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Abstract Homophily, the tendency for individuals to preferentially interact with others similar to themselves is typically documented via self-report and, for children, adult report. Few studies have investigated homophily directly using objective measures of social movement. We quantified homophily in children with developmental disabilities (DD) and typical development (TD) using objective measures of position/orientation in preschool inclusion classrooms, designed to promote interaction between these groups of children. Objective measurements were collected using ultra-wideband radio-frequency tracking to determine social approach and social contact, measures of social movement and interaction. Observations of 77 preschoolers (47 with DD, and 30 TD) were conducted in eight inclusion classrooms on a total of 26 days. We compared DD and TD groups with respect to how children approached and shared time in social contact with peers using mixed-effects models. Children in concordant dyads (DD-DD and TD-TD) both moved toward each other at higher velocities and spent greater time in social contact than discordant dyads (DD-TD), evidencing homophily. DD-DD dyads spent less time in social contact than TD-TD dyads but were comparable to TD-TD dyads in their social approach velocities. Children’s preference for similar peers appears to be a pervasive feature of their naturalistic interactions.

Abstract Current models of COVID-19 transmission predict infection from reported or assumed interactions. Here we leverage high-resolution observations of interaction to simulate infectious processes. Ultra-Wide Radio Frequency Identification (RFID) systems were employed to track the real-time physical movements and directional orientation of children and their teachers in 4 preschool classes over a total of 34 observations. An agent-based transmission model combined observed interaction patterns (individual distance and orientation) with CDC-published risk guidelines to estimate the transmission impact of an infected patient zero attending class on the proportion of overall infections, the average transmission rate, and the time lag to the appearance of symptomatic individuals. These metrics highlighted the prophylactic role of decreased classroom density and teacher vaccinations. Reduction of classroom density to half capacity was associated with an 18.2% drop in overall infection proportion while teacher vaccination receipt was associated with a 25.3% drop. Simulation results of classroom transmission dynamics may inform public policy in the face of COVID-19 and similar infectious threats.

The high speed and low latency of 5G mobile network have accelerated the speed and amount of information transmission. Web video is likely to become the main mode of news production and dissemination in the future for its richer information and more convenient dissemination, which will subvert the traditional mode of event mining. Therefore, event mining based on web videos has become a new research hotspot. However, web videos are vulnerable to video editing, lighting, shooting perspective and shooting angle, and other factors, resulting in the inaccurate visual similarity detection problem. Generally speaking, effectively integrating humungous volumes of cross-model information would give a great help. However, web videos are described with few terms, and thus sparse text information becomes a challenge for cross-model information combination. To address this issue, this paper proposes a new collaborative optimization framework with the combination of inaccurate visual similarity detection information and sparse textual information. This framework is composed of three steps. After obtaining the statistics of the distribution characteristics of each word in all Near-Duplicate Keyframes (NDKs), the high-level semantic cross-correlation between NDKs is first mined with the help of textual features, forming a new set of semantic relevant NDKs with different visual expressions. Next, textual distribution features are enriched through finding more semantically related words by the new NDK set with various forms of visual expressions, solving the sparse distribution problem for each word in all NDKs. Finally, Multiple Correspondence Analysis (MCA) is used to mine the events. Experimental results with a large number of real data demonstrate that the proposed model outperforms the existing methods for web video event mining.

Best practice for the assessment of autism spectrum disorder (ASD) symptom severity relies on clinician ratings of the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2), but the association of these ratings with objective measures of children's social gaze and smiling is unknown. Sixty-six preschool-age children (49 boys, M = 39.97 months, SD = 10.58) with suspected ASD (61 confirmed ASD) were administered the ADOS-2 and provided social affect calibrated severity scores (SA CSS). Children's social gaze and smiling during the ADOS-2, captured with a camera contained in eyeglasses worn by the examiner and parent, were obtained via a computer vision processing pipeline. Children who gazed more at their parents (p = 0.04) and whose gaze at their parents involved more smiling (p = 0.02) received lower social affect severity scores, indicating fewer social affect symptoms, adjusted R[superscript 2] = 0.15, p = 0.003.