They’re very sensibly priced and at any time when you are taking one out, all people will know what university you’re proud of. This work is framed in the PIUMA (Personalized Interactive City Maps for Autism)555PIUMA entails a collaboration amongst the computer Science and Psychology Departments of the University of Torino and the Adult Autism Heart of town of Torino. On this paper, now we have explored the tensions that emerge when utilizing computer imaginative and prescient to supply alt text descriptions of people, together with identification categories like race, gender, age, disability, and so forth. We proposed museums as an apt level of comparability, as museums have long navigated these tensions and have developed particular rules and pointers to aid of their determinations. Costa et al. (2017) develop a job recommender system that makes use of case-based mostly reasoning to suggest the child’s daily exercise to be performed (associated to consuming, protecting clear, and so on.) primarily based on age, gender, and time of day but it surely does not consider the child’s preferences. Most personalized recommender techniques consider the person user’s preferences and contextual conditions to pick out the Factors of Interest (PoIs) which are appropriate to the individual consumer (Adomavicius and Tuzhilin, 2015). However, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these methods should take into consideration that users have idiosyncratic sensory aversions to noise, brightness, and different features, which influence the way in which they understand gadgets, especially locations (Robertson and Simmons, 2013). Aversions should due to this fact be considered to suggest PoIs which can be at the same time interesting and suitable with the target consumer.

This dataset is a Constructive-Unlabeled dataset (PU dataset), since the sentences from HappyDB are at all times the optimistic class, but the sentences from eRisk can contain each completely happy moments and neutral (non-comfortable) text. Utilizing the HappyDB dataset of happy moments and the Constructive-Unlabeled Learning (PU-learning) framework, we robotically constructed a model to extract happy 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 requirements, two personas and two scenarios using storyboards were created in order to understand users’ goals, motivations, wants, ache points and more within the context of how they might use a attainable design resolution in order to generate concepts in the next stage. Users diagnosed with depression use extra verbs related to feelings (e.g., really feel, cry, cuddle), as opposed to users from the control group, which use more action verbs (e.g., construct, lead, run) in the texts of their blissful moments.

Expression such as ”I have depression” or ”I am depressed” weren’t taken into consideration in annotating the customers, only users with specific mentions of depression diagnosis had been labeled as having depression. Quite than finding probably the most ceaselessly occurring aspects of an merchandise in its evaluations, we intention at figuring out particular sensory options, probably reported by few users, which might reveal issues that dramatically influence ASD people. On this preliminary work, we goal to bridge this hole and develop a computational technique for extracting and analyzing completely happy moments from a big corpus of social media text. The glad moments of management topics because the background corpus. A score better than 1 signifies that the foreground corpus incorporates more phrases from a given class than the background corpus. Moreover, we consider that our results pave the method to a more in-depth analysis of expressions of happiness, by analyzing features of agency and sociality in happy moments. The control group, nevertheless, more frequently exhibit happy moments in on a regular basis situations, in contexts associated to entertainment, sports and monetary plans: ”Knowing I make more cash than you does make me completely happy.”, ”Eating pop corn and seeing film.”, ”I bought a new controller and worked good.”.

We computed dominance scores with each class within the foreground to reveal the dominant LIWC classes in the two corpora (depression and control). Our analysis shows that, for users from the management group, the main causes for happiness are related to leisure situations and monetary plans. Users have been annotated as having depression by their mention of prognosis (e.g., ”I was diagnosed with depression”) of their posts. Moreover, the authors present that highly valuing happiness is a key indicator and a possible danger factor of depression. The results present that the algorithms get hold of the very best accuracy. They also show that it helps bettering solutions to each autistic and neurotypical people. This work also compares the performance achieved by completely different recommender techniques once they employ crowdsourced data, our TripAdvisor dataset, or each to recommend items to two user groups: ASD people, and people who didn’t beforehand obtain an autism diagnosis (we denote the latter as neurotypical).