Reviewerly/exHarmony huggingface dataset:
Open Source network boot server for low-cost computer network learning

Abstract Universities in developing countries must have innovative ways of educating students to anticipate higher education costs. Computer network courses require server and client computers that are not cheap in terms of hardware and software. The most vulnerable computer hardware is the Hard Disk Drive (HDD), while the very expensive operating system license cost often forces universities to keep using outdated operating systems. This study aims to build open source network boot server by using PXE, DNSMASQ, TFTP, Casper and NFS. The PXE, DNSMASQ, TFTP, Casper and NFS will work together to become Open Source Network Boot Server (OSNBS). We conducted a simulation to measure the client boot speed from the network by using five clients and two versions of the uBuntu operating system image that have the capability of being a tool for computer networking courses. In addition, the processor server and hard disk server will be further monitored and analyzed. The simulation were carried out using virtualization on a desktop computer. We found that all clients successfully booted from the network. The latest uBuntu operating system image can efficiently improve the use of server resources for the processor and hard drive. In addition, the use of open source operating systems and existing hardware have also significantly reduced IT expenditure. This finding reinforces that OSNBS is suitable for low-cost computer network learning.
Means of visualization in teaching ukrainian as a foreign language to modern students with clip way of thinking

The purpose of the research is to prove the effectiveness of the use of visualization in the process of teaching Ukrainian as a foreign language to students with clip way of thinking. The following methods have been used, namely: analysis, synthesis, comparison, systematization and generalization of scientific literature; testing and surveys; pedagogical experiment; quantitative and qualitative analysis of data, interpretation and generalization of the research results. The essence of visualization means has been revealed; the expediency of their use in the methodology of teaching foreign students the Ukrainian language has been substantiated. It has been proven that the role of Ukrainian teachers lies in considering all new trends in teaching, integrating computer perception of information by foreign students into teaching technology and using cognitive visualization in order to intensify the learning process.
Comparing Bayesian Knowledge Tracing Model Against Naïve Mastery Model

We conducted a study to see if using Bayesian Knowledge Tracing (BKT) models would save time and problems in programming tutors. We used legacy data collected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have reduced the number of practice problems solved and time spent by the students represented in the legacy data. We found that in 64.23% of the concepts, students would have saved time with the BKT model. The savings varied among concepts. Overall, students would have saved a mean of 1.28 min and 1.23 problems per concept. We also found that BKT models were more effective at saving time and problems on harder concepts.
Research on the Impacts of Artificial Intelligence Technology on Language Teaching Innovation

Artificial intelligence has been integrated into our life and education, which is constantly changing our life style and our traditional college English teaching and learning methods.As an advanced and mature popular technology ; on the one hand, College English teaching as a traditional language and culture education on the other hand, the integration of the two can well meet the needs of training innovative talents in the new era.However, its development and application are still in the exploratory stage.This paper first analyzes and studies the role and development of artificial intelligence technology in College English teaching.Then, it illustrates the in-depth integration of the two can better serve English Teaching in the future; and after some survey of taking online courses during the pandemic COVID-19, the paper puts forward suggestions or prospects for the application and development of artificial intelligence technology in personalized College English teaching.
The Problems And Countermeasures Of Applying Multimedia Technology In College English Teaching

Multimedia technology refers to the computer-mediated software that integrates text, color, graphical images, animation, audio sound, and full motion video in a single application. Multimedia learning systems offer a potential venue for improving students’ interest, understanding and learning efficiency about the English language. Teachers try to make full use of multimedia to create an authentic language teaching and learning environment where students can easily acquire a language naturally and effectively. However, through teaching assessment system, a platform through which students can anonymously evaluate teachers’ teaching on the web, some limitation and problems of multimedia technology in English teaching is explored. For example, PPT-oriented English class leading to knowledge overlapping and time-wasting; Teachers relying too much on multimedia; Improper design of courseware stimulating distraction; Lacking interaction between teachers and students. Only when the multimedia teaching in college English is reformed and improved, can the function of multimedia assisted teaching play its best role. Aiming to optimize the function of multimedia in college English teaching, both the problems and countermeasures of applying multimedia in college English teaching are studied elaborately in this paper, which provides a theoretical basis and new way of teaching practice for the situational teaching of foreign language
Leveraging benchmarking data for informed one-shot dynamic algorithm selection

A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process. Dynamic algorithm selection and configuration are therefore well-researched topics in evolutionary computation. Two different settings are classically considered: hyper-heuristics and parameter control studies typically assume a setting in which the algorithm needs to be chosen and adjusted during the run, without prior information, other approaches such as hyper-parameter tuning and automated algorithm configuration assume the possibility of evaluating different configurations before making a final recommendation. In practical applications of evolutionary algorithms we are often in a middle-ground between these two settings, where one needs to decide upon the algorithm instance before the run ("oneshot" setting), but where we have (possibly lots of) data available on which we can base an informed decision.
Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.
An Ultra-Low-Cost Large-Format Wireless IoT Camera

This paper documents the design, construction, and experimental evaluation of an ultra-low-cost large-format digital camera. Used lenses that cover formats up to 4x5 can be surprisingly inexpensive, but large-format image sensors are not. By combining 3D printing with cheap components developed for use in IoT (Internet of Things) devices, especially the sub-$10 ESP32-CAM, a digital scanning large-format camera capable of over 2GP resolution can be constructed at very low cost. Despite the large image area, Lafodis160 is literally a wireless IoT device, fully remote controllable via Bluetooth and WiFi. This camera was originally intended to serve as a testbed for novel ways to improve capture quality for scenes that are not completely static during the scan interval, and a brief overview is given of methods employing unusual scan orderings that will be evaluated using it.
Research on the Relationships Among Learning Motivation, Learning Engagement, and Learning Effectiveness

This research mainly aims to explore the relationship between learning motivation and learning effectiveness and verify the mediating role of learning engagement in the relationship between learning motivation and learning effectiveness.Taking the undergraduates of Z University in China as the research object, this paper carried out an investigation through quantitative research, whose result reveals that the learning motivation (learning interest, self-development, professional needs, external expectations) has a significant positive effect on the learning effectiveness (learning level, response level, behavior level).In addition, to further verify the mediating effect of learning engagement in the relationship between learning motivation and learning effectiveness, this research adopted the bias-corrected bootstrap method to test the total effect, indirect effect, and direct effect respectively.The finding shows that learning engagement plays a partial mediating role in the relationship between learning motivation (intrinsic motivation, extrinsic motivation) and learning effectiveness (reaction level, learning level, and behavior level).
TO EXAMINE THE MEDIATING IMPACT OF WORK ENGAGEMENT AMONG THE RELATIONSHIP OF HUMAN RESOURCE MANAGEMENT PRACTICES AND SERVICE RECOVERY PERFORMANCE DURING PANDEMIC-19

This study seeks to examine the mediating impact of work engagement between the relationship of human resource management practices and service recovery performance of Takaful industry in Saudi Arabia during Covid-19 pandemic. For this purpose, data has been collected from 350 employees by using a convenient sampling technique constituting a response rate of 70 percent. Through a cross sectional research design, this research applies a quantitative research method to study this impact. The Structural Equation Modeling (SEM) had shown that all the HRMP namely human capital, training, rewards, internal career opportunities, job description, staff participation had a positive and significant relationship with the service recovery performance. On the other hand, work engagement had also a significant mediating effect among most of HRMP and service recovery performance except staff participation and service recovery performance. As work engagement has a mediating impact on most aspects of HRMP and service recovery performance, therefore, this contribution is considered to be substantial contribution to the existing body of literature. Based on these findings, this study could help managers and owners understand the importance of HRMP in enhancing service recovery performance. The design and context-based limitations of this study and the future directions that point to it are also discussed in the concluding sections of the study. © 2021. All Rights Reserved.
Compactness prediction of airport soil field based on artificial neural network

In order to improve the dynamic prediction and management capabilities of soil field safety in airfield, a new artificial neural network (ANN) model was established based on the evaluation data of six years in an airport. By factors analysis of the actual soil field of the airport, natural density (ND), actual water content (AW), optimal water content (OW), rainfall condition (RC) and compaction condition (CC) were chosen as the input data, and hyperbolic tangent sigmoid function was set as the transfer function. The network was trained by 400 sets of data and validated for its accuracy by 100 sets of data selected randomly from the database. The prediction capability of achieved ANN model was analyzed by Nash-Sutcliffe efficiency coefficient (NSE) method. And engineering application had been done in another airport. The results show that soil field compactness can be effectively predicted by well-trained ANN model with <italic>R</italic>-Squared of 0.98 and NSE of 0.89. The outcomes of validation test in another airport prove that the errors of most sample zones are between -5% and 5%, with only one exception of 15%, with calculated NSE of 0.86, which satisfies the requirements of engineering application. By optimization of ANN model with factor analysis method, it indicates that ND and AW are the controlling factors of model compactness prediction, and the best ways of improving the safety of soil field in airport are soil gradation and drainage control strictly.
Traditions of folk culture and education

The research on problems on preserving identity of tradition folk art and craft for strategic objective to design modern methods and tools in order to contribute to the creation of an education process. Experience was acquired in conducting relevant large-scale festivals, competitions, science conferences clearly demonstrated the younger generations growing ability in ownership to provide effective Russian Art cultural. Increased attention to realize workshops with Folk art and craft in the Moscow region system of additional education indicated positive dynamic for introducing teachers and children’s through period 2010-2020 years. It is notable for protection cultural historical heritage areas of Moscow region, which directed towards traditional Russian decorative Art especially over the past years.
Software Defect Prediction Using Optimized Cuckoo Search Based Nature-Inspired Technique

These days, software systems are very complex and versatile. Therefore it is essential to identify and fix the software error. Software error assessment is one of the most active areas of research in software engineering. In this research, we are introducing soft computing methods to assess software errors. Our proposed technique ts software gives errors and accurate results. In our proposed method, the error database is first extracted, which acts as an input. After that, the collected input (data) is clustered by the clustering technique. For this purpose, we use the modified C-Mean Algorithm. Therefore, the data is clustered. An efficient classification algorithm then groups clustered data. For this reason, we use a hybrid nervous system. Therefore, there are software bugs, and these errors are optimized using the MCS algorithm. Our proposed method for software error assessment is implemented on the Java platform. Performance measurement is measured by various parameters such as execution rate and execution time. Our proposed Cuckoo search based strategy is comparable to many existing strategies. Graphical representation of comparison results from our proposed strategy for identifying software proposals is one that effectively evaluates profitable strategy and reasonable reference rates.
Holistic Image Manipulation Detection using Pixel Cooccurrence Matrices

Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations. In this paper, we propose a novel approach to holistically detect tampered images using a combination of pixel co-occurrence matrices and deep learning. We extract horizontal and vertical co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. We train and validate our model on a dataset of more than 86,000 images. Experimental results show that our approach is promising and achieves more than 0.99 area under the curve (AUC) evaluation metric on the training and validation subsets. Further, our approach also generalizes well and achieves around 0.81 AUC on an unseen test dataset comprising more than 19,740 images released as part of the Media Forensics Challenge (MFC) 2020. Our score was highest among all other teams that participated in the challenge, at the time of announcement of the challenge results.
An efficient loop tiling framework for convolutional neural network inference accelerators

Convolutional neural networks (CNNs) have been widely applied in the field of computer vision due to their inherent advantages in image feature extraction. However, it is difficult to implement CNNs directly on embedded platforms owing to excessive calculations of CNNs. Field Programmable Gate Arrays have been popular in CNN accelerators because of their configurability and high energy efficiency. Given the highly parallel workloads of the CNN, a CNN accelerator with a 14 × 16 processing element array is designed in this study to accelerate the CNN inference. Besides, a loop tiling strategy for convolutional layers is proposed to efficiently transmit feature maps. Additionally, the roofline model is employed to explore the best tiling parameters for optimal performance. Finally, the accelerator written in Verilog-HDL language is implemented on the Xilinx Zynq-7045 evaluation platform. At an operating frequency of 200 MHz, the proposed accelerator can achieve a performance of 57.24 giga operations per second on You Only Look Once v2-tiny and 78.39 GOPS on Visual Geometry Group-16. The accelerator only consumes 224 DSPs, demonstrating a better performance compared with the previous works.
Derivation of a Virtual Machine For Four Variants of Delimited-Control Operators.

This paper derives an abstract machine and a virtual machine for the λ-calculus with four variants of delimited-control operators: shift/reset, control/prompt, shift₀/reset₀, and control₀/prompt₀. Starting from Shan’s definitional interpreter for the four operators, we successively apply various meaning-preserving transformations. Both trails of invocation contexts (needed for control and control₀) and metacontinuations (needed for shift₀ and control₀) are defunctionalized and eventually represented as a list of stack frames. The resulting virtual machine clearly models not only how the control operators and captured continuations behave but also when and which portion of stack frames is copied to the heap.
On Index Calculus Algorithms for Subfield Curves

In this paper we further the study of index calculus methods for solving the elliptic curve discrete logarithm problem (ECDLP). We focus on the index calculus for subfield curves, also called Koblitz curves, defined over $$\mathbb {F}_q$$ with ECDLP in $$\mathbb {F}_{q^n}$$ . Instead of accelerating the solution of polynomial systems during index calculus as was predominantly done in previous work, we define factor bases that are invariant under the q-power Frobenius automorphism of the field $$\mathbb {F}_{q^n}$$ , reducing the number of polynomial systems that need to be solved. A reduction by a factor of 1/n is the best one could hope for. We show how to choose factor bases to achieve this, while simultaneously accelerating the linear algebra step of the index calculus method for Koblitz curves by a factor $$n^2$$ . Furthermore, we show how to use the Frobenius endomorphism to improve symmetry breaking for Koblitz curves. We provide constructions of factor bases with the desired properties, and we study their impact on the polynomial system solving costs experimentally.
MENINGKATKAN KEMAMPUAN VOCABULARY BAHASA INGGRIS MELALUI GAMES KATA BERANTAI PADA SISWA KELAS VIII SMP NEGERI 1 KARANGKOBAR

English Vocabulary skills are important in learning English. Most students at SMP N 1 Karangkobar are still not active in the mastery of English vocabulary because they are lazy to learn, afraid, and have used to learn English. It is therefore necessary to innovate learning to change the habit. One of them is with serial word games. This research includes a type of class action research consisting of Two cycles. The data collection techniques used are observations and tests.  Based on the results of the study on cycle I gained an average score of the vocabulary ability of 67 students, and in cycle II by 76.  The increased submission of vocabulary mastery of students also increased each of its cycles from the cycle of the pre-cycle to the I-cycle of 17%, from cycle I to cycle II by 34%, and from cycle II to cycle II by 49%. Based on the results, the implementation of serial word games can improve students ' vocabulary skills in English language subjects.
A Stochastic Model Updating Gold Reserve Estimation by Using Monte Carlo Simulation

This paper elucidates a new idea and concept for exploration of the gold ore deposits.  The cyanidation method is traditionally used for gold extraction. However, this method is laborious, time-consuming, costly, and depends upon the availability of the processing units. In this work, an attempt is made in order to update the gold exploration method by the Monte Carlo-based simulation. An excellent approach always requires a high quality of the datasets for a good model. A total of 48 incomplete datasets are collected from the Shoghore district, Chitral area of Khyber, Pakhtunkhwa, Pakistan. The cyanidation leaching test is carried out in order to measure the percentage of the gold ore deposits. In this work, the mean, median, mode, and successive iteration substitute methods are employed in such a way that they can compute the datasets with missing attributes. The multiple regression analysis is used to find a correlation between the potential of hydrogen ion concentration (pH), solid content (in %), NaCN concentration (in ppm), leaching time (in Hr), particle size (in µm), and measured percentage of gold recovery (in %). Moreover, the normal Archimedes and exponential distributions are employed in order to forecast the uncertainty in the measured gold ore deposits. The performance of the model reveals that the Monte Carlo approach is more authentic for the probability estimation of gold ore recovery. The sensitivity analysis reveals that pH is the most influential parameter in the estimation of the gold ore deposits. This stochastic approach can be considered as a foundation to foretell the probabilistic exploration of the new gold deposits.