Reviewerly/exHarmony huggingface dataset:
Federated TSK Models for Predicting Quality of Experience in B5G/6G Networks

Real-time applications based on streaming data collected from remote devices, such as smartphones and vehicles, are commonly developed using Artificial Intelligence (AI). Such applications must fulfill different requirements: on one hand, they must ensure good performance and must deliver results in a timely manner; on the other hand, with the objective of being compliant with the AI-specific regulations, they shall preserve data privacy and guarantee a certain level of explainability. In this paper, we describe an AI-based application to predict the Quality of Experience (QoE) for videos acquired by moving vehicles from Beyond 5G and 6G (B5G/6G) network data. To this aim, we exploit a Takagi-Sugeno-Kang (TSK) fuzzy model learned by employing a federated approach, thus meeting, simultaneously, the requests for explainability and data privacy preservation. A thorough experimental analysis, involving also the comparison with an opaque baseline (i.e., a neural network model), is presented and shows that the TSK model can be regarded as a viable solution which guarantees on the one side an optimal trade-off between interpretability and accuracy, and on the other side preserves the data privacy.
The Use of a Fuzzy Rule-Based System in Adaptive e-Learning Content Based on Intercultural Competence

The article presents a fuzzy system which has been used in an adaptive e-learning content on effective cooperation with a Chinese business partner. The content adapts to the identified level of the student's intercultural competence and directs them to the specified education path. During the decision-making process, six variables are taken into account to establish the level of intercultural competence. The studies are conducted on an e-learning course intended for employees with different experiences in intercultural work. Control of the learning individualization is carried out using a hybrid model, in which the fuzzy controller is supplemented with a rule-based system. The adaptive e-learning content is constructed using an architecture where the whole solution is run on the student's internet browser.
Fuzzy Classifiers with a Two-Stage Reject Option

In general, fuzzy classifiers have high interpretability. They can linguistically explain the reason why an input pattern is classified as a particular class through the linguistic interpretation of each antecedent fuzzy set. A reject option that rejects patterns near the boundaries between different classes is an approach to increase the reliability of classifiers. However, the conventional threshold-based reject option may reject more patterns than necessary to achieve high reliability. In this paper, we propose a two-stage reject option where a machine learning model is used after the threshold-based decision by a fuzzy classifier. If the class labels predicted by the machine learning model and the fuzzy classifier are the same, the fuzzy classifier outputs the predicted class label without rejection. Through computational experiments, we discuss the trade-off relation between the accuracy and rejection rate by the proposed two-stage reject option using various machine learning models.
Enhancing MBTI Personality Trait Prediction from Imbalanced Social Media Data Using Hybrid Query Expansion Ranking and Glo Ve- BiLSTM

The usefulness of information obtained from social media data using machine learning methods is increasingly widespread, including predicting a person's personality. One of the personality type theories that is often used today in describing a person's personality is the Myers-Briggs Type Indicator. The challenges faced in processing text data from social media by machine learning methods are the imbalanced data for each personality type and the high dimensional features extracted from the data. Handling the problem of imbalanced data with oversampling techniques will increase the high dimension of features, which has an impact on increasing computation time. On the other hand, reducing feature dimensions will affect the quality of the prediction results because the machine learning process requires an adequate amount of data. This study develops a hybrid QER and GloVe-BiLSTM model by combining the Bidi-rectional Long Short-Term Memory (BiLSTM) classifier layer with the Global Vectors for Word Representation (GloVe) and Query Expansion Ranking(QER) as an input layer. The model works on data that has previously gone through a balancing process using the Synthetic Minority Oversampling Technique (SMOTE). The experimental findings show that the proposed model can, in fact, significantly enhance personality prediction performance in terms of prediction accuracy and computation time.
The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection

Fuzzy systems with type-1, interval type-2 and general type-2 fuzzy sets have been widely applied in various fields. Constrained Interval Type-2 (CIT2) fuzzy sets and systems are an approach designed to improve the interpretability of type-2 fuzzy inference systems (FISs) by preserving the semantic values throughout the modelling and decision process, including in the type-reduction and defuzzification stages. Whilst all the steps necessary for building CIT2 inference systems have been established, there is still a challenge for people to build data-driven models based on CIT2 sets and apply them in practice. This paper presents a step-by-step design of a CIT2 fuzzy inference system featuring a case study in credit card fraud detection, utilising the Juzzy Constrained toolkit. The framework contains three parts: creating CIT2 membership functions from data based on the K-means algorithm; deriving fuzzy rules by using a decision tree model; and implementing the CIT2 FIS with the Juzzy Constrained toolkit available online. This constitutes the first end-to-end demonstration of designing, creating and implementing a CIT2 FIS in a fresh application context. This will support other researchers to explore CIT2 fuzzy systems.
Bipolar Multi-Adjoint Relation Equations as Systems of Bipolar Multi-Adjoint Sup-Equations

Bipolar fuzzy relation equations are fuzzy relation equations enriched with a negation operator, where the unknown relation appears together with its negation. The investigation of this topic is receiving much attention in late years, from a theoretical but also from an applied approach, as it happened in the case of fuzzy relation equations. The development of bipolar fuzzy relation equations, specially from an applied perspective, demands relaxing the underlying requirements of the notion. This leads to the contribution of this paper, where the concept of bipolar multi-adjoint relation equation has been presented, allowing the use of multiple conjunctions in the composition operator. Additionally, a formal procedure to transform a bipolar multi-adjoint relation equation into a set of systems of bipolar multi-adjoint sup-equations has been presented.
Fuzzy-Based Solar Magnetogram Image Retrieval

We propose a fast retrieval of full-disk images of the Sun magnetograms collected by the Solar Dynamics Observatory spacecraft. The images are produced at a high rate which entails the need to have a method for efficient searching in this vast collection. The method uses a hand-crafted integer-valued short descriptor of magnetograms and fuzzy rules created by boosting meta-learning. Fuzzy rule base is used as a database index. The speed-up comes from the short descriptor and fuzzy rule indexing. The efficiency and accuracy of the method is proved by the experiments.
An IEEE Std 1855 Driver for Synthetizing Quantum Fuzzy Inference Engines

Over years, Fuzzy Logic established as a powerful tool for control systems as shown by the several applications both in the area of pure engineering and social, economical and political science. However, in spite of the success of fuzzy control systems, their design is originally affected by mainly two issues: the dependence on hardware and an expensive inference when the number of fuzzy variables is large. In 2016, the IEEE Std 1855 was released to address the first issue by introducing an XML-based language capable of modeling fuzzy systems in an abstract way independent from hardware. As for the second issue, recently, a quantum implementation of a fuzzy inference engine, denoted as QFIE, has been proposed to potentially provide an exponential computational advantage in the execution of fuzzy rules. This paper aims at integrating IEEE Std 1855 and QFIE to provide the fuzzy community with controllers transparent with respect to hardware and efficient in terms of fuzzy rule execution. This integration is achieved by introducing an IEEE Std 1855 driver capable of converting transparent fuzzy systems in controllers executable on quantum computers. As shown in a case study, this driver enables quantum implementation of a fuzzy system without knowledge of quantum hardware and technologies.
Handling of Component-Wise Noise in ANFIS Induced by Ellipsoidal Fuzzy Clustering

Local Principal Component Analysis (Local PCA) is a basic approach for unsupervised data summarization, which estimates local low-dimensional features through linear cluster extraction. In this paper, a novel robust ellipsoidal fuzzy clustering model is proposed with the goal of applying it to Adaptive Network-based Fuzzy Inference System (ANFIS). Besides fuzzy c-elliptotypes (FCE) simultaneously performs fuzzy c-means (FCM)-like clustering and Local PCA, the proposed method also considers rejection of component-wise noise supported by the iteratively reweighted least square (IRLS) framework. The fuzzy memberships of each object are calculated with the clustering criterion of distances from ellipsoidal-shape clusters while the responsibility of each observed element is estimated with the Geman-McClure robust measure. The derived fuzzy memberships are utilized in the adaptive fuzzy inference system with two modes: the premise part responsibility estimation and the robust consequent part regression. The characteristic features of the proposed method are demonstrated through numerical experiments.
FCM-Induced Switching Fuzzy Factorization Machine for Collaborative Filtering

Factorization machine (FM) is a powerful tool for collaborative filtering (CF), which constructs a prediction model for personalized recommendation with lower computational costs. This paper considers extension of FM for handling the mixture of different preference tendencies under the switching data analysis concept. Fuzzy c-means (FCM) clustering has been extended to switching data analysis by modifying the FCM clustering criterion with least square-type measures of various data analysis methods. The proposed method tries to construct multiple FM prediction models for fairly representing intrinsic dependencies among users' preferences and items, where fuzzy membership degrees of each user to cluster-wise models are estimated by utilizing FM prediction error measures not only for FM parameter estimation but also for cluster estimation. The characteristic features of the proposed prediction model are demonstrated through numerical experiments using real-world MovieLens benchmark datasets such that it has advantages for revealing multiple FM prediction tendencies having better prediction ability than the conventional single FM model.
Fuzzy Analysis for Consensus in Federated Learning with Simulated Heuristic Attacks

A large number of objects participating in the voting can be an advantage as well as a disadvantage. In the case of decentralized federated learning, adding the model to the aggregation is preceded by a vote. The choice of voters and their results can be falsified through various attacks such as dataset poisoning. In this paper, we propose a fuzzy consensus analyzing the results of individual voters regarding the aggregation of a given model. The consensus is based on a fuzzy controller that selects the most reliable models for aggregation. For this reason, it uses image-modifying heuristics and quick evaluations of incoming results. If a decision is made that a selected client is unreliable several times, it is blocked to reduce the number of performed operations. The proposed system was tested on selected tasks related to image classification. The results were discussed and compared to evaluate the proposed system.
A Comparative Study of Open-Source Fuzzy Modelling Toolkit Licenses and Features

The practice of distributing software as open-source, which serves as a pillar for Open Science, can benefit future research in the field of fuzzy logic by enabling access and increasing the maintainability of free fuzzy logic frameworks alternatives to closed commercial solutions. Most open-source software uses a distribution license, with a wide range of restrictions and requirements on the distributions of these tools and their derived works. Therefore, understanding each license limitation and requirement is essential to ensure an ethical research process and avoid legal issues from breaching the open-source license. However, there is a lack of comparative research on the current state of open-source licenses for fuzzy modelling toolkits and frameworks. Previous studies are outdated or incomplete, missing some of the most popular open-source fuzzy frameworks or a detailed analysis of the licenses used. Our research analysed 20 of the most prominent open-source toolkits and categorised them based on their licenses, with qualitative comparisons of their characteristics and limitations. In our study, we note a concerning scenario where a potential license violation may impede the publication and dissemination of academic works derived from fuzzy toolkits using the GPL license. Additionally, we identify a gap in open-source fuzzy toolkits with support for Fuzzy Control Language (FCL) and Type-2 fuzzy sets. Finally, this study aims to support and guide future researchers in selecting the fuzzy toolkit that best matches their research problem without infringing the open-source licenses used, with an intuitive and up-to-date overview of the licenses and features of open-source fuzzy modelling frameworks available.
A Comparison of Quantum Computer Architectures in Running Fuzzy Inference Engines

The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.
FuzzyLogic.jl: A Flexible Library for Efficient and Productive Fuzzy Inference

This paper introduces FuzzyLOGIC.JL, a Julia library to perform fuzzy inference. The library is fully open-source and released under a permissive license. The core design principles of the library are: user-friendliness, flexibility, efficiency and interoperability. Particularly, our library is easy to use, allows to specify fuzzy systems in an expressive yet concise domain specific language, has several visualization tools, supports popular inference systems like Mamdani, Sugeno and Type-2 systems, can be easily expanded with custom user settings or algorithms and can perform fuzzy inference efficiently. It also allows reading fuzzy models from other formats such as Matlab. fis, FCL or FML. In this paper, we describe the library main features and benchmark it with a few examples, showing it achieves significant speedup compared to the Matlab fuzzy toolbox.
Fuzzy Clustering for QAOA Complexity Reduction

The Quantum Approximate Optimization Algorithm (QAOA) has become one of the most widely used components in the development of modern quantum applications. It works on the paradigm of quantum variational circuits, where a quantum circuit is trained - by repeatedly adjusting circuit parameters - to adequately solve a combinatorial optimization problem. This training process, based on classical optimization algorithms, represents a significant computational bottleneck for QAOA, as it requires repeated calls to the quantum device to evaluate the cost function of the problem to solve. Therefore, there is a strong need to eliminate this computationally expensive task and identify an alternative strategy to compute good parameters for QAOA. This paper synergistically exploits the parameter concentration property of QAOA and the Fuzzy C-Means algorithm to achieve this goal. Experimental results show that the proposed approach can support QAOA to maintain high performance in solving well-known optimization problems such as MAXCUT, requiring a reduced computational effort for the parameter tuning phase.
On the Stability of Fuzzy Classifiers to Noise Induction

Tabular data classification is one of the most important research problems in the artificial intelligence. One of the most important desired properties of the ideal classifier is that small changes in its input should not result in dramatic changes in its output. However, this might not be the case for many classifiers used in present day. Fuzzy classifiers should be stronger than their crisp counterparts, as they should be able to handle such changes using fuzzy sets and their membership functions. However, this hypothesis has not been empirically tested. Besides, the concept of “small change” is somewhat imprecise and has not been quantified yet. In this work we propose to use small and progressively bigger changes in test samples to study how different crisp and fuzzy classifiers behave. We also study how to optimize classifiers to be more resistant to such kind of changes. Our results show that different fuzzy sets have different responses to this problem and have a smoother performance response compared to crisp classifiers. We also studied how to improve this and found that resistance to small changes can also result in a worse overall performance.
Attention-Bridging TS Fuzzy Rules for Universal Multi-Domain Adaptation Without Source Data

Universal source-free domain adaptation is an urgent, unsolved problem in transfer learning. Existing source-free and universal domain adaptation methods ignore soft information and data uncertainty during transfer resulting from limited precise target labels and the data shift between the source and target domains, which can lead to negative transfer. To solve this problem, we propose a deep attention-bridging model based on Takagi-Sugeno fuzzy rules for universal domain adaptation. The model is designed to cope with some of the most challenging situations in transfer learning where there are multiple source domains but access to the source data is not available. Hence, to build fuzzy rules in source domain(s), similar source classes are grouped into clusters which share a rule. Then, through an extension of fuzzy c-means clustering to deep structures, the model learns the fuzzy cluster prototypes and their memberships. To predict target task, thresholds for dividing the shared class from the unknown classes are defined through entropy assumption and fuzzy outputs. Here, the soft class centers generated from the model parameters, which are based on a set of selected shared classes, are designed to match the target data to the source feature space. Last, a combination strategy involving an attention layer provides the final predictions. Experiments with real-world datasets confirm the strength of the proposed fuzzy model.
Human-in-the-Loop (HITL) Augmented Growing Neural Gas for Labeling Aerial Image Datasets

There is a severe demand for, and shortage of, large accurately labeled datasets to train supervised computational intelligence (CI) algorithms in domains like unmanned aerial systems (UAS) and autonomous vehicles. This has hindered our ability to develop and deploy various computer vision algorithms in/across environments and niche domains for tasks like detection, localization, and tracking. Herein, we propose a new human-in-the-loop (HITL) based growing neural gas (GNG) algorithm to minimize human intervention during labeling large UAS data collections over a shared geospatial area. Specifically, we address human driven events like new class identification and mistake correction. We also address algorithm-centric operations like new pattern discovery and self-supervised labeling. The effectiveness of our algorithm is demonstrated using simulated realistic ray-traced low altitude UAS data from the Unreal Engine. Our results show that it is possible to increase speed and reduce mental fatigue over hand labeling large image datasets.
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations

Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa. These models deliver state-of-the-art results in several Natural Language Processing tasks, namely Emotion Recognition in Conversations (ERC), but suffer from the lack of interpretability and explainability. In this paper, we propose to combine the two approaches to perform ERC, as a means to obtain simpler and more interpretable Large Language Models-based classifiers. We propose to feed the utterances and their previous conversational turns to a pre-trained RoBERTa, obtaining contextual embedding utterance representations, that are then supplied to an adapted Fuzzy Fingerprint classification module. We validate our approach on the widely used DailyDialog ERC benchmark dataset, in which we obtain state-of-the-art level results using a much lighter model.
Sentiment Analysis of ChatGPT Tweets Using Machine Learning Techniques

Large language models like ChatGPT by OpenAI have gained significant attention for their impressive performance across various tasks. They have become highly popular among adopters. Gaining an understanding of the sentiment of these adopters is crucial, as it can offer valuable information about the potential outcomes of the technology, whether it will thrive or falter, and the advantages and drawbacks it possesses.In this research, data mining techniques are utilized to analyze Twitter data and automatically categorize their sentiment polarity. A dataset of 15,000 English tweets is gathered in natural language from ChatGPT users, and text mining and data mining techniques are applied to convert them into a useful format. The objective is to build a sentiment classifier capable of predicting whether a tweet conveys positive, negative, or neutral sentiments. To achieve this, we utilize two widely used data mining classifiers: K-Nearest Neighbor and Naive Bayes. These classifiers are applied to analyze the sentiment of the tweets, and their overall accuracy, precision, and recall values are assessed, providing a comprehensive comparison between the two methods.