moments and the Optimistic-Unlabeled Studying (PU-studying) framework, we automatically constructed a model 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 scenarios using storyboards were created in order to grasp users’ goals, motivations, needs, ache points and extra in the context of how they might use a attainable design answer in order to generate ideas in the following stage. Customers diagnosed with depression use more verbs associated to emotions (e.g., feel, cry, cuddle), as opposed to users from the control group, which use more motion verbs (e.g., construct, lead, run) within the texts of their glad moments.

Expression similar to ”I have depression” or ”I am depressed” were not taken into account in annotating the customers, only customers with specific mentions of depression analysis have been labeled as having depression. Quite than finding probably the most ceaselessly occurring points of an merchandise in its opinions, we aim at identifying specific sensory options, probably reported by few users, which could reveal issues that dramatically impact ASD people. On this preliminary work, we goal to bridge this hole and develop a computational technique for extracting and analyzing completely satisfied moments from a large corpus of social media textual content. The glad moments of management subjects because the background corpus. A rating higher than 1 indicates that the foreground corpus contains extra words from a given class than the background corpus. Furthermore, we believe that our outcomes pave the option to a extra in-depth analysis of expressions of happiness, by analyzing features of company and sociality in happy moments. The control group, nonetheless, extra regularly exhibit glad moments in everyday situations, in contexts associated to leisure, sports activities and monetary plans: ”Knowing I make extra money than you does make me blissful.”, ”Eating pop corn and seeing film.”, ”I bought a new controller and labored perfect.”.

We computed dominance scores with every class in the foreground to reveal the dominant LIWC classes in the two corpora (depression and management). Our evaluation shows that, for customers from the control group, the principle causes for happiness are related to leisure situations and monetary plans. Users have been annotated as having depression by their point out of prognosis (e.g., ”I was diagnosed with depression”) in their posts. Moreover, the authors show that highly valuing happiness is a key indicator and a potential danger factor of depression. The results show that the algorithms acquire the best accuracy. In addition they show that it helps improving solutions to both autistic and neurotypical people. This work also compares the efficiency achieved by different recommender techniques when they employ crowdsourced knowledge, our TripAdvisor dataset, or both to recommend objects to 2 consumer groups: ASD people, and people who did not beforehand receive an autism analysis (we denote the latter as neurotypical).