Ground Projects

They’re very sensibly priced and every time you’re taking one out, everyone will know what university you’re happy with. This work is framed in the PIUMA (Customized Interactive Urban Maps for Autism)555PIUMA includes a collaboration amongst the pc Science and Psychology Departments of the University of Torino and the Grownup Autism Middle of the city of Torino. On this paper, we’ve explored the tensions that emerge when utilizing laptop imaginative and prescient to produce alt textual content descriptions of people, including identification classes 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 particular ideas and pointers to help in their determinations. Costa et al. (2017) develop a job recommender system that makes use of case-primarily based reasoning to counsel the child’s every day activity to be performed (related to eating, preserving clean, and so forth.) based on age, gender, and time of day nevertheless it doesn’t consider the child’s preferences. Most customized recommender programs consider the individual user’s preferences and contextual circumstances to pick out the Factors of Interest (PoIs) that are suitable to the individual consumer (Adomavicius and Tuzhilin, 2015). Nevertheless, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these techniques ought to take into consideration that users have idiosyncratic sensory aversions to noise, brightness, and other features, which affect the way they understand items, especially places (Robertson and Simmons, 2013). Aversions should therefore be considered to counsel PoIs which are at the identical time attention-grabbing and suitable with the target user.

This dataset is a Positive-Unlabeled dataset (PU dataset), because the sentences from HappyDB are at all times the constructive class, but the sentences from eRisk can contain both completely happy moments and impartial (non-pleased) text. Utilizing the HappyDB dataset of blissful moments and the Optimistic-Unlabeled Learning (PU-learning) framework, we robotically constructed a mannequin 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. Based on these requirements, two personas and two scenarios using storyboards have been created so as to understand users’ targets, motivations, wants, ache points and more within the context of how they’d use a possible design solution as a way to generate ideas in the following stage. Customers diagnosed with depression use extra verbs related to emotions (e.g., feel, cry, cuddle), versus customers from the control group, which use extra action verbs (e.g., build, lead, run) within the texts of their completely satisfied moments.

Expression akin to ”I have depression” or ”I am depressed” weren’t taken into account in annotating the customers, solely users with specific mentions of depression prognosis had been labeled as having depression. Reasonably than discovering the most regularly occurring elements of an item in its critiques, we intention at identifying particular sensory features, possibly reported by few users, which could reveal points that dramatically affect ASD people. In this preliminary work, we intention to bridge this gap and develop a computational methodology for extracting and analyzing glad moments from a big corpus of social media text. The completely happy moments of management subjects because the background corpus. A rating larger than 1 indicates that the foreground corpus contains extra words from a given class than the background corpus. Moreover, we consider that our outcomes pave the technique to a more in-depth evaluation of expressions of happiness, by analyzing elements of agency and sociality in pleased moments. The control group, nonetheless, extra steadily exhibit glad moments in on a regular basis situations, in contexts associated to leisure, sports and financial plans: ”Knowing I make more cash than you does make me blissful.”, ”Eating pop corn and seeing film.”, ”I purchased a new controller and labored excellent.”.

We computed dominance scores with each class in the foreground to reveal the dominant LIWC categories in the two corpora (depression and control). Our evaluation shows that, for customers from the control group, the main reasons for happiness are associated to entertainment situations and monetary plans. Customers were annotated as having depression by their point out of diagnosis (e.g., ”I was diagnosed with depression”) in their posts. Furthermore, the authors present that extremely valuing happiness is a key indicator and a possible danger issue of depression. The results show that the algorithms get hold of the highest accuracy. In addition they show that it helps improving solutions to each autistic and neurotypical people. This work also compares the performance achieved by different recommender systems after they employ crowdsourced data, our TripAdvisor dataset, or each to recommend gadgets to 2 user groups: ASD people, and people who didn’t beforehand obtain an autism diagnosis (we denote the latter as neurotypical).