Reviewerly/exHarmony huggingface dataset:
Pseudo-Reconfigurable Heterogeneous Solution for Accelerating Spectral Clustering

Spectral clustering is a Machine Learning technique intensively used in Big Data applications. It makes extensive use of linear algebra. This article introduces the concept of MapReduce Accelerator (MRA) as the reconfigurable part of a heterogeneous computing system. Although the accelerator we propose is a general purpose one, it has some specific features related to the targeted application. This is possible due to the pseudo-reconfigurable environment which deploys in FPGA a parameterizable programmable accelerator. The main specific characteristics of the accelerator are proposed as a result of the analysis performed on the spectral clustering algorithms. The architecture is described and the spectral clustering algorithms are evaluated. The proposed solution is compared, in terms of computing performance and energy consumption, with other solutions published in the literature. The increase in computing performance is accompanied by a 3-5 times reduction in energy consumed. The accelerator is a linear array of cells controlled by a sequencer loop closed through a reduction network. Each cell is a simple, accumulator-based execution unit with a big two-port register file. The reduction network is a log-depth pipelined circuit performing few reduction functions such as add, min, max. The experimental system is a PYNQ-Z2 board equipped with Zinq 7020 SoC; it is used to implement and evaluate the acceleration provided by an 128-cell MRA.
Is Single Scan based Restructuring Always a Suitable Approach to Handle Incremental Frequent Pattern Mining?

Incremental mining of frequent patterns has attracted the attention of researchers in the last two decades.The researchers have explored the frequent pattern mining from incremental database problems by considering that the complete database to be processed can be accommodated in systems' main memory even after the database gets updated very frequently.The FP-tree-based approaches were able to draw more interest because of their compact representation and requirement of a minimum number of database scans.The researchers have developed a few FP-tree based methods to handle the incremental scenario by adjusting or restructuring the tree prefix paths.Although the approaches have managed to solve the re-computation problem by constructing a complete pattern tree data structure using only one database scan, restructuring the prefix paths for each transaction is a computationally costly task, leading to the high tree construction time.If the FP-tree construction process can be supported with suitable data structures, reconstruction of the FP-tree from scratch may be less time consuming than the restructuring approaches in case of incremental scenario.In this study, we have proposed a tree data structure called Improved Frequent Pattern tree (Improved FP-tree).The proposed Improved FP-tree construction algorithm has immensely improved the performance of tree construction time by resourcefully using node links, maintained in header table to manage the same item node list in the FP-tree.The experimental results emphasize the significance of the proposed Improved FP-tree construction algorithm over a few conventional incremental FP-tree construction algorithms with prefix path restructuring.
Transformation of Cognitive Assistance Systems into Augmented Reality

The field of Augmented Reality (AR) has received increasing attention in recent years, as AR can be applied to a wide range of problems. The use of AR offers great potential, especially in the industrial sector. Among many applications in this area, one promising application is the augmentation of cognitive assistance systems. Much research has already been done on the development of augmented support systems but it is still lacking on how to transform existing assistance systems into an AR application. This paper focuses on this transformation and presents a process model that intends to support the migration or digitalization of existing support systems into an AR application. An experimental study validates the proposed approach and derives recommendations for action.
Boolean Hierarchical Tucker Networks on Quantum Annealers

Quantum annealing is an emerging technology with the potential to solve some of the computational challenges that remain unresolved as we approach an era beyond Moore's Law. In this work, we investigate the capabilities of the quantum annealers of D-Wave Systems, Inc., for computing a certain type of Boolean tensor decomposition called Boolean Hierarchical Tucker Network (BHTN). Boolean tensor decomposition problems ask for finding a decomposition of a high-dimensional tensor with categorical, [true, false], values, as a product of smaller Boolean core tensors. As the BHTN decompositions are usually not exact, we aim to approximate an input high-dimensional tensor by a product of lower-dimensional tensors such that the difference between both is minimized in some norm. We show that BHTN can be calculated as a sequence of optimization problems suitable for the D-Wave 2000Q quantum annealer. Although current technology is still fairly restricted in the problems they can address, we show that a complex problem such as BHTN can be solved efficiently and accurately.
Motivation toward English Language Learning among Students of Different Fields of Study: A Case of Iraqi University Students

This study aims at identifying motivation toward English language learning among students ofdifferent fields of study. More specifically, it tries to identify the two types of motivation (integrative/ instrumental) among those students and also the preferred activities used by them. This study is qualitative and quantitative, which uses two types of instruments: a questionnaire and a semi-structured interview. In the survey, there are 150 responses collected from the participants who answered the questionnaire questions. In the interview, the researcher interviewed three participants. The results of this study show that the students of different fields of study were motivated instrumentally rather than integratively. Finally, the results also indicate that the participants identified different learning activities.
Impact of Big Data Analysis on Accounting Profession Field – Study in Jordanian Business Environment

This study aims to identifying the impact of big data analysis on the accounting Profession in Jordanian business environment. To achieve the study objectives researcher distributed a questionnaire to (147) out of certified public accounts, financial analysis and experts in big data analysis in the kingdom of Jordan. (108) questionnaires were returned. The response rate was (51.7%) of the population. In addition, the study sought to verify the hypothesis of the study. In order to analysis the data, the researcher used means, standard deviation and T-test. The result of the study revealed that the big data analysis have a significant role on the accounting roles and improve the quality of accounting characteristics in Jordan with an overall means (4.52). Based on the results of hypotheses, rejected the null basic hypothesis of the study, and the two null sub-hypotheses were rejected. In light of findings the researcher gave a number of recommendations:1- The necessity of teaching big data and business analysis in the accounting education curricula at the undergraduate level to enhance students' knowledge of the importance of that data.2- Holding workshops and training courses for researchers and academics to knowing them the importance of analyzing big data and how to process, store, manage and invest it in the accounting and financial field.
Image semantic segmentation method based on GAN network and ERFNet model

This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small-scale targets and weak anti-noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U-shaped network is used to improve the generator of GAN to avoid low-level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.
Wind speed monitoring and alert system using sensor and weather forecast

Abstract Strong wind allows uproot the trees and causes human victims to be injured or dead. Unfortunately, the strong wind comes suddenly to the trees on the street and many people and traffic are around there. It needs wind speed monitoring and strong wind early warning to avoid the victims due to fallen tree. This paper explains the development of wind monitoring and early warning system using wind speed sensor and weather forecasting. The steps involve investigating the wind speed, designing the system, developing the system, testing, and validating. The result shows that the system can measure the wind speed on the spot in real time and trigger the alert to the people around if the strong wind was occurred. On the other hand, it also informs the forecast of wind speed according to weather web services data few hours before. Applying this system will help the government to prevent the victims of fallen tree due to strong wind.
Educational platforms for distance education in russ ian and chinese universities : new challenges for higher education

The article provides a comparative analysis of digital educational platforms used in the practice of Russian and Chinese universities. Our conclusions are based on the comparative data on Russia and China published in the reports of UNESCO, International Labour Organization, Organization for Economic Cooperation and Development (OECD), Council of Europe and European Economic Community (EEC), Asia-Pacific Programme of Innovation in Education for Development (APEID). In addition, the article provides data from two sociological studies carried out by the authors themselves. The first study was conducted between January and February 2020. The study was conducted by means of a survey, personal interview (N = 600). Students from Moscow, Ekaterinburg, Chelyabinsk and Tyumen took part in the survey. The quantitative research data were supplemented by in-depth interviews with Chinese graduates of Russian universities (postgraduates, master’s students) in the amount of 50 pieces (N = 50). Also in March — May 2020, we conducted a sociological survey in the universities of Ekaterinburg, Chelyabinsk, and Moscow. In the course of the research we interviewed 500 students who had gained experience in distance learning during the pandemic. Qualitative research — in-depth interview method (N = 20), average duration of the interview is 90 minutes. Informants were the faculty of higher educational institutions of Yekaterinburg, Moscow and St. Petersburg. Frame analysis of cases was also conducted (N = 42). The analysis of these cases made it possible to identify the most popular digital technologies in the educational process, as well as their main advantages and disadvantages.
Development of a Gold-standard Pashto Dataset and a Segmentation App

The article aims to introduce a gold-standard Pashto dataset and a segmentation app. The Pashto dataset consists of 300 line images and corresponding Pashto text from three selected books. A line image is simply an image consisting of one text line from a scanned page. To our knowledge, this is one of the first open access datasets which directly maps line images to their corresponding text in the Pashto language. We also introduce the development of a segmentation app using textbox expanding algorithms, a different approach to OCR segmentation.&#x0D; The authors discuss the steps to build a Pashto dataset and develop our unique approach to segmentation. The article starts with the nature of the Pashto alphabet and its unique diacritics which require special considerations for segmentation. Needs for datasets and a few available Pashto datasets are reviewed. Criteria of selection of data sources are discussed and three books were selected by our language specialist from the Afghan Digital Repository. The authors review previous segmentation methods and introduce a new approach to segmentation for Pashto content. The segmentation app and results are discussed to show readers how to adjust variables for different books. Our unique segmentation approach uses an expanding textbox method which performs very well given the nature of the Pashto scripts.&#x0D; The app can also be used for Persian and other languages using the Arabic writing system. The dataset can be used for OCR training, OCR testing, and machine learning applications related to content in Pashto.
Design Web-Based Registration And Data Management Of Student Thesis Information System

&lt;p&gt;The thesis data management process at STIMIK Sepuluh Nopember Jayapura currently still uses Microsoft Excel, so the processing takes a lot of time, another obstacle that arises is the title submission process, where students have to come to campus to submit titles then announcements still use the announcement board so that students who want to get the information must go to the STIMIK Sepuluh Nopember campus. The purpose of making the system is to solve the above problems and at the same time make it easier for students in the process of submitting titles. The design of this system uses the waterfall method, while the data collection method uses several methods such as observation, interviews and literature study. For web development using the PHP programming language. The final result of this design is a web that can be used by students to submit titles, see a list of thesis titles that have been taken and so on. While the output from the web that is made is a list of student thesis titles STIMIK Sepuluh Nopember Jayapura and announcements such as approval of titles submitted, supervisors, examiners and payment issues.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
The Effect of COVID 19 Pandemic on English Proficiency Level in Higher Education

&lt;em&gt;This study is to find out the effect of COVID 19 pandemic on English proficiency .This research is an experimental research using quantitative approach. The students in higher education are as parts of population in this research. A random sampling technique is used to decide the sampling size. The subjects of this study are 36 students. The data are analyzed by paired t test technique. The result of the study shows sig (p) is significant. It is smaller than 0.05 (it is 0.000). The difference can be considered true. The mean is 59.30 before COVID 19; 49.33 is the mean during COVID 19. It indicates that the students at higher education with before COVID 19 pandemic have better English aptitude than in COVID 19 virus. It implies that the teachers and members of educational society in higher education be supposed to assist out students with the suitable atmosphere to be of assistance students with consequential input in addition to offer students with interface in an attempt to the regularity of experiencing English; additionally, it is suggested that the members of scholar society are being release significant input for the students.&lt;/em&gt;
A Graph Convolutional Network-Based Sensitive Information Detection Algorithm

In the field of natural language processing (NLP), the task of sensitive information detection refers to the procedure of identifying sensitive words for given documents. The majority of existing detection methods are based on the sensitive-word tree, which is usually constructed via the common prefixes of different sensitive words from the given corpus. Yet, these traditional methods suffer from a couple of drawbacks, such as poor generalization and low efficiency. For improvement purposes, this paper proposes a novel self-attention-based detection algorithm using the implementation of graph convolutional network (GCN). The main contribution is twofold. Firstly, we consider a weighted GCN to better encode word pairs from the given documents and corpus. Secondly, a simple, yet effective, attention mechanism is introduced to further integrate the interaction among candidate words and corpus. Experimental results from the benchmarking dataset of THUC news demonstrate a promising detection performance, compared to existing work.
THE UTILIZATION OF KINEMASTER APPLICATIONS IN THE MAKING OF MULTIMEDIA BASED TEACHING MATERIALS FOR ENGLISH E-LEARNING IN NEW NORMAL (COVID-19)

The objectives of this study are; 1) to determine the ability in implementing online learning and multimedia use, 2) to determine the difficulties in online learning and difficulties in providing multimedia-based learning. 3. to assist participant in making multimedia-based learning media with the Kinemaster application. Researchers use descriptive qualitative methods. Respondents in this study were prospective students participating of English Education study program. 62.5% of respondents when teaching always use multimedia, 25% sometimes use multimedia in teaching, and 12.5% rarely use multimedia. Regarding difficulties in class management as much as 50%, in time management 25% and difficulties in operating software 25%. Other obstacles experienced are; 42.9% of the obstacles are networking, then 42.9% of the software application systems are not easy to operate and electrical energy sources 14.3%. The number of respondent 100% not only know but have also used the kinemaster application in making multimedia-based learning media by 62.5%, and 37.5% who never used the kinemaster, 62.5% of respondents said it was easy and 37.5% still had difficulties,. kinemaster really helps respondents in learning as much as 50% stated that they were helped, 12.5% did not feel helped, and 37.5% stated that it was normal. Keywords: Kinemaster, Multimedia, English Online Learning
Convolution Neural Network Based Image Classifier

The concept of Deep Learning is emanated in machine learning as an enhanced research area and is empirical to various image applications.The objective of the project propounded in the paper, is applying the abstract of an algorithm of Deep Learning, viz, Convolutional neural networks (CNN) for multiple image classification.The algorithm is assessed on variegated datasets, which consist 2399 images taken from google, myntra fashion clothes, etc.The algorithm's performance is gauged based on the quality metric known as Confusion Matrix.The analysis is done and the model successfully classifies each image using VGG19 model of CNN.
Selective Image Encryption of Medical Images Based on Threshold Entropy and Arnold Cat Map

In medical image applications, selective image encryption plays an important role as it reduces computational cost and time.Lot of existing full image encryption algorithms may be more complex and uses traditional techniques.Effective method has been developed for protection of medical images.Combination of threshold entropy and Arnold Cat Map (ACM) are used in the algorithm to encrypt only selected part of medical image.Threshold entropy value used for separating the significant and insignificant blocks in the original image.In order to reduce the blocking artifacts after the partition of medical image, neglect the insignificant pixels present in the image sub blocks based on the thresholding of Lower four binary planes.At last Least significant Bit (LSB) embedding algorithm employed to achieve lossless encryption.The proposed technique is achieves the low computational complexity and also fast execution time.
How to Distribute Data across Tasks for Meta-Learning?

Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are trained on benchmarks with a fixed number of data points per task. This number is usually arbitrary and it is unknown how it affects performance at testing. Since labelling of data is expensive, finding the optimal allocation of labels across training tasks may reduce costs. Given a fixed budget of labels, should we use a small number of highly labelled tasks, or many tasks with few labels each? Should we allocate more labels to some tasks and less to others? We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks. Interestingly, Neuroscience experiments have shown that human visual skills also transfer better from easy tasks. We prove these results mathematically on mixed linear regression, and we show empirically that the same results hold for few-shot image classification on CIFAR-FS and mini-ImageNet. Our results provide guidance for allocating labels across tasks when collecting data for meta-learning.
Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

The article describes a new estimation method of regression functions in a multi-response semiparametric regression model based on smoothing spline. The multi-response semiparametric regression model is a combined model between a parametric regression model and a nonparametric regression model, where there is a correlation between responses. The proposed estimation method enhances the flexibility of the multi-response semiparametric regression model by combining a goodness of fit function and a penalty function to calculate an estimation which not only considers the goodness of fitting of the model, but also the smoothness of the estimation model curve. The optimal trade-off between goodness and smoothness can be achieved by selecting the optimal smoothing parameters. The article discusses a theoretically proposed method for estimating this multi-response semiparametric regression model regression function of parametric and nonparametric components. We use the weighted least squares method to estimate the parametric component parameters, we determine the goodness of fit and penalty functions using the reproducing kernel Hilbert space method, and then take the result of penalized weighted least squares optimization to obtain an estimate of the nonparametric component. The new research results are a weighted least squares estimator of parameters of parametric components, and a weighted partial smoothing spline estimator of the nonparametric component. The result shows that the estimated multi-response semiparametric regression model is linear to the observation, and is a combination of the estimations of the parametric and nonparametric components. The research results of the estimation of this model can be applied to medical fields for predictive purposes.