Education and self-education of students in the socionamic activity in the context of "new reality"

The current study addresses the issues of education and self-education of nurturant students in the transition to distance learning during the coronavirus pandemic, considering the purposeful and rational nature of this phenomenon, substantiates the need to manage this process; attention is focused on the fact that the educational system of universities should be responsible not only for training but also for upbringing, for the development of the creative potential of the individual, for the formation of professional skills; the possibility of changing the format of training is analysed based on their equipment of teachers with methodological developments and recommendations, modified programs prepared in accordance with the nature of the discipline; the authors note that distance education of artistic and pedagogical students should be aimed at the formation of professional skills based on the "live" interaction of the subjects of the educational process.

From Blackboard to Green Screen

We report on the conversion of two advanced database courses from their classical in-lecture-hall setup into an all-digital remote format that was delivered via YouTube. While the course contents were not turned on their heads, throughout the semester we adopted a video style that has been popularized by the live coding community. This new focus on the live interaction with the underlying database systems, led us (1) to adopt the idea of SQL probe queries that are specifically crafted to reveal database internals and (2) a study of database-supported computation that treats SQL like a true programming language. We are happy to share videos, slides, and code with anyone who is interested.

Interactive video-player to improve social smile in individuals with autism spectrum disorder

Purpose The purpose of this paper is to propose and develop a live interaction-based video player system named LIV4Smile for the improvement of the social smile in individuals with autism spectrum disorder (ASD). Design/methodology/approach The proposed LIV4Smile intervention was a video player that operated by detecting smile using a convolutional neural network (CNN)-based algorithm. To maintain a live interaction, a CNN-based smile detector was configured and used in this system. The statistical test was also conducted to validate the performance of the system. Findings The significant improvement was observed in smile responses of individuals with ASD with the utilization of the proposed LIV4Smile system in a real-time environment. Research limitations/implications A small sample size and clinical utilizing for validation and initial training of ASD individuals for LIV4Smile could be considered under implications. Originality/value The main aim of this study was to address the inclusive practices for children with autism. The proposed CNN algorithm-based LIV4Smile intervention resulted in high accuracy in facial smile detection.

Detection of Human Under Earthquake Rubble

Natural calamities like earthquake,flood ,volcanic eruptions , landslides ,avalanches ,etc are unavoidable circumstances .Occurance of such natural ailments is unpredictablecausing huge damageto resources and structures which is unavoidable .Measurement, eleventh hour detection, precautionary and rescue methods are onlypossible options .A proper rescue can do milestones in savinglife of people under such circumstances. In messina region of Newyork death tolls were 5000 during earthquake, nearly halfthe population. In Utharkhand during 2013 a landslide caused lots of people buried under earth. Newspaper report stated that if people were detected earlier death tolls would have been quite low. In this paper we design a rescue robotic platform which can act more quickly in detection location mapping diagnosing status post earthquake, avalanches andlandslides. The robot platformdesigned isfor disastermanagementsystemduringavalanches ,earthquakes , and landslidesfor accurate andfasterdetectionofhuman trapped inside without live interaction and notcausingdamagedue to increasedload of rescuer onthemwhiledetectionusinga robotic platformcontrolled remotely

How Do Ideal Friend Preferences and Interaction Context Affect Friendship Formation? Evidence for a Domain- General Relationship Initiation Process

This research examined how people’s ideal friend preferences influence the friendship formation process. In an extension of prior research on romantic relationship initiation, we tested whether the match between participants’ ideals and a partner’s traits affected participants’ interest in forming a new friendship in three contexts: evaluating a potential friend’s profile, meeting in-person, and chatting online. Results revealed that participants were more interested in becoming friends with a partner whose traits matched (vs. mismatched) their ideal friend preferences when evaluating his or her profile. After a live interaction, however, the effect of the ideal-perceived trait match manipulation on participants’ friendship interest was substantially reduced in both in-person and online chatting contexts. People’s ideal friend preferences may influence their friendship interest more strongly in descriptive (i.e., indirect) than interactive (i.e., direct) contexts, a finding that mirrors prior results from the romantic domain and documents a role for domain-general relationship initiation processes.

Perturbation Training for Human-Robot Teams

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.

Impressions Based on a Portrait Predict, 1-Month Later, Impressions Following a Live Interaction

When it comes to person perception, does one “judge a book by its cover?” Perceivers made judgments of liking, and of personality, based on a photograph of an unknown other, and at least 1 month later, made judgments following a face-to-face interaction with the same person. Photograph-based liking judgments predicted interaction-based liking judgments, and, to a lesser extent, photograph-based personality judgments predicted interaction-based personality judgments (except for extraversion). Consistency in liking judgments (1) partly reflected behavioral confirmation (i.e., perceivers with favorable photograph-based judgments behaved more warmly toward the target during the live interaction, which elicited greater target warmth); (2) explained, at least in part, consistency in personality judgments (reflecting a halo effect); and (3) remained robust even after controlling for perceiver effects, target effects, and perceived attractiveness. These findings support the view that even after having “read a book,” one still, to some extent, judges it by its “cover.”