Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic++: Towards Developing Fuzzy Education Curricula Using ACM/IEEE/AAAI CS2023

Fuzzy Logic education has been considered part of artificial intelligence, which in many cases, can be found in the electives curricula or, inclusively, at the graduate studies for the past decades. Because of the persistent emergence of computing areas, such as Artificial Intelligence and Machine Learning, Robotics, and others, there is an appetite for recognizing concepts on intelligent systems in undergraduate education. The ACM/IEEE/AAAI Computer Science Curricula, also known as CS2023, has proposed significant changes from the last version CS2013, particularly in artificial intelligence. These proposed recommendations will have an impact in the next ten years in computer science undergraduate education, including fundamentals of computer programming. This work presents the changes that may impact computer science education curricula, particularly the knowledge areas and competencies model that will influence fuzzy logic education and computing. We also present the intersection of knowledge areas in fuzzy logic, especially for explainable AI.
Designing Multimodal User Interfaces for Hybrid Collaboration: A User-Centered Approach

Hybrid collaboration has gained significant popularity in recent years due to globalization and international collaboration, which has been further accelerated by the COVID-19 pandemic. Although some studies have investigated the features of hybrid collaboration, there is still a lack of understanding regarding efficient and effective human-computer interactions that can facilitate this mode of collaboration. Research has shown that humans process information faster and better when it is presented in multiple modalities. Therefore, this study aims to develop a user-centered approach that integrates multimodal user interfaces (MUIs) to better support hybrid collaboration. User research was conducted to gather insights into the needs and preferences of both co-located teams and remote members. The study also investigated the benefits of multimodal interactions. Building upon the understanding of user requirements and MUIs, the research explored the most effective MUIs for two typical activities in hybrid collaboration. Subsequently, a high-fidelity prototype was developed and evaluated to validate the research hypothesis. The findings identified the advantages of MUIs and formulated design principles for MUIs in a hybrid collaboration environment. This research could contribute to the growing body of knowledge on MUI design, and practical implications were presented and tested for enhancing hybrid collaboration.
Comparison of Explanation Methods for Genetic Fuzzy Trees for Wine Quality Predictions

As advanced AI and Machine Learning models are more widely adopted, explanations about what the models learned from the data are desirable in order for human users to be able to interpret, verify, and trust the outputs. There are many different types of Machine Learning models, ranging in human interpretability from easy-to-understand if-then statements to large Neural Network-based models with complicated architectures that are effectively black boxes. A large focus in Explainable AI has been to develop methods to generate explanations about what these systems learned ex post facto, as many popular models and techniques lack inherent interpretability. Fuzzy-based Systems such as Genetic Fuzzy Trees, can offer inherent explanatory power due to their interpretable nature, while still retaining powerful approximation capabilities. In this work, we train a Genetic Fuzzy Tree on a regression dataset and then compare its own inherent explanations against explanations generated by the Local Interpretable Model-Agnostic (LIME) technique. We show that the inherent explanations given by the Fuzzy Tree are richer and more easily interpreted by human users.
Data Driven Level Set Fuzzy Classification

The paper addresses the structure of fuzzy rule-based classifiers from the point of view of a function relating membership grades of inputs with rule outputs and a discriminant function. Rule-based level set based models are employed to produce classifiers using data to find the output functions of the fuzzy rules. A simple and effective formulation consists of estimating the parameters of the output function of each rule using input and output class data. The data driven method gives an easy and efficient mechanism to produce rule-based fuzzy classifiers. Performance evaluation is done using the data sets and classifiers available in scikit-learn, currently a reference in machine learning. The results suggest that data driven level set fuzzy classifiers performance competes closely or surpasses state of the art classifiers.
A Model for Assessing and Sorting Virtual Locomotion Techniques According to Their Fidelity to Real Walking

Travel is a challenging task in virtual reality applications due to space constraints of the setup or limited range of the tracking systems. This prevents the user from moving away from the installation and requires developers to implement smart solutions that overcome these limitations. Some of these solutions, known as virtual locomotion techniques, try to mimic real walking movements, while others introduce some kind of magic. We developed a model that is meant to classify and sort these techniques as regards to their fidelity to real walking. This can be useful both for research, to find opportunities for new techniques, and for development, to find the technique that best fits a target application. Whereas previous works typically divide techniques into real and virtual classes, we propose a 12-point scale where all techniques fit into and therefore makes easier to compare and select techniques. Besides, we perform a retrospective analysis of a previous travel technique evaluation using this tool. Comparing the results of the experiment with the proposed fidelity scale allows us to both evaluate the proposal and find ways of improving it in future revisions.
Deep Learning ANFIS Architectures

To explore the capabilities and significance of neuro-fuzzy systems, we study deep learning with Adaptive Network-based Fuzzy Inference Systems (ANFIS). By incorporating ANFIS layers into neural networks and treating the fuzzy sets in antecedents as custom activation functions, we test the combination of a neural network’s flexibility with the capabilities of ANFIS architectures in applications such as control and function approximation. This approach is well-supported by existing tools for automatic differentiation.
Equivalence Between 1-D Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions and Neural Networks with ReLU Activation

In the present paper we establish the equivalence between a neural network with one input and one hidden layer with ReLU activation and a Takagi-Sugeno fuzzy system with triangular membership functions. The results verify, both from theoretical and practical point of view, that we can convert a neural network into a TS fuzzy system and make it more explainable via this method. Possible extensions to multiple dimensions and multiple layers are also discussed.
Biophilic Design of Virtual Workplaces: Effect of Animations on User Attention

The biophilic design of virtual workplaces consists of introducing additional natural elements in the virtual environment (VE) with respect to those needed for the main task. It has received increasing attention in recent years, and an increasing number of studies are showing that exposure to biophilic elements in an immersive VE produces positive effects on human well-being. However, in the literature, there are no guidelines about the setting of the different variables of biophilic design in a VE. In this work, we investigated one of these variables, i.e., the effect of the animations of biophilic elements, formulating the following research question: how do animations of biophilic virtual elements affect user attention while performing a working task? We carried out an experiment in which users performed two levels of difficulty of the n-Back cognitive test in three VEs: two identical biophilic VE, one with all static elements and another one in which some of these elements were animated, and an additional empty VE used as a baseline. Performance measurements, eye-tracking measurements, and subjective measurements were collected from 24 users. We found that introducing animations of biophilic elements does not affect user performance in the cognitive task. Furthermore, we found that users’ gaze remains fixed on the main task for more than 94% of the time. However, the use of animations does not bring added value to users in terms of user experience, whereas it causes an increase in perceived distraction and mental effort.
Genetic Fuzzy Threat Assessment for Asteroids 2600 Derived Game

A fuzzy logic controller is used to assess the threat of asteroids in a python game derived from Asteroids 2600. The original game has been used extensively for testing and training of autonomous agents using reinforcement learning and serves as a testing ground for the training of a fuzzy system. A system for aiming and firing by planning the trajectory of an asteroid is used in conjunction to eliminate the highest threat asteroid. A genetic algorithm is used to train the fuzzy logic controller’s rule base in various custom and random asteroid environments. Using the genetic algorithm, the solution space can be explored to produce a more optimal controller than by hand. Three varying-sized fuzzy controllers are trained and tested on similar environments and training procedures. All controllers outperformed a human operator and produced threat decisions across all asteroids within milliseconds. The smaller systems showed much faster training and evaluation while producing marginally worse results than the largest controller, leading us to use the smallest controller in future work.
Changes in Concentration Performance After Short-Term Virtual Reality Training in E-Athletes

Changes in concentration performance after short-term virtual reality training in e-athletes Abstract. E-sports, or electronic sports, have emerged as a popular form of competition in the digital age. With growing involvement from well-known and established enterprises, e-sports has grown into a thriving industry. Being a professional e-athlete has become a dream for many young people. A crucial aspect of e-athletic performance is the level of cognitive functions, including concentration performance and reaction time. Both conventional and virtual reality trainings have been shown to improve these skills. In this study, 66 amateur e-athletes (45 males, 21 females, mean age 22.7 ± 0.66) who confirmed in a pre- study questionnaire that they were active amateur athletes (with no participation in professional e-sports competitions) were selected to evaluate the effectiveness of training in virtual reality (VR) on concentration performance. Participants were randomly divided into E - experimental group (n = 32) and C - control group (n = 34), with a similar number of men and women in both groups (E = 9 females and 23 males, C = 12 females and 22 males). Differences in daily gaming time and e-sports experience between groups E and C were not statistically significant. The study group underwent 15-min training sessions in the VR game, Beat Saber, for eight consecutive weekdays. The study found that training in VR improved concentration performance, demonstrating the potential of VR as a valuable tool for e-athletes to enhance their cognitive functions.
How People Make Decisions Based on Prior Experience: Formulas of Instance-Based Learning Theory (IBLT) Follow from Scale Invariance

To better understand human behavior, we need to understand how people make decisions, how people select one of possible actions. This selection is usually based on predicting consequences of different actions, and these predictions are, in their turn, based on the past experience. For example, consequences that occur more frequently in the past are viewed as more probable. However, this is not just about frequency: recent observations are usually given more weight that past ones. Researchers have discovered semi-empirical formulas that describe our predictions reasonably well; these formulas form the basis of the Instance-Based Learning Theory (IBLT). In this paper, we show that these semi-empirical formulas can be derived from the natural idea of scale invariance.
Designing a Pinyin-Based Keyboard Based on the Frequency of Pinyin of Chinese Characters

The existing Chinese Pinyin keyboard does not accurately reflect the features of Pinyin. By investigating the frequency of Pinyin letters, we have designed a keyboard layout that incorporates all the distinctive elements of Pinyin, including Chinese special two-letter initials, two- or three-letter finals, and tones. Firstly, we propose dividing the letter area of the keyboard into two parts, with initials on the left and finals on the right, arranged according to their spelling order. Secondly, we list the high-frequency or core letters in the middle, with the less-frequency or assisted letters scattered around them in a fan-like pattern. Thirdly, we group letters with the same root together, allowing for easy extension on desktops and convenient folding on laptops and virtual screens. The arrangement of tone keys, based on the size of keyboards, is flexible. With this layout, the most frequently-used Pinyin can be typed within 2 to 3 keystrokes.
Digital Financial Innovation and Regulation

In recent years, digital finance has emerged as a significant trend in the financial industry.
Central Bank Digital Currency

Central Bank Digital Currency (CBDC) is a type of digital currency issued and managed by a central bank.
Neural Networks Universality, Geometric, and Dynamical System Elements for Fluid Animation

In this chapter, we start with basic elements of Boolean expressions and circuit theory to define a background to study neural networks as computational models. We present the truth tables and algebra of propositions. Then, we consider the circuit theory since neural networks can be cast into the computational model formalized by circuit families. Specifically, the neural computation process can be organized in a graph or circuit whose gates (neurons) are arranged in layers. In this way, owing to the equivalence between circuits and Boolean expression approaches, we can discuss the universality of neural computing. We end this chapter with a discussion about elements from differential geometry and dynamical systems that are part of the mathematical background for our presentation. Specifically, we formalize a data model based on the concept of differentiable manifolds. Hence, the database samples and the associated probability density distribution occupy the natural place in the proposed data model. Dynamical systems foundations are necessary to visualize such data model in the context of Hamiltonian formalism when representing fluids as particle systems.
Data and Learning Theory

In this chapter, the concept of learning on the basis of examples is formalized. The geometric-based model for data presented in the previous chapter is used to discuss some aspects of the learning problem. Learning machines encapsulate functional spaces due to their internal parameters (weights). Formal aspects related to classification, regression, and probability density estimation are discussed within this viewpoint. Special attention is given to the training process, and the steepest descent approaches to optimize the Loss function, with respect to the machine parameters, during the training stage.
Building a Knowledge Graph of Chinese Kung Fu Masters From Heterogeneous Bilingual Data

Various endeavours into semantic web technologies and ontology engineering have been made within the organisation of cultural data, facilitating public access to digital assets.Although models for conceptualising objects have reached a certain level of maturity, only a few have delved into the tacit roles of individuals in the development of knowledge.Simultaneously, the field of cultural analytics demands practical methods for integrating diverse multilingual materials into a consistent data representation.In this context, our work addresses a human-centred perspective to construct a knowledge graph presenting historical Chinese kung fu masters, aptly named MA 2 KG (Martial Arts MAsters Knowledge Graph).The data workflow is built upon an established ontology model that describes traditional martial arts.It aggregates information from heterogeneous bilingual sources through direct connections and rule-based inference, incorporating data from English Wikidata and Chinese Baidu Baike and complemented with manual annotations.In addition, we describe our methodology and process in making the dataset available with scripts for reproducing similar agent-mediated contexts, data application and inspection cases are provided to discuss our findings and concerns regarding the use of linked open data strategies.
The LiLa Lemma Bank: A Knowledge Base of Latin Canonical Forms

The dataset contains a list of 215,102 Latin dictionary forms (known as canonical forms or lemmas).The dataset is a set of 1,699,687 Resource Description Framework (RDF) triples that describe, using a series of Web Ontology Language (OWL) ontologies for Linguistic Linked Data, the morphological properties of these forms.The dataset is used to link together a series of corpora and dictionaries in the interoperable network of language resources published by the LiLa: Linking Latin project.
Adaptive Underwater Image Enhancement Guided by Generalized Imaging Components

Underwater images often exhibit strong color distortion and hazing due to various degradation factors. A majority of algorithms provide color correction for underwater images, but the color is not as vivid as it could be. In order to solve this problem, we propose a method known as adaptive underwater image enhancement guided by generalized imaging components (AUIE-GIC). To the best of our knowledge, this is the first method of utilizing deep learning to develop a generalized imaging model. The proposed method contains two stages: component generation and component guided learning. In the first stage, we obtain the components of a learning-based generalized imaging model. In the second stage, attenuation and transmission features are used to adjust color and semantic information. We propose the attenuation attention module (AAM), and transmission attention module (TAM), which can highlight heavily degraded areas. Finally, the refined network is able to restore underwater images that are rich in semantics and vivid in color. Based on further evaluation and analysis, AUIE-GIC is demonstrated to provide superior performance when compared with state-of-the-art (SOTA) methods. We make the source code available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/perseveranceLX/AUIE-GIC</uri> .
In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Believing that biometrics trends are moving to distant and contactless mode, in-air signature verification is nowadays considered as one of the principal users biometric identification in contactless mode allowing users identification by drawing their handwritten signature in the air. In-air signature verification is used in many applications like access control and forensic analysis. In this regard, we propose a novel system for in-air signature verification using Beta stroke segmentation. The Beta-elliptical approach and the fuzzy perceptual detector are used for features extraction. The proposed system defines a specific data acquisition protocol and uses preprocessing steps to prepare data. Finally, the verification phase is done based on Dynamic Time Warping. To evaluate our proposed system, we have created two in-air signature datasets with and without the use of a transparent glass plate, which we make publicly available at https://ieee-dataport.org/documents/air-signature-databases, termed respectively In-Air Signature dataset (IAS dataset) and In-Air Signature dataset using Glass Plate (IASGP dataset). Our verification system demonstrates promising outcomes, yielding an Equal Error Rate (EER) of 1.25% when applied to the IAS dataset and an EER of 2.00% when applied to the IASGP dataset in the skilled-forgery scenario. Extensive evaluations were conducted on both the 3DAirSig and the DeepAirSig datasets. The results confirm that the proposed system has a good performance compared to existing in-air signature verification systems for both skilled-forgery and random-forgery scenarios.
Seam Generation Matrix Based on a Guided Energy-Depth Map for Image and Video Stitching

An image captured by a single camera has a smaller viewing angle than that of the human eye. One method to expand this viewing angle is a technique known as image stitching, which generates a wider view from images captured with multiple cameras. Although this technique has found uses in multiple industries, it is vulnerable to parallax distortion, wherein objects disappear from or repeatedly appear in stitched images when the parallax between cameras differs significantly. To minimize parallax distortion, seam-based and multi-homography-based methods have been proposed. In particular, the seam-based method enables faster image stitching owing to its intuitive procedure; however, the seam generation matrix may still incur parallax distortion under certain restrictive circumstances, and a longer stitching time is required when this method is applied to video sequences. This motivated us to develop the Guided Energy–Depth Map, which uses the energy function, depth information, and guidance map to minimize parallax distortion from a human visual perspective and reduce the time required to apply the stitching process to video sequences. Based on Average Seam Error (ASE) evaluation, the proposed method produces better seams than energy functions in 25 out of 32 experimental datasets, and the improvement rate of ASE evaluation is 15.58%. Also, the Frame Selection module for video stitching proposed in this paper takes only 7.27% of the time to find a specific frame for seam regeneration compared to the instance segmentation-based frame selection method.
An Eye-Tracking Study on the Use of Split/Unified Code Change Views for Bug Detection

To address a bug detection task, developers often compare two versions of source code. Comparison tools support this task by showing both versions of the code in a split view or unified way. However, there is still little knowledge of how developers interact with code comparison tools during a source code change analysis task. Therefore, the strengths and weaknesses of each approach remain unknown. To address this research gap, we investigate their usefulness, limitations, and potential room for improvement. To this end, we conducted a user study involving 12 participants who analyzed the source code in two commits to detect bugs. In the study, we adopted a within-subjects approach. Each participant addressed similar tasks using the split and unified views of GitHub while using an eye-tracking device. We found that participants experienced less visual effort when using the unified view. The results suggest that a lower effort can promote a deeper analysis of the code, which often leads to the detection of more bugs.We also observed that participants predominantly looked at conditionals, class/instance variables, and code changes. However, we did not find statistically significant differences among the variables analyzed when using split or unified views. We consider the results of the study to be relevant to practitioners and researchers alike. Practitioners will be informed when choosing a view, while researchers will identify opportunities to improve code comparison tools.