Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.

Contextual associations represented both in neural networks and human behavior

Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objects (bicycle-helmet) are represented more similarly in convolutional neural networks (CNNs) used for image understanding than unrelated objects (bicycle-fork). Stimuli were of objects against a white background and consisted of a diverse set of contexts (N=73). CNN representations of contextually related objects were more similar to one another than to unrelated objects across all CNN layers. Critically, the similarity found in CNNs correlated with human behavior across three experiments assessing contextual relatedness, emerging significant only in the later layers. The results demonstrate that context is inherently represented in CNNs as a result of object recognition training, and that the representation in the later layers of the network tap into the contextual regularities that predict human behavior.

The Concept of Identity in the Technological Era

We are currently witnessing a process of redefinition of the social structures that we are part of, through the new technologies, which are gradually entering all sectors of our lives, influencing the way we think, live, and relate to others. Since man is essentially a “political animal”, designed to evolve within a community, what impact will the digitalization era have on his behavior, especially when the physical limits imposed by the body are progressively disappearing? The objective of this study is to explore some of the subtle, but sure transformations of human behavior in the technological era, with a particular emphasis on the process of communication, personal feelings, and identity. In a more connected world than ever, where absolutely everything can be quantified, physical reality is in danger of being replaced by the virtual one. In this dynamic, the body could gradually become the only real impediment on the way to progress. Engaged in this alert race, we risk being dehumanized, in an attempt to be as similar as possible to the machines, which, undisturbed by the feelings, experiences, and behavioral predispositions specific to the human being, operate more accurately and are more effective. History shows that man essentially remains the same, with each age illustrating another facet of him. This is why, a thorough education from an early age is needed both in terms of the consequences of digitization and the means to cope with it, thus preventing us from distorting our essence.

Using the Myers-Briggs Type Indicator (MBTI) for Modeling Multiagent Systems

The formation of high-performance teams has been a constant challenge for organizations, which despite considering human capital as one of the most important resources, it still lacks the means to allow them to have a better understanding of several factors that influence the formation of these teams. In this sense, studies also demonstrate that teamwork has a significant impact on the results presented by organizations, in which human behavior is highlighted as one of the main aspects to be considered in the building of work teams. The Myers-Briggs Type Indicator seeks to classify the behavioral preferences of individuals around eight characteristics, which grouped as dichotomies, describe different psychological types. With it, researchers have sought to expand the ability to understand the human factor, using strategies with multiagent systems that, through experiments and simulations, using computer resources, enable the development of artificial agents that simulate human actions. In this work, we present an overview of the research approaches that use MBTI to model agents, aiming at providing a better knowledge of human behavior. Additionally, we make a preliminary discussion of how these results could be explored in order to advance the studies of psychological factors' influence in organizations' work teams formation.

Research on the Importance of Digital Etiquette As a Competence of a Modern Employee

This article is devoted to the study of the current topic - the importance of digital etiquette as an employee competence. The percentage of modern employee's communications in the online space is increasing, more and more work issues are solved using computer technology and electronic systems. This trend requires the development of a new digital culture of communication, which includes the rules that are necessary to build clear business relationships. In order to form a relevant, understandable and succinct set of recommendations, it is required to assess the current level of knowledge of digital etiquette. To do this, a survey was compiled that included questions defining human behavior in various situations that may arise during online business communications. Based on the survey with 120 respondents, recommendations on business etiquette topics were developed.

ESSAI KAITAN LINGKUNGAN DENGAN PEMBELAJARAN BAHASA INDONESIA

The communication language is required. In its application, the language has an important role in human life. Therefore, human behavior and culture of a nation can be seen from the language used. Bahasa Indonesia has a high status, because of the Indonesian language is the official language of the nation of Indonesia. For that, the application of Indonesian language can be taught from an early age from the environment, whether that environment is formal or informal in order to instill the values of life and social early on.

Research on quantum cognition in autonomous driving

Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

Models of Identity Signaling

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. And, as with all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. The first concerns models that assert how identity functions as a signal and test the consequences of those assertions, with a focus on public health behavior and disease transmission. The second concerns models used to understand how identity signals operate strategically in different social environments, with a focus on covert or encrypted communication.