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The CHIIR 2023 conference was conducted as a hybrid event in Austin, TX, USA, attracting over 110 attendees both physically and virtually. The main conference program spanned over three days and comprised of single-track presentations. Two workshops were organized on the day prior to the main conference, while the doctoral consortium and tutorial took place one day after the main event. This report has been prepared by the conference General Co-Chairs. Date: 19--23 March 2023. Website: https://sigir.org/chiir2023/. [ABSTRACT FROM AUTHOR]

A recent report from Heidelberg University explores the implications of advances in artificial intelligence (AI) on human interaction with AI systems and robots. The study raises questions about the possibility of communicating with and understanding artificial agents, as well as whether they should be ascribed subjectivity and quasi-personal status. The research argues that understanding others is based on the assumption of their subjectivity, which allows for shared feelings and intentions. The paper also discusses the potential consequences of blurring the distinction between simulated and real encounters, particularly in the context of virtual psychotherapy. The researchers advocate for a mindful approach to language and caution against pretending that artificial agents possess subjectivity. [Extracted from the article]

Purpose: The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results. Design/methodology/approach: This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of "mutual judgment" among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function. Findings: This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM. Originality/value: Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the "mutual judgment" process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM's weight in SNGDM. [ABSTRACT FROM AUTHOR]

The article offers information on the findings of a study on wasps concerning evolution of brains. Biologists at the University of Washington observed the behavior of paper wasps and then measured the size of their brains. The researchers found that the so-called mushroom bodies, the lobes that underlie learning and memory in insects, were larger in dominant wasps than in their subordinate peers. The study suggests that complex social interaction drove the evolution of large brains in human.

This paper looks critically at how design evolves through an interaction between human and nonhuman relations. We introduce different ways of understanding this by considering the mediating role that affect has on the design process. To do this we explore a recent work of New Zealand architect Simon Twose through an ethnographical framework. By focusing on affect we highlight the affective capacities and connections between humans and nonhumans. We argue that the affective capacities of non-human objects, matter and spaces are fundamental to the design process, and how knowledge is produced through design. Thus, this paper questions the privileging of human subjectivity - of seeing humans as radically other to matter, where human life remains special and spirited, over the brute force of matter. [ABSTRACT FROM AUTHOR]

Integrating Deep Neural Networks (DNNs) into the Internet of Thing (IoT) devices could result in the emergence of complex sensing and recognition tasks that support a new era of human interactions with surrounding environments. However, DNNs are power-hungry, performing billions of computations in terms of one inference. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures such as systolic arrays. Energy-efficient DNN accelerators skip bit-wise or word-wise sparsity in the input feature maps (ifmaps) and filter weights which means ineffectual computations are skipped. However, there is still room for pruning the effectual computations without reducing the accuracy of DNNs. In this paper, we propose a novel real-time architecture and dataflow by decomposing multiplications down to the bit level and pruning identical computations in spatial designs while running benchmark networks. The proposed architecture prunes identical computations by identifying identical bit values available in both ifmaps and filter weights without changing the accuracy of benchmark networks. When compared to the reference design, our proposed design achieves an average per layer speedup of ×1.4 and an energy efficiency of × 1.21 per inference while maintaining the accuracy of benchmark networks. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Arbeitswissenschaft is the property of Springer Nature 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.)

Purpose: Consumers are inclined to join longer queues due to social interactions in service consumptions. This purchase behavior brings in operational challenges in terms of capacity planning, which affects consumers' demand, leading to an unstable and fluctuated arrival process. This paper aims to investigate the dynamic characteristics of the arrival process of a service system with boundedly rational consumers whose purchase decisions are influenced by the queue length under social interactions. Design/methodology/approach: Consumers' bounded rationality is modeled based on the random utility theory. Due to social interactions, the equilibrium queue length and its interaction with the expected waiting time affect consumers' value perception. The authors first analyze the optimal service capacity decision with or without considering the influence of social interactions in a static setting. They then focus on the dynamic characteristics of the arrival process by a one-dimensional dynamical model in terms of the arrival rate. Findings: This paper finds that the service system can behave chaotic in terms of arrival rate dynamics under social interactions. The results highlight the dynamical complexity of a simple service system due to consumers' behavioral factors and the influence of social interactions, which may be the critical drivers leading to fluctuated and uneven demand. Originality/value: The findings demonstrate that due to consumers' limited cognitive ability and the influence of social interactions, the demand to a service system can be stable, periodic or even chaotic in terms of the arrival process. This study provides an alternative explanation to the observed demand fluctuations in various service processes under the influence of social interactions, which is important for service providers to effectively manage service capacity to achieve a stable service process and improve operational efficiency. [ABSTRACT FROM AUTHOR]

Recent advancements in generative artificial intelligence (AI) have raised concerns about the potential for AI to outperform humans in creative tasks and make knowledge workers redundant. In a new paper, researchers from Aarhus University and international collaborators argue for a focus on co-creativity, the interaction between humans and machines, to develop a "human-centered AI" and "hybrid intelligence." They emphasize the need to balance predictive accuracy with theoretical understanding and to involve multiple disciplines in understanding and developing human-AI co-creative systems. The researchers also highlight the ethical and legal considerations, as well as the implications for the future of work. [Extracted from the article]

The First Early Career Researchers Roundtable for Information Access Research Workshop, in conjunction with the Seventh ACM Conference on Human Information Interaction and Retrieval (CHIIR) 2022, looked into the future of research, collaborations, and self-development to ask the following. Where are the opportunities for researchers in a (post-)pandemic environment, especially for Early Career Researchers (ECRs)? What do we need to do to get there? Which practical implementations can the broader CHIIR community support? The workshop started with an invited talk. Instead of conventional paper presentations, the attendees discussed the lessons learned from working in a pandemic. This report, co-authored by the workshop's organisers and its participants, summarises the discussion. This report aims to provide the broader CHIIR community with feedback on the workshop and foster ideas raised by ECRs to support ECRs. Two primary outcomes are (i) ECRs are often enthusiastic about taking on roles within a community, but formal validation and recognition are needed for their efforts and (ii) that the role of a conference needs to be reevaluated optimising the benefits of attending the event. Date: 14 March 2022. Website: https://sites.google.com/view/ecrs4ir/home. [ABSTRACT FROM AUTHOR]

Researchers from Institut Teknologi Bandung have conducted a study on epidemiology, specifically focusing on the impact of virus mutations and the spread of disease. They developed a computational model that incorporates a synthesized human interaction network and an epidemiological model to analyze the epidemic process with three variants. The study provides computational results from various scenarios, highlighting the importance of considering social structure in understanding disease spread. The research article is available in Scientific Reports, and more information can be obtained from the Department of Mathematics at Institut Teknologi Bandung. [Extracted from the article]

Purpose: As users often lack the motivation to contribute their ideas and knowledge in open innovation communities, it is necessary to identify the determinants of users' contribution. This paper aims to examine users' contribution in open innovation communities based on the social capital theory. Design/methodology/approach: The authors collected 474 valid responses from a survey and adopted structural equation modeling (SEM) to conduct data analysis. Findings: The results indicated that social interaction, which includes informational and emotional interaction, has a significant effect on social capital, which in turn affects users' contribution. Research limitations/implications: The results imply that companies need to facilitate users' interactions and develop social capital to promote their contribution in open innovation communities. Originality/value: Although previous research has found the effect of individual motivations such as perceived benefits and behavioural control on innovation community users' behaviour, it has seldom considered the effect of social capital embedded within the social relationship networking. This research tries to fill the gap and the results disclosed the mechanism underlying open innovation community users' contribution. [ABSTRACT FROM AUTHOR]

This paper argues that the Belt and Road Initiative (BRI) as a de-bordering mechanism makes a positive contribution to border security. Three central concepts—de-bordering, re-bordering and co-bordering—are used to examine the future of borders, and further develop the notion of the "cycle" of borders. This research adopts a political science perspective and combines the theory of borders with the Copenhagen School and Paris School of security, bearing in mind the BRI as an access strategy. Furthermore, the BRI elements of connectivity are central to de-bordering. Finally, this paper maintains that the BRI makes different contributions to border security, that borders are "alive", and that they are the result of cooperative or antagonistic human interactions in which asymmetry in perception is the leading cause of conflict. [ABSTRACT FROM AUTHOR]

This paper attempts to build up the sociological groundwork for theoretically addressing a critical theme of our time: human togetherness and global governance. It synergizes most major sociological theories by digging into their underlying frame of thinking and piecing together their seemingly different, even opposing arguments. It therefore highlights the essence of human togetherness as connection of social forces based on the three pillars, the communal binding, the coercive mastership, and the interdependent partnership. To achieve a better human togetherness, we need to exercise human wisdom to maintain and improve the three pillars. It might be the theoretical foundation for sensible solutions to the practical issue of global governance. [ABSTRACT FROM AUTHOR]

The popularity bias is an outstanding challenge in recommendation systems. Prevalent work based on contrastive learning (CL) alleviates this issue but neglects the relationship among data, which limits the ability of CL and leads to a loss of personalized features of users/items, and thus degrades the performance of the recommendation systems. In this paper, we propose a model-agnostic contrastive learning framework, called Relationship-aware Contrastive Learning (ReACL), to make recommendations to users. ReACL framework utilizes the relationship homophily among data to achieve a uniform distribution for node and align the relevant features to preserve personalized features and avoids the problem of popularity bias. We first combine a graph of user-item interaction with that of a social network to mine views on the relationships of users on social networks as well as those of item commonality. We then apply these two views to construct contrastive learning pairs on the user and item sides, respectively. We design two sample selectors and an augmented contrastive loss function to preserve personalized features. Furthermore, we jointly optimize the tasks of supervised learning and contrastive learning. Finally, we conduct extensive experiments on four public datasets, and the results show that the proposed ReACL framework outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• An adaptive consensus model based on feedback mechanism and social interaction is proposed to improve the consensus efficiency. • The stubbornness of decision makers to the initial opinions is considered in the consensus model. • The evolution of weights and self-confidence degrees of decision makers is integrated into the consensus model. • Social interaction may have a double-sided impact on the consensus reaching. Many consensus models in social network group decision making (SNGDM) have been reported to obtain a collective solution despite the initial opinions of decision makers (DMs) may be different. However, these models ignore the obstinacy of DMs to their initial opinions, which violates the sociological research results. Aiming at the consensus reaching of SNGDM where DMs are stubborn to their initial opinions, this paper proposes a novel consensus model based on the passive adjustment based on feedback mechanism (PA-FM) and active adjustment based on social interaction (AA-SI), which adaptively adopts AA-SI or PA-FM in each round according to the opinion distribution of DMs. Specially, the proposed consensus model assimilates the advantage of PA-FM and AA-SI, where adjustment intensity of DMs affects the consensus level of PA-FM, and stubbornness degree of DMs affects that of AA-SI. To elucidate the performance and advantages of the proposed consensus model, a hypothetical application and three simulation analyses are constructed. The results show that (1) In the decision-making group that is stubborn to the initial opinions, social interaction has a beneficial or harmful effect on the consensus reaching, depending on the stubbornness degree and opinion distribution of DMs; and (2) Given any stubbornness degree and adjustment intensity, the proposed consensus model outperforms existing consensus methods in consensus efficiency and success rate. [ABSTRACT FROM AUTHOR]

When only primaries or some subset of primary year groups are back, results can be sensiti ve to assumpti ons on adult mixing, whether more like lockdown 2 or lockdown 3 as schools return. Utm medium=email&utm campaign=govuk-noti ficati ons&utm source=049e7d84-2ca2-4c4 c-89e0-e46b713aac09&utm content=immediately T his brief paper from the JUNIPER consorti um for SAGE drew on the paper The Effect of School Opening or Closure on Social Contacts in England, from the CoMix social contact survey, Report for survey week 43b- Updated" - Munday et al., CMMID COVID- 19 Working Group, LSHTM. Impact of parti al school openings Impact of Parti al School Openings, (paper S1110), by Julia Gog, Mike Tildesley, Jon Read, Edwin van Leeuwen, JUNIPER (the Joint Universiti es Pandemic and Epidemiological Research) consorti um, Wednesday 10 February 2021, presented to the Scienti fic Advisory Group for Emergencies (SAGE) at its 80th meeti ng on COVID-19 on Thursday 11 February 2021. [Extracted from the article]

Purpose: The purpose of this paper is to examine the effect of flow experience on users' social commerce intention. Design/methodology/approach: Based on the 287 valid responses collected from a survey, structural equation modeling was used to examine the research model. Findings: The results indicated that social interaction, which includes human–computer interaction and human–human interaction, has a significant effect on the flow experience, which in turn affects social purchase and social sharing intention. Research limitations/implications: The results imply that companies need to facilitate social interaction to improve users' experience and promote their social commerce intention. Originality/value: Although prior research has examined social commerce user behaviour from multiple perspectives such as trust, perceived value and technological perceptions, it has focused on the effect of cognitive beliefs and neglected the effect of affective beliefs such as flow experience. This research tries to fill the gap. [ABSTRACT FROM AUTHOR]

Purpose Most existing recommendation systems or technologies are functional-oriented. Recommending services, nevertheless, requires the consideration of service experiences perceived by customers being individually unique and emphasizing the emotional experiences and the co-created value-in-use. This paper aims to present a new recommender system to capture customer emotional needs and address social interactions among service stakeholders.Design/methodology/approach This paper presents a color imagery-based recommender system (CIRS) capable of capturing customer emotional needs and addressing social interactions among service stakeholders that can collectively co-create the individual value-in-use and beneficial outcomes for customers. Based on the Color Image Scale, the recommender system uses the color imagery format as the uniform representation of customers’ psychological expectations, service providers and the service system, to facilitate the scoring and ranking of recommendations.Findings This study uses an application context of destination tourism to demonstrate and justify the recommender system’s attempted contributions preliminarily. That is, CIRS can recommend destinations and tour services that meet tourists’ emotional needs with a satisfactory precision of 70 per cent. CIRS can also make stakeholders’ image models evolve over time considering the dynamic interactions among stakeholders. CIRS can also help lesser-known tourism destinations be discovered by tourists who can be emotionally satisfied.Originality/value CIRS uses the color imagery as the uniform representation for customers’ expectations, service providers (e.g. small and medium enterprises) and service system (e.g. tourism destinations), considering the continued interactions among the service stakeholders that collectively co-create the individual value-in-use and beneficial outcomes for each customer. [ABSTRACT FROM AUTHOR]

Electronics, Machine Learning, Robotics and Artificial Intelligence, Technology Keywords: Electronics; Machine Learning; Robotics and Artificial Intelligence; Technology EN Electronics Machine Learning Robotics and Artificial Intelligence Technology 309 309 1 03/24/23 20230302 NES 230302 2023 MAR 2 (NewsRx) -- By a News Reporter-Staff News Editor at Blood Weekly -- Fresh data on robotics and artificial intelligence are presented in a new report. [Extracted from the article]

Copyright of Zeitschrift für Arbeitswissenschaft is the property of Springer Nature 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.)

In this paper, we consider a network scenario in which agents can evaluate each other according to a score graph that models some physical or social interaction. The goal is to design a distributed protocol, run by the agents, allowing them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We prove that each agent can learn its state by combining a local Bayesian classifier with a (centralized) Maximum Likelihood (ML) estimator of the parameter–hyperparameter. To overcome the intractability of the ML problem, we provide two relaxed probabilistic models that lead to distributed estimation schemes with affordable complexity. In order to highlight the appropriateness of the proposed relaxations, we demonstrate the distributed estimators on a machine-to-machine testing setup for anomaly detection and on a social interaction setup for user profiling. [ABSTRACT FROM AUTHOR]

The article presents a research paper on the influence of maternal odor on infants' response to strangers, which references a research paper by Yaara Endevelt-Shapira et al., published in an issue of "Science Advances". Topics discussed include the method used in the study, findings on the effect of maternal odor on infants' brainwaves, and the implication of the study for research on infants' social behavior.

Recent vision Transformer has been applied to human pose estimation and has achieved excellent performance by two-order spatial interaction with self-attention. However, it is still unclear whether higher-order spatial interaction can facilitate pose estimation. In this paper, we propose a novel approach based on multi-order spatial interactions and confirm that the combination of different orders is beneficial for human pose estimation task. We first build a Triple Interaction Module (TIM) by pure convolutions to make spatial information interactions three times. In contrast to Transformer, the TIM is compatible with several pure convolutions and extends two-order interaction in Transformer to triple-order without extensive additional computation, which makes it easier to explore inter-related features between keypoints in the human body. In addition, we combine TIM with traditional CNN and Transformer to form Multi-order Spatial Interaction Network (MSIN). This paper takes advantage of MSIN to extract keypoint heatmaps and certifies that the order-by-order structure can enhance the overall performance of locating human keypoints. Experimental results demonstrate that MSIN performs favorably against the most state-of-the-art CNN-based and Transformer-based counterparts on the COCO and MPII datasets, while being more lightweight. • The Triple Interaction Module can be obtained by pure convolutions to make spatial information interactions three times. • Combining the Triple Interaction Module with traditional CNN and Transformer can improve the performance of locating keypoints. • The Triple Interaction Module can effectively better to explore inter-related features between keypoints in the human body. • The order-by-order structure can enhance the overall performance of locating human keypoints. [ABSTRACT FROM AUTHOR]

• A novel dynamic social interaction modeling mechanism based on structure entropy minimization. • A separate scene node to model the motion patterns and behavioral preferences. • A multi-time-scale motion tendency modeling module. • State-of-the-art results on the ETH, UCY and SDD datasets. Trajectory is an important basis for reflecting the behavior of moving agents and can be used for various applications. Autonomous systems navigating in complex scenes should have the ability to predict the future locations of surrounding agents and avoid collisions. Agents in a scene interact with each other constantly, which is the greatest challenge of trajectory prediction. However, existing methods have problems with insufficient, superfluous or inaccurate interactions, whether they are distance-, attention- or dense graph-based interactive mechanisms. Moreover, the dynamic complexity and validity of social interactions cannot be quantified. In this paper, we propose a dynamic social interaction modeling mechanism based on structure entropy minimization. Specifically, structure entropy minimization provides a principle for detecting and quantifying the natural or true interactions between agents. Additionally, considering that most agents under the same scenario often have similar behavioral preferences, we introduce scene features to model the interscene variance and intrascene consistency of motion patterns. Finally, through multi-time-scale motion tendency modeling, we can simultaneously model short-term and long-term intentions to alleviate accumulated errors for pedestrians avoiding collisions. We evaluate our proposed method on three trajectory datasets, and the experiments and comparisons demonstrate that our method outperforms comparative state-of-the-art methods while demonstrating the capacity to model social interaction and motion patterns. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to show how behavioral descriptions in psychology and group dynamics can be related to four goal-setting processes and to four mode of existence. Design/methodology/approach – Some person A can approach a person B with an inclination to realize one of four goal-setting processes: (1) A sets goals for B; (2) A sets no own goals; (3) A pursues own goals alone; (4) A and B develop mutual goals. Depending on their choice of inclinations an interaction of A and B can lead to four modes of coexistence: (1) Conflict – A and B fight; (2) Hierarchy – A submits to B; (3) Independence in niches – A and B do not interact; (4) Cooperation – A and B work together. The paper investigates how these theoretical options – four inclinations for different goal-set processes and four modes of coexistence – show in behavioral descriptions in psychology and group dynamics. Findings – Psychic states studied in psychology (e.g. by Freud, Berne, Horney) can be related to one of the four inclinations. Interaction patterns studied in group dynamics (e.g. by Steiner, Schindler, Bion) describe aspects of the four modes of coexistence. Practical implications – Behavioral descriptions of various schools of psychology and group dynamics can be classified according to theoretically derivable basic options of goal-orientated behavior. Originality/value – The paper shows the application of a theoretical framework covering all options of goal-orientated behavior available in the behavioral sciences. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to develop some 2-tuple linguistic aggregation operators based on Muirhead mean (MM), which is combined with multiple attribute group decision making (MAGDM) and applied the proposed MAGDM model for supplier selection under 2-tuple linguistic environment. Design/methodology/approach – The supplier selection problem can be regarded as a typical MAGDM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MAGDM problems with 2-tuple linguistic information based on traditional MM operator. The MM operator is a well-known mean type aggregation operator, which has some particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MM operator is that it can capture the whole interrelationship among the multi-input arguments. Motivated by this idea, in this paper, the authors develop the 2-tuple linguistic Muirhead mean (2TLMM) operator and the 2-tuple linguistic dual Muirhead mean (2TLDMM) operator for aggregating the 2-tuple linguistic information, respectively. Some desirable properties and special cases are discussed in detail. Based on which, two approaches to deal with MAGDM problems under 2-tuple linguistic information environment are developed. Finally, a numerical example concerns the supplier selection problem is provided to illustrate the effectiveness and feasibility of the proposed methods. Findings – The results show that the proposed can solve the MAGDM problems within the context of 2-tuple linguistic information, in which the attributes are existing interaction phenomenon. Some 2-tuple aggregation operators based on MM have been developed. A case study of supplier selection is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the linguistic decision information in which the attributes are not independent so as to select the most suitable supplier. Practical implications – The proposed methods can solve the 2-tuple linguistic MAGDM problem, in which the interactions exist among the attributes. Therefore, it can be used to supplier selection problems and other similar management decision problems. Originality/value – The paper develop some 2-tuple aggregation operators based on MM, and further present two methods based on the proposed operators for solving MAGDM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to show how individual acts of goal-setting of two persons or systems A and B determine, which modes of coexistence become possible in an interaction of the two. Design/methodology/approach – Some person or system A can approach another person or system B with an inclination to realize one of four goal-setting processes: first, A sets goals for B; second, A sets no own goals; third, A pursues own goals alone; and fourth, A and B develop mutual goals. And an interaction of A and B can lead to just four modes of coexistence: first, conflict – A and B fight; second, hierarchy – A submits to B; third, independence in niches – A and B do not interact; and fourth, cooperation – A and B work together. Findings – Placing the inclinations of A and B to realize one of the four goal-setting processes in a 4×4 matrix leads to the interaction matrix. It shows that individual goal-setting processes predetermine and limit the available modes of coexistence, i.e. cause certain patterns of interactions. Practical implications – The interaction matrix can be applied to all interactions between persons, groups and social units generally. Originality/value – The paper introduces a theoretical framework covering all options of goal-orientated behavior. It explains the interrelation between individual goal-setting of persons and systems and the resulting behavioral options in interactions. It is applicable to all behavioral sciences. [ABSTRACT FROM AUTHOR]

Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents. [ABSTRACT FROM PUBLISHER]

As a powerful tool for exploring non-pairwise interactions, higher-order networks deepen our understanding of how cooperation emerges in complex systems. However, nearly all researches concerning the cooperative evolution on higher-order networks have neglected the prevalence of nested structures in real-world interactions — for instance, students are members of classes, which are components of grades, and these grades, in turn, are part of schools and so on. In this paper, we propose a general and scalable method to investigate the influence of nested structures in higher-order interactions on group cooperation. Specifically, by adjusting the proportion of lower-order nested relations (e.g., pairwise links) within higher-order structures, we find that higher-order interactions are more favorable to the cooperation if they comprise a rich nested dyadic structure. In addition, for the first time, we introduce higher-order network motifs to examine the contributions of various nested structures to cooperation. It is interesting to observe that the more uniform the nested structure, the more it promotes the cooperation. Our study offers a novel perspective to examine the evolution of cooperative behaviors in more complex realistic systems. • We propose a general method to study the effect of nested structures on cooperation. • We find that nested structures of higher-order interactions can promote cooperation. • We add higher-order motifs to study how various nested structures affect cooperation. • We reveal that the more uniform the nested structure, the more it favors cooperation. [ABSTRACT FROM AUTHOR]

It has a long tradition to study trust behavior among humans or artificial agents by investigating the trust game. Although previous studies based on evolutionary game theory have revealed that trust and trustworthiness can be promoted if network structure or reputation is considered, they often assume that interactions among agents are one-shot and investors do not consider the investment environment before making decisions, which collide with many realistic situations. In this paper, we introduce the conditional investment strategy into the repeated N -player trust game, in which conditional investors decide to invest or not depending on their assessment of the trustworthiness level of the group. By using the approach of the Markov decision process, we study the evolutionary dynamics of trust in repeated group interactions with the conditional investment strategy. We find that conditional investors can form an effective alliance with trustworthy trustees, hence they can sweep out untrustworthy trustees. Moreover, we verify that such alliance can exist in a wide range of model parameters. These results may explain why trusting in others and reciprocating them with trustworthy actions can be sustained in game interactions among intelligent agents. [ABSTRACT FROM AUTHOR]

The field of international business (IB) largely focuses on the inter-country institutional differences, however, very little focus and attention have been given to the social interactions of sub-cultural groups within the country. Could we be overlooking greater opportunities for understanding differences across the diversity within countries? This article aims to provide insights and practical suggestions on: (1) How does leveraging intra-country diversity affect firm performance outcomes globally? (2) Why understanding intra-country diversity is important to the field of IB? and (3) How can IB scholars, managers, and educators engage further as a discipline to explore strategies that achieve more diverse and equitable societal and economic outcomes? [ABSTRACT FROM AUTHOR]

Washington is beyond doubt a difficult newspaper field. Unlike the other great capitals, it has been merely the seat of the national government and not until comparatively recently has it even taken on the outward aspects of an impressive residential city. A dozen years ago its fashionables never shopped except in New York or Baltimore, today its shopping district impresses. Only within the last twenty years has it been discovered as a delightful winter resort for retired civilians or the idle rich who are beginning to play such a part in its social life.

Keywords: Behavioral Science; Science; Social Media EN Behavioral Science Science Social Media 981 981 1 09/19/23 20230922 NES 230922 2023 SEP 24 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Fresh data on behavioral science are presented in a new report. Our news editors obtained a quote from the research from University of Padova: "This paper illustrates research aimed at describing and measuring human interactions in the Veneto community and its changing during the COVID-19 pandemic. [Extracted from the article]

In social networks, decision makers of group decision-making (GDM) can have interactive behaviors with different individual relationships. With large-scale group participation, competitive behaviors resulted from competition relationship of the decision makers represent one of the important influences. In this case, the effects of competitive behaviors also have an impact on consensus reaching process (CRP) of GDM. Thus, considering the individual competition behaviors in large-scale group social networks, this paper deeply investigates large-scale group consensus decision with increased complexity of GDM in the framework of three-way decision (TWD). More specifically, we firstly improve the K-L algorithm by considering the frequent connections between leaders and followers as well as few connection in own sub-networks under the condition of large-scale group social networks and partition networks for classifying the social roles of leaders and followers. Then, we design the identification rules of competition relationship with consideration of social roles and interest conflict. With respect to the competition relationship, referring to the decision styles of the dependent, biased and rational decision makers, this paper further explores the corresponding competitive behaviors in information interaction of social networks. According to the competitive behaviors, we construct a new optimization consensus model in CRP for the evaluation adjustment of large-scale group TWD and obtain the decision result with Bayesian decision procedure. Finally, we apply our proposed model to analyze an example of product promotion and verify its effectiveness. [ABSTRACT FROM AUTHOR]

Okinawa Institute of Science and Technology (OIST) Graduate University, Science And Technology, Technology Keywords: Okinawa Institute of Science and Technology (OIST) Graduate University; Science And Technology; Technology EN Okinawa Institute of Science and Technology (OIST) Graduate University Science And Technology Technology 1999 1999 1 09/04/23 20230908 NES 230908 2023 SEP 8 (NewsRx) -- By a News Reporter-Staff News Editor at Respiratory Therapeutics Week -- At first glance, the term "digital hug" might seem paradoxical. -- Written by Megha Kalra Keywords for this news article include: Okinawa Institute of Science and Technology (OIST) Graduate University. [Extracted from the article]

Feature importance and interaction are among the main issues in explainable artificial intelligence or interpretable machine learning. To measure feature importance and interaction, several methods, such as H-statistic and partial dependency, have been proposed. However, it is difficult to understand the practical implications of importance and interaction. In this paper, a new method for measuring feature importance and interaction is proposed. For the classification model, we observed correctly predicted cases in a predictive model and grouped them according to the characteristics of the cases. We derived a method for feature importance and interaction from group information. For the regression model, we grouped cases according to the change in the size of the prediction error. The proposed method supports the same rationale for feature importance and interaction. It also supports the decomposition of feature importance to feature power and feature interactions. To implement the proposed method, three visualization tools, including a feature interaction graph, are implemented. Through the proposed work, we can better understand the working mechanism of a predictive model. [ABSTRACT FROM AUTHOR]

• We propose a SocialLGN system based on GCN for social recommendation problems. • We design a customized graph fusion component to fuse the user representations. • Empirical experiments verify the effectiveness of the proposed system. Graph Neural Networks have been applied in recommender systems to learn the representation of users and items from a user-item graph. In the state-of-the-art, there are two major challenges in applying Graph Neural Networks to social recommendation: (i) how to accurately learn the representation of users and items from the user-item interaction graph and social graph, and (ii) based on the fact that each user is represented simultaneously by the two graphs, how to integrate the user representations learned from these two graphs. Aiming at addressing these challenges, this paper proposes a new social recommendation system called SocialLGN. In SocialLGN, the representation of each user and item is propagated in the user-item interaction graph with light graph convolutional layers; in the meantime, the user's representation is propagated in the social graph. Based on this, a graph fusion operation is designed to aggregate user representations during propagation. The weighted sum is applied to combine the representations learned by each layer. Comprehensive experiments are conducted on two real-world datasets, and the result shows that the proposed SocialLGN outperforms the SOTA method, especially in handling the cold-start problem. Our PyTorch implemented model is available via https://github.com/leo0481/SocialLGN. [ABSTRACT FROM AUTHOR]

The article highlights the demand of Academy of Medical Sciences to prepare a paper for the Government Office of Science on preparing for winter challenges due to COVID-19 pandemic as it mandated schools and business to shut down. It focuses on improving of health service and social care along with the estimate of the rising reproduction rate of SARS-CoV-2; also focuses on plans as developing effective policies for social interaction and social distancing.

Recently, friend recommendation has gained widespread popularity in location-based social networks (LBSNs), which provides more opportunities for users to forge new friendships. Most existing studies exploit user trajectories or check-ins of Point-Of-Interests (POIs) to predict friendships based on geographic homophily. However, the dynamics of social relationships are left insufficiently considered in modeling user preferences. In this paper, we explore how geographical and social preferences influence each other in a dynamic manner. Specifically, we propose a Meta-path aware Dynamic Graph with Subgraph Inference, named MDyGSI, which models the evolution of user preferences with time-phased sequences of POIs and social relationships for friend recommendation in LBSNs. In each time step of the evolution, geographical and social preferences are modeled through behavior-specific meta-paths in a dynamic heterogeneous graph. The formations of different meta-paths are facilitate by each other to explore mutual influences of dual preferences. To keep the dynamics of social relationships aligned with check-in history, reliable User - User connections are sampled from social graphs based on geographical collaborative filtering in each step, which also avoids noisy social interactions. Furthermore, the dual preferences are concatenated with evolutionary weights measured by a Gated Recurrent Unit for final recommendation. Experimental results on two real-world datasets show significant improvements in MDyGSI over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the H yperbolic S ocial R ecommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top- K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space. [ABSTRACT FROM AUTHOR]

Mariah Carey, known as the Christmas queen, is reportedly going through a difficult time. Speculation has arisen about a possible breakup with her backup dancer boyfriend, Bryan Tanaka, as he has been absent from her tour and their social media interactions have ceased. Fans have expressed concern about Mariah's lackluster performances and low energy. Additionally, Mariah is facing a $30 million lawsuit accusing her of stealing her hit song "All I Want For Christmas Is You." There are also concerns about Mariah's spending habits, as she is said to be spending extravagantly and potentially jeopardizing her fortune. [Extracted from the article]

The article offers suggestions for having an eco-friendly Diwali, a Hindu festival of lights in India. It includes giving the unused clothes, toys and books to the orphanage and underprivileged books, decorating the house with electric and colourful lights and gifting feng shui plants and kitchen herbs. Also emphasized is the significant of putting up room fresheners to remove toxic and hazardous compounds.

The article focuses on the book "Practicable Socialism," by Canon Barnett and S.A. Barnett. Canon and S.A. Barnett, indeed, not merely are not "Socialists," but are at the very antipodes of Socialism. The entire series of papers covering subjects as little related as is possible in a general discussion of human activities rests on the fundamental notion that betterment in human social relations must come from within the individual heart, not from external social decree. It Is significant that the book opens with Canon Barnett's paper on "Religion of the People," first published in 1907.

This article presents an argument pertaining to the absence of perfection in anybody's work. A man may be led, by the connections of things themselves, far from his starting point, just as "Sainte-Beuve" as literary critic, was led to study the whole of social life. Where there is no vital connection, the man may be a brilliant virtuoso, but is probably nothing more.

The article discusses the impact that social interaction between men and women has on gender inequality, or the gender gap, in leadership, referencing an article coauthored by management research Katie Badura in the "Personnel Psychology" journal.

The formation of public opinions is a complex phenomenon that revolves around the aggregation of individuals' beliefs. To accurately capture this phenomenon, one needs to build links from individualistic experiences to personal beliefs, which evolve in a social space through information exchange and belief revision. Despite many efforts to model opinion dynamics, the role of personal experiences and beliefs has often been overlooked. In this paper, we address this issue and propose an agent-based model in a social network. We explicitly model belief acquisition as a learning process from experiences that take the form of local data sets. Agents interact through a social network and update their beliefs based on how accurately the belief reflects experiences. Through iterations of interactions, the agents are able to arrive at a unified belief. We then focus on the accuracy of the personal beliefs during their evolution and the impact of the social network structure. On a micro-level, we investigate positional attributes such as the centrality of nodes that affect belief accuracy. On a macro-level, we investigate structural features that affect the overall performance. We then investigate a method to intervene in opinion formation through expert agents. Experiments are performed on real-world and synthetic data sets, which validate a number of important structural insights. [ABSTRACT FROM AUTHOR]

Spreads can be contained through tuning the underlying contact networks, such as the social distance policy reducing the average degree of social contacts and the suspension of flights increasing the average length of travel distances. This paper studies how to find the optimal removal edge set of minimum size, such that the resulted network can survive from varied outbreaks. Specifically, we investigate the containment strategies from network epidemiology and immunization, and propose three novel methods that can well balance the transmission characteristics and topology of the remaining network and can thus effectively suppress varied spreads. In particular, the developed methods use the epidemic threshold to characterize the transmission characteristics and the largest connected component to measure the topology, and obtain the edge set by simultaneously optimizing them. We further introduce a bound strategy to scale up our methods, providing a time complexity of O (m log ω (n / ℓ)). We also conduct extensive experiments to evaluate the proposed methods. Results show that the developed approaches outperform the state-of-the-art by a large margin. Meanwhile, our methods are also much faster than those compared strategies. We are convinced that the proposed containment approaches promise to be effective tools to suppress spread on large-scale networks. • Three novel edge immunization methods are presented for diffusion containments. • A bound strategy is further introduced to scale up the proposed methods. • Thereby the proposed methods are applicable for tackling large-scale networks. • Experiments on 28 empirical networks demonstrate the superiority of our approaches. [ABSTRACT FROM AUTHOR]