Reviewerly/exHarmony huggingface dataset:
A Proposal for a Taxonomy of Augmented Humanity Based on Current Needs, Topics and Challenges

In recent years, the term Augmented Humanity (AH) has been increasingly used in the research community. AH is a term that refers to the use of advanced technologies to enhance human capabilities, whether physical, cognitive, or social. Technologies used to achieve AH range from wearable devices such as smart watches to cybernetic implants and advanced prostheses. The purpose of this article is to present a proposal for a taxonomy of AH. After describing the main research topics that have been worked on in this area, the reasons why such a taxonomy is needed are explained. Then, this article presents three main approaches to AH: the enhancement of physical, intellectual, and social capabilities. In addition, several challenges of providing a taxonomy of AH are introduced. Finally, it may be concluded that this proposed AH taxonomy will help to organize the field of research and establish a common knowledge base and terminology for future studies.
Review of Physical Aggression Detection Techniques in Video Using Explainable Artificial Intelligence

Physical aggressions are a problem that affects our society throughout the world. This study is a review of the work done so far on the physical detection of aggressions in video using XAI techniques (XAI). In the Related Work section, other reviews have been presented that compile physical aggression detection, XAI techniques, and the combination of both: physical aggression detection using XAI techniques. The state of the art section, compiles papers on video assault detection, an explanation of XAI and the different ways of categorising and grouping existing techniques, and a review of papers dealing with video assault detection using XAI. The search methodology used consisted of using Google Scholar, reviewing articles with a publication date of no more than two years, with high quality and with a theme as close as possible to the subject matter of the article. As far as it has been possible to find, no works have been found on the detection physical aggressions or actions using XAI techniques; however, some works have been found in the context of object detection using XAI techniques. This opens the possibility of creating a research line on the detection of physical aggressions in video using XAI.
Analysis of Techniques for the Augmentation and Correction of Unbalanced Data in Binary Cybersecurity Datasets for Machine Learning

This document presents an analysis of different methods for correcting an unbalanced binary dataset with a limited number of observations associated with cybersecurity, specifically with Mitre Att&ck. To this end, the application of data manipulation techniques, oversampling, undersampling and synthetic generation of observations is considered. This research contributes to having a balanced dataset with sufficient observations for the subsequent application of machine learning algorithms.
Human Action Recognition in Uncontrolled Environments: Application from Artificial Intelligence to Contactless Interfaces

The recognition of human gestures and actions in images and videos is an active area of research in computer vision. This field has made great advances in the last decade thanks to the use of deep learning techniques. In addition, the recent diffusion of low-cost video camera systems, including depth cameras has enhanced the development of observation systems in a variety of application areas, such as video surveillance, home security, healthcare, etc. However, most of these developments are in closed and controlled environments. The recognition of movements and gestures in real-time through a camera that acquires its images in an uncontrolled environment (such as a shopping mall, a university lobby or a museum hall) and that allows interaction with passers-by in these spaces, contains challenges in various areas that include at least technological, social and legal challenges that need a careful approach. Within this framework, we set as objectives of this project the design of a web interface adapted to interaction without physical contact (i.e., through video images captured with a camera in real-time) and the construction of artificial intelligence models (based on deep learning) that guarantee, in an uncontrolled environment, an interaction with this web interface. As a first step in this work, we propose a literature review in this area. In addition, we include model recognition for a given set of gestures to explore the possibilities of different approaches.
Leadership and Social Intelligence Relationships in the Educational Space

The relevance of research into the relationships between social intelligence and leadership in the learning environment is currently very high, especially in the context of the development of learning and teaching approaches in scientific doctrine. Thus, it can be established that the article aims to determine the dependence and consequently the relationship between the category of social intelligence and leadership directly in the educational sphere. To carry out the research and achieve the goal, the paper used various scientific and methodological tools, including functional and systemic approaches, and also the method of analysis and synthesis, comparison, deduction, and generalization. After conducting the research, the article obtained several important results, which, in turn, fully reveal both the theoretical and practical essence of the issue under study. In particular, the first includes the analysis of basic general theoretical concepts, their properties, and attributes, which allowed us to characterize the object of research. The second component focused on the direct interaction and its result, which is reflected in the process of socialization of the subject and his psychological and emotional state. The practical value of the article lies in the fact that its substantive content is an important source for future in-depth study of this issue and can also be used in methodological teaching materials for teachers.
Project based learning in post-primary school in Ireland – a narrative literature review of the transition year programme to understand evolving digital spaces

Aim: Initially, this paper delves into the connection between Irish society and Project-Based Learning (PBL) and curriculum, exploring how this connection has influenced the evolution of Digital Spaces (DS). It positions the origins of the Transition Year Program (TYP) in Ireland within the context of societal and political changes that are relevant to the education field. Lastly, the focus shifts to a particular change, the integration of PBL, curriculum, and DS within the framework of the TYP. No review of research on the TYP in Ireland is currently available in relation to PBL in DS and this is timely given the policy context's recent focus on the digital world and the development of students’ digital competence.
Personality Enhanced Emotion Generation Modeling for Dialogue Systems

Abstract Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user’s unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.
Robust steady‐state <scp>matrix‐weighted fusion</scp> filtering for multisensor multichannel <scp>autoregressive</scp> signal with multiple uncertainties

Summary This article investigates the robust steady‐state matrix‐weighted fusion (MWF) filtering problem for multisensor multichannel autoregressive (AR) signal with multiple uncertainties, which can be transformed into the state estimation problems in the state space representation. The uncertainties include the random parameter matrices, uncertain noise variances (UNVs), one‐step random measurement delay, missing measurements, and packet dropouts. Three Bernoulli distributed stochastic variables with known conditional probabilities are introduced to describe the networked stochastic uncertainties. The AR signal model under consideration is transformed into a state space model only with UNVs via using a combined method, which is composed of the state space method, the augmented method, and the fictitious noise method. Based on the worst‐case system with conservative upper bounds of UNVs, the robust local steady‐state one‐step and multi‐step signal predictors are proposed by using the minimax robust estimation method. Subsequently, the distributed fusion signal predictors weighted by matrices are addressed. The robustness of the proposed signal predictors is proved by the introduced augmented noises and permutation matrices. Simulation experiment demonstrates the correctness and effectiveness of the proposed methods.
Optimal, hardware native decomposition of parameterized multi-qubit Pauli gates

Abstract We show how to efficiently decompose a parameterized multi-qubit Pauli (PMQP) gate into native parameterized two-qubit Pauli (P2QP) gates minimizing both the circuit depth and the number of P2QP gates. Given a realistic quantum computational model, we argue that the technique is optimal in terms of the number of hardware native gates and the overall depth of the decomposition. Starting from PMQP gate decompositions for the path and star hardware graph, we generalize the procedure to any generic hardware graph and provide exact expressions for the depth and number of P2QP gates of the decomposition. Furthermore, we show how to efficiently combine the decomposition of multiple PMQP gates to further reduce the depth as well as the number of P2QP gates for a combinatorial optimization problem using the Lechner–Hauke–Zoller mapping.
A non-linear optimization based robust attribute weighting model for the two-class classification problems

This article aims to determine the coefficients that will reduce the in-class distance and increase the distance between the classes, collecting the data around the cluster centers with meta-heuristic optimization algorithms, thus increasing the classification performance.The proposed mathematical model is based on simple mathematical calculations, and this model is the fitness function of optimization algorithms. Compared to the methods in the literature, optimizing algorithms to obtain fast results is more accessible. Determining the weights by optimization provides more sensitive results than the dataset structure. In the study, the proposed model was used as the fitness function of the metaheuristic optimization algorithms to determine the weighting coefficients. In this context, four different structures were used to test the independence of the results obtained from the algorithm: the particle swarm algorithm (PSO), the bat algorithm (BAT), the gravitational search algorithm (GSA), and the flower pollination algorithm (FPA).As a result of these processes, a control group from unweighted attributes and four experimental groups from weighted attributes were obtained for each dataset. The classification performance of all datasets to which the weights obtained by the proposed method were applied increased. 100% accuracy rates were obtained in the Iris and Liver Disorders datasets used in the study. From synthetic datasets, from 66.9% (SVM classifier) to 96.4% (GSA Weighting + SVM) in the Full Chain dataset, from 64.6% (LDA classifier) to 80.2% in the Two Spiral datasets (weighted by BA + LDA). As a result of the study, it was seen that the proposed method successfully fulfills the task of moving the attributes to a linear plane in the datasets, especially in classifiers such as SVM and LDA, which have difficulties in non-linear problems, an accuracy rate of 100% was achieved.
Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer

The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-Labelsets) algorithm for the fault diagnosis of high-speed train traction transformers. Firstly, this article starts from the large amount of "sleeping" fault maintenance data accumulated by the railway department, takes a single maintenance record as an instance, uses specific monitoring values to construct an instance vector, and uses the fault phenomena corresponding to the monitoring indicators as labels. Then, this paper improves the step of selecting k-labelsets in RAkEL, and extracts associated faults using the Relief algorithm. Finally, this paper excavates and uses the association rules between data and faults to identify traction transformer faults. The results showed that the improved RAkEL diagnostic method had a significant improvement in the evaluation indicators. Compared with other multi-label classification algorithms, including BR (Binary Relevance) and CLR (Calibrated Label Ranking), this method performs well on multiple evaluation indicators. It can further help engineers perform timely maintenance work in the future.
Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out.
Beyond Weisfeiler–Lehman with Local Ego-Network Encodings

Identifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Weisfeiler–Lehman (1-WL) test. We introduce IGEL, a preprocessing step to produce features that augment node representations by encoding ego networks into sparse vectors that enrich message passing (MP) graph neural networks (GNNs) beyond 1-WL expressivity. We formally describe the relation between IGEL and 1-WL, and characterize its expressive power and limitations. Experiments show that IGEL matches the empirical expressivity of state-of-the-art methods on isomorphism detection while improving performance on nine GNN architectures and six graph machine learning tasks.
A geological and geotechnical information system (GEO-GIS) for urban planning and its application to the Acıpayam Basin (Denizli, Turkey) with some observations from a M <sub>w</sub> 5.5 earthquake on 20 March 2019

There are many site investigation projects used for the urban planning of a city. They are archived in hard copy or scanned in image files stored on computers. In this study, geological, geotechnical and geophysical data obtained from different site investigation projects in the Acıpayam district were evaluated in the same coordinate system using a geological and geotechnical information system (GEO-GIS) computer program. The Acıpayam GEO-GIS database includes field tests in 82 boreholes, 85 observation pits, laboratory test results of soil samples, 99 seismic refraction measurements and 70 microtremor measurements. Required information from the database can be displayed at its spatial coordinates on a base map and new maps can also be drawn using the GEO-GIS database system. Resultant maps drawn using the Acıpayam GEO-GIS database show that in the northern and eastern parts of the Acıpayam district, where the Oğuz, Kırca, Yassıhöyük, Apa, Ovayurt, Karahüyük, Ucarı and Gedikli villages are located, weak soil layers exist. This means that some damage may be caused to adobe and masonry buildings in the villages during shaking associated with strong earthquakes, as observed after the 5.5 (M w ) earthquake that occurred on 20 March 2019. The Acıpayam GEO-GIS database system is a useful tool for engineers and planners that can assist in drawing GIS-produced maps for geotechnical evaluations of the basin. These maps can be used in decision-making projects for urban planning. Thematic collection: This article is part of the Leading to Innovative Engineering Geology Practices collection available at: https://www.lyellcollection.org/topic/collections/leading-to-innovative-engineering-geology-practices
Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical methodis appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.
Perfect state transfer and maximal entanglement in a trimer of three-level systems

We analyze a quantum wire that consists of a linear chain of three qutrits with nearest-neighbor interactions and detuning on the central qutrit. We derive the detuning conditions for perfect state transfer of a double excitation and for maximal entanglement between the first and third qutrits. We also analytically determine the times for perfect state transfer and maximal entanglement, which are found to be periodic. We also show that perfect state transfer is a necessary condition for maximal entanglement, with the first occurrence of maximal entanglement at half the time for the first occurrence of perfect state transfer.
On the visual analytic intelligence of neural networks

Visual oddity task was conceived to study universal ethnic-independent analytic intelligence of humans from a perspective of comprehension of spatial concepts. Advancements in artificial intelligence led to important breakthroughs, yet excelling at such abstract tasks remains challenging. Current approaches typically resort to non-biologically-plausible architectures with ever-growing models consuming substantially more energy than the brain. Motivated by the brain's efficiency and reasoning capabilities, we present a biologically inspired system that receives inputs from synthetic eye movements - reminiscent of saccades, and processes them with neuronal units incorporating dynamics of neocortical neurons. We introduce a procedurally generated visual oddity dataset to train an architecture extending conventional relational networks and our proposed system. We demonstrate that both approaches are capable of abstract problem-solving at high accuracy, and we uncover that both share the same essential underlying mechanism of reasoning in seemingly unrelated aspects of their architectures. Finally, we show that the biologically inspired network achieves superior accuracy, learns faster and requires fewer parameters than the conventional network.
An empirical study of the relationship between refactorings and merge conflicts in Javascript code

Maintenance activities are crucial to prolong the lifecycle of a software. An important activity during software maintenance is refactoring, which is a transformation that improves the quality of a program without changing its behavior. During software development, Version Control Systems (VCS) are used to integrate changes made by developers. These integration procedures, known as merge processes, may result in conflicts if changes are made in the same place in the code. This work aims to analyze the possible relationship between refactorings and merge conflicts in JavaScript code. We analyzed 50 JavaScript repositories, including 56,966 merge scenarios, which 4,816 of them have conflicts. We discovered a moderate positive correlation between the number of conflicts and the number of refactoring transformations within the conflicting file. Additionally, this correlation captured a specific JavaScript refactoring type, "Internal Move", that was most strongly associated with conflicting merge scenarios.
FORMATION OF SPIRITUAL AND MORAL VALUES AMONG UNIVERSITY STUDENTS

In the context of a decrease in the level of personality culture in a post-industrial society, the problem of the formation of spiritual and moral values in the younger generation is becoming increasingly important. The essence of such concepts as spirituality, morality, spiritual and moral values is considered. The ways and conditions for the formation of spiritual and moral values among students are determined.