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Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC 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.)

The rapid progress in conversational AI has given rise to advanced language models capable of generating human‐like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human‐like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting‐edge language models. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive literature review and real-world survey analysis examining the intricate relationship between communal media and psychological concerns within human–computer interaction (HCI). By systematically categorizing research from 1995 to 2023, we observe a significant increase in studies, reflecting the growing recognition of communal media's impact on psychological well-being. Our review delves into various HCI aspects, including demographic effects, emotional sentiment analysis, online social networking, and communal media use intensity. A real-world survey focusing on TikTok users reveals diverse user perspectives on safety, privacy, and the platform's impact on creativity and mental health. The data highlights a demographic predominance of young adults aged 16–24, with creativity, leisure, and content discovery being primary engagement motivators. Positive impacts such as creative inspiration and distraction from worries are contrasted with concerns over privacy invasion, harm to underaged users, and addiction. Correlation analysis underscores the complex interplay between user experiences and perceptions. Future research should prioritize enhancing privacy and safety measures, educational campaigns, and in-depth studies on the long-term psychological impacts of TikTok usage. Additionally, exploring platform modifications and conducting longitudinal and cross-platform studies will help create a safer and more engaging social media environment. This work contributes significantly to the expanding HCI body of knowledge, providing a robust foundation for future research to inform policies and interventions promoting positive psychological outcomes in the digital age. [ABSTRACT FROM AUTHOR]

Objectives. Research literature has not given much evaluation to why the proposed noise control actions in workplaces are not being implemented. There are views of the different personnel groups in the workplace and the reasons why the possibilities for solutions have not been examined. Methods. The companies under study were a welding machine shop, an assembly plant, a heavy steel company and an art glass factory. Noise management was approached by combining the perspectives of technology, acoustics and occupational safety with the examination of sociocultural factors at the workplace level. Results. Noise was not well represented in occupational safety practices. The productivity aspect was strongly emphasized. The use of personal protective equipment created the illusion that everything possible has been done and noise controlled. Conclusion. The study developed a model in which experts in the field of acoustics and sociocultural researchers together evaluated the state and development needs of noise abatement. The results of the project show that successful noise control solutions require a process involving all staff in the workplace. [ABSTRACT FROM AUTHOR]

Outdoor navigation often poses significant challenges for blind individuals due to the lack of accessibility features. Public accessibility systems often overlook the needs of the blind, leading to exclusionary experiences. Blind individuals frequently rely on alternative senses, such as creating mental maps, to navigate known spaces. Even commonplace activities like using transportation can be complex, requiring the blind to map known places with maneuvers made by the driver. Integrating a speech and gesture-based interface within accessibility frameworks is a crucial step towards inclusivity and improved user experiences. This approach offers alternative interaction methods beyond traditional graphical interfaces, empowering individuals with diverse abilities to navigate outdoor environments effortlessly. By implementing multi-modal feedback interfaces, our effort aims to create an intuitive interaction method that seamlessly provides navigation instructions, guiding users step-by-step along their routes. We prioritize minimizing cognitive load and architectural complexities by designing a notification system that complements the user's actions without causing distraction. [ABSTRACT FROM AUTHOR]

There is a correlative relationship between mediatization and social interaction. Communication scholars have paid attention to this relationship. We argue that mediatization involves the incorporation of various patterns of interaction among users of social media platforms. We used two theoretical approaches, namely mediatization and symbolic interaction. We surveyed a purposive sample that we selected according to certain criteria. Regression analysis revealed the correlative relationship between the dimensions of mediatization over social media platforms and the patterns of symbolic interaction among users. [ABSTRACT FROM AUTHOR]

Mobile robots have been incorporated into human society to help perform tasks that can affect or endanger health and life. One of the challenges is in the mobile robot-human interaction, as unexpected movements made by people can cause collisions with the robots when accomplishing a task. In this paper an optimized algorithm based on TD3 (Twin Deep Deterministic Policy gradient) used in Deep Reinforcement Learning is proposed, which allows the robot to take its own actions based on the observations it makes, without defining any trajectory beforehand. Using this algorithm that uses the actor-critical policy that helps to determine the linear and angular velocity of the robot, which allows the robot to move in unknown dynamic environments avoiding collisions. It is proposed to use a buffer that stores the values sent by the neural network and analyses them together with the odometry parameters of the robot to send the best decision to the robot to achieve a collision-free path and meet the objectives. The purpose of this algorithm is to meet as many consecutive targets as possible, i.e. the robot never returns to its initial position, it is assigned new targets regardless of the position it has reached. Finally, the results obtained by training the algorithm correspond nearly 0 for the actor and critic training, with a training of 12,000 episodes, and with an evaluation results 92 % of effectivity of our algorithm, based on 772 steps performed by the rob ten in a time of 11 s.

Background: There is an increasing interest in big data. However, challenges shape and affect the gathering, retrieval, use and management of big data in many organisations. Some of the challenges are linked to a lack of architecture that is specific to big data. Attempts have been made from both business and academic fronts, yet the challenges persist. The challenges are attributed to a lack of an understanding of the factors that influence the design of architecture for big data in an organisation. Objectives: The study aims to propose big data architecture for enterprises. Method: We employed the qualitative method, using document analysis to gather data. Activity theory (AT) was employed in the analysis of the data. Results: From the analysis, governance, interactions, relationships and allocative were found to be the factors that influence the design of big data architecture. An interpretation was conducted following the inductive reasoning approach to gain a deeper insight of how the factors manifest themselves. Conclusion: Big data architecture is proposed. The architecture is intended to address some of the challenges encountered in gathering, retrieving, using or managing big data in organisations. Contribution: This study advances our understanding of the complex interplay of factors influencing the architecture of big data. Applying AT, the study fortifies our understanding of complex interactions between humans and big data including the architecture design.

AbstractAs technologies evolve, businesses have focused on enhancing human interactions beyond simple satisfaction. This new research builds on recent exploratory work, responding to calls for testing antecedents and consequences of Smart Retail Technology (SRT) interactions. An online survey was administered to 338 respondents. In addition to structural equation modelling (SEM), fuzzy set Qualitative Comparative Analysis (fsQCA) was employed to identify value outcomes. Results demonstrate that the more individuals perceive a business’s technologies to be distinctive, the more likely they are to interact with those technologies. Respondents’ innovativeness was positively associated with their intentions to interact with SRT. The fsQCA outcomes offer an understanding of the types of individuals who perceive value through SRT interaction. Findings offer managerial implications and extend current knowledge about adopting interactive technologies. This is the first study to contribute a comprehensive SRT model that examines the inter-relationships between antecedents and consequences of interacting with SRT. [ABSTRACT FROM AUTHOR]

The paper studies the influence of human factor and clinical constraints specific to mechanical devices for medical purpose. environment (with a limited workspace, inside or outside the patient body). A medical robot is usually a complex system including mechanical structure, human-machine interface (HMI), electronic components, and software controller. All these components are integrated to perform various tasks for daily medical applications. Because any failure may become very critical, medical robots must function safely and with high reliability, so that designing medical robots for safe human interaction can be considered a real challenge. In order to respect the clinical constrains, a safety real-time controller has been designed. [ABSTRACT FROM AUTHOR]

This study combined the Human-centered design (HCD) approach with the immersive VR techniques to design and evaluate a new evacuation alarm system with improved warning efficiency for building emergency evacuation, considering the negative influence of pre-emergency activity. Four main HCD design activities were performed, the context-of-use, user requirements were defined through a theoretical perspective, technology-based solution research, expert group meeting, and focus group were used to produce the final concept. A smart phone-based evacuation alarm system concept, consisting of a control center and a smartphone-based user interface, featuring warning redundancy and multimodality was proposed. A developed virtual environment (VE) was used to evaluate and refine the final design in an iteration manner. This study widened the application of the HCD approach by using alternative simulator methods for evaluation, and added a step for the validation of VR as an adequate tool for designing and evaluating products. [ABSTRACT FROM AUTHOR]

The use of modern technologies by preschoolers is a complex and current topic. In an increasingly digitized society, children are exposed to electronic devices from an early age. Modern communication technologies have become an integral part of everyday life, profoundly influencing the way preschool children interact and learn. While technology can provide opportunities for learning and development, there are also concerns about its overuse, impact on social-emotional skills, and potential health risks. This paper examines the benefits and risks associated with early exposure to technology and proposes strategies, effective educational practices, and recommended methods to effectively manage technology use and to promote the healthy and balanced development of preschool children. [ABSTRACT FROM AUTHOR]

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research's sample size may restrict its findings' generalizability. Furthermore, the study's focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system's chatbots and AI features must be user-friendly, considering the customers' familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs' perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising. [ABSTRACT FROM AUTHOR]

Over the past several decades, research in the cognitive sciences has foregrounded the importance of active bodies and their continuous dependence on the changing environment, strengthening the relevance of dynamical models. These models have been steadily developed within the ecological psychology approach to cognition, which arguably contributes to the “ecological turn” we are witnessing today. The embodied and situated nature of cognition, regarded by some as a passing trend, is presently becoming a largely accepted assumption. In this paper, I claim that in light of these developments, ecological psychology, in alliance with related approaches, such as enactivism and interactivism, has the potential to deeply transform our perspectives on cognition and action, restoring their pertinence to humans as persons. However, an important challenge to the realization of this potential has to be noted: neither the mainstream information‐processing approach nor the dynamics‐oriented perspective on cognition provides an account of how the capacity of humans to use language and think “symbolically” can be derived from the continuous flow of agent−environment interaction. I will attempt to show that posing the “dynamical” and “computational” hypotheses about the nature of cognition as mutually exclusive approaches to cognition results in undesirable reductionism, which makes it difficult to meet this challenge. There are good reasons, advanced over half a century ago by, for example, Michael Polanyi or Howard Pattee, to think that we need complementary descriptions to understand cognizing systems, in order to grasp the fact that they are governed both by physical laws and by emergent historical constraints. Details of such a complementarity‐based approach still await elucidation, but some proposed solutions have the potential to ease the tension between the information‐processing and dynamical approaches to cognition and to lead to a better understanding of their interrelation. [ABSTRACT FROM AUTHOR]

Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) datasets. A deep belief network (DBN) model was then applied to integrate the segment-level spatial and temporal features. Deep fusion networks were jointly used to learn spatiotemporal features for discrimination purposes. Due to its generalization capabilities, we used a multi-class support vector machine classifier to classify the seven basic emotions in the proposed model. The proposed model exhibited 98.14% recognition performance for the JaFFE database, 95.29% for the KDEF database, and 98.86% for the RaFD database. It is shown that the proposed method is effective for all three databases, compared with the previous schemes for JAFFE, KDEF, and RaFD databases. [ABSTRACT FROM AUTHOR]

A few studies that investigated the application of robotics in restaurants explored benefits such as improved financial performance and quality control. The COVID-19 pandemic magnified the significance of this topic because robotics may save labor costs, replace unfilled positions, and improve sanitation through decreased human contact. However, previous research found that human interaction and other elements are important to restaurant customers. Built upon diffusion of innovation theory and expectancy-value theory, this study examined psychological, technological, and social factors that might affect diners’ intention to use service robots in quick-service restaurants (QSR). A sample of 615 self-reported survey responses was analyzed using regression models. The results indicate that perceived benefits, ease of use, social influence, and attitude toward service robots are positively associated with usage intention. While usage intention is lower for diners who value human interactions, it is not significantly influenced by diners’ novelty-seeking tendencies. The findings enhance the restaurant literature and provide important implications for QSR operators. [ABSTRACT FROM AUTHOR]

Customer experience has been a central component in hospitality management literature since it is pivotal to the success of the service industry. Customer experience facilitates service providers with an effective tool for competitive advantage, differentiation, and generating valuable customer relationships. Based on the customer experience concept and its demonstrated relevance, the study investigates the customer experience dimensions: physical interaction, human interaction, and digital interaction. The moderating role that hotel image has in the relationship between customer experience and brand loyalty is also explored in this study, along with the impact of customer experience and its aspects on brand loyalty. 360 customers/guests were surveyed via both online and offline questionnaires as part of this study using a purposive sampling technique. Using structural equation modelling, the proposed relationships were tested, which suggests that the dimensions of customer experience influence brand loyalty. The main dimension impacting brand loyalty is the human interaction dimension, followed by physical and digital interaction. These results contribute handsomely to experiential marketing literature by adding new insights into the existing body of knowledge and also building a further understanding of the customer experience management concept that assists in managerial decision-making. By emphasising their customers' experiences, this study adds to the knowledge already known about the interaction management approach in the hospitality sector. It makes suggestions for how to consider it when increasing brand loyalty. This study will be immensely useful for marketing practitioners and provide avenues for further research. [ABSTRACT FROM AUTHOR]

Working from vague client instructions, how do audio producers collaborate to diagnose what specifically is wrong with a piece of music, where the problem is and what to do about it? This paper presents a design ethnography that uncovers some of the ways in which two music producers co-ordinate their understanding of complex representations of pieces of music while working together in a studio. Our analysis shows that audio producers constantly make judgements based on audio and visual evidence while working with complex digital tools, which can lead to ambiguity in assessments of issues. We show how multimodal conduct guides the process of work and that complex media objects are integrated as elements of interaction by the music producers. The findings provide an understanding how people currently collaborate when producing audio, to support the design of better tools and systems for collaborative audio production in the future. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Polonorum seria Zootechnica is the property of West Pomeranian University of Technology 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 ZARCH: Journal of Interdisciplinary Studies in Architecture & Urbanism is the property of Universidad de Zaragoza 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 scoping review investigates the applications and implications of the conversation analysis approach in the Arab world. Specifically, it examines the implementation of conversation analysis by Arab linguists in both foreign language classrooms and social interaction settings. The review identifies gaps in the relevant literature to guide future research. This scoping review is underpinned by the five-stage framework developed by Arksey and O’Malley. The findings reveal that, compared to non-Arab contexts, the literature on this topic in the Arab world is scarce, but there is a growing interest among Arab linguists. The majority of studies reviewed were conducted in college-level English classes, and there is a dearth of studies in school contexts. The studies were categorized based on themes, including the implementation of conversation analysis, context, educational level, type of methodology, and the journal. The findings from interaction in foreign language classrooms were promising, as they provided insights into how learning opportunities might be developed to facilitate teachers’ professional training. Findings related to social interaction, although limited in number, revealed important features of human interaction, such as topic shifting, ending conversations, and the functions of religious markers. The paper concludes with a discussion of the challenges in implementing conversation analysis and calls for future research on this promising approach in the Arab world.

Graph Convolutional Networks (GCNs) have gained widespread adoption in modeling human skeleton sequences for two-person interaction recognition. Most GCN-based models achieve state-of-the-art results by leveraging either intra-body or inter-body connections. However, using only intra-body relations may ignore important interactive features between two individuals, whereas relying on inter-body relations may weaken the specific motion dynamics of each skeleton. To address these shortcomings, we propose a Distinct Motion-Preserving GCN (DMP-GCN) that utilizes intra-body and inter-body graphs to extract interactive features from two human bodies while preserving the distinct motion characteristics of each skeleton. Specifically, two motion-specific streams are adopted to capture specific motion features of each human skeleton and an interactive stream is applied to model the interactive dynamics of two bodies. In addition, we introduce a new graph labeling strategy, namely Distance Variation Labeling, which is a data-driven approach for defining the edge strength in the skeleton graph. Extensive experiments show that our proposed approaches outperform state-of-the-art methods on two large-scale human interaction datasets, NTU RGB+D (mutual) and NTU RGB+D 120 (mutual).

Due to the lack of accessibility features, the process by which a blind person navigates in space is sometimes regarded as difficult. A large part of the accessibility systems, for the public, are not designed to consider the usage of the blind, which can lead to exclusion. The majority of them must rely on other senses for space location awareness, an example that is followed frequently is the creation of mental maps for certain already-known places. Using the means of transportation, which is a necessary activity for a blind person but uncomplicated for a sighted person, may require studying every move the driver makes in order to accurately determine the present path. Crowdsourcing systems offer the potential to develop a social network through which some of the issues faced by blind people may be resolved. Over time, this leads to the development of a community that fully comprehends the issues and requirements that visually impaired people (VIPs) encounter on a daily basis. With the use of multi-modal feedback interfaces, this effort intends to demonstrate a collaborative system that pairs volunteers with blind persons and guides them along a route. We address the issue of minimizing the cognitive load in addition to the architecture-related issues of such a system by designing a notification system that does not distract the user from their current action. [ABSTRACT FROM AUTHOR]

We see the world as continuous with smooth movements ofobjects and people, even though visual inputs can consist ofstationary frames. The perceptual construction of smoothmovements depends not only on low-level spatiotemporalfeatures but also high-level knowledge. Here, we examined therole of causality in guiding perceptual interpolation of motion inthe observation of human actions. We recorded videos of naturalhuman-object interactions. Frame rate was manipulated to yieldshort and long stimulus-onset-asynchrony (SOA) displays for ashort clip in which a catcher prepared to receive a ball. Thefacing direction of the catcher was either maintained intact togenerate a meaningful interaction consistent with causality, orwas transformed by a mirror reflection to create a non-causalsituation lacking a meaningful interaction. Across threeexperiments, participants were asked to judge whether thecatcher’s action showed smooth movements or sudden changes.Participants were more likely to judge the catcher’s actions to becontinuous in the causal condition than in the non-causalcondition, even with long SOA displays. This causalinterpolation effect was robust to manipulations of bodyorientation (i.e. upright versus inverted). These findings indicatethat causality in human actions guides interpolation of bodymovements, thereby completing the history of an observedaction despite gaps in the sensory information. Hence, causalknowledge not only makes us see the future, but also fills ininformation about recent history.

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first-person vision (FPV) or third-person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely FT-HID dataset. FT-HID contains pair-aligned samples of first-person and third-person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first-person and third-person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) and machine learning (ML) is increasingly becoming prevalent in libraries and information centres, promising to improve service delivery and efficiency. However, the use of these technologies also poses significant ethical challenges and risks, including bias and discrimination, privacy and security, automation and job displacement, and the lack of human interaction in service delivery. This conceptual paper provides an overview of the key ethical frameworks and principles relevant to the use of AI and ML in libraries and information centres, and analyses how these frameworks can be applied to this specific context. The paper also discusses the potential benefits and risks of these technologies, and provides best practices and strategies for mitigating the ethical risks associated with their use. Finally, the paper highlights the implications of these findings for libraries and information centres, and recommends future research and practice in this area, including on-going evaluation and monitoring of the ethical implications of AI and ML. Overall, this paper underscores the importance of taking a proactive and ethical approach to the use of AI and ML in libraries and information centres to ensure that these technologies are used in a way that aligns with their mission and values, and serves the best interests of their users and society as a whole. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic took everyone by surprise and changes were needed in the life of humans in order to continue living in a safe environment. The systems had to adapt, innovate and evolve. And, as expected, there were lots of problems that occurred with the changes. Taking a closer look at the problems distance learning faced when the switch to an online environment happened it was clear that some improvements were required. This paper proposed the identification of methods that work for streamers in keeping online viewers engaged and that could easily be borrowed by teachers. Results showed that engagement methods like building a community, fostering interactions, the usage of an avatar, or defining commands, can be easily borrowed and help educators to captivate the pupils and transform them into active participants, increasing the attention spam rate and the rate of success of online lessons. [ABSTRACT FROM AUTHOR]

Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) datasets. A deep belief network (DBN) model was then applied to integrate the segment-level spatial and temporal features. Deep fusion networks were jointly used to learn spatiotemporal features for discrimination purposes. Due to its generalization capabilities, we used a multi-class support vector machine classifier to classify the seven basic emotions in the proposed model. The proposed model exhibited 98.14% recognition performance for the JaFFE database, 95.29% for the KDEF database, and 98.86% for the RaFD database. It is shown that the proposed method is effective for all three databases, compared with the previous schemes for JAFFE, KDEF, and RaFD databases.