Visual Non-verbal Social Cues Data Modeling

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

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Does context matter? Effects of robot appearance and reliability on social attention differs based on lifelikeness of gaze task

10.31234/osf.io/aqk5s ◽

2020 ◽

Author(s):

Abdulaziz Abubshait ◽

Patrick P. Weis ◽

Eva Wiese

Keyword(s):

Cognitive Processing ◽

Social Cognitive ◽

Physical Appearance ◽

Mental States ◽

Social Attention ◽

Social Cues ◽

Social Signals ◽

Mind Perception ◽

The Social ◽

Behavioral Features

Social signals, such as changes in gaze direction, are essential cues to predict others’ mental states and behaviors (i.e., mentalizing). Studies show that humans can mentalize with non-human agents when they perceive a mind in them (i.e., mind perception). Robots that physically and/or behaviorally resemble humans likely trigger mind perception, which enhances the relevance of social cues and improves social-cognitive performance. The current ex-periments examine whether the effect of physical and behavioral influencers of mind perception on social-cognitive processing is modulated by the lifelikeness of a social interaction. Participants interacted with robots of varying degrees of physical (humanlike vs. robot-like) and behavioral (reliable vs. random) human-likeness while the lifelikeness of a social attention task was manipulated across five experiments. The first four experiments manipulated lifelikeness via the physical realism of the robot images (Study 1 and 2), the biological plausibility of the social signals (Study 3), and the plausibility of the social con-text (Study 4). They showed that humanlike behavior affected social attention whereas appearance affected mind perception ratings. However, when the lifelikeness of the interaction was increased by using videos of a human and a robot sending the social cues in a realistic environment (Study 5), social attention mechanisms were affected both by physical appearance and behavioral features, while mind perception ratings were mainly affected by physical appearance. This indicates that in order to understand the effect of physical and behavioral features on social cognition, paradigms should be used that adequately simulate the lifelikeness of social interactions.

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METHOD FOR SYNTHESIS OF CONCEPTUAL DATA MODELS BASED ON PROCESS DOMAIN MODELS

СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ ◽

10.36622/vstu.2020.75.23.019 ◽

2020 ◽

pp. 80-87

Author(s):

С.И. Рябухин

Keyword(s):

Data Modeling ◽

Database Design ◽

Data Models ◽

Process Models ◽

Domain Models ◽

Conceptual Data

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.

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Advance in Moving Object Data Modeling under Geographic Network Environment

Geo-information Science ◽

10.3724/sp.j.1047.2013.00328 ◽

2013 ◽

Vol 15 (3) ◽

pp. 328

Author(s):

Hengcai ZHANG ◽

Feng LU ◽

Jie CHEN

Keyword(s):

Data Modeling ◽

Moving Object ◽

Network Environment

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