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But For One Week In August

They’re very sensibly priced and whenever you are taking one out, everyone will know what university you’re happy with. This work is framed within the PIUMA (Personalised Interactive Urban Maps for Autism)555PIUMA entails a collaboration amongst the computer Science and Psychology Departments of the University of Torino and the Grownup Autism Middle of town of Torino. On this paper, we have now explored the tensions that emerge when utilizing computer imaginative and prescient to produce alt text descriptions of people, including identity categories like race, gender, age, incapacity, and so on. We proposed museums as an apt level of comparison, as museums have lengthy navigated these tensions and have developed specific rules and pointers to aid of their determinations. Costa et al. (2017) develop a activity recommender system that uses case-primarily based reasoning to counsel the child’s every day exercise to be carried out (related to eating, conserving clean, and many others.) primarily based on age, gender, and time of day but it does not consider the child’s preferences. Most customized recommender programs consider the individual user’s preferences and contextual circumstances to pick the Factors of Curiosity (PoIs) that are appropriate to the individual person (Adomavicius and Tuzhilin, 2015). Nevertheless, when suggesting PoIs to people with Autism Spectrum Disorders (ASD), these methods ought to take into consideration that customers have idiosyncratic sensory aversions to noise, brightness, and other options, which affect the way in which they perceive objects, especially locations (Robertson and Simmons, 2013). Aversions should subsequently be considered to counsel PoIs which can be at the same time interesting and compatible with the target consumer.

This dataset is a Positive-Unlabeled dataset (PU dataset), since the sentences from HappyDB are at all times the optimistic class, but the sentences from eRisk can comprise each joyful moments and neutral (non-blissful) textual content. Using the HappyDB dataset of joyful moments and the Positive-Unlabeled Studying (PU-studying) framework, we mechanically constructed a mannequin to extract completely 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 eventualities utilizing storyboards were created in order to grasp users’ targets, motivations, wants, pain points and extra within the context of how they would use a attainable design solution to be able to generate concepts in the next stage. Customers diagnosed with depression use more verbs related to feelings (e.g., feel, cry, cuddle), versus users from the control group, which use more action verbs (e.g., construct, lead, run) within the texts of their blissful moments.

Expression such as ”I have depression” or ”I am depressed” were not taken into account in annotating the customers, only users with specific mentions of depression diagnosis were labeled as having depression. Moderately than finding probably the most frequently occurring facets of an item in its opinions, we intention at identifying specific sensory features, possibly reported by few customers, which could reveal issues that dramatically impact ASD people. In this preliminary work, we intention to bridge this hole and develop a computational technique for extracting and analyzing blissful moments from a big corpus of social media textual content. The happy moments of management subjects because the background corpus. A score larger than 1 indicates that the foreground corpus comprises more phrases from a given class than the background corpus. Moreover, we believe that our outcomes pave the approach to a more in-depth evaluation of expressions of happiness, by analyzing points of company and sociality in completely happy moments. The control group, nonetheless, more incessantly exhibit joyful moments in on a regular basis situations, in contexts associated to entertainment, sports and monetary plans: ”Knowing I make extra money than you does make me blissful.”, ”Eating pop corn and seeing film.”, ”I purchased a new controller and labored perfect.”.

We computed dominance scores with every class within the foreground to reveal the dominant LIWC categories in the two corpora (depression and control). Our analysis reveals that, for users from the management group, the main reasons for happiness are associated to leisure conditions and financial plans. Customers had been annotated as having depression by their mention of diagnosis (e.g., ”I was diagnosed with depression”) of their posts. Moreover, the authors show that highly valuing happiness is a key indicator and a possible risk factor of depression. The outcomes show that the algorithms receive the best accuracy. Additionally they show that it helps bettering ideas to both autistic and neurotypical people. This work additionally compares the performance achieved by different recommender techniques after they employ crowdsourced data, our TripAdvisor dataset, or both to recommend gadgets to two user groups: ASD people, and people who didn’t previously obtain an autism diagnosis (we denote the latter as neurotypical).