We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD). The interface is intended to enable private conversation practice anywhere, anytime using a web-browser. Users converse informally with a virtual agent, receiving feedback on nonverbal cues in realtime, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using a hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers. Through a thematic analysis of post-experiment interviews, identified several key design considerations, notably: 1) Users should be fully briefed at the outset about the purpose and limitations of the system, to avoid unrealistic expectations. 2) An interface should incorporate positive acknowledgment of behavior change. 3) Realistic appearance of a virtual agent and responsiveness are important in engaging users. 4) Conversation personalization, for instance in prompting laconic users for more input and reciprocal questions, would help the teenagers engage for longer terms and increase the system's utility. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI .

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We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients interacting with our system. The conversational agent, represented by a virtual avatar, is designed to hold multiple sessions of casual conversation with older adults. Throughout each interaction, the system analyzes the prosodic and nonverbal behavior of users and provides feedback to the user in the form of periodic comments and suggestions on how to improve. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics – 27 topics in three groups, developed in collaboration with a team of gerontologists. The three groups vary in “degrees of intimacy”, and as such in degrees of cognitive difficulty for the user. After collecting data from 9 participants who interacted with the avatar for 7-9 sessions over a period of 3-4 weeks, we present results concerning dialogue behavior and inferred sentiment of the users. Analysis of the dialogues reveals correlations such as greater elaborateness for more difficult topics, increasing elaborateness with successive sessions, stronger sentiments in topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication and practical applicability of our dialogue system.

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In this paper we address the problem of turn-taking prediction in open-ended communication between humans and dialogue agents. In a non-task-oriented interaction with dialogue agents, user inputs are apt to be grammatically and lexically diverse, and at times quite lengthy, with many pauses; all of this makes it harder for the system to decide when to jump in. As a result recent turn-taking predictors designed for specific tasks or for human-human interactions will scarcely be applicable. In this paper we focus primarily on the predictive potential of linguistic features, including lexical, syntactic and semantic features, as well as timing features, whereas past work has typically placed more emphasis on prosodic features, sometimes supplemented with non-verbal behaviors such as gaze and head movements. The basis for our study is a corpus of 15 "friendly" dialogues between humans and a (Wizard-of-Oz enabled) virtual dialogue agent, annotated for pause times and types. The model of turn-taking obtained by supervised learning predicts turn-taking points with increasing accuracy using only prosodic features, only timing and speech rate features, only lexical and syntactic features, and achieves state-of-the art performance with a mixture-of-experts model combining these features along with a semantic criterion.

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Background: Although there is a growing consensus that hydroxychloroquine may not be effective in the treatment of COVID-19 patients, there is still little high-quality evidence about the prophylactic effects of this medication. In this study, we aimed to evaluate the efficiency of hydroxychloroquine in preventing COVID-19 infection among healthcare workers. Methods: In this clinical trial, 90 healthcare providers from two referral hospitals of COVID-19 were divided into the hydroxychloroquine group (400 mg/week for eight weeks) and the routine-care group. Serum CRP levels and the frequency of T-helper (CD4+ cells) and T-cytotoxic (CD8+ cells) were assessed at the beginning and end of the study. The groups were compared in terms of White Blood cells (WBCs), polymorph nuclear cells (PMNs), lymphocytes (LYM), hemoglobin (Hb), and platelets (Plt.). Results: The results revealed no significant differences between the two groups in terms of WBC, PMN, LYM, Hb, Plt., CD4, and CD8. The mean difference of the CD4:CD8 ratio showed a significantly higher decrease (P=0.05) in hydroxychloroquine group than in the control group (0.18 vs. 0.02). The incidence of COVID-19 was 15% (95%CI: 12-18%) in the control group and 10% (95%CI: 8-12%) in the intervention group; however, no significant difference was observed between the two groups in this regard (P=0.45). Conclusion: Our study findings boost an increasing level of evidence that hydroxychloroquine is not an effective prophylactic medication against COVID-19 and might even exacerbate the profile of pandemic containment efforts by adding more pain to patients’ life and healthcare services.