14 research outputs found

    Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis

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    Effective Brain Connectivity for fNIRS with Fuzzy Cognitive Maps in Neuroergonomics

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    Effective connectivity (EC) amongst functional near-infrared spectroscopy (fNIRS) signals is a quantitative measure of the strength of influence between brain activity associated with different regions of the brain. Evidently, accurate deciphering of EC gives further insight into the understanding of the intricately complex nature of neuronal interactions in the human brain. This work presents a novel approach to estimate EC in the human brain signals using enhanced fuzzy cognitive maps (FCMs). The proposed method presents a regularized methodology of FCMs, called effective FCMs (E-FCMs), with improved accuracy for predicting EC between real, and synthetic fNIRS signals. Essentially, the revisions made in the FCM methodology include a more powerful prediction formula for FCM combined with independent tuning of the transformation function parameter. A comparison of EC in fNIRS signals obtained from E-FCM with that obtained from standard FCM, general linear model (GLM) parameters that power Dynamic Causal Modelling (DCM), and Granger Causality (GC) manifests the greater prowess of the proposed E-FCM over the aforementioned methods. For real fNIRS data, an empirical investigation is also made to gain an insight into the role of oxyhemoglobin and deoxyhemoglobin (oxy-Hb, deoxy-Hb) in representing the cognitive activity. We believe this work has profound implications for neuroergonomics research communities

    Privacy-preserving document similarity detection

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    The document similarity detection is an important technique used in many applications. The existence of the tool that guarantees the privacy protection of the documents during the comparison will expand the area where this technique can be applied. The goal of this project is to develop a method for privacy-preserving document similarity detection capable to identify either semantically or syntactically similar documents. As the result two methods were designed, implemented, and evaluated. In the first method privacy-preserving data comparison protocol was applied for secure comparison. This original protocol was created as a part of this thesis. In the second method modified private-matching scheme was used. In both methods the Natural Language processing techniques were utilized to capture the semantic relations between documents. During the testing phase the first method was found to be too slow for the practical application. The second method, on the contrary, was rather fast and effective. It can be used for creation of the tool for detecting syntactical and semantic similarity in a privacy-preserving way

    9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)

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    International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at classification and knowledge discovery that can be used for many purposes in Artificial Intelligence (AI). The objective of the ninth edition of the FCA4AI workshop (see http://www.fca4ai.hse.ru/) is to investigate several issues such as: how can FCA support various AI activities (knowledge discovery, knowledge engineering, machine learning, data mining, information retrieval, recommendation...), how can FCA be extended in order to help AI researchers to solve new and complex problems in their domains, and how FCA can play a role in current trends in AI such as explainable AI and fairness of algorithms in decision making.The workshop was held in co-location with IJCAI 2021, Montréal, Canada, August, 28 2021

    Achieving while maintaining:A logic of knowing how with intermediate constraints

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    In this paper, we propose a ternary knowing how operator to express that the agent knows how to achieve ϕ\phi given ψ\psi while maintaining χ\chi in-between. It generalizes the logic of goal-directed knowing how proposed by Yanjing Wang 2015 'A logic of knowing how'. We give a sound and complete axiomatization of this logic.Comment: appear in Proceedings of ICLA 201

    Fuzzy Techniques for Decision Making 2018

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    Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches
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