Reviewerly/exHarmony huggingface dataset:
The impact of implementing green human resources management in government institutions in the Jordanian Ministry of Justice

This paper aims to study the impact of implementing green human resources management (GHRM) in government institutions in the Jordanian Ministry of Justice. The population of this paper is all the Jordanian government institutions and the sample is the Jordanian Ministry of Justice, the questionnaire was designed with four aspects to collect the data related to environmental sustainability, organization performance, recruitment, and competitive advantages for the year 2021 and distributed to 75 employees of Jordanian Ministry of Justice. The paper has one independent variable that represents the GHRM and four dependent variables and they are environmental sustainability, performance, recruitment of human resources, and competitive advantages, to measure the data collected SPSS packages such as descriptive statistical analysis, Cronbach Alpha, the normal distribution and One-sample T-test were used. The researcher concluded that the implementation of green human resources management has a positive effect on environmental sustainability by performing green practices, improving performance by reducing cost while maintaining the quality of performed services, recruiting human resources by increasing employee satisfaction and competitive advantages by the good reputation of Jordanian government institutions. The researcher recommends the need to strengthen awareness among employees through guidance programs about the importance of implementation of this relatively modern type of management in Jordanian government institutions.
High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics

In Neural Machine Translation, it is typically assumed that the sentence with the highest estimated probability should also be the translation with the highest quality as measured by humans. In this work, we question this assumption and show that model estimates and translation quality only vaguely correlate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples to optimize diverse automated metrics of translation quality as an alternative inference strategy to beam search. Instead of targeting the hypotheses with the highest model probability, MBR decoding extracts the hypotheses with the highest estimated quality. Our experiments show that the combination of a neural translation model with a neural reference-based metric, BLEURT, results in significant improvement in human evaluations. This improvement is obtained with translations different from classical beam-search output: these translations have much lower model likelihood and are less favored by surface metrics like BLEU.
Starting positions of university applicants and features of their further education: A sociological analysis

The article considers the features of students’ educational trajectories as depending on how they entered the university. A distinctive feature of the Russian system of higher education is various grounds for the educational transition “school-university”: the results of the Unified State Exam, school Academic Olympics in key subjects, employer-sponsored education, and quotas. These grounds seem to affect the further education of students and the quality of training and professional trajectories. At the same time, the resistance of the higher education system to external challenges, its sustainable competitiveness, modernization and the search for new forms and models of work with the talented youth are priority development tasks of the national higher education system. The article presents the results of the comparative analysis of the students’ motivation for learning, educational achievements, ideas about possible professional and educational trajectories. The analysis is based on the data of the Russian students survey conducted by the Center for Educational Development Strategy of the Lomonosov Moscow State University and RUDN University in November-December 2021 (N = 37457). The research findings show that at the stage of the transition from school to university, there is a very heterogeneous student inflow by quality and forms of training, individual abilities and inclinations, motivation and requests to the higher education. There is a special group - prize-winners of school Academic Olympics - of the most talented and promising applicants. The admission companies show that variable grounds for entering universities lead to certain conflicts and contradictions in the higher education system.
The organization of educational-project activities of students to create chatbots as a condition for training of future teachers

Problem and goal. The training of future teachers in the field of education involves the formation of competencies in the development of software for various purposes, the design of automated control systems for real objects. The development of relevant qualities in the conditions of higher education is complicated by a number of problems of various nature. The authors suggest to improve the training of future specialists to organize project activities of students to create dialogue programs (training bots). Methodology. Project-based training is used for the formation of competencies in the field for informatization of education, obtaining in-demand soft skills, team building. The construction of a chatbot takes place in teamwork. The experiment involved 43 students of the North Caucasus Federal University in the direction of training 44.03.05 “Pedagogical Education with Two Training Profiles.” The assessment of the quality of educational results was carried out using the author's testing of 10 tasks (“credited”/“not credited”). Fischer's criterion was used to establish statistically significant differences. Results. Students studied the services for creating chatbots, the didactic potential and functionality of the received programs, used them to solve problems. Statistically significant differences were revealed in the changes that occurred in the system in terms of the quality of vocational training. The features of the presented variant of the organization of educational activities for the creation of chatbots are described: teamwork, the use of templates and scripts, and the choice of project topics. Conclusion. Problematic questions are formulated, the answers to which allow us to determine the directions of work on the construction of chatbots: discussion of the development goal and didactic goal, the use of blocks and actions, modeling interaction scenarios, etc.
Digital transformation of the university library in the context of information abundance

Problem and goal. In the context of digital transformation of education, the importance of digital library formation is justified, taking into account the training areas, research topics and other requests of all target groups of the educational institution. Active use by students and teachers of Internet sources raises the problem of popularization and disclosure of electronic collections, organization of problem-free access to valuable, reliable information from legitimate sources. Implementation of the practice of constant use of verified content by students, teachers, scientists has become the main task of the library. In this regard, the principles and methods used in the scientific library of the university for the collection and dissemination of information, organization of events, implementation of academic standards and new forms of work for the use of information resources in the educational process and research activities are studied. Methodology. As a generalization and systematization of the accumulated practical experience of working with electronic information, the stages of formation of a classical university digital library, methods of statistical analysis to evaluate the use of collections, forms of content promotion and work with target user groups are described. Results. The author analyzes the experience of the project on the formation and promotion of the university digital library, accumulates data on the effectiveness of content use, promotional mechanisms, achieved results for use in the work of libraries in the field of education. Proved the necessity of formation of university digital library using cluster approach - universal, specialized and profile resources, regarding peculiarities of promotion in different target groups of university, importance of analytical work on content use for optimization of subscriptions and elimination of information gaps, obligatory introduction of information culture and activation of digital educational resources use in the context of professional activity of the teacher. Conclusion. Using theoretical and practical data, statistical analysis and various aspects of both Russian and foreign experience of digital transformation of the university libraries in the conditions of information abundance, the stages of modification of the library collections, communication channels, and the results in a particular library were considered and analyzed.
Quantum tangent kernel

Quantum kernel method is one of the key approaches to quantum machine learning, which has the advantages that it does not require optimization and has theoretical simplicity. By virtue of these properties, several experimental demonstrations and discussions of the potential advantages have been developed so far. However, as is the case in classical machine learning, not all quantum machine learning models could be regarded as kernel methods. In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method. In this case, the representation power and performance are expected to be enhanced, while the training process might be a bottleneck because of the barren plateaus issue. However, we find that parameters of a deep enough quantum circuit do not move much from its initial values during training, allowing first-order expansion with respect to the parameters. This behavior is similar to the neural tangent kernel in the classical literatures, and such a deep variational quantum machine learning can be described by another emergent kernel, quantum tangent kernel. Numerical simulations show that the proposed quantum tangent kernel outperforms the conventional quantum kernel method for an ansatz-generated dataset. This work provides a new direction beyond the conventional quantum kernel method and explores potential power of quantum machine learning with deep parameterized quantum circuits.
Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge

A key bottleneck for developing dialog models is the lack of adequate training data.Due to privacy issues, dialog data is even scarcer in the health domain.We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data.We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model.We show that our data creation method has several advantages.Not only does it allow for the semi-automatic creation of large quantities of training data.It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions.
Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%.
A CONCEPTUAL MODEL FOR A DECENTRALIZED CENTRAL BANK DIGITAL CURRENCY

Central bank digital currency (CBDC) is a digitized fiat currency. As the nature of the central bank is centralized, the CBDC is also centralized. This paper proposes a conceptual model for a decentralized CBDC controlled by many central banks or countries in the world. It is only for international transactions between member countries. While domestic transactions continue to use the national currency of each country. A decentralized CBDC could potentially explore the advantages of digital technologies more deeply than centralized ones by making macro-fundamentals reconciliations between economies in real-time. Furthermore, this concept could also potentially provide international liquidity for all (member) countries in the world sustainably, align macro-fundamental, eliminate global imbalances, and makes the international monetary system naturally more stable. This paper focuses on discussing the conceptual model and theoretical framework of how this system works in providing international liquidity and making the system balanced and stable.
The Duality of Ray-Based and Pinhole-Camera Modeling and 3D Measurement Improvements Using the Ray-Based Model

Geometrical camera modeling is the precondition for 3D-reconstruction tasks using photogrammetric sensor systems. The purpose of this study is to describe an approach for possible accuracy improvements by using the ray-based-camera model. The relations between the common pinhole and the generally valid ray-based-camera model are shown. A new approach to the implementation and calibration of the ray-based-camera model is introduced. Using a simple laboratory setup consisting of two cameras and a projector, experimental measurements were performed. The experiments and results showed the possibility of easily transforming the common pinhole model into a ray-based model and of performing calibration using the ray-based model. These initial results show the model’s potential for considerable accuracy improvements, especially for sensor systems using wide-angle lenses or with deep 3D measurements. This study presents several approaches for further improvements to and the practical usage of high-precision optical 3D measurements.
On the Convergence of Median Versions of the Expectation-Maximization Algorithm for the Separation of Finite Normal Mixtures

In the paper, justification is given for convergence of the median modification of the classical expectation-maximization (EM) algorithm for separation of finite mixtures of normal distributions. This modification is designed to overcome the instability of the classical EM algorithm with respect to initial data.
Cricket Video Highlight Generation Methods: A Review

The key events extraction from a video for the bestrepresentation of its contents is known as video summarization.In this study, the game of cricket is specifically consideredfor extracting important events such as boundaries, sixes andwickets. The cricket video highlight generation frameworksrequire extensive key event identification. These key events canbe identified by extracting the audio, visual and textual featuresfrom any cricket video.The prediction accuracy of the cricketvideo summarization mainly depends on the game rules, player’sform, their skill, and different natural conditions. This paperprovides a complete survey of latest research in cricket videosummarization methods. It includes the quantitative evaluationof the outcomes of the existing frameworks. This extensive reviewhighly recommended developing deep learning-assisted videosummarization approaches for cricket video due to their morerepresentative feature extraction and classification capabilitythan the conventional edge, texture features, and classifiers. Thescope of this analysis also includes future visions and researchopportunities in cricket highlight generation.
SOCIOLOGICAL SUPPORT AS A FACTOR OF IMPROVING THE QUALITY OF EDUCATION MANAGEMENT

Within the framework of this research work, an attempt is made to combine fundamental and applied research developments and the real needs of the management system in order to achieve the only possible goal of education – to educate a harmoniously developing competitive personality with modern competencies. The article presents a synergetic analysis of the determinism of the theoretical and methodological base, sociostructural changes, as well as the modernizing substantive practices of state educational policy for the development of a qualitatively new model of socio-political management of the system of intellec-tual resources in the context of the implementation of the national project "Education" with the help of sociological support of educational actors.
The Scientific Productivity of Collective Subjects Based on the Time-Weighted Pagerank Method with Citation Intensity

This study aims to estimate the scientific productivity of collective subjects. The objective is to build a method for evaluating scientific productivity that allows calculating productivity, including for new collective subjects with a small citation network&amp;mdash;the paper purposes the Time-Weighted PageRank method with citation intensity (TWPR-CI). The Citation Network Dataset (ver. 13) has been analyzed to verify the method. The dataset includes more than 5 million scientific publications and 48 million citations. There have been allocated four classes of collective subjects (more than 27,000 collective subjects in total). For each class, scientific productivity estimates from 2000 to 2021 were calculated using the PageRank, Time-Weighted PageRank, and TWPR-CI methods. It is shown that the advantage of the TWPR-CI method is the higher sensitivity of the scientific productivity estimates for new collective subjects on average during the first ten years of observation. At the same time, the assessment of scientific productivity for other collective subjects according to this method is stable. However, the small citation network of the new collective subjects does not allow an adequate assessment of scientific productivity during the first years of its operation. Therefore, the TWPR-CI method can be used to assess the scientific productivity of collective subjects, in particular the productivity of new ones.
Teachers' digital competence and inclusive education at school: An analysis of teacher attitudes

Inclusive education and the implementation of Information and Communication Technologies in the 21st century classroom are priority objectives in global agendas. However, few studies focus on and combine the following areas of study: Inclusive education and Digital Competence (IE and DC). This research gathers the opinion of 221 active teachers on these two variables through a sample composed of participants from Spain and 10 Latin American countries. These educational professionals responded to a questionnaire created ad hoc, tested by a group of experts, and based on instruments previously validated by the scientific community. There were 30 items scored on a scale of 1-7 for the areas of study. The results indicate that the participating teachers attach high importance to the study variables, although they lack the training and competence necessary for their performance. Furthermore, basic and advanced levels of perception regarding the importance of inclusive education and digital competence are identified, which are similar in the basic levels and increasingly disparate at the advanced levels due to specialization. Keywords: Compulsory Education; Digital Competences; Inclusive Education; Technology; Teachers.
A Novel Recommender System Using PSO, FCM and, Autoencoder

Abstract The recommender system is the most developing application in the web environment. A good Recommendation system needs to make choices and decisions to provide recommendations to the user. The Recommender system filters the information and provides accurate recommendations of products or services for a concerned individual. In this research, we propose a model, which employs three approaches; clustering, nature-inspired optimizer, and deep learning. The fuzzy c-means clustering technique is combined with the particle swarm optimization algorithm. The particle swarm optimizer works as a feature extractor. The particle swarm optimizer is applied to the Movielens dataset to get the initial cluster positions. The fuzzy c-means is used to classify the users in the dataset and to reduce data redundancy. The optimized and clustered data is provided as input to the autoencoder to get the final recommendations of the movies to the users. We have proposed our recommender system in two parts; 1) Using a single-layer autoencoder, and 2) Using a deep autoencoder. We analyzed our proposed system over a publicly available dataset- Movielens. The proposed system is compared with the existing recommender systems. The experimental results of our system revealed that the performance and efficiency are improved, and offered better recommendations.
Class Gradient Projection For Continual Learning

Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL). Recent approaches tackle this problem by projecting the gradient update orthogonal to the gradient subspace of existing tasks. While the results are remarkable, those approaches ignore the fact that these calculated gradients are not guaranteed to be orthogonal to the gradient subspace of each class due to the class deviation in tasks, e.g., distinguishing "Man" from "Sea" v.s. differentiating "Boy" from "Girl". Therefore, this strategy may still cause catastrophic forgetting for some classes. In this paper, we propose Class Gradient Projection (CGP), which calculates the gradient subspace from individual classes rather than tasks. Gradient update orthogonal to the gradient subspace of existing classes can be effectively utilized to minimize interference from other classes. To improve the generalization and efficiency, we further design a Base Refining (BR) algorithm to combine similar classes and refine class bases dynamically. Moreover, we leverage a contrastive learning method to improve the model's ability to handle unseen tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed approach. It improves the previous methods by 2.0% on the CIFAR-100 dataset.