Reviewerly/exHarmony huggingface dataset:
Using the electronic portfolio as a means of formation the foreign language communicative competence when training future tutors-teachers

The problem and the aim. Intercultural, foreign language communication is an integral part of the modern electronic learning environment. The professional competence of the future teacher as a subject of information interaction is manifested in the ability and readiness to speak a foreign language while performing official duties, in research activities, for self-realization. The authors propose to use the capabilities of the electronic portfolio for the formation of foreign language communicative competences of students of pedagogical training programs in the information educational space. Research methods. Theoretical analysis and generalization of scientific literature were used to determine the components of the foreign language communicative competence, the content and structure of the electronic portfolio. The method of pedagogization of the information technology, group interaction, assessment by external experts, mutual assessment and self-assessment are used for designing and scaffolding of the electronic portfolio with digital tools. The experiment involved 64 students of the Vyatka State University of the training program 44.03.01 Pedagogical education (bachelor level). The software is the Tilda service (https://tilda.cc/ru/). As a method of statistical processing of the experimental data, the χ2 (chi-square) Pearson test was used. Results. In the experimental group, future tutors-teachers used Tilda tools to determine the structure and content of the electronic portfolio (categories, sections, projects), which was subsequently provided for discussion and analysis by other students, external experts. The assessment of the levels of formation of the foreign language communicative competence was carried out and statistically significant differences in the qualitative changes that occurred in the pedagogical system were revealed, χ2obs. 2 &gt; χ2crit0.05 (7,07 &gt; 5,99). In conclusion features of designing and using electronic portfolios, implemented on the Tilda platform to present achievements in the foreign language communicative, educational, cognitive, professional activity are summarized.
Strength Analysis and Assessment of Ina-TEWS Wave Glider

Indonesia as a country that often experiences tsunami disasters needs to have an early warning system against tsunami disasters. This system can use various existing technologies, one of which is the tsunami buoy system. The new tsunami buoy system does not use the natural mooring system but uses the wave glider system. This paper discusses the structural strength of the surface floater of wave glider using Eva Foam and Fiberglass material for skin and Alluminium material for frame and kell. The surface floater using 16 pieces for frame and 1 piece for keel. Enviromental loads is use in this paper like hydrodynamics load and weight load. The results from this paper is material from Eva Foam has a maximum principle stress is 12693 Pa and shear stress is 6114.6 Pa. For material from Fiberglass has maximum principle stress is 11.875 Pa and shear stress is 6076.3 Pa. Safety factor (SF) from maximum principle stress and shear stress for Eva Foams is up to 6x and SF for maximum principle and shear stress for Fiberglass is up to 26x. Conclusions for this paper is the desain for surface floater of wave glider it can be operated in the sea with draugh 0.18 m.
Dual Immersion Digital Instruction: A Theoretical Model for Equitable and Inclusive Classrooms

Dual immersion programs have proven effective in achieving biliteracy for all students. However, maintaining equitable practices at the core of such programs has become more challenging in remote learning due to the pandemic. It is necessary, therefore, to revise some of the benefits and challenges of digital instruction mediated by technology in these settings. Using a middle school Dual Immersion (di) program in Southern California as a background, and from the perspective of bilingual education teachers and professors, this article presents a theoretical model called Dual Immersion Digital Instruction (di2) that could serve that purpose. The model includes the five dimensions involved in just, equitable, and inclusive education: Technological, content, social, linguistic, and pedagogical. The article also analyzes the pedagogical opportunities and challenges that teachers in di programs face in regards to each of these dimensions when all instruction becomes fully online. Finally, the article discusses how the shift to online teaching in di classrooms could impact bilingual teacher education programs.
The “Gandalf” soil sampling project at a former industrial site in Copenhagen, Denmark: evaluating soil classification reliability

When previously industrialised or urbanised sites are redeveloped, the contamination of the soil is a vital consideration. It is essential that it is classified correctly as being fit for reuse or only for landfill, or even needing decontamination. When dealing with truck loads of soil, correct, representative sampling is essential to assure safety and to minimise unnecessary costs.
Mining Weighted Periodic Patterns by a Weighted Direction Graph Based Approach for Time-Series Databases

Periodic pattern mining in time series database plays an important part in data mining. However, most existing algorithms consider only the count of each item, but do not consider about the value of each item. To consider the value of each item on periodic pattern mining in time series databases, Chanda et al. proposed an algorithm called WPPM. In their algorithm, they construct the suffix trie to store the candidate pattern at first. However, the suffix trie would use too much storage space. In order to decrease the processing time for constructing the data structure, in this paper, we propose two data structures to store the candidates. The first data structure is Weighted Paired Matrix. After scanning the database, we will transform the database into the matrix type, and it is used for the second data structures. Therefore, our algorithm not only can decrease the usage of the memory space, but also the processing time. Because we do not need to use so much time to construct so many nodes and edges. Moreover, wealso consider the case of incremental mining for the increase of the data length. From the performance study, we show that our proposed algorithm based on the Weighted Direction Graphis more efficient than the WPPMalgorithm.
Unsupervised Object Segmentation Based on Bi-Partitioning Image Model Integrated with Classification

The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label.
Digital Ludeme Project Database Guide

This document outlines the types of data collected for the Digital Ludeme Project – how it is collected, how it is formatted, how it is stored, and how it can be accessed – with special reference to the FAIR Guiding Principles for scientific data management and stewardship.
Artificial Intelligence in Architectural Design

Artificial intelligence is one of the methods in elaborating design, especially on architectural design. This method has been adapted to many designs, such as building complex, massing models. This study aims to determine the influence of artificial intelligence in forming complex conceptual designs in architecture. We used a case study method by examining documents or journals related to research to conduct this research. The results show that more than 50% of artificial intelligence helps architects make conceptual designs. This is due to artificial intelligence's ability to analyze data and turn it into a draft plan. Then, it will be analyzed by the architect will so that they can make the final plan. However, there is still a bad influence that allows an architect to be replaced by automation. Therefore, that means artificial intelligence must be used with great responsibility.
The Role of Machine Translation Quality Estimation in the Post-Editing Workflow

As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.
An Improved Whale Optimization Algorithm for Dinosaur Lantern Festival Color Image Multilevel Thresholding Segmentation

The whale optimization algorithm has the shortcomings of precocity and insufficient diversity when it is used to segment the color lantern image. A chaotic levy whale optimization algorithm is proposed to search the optimal Kapur's entropy and Renyi entropy. In this algorithm, particles are used to represent a feasible one-dimensional multi threshold vector, and chaos initialization strategy is adopted to improve the randomness of the algorithm. Based on the whale optimization algorithm, Levy flight strategy is used to enhance the diversity and global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the other five algorithms. In addition, the algorithm using Renyi entropy has better performance than Kapur's entropy in terms of Signal to Noise Ratio (PSNR), Feature Similarity index (FSIM), and best fitness. It also provides a reference for the application of swarm intelligence algorithm in intangible cultural heritage image segmentation.
The Role of Vidura Chatbot in the Diffusion of KnowCOVID-19 Gateway

The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot to assist users to overcome infodemic, low usability, and high learning curve. We argue that human-machine communication via a chatbot play a critical role in enabling the diffusion of innovations.
Analysis of the infectional profile of diabetic patients infected by COVID 19 in the period of 2020 – 2021 Análise do perfil de infecção dos pacientes diabéticos infectados pela COVID 19 no período de 2020-2021

The COVID-19 disease has as its etiological agent the SARS-CoV-2, a RNA virus that causes severe respiratory syndrome. Diabetes mellitus is one of the significant risk factors for COVID-19 due to the weakened immune response of these patients and widely distribution of ACE2 in diferent organs. ACE2 is a receptor that antagonizes the RAS system, and is related to the clinical symptoms of COVID 19 .Its expression in the islet cells and exocrine pancreas suggest the susceptibility for SARS-CoV 2 infection in diabetic patients. This stydy analyses the infeccional profile of diabetic patients infeccted by COVID-19 in the city of Maceió. A research was performed in the year of 2020-2021 in english idiom in the database PubMed using the descriptors “ACE receptor” AND “Covid”, “Covid” AND “Diabetes” AND “ ACE receptor” .The criteria of inclusion and exclusion established was the relevance and approach based on the theme to be discussed.It was collected data about confirmed deaths by Covid 19 according to preexisting comorbidities in the city of Maceió – AL shown in the graphics , expressing an average mortality among patients infected by Covid and preexisting Diabetes Mellitus. This study shows a reduction in the percentage of mortality in diabetic patients infected by Covid-19 seen in view of the adoption of measures to restrict the spread of the virus, reducing the numbers of infecion, especially the risk group, in addition to the vaccination campaign which culminated in positive results to the reduction in the mortality in infected diabetics.
GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Recommendation system has been paid growing attention in the academia community and industry community because it can solve the problem of information overload. Among a variety of methods, the click-through rate prediction model plays an important role in predicting user’s attention to a specific item. To predict click-through rate, high-dimensional and sparse features are usually adopted, and the accuracy of the prediction result depends on the combination of high-order features to a great extent. Therefore, many methods have been proposed to find the low-dimensional representation from sparse high-dimensional original features, and the meaningful way of feature combination has also been mined to improve the accuracy of the model. However, the click-through rate prediction models generally have two problems. One is that they can’t extract the feature interaction of non-Euclidean features very well. Another one is that it is hard to explain the inward meaning of feature interaction. In this paper, a GCN-int model based on the interaction of Graph Convolutional Network is proposed to solve the above problems. The proposed model simplifies the complex interaction among multiple features, gets a better representation of the interaction between high-order features, and improves the interpretability of feature interaction. The experimental results on the public movie recommendation dataset and our own IPTV movie recommendation dataset show that the proposed GCN-int model gets higher accuracy and efficiency compared with the state-of-the-art models.
Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos

Smart traffic management is being proposed for better management of traffic infrastructure and regulate traffic in smart cities. With surge of traffic density in many cities, smart traffic management becomes utmost necessity. Vehicle categorization, traffic density estimation and vehicle tracking are some of the important functionalities in smart traffic management. Vehicles must be categorized based on multiple levels like type, speed, direction of travel and vehicle attributes like color etc. for efficient tracking and traffic density estimation. Vehicle categorization becomes very challenging due to occlusions, cluttered backgrounds and traffic density variations. In this work, a traffic adaptive multi-level vehicle categorization using deep learning is proposed. The solution is designed to solve the problems in vehicle categorization in terms of occlusions, cluttered backgrounds.
Digital Education and Society 5.0

Today's world is dominated by artificial intelligence, robotics, big data, and machine learning, and educating people for future “digital education” is considered crucial. The incorporation of technology in education has created a new form of space that enables remote synchronous, real-time interaction between individuals, territories, and organizations in a variety of educational, cultural, economic, and political fields. The chapter examines Society 5.0 (where people can address different social problems by integrating innovations) and its implementation in today's education policy and the national curriculum and addresses how learners and teachers can benefit from it. The chapter concludes by considering a few ways in which we might begin to think about the future impact of technology on classroom teaching and learning practices.
edTPA During Student Teaching: PETE Teacher Candidates’ Experiences and Future Support

Many physical education teacher education programs require teacher candidates to successfully complete edTPA during student teaching. Considering that research remains sparse regarding best practices in edTPA, the purpose of this study was to understand the experiences of teacher candidates completing edTPA during student teaching and of university supervisors supervising teacher candidates during the process. Twenty-four teacher candidates and 11 university supervisors participated in a focus group discussion for approximately 1 hr. Data were analyzed and categorized as (1) negative experiences, (2) positive experiences, and (3) future support. Overall, the teacher candidates felt that completing edTPA diminished their student teaching experience, as it was tedious and time-consuming pertaining to technological difficulties in videotaping, video processing, and video uploading. Nonetheless, the participants felt that completing edTPA was valuable in providing teacher candidates with detailed feedback to improve on their pedagogical skills. Future support for teacher candidates includes integrating edTPA early in their curriculum of study to better prepare them to successfully complete edTPA during student teaching. The university supervisors felt that with more in-depth training in edTPA and experience supervising teacher candidates on edTPA, they would be more competent in the future. Subscribe to TPE
Experimental Study on Reduction of Water Level behind a Caisson-Type Submerged Breakwater

Hur, D.-S.; Lee, W.-D.; Lee, S.-Y., and Jeong, Y.-M., 2021. Experimental study on reduction of water level behind a caisson-type submerged breakwater. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 211–215. Coconut Creek (Florida), ISSN 0749-0208. In this study, hydraulic model experiments were performed for four types of cross sections depending on the presence or absence of drainage channel of Caisson-Type submerged breakwaters, in which slit-upper-level and lower-level drainage channels are installed to lower the water level behind the submerged breakwaters. Additionally, based on the measured water level data, compared the changes in the average water level behind the submerged breakers for cases with or without drainage channels with a Solid-Caisson that has no slit-upper-level and lower drainage channels. Hence, in submerged breakwaters with a slit-upper-level drainage channel, the rear water level was reduced by 27.1∼52.5% compared to that with Solid-Caisson, and it was reduced by 36.8∼62.2% and 67.4∼81.5%, in the case of lower-level drainage channel and the combined case, respectively. Therefore, lowering of water level behind the submerged breakwaters is better using the lower-level drainage channel than with the slit-upper-level drainage channel. Moreover, its effect is excellent when both are used. The Caisson-Type submerged breakwater proposed herein was found to be effective in reducing the rear water level and the flow in the openings, thereby securing the stability of the submerged breakwaters.
Sentiment Analysis Using Machine Learning Approach

Customers feedback is a valuable asset for businesses, that can be used in order to improve their performance. One of the fastest spreading areas today in computer science - Sentiment Analysis, helps to extract precious information from textual data, in order to identify the feeling of a statement. This research aims to build a classifier to predict customersâ€™ satisfaction, based on Amazon reviews dataset, for different brands of mobile phones. The paper proposes a comparison between four text classification algorithms - NaA¯ve Bayes, Support Vector Machine, Decision Tree and Random Forest, using different feature extraction techniques, such as Bag of words and TF-IDF. In addition, the models are evaluated using accuracy, precision, recall and F-score metrics. Our experiments revealed that Support Vector Machine achieves the best results and is very suitable for classification of the sentiment on product reviews.
A study on the effect of VR contents characteristics on educational effects - Focusing on the perceived characteristics of VR content

Recently, as awareness of educational service quality is increasesing, various studies are being conducted to improve educational service quality. In this study, to verify the educational effect of students on VR content used in the educational field, and to explore the relationship between the perceived characteristics of users who use immersive content and the effect on the need for immersive content and educational effects. As a result of the study, it was found that presence and interactivity had a positive(+) effect on necessity, and that complexity had a negative(-) effect. Also, presence, interactivity, and necessity were found to have a positive(+) effect on the educational effect, and VR sickness was derived to be statistically insignificant for both necessity and educational effect. This model explains 74.8%. Based on these results, when VR contents are used for education in the future, concerns about VR sickness can be resolved, and it was possible to discuss the need to plan and produce sophisticated interactivity.
Enterprise Cloud Native Software Engineering

As a software engineer, you get to see your work being used in every aspect of life, and you are responsible for how it is developed and deployed in an environment. According to recent global surveys, a large percentage of software deployed around the world is poorly designed and executed and the people using it are unaware of the socially engineered risks of software exploitation. Software is not just about an executable deployed on a server; it is about adaptability, agility, accessibility, security, and intelligence.