Reviewerly/exHarmony huggingface dataset:
Implementation of the agricultural university educational programs during a pandemic: participants’ opinions

The article presents the results of a study of the opinions of students and teachers of the southern region of Russia about the learning process in higher education programs during the COVID-19 pandemic. We carried out a comparative analysis of the quality of the implementation of educational programs in the assessments of participants in the process during the onset of the COVID-19 pandemic (April-May 2020) and its peak values (November-December 2020). The positive and negative consequences of transferring training to a distance learning format are formulated. Among the positive consequences are: the availability of training from anywhere; more flexible training, focused on the educational needs of students; new formats of social student life; more accessible communication with teachers. We analyzed the experience of the educational community presented in publications during the COVID-19 pandemic. The data obtained provide an understanding of the importance of the development and implementation of new organizational and methodological approaches for the effective response of higher education to external global challenges of our time.
Adaptive Pauli Shadows for Energy Estimation

Locally-biased classical shadows allow rapid estimation of energies of quantum Hamiltonians. Recently, derandomised classical shadows have emerged claiming to be even more accurate. This accuracy comes at a cost of introducing classical computing resources into the energy estimation procedure. This present note shows, by adding a fraction of this classical computing resource to the locally-biased classical shadows setting, that the modified algorithm, termed Adaptive Pauli Shadows is state-of-the-art for energy estimation.
Evaluating Complexity of Digital Learning in a Multilingual Context: A Cross-Linguistic Study on WHO’s Emergency Learning Platform

Reproduction of knowledge, especially tacit knowledge can be expensive during a pandemic. One of the most common causes is the reduced information accessibility during the translation process. Having the ability to assess the linguistic complexity of any given contents could potentially improve knowledge reproduction. Authors conduct two cross-linguistic studies on the World Health Organization (WHO)’s emergency learning platform to assess the linguistic complexity of two online courses in 10 languages. Morpho-syntactically annotated treebanks, unannotated materials from Wikipedia and language-specific corpora are set as control groups. Preliminary findings reveal a clear reduced complexity of learning contents in the most candidate languages while retaining the maximum amount of information. Creating a baseline study on low-resourced languages on the learning genre could be potentially useful for measuring impact of normative products at country and local level.
Towards Understanding Knowledge Distillation

Knowledge distillation, i.e., one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifiers. It has even been observed that classifiers learn much faster and more reliably if trained with the outputs of another classifier as soft labels, instead of from ground truth data. So far, however, there is no satisfactory theoretical explanation of this phenomenon. In this work, we provide the first insights into the working mechanisms of distillation by studying the special case of linear and deep linear classifiers. Specifically, we prove a generalization bound that establishes fast convergence of the expected risk of a distillation-trained linear classifier. From the bound and its proof we extract three key factors that determine the success of distillation: * data geometry -- geometric properties of the data distribution, in particular class separation, has a direct influence on the convergence speed of the risk; * optimization bias -- gradient descent optimization finds a very favorable minimum of the distillation objective; and * strong monotonicity -- the expected risk of the student classifier always decreases when the size of the training set grows.
Digital image analysis of immunohistochemistry KI-67 using QuPath software in breast cancer

Background: Today, pathology services are more developed for quantitative diagnostic evaluation. The quantitative diagnostic evaluation requires detailed accuracy and can be done using digital image analysis (DIA). Assessment of the Ki67 labelling index (LI) in breast carcinoma needs to be done quantitatively. A visual evaluation of Ki67 LI using light microscopy has high inter-observer variability. The evaluation of Ki67 LI could be done digitally with the DIA technique to overcome the inter-observer variability. The DIA technique is carried out by counting the Ki67 LI manually or automatically with bioimage analysis software. QuPath is one of the bioimage analysis software, has characteristics of cross-platform, intended for bioimage analysis and digital pathology. Objective: This study aims to compare the manual and automatic calculation of Ki67 LI digitally. Methods: This study was a cross-sectional study; a total of 240 digital Ki67 images from 30 slides were analyzed by counting manually and automatically using QuPath. Results: Statistical analysis using the T-test showed no significant difference between the manual and automatic counting of Ki67 LI (p = 0,801, α = 0,05). Conclusion: Digital image analysis using QuPath can be used to calculate the Ki67 LI automatically.
Specific radar emitter identification based on two stage multiple kernel extreme learning machine

To make full use of the discriminative information containing in the whole ambiguity function (AF) plane, a novel two stage multiple kernel extreme learning machine (TSMKELM) method for specific radar emitter identification is proposed. Firstly, the AF plane is segmented into the non-overlapping Doppler shift stripes and each stripe is encoded as a kernel. Next, the discrimination of these stripes is evaluated and sorted according to the kernel discriminant ratio (KDR) criterion, which is in line with the large margin principle of KELM. Then, only the stripes with large KDRs are kept and the combined kernel is calculated by directly using the normalized KDRs as combination weights. At last, the KELM classifier is employed to fulfil the individual identification task. The proposed algorithm, named as KDR-TSMKELM, solves the kernel combination weights and kernel classifier parameter separately, bringing much efficiency in practice. Experiments on two real radar datasets validate the proposed algorithm.
A Unified Framework for Neuroscience Morphological Data Visualization

The complexity of the human brain makes its understanding one of the biggest challenges that science is currently confronting. Due to its complexity, the brain has been studied at many different levels and from many disciplines and points of view, using a diversity of techniques for getting meaningful data at each specific level and perspective, producing sometimes data that are difficult to integrate. In order to advance understanding of the brain, scientists need new tools that can speed up this analysis process and that can facilitate integrating research results from different disciplines and techniques. Visualization has proved to be useful in the analysis of complex data, and this paper focuses on the design of visualization solutions adapted to the specific problems posed by brain research. In this paper, we propose a unified framework that allows the integration of specific tools to work together in a coordinated manner in a multiview environment, displaying information at different levels of abstraction and combining schematic and realistic representations. The two use cases presented here illustrate the capability of this approach for providing a visual environment that supports the exploration of the brain at all its organizational levels.
Resource Analysis of the Log Files Storage Based on Simulation Models in a Virtual Environment

In order to perform resource analyses, we here offer an experimental stand on virtual machines. The concept of how to measure the resources of each component is proposed. In the case of system design, you can estimate how many resources to reserve, and if external modules are installed in an existing system, you can assess whether there are enough resources and whether the system can scale. This is especially important for large software systems with web services. The dataset contains a set of experimental data and the configuration of virtual servers of the experiment in order to conduct resource analyses of the logs.
The effectiveness of using distance learning from the viewpoint of secondary school teachers in the period of the Corona pandemic – A comparative study between the Hashemite Kingdom of Jordan and the Syrian Arab Republic –: فاعلية استخدام التعلم عن بعد من وجهة نظر مدرسي المرحلة الثانوية في فترة جائحة كورونا – دراسة مقارنة بين المملكة الأردنية الهاشمية والجمهورية العربية السورية –

The aim of the study is to explore the effectiveness of distance learning from the viewpoint of high school teachers in the educational process in the Hashemite Kingdom of Jordan and the Syrian Arab Republic within the Corona pandemic, and study the significance of differences in the effectiveness of distance learning according to the gender variable, specialization and years of experience. To achieve the goals of the study, the researcher prepared a questionnaire to survey the views of teachers about the effectiveness of distance learning and prepare it electronically, and applied it to a random sample consisting of (300) teachers and schools in the Hashemite Kingdom of Jordan in Irbid Governorate, and (300) teachers and schools in the Syrian Arab Republic in Aleppo Governorate. Results showed: The effectiveness of distance learning in the Corona pandemic period was lower than the average for the study sample, There are statistically significant differences in the extent of the effectiveness of distance learning in the educational process due to the state factor and to the Hashemite Kingdom of Jordan, There are statistically significant differences in the effectiveness of distance learning in the educational process due to the sex factor and in favor of females, There are statistically significant differences in the extent of the effectiveness of distance learning in the educational process due to the specialty factor and in favor of theoretical major, There are statistically significant differences in the effectiveness of distance learning in the educational process due to the factor of years of experience in favor of (from 5 to 10 years) followed by (less than 5 years) and then (more than 10 years).
Feasibility evaluation of the 3D-DIC non contact measurement system using small-scaled model test of earth retaining wall

Abstract In this study, a small scale experiment of an earth retaining wall was conducted to develop a real-time and full-field noncontact measurement system for earth retaining walls in urban areas. The collapse behavior of the structure was reproduced using a model of an earth retaining wall, and the deformation and displacement of the entire surface of the small scale structure were measured using a 3D image-based digital-image correlation method employing a stereo camera system. The measured displacement was compared with the actual displacement by using high-precision hexapod equipment in the experiment, and the results confirmed a high level of precision.
Convolutional neural Network-XGBoost for accuracy enhancement of breast cancer detection

Abstract Computer programs can work by imitating the human brain to make decisions that can be used in the health sector. One of them is the Convolutional Neural Network (CNN) which is combined with XGBoost as the classifier. CNN-XGBoost can be implemented for the accuracy of early detection of breast cancer. The problem is how to improve the accuracy of breast cancer detection on mammogram images. The stages of the research method: (1) Collecting the MIAS 2012 dataset, (2) Dividing data into training data and testing data. (3) preprocessing: cropping, resizing, and reshaping. (4) Data Augmentation (5) Transfer Learning (6) Classification using CNN-XGBoost (7) Testing the accuracy. Based on the research that has been carried out, the results are: (1) Obtained data on the accuracy of using CNN-XGBoost on mammogram image analysis in early detection of breast cancer. (2) Further testing is needed to improve accuracy. Further testing is needed with the use of other method or by improving the quality of mammogram images.
Design of a High Precision Digital Clock Based on Single Chip Microcomputer

Compared with traditional mechanical clocks, digital clocks have higher accuracy and durability and are widely used in people's daily lives.This paper designs a digital clock which uses AT89S52 single-chip microcomputer as the main control chip.It can count hours, minutes, and seconds, and can calibrate the time.It can switch between 24-hour and 12-hour systems.As a smart clock, while displaying the time, it also adds the function of temperature display.The intelligent digital clock designed in this paper has stable performance in theory and has certain practical value.
Cataloging Consistency in Access Points: Academic Libraries of Turkey

Cataloging quality plays a crucial role in libraries. In this context, this study investigates the adherence of access points in bibliographic records to content and format standards. We employed the qualitative descriptive method and analyzed 2,695 catalog records from seven academic libraries in Turkey. We analyzed MARC fields for access points to see whether the bibliographic records adhere to AACR2 and MARC standards. Furthermore, we provide exemplary cases of common nonadherent records. Results demonstrated that analyzed records have inconsistencies from 2% to 75% for the analyzed MARC fields. This study recommends that academic libraries provide ongoing training for catalogers.
On (Semi, Pre, Semi-pre, b)-open subgraph

"Due it difficult to find applications in topological spaces, which are branches of pure mathematics. The importance of this paper is to find applications in graph theory. So, We Introduced (semi, pre, b, semi-pre)-open subgraph to graph theory. The relations among them were studied. At least many theorems were proofed as a characterization and some examples introduced to explain the subject.
Feature-level Incongruence Reduction for Multimodal Translation

Caption translation aims to translate image annotations (captions for short).Recently, Multimodal Neural Machine Translation (MNMT) has been explored as the essential solution.Besides of linguistic features in captions, MNMT allows visual (image) featuresto be used.The integration of multimodal features reinforces the semantic representation and considerably improves translation performance.However, MNMT suffers from the incongruence between visual and linguistic features.To overcome the problem, we propose to extend MNMT architecture with a harmonization network, which harmonizes multimodal features (linguistic and visual features) by unidirectional modal space conversion.It enables multimodal translation to be carried out in a seemingly monomodal translation pipeline.We experiment on the golden Multi30k-16 and 17.Experimental results show that, compared to the baseline, the proposed method yields the improvements of 2.2% BLEU for the scenario of translating English captions into German (En→De) at best, 7.6% for the case of Englishto-French translation (En→Fr) and 1.5% for English-to-Czech (En→Cz).The utilization of harmonization network leads to the competitive performance to the-state-of-the-art. Ground-truth:Two brown horses pulling a sleigh through snow. Ground-truth:Sled dogs running and pulling a sled. Counterfeit:Two brown horses running and pulling a sled.
Transfer Learning for Instance Segmentation of Waste Bottles Using Mask R-CNN Algorithm

This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the mask region proposal convolutional neural network (Mask R-CNN). Plastic bottles constitute one of the major pollutants posing a serious threat to the environment both in oceans and on land. The automated identification and segregation of bottles can facilitate plastic waste recycling. We prepare a custom-made dataset of 192 bottle images with pixel-by pixel-polygon annotation for the automatic segmentation task. The proposed transfer learning scheme makes use of a Mask R-CNN model pre-trained on Microsoft COCO dataset. We present a comprehensive scheme for fine-tuning the base pre-trained Mask-RCNN model on our custom dataset. Our final fine-tuned model has achieved 59.4 mean average precision (mAP), that corresponds to the MS COCO metric. The results indicate promising application of deep leaning for detecting waste bottles.
Comparison of Grammatical Error Correction Using Back-Translation Models

Grammatical error correction (GEC) suffers from a lack of sufficient parallel data.Therefore, GEC studies have developed various methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences.Currently, a mainstream approach to generate pseudo data is back-translation (BT).Most previous GEC studies using BT have employed the same architecture for both GEC and BT models.However, GEC models have different correction tendencies depending on their architectures.Thus, in this study, we compare the correction tendencies of the GEC models trained on pseudo data generated by different BT models, namely, Transformer, CNN, and LSTM.The results confirm that the correction tendencies for each error type are different for every BT model.Additionally, we examine the correction tendencies when using a combination of pseudo data generated by different BT models.As a result, we find that the combination of different BT models improves or interpolates the F 0.5 scores of each error type compared with that of single BT models with different seeds.
Lossless instruction-to-object memory tracing in the Linux kernel

The lack of visibility into Linux's behavior makes it hard to refactor and maintain. To peer inside the box, we present Memorizer, a self-contained, low-level tracing framework that tracks (most) object allocations, data accesses, and function calls within the kernel. The core insight is a low-level object-centric representation that records detailed lifetime information while linking each operation (call/read/write) with its intended target. We evaluate Memorizer using extensive input programs and demonstrate its value by showing how Memorizer can (1) aid in refactoring, (2) extend code coverage with object coverage to improve testing and analysis, and (3) identify leaky abstractions. We also release a large data set, visualization tools, and Memorizer's source. This generic, object-centric approach is the first to provide loss-less instruction-to-object tracing, adding an essential software engineering capability to the overly complex Linux kernel.
Deductive Synthesis of Networks Hierarchical Knowledge Graphs

The article focuses on developing of a deductive synthesis method for building telecommunications networks (TN) hierarchical knowledge graphs (KG). Synthesized KGs can be used to solve search, analytical, and recommendation (forecast) problems. TNs are complex heterogeneous objects. The synthesis of knowledge graphs of such objects requires much computational resources. The proposed method provides a low complexity of the synthesis of KG of TN by taking into account their hierarchical structure. The authors propose to do synthesis by direct downward multilevel inference and reverse multilevel inference. The article analyses existing graph models of TNs and methods for their building. Detailed description of the proposed method of networks hierarchical KGs synthesis is given. In order to evaluate the deductive synthesis method, a prototype of the system is developed. The provided real-world example shows how telecommunications networks hierarchical knowledge graphs are synthesized and used in practice. Finally, conclusions are formulated, and the areas of further research are identified.
The Development of a Virtual Module Based on the Infographic Dynamics of Art Materials

The COVID-19 pandemic in Indonesia has had a very significant impact on learning activities in schools, including learning at the UM Lab SMP. Teachers who are accustomed to using technology can make their own or take teaching materials from various sources on the internet. There are not many teaching materials that can be found on the internet that discuss specifically about junior high school art material. Teachers still have to reduce text material into easily digestible material for students to learn. This research used Sugiyono’s development method which has 10 research steps. The virtual module developed in this study is in the form of an application that can be operated on a smartphone or laptop, and contains junior high school art material in a systematic and attractive learning package that makes the learning experience easier for teachers and students. In using the module, the teacher becomes the facilitator, and students can understand the steps that must be taken in each basic competency, without even asking the teacher. The module directs students to follow the learning flow like playing a game. The interactive digital module was tested on UM Lab SMP students and showed good results. It got a score of 90.6% for the material expert validation test, 92.3% for the media expert validation test, 85% for the product trial with a small group, and 87.5% for the trial with a large group. This module can minimize the role of the teacher, enable students to learn independently, and can increase student activity with media in learning and learning activities.&#x0D; Keywords: Virtual module, Dynamix module, art module