Reviewerly/exHarmony huggingface dataset:
Formative assessment as one of the ways to support the child in the learning process

The aim of this article is to show formative assessment as one of the ways to support first graders in guiding their learning process, an important element of which is self-regulation. It is the core around which the skills of first graders are built up. It becomes possible by directing their actions in speech and practice. Particular attention was paid to one of the elements of the formative assessment: feedback, appearing during the dialogue between the teacher and the student. I refer to John Hattie’s model (2015), describing four levels of feedback: task, process, self-regulation and “me”, and the main questions functioning at each of these levels. Levels and questions allow us to notice that feedback is not related to grading, but is information current in action, helping students to make progress on the basis of data reducing the gap between where they are and where they should be. It is important because if the student receives it on an ongoing basis, then he/she is best able to perform his/her tasks and activities. Self-regulation is the basis of education in the school class, which should be a space for its shaping. This approach of self-regulation allows us to see the first grader as an active, innovative and curious person. This requires a shift from summative grading to equipping students with a variety of self-regulated learning strategies.
Stiffness prediction on elastography images and neuro-fuzzy based segmentation for thyroid cancer detection

The elastography method detects metastatic changes by measuring the stiffness of tissues. Estimation of elasticities from elastography images facilitates more precise identification of the metastatic region and detection of the same. In this study, an automated segmentation algorithm is proposed that calculates pixel-wise elasticity values to detect thyroid cancer from elastography images. This intensity to elasticity conversion is achieved by constructing a fuzzy inference system using an adaptive neuro-fuzzy inference system supported by two meta-heuristic algorithms: genetic algorithm and particle swarm optimization. Pixels of the input color images (red, green, and blue) are replaced by equivalent elasticity values (in kilo Pascal) and are stored in a two-dimensional array to form an “elasticity matrix.” The elasticity matrix is then segmented into three regions, namely, suspicious , near-suspicious , and non-suspicious , based on the elasticity measures, where the threshold limits are calculated using the fuzzy entropy maximization method optimized by the differential evolution algorithm. Segmentation performances are evaluated by Kappa and the dice similarity co-efficient, and average values achieved are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.94</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.11</mml:mn> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.93</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.12</mml:mn> </mml:math> , respectively. Sensitivity and specificity values achieved by the proposed method are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>86.35</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.34</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>97.67</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.40</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> , respectively, showing an overall accuracy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>93.50</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>0.42</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> . Results justify the importance of pixel stiffness for segmentation of thyroid nodules in elastography images.
An Unsupervised Semantic Model for Arabic/French Terminology Extraction

We provide in this work, a new approach for automatic bilingual terminology extraction involving Arabic and French languages. We aim to build a bilingual list of terms for a specific field, namely “Algerian Culture”. The approach is based on building a semantic space for context understanding, adopting COALS (Correlated Occurrence Analogue to Lexical Semantics). COALS is an interesting semantic model which combines the advantages of LSA and HAL models. We use “Wikipedia” to build a comparable corpus for “Algerian heritage and culture” and a translation tool for term alignment.
An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high; Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes; explainable CNNs remains a challenge; explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient.
Emplacement of the Photovoltaic Water Pumping System in Remote Areas by a Multi-Criteria Decision-Making Method: A Case Study

Utilizing the solar energy from photovoltaic panels integrated into a water pumping system to provide electricity for dispersed villages that have no access to backup systems not only reduces the electricity grid costs and environmental pollutants but also is an innovative approach in areas with sufficient solar irradiation. Therefore, identifying and studying the feasibility of stand-alone solar power plants in various locations is the first step in using this reliable source of energy. For this reason, 93 sites in the provinces of Razavi Khorasan, Northern Khorasan, and Southern Khorasan, Iran, were chosen by the renewable energy organization engineers as regions with high potential for establishing a solar water pumping station. The goals of this research were to prioritize the 93 sites and find the perfect location for the establishment of the aforementioned station. In order to investigate and prioritize these sites, a multi-criteria decision-making method, namely, TOPSIS (technique for order preference by similarity to ideal solution), has been used. Therefore, the geographical locations of the sites and the weather in these provinces have been studied; consequently, 15 important and essential criteria, including slope, solar irradiation, precipitation, wind speed, relative humidity, altitude, distance from substation, distance from grid, obtainable solar energy capacity, distance from river, population, distance from city center, distance from road, average temperature, and number of dams in the vicinity of the site, have been chosen. All the aforementioned criteria have been weighted using the Shannon entropy method, and then the sites have been prioritized using the TOPSIS method. The results indicated that Sarayan, Isk, Esfadan, and Dasht Byaz in Southern Khorasan and then Beydokht 2 in Razavi Khorasan are the top five locations and Shosef in Southern Khorasan is the least favorite location for establishing a solar pumping station. In order to achieve more reliable results, sensitivity analysis was implemented.
Analisis Keterampilan Membaca Permulaan dan Berhitung pada Siswa Sekolah Dasar

This study aims to determine how the reading and numeracy skills possessed by elementary school students in grade 1 SD Kendalagung, RembangThe research approach used in this study is a qualitative approach (Qualitative Research). This research was conducted at SD Kendalagung, Rembang. The research informants were 6 grade 1 students at SD Kendalagung. Methods of data collection in this study using interviews, documentation, and field notes. The data analysis technique used is qualitative data which consists of data reduction, data presentation, and drawing conclusions.The results showed that the initial reading skills possessed by grade 1 students at SD Kendalagung were students able to voice pronunciation, students were able to understand the letters of the alphabet, students were able to read using appropriate intonation and fluency, students were able to read syllables, and students were able to understand word stops. . While the numeracy skills possessed by grade 1 SD Kendalagung students are students able to recognize numbers, students are able to sort numbers, students are able to count objects around, students are able to operate numbers (addition, subtraction, multiplication and division), and students are able to implement mathematical concepts in life daily.
Hardware Implementation of Stockwell Transform and Smoothed Pseudo Wigner Ville Distribution Transform on FPGA using CORDIC Algorithm

A comparison of linear and quadratic transform implementation on field programmable gate array (FPGA) is presented. Popular linear transform namely Stockwell Transform and Smoothed Pseudo Wigner Ville Distribution (SPWVD) transform from Quadratic transforms is considered for the implementation on FPGA. Both the transforms are coded in Verilog hardware description language (Verilog HDL). Complex calculations of transformation are performed by using CORDIC algorithm. From FPGA family, Spartan-6 is chosen as hardware device to implement. Synthetic chirp signal is taken as input to test the both designed transforms. Summary of hardware resource utilization on Spartan-6 for both the transforms is presented. Finally, it is observed that both the transforms S-Transform and SPWVD are computed with low elapsed time with respect to MATLAB simulation.
Robust Classification of Histology Images Exploiting Adversarial Auto Encoders

Deep learning (DL) thrives on the availability of a large number of high quality images with reliable labels. Due to the large size of whole slide images in digital pathology, patches of manageable size are often mined for use in DL models. These patches are variable in quality, weakly supervised, individually less informative, and noisily labelled. To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature generation using an adversarial autoencoder (AAE). We utilize the likelihood of the features in the latent space of AAE as a criterion to weigh the training samples. We propose different weighting schemes for our framework and evaluate the effectiveness of our methods on the publically available BreakHis and BACH histopathology datasets. We observe consistent improvement in AUC scores using our methods, and conclude that robust supervision strategies should be further explored for computational pathology.
Fencing tracking and visualization system

We developed the "Fencing tracking and visualization system." It detects the tips of sabers (fencing swords) to visualize the trajectory of the sabers in real time, which doesn't require any markers but works only with the input of the images from cameras. This is the only fencing visualization technology that has been used in actual international fencing matches, such as the H.I.H. Prince Takamado Trophy JAL Presents Fencing World Cup 2019.
Research on Evaluation Model of System Optimization Using Statistical Metrology and Coefficient Variation Method

With the rapid development of society, the healthy and sustainable higher education system is particularly important. As the level of higher education varies in different countries, it is therefore necessary to assess the health of the higher education system comprehensively, in order to make up for its deficiencies. The aim of this contribution is to establish a series of models for assessment, as well as put forward specific policy recommendations to help improve the education index of designated countries or regions. In this work, we collect plenty of data related to higher education. Based on Principal Component Analysis (PCA), we select 20 second-level indicators out of 36 initial indicators. Then, we divide these 20 indicators into 5 groups and construct a three-level evaluation index system to get Higher Education Evaluation Index (HEEI). Next, we determined weight of each indicator using both the Entropy Weight Method (EWM) and Coefficient Variation Method (CVM). And then we set three levels for the HEEI through K-means algorithm to establish the Higher Education Evaluation Model.
On the seven-diagonals splitting for the cubic spline wavelets with six vanishing moments on an interval

Abstract This study uses a zeroing property of the first six moments for constructing a splitting algorithm for the cubic spline wavelets. First, we construct a system of cubic basic spline-wavelets, realizing orthogonal conditions to all polynomials up to any degree. Then, using the homogeneous Dirichlet boundary conditions, we adapt spaces to the orthogonality to all polynomials up to the fifth degree on the closed interval. The originality of the study consists of obtaining implicit finite relations connecting the coefficients of the spline decomposition at the initial scale with the spline coefficients and wavelet coefficients at the nested scale by a tape system of linear algebraic equations with a non-degenerate matrix. After excluding the even rows of the system, the resulting transformation matrix has seven diagonals, instead of five as in the previous case with four zero moments. A modification of the system is performed, which ensures a strict diagonal dominance, and, consequently, the stability of the calculations. The comparative results of numerical experiments on approximating and calculating the derivatives of a discrete function are presented.
Cryptocurrency

The recent development of communication and information technologies has transformed the lives of people. The internet has supported people in various ways to become more flexible and effective for carrying out tasks, from booking a cab to transferring huge amounts of money in just a few clicks. A surge in the users of the internet has led to the inception of notions and devised a new exchange dimension of cryptocurrency. This paper provides diverse perspectives of cryptocurrency and its adoption. It also aims to scrutinize the terms of regulations and legislation, issues faced, and various scams of cryptocurrencies. It also discourses on the potential and implications of cryptocurrency.
Synthesis of Digital Medical Student Characteristics

The objective of this paper is to study, analyze and synthesize the characteristics of medical students together with digital competencies. It can be concluded that there are 4 characteristics of digital medical students: 1) Digital technology ethics 2) The use of digital technology 3) Creativity, innovation, and digital communication 4) Digital leadership. A compelling visual model is presented that defines the characteristics of medical students needed to become global citizens in the digital era.
A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.
Cataloging for a Celebration: Metadata for an Institutional Repository from the Ground Up

The Adelphi University Libraries recently adopted Esploro as its institutional repository. By engaging in a pilot project, cataloging assets for Adelphi’s Celebration of Scholarly Research and Creative Works, the Catalog and Metadata Strategies Unit created metadata for this collection from the ground up. We looked at workflows, automated processes, cataloging oversight, and the limitations of Esploro. We determined that authority control for both name and subject was essential to an effective institutional repository. This case study illustrates how the drawbacks of our institutional repository led us to find local solutions to serve the needs of our users.
Factors Influencing Employee Satisfaction of Educational Institutions

Generally speaking, education plays a potential role in shaping the economy. Like other organizations and education, employee development initiatives play a vital role in employee satisfaction. Therefore, this paper aims to study the influence of employee development on employee satisfaction in the education sector. Employee development is attached to empowerment, motivation, and training as independent variables. The data collected from 261 sample respondents from the education institutions of the Kingdom of Saudi Arabia. To prove the construct's hypothesis, SmartPLS 3.3.2 was utilized to analyze the measurement model and the structural model. Therefore, the study utilized the combinative PLS method that fulfills the characteristics of the model. Empowerment, motivation, and training were found to be conducive to employee satisfaction. Employee development (empowerment, motivation, and training) measures on employee satisfaction can help decision-makers emphasize their actions. The findings revealed that employee development positively influences employee satisfaction and commitment among employees in the education sector.
A Novel Sequence-to-Sequence Method for General Aviation Aircraft Normal Acceleration Prediction Using Flight Recording Data

Hard landing prediction is one of the most typical landing safety incidents analyses, which is the crucial part in the flight operational quality assurance of civil aviation. Although prediction performances based on quick access recorder data have been improved using machine learning and deep learning methods, there have been few studies on hard landing predictions using SD card data from general aviation aircraft recording systems. To address the problem of normal acceleration prediction in hard landing analyses, a novel sequence to sequence prediction method was developed based on SD card flight data. First, the original data was preprocessed and then an attention mechanism-based GRU encoder-decoder model was designed for the normal acceleration sequence predictions. Experiments were conducted based on real SD card flight samples for training flight aircrafts to compare the proposed method with previous approaches, the comparison results from which showed that even when using the relatively low sampling rate flight recording data, the proposed method had significant performance improvements for general aviation aircraft landing safety incidents analyses.
Research on Speech Feature Extraction and Synthesis Algorithm Based on EEMD

In the speech synthesis system based on statistical acoustic modeling, the parameter generation method based on Hidden Markov Model (HMM) has been fully developed. The parameters are mainly Mel Frequency Cepstrum Coefficient (MFCC), so the accuracy of extracting the speech signal MFCC directly affects the accuracy of speech synthesis. In this paper, based on the traditional method of extracting MFCC features, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is added, and the Mel filter bank in the traditional algorithm is changed to an adaptive Mel filter bank. The EEMD algorithm helps to enhance the ability of MFCC to describe the original speech signal, and the adaptive Mel filter bank can improve the utilization of the filter, thereby further improving the accuracy of MFCC. The experimental results show that compared with the traditional algorithm, the proposed algorithm can significantly reduce the MFCC root mean square error (RMSE) of the synthesized speech sequence.
A Moving Target Tracking Algorithm Based on Multi-camera Coordination

In this paper, an improved moving target tracking algorithm with multiple cameras based on the FOV line is presented. Firstly, a SIFT algorithm is used to select matching points automatically and generate the FOV line. When the target is through the line, the target’s color histogram matches the real target’s color histogram and endows the best matching with identification to achieve the target handover. Then the Mean Shift algorithm is used to achieve the tracking of the subsequent target. Real-time tracking is carried out. The experimental results show that the algorithm effectively realizes the target continuity tracking among multiple cameras.
Design of feasibility analysis platform for college physical education based on mobile social network

In order to promote the development of sports distance network education, a college sports teaching feasibility analysis platform based on mobile social network is designed. The key technologies such as Ajax, database and XML, Java Web containers applied to mobile social networks provide a technical basis for the platform design of this paper. Design the feasibility analysis platform framework using MVC mode, three platform functional modules were designed. Among it, the member function module is responsible for the registration and modification of the platform personal information of members; The feasibility analysis interactive discussion module is based on the cloud database storage and analysis platform user discussion records; The feasibility evaluation module of mobile social network is based on mobile social network, builds the hierarchical model of feasibility analysis and completes the design of feasibility analysis of distance teaching in universities. Experimental performance tested the platform, with an average response time of 2.3 ms and an analysis time of 18 ms. The feasibility analysis results of the platform have high reliability and effectiveness to meet the practical application requirements.