Home Butler Creating a Virtual Home Assistant

Virtual butlers, or virtual companions, try to imitate the behaviour of human beings in a believable way. They interact with the user through speech, understand spoken requests, are able to converse with the user, and show some form of emotion and personality. Virtual companions are also able to remember past conversations, and build some sensible knowledge about the user. One major problem with virtual companions is the need to manually create dialogues. We shall introduce a system which automatically creates dialogues using television series scripts.peer-reviewe

Assessing Sentiment of the Expressed Stance on Social Media

Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing. One may express a viewpoint towards a topic by using positive or negative language. This paper examines how the stance is being expressed in social media according to the sentiment polarity. There has been a noticeable misconception of the similarity between the stance and sentiment when it comes to viewpoint discovery, where negative sentiment is assumed to mean against stance, and positive sentiment means in-favour stance. To analyze the relation between stance and sentiment, we construct a new dataset with four topics and examine how people express their viewpoint with regards these topics. We validate our results by carrying a further analysis of the popular stance benchmark SemEval stance dataset. Our analyses reveal that sentiment and stance are not highly aligned, and hence the simple sentiment polarity cannot be used solely to denote a stance toward a given topic.Comment: Accepted as a full paper at Socinfo 2019. Please cite the Socinfo versio

Comparison of outcomes of a community-based education program executed with and without active community involvement

Objective : The aim of this study was to evaluate the applicability of a previously designed set of generic objectives for community-based education (CBE) emphasising community involvement. Methods : The study was designed as a non-blinded, randomised trial. Experimental and conventional groups of students following CBE programmes either closely or weakly matching the set of generic objectives were compared. Student groups were subjected to passive participatory observation. Students evaluated their programmes through questionnaires. The impact of student interventions was assessed by community compliance. Community perception of the programmes was evaluated through structured interviews with community representatives. Results : Students in experimental groups appreciated their programme more than students in conventional groups. High compliance and appreciation were recorded in communities hosting the modified programme. Most students in conventional groups judged their posting negatively, largely because of the high number of households to be visited. Health interventions performed by conventional groups lacked co-operation between students and the community. Communities hosting conventional groups felt their health needs were scarcely discussed and addressed. Conclusions The modification of an existing CBE programme to better match a set of generic CBE objectives emphasising community involvement had a positive effect on programme outcomes and levels of appreciation by both students and hosting communities. However, some confounding variables could not be controlled. Colleagues planning comparable studies may take advantage of the lessons we learned while performing this study

Public acceptance of using artificial intelligence-assisted weight management apps in high-income Southeast Asian adults with overweight and obesity: A cross-sectional study

National University Health System (NUHS

Using Clustering to Improve the Structure of Natural Language Requirements Documents

[Context and motivation] System requirements are normally provided in the form of natural language documents. Such documents need to be properly structured, in order to ease the overall uptake of the requirements by the readers of the document. A structure that allows a proper understanding of a requirements document shall satisfy two main quality attributes: (i) requirements relatedness: each requirement is conceptually connected with the requirements in the same section; (ii) sections independence: each section is conceptually separated from the others. [Question/Problem] Automatically identifying the parts of the document that lack requirements relatedness and sections independence may help improve the document structure. [Principal idea/results] To this end, we define a novel clustering algorithm named Sliding Head-Tail Component (S-HTC). The algorithm groups together similar requirements that are contiguous in the requirements document. We claim that such algorithm allows discovering the structure of the document in the way it is perceived by the reader. If the structure originally provided by the document does not match the structure discovered by the algorithm, hints are given to identify the parts of the document that lack requirements relatedness and sections independence. [Contribution] We evaluate the effectiveness of the algorithm with a pilot test on a requirements standard of the railway domain (583 requirements)

Table_1_Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study.docx

IntroductionWith in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity.Methods280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression.Results271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2–28.4 kg/m2) and 86.4 (80.0–94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity.ConclusionUTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.</p