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They’re very sensibly priced and whenever you’re taking one out, everyone will know what university you’re pleased with. This work is framed within the PIUMA (Customized Interactive Urban Maps for Autism)555PIUMA involves a collaboration among the pc Science and Psychology Departments of the University of Torino and the Adult Autism Center of town of Torino. On this paper, we’ve got explored the tensions that emerge when utilizing laptop imaginative and prescient to provide alt textual content descriptions of people, together with identity categories like race, gender, age, incapacity, and so forth. We proposed museums as an apt level of comparability, as museums have lengthy navigated these tensions and have developed particular ideas and guidelines to aid in their determinations. Costa et al. (2017) develop a activity recommender system that uses case-based mostly reasoning to suggest the child’s day by day exercise to be performed (related to eating, retaining clear, etc.) based mostly on age, gender, and time of day nevertheless it does not consider the child’s preferences. Most customized recommender systems consider the person user’s preferences and contextual conditions to pick the Factors of Interest (PoIs) which can be appropriate to the individual consumer (Adomavicius and Tuzhilin, 2015). However, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these techniques ought to take into consideration that customers have idiosyncratic sensory aversions to noise, brightness, and other options, which influence the way in which they understand gadgets, particularly locations (Robertson and Simmons, 2013). Aversions ought to subsequently be thought of to recommend PoIs which might be at the identical time fascinating and compatible with the goal consumer.

This dataset is a Optimistic-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 completely satisfied moments and impartial (non-joyful) text. Using the HappyDB dataset of blissful moments and the Positive-Unlabeled Studying (PU-studying) framework, we robotically constructed a model to extract glad 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 requirements, two personas and two scenarios utilizing storyboards have been created so as to know users’ targets, motivations, wants, pain factors and more within the context of how they’d use a potential design answer with a view to generate ideas in the next stage. Customers diagnosed with depression use more verbs related to emotions (e.g., feel, cry, cuddle), versus users from the control group, which use extra action verbs (e.g., construct, lead, run) in the texts of their blissful moments.

Expression such as ”I have depression” or ”I am depressed” were not taken into consideration in annotating the users, only users with express mentions of depression prognosis have been labeled as having depression. Rather than finding probably the most ceaselessly occurring aspects of an item in its reviews, we purpose at figuring out particular sensory features, probably reported by few users, which might reveal issues that dramatically impression ASD people. In this preliminary work, we aim to bridge this hole and develop a computational technique for extracting and analyzing pleased moments from a large corpus of social media textual content. The happy moments of control topics as the background corpus. A score greater than 1 signifies that the foreground corpus accommodates extra words from a given class than the background corpus. Moreover, we believe that our outcomes pave the option to a extra in-depth analysis of expressions of happiness, by analyzing aspects of agency and sociality in glad moments. The management group, nonetheless, more steadily exhibit glad moments in on a regular basis conditions, in contexts related to entertainment, sports and monetary plans: ”Knowing I make extra money than you does make me happy.”, ”Eating pop corn and seeing film.”, ”I bought a new controller and worked excellent.”.

We computed dominance scores with each class within the foreground to reveal the dominant LIWC categories in the 2 corpora (depression and management). Our analysis exhibits that, for users from the control group, the main causes for happiness are associated to leisure situations and financial plans. Customers have been annotated as having depression by their point out of diagnosis (e.g., ”I was diagnosed with depression”) of their posts. Furthermore, the authors show that highly valuing happiness is a key indicator and a potential threat factor of depression. The outcomes show that the algorithms receive the very best accuracy. Additionally they show that it helps improving suggestions to each autistic and neurotypical people. This work also compares the performance achieved by completely different recommender techniques when they make use of crowdsourced data, our TripAdvisor dataset, or both to counsel gadgets to 2 person groups: ASD people, and people who did not beforehand receive an autism analysis (we denote the latter as neurotypical).