Reviewerly/exHarmony huggingface dataset:
Interval-Valued Observations-Based Multi-Source Domain Adaptation Using Fuzzy Neural Networks

Unsupervised domain adaptation aims to learn a well-performed classifier in one domain (target) by leveraging knowledge from another domain (source) that has different distribution compared with the previous one. Although researchers have gained significant achievements in addressing the domain adaptation problems, existing works hold a common assumption that both the source and target domains only contain crisp-valued observations. However, in many real-world scenarios, such crisp-valued observations are not always available. For example, interval-valued data, where all of the observations' features are described by intervals, is a common type of data. Hence, in this paper, we focus on a more realistic problem called interval-valued observations-based multi-source domain adaptation (IMSDA), where we aim to train a classifier with high classi-fication accuracy on an unlabeled target domain with interval-valued observations by utilizing previously-acquired knowledge in multiple labeled source domains with crisp-valued observations. We develop a practical model called fuzzy multi-adversarial training neural networks (FUMAT-Net), which integrates fuzzy neural networks and adversarial training to solve the proposed problem. Further, a new fuzzy relation, which can measure the correlation between the multiple source domains and the target domain, is proposed to improve the performance of our model. The experiment results on both synthetic and real-world datasets verify the efficacy of FUMAT-Net and the rationality of the proposed fuzzy relation.
Semantic Annotation of Sensor Data Using a Sequential Possibilistic Clustering Methodology

The rapid increase of the older population has gained significant healthcare attention in recent years. Early detection of illness might result in better outcomes and reduced healthcare cost. The use of health monitoring sensors such, motion or bed or depth ones, may be able to detect early sign of illness but they are hard to interpret. To enhance caretakers understanding of health sensor data we propose to link it to signs and symptoms extracted from related electronic health record (EHR). The proposed methodology is based on detecting changes in sensor patterns using an unsupervised method (Sequential Possibilistic Gaussian Mixture Model, SPGMM), and associate health patterns from the person's EHR extracted using the clinical language annotation, modeling, and processing (CLAMP) toolkit. However, the evaluation of these health algorithms requires substantial time and effort by clinicians to examine the related electronic health record (EHR) notes. We validated our approach on three nursing home residents monitored with a collection of motion, bed, and depth sensors over several years. The obtained results show that the agreement of CLAMP-clinician and clinician-clinician evaluation are substantial, and provide strong support for our proposed methodology.
Perceptual System of a Mobile Robot for Autonomous Adaptation to Environmental Changes

In the “ultra-smart society”, which aims for symbiosis between humans and robots, mobile robots that can move freely in a variety of environments are expected. However, the appropriate parameters for any navigation method depend on the characteristics of the environment and often need to be adjusted to the environment in which it is implemented. Especially in dynamic environments that include pedestrians, avoidance is difficult and it cannot be handled only by adjusting parameters. Therefore, we propose a perceptual system for a mobile robot that can autonomously adapt to environmental changes by adjusting internal parameters based on perceptual information obtained by the robot in environments with multiple different characteristics, such as a space where many pedestrians freely pass by and a complex space with many obstacles.
Additively Generated (a,b)-Implication Functions<sup>*</sup>

Some problems involving classification through neural networks are known to use inputs out of the scope of the unit interval. Therefore, defining operations on arbitrary closed real intervals may be an interesting strategy to tackle this issue and enhance those application environments. In this paper we follow the ideas already discussed in the literature regarding (a,b)-fusion functions, and (a,b)-negations, to provide a new way to construct implication functions. The main idea is to construct an operator using additively generated functions that preserve the properties required by implication functions.
Towards Dynamic Fuzzy Rule Interpolation via Density-Based Spatial Clustering of Interpolated Outcomes

Fuzzy rule interpolation (FRI) provides an innovative approach to deducing conclusions for observations that do not match existing rules in a sparse fuzzy rule base. Conventional fuzzy rule-based systems that rely on sparse rule bases often encounter limitations due to the lack of sufficient data or the absence of adequate human expertise necessary to formulate rules encompassing the majority of a given problem domain. Beginning with a sparse rule base, the Fuzzy Rule Interpolation (FRI) approach constructs interpolated rules, specifically when such observations fail to trigger any rule present in the sparse rule base. Once the inference results through interpolation are found, the interpolated rules that might involve valuable information about the knowledge space are discarded. The system's overall efficiency and robustness will never improve by only performing FRI. This raises the need for dynamic fuzzy rule interpolation (D-FRI). That is, after the FRI process, interpolated rules that cover certain domain regions can be adapted to match future observations directly. Thus, a dynamic fuzzy rule promotion approach that evaluates interpolated rules and promotes potential valuable ones to the sparse rule base for future inference can improve the overall coverage of the domain knowledge and inference efficiency. This paper proposes a dynamic fuzzy rule interpolation framework that joins the popular Transformation-based Fuzzy Rule Interpolation (T-FRI) and Density-Based Spatial Clustering of interpolated outcomes. Experimental results demonstrate that the approach works effectively.
Fuzzy Inference on Quantum Annealers

Quantum computers can potentially perform certain types of optimisation problems much more efficiently than classical computers, making them a promising tool for solving complex fuzzy logic problems. In two recent developments, based on solving Quadratic Unconstrained Binary Optimization (QUBO) problems on a type of quantum computers known as quantum annealers, we have introduced novel representations of a) fuzzy sets; b) implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) and; c) the centroid defuzzification. In this paper, the previous works are further extended by presenting an implementation of Mamdani inference on the quantum annealer machines. We first present how the fuzzy rules can be formulated for such an implementation, then we present how to cascade different quantum-fuzzy operators in order to implement the quantum-fuzzy inference, and finally, a sample implementation of the inference on a real quantum computer is demonstrated. Having the main components of a rule-based fuzzy logic system implemented on quantum computers, this paper provides an integrated solution for implementing a whole fuzzy rule-based system on quantum computers.
Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor results. Therefore, it is important to design algorithms for deep learning models to reliably detect out-of-distribution (OOD) data. In this paper, we propose a novel fuzzy-uncertainty-based method to detect OOD samples for semantic segmentation. Firstly, to capture both data and model uncertainties, test-time augmentation and Monte Carlo dropout are applied to a ready-trained image segmentation model for generating multiple predicted instances of a given test image. Then interval fuzzy sets are generated from these multiple predictions to describe the captured uncertainty via distance transform operators. Finally, an image-level uncertainty score, which is calculated from the generated interval fuzzy sets, is used to indicate if it is an OOD sample. Experiments on testing three OOD test sets on a skin lesion segmentation model show that our proposed method achieved significantly higher classification accuracy in detecting OOD samples than three other state-of-the-art uncertainty-based algorithms.
Knowledge Integration in XAI with Gödel Integrals

Counterfactual examples constitute a popular form of explanations that are most often generated through the optimisation of a cost function that combines different components of the explanation quality. This paper focuses on the final aggregation of the objective term, that depends on the considered machine learning task, and the subjective term, that depends on the targeted user and more precisely on their knowledge. It discusses the desired properties of this aggregation operator and proposes to use two forms of the Gödel integral operator, highlighting the expressiveness and appropriateness they offer.
Towards Causal Fuzzy System Rules Using Causal Direction

Generating (fuzzy) rule bases from data can provide a rapid pathway to constructing (fuzzy) systems. However, direct rule generation approaches tend to generate very large numbers of rules. One reason for this is that such techniques are not designed to differentiate between relationships of variables reflecting a causal link and those where such a link reflects a spurious correlation in the data set. In prior work, we discussed how causal discovery techniques, and specifically the subset resulting of variables within the Markov blanket can be leveraged to focus on the generation of rules for variables with a causal link. In this paper, we broaden this discussion, outlining a road-map to explain how causal discovery and its outputs-causal graphs-can be used towards refining (fuzzy) rule generation techniques. As a next step on this road-map, we present an initial approach which leverages the causal direction captured in the graph to further reduce the set of variables from those captured in the Markov blanket. Initial results show that the approach, combined with a traditional fuzzy rule generation technique such as the Wang-Mendel approach, produces competitive performance and concise rule bases-highlighting a path towards improved fuzzy system interpretability.
Cohort Discovery from Bed Sensor Data with Fuzzy Evidence Accumulation Clustering

In numerous settings, humans are observed in a variety of modalities by connected (e.g., wearables) and unconnected sensors (e.g., stand-off sensors). Smart facilities that leverage unobtrusive sensors for longitudinal health monitoring are able to capture rich data regarding resident health in real-time, a particularly important ability in eldercare. When providing eldercare in technology-enabled living environments, using this wealth of data to detect health changes and identify health conditions is a considerable challenge. Herein, we first examine how data collected from unobtrusive, hydraulic bed sensors can be decomposed into restlessness, respiration, and ballistocardiogram features and subsequently used in clustering algorithms. We apply fuzzy Evidence Accumulation Clustering (Fuzzy-EAC) to the results of clustering these feature sets to discover resident cohorts with similar manifestations of sleep health through unsupervised machine-learning. These cohorts allow clinicians to discover resident links and can provide diagnosis aid based on known medical histories of cohort members. Additionally, the intra-person cluster analysis described here may provide useful insights for enhancing clinical care and improving health outcomes for independent living residents.
Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series

Partial periodic patterns are an important class of regularities in multiple time series data. Most previous works focused on finding these patterns in a binary series by disregarding the quantities of objects. This paper explores the concept of “fuzzy sets” and proposes a novel model of fuzzy partial periodic patterns (F3Ps) that may exist in a quantitative series. F3Ps have value because they represent regularities that are predictable in a series. Unfortunately, finding F3Ps is challenging due to its colossal search space. We introduce a novel pruning technique to reduce the search space and computational cost of finding these patterns. We also present an efficient depth-first search algorithm, F3P-Miner, to find all F3Ps in a series. We also describe a new pattern representation technique, temporal ordering and grouping with a wildcard character, to visualize long patterns easily. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we present the real-world applicability of F3Ps by finding helpful information about polluted areas in the one year Japan's air pollution database.
Gaussian Process to Takagi-Sugeno Fuzzy Model Using Supervised Clustering

In this paper, we present a novel approach for transforming Gaussian process models into Takagi-Sugeno fuzzy models (GP2TS). Our approach utilizes a supervised clustering algorithm to gradually merge data points into larger and larger clusters based on the errors of the local models and the prediction intervals that capture the underlying nonlinearities in the data. This results in a reduced number of local models that accurately describe the problem in their respective vicinity. Compared to the Gaussian process models, our GP2TS approach offers several advantages. First, it enhances the interpretability of local models, making it easier to understand the behavior of the model and its predictions. Secondly, it improves the applicability of the model by reducing computational demand, making it more practical for real-world applications. To demonstrate the potential of our proposed approach, we apply it to two benchmark problems from the literature. The results show that the GP2TS approach performs well in practice and can achieve similar levels of accuracy compared to other state-of-the-art fuzzy model identification techniques.
An Uncertainty-Accuracy-Based Score Function for Wrapper Methods in Feature Selection

Feature Selection (FS) is an effective preprocessing method to deal with the curse of dimensionality in machine learning. Redundant features in datasets decrease the classification performance and increase the computational complexity. Wrapper methods are an important category of FS methods that evaluate various feature subsets and select the best one using performance measures related to a classifier. In these methods, the accuracy of classifiers is the most common performance measure for FS. Although the performance of classifiers depends on their uncertainty, this important criterion is neglected in these methods. In this paper, we present a new performance measure called Uncertainty-Accuracy-based Performance Measure for Feature Selection (UAPMFS) that uses an ensemble approach to measure both the accuracy and uncertainty of classifiers. UAPMFS uses bagging and uncertainty confusion matrix. This performance measure can be used in all wrapper methods to improve FS performance. We design two experiments to evaluate the performance of UAPMFS in wrapper methods. In experiments, we use the leave-one-variable-out strategy as the common strategy in wrapper methods to evaluate features. We also define a feature score function based on UAPMFS to rank and select features. In the first experiment, we investigate the importance of considering uncertainty in the FS process and show how neglecting uncertainty affects FS performance. In the second experiment, we compare the performance of the UAPMFS-based feature score function with the most common feature score functions for FS. Experimental results show the effectiveness of the proposed performance measure on different datasets.
Cluster Validity for Fuzzy Text Segmentation

Topical text segmentation is an unsupervised learning process of separating documents, transcripts, and other text streams into segments-i.e., clusters-where the text in each segment is considered to be topically similar, and distinct from other segments. In this paper, we consider the task of fuzzy text segmentation, where words, or utterances, have shared membership in all segments. This is especially nascent for text sources like transcripts, where multiple topics are often simultaneously discussed: e.g., cost and deliverables in a sales meeting. One challenge in segmentation and clustering is how to choose the hyperparameters-e.g., number of clusters-in the algorithm. Hence, here we propose a fuzzy cluster validity metric, a modified Davies-Boudin index, and demonstrate how this index can be used to tune a fuzzy text segmentation algorithm. We demonstrate how fuzzy clustering can be used as a form of text segmentation and show some applications on benchmark data.
Proof of Accuracy-Transparency Trade-Off in Takagi-Sugeno Model and its Implications

In the literature, Takagi-Sugeno (TS) models are highly-regarded for their accuracy and interpretability. However, several studies have observed the phenomenon of an accuracy-interpretability trade-off, where a model can be either accurate or interpretable, but not both. After providing an overview of the phenomenon described in the literature, this paper presents the proof of the presence of mentioned trade-off in Takagi-Sugeno models with local affine models under loose assumptions. The implications of proof to local nonlinear models is also presented. The cause of this phenomenon is proposed to be found in the core structure of the TS models. Since TS models continue to achieve state-of-the-art results in various fields, this paper explores how the model developers overcame this trade-off and what they sacrificed to do so. Finally, further research in a completely new fuzzy structure is proposed that retains the idea of TS models but can achieve arbitrary accuracy without compromising the model interpretability.
Using the Pre-Large Concept for Maintaining High Fuzzy Utility Itemsets

Utility mining is a popular research field in data mining. It uses item utilities and quantities to fit more into real applications. Fuzzy utility mining has also been proposed to reflect the linguistics of human perceptron for item association. In the past, we proposed a fast-up date-based (FUP-based) approach to maintain high fuzzy utility itemsets for continuously coming transaction data. This paper proposes an algorithm that applies the pre-large strategy on fuzzy utility mining to raise the maintenance performance. Nine cases are considered to maintain the current high fuzzy utility itemsets based on the fuzzy upper-bound utility. The results of the numerical experiments show that our proposed algorithm has better efficiency than the batch and the FUP-based approach in the execution time.
FuzzySentClass: Interval-Valued Fuzzy Approach to the Sentiment Analysis Problem via SentiWordNet

Sentiment analysis, especially social network analysis (SNA), is a relevant research area that consists of analyzing and extracting emotions, opinions, or attitudes from reviews of products, services, music, and movies, classifying them into positive, neutral, and negative. In this article, we propose a new approach using the Interval-valued Fuzzy Logic called FuzzySentClass to classify tweets based on lexicon using SentiWordnet. The obtained results are evaluated based on the accuracy of the classifications obtained in the executions varying the type reducer in the Defuzzification step of FuzzySentClass. In addition, the interval entropy approach is used to measure the imprecision information of achieved results. Our approach reached an accuracy of 83.22 % with the centroid type reducer and 82.63 % with the center of sets type reducer. And, this results in the values of 0.117469 and 0.149853 as the maximum diameter of interval entropy for IvFS related to input and output variables, respectively.
Comparing Different Approaches to Entropy for Interval Valued Fuzzy Sets

In a general sense, entropy is a measure of disorder. When adapted to information theory, the considered disorder is mostly related to information uncertainty, and in particular to randomness. Finally, when applied to fuzzy sets, this disorder is interpreted also in terms of uncertainty, but now the uncer-tainty relates to fuzziness. Consequently the fuzzy entropy of a fuzzy set defines how fuzzy is the set. Since its introduction, different approaches to fuzzy entropy has been considered. Those approaches usually measure the distance from the considered fuzzy set, to a minimum entropy set. In general, the minimum entropy set is a crisp set, but when analysing fuzzy entropy in the different extensions of fuzzy sets (interval valued, intuitionistic, type-2 and so on) this is not always the case. The present paper will concentrate on Interval Valued Fuzzy Sets, considering the different definitions of entropy that has been introduced, and analysing them in terms of the set used for comparison and the way to measure the distance. As a result, a certain ordering among the considered measures will be achieved.
Knowledge Graph-Based Genetic Fuzzy Agent for Human Intelligence and Machine Co-Learning

This paper proposes a novel approach for evaluating the co-learning performance of human intelligence (HI) and machine intelligence (MI) using a Knowledge Graph-based genetic fuzzy agent. The agent utilizes a Knowledge Graph structure to represent a specific knowledge domain related to human learning and employs a genetic fuzzy learning mechanism to construct a personalized learning model. Human learners can engage in co-learning with machines using state-of-the-art AI tools such as the Meta AI S2ST Taiwanese-English language model and the OpenAI ChatGPT text model. The proposed approach was evaluated using human learning data from an undergraduate computer science course and a series of Taiwanese and English language translation experience activities. The experimental results indicate that the proposed approach can effectively enhance the co-learning process for both human and machine learners.
A New Linguistic Fuzzy PRISM Algorithm

Inductive algorithms for generating fuzzy rules are based on the division of the data set according to fuzzy values created for individual attributes. Its main advantage is the ability to generate interpretable systems. However, it can be degraded if the complexity of the system increase. In this paper, we will propose a new version of the Fuzzy Prism algorithm, which allows for obtaining a set of readable and interpretable linguistic fuzzy rules. It also uses linguistic modifiers, which a llows them to increase their expressiveness and reduce their number.