Reviewerly/exHarmony huggingface dataset:
Evaluation of Enrollment Trends in Technological Subjects at Secondary Level in Punjab

The major aim of this study was to evaluate the enrollment trends in Technical subjects at secondary level. The study was descriptive in nature and quantitative approach was applied for conducting this study. All the teachers, students and parents of Gujranwala Division in Punjab were the population of the study. Stratified random sampling technique was applied for the selection of sample. Three questionnaires were used in this study for the collection of data. Data were collected through personal visits of sampled schools. Data were analyzed by using percentage, mean and independent sample t-test. It was found that majority of students were enrolled in session 2019-20. Furthermore, majority of the students were enrolled in technical subjects on the demand of their parents and technical subjects have fame among the students. It is recommended that the government may arrange some kind of public awareness seminars at local level.
The Mediation effect for Bitcoin, Evidence from China Market on the Period of Covid-19 Outbreaking

The outbreak of Wuhan pneumonia in China in January 2020, we observed that sharp falls in Chinese stock markets were often followed by a brief drop in the price of Bitcoin followed by a notable increase The fact that the outbreak of infectious disease in China had little impact on US markets and at least a portion of the funds flowed back to the US through Bitcoin transactions suggests that the price of Bitcoin is related to an outflow of Chinese funds to the US Our analysis combining the computational aspects of cumulative prospect theory with the stochastic dominance method indicates that investors facing instability on Chinese markets use Bitcoin for hedge trading, perhaps as an intermediary in times of emergency
Methodological Culture as A Means of Professional and Personal Development of Bachelors of Pedagogical Education

The logic of the organization of the professional and personal development of bachelors of pedagogical education based on the effective development of their methodological culture is scientifically grounded in the article. The authors have set a goal to define and substantiate the essence and structure of the category of methodological culture, as well as to define its role in personal development. Moreover, the authors rely not only on consolidating but also on alternative theoretical approaches and conclusions. The methodological basis of the research is a systematic approach, which has allowed identifying and substantiating the place of methodological culture in the system of training future educators. The components and characteristics of the methodological culture revealed during the research have proved that the methodological culture is the basic means of the professional and personal development of bachelors in the system of teacher education. The scientific novelty of the work consists in logical construction of stages of the professional and personal development of bachelors of pedagogical education during the formation of methodological culture. As prospective research on the development of the methodological culture of future educators, the authors propose relevant directions for its further development.
An Adaptive Arbitrary Multiresolution Decomposition for Multiscale Geometric Analysis

The nonsubsampled Laplacian pyramid (NSLP) is widely used as a common multiresolution decomposition method in various nonsubsampled image transforms. However, the NSLP has a fixed spectrum partition, and thus cannot represent images accurately, and flexibly. We propose a new adaptive arbitrary multiresolution decomposition to solve these problems. First, using the affine characteristics of a pseudopolar Fourier transform (PPFT), we apply a 1-D nonuniform filter bank to the modulated PPFT to obtain a 2-D arbitrary resolution filter bank. This filter surpasses the limitation of fixed spectrum partitioning of the traditional tree structure. We then demonstrate that the proposed method satisfies the compact frame condition, and has translation invariance, and a linear phase. Furthermore, we propose an adaptive spectrum division approach at various scales based on image spectrum information based on a 2-D filter bank of arbitrary multiresolution, so our method can capture important visual information more accurately. Finally, we combine our method with a nonsubsampled directional filter bank of the nonsubsampled contourlet transform to create a new multiscale geometric analysis (MGA) method, and verify that the new method can have perfect reconstruction properties. The new MGA also performs better in image denoising, and recognition experiments than state-of-the-art MGA methods with translation invariance.
Study of Text Clustering in Semantic Web

Clustering is a Widely used information acquisition method. In order to solve the traditional text clustering Which is impossible to fully exploit the semantic information of text resources and the high dimensional and sparseness of similarity matrix, this paper proposes a text clustering method based on semantic similarity in semantic Web so as to further improve the quality of text clustering. By calculating the semantic similarity of Words so as to obtain the text semantic similarity matrix, spectral clustering is carried out according to the text semantic similarity matrix (SS-SC). The proposed method in this paper takes into account the semantic relations between Words, fully mines the potential information of the subject text, improves the quality of the clustering, and provides a new method for text clustering and recommendation. This paper verify the effect of the improved Weight calculation method on improving the clustering effectiveness. Thinking of the text resources of Google text corpus as data source, the traditional clustering K-Means algorithm, TCUSS (Text ClUstering based on Semantic Similarity) algorithm and the SS-SC algorithm are respectively tested. The results show that the precision value is higher than that of the traditional clustering algorithm.
Academic Library Services in Post Covid-19 Era

Academic libraries are considered the backbone of the institutions to which they are attached. It is seen in the past, a lot of services were being provided by them-either in manual mode or online in the digital era but Covid19 disease has completely changed the scenario. It is not sure how long it will take time to become normal and as per WHO and other sources, we have to live with it for a long time. So, it is expected that academic libraries will also have to reform their services and environment to provide services in the post Covid-19 environment. This paper suggests some measures that can be fruitful to cope with the effect of Covid-19 and to sustain the libraries in post Covid-19 era.
A Chatbot for supporting users in Cultural Heritage contexts (S)

The distance education of today is performed through new technologies, which assist whit the aim to improving the educational process.New technologies brought elearning environments more and more complete and able to supply learning experiences increasingly similar to traditional educational processes.The introduction of chatbots could represent an important aspect of the students' educational process.Chatbots are particular applications able to simulate typical human being conversations; through inference engines and modern Natural Language Processing techniques, a chatbot could be able to provide tailored educational paths.In this paper is presented a chatbot prototype in order to support students in the study of Cultural Heritage.This system takes advantage of inference techniques and approaches based on Natural Language Processing in order to provide a fluid conversation responding to the student's needs.The real experimentation was performed in the application context of the archaeological park of Paestum, where students were able to experience contextualized and personalized learning paths based on the recommendation capacity of the proposed system.In terms of student satisfaction, our proposed system shows very satisfactory results.
An Algorithm for the Detection of Circular Elements in Engineering Design

Various concentrated works have been done in the area of computational vision regarding the circle and texture detections. Detection of circles in images can be beneficial for PCB components industries for the detection of capacitors in printed circuit boards, also for medicine in the detection of red cells, white blood cells, and leukocytes, and for applications which requires precision and assignments regarding the detection of circles in a digital image. In this work is utilized a benchmarking of images to detection circle boards of different radio values for the comparison with the work [1] of this article. The benchmarking of images is composed of five main images that are tested in the algorithm of detection of circles in MATLAB with different values of radio for each image. The results appoint an enhancement of 300 % concerning the algorithm proposed in work [1] showed in this article. In this work also would be plotted graphs concerning the accuracy of the new proposed algorithm with relation to the algorithm proposed in work [1], indicating better results concerning the GUI interfaces and capacity of detection circles. Keywords: computer vision, pattern recognition, an algorithm of detection, circle detection, parameter identification.
Hierarchical Region Merging for Multi-scale Image Segmentation

Image segmentation is a key computer vision technique that divides the pixels of an image into different blocks of distinct transactions. The multi-scale segmentation method is one of the image segmentation methods, which can extract the object regions of different scales. It has the potential to fully exploit the application of high resolution and complex scene images and is the research hotspots direction of image segmentation technology. In this work, a feasible image scale-aware algorithm is proposed. By using the segmentation results of the existing multi-scale segmentation algorithm, the global region’s hierarchical region is merged by the quantitative description of each hierarchical region feature to achieve the optimal scale of multi-scale segmentation. We validate the proposed method on different algorithms and data sets. The results have shown that the proposed method can solve the error caused by manual threshold setting and achieve the optimal selection of individual goals to a certain extent.
About the Impact of The Concept Of E-Government on Modern Education in Russia

The hermeneutics of the concept of “e-government” is implemented, and the results are interpreted in the article using the method of expert survey. The goal of the study was to find out the relation between the digitalization of public administration in modern Russia and higher education. The conclusions on the impact of the concept of e-government on Russian education have been drawn, based on the interpretation of the experts’ answers. Some conclusions from a pedagogical experiment conducted on an ongoing basis at the Academy of Civil Defense of the Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters since 2016 have been also provided in the article. The goal of this experiment is to find out which pedagogical and information technologies are the most efficient for teaching at a university. The authors are confident that the problems of the mutual influence of the e-government and modern education in Russia described in the article are inexhaustible and will be continued by them.
A Relational Network Framework for Interoperability in Distributed Energy Trading

Blockchain-based approaches are increasingly being used to provide distributed trust and security in Distributed Energy Trading (DET). However, the state-of-the-art solutions lack scalability, privacy, interoperability, and often have large computational overheads hindering their mainstream adoption for sustainable development. To address these challenges, this paper proposes a multi-relationship network framework (RNF) that uses hypergraphs to organise participants in energy trading networks based on high-order relationships (rather than pairwise) for flexibility, interoperability, data privacy, and reduced resource consumption. Results indicate that the proposed framework outperforms the baseline in terms of: enabling value transfer across multiple blockchain-based DET systems; reducing computational costs and achieving energy efficiency for sustainable development.
An Expert System of Chicken Disease Diagnosis by Using Dempster Shafer Method

Chicken is an animal that can provide many benefits for human life, meat and eggs can be used as food to fulfill the needs of human food, the excrement can be made fertilizer, and frequently its be used as a farm animal. Although it can provide many benefits, but for chicken farmers, the maintenance of chicken meet some obstacles that must be faced such as disease, poor environmental sanitation, and the production of eggs are declining. From some of the obstacles that have been mentioned, the most frequently encountered are animals infected with the disease. Based on the results of interviews that have been done to some chicken farmers, it can be said that the knowledge of chicken farmers against chicken disease and its handling is still very lacking. But the number of experts who understand and know about the type of chicken disease and the way of handling is limited, then it takes an expert system that can simulate knowledge and understanding of experts to overcome the problem. Based on the study of the libraries, the method suitable for use in the expert system is the Dempster shafer method by processing the value of belief in a disease. Dempster shafer method is a method used to calculate uncertainty due to the addition or reduction of new facts that will change the existing rules. Based on tests in 40 cases using an expert system applying the Dempster Shafer method, obtained the percentage of diagnostic compatibility result given by experts and system is 95%.
Research on Optimization Scheme of Ship Radar Information Storage Based on Big Data

With the development of marine information technology, ship radar data increases geometrically, and the traditional local server storage architecture has become increasingly unable to meet the requirements of modern ship radar data capacity, so cloud storage has gradually become the mainstream technology of radar data storage.At the same time, because the marine communication network is more vulnerable to external attacks and environmental interference, high-density encryption technology suitable for cloud storage is needed.In this paper, a ship radar cloud storage platform based on big data is studied, and a high-density encryption algorithm CP-ASBE is designed.Finally, simulation is carried out.
Tolerance Rough Set-Based Bag-of-Words Model for Document Representation

Document representation is one of the foundations of natural language processing.The bag-of-words (BoW) model, as the representative of document representation models, is a method with the properties of simplicity and validity.However, the traditional BoW model has the drawbacks of sparsity and lacking of latent semantic relations.In this paper, to solve these mentioned problems, we propose two tolerance rough set-based BOW models, called as TRBoW1 and TRBoW2 according to different weight calculation methods.Different from the popular representation methods of supervision, they are unsupervised and no prior knowledge required.Extending each document to its upper approximation with TRBoW1 or TRBoW2, the semantic relations among documents are mined and document vectors become denser.Comparative experiments on various document representation methods for text classification on different datasets have verified optimal performance of our methods.
Sparse Autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

Automatic target recognition (ATR) has always been an important research topic, and the performance is affected by feature extraction. High-resolution range profiles (HRRP) contains structural information of target from different angles. Existing target recognition algorithms are mostly adopt the supervised learning and verify the validity through classification accuracy. However, these methods cannot satisfy the information acquisition such as correlation, composition analysis and attitude analysis of unlabeled or uncoordinated targets. Therefore, we are committed to researching new intelligent feature extraction and analysis method. In this article, a novel learning framework is proposed to realize the angle correlation feature extraction and feature analysis of multi-angle HRRP targets. A multi-layer sparse autoencoder is applied to extract HRRP features and the extracted features are mapped to the low-dimensional space by manifold learning method to find the correlation distribution of HRRP data. The effectiveness of the proposed framework is demonstrated by experiments with simulated and measured data. The experimental results show that the framework realizes the extraction of angle-invariant features, and the analysis of distribution relationship between HRRP data and angles. The research results of the correspondence between the assembly and the related geometry provide the basis and possibility for further identification and composition determination.
MEMOTIVASI SISWA UNTUK BELAJAR DENGAN VARIASI METODE DAN PENERAPAN PAIKEM

Good classroom management will produce good teaching and learning interactions. Learner goals can be achieved without finding significant obstacles. But it is unfortunate that good classroom conditions cannot always be maintained. There is often a lack of learning in students, their confusion here is different, and various other learning problems. This may be due to their routine in learning. They routinely enter class, take a break, go home according to the time determined by the school. At the same time, the learning methods used by the teacher are also routine, aka not changing. So, this article is arranged to contribute ideas about solutions to these learning problems. After a literature review, a solution to the problem is offered, namely the use of a variety of learning methods and the application of PAIKEM.
Machine Learning in Sports 101

In this chapter I introduce concepts and areas of machine learning we will be covering further in detail in this book. If you are a data scientist or a sport science practitioner interested in machine learning, you will find many practical examples that can help you in real life. From deep computer vision to working and applying sensors in the field of various sports, analyzing and processing data and training models, new technologies and projects collected in this book should help adding machine learning as a tool.
Solving Competitive Traveling Salesman Problem Using Gray Wolf Optimization Algorithm

In this paper a Gray Wolf Optimization (GWO) algorithm is presented to solve the Competitive Traveling Salesman Problem (CTSP). In CTSP, there are numbers of non-cooperative salesmen their goal is visiting a larger possible number of cities with lowest cost and most gained benefit. Each salesman will get a benefit when he visits unvisited city before all other salesmen. Two approaches have been used in this paper, the first one called static approach, it is mean evenly divides the cities among salesmen. The second approach is called parallel at which all cities are available to all salesmen and each salesman tries to visit as much as possible of the unvisited cities. The algorithms are executed for 1000 times and the results prove that the GWO is very efficient giving an indication of the superiority of GWO in solving CTSP.
Data Science for Motion and Time Analysis with Modern Motion Sensor Data

Data Science for Motion and Time Analysis Motion and time analysis has been a popular tool in operations research for analyzing work performance in manufacturing and service operations. The current practice in motion and time analysis involves many labor-intensive steps such as stop-watching, videotaping, and manual data analysis. It is too inefficient to be practiced regularly for continuous improvement. Whereas modern sensing devices have automated and eased motion measurements, the motion analytics transforming the new data into knowledge are largely underdeveloped. Unsolved technical questions include: How can the motion and time information be extracted from the motion sensor data? How are work motions and work rates statistically modeled and compared? How are the motions correlated to the rates? This paper develops solutions to the technical questions into a novel data science framework for motion and time analysis. The new framework is demonstrated with industrial use cases for a smart factory.
Super Resolution for Digital Rock Core Images via FSRCNN

Digital rock images have been widely used for rock core analysis in petroleum industry. Petroleum engineers are interested in high resolution digital rock images which are fine enough for them to overcome complex real-world problems. The higher resolution of digital rock images, the more accurate the parameters they can get, such as porosity and permeability which statistically describe porous materials. We applied Fast Super-Resolution Convolutional Neural Networks (FSRCNN) to increase resolution for Digital Rock Images. And we compared it with the benchmark named Super-Resolution Convolutional Neural Networks (SRCNN) which is the first deep learning method for image super resolution. The results demonstrate that the FSRCNN can produce high resolution digital rock images in a faster speed and it is valuable processing step for digital rock workflow.
Research on Constructing “Parallel Contrast Corpus of Grammatical Errors”

Error analysis and interlanguage are two cores in second language acquisition research. Researchers have conducted studies and established corpora from various perspectives based on Big Data. However, most of the existing interlanguage corpora provide no feedback for students, which resulted in the barrier of improving self-study efficiency. Additionally, interlanguage systems are influenced by nationalities, while there is a vacancy on the construction of divisional interlanguage corpora. Based on previous studies and error analysis of BNU-Cardiff Chinese College students, this study proposes an idea and model of “Parallel Contrast Corpus of Grammatical Errors” for native English speakers in Chinese learning.