Reviewerly/exHarmony huggingface dataset:
Nurse's protagonism in structuring and managing a specific unit for Covid-19/ Protagonismo del enfermero en la estructuración y gestión de una unidad específica para Covid-19/ Protagonismo do enfermeiro na estruturação e gestão de uma unidade específica para Covid-19

ABSTRACT Objective: to report the experience in the structuring and managing process of a specific unit for COVID-19, highlighting the role of nurses in decision-making. Method: an experience report on the creation and management of the Coronavirus Disease 2019 (COVID-19) unit, in March 2020, in 2020, in a philanthropic hospital of the state of Santa Catarina. Results: the unit was structured with 10 intensive care beds and 20 infirmary beds. Meetings were held to make decisions, as well as to create protocols and flows with active participation of the nurse. In questions related to direct assistance, adaptations were developed in the nursing process performed at the hospital and the organization of new flows and routines. The physical space was structured, considering the high risk of transmissibility for the disease. Professionals were hired with staffing readjustment according to the complexity of the service, making up a team of professionals with experience in critical care. There were trainings for developing knowledge and skills prior to the first cases, which were systematically maintained. In addition, it was observed that the nurses were concerned about the mental health of the professionals working in this unit and, therefore, support actions were programmed. Conclusion: the foundation in the scientific evidence and recommendations of the competent bodies at the world and national levels for the structuring of the COVID-19 unit is emphasized. The role of the nurse in all the interfaces stands out, assuming a fundamental role from the composition of the commissions, going through the planning and functioning of the physical structure, management of human resources, and construction of care protocols and flows, in addition to acting directly in the care provided.
Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data

Transfer learning has been considered a key solution for the problem of learning when there is a lack of knowledge in some target domains. Its idea is to benefit from the learning on different (but related in some way) domains that have adequate knowledge and transfer what can improve the learning in the target domains. Although incompleteness is one of the main causes of knowledge shortage in many machine learning real-world tasks, it has received a little effort to be addressed by transfer learning. In particular, to the best of our knowledge, there is no single study to utilize transfer learning for the symbolic regression task when the underlying data are incomplete. The current work addresses this point by presenting a transfer learning method for symbolic regression on data with high ratios of missing values. A multi-tree genetic programming algorithm based feature-based transformation is proposed for transferring data from a complete source domain to a different, incomplete target domain. The experimental work has been conducted on real-world data sets considering different transfer learning scenarios each is determined based on three factors: missingness ratio, domain difference, and task similarity. In most cases, the proposed method achieved positive transductive transfer learning in both homogeneous and heterogeneous domains. Moreover, even with less significant success, the obtained results show the applicability of the proposed approach for inductive transfer learning.
Hybridising Particle Swarm optimisation with Differential Evolution for Feature Selection in Classification

Classification has been widely studied due to its practical applications. Feature selection aims to improve the classification accuracy by selecting a small feature subset from the original full feature set. However, identification of relevant features is not trivial due to the large search space. Particle swarm optimisation (PSO) is an efficient meta-heuristic algorithm which has shown to be promising in feature selection. However, traditional PSO uses its personal best experience and its historical best experience to determine its search direction, but this learning strategy may limit its performance for feature selection due to the premature convergence. Therefore, the potential of PSO needs to be further explored. In this paper, a new evolutionary learning algorithm termed hybridising PSO with differential evolution (HPSO-DE) is proposed to develop new feature selection methods. In HPSO-DE, differential evolution is applied to breed promising and efficient exemplars for PSO to guide its search, which is expected to not only preserve the diversity of the population but also guide particles to fly to promising areas. HPSO-DE is compared with three classic PSO variants and five traditional feature selection methods on 15 classification problems. The results show that the proposed algorithm can effectively achieve a higher classification accuracy with a smaller feature subset than the compared methods.
SR-HGAT: Symmetric Relations Based Heterogeneous Graph Attention Network

Graph neural network, as a deep learning based graph representation technology, can capture the structural information encapsulated in graphs well and generate more effective feature embedding.We have recently witnessed an emerging research interests on it.However, existing models are primarily focused on handling homogeneous graphs.When designing graph neural networks for heterogeneous graphs, heterogeneity and rich semantic information bring great challenges.In this paper, we extend graph neural network to heterogeneous graph scenes, and propose a novel high-order Symmetric Relation based Heterogeneous Graph Attention Network, denoted as SR-HGAT, which takes into account the features of nodes and high-order relations simultaneously, and exploits the two-layer attention mechanism based aggregator to efficiently capture essential semantics in an end-to-end manner.The proposed SR-HGAT first identifies the latent semantics underneath the observed explicit symmetric relations guided by different meta-paths and meta-graphs in a heterogeneous graph.The nested propagation mechanism for aggregating semantic and structural features that different links contain is then designed to calculate the interaction strength of each symmetric relation.As the core of the proposed model, to comprehensively capture both the structural and semantic feature information, a two-layer attention mechanism is applied to learn the importance of different neighborhood information as well as the weights of different symmetric relations.These latent semantics are then automatically fused to obtain unified embeddings for specific mining tasks.Extensive experimental results offer insights into the efficacy of the proposed model and have demonstrated that it significantly outperforms state-of-the-art baselines across three benchmark datasets on various downstream tasks.
Comparative Study of Three Mixing Methods in Fusion Technique for Determining Major and Minor Elements Using Wavelength Dispersive X-ray Fluorescence Spectroscopy

Accurate analysis using a simple and rapid procedure is always the most important pursuit of analytical chemists. In this study, a new sample preparation procedure, namely the shaker cup (SH) method, was designed and compared with two sample preparation procedures, commonly used in the laboratory, from three aspects: homogeneity of the sample–flux mixture, potential for sample contamination, and sample preparation time. For the three methods, a set of 54 certified reference materials (CRMs) was used to establish the calibration curves, while another set of 19 CRMs was measured to validate the results. In the calibration procedures, the matrix effects were corrected using the theoretical alpha coefficient method combined with the experimental coefficient method. The data of the major oxides (SiO2, TiO2, Al2O3, TFe2O3, MnO, MgO, CaO, Na2O, K2O, and P2O5) and minor elements (Cr, Cu, Ba, Ni, Sr, V, Zr, and Zn) obtained by wavelength dispersive X-ray fluorescence spectroscopy (WD-XRF) were compared using two derivative equations based on the findings by Laurence Whitty-Léveillé. The results revealed that the WD-XRF measured values using the SH method best agreed with the values recommended in the literature.
The New Combined Method of the Generation of a 3d Dense Map of Evironment based on History of Camera Positions and the Robot's Movements

A combined solution is proposed for solving the point-plane variational problem in a closed-form based on fusion visual features and 3d point clouds.An accurate method for reconstructing 3d scene is introduced, and the closed-form solutions of registration task for orthogonal transformation are presented.This method is used to reconstruct a threedimensional model of the environment from a set of images and depth map.The given method is used to match and register point clouds with an arbitrary spatial resolution and an arbitrary scale with respect to each other.The suggested method improves the convergence and accuracy of reconstruction methods for dynamic, bulky scenes.
Line-scan camera calibration: a robust linear approach

Camera calibration requires three steps: estimation of correspondences between world and image coordinates, computation of a linear solution, and nonlinear optimization using the linear estimate as a starting point. The resulting accuracy depends mostly on the first and final steps. However, the nonlinear optimization method can achieve an accurate result only when given an initial estimate close to the global solution. Therefore, obtaining a good linear estimation is crucial for the performance of the camera calibration procedure. This work proposes a robust method to estimate a linear solution for the calibration of line-scan cameras, resulting in individual intrinsic and extrinsic parameters by using only a single line scan. The calculated parameters can then be used by nonlinear optimization methods to finely adjust the estimation of all the line-scan camera parameters, including distortions. The proposed procedure does not impose restrictions on particular orientations, and always generates a well-conditioned problem that can be solved analytically with no optimization required. Extensive experiments are performed to verify the robustness and accuracy of the proposed method. The comparative results demonstrate that the proposed method provides excellent performance.
Survey on Federated Learning Towards Privacy Preserving AI

One of the significant challenges of Artificial Intelligence (AI) and Machine learning models is to preserve data privacy and to ensure data security. Addressing this problem lead to the application of Federated Learning (FL) mechanism towards preserving data privacy. Preserving user privacy in the European Union (EU) has to abide by the General Data Protection Regulation (GDPR). Therefore, exploring the machine learning models for preserving data privacy has to take into consideration of GDPR. In this paper, we present in detail understanding of Federated Machine Learning, various federated architectures along with different privacy-preserving mechanisms. The main goal of this survey work is to highlight the existing privacy techniques and also propose applications of Federated Learning inIndustries.Finally, we also depict how Federated Learning is an emerging area of future research that would bring a new era in AI and Machine learning.
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control

Faults are endemic to all systems. Adaptive fault-tolerant control accepts degraded performance under faults in exchange for continued operation. In systems with abrupt faults and strict time constraints, it is imperative for control to adapt fast to system changes. We present a meta-reinforcement learning approach that quickly adapts control policy. The approach builds upon model-agnostic meta learning (MAML). The controller maintains a complement of prior policies learned under system faults. This ``library" is evaluated on a system after a new fault to initialize the new policy. This contrasts with MAML where the controller samples new policies from a distribution of similar systems at each update step to achieve the new policy. Our approach improves sample efficiency of the reinforcement learning process. We evaluate this on a model of fuel tanks under abrupt faults.
JMLNet: Joint Multi-Label Learning Network for Weakly Supervised Semantic Segmentation in Aerial Images

Weakly supervised semantic segmentation in aerial images has attracted growing research attention due to the significant saving in annotation cost. Most of the current approaches are based on one specific pseudo label. These methods easily overfit the wrongly labeled pixels from noisy label and limit the performance and generalization of the segmentation model. To tackle these problems, we propose a novel joint multi-label learning network (JMLNet) to help the model learn common knowledge from multiple noisy labels and prevent the model from overfitting one specific label. Our combination strategy of multiple proposals is that we regard them all as ground truth and propose three new multi-label losses to use the multi-label guide segmentation model in the training process. JMLNet also contains two methods to generate high-quality proposals, which further improve the performance of the segmentation task. First we propose a detection-based GradCAM (GradCAMD) to generate segmentation proposals from object detectors. Then we use GradCAMD to adjust the GrabCut algorithm and generate segmentation proposals (GrabCutC). We report the state-of-the-art results on the semantic segmentation task of iSAID and mapping challenge dataset when training with bounding boxes annotations.
Teaching the Old Dog New Tricks: Supervised Learning with Constraints

Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver: this enables taking advantage of decades of research on constrained optimization with limited effort. In practice, we use a decomposition scheme alternating master steps (in charge of enforcing the constraints) and learner steps (where any supervised ML model and training algorithm can be employed). The process leads to approximate constraint satisfaction in general, and convergence properties are difficult to establish; despite this fact, we found empirically that even a naive setup of our approach performs well on ML tasks with fairness constraints, and on classical datasets with synthetic constraints.
Visual Analytics Approach to Comprehensive Meteorological Time-Series Analysis

In some of the domain-specific sectors, such as the climate domain, the provision of publicly available present-day high-resolution meteorological time series is often quite limited or completely lacking. This repeatedly leads to excessive deployment of synthetically generated (historical) meteorological time series (TMY) to support thermal performance assessments on both building and urban scale. These datasets are generally a misrepresentation of current weather variability, which may lead to erroneous inferences drawn from modelling results. In this regard, we outline the application potential of a visual analytics approach in the context of data quality assessment and validation of TMYs. For this purpose, we deployed a standalone visual analytics tool Visplore, enriched with interlinked dashboards, customizable visualizations, and intuitive workflows, to support continuous interaction and early visual feedback. Driven by such integrated visual representations and visual interactions to enhance the analytical reasoning process, we were able to detect critical multifaceted discrepancies, on different levels of granularity, between TMY and present-day meteorological time series and synthetize them into cohesive patterns and insights. These mainly entailed diverging temporal trends and event time lags, under- and overestimation of warming and cooling regimes, respectively, and seasonal discrepancies, in particular meteorological parameters, to name a few.
WhatsApp as a resource for group work in university students

The objective of the research was to analyze the group work carried out by students of a public university in the State of Hidalgo, Mexico, through the use of WhatsApp, a descriptive study with a quantitative approach was carried out, and a survey was applied to 299 students of the Bachelor’s degrees in Education Sciences and Social Work. Among the main findings, the usefulness of the technological tool for the organization of work stands out, and the development of efficient communication between students and students, as well as students and teachers, which strengthens interpersonal relationships, among the limitations are the distractions that generated by sending information that does not correspond to the subject of study.
Intelligent edge processing with photonic multilevel memory

Here we demonstrate an on-chip programmable multi-level non-volatile photonic memory used as node in a photonic neural network that effortlessly perform inference at the edge of the network as a passive and reprogrammable filter.
Production Analysis and Scientific Mapping on Active Methodologies in Web of Science

Teaching and teaching trends are in continuous transformation. Teachers and the teaching community play new roles in the teaching and learning processes. This situation implies the need of new competencies, knowledge, and skills in teachers to carry out their tasks, thus adjusting them to the requirements of contemporary society. The incidence of technology and new training resources and resources has led to an increase in innovative practices in learning spaces. This study focuses on analyzing the production of active methodologies in the Web of Science database. All this with the purpose of determining the state of the art on a field in the scientific literature from a bibliometric perspective. In addition to the study of the main indicators of bibliometric production, a word analysis has been carried out. To do this, 2208 documents has been analyzed. The study was carried out with the SciMAT software. The results reveal that scientific production has been increasing. The publications are mainly in English with a wide variety of topics, outstanding among them Higher Education in the early years. The study concludes that the research has specialized in topics such as the active profile of students and the influence of innovative practices on the learning process.
Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids Dan Backpropagation Pada Kasus Pandemi COVID-19 Di Indonesia

The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.
Facial Expression Recognition Based on Sobel Operator and Improved CNN-SVM

Aiming at the problem that traditional convolutional neural networks (CNN) have incomplete facial expression feature extraction and optimizable recognition accuracy in facial expression recognition (FER), this paper proposes a FER model based on improved CNN for Sobel edge detection and fused support vector machine (SVM). In this model, the VGG11 network is used to realize the feature extraction of facial expressions, and the L2-SVM is used to replace the softmax activation function to realize facial expression classification. This paper has been verified on the CK+ and JAFFE data sets, and the experimental results show that on the CK+ data set, after replacing the softmax activation function with L2-SVM, the accuracy rate has been improved by 3.02%. On this basis, in the image preprocessing stage, after adding Sobel edge detection, the accuracy has been improved by 3.71%. On the JAFFE data set, after using L2-SVM, the accuracy rate has increased by 2.16%, and after adding Sobel edge detection, it has increased by 2.17%.
Eye-Tracking to Predict User Cognitive Abilities and Performance for User-Adaptive Narrative Visualizations

We leverage eye-tracking data to predict user performance and levels of cognitive abilities while reading magazine-style narrative visualizations (MSNV), a widespread form of multimodal documents that combine text and visualizations. Such predictions are motivated by recent interest in devising user-adaptive MSNVs that can dynamically adapt to a user's needs. Our results provide evidence for the feasibility of real-time user modeling in MSNV, as we are the first to consider eye tracking data for predicting task comprehension and cognitive abilities while processing multimodal documents. We follow with a discussion on the implications to the design of personalized MSNVs.
Domain-Invariant Speaker Vector Projection by Model-Agnostic Meta-Learning

Domain generalization remains a critical problem for speaker recognition, even with the state-of-the-art architectures based on deep neural nets. For example, a model trained on reading speech may largely fail when applied to scenarios of singing or movie. In this paper, we propose a domain-invariant projection to improve the generalizability of speaker vectors. This projection is a simple neural net and is trained following the Model-Agnostic Meta-Learning (MAML) principle, for which the objective is to classify speakers in one domain if it had been updated with speech data in another domain. We tested the proposed method on CNCeleb, a new dataset consisting of single-speaker multi-condition (SSMC) data. The results demonstrated that the MAML-based domain-invariant projection can produce more generalizable speaker vectors, and effectively improve the performance in unseen domains.
Evolution of Quantum Radar Concept to Noise Radar Concept

Preliminary results of the comparative analysis of quantum radar (QR) based upon quantum entanglement phenomenon and noise radar (NR) based upon classical coherence and correlation processing of random signals are presented in this article. Common and distinguishing features of NR and QR are briefly discussed. A novel concept of a stepped-frequency quantum radar (SF-QR) for design of QR with range resolution capability is suggested. Field programmable gate array based digital generation of entangled signals is proposed as well.