Reviewerly/exHarmony huggingface dataset:
A Critical Review of Basic Literacy Learning Materials on ‘KU-CU’ site

Objectives The purpose of this study is to find problems with basic literacy learning materials and to find directions for improvement by critically reviewing basic literacy learning materials provided by the basic education improvement support site. Methods Through literature research, the analysis criteria for basic literacy learning materials were prepared in terms of ‘learning content’ and ‘instruction principle’. The analysis targets 13 volumes of 5 basic literacy learning materials provided by the basic education improvement support site (Korean basic learning, hard-pressed Korean, dream-growing Korean, Chanchan Hangul, Hangul Hanmadang), and the analysis results were cross-reviewed. Results In terms of learning content, the problems were that basic literacy learning contents were not selected in a balanced manner, lack of learning materials related to phonological recognition, failure to present basic vocabulary considering the learner's level, and neglect detailed learning elements of fluency. In terms of guiding principles, the lack of explicit presentation of basic learning concepts, lack of guidance on functions and strategies, failure to provide opportunities for repetitive exercise activities, failure to subdivide and present learning units, and lack various sensory activities to promote learning motivation. Conclusions Based on the above critical review, first, when constructing basic literacy learning content, the basic literacy learning content should be evenly reflected and the level of learners should be considered. Second, in order to minimize learners' cognitive burden, explicit presentation of basic concepts, presentation of consistent functions and strategies, provision of opportunities for repetitive practice, presentation of segmented learning units, and composition of activities using various senses should be reflected. Third, in consideration of the utilization of learning materials, modifiable formats, individual files, and combined files should be provided together. Fourth, in order to secure learning materials that reflect the latestness, new materials should be steadily developed and pre-developed materials should be regularly updated.&#x0D;
Connectivity-informed drainage network generation and deep learning for inverse modeling.

are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.
COVID-19 and Virtual Nutrition: A Pilot Study of Integrating Digital Food Models for Interactive Portion Size Education

Background and aims: Digital food viewing is a vital skill for connecting dieticians to e-health. The aim of this study was to integrate a novel pedagogical framework that combines interactive three- (3-D) and two-dimensional (2-D) food models into a formal dietetic training course. The level of agreement between the digital food models (first semester) and the effectiveness of educational integration of digital food models during the school closure due to coronavirus disease 2019 (COVID-19) (second semester) were evaluated. Method: In total, 65 second-year undergraduate dietetic students were enrolled in a nutritional practicum course at the School of Nutrition and Health Sciences, Taipei Medical University (Taipei, Taiwan). A 3-D food model was created using Agisoft Metashape. Students’ digital food viewing skills and receptiveness towards integrating digital food models were evaluated. Results: In the first semester, no statistical differences were observed between 2-D and 3-D food viewing skills in food identification (2-D: 89% vs. 3-D: 85%) and quantification (within ±10% difference in total calories) (2-D: 19.4% vs. 3-D: 19.3%). A Spearman correlation analysis showed moderate to strong correlations of estimated total calories (0.69~0.93; all p values < 0.05) between the 3-D and 2-D models. Further analysis showed that students who struggled to master both 2-D and 3-D food viewing skills had lower estimation accuracies than those who did not (equal performers: 28% vs. unequal performers:16%, p = 0.041), and interactive 3-D models may help them perform better than 2-D models. In the second semester, the digital food viewing skills significantly improved (food identification: 91.5% and quantification: 42.9%) even for those students who struggled to perform digital food viewing skills equally in the first semester (equal performers: 44% vs. unequal performers: 40%). Conclusion: Although repeated training greatly enhanced students’ digital food viewing skills, a tailored training program may be needed to master 2-D and 3-D digital food viewing skills. Future study is needed to evaluate the effectiveness of digital food models for future “eHealth” care.
ONBOARDING, A LEARNING APPROACH TO OVERCOME ADOPTION THRESHOLDS WITH EXTENDED REALITY

Never before has restrictions at a global scale due to the Covid-19 pandemic put industry at such unprecedented transformation need.Sustainability by using resources with greater consideration in order to continue to stay resilient, and to thrive post-pandemic has put attention on how benefit the most out of emerging technologies.The advent of extended reality (XR) is today reshaping the way people experience the physical and the virtual environments, from observation to immersion.Due to the turmoil caused by the pandemic technologies that minimize equipment threshold has increased in popularity.Today XR enable experts to be more productive to detect and debug problems and resolve production issues remotely.Currently, both clear advantages and potential disadvantages may exist based on degree of the maturity level of integration.XR provides opportunities for immersive learning, upskilling and renewal.Still, it involves several uncertainties in benefits.XR implementation therefore remain very dispersed among companies.Implementation of these technologies has been considered more to the level of having an inhouse highly sophisticated playground, with potential for later consideration.However, what is missing in many instances is a clear understanding of the impact and transformative mechanisms that AR, VR and MR could provide if only equipped with learnings of how to introduce and adopt knowledge embedded in various use-cases.With a growing experience in testing these technologies the possibilities and requirements needed for successful decision-making are also starting to emerge.Consequently, by understand learning adoption and value drivers in industrial settings, skills practiced in academia can be sharpened not only from a contextual understanding but also to new academic domains and to incentivize implementation initiatives.This paper is looking at distinct value indicators for onboarding as a way for industry professionals to learn new XR skills.
CCANet: Classifcation of Colorectal Tumor Histopathological Images Using a CNN with Channel Attention Mechanisms

Histopathological imaging analysis of colorectal polyps is the gold standard for tumor diagnosis. Nevertheless, the diagnostic process of organizing images is time-consuming and laborious. So a fast and effective computer automatic analysis method is needed. In recent years, Convolutional Neural Networks (CNN) have been frequently used for cancer classification tasks. Nevertheless, the potential global and channel relationships of the image may be overlooked and influence the representation capability of features.Also, the results are often not convincing for pathologists due to the un-interpretability. We propose a lightweight colorectal tumor classification network with only 10.02M parameters based on CNN and Channel Attention Mech-anisms(CCANet). The four classification tasks performed on the public dataset Warwick_qu reached an accuracy of 80.49%. Moreover, the visual analysis of Grad_CAM rationalizes the results and makes them more convincing to pathologists.
Construction of English Translation Teaching Model Based on Cloud Data Edge Detection Algorithm and Output-Oriented Method

In the context of globalization, English, as a unique passport to the world, is an important way for countries to communicate in information, technology, and culture. The output-oriented approach is a new theory of foreign language learning and teaching constructed by Chinese scholars, which has attracted widespread attention from foreign language scholars and front-line teachers and is expected to address the issue of “separation of learning and use” of English in Chinese universities. In the context of globalization, English, as a unique passport to the world, is an essential way for countries to exchange ideas about information, technology, and culture. Therefore, there is no doubt that English education plays a crucial role in nurturing the younger generation, especially at the high school level. This paper introduces an edge detection algorithm and an output-oriented approach to the teaching model of English translation. Questionnaires were administered to students in the control and experimental classes. The percentages of students who strongly disagreed, disagreed, neutral, agreed, and strongly agreed with the translation task based on their interest were 1.9%, 13%, 25.1%, 36.8%, and 23.2%, respectively.
IMPROVING THE QUALITY OF TRAINING SECONDARY VOCATIONAL EDUCATION SPECIALISTS IN THE FRAMEWORK OF NATURAL SCIENCE AND GENERAL PROFESSIONAL DISCIPLINE CYCLES

Statement of the problem. The relevance of the study is due to the need to find effective approaches to assessing the quality of training of specialists in secondary vocational education (SVE), dictated by the growing demands of the labor market and technological progress in the economy. The purpose of the article is to develop proposals for determining the criteria and parameters for the diagnostic assessment of the quality of training of specialists in secondary vocational education in the framework of the implementation of the natural science and general professional cycles of disciplines using pedagogical measuring materials of the Federal Internet Exam. The research methodology is based on legal acts in the field of secondary vocational education, competence-based approach, analysis of scientific publications and educational experience in the field of the formulated problem, practical research methods (questionnaires, surveys, expert assessments). The study is based on the methodology of V.P. Bespalko on the levels of knowledge assimilation and the gradual ranking of students along educational trajectories through testing in the form of testing on certain pedagogical measuring materials (PMM), which made it possible to clarify the content and structure of training students in the SVE system and the methodology for assessing the main educational results within the framework of the level model developed by the authors. Research results. On the basis of the proposals put forward, proposals were developed for conducting a diagnostic assessment of the quality of training of specialists in secondary vocational education at the Aerospace College, the content of the work programs of the natural science and general professional cycles of disciplines and assessment funds were revised. Organizational and pedagogical conditions for the implementation of the level model were developed and tested. An experimental confirmation of the effectiveness of the model of level assessment of the quality of training of SVE specialists in the educational process of the Aerospace College was carried out using pedagogical measuring materials of the Federal Internet Exam. Conclusion. The implementation of the developed organizational and pedagogical conditions for a level model for controlling the formation of general and professional competencies of SVE specialists contributes to the enrichment of the content of the disciplines in natural science and general professional cycle with production and situational tasks and the use of integrative design in the educational process, which contributes to improving the quality of training of SVE specialists. The implementation of the developed proposals in preparation for the FEPO in 2021 made it possible to increase learning outcomes by more than 10%, according to the pedagogical monitoring based on the results of the FEPO for March-July 2021.
The Use of the Fuzzy Inference System in Determining the Quality of Goatskin as Raw Material for the Leather Industry

Leather products are affected by the quality of the raw hide, the pre-treatment, and the tanning process. Furthermore, some leather products use quality goatskins with the right determination as raw materials. This study aims to determine the quality of goatskin through the use of Fuzzy Logic science. It was conducted using the Sugeno Fuzzy Inference System (FIS), while data analysis was carried out through the Matlab R2018a program. The criteria for using this method were the factors that affected the quality of raw goatskin such as thickness, length of the backline, skin area, number of scratches on the skin, number of holes, presence of boils, lice bumps, and hair loss. The results of the analysis showed that Sugeno FIS could be used to help determine the quality of goatskin with a good accuracy level with a MAPE value of 18%. Furthermore, the FIS method could be used as an appropriate modeling system for determining the quality of goatskin as a raw material for the leather tanning industry.
Noise-Resistant Video Channel Identification

As the video streaming traffic grows exponentially nowadays, variable bitrate (VBR) encoding has been widely utilized by modern live video streaming service providers, such as YouTube, TikTok, and Twitch. However, video bitrate can be a delicate fingerprint of the video streaming, leading to risks of privacy leakage. There are several studies that attempt to eavesdrop the privacy from encrypted video streaming, but most of them presume strict requirements on the implementation environments and have great limitations when noise interference exists. Actually, the video traffic from the multimedia edge server is distinct from interapplication traffic flows due to device customization and can be identified even if there are noise interferences or the victim in a weak network condition. In this paper, a video traffic identification method is proposed to identify the encrypted video streaming from multimedia edge server under the interference of irrelevant traffic flows. Initially, we use an interapplication filter to identify the traffic from the edge server. Then, a longest-common-subsequence (LCS)-based method is developed for similarity matching to resist the noise interference from unpredictable burst traffic and network environment variations. In order to evaluate the system performance, we setup the prototype system with an AWS EC2 server and a raspberry pi device, then utilize the real-world trace data for pushing movies to victims. The experimental results show that the accuracy of our proposed strategy can reach 89.1% within 140 seconds eavesdropping even mixed with 14% noise interference.
Dual Fusion Mass Detector for Mammogram Mass Detection

Mammogram mass detection is a difficult task due to the mass character of the tiny area, fuzzy boundary, and occlusion. To address these problems, this paper proposes a novel detection network for mammogram mass detection. Firstly, we propose a novel feature fusion structure and Small Target Attention Module (STAM) to improve the model's ability to detect small masses. Secondly, Results-oriented Loss (ROL) is adopted to obtain better model performance. Finally, Incremental Positive Selection (IPS) is used to divide positive and negative anchors. The scarcity of breast mammogram images for training aggravates the difficulty of mass detection. Thus, we open our collected dataset, which contains 1456 mammogram images from 400 patients. Since the model includes a double feature fusion structure, the proposed network is named Dual Fusion Mass Detector (DFMD). Experiment results show that DFMD is robust to various variations on scale, blurry and occlusion.
Breast Lesions Segmentation using Dual-level UNet (DL-UNet)

Breast disease is one of the primary diseases endangering women's health. Accurate segmentation of breast lesions can help doctors diagnose breast diseases. However, the size and morphology of breast lesions are different, and the intensity of breast tissue is uneven. Thus, it is challenging to segment the lesion area accurately. In this paper, we propose Dual-scale Feature Fusion (DSFF) module and Edgeloss to segment breast lesions. The DSFF module aims to integrate two-scale features and design another effective skip connection scheme to reduce false positive regions. To solve the problem of unclear segmentation boundary, we design Edgeloss for additional supervision on the boundary region to obtain a finer segmentation boundary. The experiment results show that the proposed DL-UNet with the DSFF module and new Edgeloss performs best in several classic networks.
Vibration Signal Analysis Based on Spherical Error Compensation

A vibrating screen is important equipment in industrial production. According to the principle of bionics, a vibrating screen can be divided into a linear vibrating screen, elliptical vibrating screen, ball vibrating screen, and banana vibrating screen. There are also great problems with the use of a vibrating screen. The vibrating screen works due to the vibration excitation force generated by vibration. This work studies the motion trajectory of a vibrating screen by taking the vibrating screen with line motion trajectory as the research object. In this study, the vibration information is detected by an intelligent sensor, and the signal is filtered by an intelligent algorithm. Then, the spherical error compensation is used to improve the calculation accuracy, and the least square method is used to evaluate the error. Finally, the accurate vibration trajectory of the vibrating screen is obtained. The acquisition of a vibration track can provide the working efficiency and safety performance of the vibrating screen, and has social and economic benefits.
Islands of the Tectonic Origin in the Shatt Al-Arab River, Southern Iraq

Several factors control the formation of river islands, such as tectonic factors, sedimentation, erosion, and human activities. This research is concerned with islands in the Shatt Al-Arab channel, which are likely to be traced back to tectonic factors in their formation. The islands of the Shatt Al-Arab River are distributed along its axis starting from the islands of Sindbad and Muhammadiyah, 15 km to the north of the city center of Basra, to the islands of Shatit, Al-Dawasir and Minauhi, 70 km south of the city center of Basrah. This study area is part of the Mesopotamia Plain, southern Iraq. The fieldwork period extended from the beginning of September 2012 to June 2013, which included making subsurface sections (longitudinal and transverse) of the bottom of the Shatt Al-Arab channel using the Sub-bottom profile Strata box device, imaging the topography of the river bottom in a side scan sonar, collecting the bottom sediments by a grab sampler. The volumetric analysis method uses the Master Sizer device based on the Folk classification to calculate the volumetric gradient of sediments. The study showed that the tectonic factor has a prominent role in the growth island Maino and as a result of the presence of sub-surface structures for the structure of the Siba, which appeared to affect the subsurface layers of the river bed near the island as it appeared in the survey sections of the Strata box device. There are some islands that more than one factor emerged as a reason for their formation and development, as in the islands of Muhammadiyah and Umm al-Rasas, where tectonic and sedimentary factors interfered in their formation and development due to the influence of sub-surface structures as well as the influence of nearby rivers such as the Karma and Karun Rivers.
PENINGKATAN KOMPETENSI GURU SD MELALUI PENGEMBANGAN MEDIA BELAJAR INTERAKTIF

During this pandemic, teachers are required to be more creative and innovative in carrying out learning. It is still very rare for teachers to use the computer for the purpose of improving learning, let alone using Powerpoint-based learning media with various applications. Facts on the ground, namely in Cluster 6 Blimbing do not have sufficient competence in operating the tools available in Microsoft Office Powerpoint. Based on this, a learning media workshop is needed that is able to support this, does not require high specifications for laptop devices, and provides easy access guidebooks for educators. Powerpoint is an application that easily bridges users in designing various types of creative learning materials. In particular, PowerPoint has new features that can make it easier for educators to create media slides for presentations, learning videos, lesson plans, integration with virtual classroom platforms that support creativity and collaboration, etc. There are several specific activities that will be carried out to overcome the above problems through this Community Service, namely: 1) providing interactive learning media guidebooks to elementary school teachers; 2) holding a workshop “Development of Learning Media for Elementary School Teachers Using PowerPoint and Slidesgo”; and 3) Make a video tutorial "Development of Learning Media for Elementary School Teachers Using Interactive Learning Media".
Predicting the Amount of Pineapple Production in Sumatra Using the Fletcher-Reeves Algorithm

Pineapple is a kind of organic product from the Bromeliaceae family which has the logical name Ananas comosus Merr. Pineapple plants have weathered skin and pointed leaves on top. The taste of new pineapple is a combination of sweet and slightly sharp. Pineapple is high in L-ascorbic acid, which helps cells fight damage, according to the Linus Pauling Organization at Oregon State College. L-ascorbic acid is also useful in managing medical conditions, such as heart disease and joint pain. However, due to the absence of consideration from the regions and local governments regarding pineapple on the island of Sumatra, it has caused several problems, especially data on pineapples related to the advantages, content, and uniqueness of pineapples to be used as pineapples. chaotic and diminishing pineapple production, especially on the island of Sumatra. Therefore, it is important to make a wish to know the assessed amount of Pineapple Organic Product Crop Creation on the island of Sumatra so that the public authorities on the island of Sumatra have endlessly clear references to decide on an approach or make major progress sothat the development of pineapple on the island of Sumatra does not diminish. The method used in making predictions is the FletcherReeves algorithm and is a method in ANN. In this study, the data used was the number of pineapple fruit plants on the island of Sumatra in 2012-2021 obtained from BPS. Given this information, organizational design models will not be fully defined, including 4-10-1, 4-15-1, 4-20-1, 4-25-1 and 4-30-1. Of these 5 models, then Training and Testing is done and the best architectural model result is 4-15-1 with the least (less) Performance/MSE test. With the lowest Performance/MSE level of 0.005488189 compared to the other 4 models.