Human Interaction Patterns Research Articles

An Empirical Study on Teaching Management and Quality Feedback Based on Human Behavior Detection

In the current education field, the assessment of teaching management quality mostly relies on subjective judgment and static data, and lacks a real-time and dynamic feedback mechanism. In this study, we propose a deep learning-based human behavior analysis method, which aims to assess teaching management quality in real time by analyzing the behaviors of teachers and students in the classroom. First, in order to detect individual students in the video stream, an augmented detection framework based on YOLO v5s is introduced to process and analyze human actions and interaction patterns in the video data. Immediately after that, we design a channel residual decoupled convolutional neural network to recognize the different states of students. Teaching management quality is assessed by detecting students' classroom attention scores. By conducting experiments in different disciplines and teaching management environments to collect and train the model, the results show that the method can effectively improve the objectivity and accuracy of teaching management quality assessment.

New Technology, Warm Care: Social Robots in Nursing Practice and Research

Recent, rapid advancements in technology have enabled the introduction and integration of robots into everyday life. Different from the traditional image of robots as cold and mechanical, social robots are designed to emulate human interaction patterns, improving the user experience and facilitating social interactivity. Thus, social robots represent a promising new care intervention. In this article, after defining social robots and explaining the factors influencing "human-robot interaction", the authors discuss the effectiveness of social robots in the context of providing care to patients with dementia and autism as well as to pediatric patients. Finally, current cases in which PARO, a social robot, has been used in nursing are described, and key challenges and suggestions for future social robot applications are given. Current evidence indicates social robots must be developed and designed to adhere to a people-centered approach to achieve better robot-assisted care outcomes, be better accepted by patients, and better enable patients to open up emotionally and maintain good physical, mental, and social well-being.

Evaluating the Impact of Social Networks on Human Communication in the Digital Era

The widespread adoption of social networks has significantly transformed human communication and interaction patterns in contemporary society. The purpose of the study is to examine how individuals perceive and experience social network usage, aiming to shed light on its implications for interaction frequency, communication styles, social unity, collective action participation, and overall well-being. Employing an observational and descriptive research design, data was collected from a diverse sample of 120 participants using surveys administered online or in-person. Convenience sampling techniques ensured a balanced representation across age groups, genders, occupations, and geographical locations. Data analysis involved a combination of quantitative and qualitative approaches, with descriptive statistics summarizing demographic characteristics and survey responses. Thematic analysis was employed to identify recurring themes and patterns in qualitative data. Results reveal diverse perceptions regarding social network impact, with Facebook emerging as the most preferred platform for human communication. Participants reported varying levels of interaction frequency, communication styles, and concerns about negative effects on mental health. In conclusion, the study underscores the nuanced nature of social network dynamics and their implications for human communication and well-being. Recommendations include promoting digital literacy and mindfulness to mitigate potential negative impacts while harnessing the positive aspects of social network usage for enhancing social connections and community engagement.

Analisis Pengenalan Literasi Digital pada Anak di Lingkungan Keluarga

Human interaction patterns with digital media are becoming more intense as a result of digital disruption. Digital media can be beneficial, but it can also be detrimental. Therefore, digital literacy skills are needed to help people use digital media correctly, especially children. This can start from the family environment as the smallest organization closest to the child. This research aims to analyze the introduction of digital literacy to children in the family environment. This research was conducted by conducting a systematic literature review using a qualitative approach. Data collection techniques by examining Google Scholar-based journal article sources that are relevant to the research study. After collecting and obtaining data that is in accordance with the research study, the data is then analyzed using the content analysis method. The results of research on introducing digital literacy to children in the family environment can be divided into 4 parts, including 1) children are good imitators, 2) parents' digital literacy skills, 3) guidance and control, 4) developing digital literacy habits.

Analyze How Artificial Intelligence Affects Human Personality

Artificial intelligence (AI) is a rapidly growing field of study with very prominent effects on everyday life. However, there are concerns about how AI can change a person's personality. This study attempts to examine literacy on how AI affects human personality from various points of view. First, the study examines how AI affects human personality development, concentrating on thought patterns and behaviors. Second, the study takes into account the moral consequences of using AI on human personality, especially given personal freedom and privacy, as well as how AI can affect human interaction patterns and personalities. The findings suggest that ethical use of artificial intelligence (AI) can advance human intelligence and skills, but it also requires consideration of potential negative effects on human life, especially in terms of personality. As a result, although education should be improved to inform people about the responsible use of AI, AI development should have to do with ethical issues and their impact on the development of human personality.

Parental Perception of Remote Education in Pandemic: An Analysis Based on Children's Cognitive Performance.

The COVID-19 pandemic significantly changed patterns of human interaction, including in the educational sector, which was forced to transform relationships among students, families, and the academic community. The present study sought to establish the interrelationships between performance on cognitive tests during the preschool stage and the perceptions of parents about remote education in school children during the pandemic. The study included 100 preschool children from socially vulnerable sectors who underwent remote and distance learning in 2020 and 2021. The reliability of the applied questionnaire was determined through a confirmatory factor analysis. A structural equation model was constructed to determine the perceptions of parents about remote education based on cognitive performance during the preschool stage. The model fit yielded favorable results for predictive variables (χ2 = 7.734, DF = 9 [p = 0.561], the comparative goodness-of-fit index [CFI] = 1.000, root mean square error of approximation [RMSEA] = 0.000, standardized mean square residual [SRMR] = 0.069), and executive function (χ2 = 3.711, DF = 5 [p > 0.592], CFI = 1.000, RMSEA = 0.000, SRMR = 0.039) as latent variables that affected parents' perceptions. These results indicate that parents' perceptions of remote education are mediated by predictive aspects of learning and executive function during the preschool stage.

TIKTOK SHOP: CONSUMER CULTURE TRANSFORMATION IN THE ERA OF SOCIAL MEDIA

Background. The presence of social media has transformed human communication and interaction patterns. Communication typically occurred through face-to-face interactions. However, in the modern era, thanks to technological advancements, communication can take place without the need for physical meetings. Research Purpose. This research aims to analyze changes in consumer culture in the context of TikTok Shop, a social media-based video platform connecting users to virtual stores, particularly among millennial female students in the Blitar Raya area. Research Method. This study employs a qualitative descriptive method. Data collection is carried out through unstructured interviews using purposive sampling. This research was conducted on students in the Blitar Raya area using sample selection criteria. The data analysis in this research is conducted using scaling measurement. Findings. Consumption patterns on the TikTok Shop social media platform include the intensity of watching live streaming and short videos containing product reviews from buyers. Millennial students in Blitar tend to consider online shopping as an effective and efficient method. They trust the authenticity of products more because promotions on the TikTok Shop social media platform create the impression that they can see the products as if in a physical store. Conclusion. Through this research, it is hoped that society can become more aware of the influence of social media on consumer behavior, avoiding falling into false consciousness and resisting the allure of advertising. Furthermore, the role of capitalism in fostering consumer behavior by blurring the line between wants and needs should also be given attention.

Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies

We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.

Time tracing the earliest case of local pandemic resurgence

Origin identification of the earliest cases during the pandemic is crucial in containing the transmission of the disease. The high infectiousness of the disease during its incubation period (no symptom yet) and underlying human interaction pattern make it difficult to capture the entire line of the spread. The hidden spreading period is when the disease is silently spreading, for the “silent spreaders” showing no symptoms yet can transmit the infection. Being uncertain of the hidden spreading period would bring a severe challenge to the contact tracing mission. To find the possible hidden spreading period span, we utilized the SEITR (susceptible–exposed–infected–tested positive–recovered) model on networks where the relation between E state and T state can implicitly model the hidden spreading mechanism. We calibrated the model with real local resurgence epidemic data. Through our study, we found that the hidden spreading period span of the possible earliest case of local resurgence could vary according to the people interaction networks. Our modeling results showed the clustering and shortcuts that exist in the human interaction network significantly affect the results in finding the hidden spreading period span. Our study can be a guide for understanding the pandemic and for contact tracing the origin of local resurgence.

Review of animal transmission experiments of respiratory viruses: Implications for transmission risk of SARS-COV-2 in humans via different routes.

Exploring transmission risk of different routes has major implications for epidemic control. However, disciplinary boundaries have impeded the dissemination of epidemic information, have caused public panic about "air transmission," "air-conditioning transmission," and "environment-to-human transmission," and have triggered "hygiene theater." Animal experiments provide experimental evidence for virus transmission, but more attention is paid to whether transmission is driven by droplets or aerosols and using the dichotomy to describe most transmission events. Here, according to characteristics of experiment setups, combined with patterns of human social interactions, we reviewed and grouped animal transmission experiments into four categories-close contact, short-range, fomite, and aerosol exposure experiments-and provided enlightenment, with experimental evidence, on the transmission risk of severe acute respiratory syndrome coronavirus (SARS-COV-2) in humans via different routes. When referring to "air transmission," context should be showed in elaboration results, rather than whether close contact, short or long range is uniformly described as "air transmission." Close contact and short range are the major routes. When face-to-face, unprotected, horizontally directional airflow does promote transmission, due to virus decay and dilution in air, the probability of "air conditioning transmission" is low; the risk of "environment-to-human transmission" highly relies on surface contamination and human behavior based on indirect path of "fomite-hand-mucosa or conjunctiva" and virus decay on surfaces. Thus, when discussing the transmission risk of SARS-CoV-2, we should comprehensively consider the biological basis of virus transmission, environmental conditions, and virus decay. Otherwise, risk of certain transmission routes, such as long-range and fomite transmission, will be overrated, causing public excessive panic, triggering ineffective actions, and wasting epidemic prevention resources.

An Overview of Self-Adjustment in Students of the Faculty of Psychology Who Are Activelyinvolved in Student Organizations

Self-adjustment is the process of the results of a group or individual facing a new situation in the environment and whose behavior can be accepted in living together with the surrounding community. The purpose of this study is to find out how the design picture of diri Students of the Faculty of Psychology who are actively involved in student organizations. This research usesqualitative research, a research method that has the aim of promoting an understanding of human experience, interaction, and behavior patterns. The technique used in this study is the snowball sampling technique with a total of five subjects that have characteristics The qualitative research method used is phenomenology which has the characteristics of students aged 20-22 years, active students, experiencing insufficient self-adjustment. Data collection techniques were carried out with semi-structured interviews and direct observation. Data analysis techniques used in this study, namely data reduction, data presentation, drawing conclusions. The data credibility used in this study is a data credibility test with a type of source triangulation testing and engineering traingulation.

Data needs for integrated economic-epidemiological models of pandemic mitigation policies

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has led to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.

Patterns of verbal interaction in newly formed music ensembles.

Ensemble rehearsal in the European classical music tradition has a relatively homogenised format in which play-through, discussion, and practice of excerpts are employed to establish and agree on performance parameters of notated music. This research analyses patterns in such verbal communication during rehearsals and their development over time. Analysing two newly established ensembles that work over several months to a performance, it investigates the interaction dynamics of two closely collaborating groups and adaptation depending on task demands, familiarity with each other and an upcoming deadline. A case study approach with two groups of five singers allowed in-depth exploration of individual behaviours and contributions; results are reported descriptively and supported by qualitative data. The results highlight changes over time that reflect the development of implicit (faster decisions) interactions from explicit (slower decisions). They show a trajectory of opening up and closing down in terms of interactional flexibility, enabling members to significantly contribute to the group, followed by tightening the interaction to establish stability for performance. These findings and novel employment of T-pattern analysis contribute to the understanding of human group behaviour and interaction patterns leading to expert team performance.

Pengaruh Proses Transnasionalisasi Coffehouse Company “Starbucks Coffee” Terhadap Budaya Tradisional Minum Teh di China

The phenomenon of globalization has dominated various aspects of human life today. Globalization has changed the patterns of human interaction and activities to be faster, more modern, and more innovative. In the economic aspect, globalization has led to the process of economic transnationalism, where TNCs and MNCs have become key players in the global flow of goods and services in various sectors. These transnational corporations are able to create new, modern, and exclusive consumerist trends that attract everyone and make them want to follow them. This is what happened to the trend of coffee drinking, which has now become a global coffee culture, one of which is pioneered by Starbucks Coffee Company. The presence of Starbucks Coffee Company in various countries around the world, including China, is predicted to be a challenge to the existence of local culture, such as the traditional culture of drinking tea in China, which has been a long-standing tradition. This paper will analyze the influence of Starbucks on the traditional culture of drinking tea in China using the concept of Globalization and Three H Scenario. The results of the analysis show that Starbucks has been able to internalize the modern values of coffee drinking culture and change the perception of the Chinese people, who previously only oriented themselves towards tea, to now consume coffee, and gradually Starbucks can displace the traditional culture of drinking tea in China.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks.

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

Dynamics of macroscopic diffusion across meta-populations with top-down and bottom-up approaches: A review

<abstract><p>Human interaction patterns on the Web over online social networks vary with the context of communication items (<italic>e.g.,</italic> politics, economics, disasters, celebrities, and etc.), which leads to form unlimited time-evolving curves of information adoption as diffusion proceeds. Online communications often continue to navigate through heterogeneous social systems consisting of a wide range of online media such as social networking sites, blogs, and mainstream news. This makes it very challenging to uncover the underlying causal mechanisms of such macroscopic diffusion. In this respect, we review both top-down and bottom-up approaches to understand the underlying dynamics of an individual item's popularity growth across multiple meta-populations in a complementary way. For a case study, we use a dataset consisting of time-series adopters for over 60 news topics through different online communication channels on the Web. In order to find disparate patterns of macroscopic information propagation, we first generate and cluster the diffusion curves for each target meta-population and then estimate them with two different and complementary approaches in terms of the strength and directionality of influences across the meta-populations. In terms of the strength of influence, we find that synchronous global diffusion is not possible without very strong intra-influence on each population. In terms of the directionality of influence between populations, such concurrent propagation is likely brought by transitive relations among heterogeneous populations. When it comes to social context, controversial news topics in politics and human culture (<italic>e.g.,</italic> political protests, multiculturalism failure) tend to trigger more synchronous than asynchronous diffusion patterns across different social media on the Web. We expect that this study can help to understand dynamics of macroscopic diffusion across complex systems in diverse application domains.</p></abstract>

Indonesian National Security Policy in Fighting Terrorism Among the Youth Generation

At the beginning of the 21st century, Indonesia was marked by terrorist attacks that caused victims, such as the Bali Bombings in 2002. The threat and violence of terrorism cannot be separated from the influence of international terrorist organizations that attack Indonesia through targeted attacks on vulnerable individuals or groups, more specifically the youth generation. The perpetrators of terrorism have taken advantage of the technological network of the online radicalization era. The Industrial Revolution 4.0 has inspired many patterns of human interaction from domestic interactions to global interactions. This study explains various ideas about implementing national security policies in countering terrorism among the youth generation with qualitative methods using literature analysis so that several tactical steps are found to counter terrorism through critical and open education, exemplary, eradicating injustice, transcendence, and international cooperation. An important finding in this study is the importance of the joint commitment of elements of society to implement Indonesia's national security policy through actions that have small dimensions in the school and household environment and large dimensions at the national level.

Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model

In this paper, we have implemented a large-scale agent-based model to study the outbreak of coronavirus infectious diseases (COVID-19) in Singapore, taking into account complex human interaction pattern. In particular, the concept of multiplex network is utilized to differentiate between social interactions that happen in households and workplaces. In addition, weak interactions among crowds, transient interactions within social gatherings, and dense human contact between foreign workers in dormitories are also taken into consideration. Such a categorization in terms of a multiplex of social network connections together with the Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model have enabled a more precise study of the feasibility and efficacy of control measures such as social distancing, work from home, and lockdown, at different moments and stages of the pandemics. Using this model, we study an epidemic outbreak that occurs within densely populated residential areas in Singapore. Our simulations show that residents in densely populated areas could be infected easily, even though they constitute a very small fraction of the whole population. Once infection begins in these areas, disease spreading is uncontrollable if appropriate control measures are not implemented.

Patterns of human interaction with snow leopard and co-predators in the Mongolian western Altai: Current issues and perspectives

Large carnivores can cause considerable economic damage, mainly due to livestock depredation. These conflicts instigate negative attitude towards their conservation, which could in the extreme case lead to retaliatory killing. Here we focus on the snow leopard (Panthera uncia), a species of conservation concern with particularly large spatial requirements. We conducted the study in the Bayan Ölgii province, one of the poorest provinces of Mongolia, where the majority of the human population are traditional herders. We conducted a survey among herders (N = 261) through a semi-structured questionnaire with the aim to assess: the current and future herding practices and prevention measures, herders’ perceptions and knowledge of the environmental protection and hunting laws; the perceived livestock losses to snow leopard, wolf (Canis lupus), and wolverine (Gulo gulo), as well as to non-predatory factors; the key factors affecting livestock losses to these three large carnivores; and, finally, the attitudes towards these three large carnivores. Non-predatory causes of mortality were slightly higher than depredation cases, representing 4.5% and 4.3% of livestock holdings respectively. While no depredation of livestock was reported from wolverines, snow leopard and wolf depredation made up 0.2% and 4.1% of total livestock holdings, respectively. Herders’ attitudes towards the three large carnivores were negatively affected by the magnitude of the damages since they had a positive overall attitude towards both snow leopard and wolverine, whereas the attitude towards wolf was negative. We discuss conservation and management options to mitigate herder-snow leopard impacts. To palliate the negative consequences of the increasing trend in livestock numbers, herd size reduction should be encouraged by adding economic value to the individual livestock and/or by promoting alternative income and/or ecotourism. Furthermore, co-management between government and stakeholders would help tackle this complex problem, with herders playing a major role in the development of livestock management strategies. Traditional practices, such as regularly shifting campsites and using dogs and corrals at night, could reduce livestock losses caused by snow leopards.

How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design

ABSTRACT Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 56 papers from various domains to understand how social characteristics can benefit human–chatbot interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human–chatbot interactions.