A Social Force Model for Adjusting Sensing Ranges in Multiple Sensing Agent Systems

In previous work of multiple sensing agent systems (MSASs), they mainly adjust the sensing ranges of agents by centralized heuristics; and the whole adjustment process is controlled in centralized manner. However, such method may not fit for the characteristics of MSASs where the agents are distributed and decide their activities autonomously. To solve such problem, this paper introduces the social force model for adjusting the sensing ranges of multiple sensing agents, which can make the agents adjust their sensing ranges autonomously according to their social forces to other agents and the sensing objects. Based on the social force model, the coverage and optimization models are presented for both point-type and area-type objects. The presented model can produce appropriate social forces among the sensing agents and objects in MSASs; thereby the system observability and lifetime can be improved

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Engineering Problem Solving And Sustained Learning: A Mixed Methods Study To Explore The Dynamics Of Engineering Knowledge Creation

This dissertation research explores processes by which engineering problem solving (EPS) results in sustained organizational learning. Approaching from a constructionist perspective, the study empirically examines the knowledge creation dynamics instigated by product-related problems using a mixed methods research approach. The research has identified the Japanese concept of ba, defined in this study as shared experiential space, as a key construct that explains the phenomena of interest. A new framework that the study has developed, which interprets EPS as an epistemic journey to attain system-wide improvements, is highly complementary to the traditional structured routine based approaches to engineering operations and management. Operational sustainability is an important issue for every enterprise\u27s survival, to which engineering contributes by managing product and customer requirements. Effective product management is made possible by seamless feedback of lessons learned, which are generated by problem solving. While the literature offers ample evidence of the relationship between problem solving and organizational improvements, however, how this linkage is actually facilitated is not well understood. Studies in industrial engineering and operations research have traditionally emphasized measurable outcomes and the rational aspects of technical problem solving but have yet to saturate the research landscape with more qualitative exploration of the actual processes that leverage engineering knowledge embedded in local contexts. Motivated by the gaps in research, a two-stage empirical study was conducted to probe deeply into the black box of engineering knowledge creation. The study used the exploratory sequential mixed methods research approach to uncover potentially relevant factors for EPS efforts to attain sustained learning, which was defined and subsequently operationalized as positive system changes. In the first phase, a qualitative investigation using grounded theory helped to develop a conceptual model of EPS dynamics. In the second and last phase, this model was tested quantitatively using partial least squares analysis to assess the extent to which the theorized concept can be generalized across a larger engineering sample. The study findings show that contextual factors alone are not sufficient for EPS efforts to result in sustained learning. While these factors have direct effects on operational efficiency and partially affect the effectiveness of problem correction, the EPS processes do not accomplish system changes without first carrying out knowledge creation routines. These routines are a form of sensemaking posited as necessary for cognitive convergence and achievement of a unified interpretation. To the best of our knowledge, this study is first to quantitatively model the concept of ba as a deliberately created environment that promotes such routines, as well as to apply it in a U.S. engineering context. A set of recommendations for engineering knowledge management are provided for practice. For theory, the outcomes of this research illuminate the little addressed link that connects EPS to organizational learning and by so doing contribute to a more complete epistemology of engineering practices

Structured film-viewing preferences and practices : a quantitative analysis of hierarchies in screen and content selection amongst young people in Flanders

Aleit Veenstra, Philippe Meers and Daniel Biltereyst address a specific segment of a typical small-market audience—Flemish youth film viewers. Their study “Structured Film Viewing Preferences and Practices: A Quantitative Analysis of Hierarchies in Screen and Content Selection among Young People in Flanders” deals with one of the symptomatic problems of the era of convergent audiences, the multiplication of screens used for domestic consumption of audiovisual content. Building an intriguing empirical design, Veenstra and her colleagues aim to identify patterns of screen selection and their relation to the perceived value of Hollywood, European and domestic Flemish films. Their conclusion is that there are well-articulated hierarchies applied by the audience members in the selection of both film titles and reception screens and that, to put it simply, in the case of screens, size matters

The rhythm that unites: an empirical investigation into synchrony, ritual, and hierarchy

Synchrony, or rhythmic bodily unison activities such as drumming or cadence marching, has attracted growing scholarly interest. Among laboratory subjects, synchrony elicits prosocial responses, including altruism and empathy. In light of such findings, researchers in social psychology and the bio-cultural study of religion have suggested that synchrony played a role in humanity’s evolutionary history by engendering collectivistic commitments and social cohesion. These models propose that synchrony enhances cohesion by making people feel united. However, such models overlook the importance of differentiated social relations, such as hierarchies. This dissertation builds on this insight by drawing on neuroscience, coordination dynamics, social psychology, anthropology, and ritual studies to generate a complex model of synchrony, ritual, and social hierarchy, which is then tested in an experimental study. In the hypothesized model, shared motor unison suppresses the brain’s ability to distinguish cognitively between self-caused and exogenous motor acts, resulting in subjective self-other overlap. During synchrony each participant is dynamically entrained to a group mean rhythm; this “immanent authority” prevents any one participant from unilaterally dictating the rhythm, flattening relative hierarchy. As a ritualized behavior, synchrony therefore paradigmatically evokes shared ideals of equality and unity. However, when lab participants were assigned to either a synchrony or asynchrony manipulation and given a collaborative task requiring complex coordination, synchrony predicted a marginally lower degree of collaboration and significantly lower interpersonal satisfaction. These findings imply that unity and equality can undercut group cohesion if the collective agenda is a shared goal that requires interpersonal coordination. My results emphasize that, despite the inevitable tensions associated with social hierarchy, complementary roles and hierarchy are vital for certain aspects of social cohesion. Ritual and convention institute social boundaries that can be adroitly negotiated, even as egalitarian effervescence such as communitas (in the sense of Victor Turner) facilitates social unity and inspires affective commitments. These findings corroborate theories in ritual studies and sociology that caution both against excessive emphasis on inner emotive states (such as empathy) and against excessively rigid conventions or roles. An organic balance between unity and functional differentiation is vital for genuinely robust, long-term social cohesion.2018-06-21T00:00:00

Team Interaction Dynamics During Collaborative Problem Solving

This dissertation contributes an enhanced understanding of team cognition, in general, and collaborative problem solving (CPS), specifically, through an integration of methods that measure team interaction dynamics and knowledge building as it occurs during a complex CPS task. The need for better understanding CPS has risen in prominence as many organizations have increasingly worked to address complex problems requiring the combination of diverse sets of individual expertise to achieve solutions for novel problems. Towards this end, the present research drew from theoretical and empirical work on Macrocognition in Teams that describes the knowledge coordination arising from team communications during CPS. It built from this by incorporating the study of team interaction during complex collaborative cognition. Interaction between team members in such contexts has proven to be inherently dynamic and exhibiting nonlinear patterns not accounted for by extant research methods. To redress this gap, the present research drew from work in cognitive science designed to study social and team interaction as a nonlinear dynamical system. CPS was examined by studying knowledge building and interaction processes of 43 dyads working on NASA\u27s Moonbase Alpha simulation, a CPS task. Both non-verbal and verbal interaction dynamics were examined. Specifically, frame-differencing, an automated video analysis technique, was used to capture the bodily movements of participants and content coding was applied to the teams\u27 communications to characterize their CPS processes. A combination of linear (i.e., multiple regression, t-test, and time-lagged cross-correlation analysis), as well as nonlinear analytic techniques (i.e., recurrence quantification analysis; RQA) were applied. In terms of the predicted interaction dynamics, it was hypothesized that teams would exhibit synchronization in their bodily movements and complementarity in their communications and further, that teams more strongly exhibiting these forms of coordination will produce better problem solving outcomes. Results showed that teams did exhibit a pattern of bodily movements that could be characterized as synchronized, but higher synchronization was not systematically related to performance. Further, results showed that teams did exhibit communicative interaction that was complementary, but this was not predictive of better problem solving performance. Several exploratory research questions were proposed as a way of refining the application of these techniques to the investigation of CPS. Results showed that semantic code-based communications time-series and %REC and ENTROPY recurrence-based measures were most sensitive to differences in performance. Overall, this dissertation adds to the scientific body of knowledge by advancing theory and empirical knowledge on the forms of verbal and non-verbal team interaction during CPS, but future work remains to be conducted to identify the relationship between interaction dynamics and CPS performance

AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems

The evolution towards 6G architecture promises a transformative shift in communication networks, with artificial intelligence (AI) playing a pivotal role. This paper delves deep into the seamless integration of Large Language Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems. Their ability to grasp intent, strategize, and execute intricate commands will be pivotal in redefining network functionalities and interactions. Central to this is the AI Interconnect framework, intricately woven to facilitate AI-centric operations within the network. Building on the continuously evolving current state-of-the-art, we present a new architectural perspective for the upcoming generation of mobile networks. Here, LLMs and GPTs will collaboratively take center stage alongside traditional pre-generative AI and machine learning (ML) algorithms. This union promises a novel confluence of the old and new, melding tried-and-tested methods with transformative AI technologies. Along with providing a conceptual overview of this evolution, we delve into the nuances of practical applications arising from such an integration. Through this paper, we envisage a symbiotic integration where AI becomes the cornerstone of the next-generation communication paradigm, offering insights into the structural and functional facets of an AI-native 6G network