Reviewerly/exHarmony huggingface dataset:
The Effect of Human Resource Information Systems on Innovation –Study at Jordanian Telecommunication Companies

Telecommunication companies is a very important sector in today's economy.They contribute heavily to the national economy of developed and developing countries.They are considered a motive for all sectors in any country.Therefore, human resources should be managed well and the consolidated human resources information systems are very important in this pursuit.They need support from top management at the telecommunications companies.As such, this study aimed to know the effect of human resources information systems on innovation from the employees' point of view at the Jordanian telecommunications companies.To achieve this, (384) questionnaires were distributed to the sample at Jordanian telecommunications companies, with (336) valid questionnaires were recovered.The study concluded with some results, most importantly are the level of human resources information systems, which scored high, in addition to innovation, which scored high as well.There is a significant statistical effect of human resources information systems and all its components on innovation at (α ≤ 0.05) at Jordanian Telecommunications companies, and interpreted (0.60) variance for innovation, except for selection and recruitment information systems which had no effect.The researcher recommended that the telecommunications companies adopt human resources information systems as a strategy to maximize benefits gained from the strategy of their use.In addition, the researcher recommended that the telecommunication companies increase the level of relying on human resources information systems to get information about employees, competencies, and human resources as a whole, which benefit in planning, training, compensation and so on to get benefits for their companies.
Rotation forest of random subspace models

During the last decade, a variety of ensembles methods has been developed. All known and widely used methods of this category produce and combine different learners utilizing the same algorithm as the basic classifiers. In the present study, we use two well-known approaches, namely, Rotation Forest and Random Subspace, in order to increase the effectiveness of a single learning algorithm. We have conducted experiments with other well-known ensemble methods, with 25 sub-classifiers, in order to test the proposed model. The experimental study that we have conducted is based on 35 various datasets. According to the Friedman test, the Rotation Forest of Random Subspace C4.5 (RFRS C4.5) and the PART (RFRS PART) algorithms exhibit the best scores in our resulting ranking. Our results have shown that the proposed method exhibits competitive performance and better accuracy in most of the cases.
PROFESSIONAL TRAINING OF FUTURE TEACHERS OF MATHEMATICS: THE PROBLEM OF FORMING CRITICAL THINKING

The relevance of the problem under consideration. In the process of training at the pedagogical university, the educational activities of future mathematics teachers should acquire the features of his professional activity, so in addition to mastering knowledge, must have the ability to control mental processes, justify, identify problems and find their own solutions, be able to defend their opinions, choice of elements of educational content and teaching methods in lesson design. These provisions intensify the problem of developing critical thinking of future mathematics teachers in the process of their professional training. The purpose of the study is to reveal ways to organize the process of professional training of future teachers of mathematics, which will contribute to the effective formation of their critical thinking in the study of professional mathematical disciplines. Research methods: theoretical: analysis, systematization and generalization of pedagogical and psychological research. Critical thinking is the most important component of the training of future mathematics teachers. Critical thinking of the teacher is generally defined as a type of evaluative thinking activity, based on the formed skills of a certain type, which contributes to the understanding of theoretical knowledge, translating them into practical activities and predicting results. Methods of training future teachers of mathematics, focused on the formation of critical thinking skills, includes the organization of the educational process based on involving students in active cognitive activities using various methods of working with information and development of specially designed sets of tasks.
Optimal Polarization-Entanglement Source: Frequency-Converted SPDC with Degeneracy, Indistinguishability, and Ultrahigh Purity That is Configurable Over a Large Spectral Range

Modeling and simulations of entanglement-based quantum-networking protocols commonly assume perfect entangled states. Some investigations have been performed, which show how imperfections cause the efficiency of the protocols to rapidly deteriorate. For polarization-entangled states created by spontaneous-parametric down-conversion (SPDC), the fundamentals of phase matching lead to a trade-off problem for the optimal properties of the state. We present a SPDC method, which circumvents the trade-off problem and allows one to obtain degeneracy, indistinguishability, and heralded-single-photon spectral purity greater than $99\mathrm{%}$ for any target SPDC wavelength in the visible and near-infrared spectrum. Therefore, our method can, in principle, generate polarization-entangled states that are optimal for polarization-entanglement-based quantum-networking protocols.
Introduction to this special section: Physics-driven machine learning

Machine learning has gained widespread popularity in fields such as research, finance, behaviorism, marketing, economics, and communications, where large volumes of training data are available and where an underlying model or system may not be readily accessible or easily parameterized. On the other hand, breakthroughs into scientific applications where underlying physical models exist and are well understood are harder to find. This is presumably because the physics-based solution, while imperfect, exploits the intrinsic relation between inputs and outputs and can often outperform a purely data-driven solution. Furthermore, adoption of machine learning may encounter resistance because these new algorithms are often opaque, and it is not always clear under what circumstances the methods will fail.
SAMPURNA- A Multi-Functional Medical Assistant Application Framework

Abstract: In recent times, the need for the advancement of medical facilities has been majorly realised and a lot of efforts have been made in the same direction. Healthcare is vital to leading a healthy and prosperous life. However, there are still certain arenas where there is a need for improvement so that people can benefit the most. The COVID-19 pandemic made everyone realise that there is nothing more important than their own health and family. During the peak times when India was trembling with the COVID waves, in spite of the ceaseless efforts from the doctors, there were still many people who remained unattended and eventually perished because of the lack of awareness and aid facilities. Had there been a system through which they could have gotten the aid on time, they wouldn’t have died a helpless death. Here we bring a system which has the capability of solving such issues for one and all. This Multi-Functional Medical Assistant Framework, as the name suggests, would provide the patient or the person in need with medical aid. The Chatbot will provide the exact medical composition that would suite their metabolism in an orchestrated and expedited manner. In this paper we have provided the design, the internal functioning, and the implementation of our application. General Terms Natural Processing Language Pattern Matching Artificial Intelligence Medicine. Keywords: Chatbots, Health, Bots, Natural Language, Covid-19
Linked and Coordinated Visual Analysis of Eye Movement Data

Eye movement data can be used for a variety of research in marketing, advertisement, and other design-related industries to gain interesting insights into customer preferences. However, interpreting such data can be a challenging task due to its spatio-temporal complexity. In this paper we describe a web-based tool that has been developed to provide various visualizations for interpreting eye movement data of static stimuli. The tool provides several techniques to visualize and analyze eye movement data. These visualizations are interactive and linked in a coordinated way to help gain more insights. Overall, this paper illustrates the features and functionality offered by the tool by using data recorded from transport map readers in a previously conducted experiment as use case. Furthermore, the paper discusses limitations of the tool and possible future developments.
Challenges to the Implementation and Enforcement of Data Protection in Nigeria

The formation of laws that contain the order of social, national and state life is part of the public decisionmaking process.Public decisions in the form of this law will be binding and apply to all people in a country.Its formation must provide a public space for the wider community to participate in the process of its formation.Pancasila as the root of the legal ideals of the Indonesian nation has the consequence that in the dynamics of the life of the nation and state, as a view of life adopted will provide coherence and direction (direction) to thoughts and actions.Reform of national law is directed at realizing laws based on the will of the people (democratic), then laws which are part of the national legal system in the process of their formation must reflect democratic values which are characterized by actively and comprehensively involving all levels of society in order to participate.inside it.
ANALISIS KEMAMPUAN MATEMATIS SISWA SEKOLAH DASAR PADA PEMBELAJARAN DARING

This study aims to analyze the ability to understand mathematical concepts of students in inline learning who have low, mdium, and high levels of mathematical ability. This study used a qualitative method with the type of case study research. This research was conducted by taking 6 research subjects from grade 4. Data collection techniques in this study were observation, tests, interviews, and documentation. The results of this study indicate that students who have low mathematical abilities have achieved 2 indicators, students who have moderate mathematical abilities have reached 4 indicators, and students who have high mathematical abilities have been able to achieve 6 indicators.
Loan Fraud Detection using Machine Learning Algorithm

The widespread usage of the Web has had a massive effect on the growth of online card transactions, especially at the beginning of the previous few years. Because of the increase in internet transactions, the global banking system has been forced to deal with or confront an unexpected number of fraudulent operations. As a reason, rule-based algorithms were created to detect high-risk transactions and allow specialists to authenticate whether or not they were fraudulent. The present intruders used the static nature of rule-based systems as a defensive measure to avoid detection. As a result, researchers set out to develop adaptive fraud detection systems based mostly on machine learning techniques, including deep learning, which is a relatively new application. The widespread use of the Internet, notably at the start of the previous decade, had a considerable impact on the rise in online card transactions. The rise of internet transactions has forced the worldwide banking system to deal with or confront an unexpected amount of fraudulent operations. As a result, rule-based algorithms were developed to identify high-risk transactions and allow specialists to confirm whether they were genuine or not. To avoid detection, the current attackers used the static nature of rule-based systems as a countermeasure. As a result, researchers set out to develop adaptive fraud detection systems based mostly on machine learning techniques, including deep learning, which is a relatively new application.
Development of Children With Superior Cognitive Abilities

This chapter provides the reader with an analysis of how the major theories of cognitive development in childhood apply to cognitively advanced children. Particular attention is given to explaining how these children differ from and how they are similar to all children in their development. Cognitive psychology is a broad area of psychology, and thus this chapter ranges from the appropriate application of cognitive neuroscience to the world systems perspective of Bronfenbrenner’s Ecological Systems Theory. Perspectives addressed include information systems (attention, working memory, and long-term memory), cognitive constructivism, and social constructivism. Each perspective includes guidance for how one would apply the information in the classroom when working with advanced students along with learning activities to help the reader synthesize the information.
Support vector clustering for face-based emotion predictions

A person’s face is an extremely essential biometric trait. Automatic recognition and analysis of face-based emotions in the field of research have initiated great inquisitiveness. A human face is multi-faceted, multi-dimensional, and contains a variety of important visual messages. The lack of unique classifiers for individuals due to the lack of dataset remains a big challenge. The performance of several classifier techniques like SVC and single-layer neural network, with principal component analysis (PCA) used for dimensional reduction in the paper using F1 score, is evaluated using labelled faces from the wild dataset. The ratio 70/30 of the train and test set requires less time for execution as well as a number of iterations.
Human-centered Evaluation of 3D Radial Layouts for Centrality Visualization

In this paper we propose improvements to the 3D radial layouts that make it possible to visualize centrality mea- sures of the nodes in a graph. Our improvements mainly relate edge drawing and the evaluation of the 3D radial layouts. First, we projected not only the nodes but also the edges onto the visualization surfaces in order to reduce the node overlap that could be observed in previous 3D radial layouts. Secondly, we proposed a human-centered evaluation in order to compare the efficiency score and the time to complete tasks of the 3D radial layouts to those of the 2D radial layouts. The evaluation tasks proposed are related to the central nodes, the peripheral nodes and the dense areas of a graph. The results showed that 3D layouts can perform significantly better than 2D layouts in terms of efficiency when tasks are related to the central and peripheral nodes, while the difference in time is not statistically significant between these various layouts. Additionally, we found that the participants preferred interacting with 3D layouts over 2D layouts.
FORMATION OF CRITICAL THINKING SKILLS IN METHODS OF METHODICAL PREPARATION STUDENTS OF PHILOLOGISTS

The article deals with lingual didactic problems of the development of critical thinking skills of students at lessons on methodic Ukrainian language learning. The author proposes educational methods, which of develop the critical thinking of students-philology.
Prof. Dr. Paolo Giubellino Starts Second Term as Scientific Managing Director of GSI and FAIR

"Prof. Dr. Paolo Giubellino Starts Second Term as Scientific Managing Director of GSI and FAIR." Nuclear Physics News, 32(2), p. 36
Design and Implementation of a Custom, Web-Based Cooperative Loan Application Management System

Cooperative societies are growing in Nigerian Educational institutions. Members of different groups within theseacademic settings have been coming together to support each other financially through saving and incidental accessto loans. A good example of such cooperative groups is ASUP SICHST Makarfi cooperative society limited. Oneof the key challenges that most of these societies face in their day-to-day administration is the use of manualtechniques for management of cooperative records. ASUP SICHST Markarfi cooperative society limited is used asthe case study in this work. The society is registered by government of Kaduna State and is operating at Shehu IdrisCollege of Health Sciences and Technology, Makarfi. Despite having some proposals for deploying ICT solutionsto manage cooperative activities, it was observed that there is paucity of works that specifically identify the needto provide solutions to loan application management problem in a custom scenario. This study proposed a webbased loan application management system that addresses the identified problems in the current manual system.The solution was developed with focus on features that cater for loan application management function in thecooperation society, in a custom manner. MySQL, PHP, HTML, CSS and JQuery were the tools used for theimplementation. The new system is found to be custom-developed and can promote the creation and managementof members’ loan applications in the cooperative society. It is believed that this approach is better when it comesto deploying solutions for the management of custom operations of cooperative society activities.
IMPLEMENTASI ALGORITME SUPPORT VECTOR MACHINE DAN FITUR SELEKSI MRMR UNTUK PREDIKSI GLIKOSILASI

During the protein formation process, there are post-translational modifications that provide additional properties and produce new R groups in the polypeptide chain. One of the results of post-translational modifications is glycosylation. Glycosylation reactions occur between protein and glucose at high concentrations. There are 3 categories of glycosylation found in the human body, namely N-glycosylation, O-glycosylation and C-glycosylation. To determine the functional role of glycosylation, that is by predicting the substrate of each glycosylation site. A computational approach is a way to predict the glycosylation site, using the Support Vector Machine (SVM) algorithm. In this study there are 2 types of data, namely independent data and benchmark data. The features used are feature extraction and feature selection using Maximum Redundancy Minimum Relevance (MRMR) of 25, 50 and 75 columns. SVM classification test using 5-fold cross validation. The highest accuracy result lies in the use of the 75 column MRMR selection feature. In Independent N data, the greatest accuracy lies in the sigmoid kernel with a causation value of 86.66%, while for independent C data, the accuracy is 87.5% in the sigmoid kernel and for independent O data, the largest accuracy is 89.31% which is in the RBF kernel. For benchmark N data, the highest accuracy is 70.54% in the RBF kernel, for benchmark C data the greatest accuracy lies in the RBF kernel with a value of 95.06% and for benchmak O data it is in the RBF kernel with the greatest accuracy, which is 92.64%.
Breast Cancer Prognosis Using Machine Learning Approaches

Cancer is a group of diseases and breast cancer is one of the leading cancer types in which cells in breast tissue change or reform abnormally and divides in an uncontrolled manner, typically emerged in a lump or mass. Every year more than 2.7 million new cases of breast cancer are enrolled worldwide, and according to GLOBOCAN statistics of 2020, breast cancer is the leading type of cancer in the world followed by lung cancer. In the present medical scenario, detection of breast cancer in its premature stage is immensely challenging. So, early detection and diagnosis are required to reduce mortality gains demand. Thanks to artificial intelligence and machine-learning–based automatic approaches which make it possible to detect and diagnose breast cancer using radiographic mammograms in early stages without human intervention. This study aims to provide a deep insight into breast cancer and the role of machine learning approaches to early detection and diagnosis of tumour masses. Different state-of-the-art techniques and algorithms are overviewed to get a proper understanding of how to utilize them for breast cancer prognosis. The pipeline of the general approach has also been explored which proves valuable to help the expert medical community and researchers in automatic diagnosis and detection of breast cancer at an early stage to reduce death rate.
The Effect of Organizational Culture on Organizational Performance of Saudi Higher Education: The Mediating Role of Human Resource Development

Organizational culture is essential to implement organizational policies and strategies. In addition, human resource development (HRM) is critical for the success of any organization. However, most of previous studies have focused on these variables in developed nations. In Saudi Arabia, the number of non-Saudis employed as academic staff is greater than Saudis and these issues are related to organizational culture and HRD. The purpose of this study is to examine the effect of organizational culture on HRD and organizational performance (OP). The study also aims at examining the effect of HRD on OP. The study also discusses the mediating role of HRD between organizational culture and OP. Public universities in Riyadh, Saudi Arabia were chosen as the population of this study. Using a stratified random sampling, 425 responses were collected. The data have been analysed using the Partial Least Square (PLS). This study has found that organizational culture affected the HRD and OP. HRD affected the OP and mediated the effect of organizational culture on OP. The findings of this study could foster a strategic plan for the achievement of HRD and improve the OP of universities.
ULTRA: Uncertainty-Aware Label Distribution Learning for Breast Tumor Cellularity Assessment

Neoadjuvant therapy (NAT) for breast cancer is a common treatment option in clinical practice. Tumor cellularity (TC), which represents the percentage of invasive tumors in the tumor bed, has been widely used to quantify the response of breast cancer to NAT. Therefore, automatic TC estimation is significant in clinical practice. However, existing state-of-the-art methods usually take it as a TC score regression problem, which ignores the ambiguity of TC labels caused by subjective assessment or multiple raters. In this paper, to efficiently leverage the label ambiguities, we proposed an Uncertainty-aware Label disTRibution leArning (ULTRA) framework for automatic TC estimation. The proposed ULTRA first converted the single-value TC labels to discrete label distributions, which effectively models the ambiguity among all possible TC labels. Furthermore, the network learned TC label distributions by minimizing the Kullback-Leibler (KL) divergence between the predicted and ground-truth TC label distributions, which better supervised the model to leverage the ambiguity of TC labels. Moreover, the ULTRA mimicked the multi-rater fusion process in clinical practice with a multi-branch feature fusion module to further explore the uncertainties of TC labels. We evaluated the ULTRA on the public BreastPathQ dataset. The experimental results demonstrate that the ULTRA outperformed the regression-based methods for a large margin and achieved state-of-the-art results. The code will be available from https://github.com/PerceptionComputingLab/ULTRA .