165,159 research outputs found

    Predictive intelligence to the edge through approximate collaborative context reasoning

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    We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

    Task Complexity and Linguistic Complexity: An Exploratory Study

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    Central to any task-based syllabus is the notion of complexity. Proponents of task-based language teaching (TBLT) have argued that tasks be sequenced according to their inherent cognitive complexity, partially because learner performance changes according to the complexity of the task. This exploratory study examines the effect of task complexity on the linguistic complexity of task performance. The participants in the study were a group of ten advanced- level second language (L2) English speakers, and two groups of native speakers of English. Task complexity was operationalized by manipulating two independent variables – reasoning demand and contextual support – in a series of picture narration tasks. The study thus had a 2 x 2 factorial design, with participants completing the tasks under four different sets of conditions. Each set provided the participants with different reasoning demands and/or contextual support. Repeated measures ANOVA were used to analyze the initial data. Following these analyses, separate ANOVAs were calculated to distinguish between the types of reasoning that may have contributed to differences in task performance. It was found that contextual support had little influence on the complexity of task performance, but that reasoning demands, specifically causal and spatial reasoning, may have contributed to differences in the linguistic complexity of participants‟ task performance

    Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections

    The Bobath Clinical Reasoning Framework: A systems science approach to the complexity of neurodevelopmental conditions, including cerebral palsy

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    The current recommended developmental Bobath practice within the Bobath Clinical Reasoning Framework (BCRF) can be conceptualized using the lens of systems science, thereby providing a holistic perspective on the interrelatedness and interconnectedness of the variables associated with childhood-onset disability. The BCRF is defined as an in-depth clinical reasoning framework that can be applied to help understand the relationships between the domains of the International Classification of Functioning, Disability and Health, how those domains can be influenced, and how they impact each other. The BCRF is a transdisciplinary observational system and practical reasoning approach that results in an intervention plan. This provides a holistic understanding of the complexity of situations associated with disorders such as cerebral palsy (CP) and the basis for the lifelong management and habilitation of people living with neurological disorders. The clinical reasoning used by the BCRF draws on the important contextual factors of the individual and their social environment, primarily the family unit. It is rooted in an understanding of the interrelationships between typical and atypical development, pathophysiology (sensorimotor, cognitive, behavioural), and neuroscience, and the impact of these body structure and function constructs on activity and participation. The systems science model integral to the BCRF is a useful way forward in understanding and responding to the complexity of CP, the overarching goal being to optimize the lived experience of any individual in any context

    Complex Logical Reasoning over Knowledge Graphs using Large Language Models

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    Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and abstract logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area.Comment: Code available at https://github.com/Akirato/LLM-KG-Reasonin

    The systemic mind and a conceptual framework for the psychosocial environment of business enterprises: Practical implications for systemic leadership training

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    This chapter introduces a research-based conceptual framework for the study of the inner psychosocial reality of business enterprises. It is called the Inner Organizational Ecosystem Approach (IOEA). This model is systemic in nature, and it defines the basic features of small and medium-size enterprises, such as elements, structures, borders, social actors, organizational climate, processes and resources. Further, it also covers the dynamics of psychosocial reality, processes, emergent qualities and the higher-order subsystems of the overall organizational ecosystem, including the global business environment, which is understood as a macro-system where all the individual organizational ecosystems co-exist. In the applied part of the chapter, cognitive changes emerging within systemic leadership training are defined. Participation in systemic training causes changes in the cognitive processing of reality, more specifically improvements in layer-based framing, relativistic contextual orientation, temporality drift and meaning generation. All of these changes are components of the systemic mind, which is a concept newly proposed and defined by the present study. The systemic mind is a living matrix that is extremely open to acquiring new skills and new patterns of thinking, analyzing and meaning generation. It is processual and it can be considered as an ongoing process of continuous absorption of new cognitive patterns. Both the Inner Organizational Ecosystem Approach and the concept of the systemic mind provide a new theoretical background for empirical investigation in the fields of systemic and systems psychology, complexity psychology, organizational psychology, economic anthropology and the social anthropology of work

    Preferential Multi-Context Systems

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    Multi-context systems (MCS) presented by Brewka and Eiter can be considered as a promising way to interlink decentralized and heterogeneous knowledge contexts. In this paper, we propose preferential multi-context systems (PMCS), which provide a framework for incorporating a total preorder relation over contexts in a multi-context system. In a given PMCS, its contexts are divided into several parts according to the total preorder relation over them, moreover, only information flows from a context to ones of the same part or less preferred parts are allowed to occur. As such, the first ll preferred parts of an PMCS always fully capture the information exchange between contexts of these parts, and then compose another meaningful PMCS, termed the ll-section of that PMCS. We generalize the equilibrium semantics for an MCS to the (maximal) l≤l_{\leq}-equilibrium which represents belief states at least acceptable for the ll-section of an PMCS. We also investigate inconsistency analysis in PMCS and related computational complexity issues

    A contextual behavioral approach to the study of (persecutory) delusions

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    Throughout the past century the topic of delusions has mainly been studied by researchers operating at the mental level of analysis. According to this perspective, delusional beliefs, as well as their emergence and persistence, stem from an interplay between (dysfunctional) mental representations and processes. Our paper aims to provide a starting point for researchers and clinicians interested in examining the topic of delusions from a functional-analytic perspective. We begin with a brief review of the research literature with a particular focus on persecutory delusions. Thereafter we introduce Contextual Behavioral Science (CBS), Relational Frame Theory (RFT) and a behavioral phenomenon known as arbitrarily applicable relational responding (AARR). Drawing upon AARR, and recent empirical developments within CBS, we argue that (persecutory) delusions may be conceptualized, studied and influenced using a functional-analytic approach. We consider future directions for research in this area as well as clinical interventions aimed at influencing delusions and their expression
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