Reviewerly/exHarmony huggingface dataset:
Saturation in Fuzzy Flip-Flop Neural Networks

The aim of this paper is to investigate the phenomenon of saturation in a recurrent neural network based on J-K Fuzzy Flip-Flop neurons, and to develop a modification of the learning process to reduce this phenomenon. The work on solving this problem consists of several stages. Firstly, the concept of neuron saturation in the FFF-type network was defined and an analysis of the influence of basic internal parameters of the neural network and the Particle Swarm Optimization algorithm on saturation reduction was performed. Next, modifications to the learning algorithm were investigated. In the paper, extensions to the basic learning algorithm were then presented. These allowed reduction of the saturation of neurons inside the network. In order to perform the above analysis, the issue of data classification was addressed and, on the basis of the obtained results, the effectiveness of the introduced changes was verified in terms of the achieved neural network learning errors and saturation indices. For the numerical analysis, five benchmark data sets were exploited: Iris, Seeds, Wine, Glass, Breast Cancer and Wisconsin - drawn from the well-known UCI Machine learning repository. The aim of the validation of the developed algorithms is to demonstrate the correctness of the proposed solution in reducing the saturation of neurons, a phenomenon that affects the efficiency of learning, and which translates into confidence in the results obtained.
Modified Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: MPIFCM

The recently reported ‘Improved Probabilistic Intuitionistic Fuzzy c-Means (IPIFCM) algorithm’ is a computationally efficient algorithm that does fuzzy clustering based on Probabilistic Euclidean Distance measure (PEDM). A significant issue with the IPIFCM algorithm is that it does not consider the hesitation factor's effect while updating the membership of a datapoint for a given cluster. Therefore, the convergence of the algorithm is not optimal. In this paper, we modify the membership function by adding the hesitation component to the IPIFCM clustering algorithm's objective function to propose 'Modified Improved Probabilistic Intuitionistic Fuzzy c-Means' (MPIFCM) clustering algorithm. The proposed MPIFCM algorithm helps in achieving a realistic clustering of the datapoints. This modification leads to the improvement in the accuracy as well as the convergence rate of the algorithm. Experiments over various benchmark UCI datasets confirm that our proposed algorithm provides better performance over its existing counterparts. Popular performance metrices such as accuracy, convergence rate, partition coefficients and cluster entropy are considered for comparative analysis of the performances of the studied algorithms.
Explainable AI for Early Detection of Health Changes Via Streaming Clustering

The ability to explain the predictions of machine learning models has become increasingly important, especially in healthcare applications. Streaming clustering is an effective tool to recognize normal baseline patterns and to detect early signs of changes in data streams. However, many streaming clustering algorithms are not designed to explain to the users how predictions are made. In this paper, we extend a streaming clustering algorithm, the sequential possibilistic Gaussian mixture model (SPGMM) for early detection of health change to provide algorithm explainability for the results. Four approaches are discussed to explain either the cluster differences or the reason for the algorithm warnings: (i) linguistic summarization for warnings; (ii) annotation distribution of clusters; (iii) SHapley Additive exPlanations (SHAP); (iv) functional health score. The four approaches are validated on one older adult monitored with a collection of motion, bed, and depth sensors over three years. The results obtained on the older adult show that the four approaches aid understanding of how the clusters and warnings are generated, providing strong support for clinicians to take corresponding actions.
Vertical Slice Based General Type-2 Fuzzy Reasoning and Defuzzification for Control Applications

Two well-known representations of Generalized Type-2 Fuzzy sets, such as the z-slice and the vertical slice, are prevalent in the literature. While the z-slice based model has shown promising performance in reasoning and control applications, the vertical slice based model, until this date, is restrictedly being used for reasoning with rules containing General type-2 propositions in the antecedent and Interval type-2 propositions in the consequent. This paper attempts to overcome this restriction by proposing a general framework for automated reasoning with rules containing Vertical Slice based General Type-2 propositions in both the antecedent and the consequent. Naturally, the inference is a Vertically Sliced General type-2 fuzzy set, which is type-reduced and then (type-1) defuzzified by a simple but elegant approach. The proposed Vertical slice based defuzzification is time-efficient in comparison to its z-slice based counterpart as the former requires type-1 centroidal defuzzification only in µ-u plane, while the latter requires Karnik-Mendel defuzzfication over each z-slice. The proposed vertical slice based reasoning and defuzzification has successfully been applied in angular position control of an inverted pendulum system. Experiments undertaken confirms that the proposed vertical slice based controller outperforms the existing z-slice, Interval type-2 plus KM/CKM defuzzification, and other control strategies with respect to the settling time, peak-overshoot, and root-mean square error.
Evolutionary Multi-Objective Multi-Tasking for Fuzzy Genetics-Based Machine Learning in Multi-Label Classification

Explainable artificial intelligence (XAI) is an important research topic in the field of machine learning. A fuzzy rule-based classifier is a promising XAI technique thanks to its high interpretability. We can linguistically explain its classification result because a set of linguistically explainable fuzzy if-then rules are used for classification. In real-world data mining applications, multiple class labels are assigned to a single instance. Such a dataset is called a multi-label dataset (MLD). For MLDs, multiobjective fuzzy genetics-based machine learning for multi-label classification (MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> ) has been proposed. MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> aims to search for explainable fuzzy classifiers by explicitly considering the accuracy-complexity tradeoff that exists in explainable classifier design. In the field of multi-label classification, different accuracy metrics have been proposed to evaluate classifier performance. As a result, different multiobjective optimization problems (MOPs) can be defined using each accuracy metric together with a complexity metric. Usually, MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> solves each MOP independently. In this paper, we incorporate the idea of multi-tasking optimization into MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> so that multiple MOPs are solved simultaneously. We also propose a new information sharing method to improve the effectiveness of multi-tasking optimization in MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> . Our experimental results show that multiple accuracy metrics can be simultaneously optimized through the multi-tasking optimization framework and the proposed information sharing method improves the classification accuracy of fuzzy classifiers obtained by MoFGBML <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</inf> .
Fuzzy Index Evaluating Image Edge Detection obtained with Ant Colony Optimization

Ant Colony Optimization is one of the most used methods applied to complex problems. Image processing is a difficult task in particular when complex images are involved. This paper uses a fuzzy euclidean metric as a distance measure between pixels from two images. This is the first evaluation of Ant Colony Optimization image edge detection in the context of fuzzy index and fuzzy euclidean metrics. The Canny edge detection is considered as the ground truth when evaluating similarities between the considered fuzzy images including medical ones. Experiments were run and successful comparisons were conducted using existing data sets as well as well-known non-fuzzy similarity metrics such as Jaccard’s index, Dice’s coefficient and the Pratt’s Figure of Merit were applied.
A Neutrosophic Evaluation Model for Blockchain Technology in Supply Chain Management

Nowadays, firms are trying to execute and use blockchain technology (BT) for rising the products and service goodness in the supply chain (SC). Based on the specific requirements, the BT can be used in several areas. The BT assets various segments of Supply Chain Management (SCM) with consideration of several effective features that are characterized to be multi-criteria decision making (MCDM) issues with environmental restrictions of uncertainty conditions. This research illustrates the suitability of BT in SCM for various segments that are assessed using a neutrosophic model according to single-valued neutrosophic sets (SVNSs). Also, contributes as an evaluation model that combines neutrosophic set, with MCDM methods of Analytic Hierarchy Process (AHP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). The proposed study uses the AHP method to generate weights of criteria considering the expert's perspectives. Moreover, uses the neutrosophic theory to handle uncertain situations. The alternatives ranked based on outcomes of applying TOPSIS and VIKOR methods. A case study presents a hierarchical MCDM issue of 7 criteria and 20 sub-criteria with alternative segments assessed. As a result, the medicine segment is recommended to be the best alternative according to the proposed methods of AHP, TOPSIS, and VIKOR while the insurance segment is not recommended by AHP and TOPSIS methods and jewelry segments are not recommended in the VIKOR method.
Analysis of Sub-Integral Functions in the Aggregation of Classification Results Using Generalizations of the Choquet Integral on the Example of Emotion Classification

Speech emotion recognition is a complicated and challenging task in the human-computer interaction. Its performance depends on the relevance of the considered features in addition to the extent by which the speakers express their emotions. The well-known methods for determining the characteristics of a speech signal for the classification of emotional states are: The Mel Frequency Cepstral Coefficients, spectral methods like Short Time Fourier Transform and wavelet transform. However, the conventional approach to classifying emotions encounters problems, such as lack of sharp boundaries between separate emotional states, features correlation or expressing the same emotions differently depending on gender, culture orage. To prevent these issues, the fuzzy techniques such as an aggregation on a basis of the Choquet integral can be efficiently applied. Therefore, the main goal of this study is to determine the most efficient integrand of the popular Choquet integral generalizations. To find such a kind of functions, we run six separate tests for non-fuzzy classifiers: Multilayer Perceptron, Naive Bayes Network, Decision Trees, Probabilistic Neural Network, Random Forest and fuzzy ones: Fuzzy Multilayer Perceptron, Fuzzy Rule Classifier and Fuzzy Decision Trees. Additionally, the aggregation of the classification processes using 25 families of t-norms serving as integrands was performed. The results obtained in this extensive set of numerical experiments helped to establish the most effective operators which can be applied successfully in the multi-modal emotion recognition tasks. The used aggregation increased the emotions recognition accuracy for non-fuzzy classifiers from 11.10% to 15.50%, and for fuzzy classifiers from 3.31% to 6.00% depending on tested dataset.
Living Labs in Social Service Institutions: An Effective Method to Improve the Ethical, Reliable Use of Digital Assistive Robots to Support Social Services

The measures taken to prevent further damage by the novel coronavirus (SARS-CoV-2) accelerated the negative impact of the shortage of carers on the availability of social services, especially in long-term care institutions. Although much has been done in recent years to develop digital solutions to assist people who need care, such as socially assistive robots (SAR) nothing can substitute for human carers in times of crisis. The current paper describes a planned research study to work out answers to three research questions of the CareSAR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> research program: (A)What is the status of digitized assistive tools that are primarily designed and developed to support carers in long-term care institutions and home care?(B)What are the main reasons why existing digitized tools and equipment/devices using artificial intelligence are not yet used to the extent that is needed to mitigate the shortage of carers in a crisis and critical situations?(C)What kind of support can be provided by a living lab that is designed to develop effective, ethical, and socially acceptable digital devices to assist carers in their caring environment? The analysis of projects, that were designed to develop assisting robots to mitigate the lack of carers and the research of existing living labs conducted in social care institutions shall evaluate the opportunities how social assistive robots get the chance to be used largely.
State-of-Art Methods Used in Sentiment Analysis: A Literature Review

Understanding human sentiments on social media is a hot topic. Due to its practical applications, many organizations across several domains are involved and invested in sentiment analysis. Marketers are interested in extracting the voice of customers to gain insights. The challenges to the gathering, decoding, and extracting actionable knowledge from the huge volume of data that is constantly generated has increased manifold. Even with newer technologies, increased computing capacity, crunching social data remains a challenge. This organized study is dedicated to understanding the general state of the art of sentiment analysis including common methods, approaches, and gaps. The contribution of this paper is significant because firstly it identifies the types and techniques of sentiment analysis, and secondly, this study adopts a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions about the defined research domain.
The Role of Emotional Intelligence in Agile Project Management

This paper is one skeptical look at the role of emotional intelligence in project “outcome value” success and whether emotional intelligence can be measured. The emotional intelligence framework defined by Daniel Goleman and behavioral neuroscience is included in the analysis. One study shows the Self-Assessment measurement of emotional intelligence. Three studies show patterns and correlation of emotional intelligence “peoplefactors” to the outcome success of agile projects. The “accidental technical project managers” practitioners may find interesting areas and opportunities for better practices to enhance their emotional intelligence to become more compassionate and resilient under complexity and ambiguity. Project managers may also identify a genuine language of appreciation model to express words of affirmation, quality of time, acts of service and gifts with team members, adapted to their individual trait which motivates them to stay in their organization and reduce organization turnover. The project management principles and domains work in the context of a system of value delivery. The emotional intelligence framework is integrated into the project management principles and performance domains. The project manager behavior facilitates the environment for a project outcome value for the whole organization value delivery system. The project management leadership skills can be critical especially in the challenging pandemic times.“People work better when they know what the goal is and why” -Elon Musk.
Critical Analysis of “Sustainability” in Korean Early Childhood Curriculum from Posthuman Perspectives

This study analyzed how the recently revised Korean early childhood curriculum describes and discusses sustainability through a critical perspective using posthumanism to destabilize anthropocentric thinking and expand the concept of sustainability. The notion of sustainability described in the General Guidelines and Commentaries of the Korean Early Childhood Curriculum reveal limitations and the potential of the ontological, epistemological, and ethical foundations which serve as the basis of early childhood education. While sustainability was expressed as a new value from which the curriculum stems, it was presented as traditional nature, resource conservation, and raising animals and plants based on human-centered thinking written as educational content. However, through the vison of an educated person of the curriculum, children were found to be relational beings, and in everyday encounters they influenced and interacted with various more-than-human others; there was room for the interpretation that not only children but also other beings exert agency. We live entangled with multiple species. This implies that early childhood pedagogy has the potential to broaden understanding and practice and generate vigorous debate on sustainability.
Variance‐SNR Based Noise Suppression on Linear Canonical Choi‐Williams Distribution of LFM Signals

By solving the existing expectation-signal-to-noise ratio (expectation-SNR) based inequality model of the closed-form instantaneous cross-correlation function type of Choi-Williams distribution (CICFCWD), the linear canonical transform (LCT) free parameters selection strategies obtained are usually unsatisfactory. Since the second-order moment variance outperforms the first-order moment expectation in accurately characterizing output SNRs, this paper uses the variance analysis technique to improve parameters selection strategies. The CICFCWD's average variance of deterministic signals embedded in additive zero-mean stationary circular Gaussian noise processes is first obtained. Then the so-called variance-SNRs are defined and applied to model a variance-SNR based inequality. A stronger inequalities system is also formulated by integrating expectation-SNR and variance-SNR based inequality models. Finally, a direct application of the system in noisy one-component and bi-component linear frequency-modulated (LFM) signals detection is studied. Analytical algebraic constraints on LCT free parameters newly derived seem more accurate than the existing ones, achieving better noise suppression effects. Our methods have potential applications in optical, radar, communication and medical signal processing.
DeepHGNN: A Novel Deep Hypergraph Neural Network

With the development of deep learning, graph neural networks (GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured data. In many real-world networks, the relations between objects are complex and go beyond pair-wise. Hypergraph is a flexible modeling tool to describe intricate and higher-order correlations. The researchers have been concerned how to develop hypergraph-based neural network model. The existing hypergraph neural networks show better performance in node classification tasks and so on, while they are shallow network because of over-smoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel deep hypergraph neural network (DeepHGNN). We design DeepHGNN by using the technologies of sampling hyperedge, residual connection and identity mapping, residual connection and identity mapping bring from graph convolutional neural networks. We evaluate DeepHGNN on two visual object datasets. The experiments show the positive effects of DeepHGNN, and it works better in visual object classification tasks.
Data Safety Prediction Using Bird’s Eye View and Social Distancing Monitoring for Penang Roads

In terms of fatalities, Malaysia ranks third among ASEAN countries. Every year, there is an increase in accidents and fatalities. The state of the road is one factor contributing to near misses. A near miss is an almost-caused accident, an unplanned situation that could result in injury or accidents. The Majlis Bandar Pulau Pinang (MBPP) has installed 1841 closed-circuit television (CCTV) cameras around Penang to monitor traffic and track near miss incidents. When installing CCTVs, the utilisation of video allows resources to be used and optimised in situations when maintaining video memories is difficult and costly. Highways, industrial regions, and city roads are the most typical places where accidents occur. Accidents occurred at 200 per year on average in Penang from 2015 to 2017. Near misses are what create accidents. One of the essential factors in vehicle detection is the “near miss.” In this study, You Only Look Once version 3 (YOLOv3) and Faster Region-based Convolutional Neural Network (Faster RCNN) are used to solve transportation issues. In vehicle detection, a faster RCNN was used. Bird’s Eye View and Social Distancing Monitoring are used to detect the only vehicle in image processing and observe how near misses occur. This experiment tests different video quality and lengths to compare test time and error detection percentage. In conclusion, YOLOv3 outperforms Faster RCNN. In high-resolution videos, Faster RCNN outperforms YOLOv3, while in low-resolution videos, YOLOv3 outperforms Faster RCNN.
Digital transformation in local government organisations: empirical evidence from blockchain initiatives

Local government organisations are the first contact between the citizen and state authorities. However, the rapid technological development in the private sector raises questions on how public actors can keep up. Seeking improvement, local governments undergo the process of digital transformation (DT). This encompasses a variety of processes and initiatives, including experimenting with new technological solutions. We focus here on experiments on one of these emerging technologies: blockchain. We report the results of a multi-case study investigating the DT processes that experiment with blockchain technology in three municipalities in Western Europe. We present our theoretical perspective (institutional logics and digital transformation), describe our qualitative comparative case methodology that relies on key-respondent interviews, and discuss our distilled categorisation of three activities (including excerpts from data).
Superconducting Microbridges for large area single photon detectors

In this work we discuss the result of the fabrication with photolithography process of microstrips made of MoSi/Al superconducting films, with different widths and patterns suitable for single photon detection. The different designs presented are adopted to increase the sensitive area.