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Nowadays the prediction and change of waste-related behaviors represent a key topic for scholars and policy makers. The theoretical mainstays applied to waste separation behavior, such as the Theory of Planned Behavior (TPB), the Norm Activation Model and the Value Belief Norm, do not include the construct of goal in their formulation. Other goal-focused theories, such as the Goal Systems Theory (GST), lack applications on separation behavior. Recently, Ajzen and Kruglanski (2019) have proposed the Theory of Reasoned Goal Pursuit (TRGP) which combines TPB and GST. Considering TRGP has the potential to offer further insights on human behavior and, to our knowledge, there is no application of TRGP to recycling behavior yet, this paper analyses waste separation behavior of households in Maastricht and Zwolle (The Netherlands) under the lens of TRGP. Although waste separation behavior represents a kind of habitual behavior, this paper highlights the influence of goals and motivation on intention to separate waste. Furthermore, it offers some indications to promote behavior change and some suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Purpose: The correct interaction between the workforce and augmented, virtual, and mixed reality technologies represents a crucial aspect of the success of the smart factory. This interaction is, indeed, affected by the variability of human behavior and its reliability, which can strongly influence the quality, safety, and productivity standards. For this reason, this paper aims to provide a clear and complete analysis of the impacts of these technologies on the worker’s performance. Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify peerreviewed papers that focused on the implementation of augmented and virtual technologies in manufacturing systems and their effects on human performance. Findings: In total, 61 papers were selected and thoroughly analyzed. The findings of this study reveal that augmented, virtual and mixed reality can be applied for several applications in manufacturing systems with different types of devices, that involve various advantages and disadvantages. The worker’s performances that are influenced by the use of these technologies are above all time to complete a task, error rate, and mental and physical workload. Originality/value: Over the years augmented, virtual and mixed reality technologies in manufacturing systems have been investigated by researchers. Several studies mostly focused on technological issues, have been conducted. The role of the operator, whose tasks may be influenced positively or negatively by the use of new devices, has been hardly ever analyzed and a deep analysis of human performance affected by these technologies is missing. This study represents a preliminary analysis to fill this gap. The results obtained from the SLR allowed us to develop a conceptual framework that investigates the current stateof-the-art knowledge about the topic and highlights gaps in the current research. [ABSTRACT FROM AUTHOR]

An editorial is presented in the Journal of West African Languages, celebrating Volume 50 as a jubilee edition that combines usual volumes into one. Topics include diverse linguistic areas such as phonetics, morphology, and sociolinguistics, with specific papers exploring West African language vowel systems, Igbo vowel elision and assimilation, and Yorùbá noun ontological annotation.

This article explores the design and ethical considerations of social robots in human-robot interaction (HRI). The authors argue that social robots should have an honest design that accurately reflects their capabilities and aligns with user needs in specific use cases. They suggest that an honest design can reduce uncertainty and shape users' expectations during interaction. The article raises questions about categorizing user needs, adjusting a robot's capabilities, perceiving a robot with an honest design, and developing an evaluation framework for a robot's affordances, capabilities, and usability. [Extracted from the article]

This paper provides a rationale and convention for discussing the true limits and interpretation of data collected using unidimensional, summated, Likert-type, and attitudinal scales used in research investigating human behavior, sociology, education, psychology, and other related fields of study. All vague quantifiers must be described in methods and findings. The true limits of the scale and of each vague quantifier should be described. This information should be placed in the methods section. A five-point summated scale, for example, can be interpreted as follows: Strongly Agree = 5 - 4.51, Agree = 4.5 - 3.51, Neither Agree nor Disagree = 3.5 - 2.51, Disagree = 2.5 - 1.51, Strongly Disagree = 1.5 - 1. This paper also provides a rationale and convention for the use of nonstandardized effect size (ES) estimates to describe the magnitude and strength of the effect. This is accomplished by subtracting one summated M from another summated M and interpreted using the following convention: Small (ES = .19 and lower); Medium (ES = .20 - .49); and Large (ES = .50 and higher). The rationale for this is based on the intuitiveness of the measure, true limits of the scale, and scale intervals. [ABSTRACT FROM AUTHOR]

In this paper, we reviewed 126 papers published in 13 top journals in information systems from 2008 to 2022 that focused on the adoption of social networking sites (SNS) and its consequences. The paper presents a summary of the literature review. The literature review indicated that most scholars studied the adoption and the consequences of SNS adoption in silos. Human behavior is complex. Skinnerian behaviorism posits that human behavior is triggered by environmental stimulus and reinforced by an appraisal of consequences to the behavior. It evinces a need for the development of an integrative framework to model the stimulus for adoption of SNS and an appraisal of the reinforcement of the consequences of adoption in a single frame. We analyze the associated constructs and theories to propose the integrative Social Networking Site Exploitation Framework (SNSEF), based on reinforcement theory, social psychology, and social capital theory. The SNSEF constitutes nine propositions to explain human motivations and behavior that leads to value creation on SNS. SNS providers will find the framework useful to assess human motivations of SNS usage and the value generation from the sites to design appropriate features of their sites to attract and retain active users. The SNSEF will help educational and business organizations as well as governmental agencies in designing appropriate policies for SNS adoption and exploiting SNS for communication. Business managers and marketers may leverage the SNSEF to understand user behavior on SNS to attain business goals. Academic scholars may gain from the literature review, propositions of the SNSEF, and the future research directions presented in this paper. [ABSTRACT FROM AUTHOR]

Harvesting usable and meaningful disaster-related, spatio-temporal data at a highly granular level poses major challenges in its cleaning and aggregation. This paper presents a strategy related to those challenges with respect to individual behavior near COVID-19 laden healthcare facilities. This is done to enable the visualizing of egress behavior data as interactive, three-dimensional (3D) scenes to investigate human behavior patterns regarding touch-based, disease transmission. Therefore, the aim is to demonstrate how this concept of 3D epidemiology may provide new mechanisms to understand the relative risk and exposure prevalence for data analysis. This paper demonstrates 3D enablement of disaster-related field data through use of first-hand observations of 1,936 individuals egressing New York City healthcare facilities during the onset of COVID-19 in the Spring of 2020. The observations capture egress behavior in terms of where people go (e.g. coffee shop, Subway) and how they physically interact with the surroundings (i.e. what they touch and how long they remain). This paper introduces a mechanism for automated extraction and 3D visualization of such data in Potree, an open-source Web Graphics Library (WebGL) point cloud viewer. Distinctive vertex shaders are used to distinguish specific destination selection and behavioral patterns (e.g. personal protective equipment usage). Two-dimensional heatmaps are paired with 3D scenes to demonstrate the potential of using 3D visualization of spatio-temporal patterns for visualizing disease transmission potential. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of human psychology and advanced technology, focusing on how intelligent and emotive technology influences human behavior and emotional intelligence, and in the process, might impact our ability to show and feel empathy. Based in Alfred Adler's theory of human motivation, we examine how feelings of inferiority -- vulnerability, powerlessness, perfectibility, and the need for affiliation -- drive our increasing dependence on technology. The human tendency to treat inanimate objects as animate is heightened by the sophisticated communication capabilities of Generative AI (Gen AI), altering our interpersonal dynamics and communication signals. We analyze how this shift impacts empathy, self-centeredness, and impatience, suggesting a need for conscious awareness of technology's limitations to preserve genuine human connections. By conducting a "technology dependency audit," we encourage individuals to reflect on the extent to which their lives are mediated by technology. Ultimately, the paper argues for reclaiming our emotional and practical autonomy from technology to maintain authentic human relationships and emotional well-being. [ABSTRACT FROM AUTHOR]

Zoos as an institution have a significant international and educational reach, with hundreds of institutions worldwide and hundreds of millions of visitors each year. But even with such reach, zoos continue to be overlooked by the field of literature. Over the years, zoos have been represented in many literary landscapes, and the same can be analyzed for understanding how zoos as an institution. Furthermore, the zoo’s representation in literature opens the venue for discourses on ethical considerations and moral dilemmas surrounding zoos, and questioning the impact of human intervention on the natural world. Authors of such work navigate themes of conservation, animal rights, and the blurred lines between entertainment and exploitation, prompting readers to reconsider their perceptions of zoological spaces. This research paper delves into the multifaceted representations of zoos in literature, examining how authors have engaged with the concept of the zoo as a cultural and societal institution. By employing a thematic approach, the paper investigates how zoos have been depicted as microcosms of society, mirrors reflecting human behavior and societal attitudes towards nature. The exploration encompasses works, such as Yann Martel's Life of Pi and Edward Albee’s The Zoo Story which delve into the dichotomy between captivity and freedom. The study spans various literary works aiming to provide a comprehensive analysis of the evolving portrayal of zoos in literature. [ABSTRACT FROM AUTHOR]

Copyright of Filozofija i Drustvo is the property of University of Belgrade, Institute for Philosophy & Social Theory and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the problem of coherence, i.e., whether the internal and external dimensions of li (礼 rituals, rites, and the observance of them) are compatible. This problem stems mainly from Mencius' seemingly conflicting statements. On the one hand, he emphasized the goodness of xing (性 human nature) in terms of the goodness of the heart-mind. On the other hand, he defended the act of following li regardless of one's heart-mind. This paper argues that Mencius held coherent moral reasons internalism, asserting that moral reasons are embedded in people's universal and potential instinct to do morally good things. Consequently, he distinguished morality from non-moral normativity and claimed that the former takes precedence over the latter. The concept of li is thus divided into moral li and non-moral li, with moral li taking precedence over non-moral li. Therefore, the act of "following li regardless of one's heart-mind" refers to following non-moral li, as long as it does not conflict with moral li. Based on the fact that Mencius' concept of li is a kind of coherent moral reasons internalism, this paper further responds to some challenges from moral reasons externalism. It does so by clarifying the meaning of "seeking the cause within oneself" (反求诸己 fan qiu zhu ji), showing that Mencius believed every individual could establish rational inner guidance and be motivated to lead a reflective and autonomous moral life. [ABSTRACT FROM AUTHOR]

Food waste is one of the most unusual behaviours related to economic systems located on the agrifood chain. In this matter, the study of the food waste and the mechanisms that lead to its development are complex and rooted within several components of the human behaviour and economic system dynamic. In this paper, we propose the development of a model which presents the dynamics of the phenomenon based on several cause-effect relationships between aspects of human behaviour and economic aspects. The model is built using a Systems Dynamic (SD) approach, based on the identification of connections between identified causes and effects and the determination of the ontologic and mathematical nature of the connections. [ABSTRACT FROM AUTHOR]

This article explores the concept of human nature from a post-essentialist, pluralist, and interactive perspective. It discusses Maria Kronfeldner's work on the topic and the application and implications of her ideas. The article includes papers by various authors who extend Kronfeldner's work and examine topics such as archeology, the origin of human language faculties, and the relationship between nature and culture. The article also addresses concerns about human exceptionalism and the social ramifications of scientific narratives. The exchange of views and criticism continues throughout the article, with Kronfeldner's paper serving as a response to the concerns raised. Overall, the article emphasizes the complexity of understanding human nature and the potential for integrating knowledge in specific contexts. [Extracted from the article]

This article explores the significance of gift-giving in human relationships, with a particular focus on traditional Korea. It discusses how gift exchanges create value and satisfaction for both parties involved, and examines the principles of gift exchange as outlined by anthropologists Marcel Mauss and Karl Polanyi. The article also explores the practical and symbolic functions of diplomatic gifts in establishing and fortifying international relationships and political order. It provides examples of diplomatic gift exchanges between China and neighboring countries, as well as between European countries and China. The article concludes by mentioning three papers that will further examine the practice of diplomatic gifts in East Asia, with a focus on Korea. [Extracted from the article]

Objectives: This study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing two key challenges: (1) extracting local spatial feature information from implicit joint connections that is unobtainable through standard graph convolutions on natural joint connections alone. (2) Capturing long-range temporal dependencies that extend beyond the limited temporal receptive fields of conventional temporal convolutions. Methods: To achieve these objectives, we propose three novel modules integrated into the spatiotemporal graph convolutional framework: (1) a connectivity feature extraction module that employs attention to model implicit joint connections and extract their local spatial features. (2) A long-range frame difference feature extraction module that captures extensive temporal context by considering larger frame intervals. (3) A coordinate transformation module that enhances spatial representation by fusing Cartesian and spherical coordinate systems. Findings: Evaluation across multiple datasets demonstrates that the proposed method achieves significant improvements over baseline networks, with the highest accuracy gains of 2.76 % on the NTU-RGB+D 60 dataset (Cross-subject), 4.1 % on NTU-RGB+D 120 (Cross-subject), and 4.3 % on Kinetics (Top-1), outperforming current state-of-the-art algorithms. This paper delves into the realm of behavior recognition technology, a cornerstone of autonomous systems, and presents a novel approach that enhances the accuracy and precision of human activity recognition. [ABSTRACT FROM AUTHOR]

Psychology, and cross-cultural psychology (CCP) in particular, plays a pivotal role in understanding the intricate relationship between culture and human behavior. This paper sheds light on the challenges of inequity and marginalization, especially concerning scholarship from the Global South, which have roots in historical colonial practices. It highlights how intellectual extractivism and the predominance of Western research methodologies often overlook the contributions of Global South scholars and indigenous ways of knowing. Such imbalances risk narrowing the scope of psychological inquiry, privileging American and European perspectives, and undermining the richness of global human experiences. This paper calls for a shift toward more equitable collaborations and the recognition of diverse epistemologies. By advocating for genuine representation in research and valuing local knowledge, it proposes pathways for a more inclusive and authentic exploration of human behavior across cultures. [ABSTRACT FROM AUTHOR]

This article explores the use of infrared thermal imaging (ITI) as a non-invasive method to study parent-infant interactions. ITI can measure changes in cutaneous temperature and capture psychophysiological arousal states during social interactions. The authors discuss the potential of ITI to provide insight into autonomic co-regulation and bio-behavioral synchrony. They also highlight the challenges and considerations in conducting ITI studies. Additionally, the document provides a list of references and citations for scientific papers related to thermal facial imprints, autonomic contagion, mother-infant synchrony, and infant stress regulation. These papers discuss the physiological and developmental aspects of these phenomena and the potential applications of thermal imaging in studying emotions and deception detection. [Extracted from the article]

Traditional human behavior recognition needs many training samples. Signal transmission of images and videos via visible light in the body is crucial for detecting specific actions to accelerate behavioral recognition. Joint sparse representation techniques improve identification accuracy by utilizing multi-perspective information, while distributional adaptive techniques enhance robustness by adjusting feature distributions between different perspectives. Combining both techniques enhances recognition accuracy and robustness, enabling efficient behavior recognition in complex environments with multiple perspectives. In this paper, joint sparse representation has been combined with distributed adaptation algorithm to recognize human behavior under the fusion algorithm, and verify the feasibility of the fusion algorithm through experimental analysis. The research objective of this article is to explore the use of the combination of joint sparse representation technology and distributed adaptive technology in the recall and accuracy of human detection, combined with the cross perspective human behavior recognition of wireless optical transmission. The experimental results showed that in the process of human detection, the recall and precision of the fusion algorithm in this paper reached 92% and 90% respectively, which are slightly higher than the comparison algorithm. In the experiment of recognition accuracy of different actions, the recognition accuracy of the fusion algorithm in this paper was also higher than that of the control algorithm. It can be seen that the fusion of joint sparse representation and distributed adaptation algorithms, as well as wireless communication light technology, are of great significance for human behavior recognition. [ABSTRACT FROM AUTHOR]

This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as a collection of sample paths from a Bayesian optimization procedure. The methodology involves defining a likelihood on observed human behavior from an optimization task, where the likelihood is parameterized by a Bayesian optimization subroutine governed by an unknown acquisition function. This structure enables us to make inference on a subject's acquisition function while allowing their behavior to deviate around the solution to the Bayesian optimization subroutine. To test our methods, we designed a sequential optimization task which forced subjects to balance exploration and exploitation in search of an invisible target location. Applying our proposed methods to the resulting data, we find that many subjects tend to exhibit exploration preferences beyond that of standard acquisition functions to capture. Guided by the model discrepancies, we augment the candidate acquisition functions to yield a superior fit to the human behavior in this task. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Academic Association for Organizational Science has a history of more than 60 years. Topics include focused on the level of a focal organization and the human behavior in it and fostered interdisciplinary studies after the demise of BABA in 1961; and FUJIMOTO tried to promote internationalization by sending Japanese research achievements overseas in English.

This paper re-assesses Ramsey's influence on Keynes. It is argued that the Standard View has restricted attention to the implications for probability theory of Ramsey's criticisms of Keynes's concepts of logical probability-relations and non-numerical probabilities. Building on the work of both Coates (1996) and Misak (2016) , an Alternative View is proposed in which Ramsey's influence on Keynes is seen as principally philosophical. Specifically, the Alternative View recognises Ramsey's adoption of the logical pragmatist philosophy of C. S. Peirce from 1924 onwards with a dispositional theory of belief in which beliefs are treated not only as useful mental habits that can successfully guide future actions but also as able to provide true explanations of observed empirical facts. The textual evidence is examined, particularly Keynes's biographical essay on Ramsey, which, it is argued, supports the contention that Keynes fully appreciated and was sympathetic to Ramsey's pragmatism especially the importance of vague knowledge and the need for the development of human logic as the study of reasonable human behaviour. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Positivity & Well-Being (IJPW) is the property of International Journal of Positivity & Well-Being (IJPW) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper compares three methods for evaluating computer-generated motion behaviour for animated characters: two commonly used direct rating methods and a newly designed questionnaire. The questionnaire is specifically designed to measure the human-likeness, appropriateness, and intelligibility of the generated motion. Furthermore, this study investigates the suitability of these evaluation tools for assessing subtle forms of human behaviour, such as the subdued motion cues shown when listening to someone. This paper reports six user studies, namely studies that directly rate the appropriateness and human-likeness of a computer character's motion, along with studies that instead rely on a questionnaire to measure the quality of the motion. As test data, we used the motion generated by two generative models and recorded human gestures, which served as a gold standard. Our findings indicate that when evaluating gesturing motion, the direct rating of human-likeness and appropriateness is to be preferred over a questionnaire. However, when assessing the subtle motion of a computer character, even the direct rating method yields less conclusive results. Despite demonstrating high internal consistency, our questionnaire proves to be less sensitive than directly rating the quality of the motion. The results provide insights into the evaluation of human motion behaviour and highlight the complexities involved in capturing subtle nuances in nonverbal communication. These findings have implications for the development and improvement of motion generation models and can guide researchers in selecting appropriate evaluation methodologies for specific aspects of human behaviour. [ABSTRACT FROM AUTHOR]

In today's age, we see the increasing influence of technology on people, which begs to raise the question: "Is society determined by technology?" Rising up within the constraints of each society, technology had its limitations, as it catered to the needs and interests of the masses. As society evolved, so did its requirements. We are at a stage where dependence on technology has gone through the roof with new innovations coming up in the sector, the rise of artificial intelligence, for instance. Technology has replaced the concept of being a mere tool and is creating/demanding space of its own. With advancements in various fields like robotics which churn out machines that can mimic human behaviour and are equipped with anthropomorphic features, it has become rather difficult to view them as mere commodities waiting for one's (human master's) command. Keeping this in view, the paper primarily focuses on the relationship between society and technology, with special emphasis on artificial intelligence, through an analysis of the films Ra One (Bollywood), Android Kunjappan (Mollywood), and Ironman 3 (Hollywood). Secondarily, an experimental study was carried out to know the perception of the Engineering students, the future technologists, who are the stakeholders of the 21st-century technological world, with an aim to know their perception of technology and bring an awareness of the limitations of technology and its role in the creation of utopian and dystopian world. Finally, the paper examines the consequence of technological determinism and autonomy from a colonial perspective and intends to bring preparedness among the students to perceive technology conscious of its limitation and bring harmony between society and technology that leads towards building a utopian world. [ABSTRACT FROM AUTHOR]

Human abnormal action recognition is crucial for video understanding and intelligent surveillance. However, the scarcity of labeled data for abnormal human actions often hinders the development of high-performance models. Inspired by the multimodal approach, this paper proposes a novel approach that leverages text descriptions associated with abnormal human action videos. Our method exploits the correlation between the text domain and the video domain in the semantic feature space and introduces a multimodal heterogeneous transfer learning framework from the text domain to the video domain. The text of the videos is used for feature encoding and knowledge extraction, and knowledge transfer and sharing are realized in the feature space, which is used to assist in the training of the abnormal action recognition model. The proposed method reduces the reliance on labeled video data, improves the performance of the abnormal human action recognition algorithm, and outperforms the popular video-based models, particularly in scenarios with sparse data. Moreover, our framework contributes to the advancement of automatic video analysis and abnormal action recognition, providing insights for the application of multimodal methods in a broader context. [ABSTRACT FROM AUTHOR]

Copyright of Filozofija i Drustvo is the property of University of Belgrade, Institute for Philosophy & Social Theory and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Filozofija i Drustvo is the property of University of Belgrade, Institute for Philosophy & Social Theory and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explores the representation of art in Willa Cather's Lucy Gayheart (1935), which is viewed as an essential aspect of the novel in terms of its vitality and dominance. The novel subtly yet persistently invokes new possibilities for general human behavior and supportive interconnectedness among women. As the female experience looms large in Cather's fiction, Lucy Gayheart illustrates the dangers of presenting women in a romanticized, ethereal light. However, the present paper intends to prove that Cather's attempt at romanticizing her heroine's stance leads to a war between artistic accomplishment and acquisitive spirit. Through examining Cather's Lucy Gayheart, this paper contends that women's perplexed possibilities are a miniature of the fragmented psyche that pervades Cather's work. [ABSTRACT FROM AUTHOR]

This review paper aimed to the study Emotional Balance and its Significance in the Prophetic Sunnah, the paper discussed Balance deeply as it is considered the foundation of upright behavior for individuals and communities. The paper used the analytical descriptive for the Quran and the Prophetic Sunnah to investigate the topic of balance and its impact. It founded that, Islam has a unique and distinctive nature, as it balances actions, words, and all aspects of human behavior, both apparent and hidden, with the standard of moderation and balance. It rejects extremism in all cases. Therefore, this review paper has been conducted to highlight Islam's concern with building the balanced Muslim personality, in compatible three phases: Muslim's Balance with His God, The Muslim's Balance with Himself, and The Muslim's Balance with His Society. And recommended Muslim to be proud of its religion, according to the approach of the Quran and the Prophetic Sunnah. [ABSTRACT FROM AUTHOR]

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices' ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks. [ABSTRACT FROM AUTHOR]

Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection. [ABSTRACT FROM AUTHOR]

Cooperation is essential for the survival of human society. Understanding the nature of cooperation and its underlying mechanisms is crucial for studying human behavior. This paper investigates the impact of leadership on public cooperation by employing repeated sequential public goods games, as well as by examining whether leading by example (through rewards and punishments) can promote cooperation and organizational success. The leaders were assigned randomly and were given the authority to reward or punish. As a result, (1) the leaders showed a strong tendency toward reciprocity by punishing free riders and rewarding cooperators at their own expense, which enhanced the intrinsic motivation for others to follow their example; and (2) both rewards and punishments were effective in promoting cooperation, but punishment was more effective in sustaining a high level of collaboration. Additionally, leaders preferred using rewards and were more reluctant to use punishments. These findings are crucial for creating organizational structures that foster cooperation. [ABSTRACT FROM AUTHOR]

Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep learning techniques for detecting abnormal human behaviors in surveillance video streams. We categorize the existing techniques into three approaches: unsupervised, partially supervised, and fully supervised. Each approach is examined in terms of its underlying conceptual framework, strengths, and drawbacks. Additionally, we provide an extensive comparison of these approaches using popular datasets frequently used in the prior research, highlighting their performance across different scenarios. We summarize the advantages and disadvantages of each approach for abnormal human behavior detection. We also discuss open research issues identified through our survey, including enhancing robustness to environmental variations through diverse datasets, formulating strategies for contextual abnormal behavior detection. Finally, we outline potential directions for future development to pave the way for more effective abnormal behavior detection systems. [ABSTRACT FROM AUTHOR]

In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively. [ABSTRACT FROM AUTHOR]

Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of automated machine learning or AutoML has raised an interesting trend of no-code and low-code machine learning where most tasks in the machine learning pipeline can possibly be automated without support from human data scientists. While it sounds reasonable that we should leave repetitive trial-and-error tasks of designing complex network architectures and tuning a lot of hyperparameters to AutoML, leading research using AutoML is still scarce. Thereby, the overall purpose of this case study is to investigate the gap between current AutoML frameworks and practical machine learning development. Case description: First, this paper confirms the increasing trend of AutoML via an indirect indicator of the numbers of search results in Google trend, IEEE Xplore, and ACM Digital Library during 2012–2021. Then, the three most popular AutoML frameworks (i.e., Auto-Sklearn, AutoKeras, and Google Cloud AutoML) are inspected as AutoML's representatives; the inspection includes six comparative aspects. Based on the features available in the three AutoML frameworks investigated, our case study continues to observe recent machine learning research regarding the background of image-based machine learning. This is because the field of computer vision spans several levels of machine learning from basic to advanced and it has been one of the most popular fields in studying machine learning and artificial intelligence lately. Our study is specific to the context of image-based road health inspection systems as it has a long history in computer vision, allowing us to observe solution transitions from past to present. Discussion and evaluation: After confirming the rising numbers of AutoML search results in the three search engines, our study regarding the three AutoML representatives further reveals that there are many features that can be used to automate the development pipeline of image-based road health inspection systems. Nevertheless, we find that recent works in image-based road health inspection have not used any form of AutoML in their works. Digging into these recent works, there are two main problems that best conclude why most researchers do not use AutoML in their image-based road health inspection systems yet. Firstly, it is because AutoML's trial-and-error decision involves much extra computation compared to human-guided decisions. Secondly, using AutoML adds another layer of non-interpretability to a model. As these two problems are the major pain points in modern neural networks and deep learning, they may require years to resolve, delaying the mass adoption of AutoML in image-based road health inspection systems. Conclusions: In conclusion, although AutoML's utilization is not mainstream at this moment, we believe that the trend of AutoML will continue to grow. This is because there exists a demand for AutoML currently, and in the future, more demand for no-code or low-code machine learning development alternatives will grow together with the expansion of machine learning solutions. Nevertheless, this case study focuses on selected papers whose authors are researchers who can publish their works in academic conferences and journals. In the future, the study should continue to include observing novice users, non-programmer users, and machine learning practitioners in order to discover more insights from non-research perspectives. [ABSTRACT FROM AUTHOR]

With increasing urban development, urban transit systems and corridors have emerged as an important aspect of the urban fabric with mixed use development in their surroundings. Urban Transit Systems are accessible to all groups of people and allow people to reach their destination, following a fixed route and schedule. The Metro transit system with an elevated corridor running along the streetscape is the preferred choice of public transport in the Indian context. However, these elevated transit systems create a shortage of space while reducing the green cover. This shortage of space results in decrease in human interaction and creates an inactive space. It is proven that a streetscape with greenery enhances the visual quality of a space, making it active and influencing human behavior positively. This paper elaborates the adverse impact of urban transit systems on the environment, particularly the greenery volumes of the streetscape, space quality, visual quality and human behavior with the help of case studies. To recapitulate, this paper reviews how transit systems, streetscape and greenery are connected to each other, how they influence one another and what could be a possible solution on the adverse effects of urban transit systems. [ABSTRACT FROM AUTHOR]

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classification algorithms to construct classification models, which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effective segmentation of multiple discontinuous human behaviors, then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is significantly improved compared with the traditional classification algorithm. [ABSTRACT FROM AUTHOR]

The shift in the critical perspective splits an episteme in two. A line of demarcation identical to the paradigm shift separates the new epistemes in a way that the critical insight should differ drastically on both sides of the line. Michel Foucault sorts these historical epistemes as Renaissance and Classical epistemes, then he included the Modern episteme as a latter historical era, and the line of distinction between one episteme and another is the critical insight that the involved mentality adopts. When it comes to the source of the influence of human behavior, the change in perspective in the consideration of the source of the influence is clarified in the comparison between the preFreudian versus the post-Freudian understandings of human beings. It means that with the consideration of two major depressions, the preFreudian thought believed that the driving forces of human beings are external. Whereas the post-Freudian understanding reconsidered the influence on human behavior as ramifications of the subconsciousness that eventually affect the consciousness of human beings. This paper examines Philip Ridley‟s Mercury Fur (2005) in terms of the Russian Formalist‟s defamiliarization aspects. The deployment of such techniques implicates the advancement of the dystopian irregularities on the stage over the regularly implied didactic methodologies. The use of the Foucauldian epistemes distinguishes the deployed defamiliarizing aspects as identified with a difference from the de Sassurean sign analogy. The paper concludes with the defamiliarized elements as the focal points of the plays that adopt Thanatos as their methodology. [ABSTRACT FROM AUTHOR]

Humans are complex systems, 'macro-entities', whose existence, behaviour and consciousness stem out of the configurations of physical entities on the micro-level of the physical world. But an explanation of what humans do and think cannot be found through 'tracking us back', so to speak, to micro-particles. So, in explaining human behaviour, including linguistic behaviour on which this paper focuses, emergentism opens up a powerful opportunity to explain what it is exactly that emerged on that level, bearing in mind the end product in the form of the intra- and inter-cultural diversity. Currently there is a gap in emergentism research. On one hand, there are discussions in philosophy of the emergent human reality; on the other, there are discussions of social, cultural, or individual variation of these emergent aspects of humanity in the fields of anthropology, sociology, linguistics or psychology. What I do in this paper is look for a way to 'trace' some such diversified emergents from what is universal about their 'coming to being', all the way through to their diversification. My chosen emergent is human time, my domain of inquiry is natural-language discourse, and the drive behind this project is to understand the link between 'real' time of spacetime on the micro-level from which we emerged and the human time devised by us, paying close attention to the overwhelming diversity in which temporal reference is expressed in human languages. The main question is, where does this diversity fit in? Does understanding of this diversity, as well as of what lurks under the surface of this diversity, aid the emergentism story? My contribution to this volume on 'the nature of structure and the structure of nature' thus takes the following take on the title. The structure of human communication is at the same time uniform, universal, and relative to culture, in that it is emergent as a human characteristic, and as such compatible with the micro-level correlates in some essential ways, but also free to fly in different directions that are specific to societies and cultures. I explore here the grey area between the micro-level and the linguistic reflections of time—the middle ground that is emergent itself but that tends to be by-passed by those who approach the question of human flowing time from either end: metaphysics and the philosophy of time on the one hand, and contrastive linguistics, anthropological linguistics and language documentation on the other. I illustrate the debate with examples from tensed and tenseless languages from different language families, entertaining the possibility of a conceptual universal pertaining to time as degrees of epistemic modality. Needless to say, putting the question in this way also sets out my (not unassailable) methodology. [ABSTRACT FROM AUTHOR]

This article explores the work of Robert Cooper, a well-known figure in organization studies, particularly in the areas of poststructuralism and process theory. Cooper's introduction of postmodernism and poststructuralism to the study of organizations has had a significant impact on the field. The article discusses Cooper's background in avant-garde poetry and how it influenced his writing style. It also examines the influence of Charles Olson's essay on Cooper's ideas about poetry and social theory. Cooper's work challenges preformed concepts and explores an open field of thought, offering a unique and compelling perspective on organization theory and human existence. [Extracted from the article]

Human activity recognition (HAR) has recently become a popular research field in the wearable sensor technology scene. By analyzing the human behavior data, some disease risks or potential health issues can be detected, and patients' rehabilitation progress can be evaluated. With the excellent performance of Transformer in natural language processing and visual tasks, researchers have begun to focus on its application in time series. The Transformer model models long-term dependencies between sequences through self-attention mechanisms, capturing contextual information over extended periods. In this paper, we propose a hybrid model based on the channel attention mechanism and Transformer model to improve the feature representation ability of sensor-based HAR tasks. Extensive experiments were conducted on three public HAR datasets, and the results show that our network achieved accuracies of 98.10%, 97.21%, and 98.82% on the HARTH, PAMAP2, and UCI-HAR datasets, respectively, The overall performance is at the level of the most advanced methods. [ABSTRACT FROM AUTHOR]

This article presented a comparative analysis of Reinhold Niebuhr's anthropology and the Seventh-day Adventist view of human nature and aims at enriching contemporary theological discourse. Niebuhr's theological framework acknowledges the limitations and fallibility of human beings while emphasizing their capacity for transcendence, susceptibility to sin, and openness to grace. On the other hand, the Seventh-day Adventist perspective emphasizes the holistic understanding of human nature, the restoration of the divine image, and the transformative potential through the indwelling of the Holy Spirit. Through a comprehensive analysis, this research explored the similarities, differences, and theological implications of these perspectives, providing valuable insights into the complexities of human existence. The paper posited a holistic understanding of human nature, acknowledging its inherent flaws and the possibility of redemption through the transformative power of the Holy Spirit. Significantly, the paper contributes to the ongoing exploration of anthropology, fostering a deeper understanding of human existential reality and its significance in contemporary theological discourse. [ABSTRACT FROM AUTHOR]

This study presents a bibliometric analysis of the literature on “space syntax.” Space syntax studies the relationship between spatial configurations and human behaviour. As the study of space syntax gains relevance in numerous fields, its bibliometric analysis becomes critical for understanding research trends, identifying influential publications, and flagging knowledge gaps. The Scopus database was searched using “title-abstract-keyword”, and a total of 2411 peer-reviewed articles were finally examined. Findings demonstrated the evolution of space syntax study over time, with a notable increase in publications in recent years. It provides an updated overview of how currently accessible papers on space syntax are distributed chronologically among journals, authors, nations, and institutions. The top 10 most cited publications on space syntax; the co-occurrence map of keywords; citation patterns of journals and articles; and the most cited journals are all shown in detail. The findings indicate that the number of publications on this subject increased dramatically between 2021 and 2022, with China being the most productive and prominent country in terms of the number of publications (n=712), followed by the United Kingdom (n=511). Citation analysis shows that the United Kingdom has the greatest number of citations (n=3948) followed by the United States (n=2089). According to keyword co-occurrence analysis, the highfrequency keywords include space syntax (n=1567), syntactic (n=1015), urban planning (n=289), spatial configuration (n=265), and spatial analysis (n=254). This study will give researchers a better grasp of the current use of space syntax and will act as a reference for researchers working on this subject to select their own future research goals. [ABSTRACT FROM AUTHOR]