Reviewerly/exHarmony huggingface dataset:
The Study on Automated Evaluation of Online Discussion Quality Based on Semantics

High-quality discussions are of great significance to promote learning and achieve teaching goals. In this study, content analysis is used to perform semantic analysis on the content of the discussion posted by learners on online learning platform from the perspective of semantic similarity. The semantic similarity of different topics under the course is weighted to obtain the online discussion quality score of the learner. Then the evaluation results from experts discussed with the learner are divided into four levels. The Kappa consistency test and the McNemar paired chi-square test are performed. The experimental results show that there is consistency between the two evaluation methods, and the accuracy rates of the two ranked lower rankings are better. Therefore, the automated evaluation of the online discussion quality of learners based on semantics will play an indispensable role in the selection and sticking of comments on online learning platforms.
Robust Two-stage Graph Convolutional Network for Face Clustering

Face clustering has been widely studied, due to its broad applications in academia and industry. Regarding researches on clustering, two vital parts are involved: one is the feature representation space of the image, and the other is the clustering algorithm. However, most of the current researches lay more emphasis on the latter and overlook the need for an appropriate feature representation space for clustering. Therefore, we propose a novel clustering framework, to address the problem of insufficiently compact features in clustering. Our method is comprised of a feature topology learning module (GCN-FT) and an auto-search clustering module (GCN-AS), which called GCN-F&A (GCN-FT & GCN-AS). Specifically, GCN-FT adds a self-adaptive learning structure to the traditional GCN to capture the internal correlation of features, so that the module can better aggregate information from itself and its neighbor nodes, and provide more gathered features for clustering. Our GCN-AS consists of two parts, one is ‘1-NN’, and the other is clustering module based on linkage prediction, namely ‘GCN-LP’. 1-NN is an effective pre-clustering method, which can automatically search hyper-parameters required in GCN-LP, thereby improving the scalability of the clustering module. Experiments on two large-scale face benchmarks and one clothing dataset demonstrate that our method significantly outperforms the state-of-the-arts.
DEVELOPMENT OF THE TEACHER TRAINING SYSTEM IN THE VOLOGDA REGION ON THE BASIS OF CONTINUITY AND CONTINUITY OF INTER-LEVEL EDUCATIONAL PROGRAMS

The article discusses the training of teaching staff based on the model of continuing education, which provides for the continuity of educational programs vertically between the levels of secondary vocational and higher education. Based on the analysis of the educational process for the continuity and continuity of pedagogical education at each level of vocational education, legal, organizational and technological gaps have been identified. The article presents an algorithm for the interaction of educational organizations of secondary vocational and higher education in the development and implementation of succession programs. To create a holistic process of teacher training, an inter-level competence model has been developed, in which the methodology of cross-cutting competencies is used. Twelve cross-cutting competencies have been identified, which should be formed among graduates of colleges and universities. Cross-cutting competencies formed the basis for the development of the competence matrix, which reflects the content of the training of teaching staff at different levels of education and is consistent with the sequence of their development by determining the level of their formation.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: Zmeeting submission system • Number of submissions received: 63 • Number of submissions sent for review: 52 Number of submissions accepted: 28 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 44.4% • Average number of reviews per paper: 2 • Total number of reviewers involved: 29 reviewers • Any additional info on review process: 1. Preliminary Review 2. The papers passed the first review will be reviewed again from the following aspects: Originality, Innovation, Technical Soundness, Applicability, Presentation &amp; English and Relevance Contact person for queries: zengzhu@yaang.cn Chengdu Young Academic Conference Co. Ltd., China Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.
Semantic Relocation Parallel Network for Semantic Segmentation

Semantic segmentation is an essential issue in the computer vision field, the difficulty of which lies in the accurate prediction of the pixel level and the edge division of similar objects. The encoder-encoder structure is widely used in many methods to capture the global information of semantic objects. However, continuous subsampling causes irreversible loss of spatial information of the feature map. A parallel semantic relocation (SRPNet) based network is proposed. Specifically, a high-resolution global spatial path is designed to extract rich spatial information in which feature maps have high resolution. In feature extraction path, a powerful feature extractor is used to expand the receptive field by fast subsampling. Besides, a semantic relocation module (SRM) is designed to compensate for the lack of context information caused by multiple subsamples. Dice loss is employed to alleviate the imbalance of positive and negative samples in the dataset and obtain better segmentation performance. Finally, the proposed network is evaluated on the Cityscapes and CamVid dataset. The results show that SRPNet can improve the previous best result by approximately 3.1% and 1.8% measured by mIoU on the CamVid dataset and the Cityscapes dataset, respectively.
Foreign Language Teachers’ Activities to Develop Students’ Digital Citizenship Competences: Findings of the Dice. Lang Project

he extreme situation connected with the outbreak of the pandemic coronavirus has forced foreign language teachers worldwide to challenge their teaching competences and approaches when teaching remotely. Now, more than ever, foreign language teachers are forced or encouraged to implement digital materials, learning objects and environments. Meanwhile, foreign language teachers’ knowledge, skills and attitudes related to Digital Citizenship Education (DCE) are tested and challenged, too. The aim of this paper is to explore how confident and knowledgeable about DCE foreign language teachers are in order to offer activities that can enhance the development of language learners’ digital citizenship competences. This study presents the survey findings of the ERASMUS+ project: “Digital Citizenship Education and Foreign Language Learning” (Dice.Lang), which brings together five European partner universities: University of Munich, University of Aveiro, University of Latvia, University of Limerick, and Siena Italian Studies. There were 627 foreign language teachers (312 pre-service teachers and 315 in-service teachers) in total who participated in the online survey representing Germany, Ireland, Italy, Latvia, and Portugal. The findings highlighted the needs foreign language teachers have to develop and apply their expertise on DCE in their language lessons. The authors of the paper present their vision to address the teachers’ needs, providing and analysing samples of a comprehensive set of open educational resources (OER) available in English and additional European languages. These OER, which have been designed by the Dice.Lang consortium and confirmed by the questionnaire results, aim at developing language learners’ digital citizenship competences. The resources intertwine the five DCE strands created by the consortium (Critical Digital Literacies; Intercultural and Transcultural Perspective on Digital Exchanges; Identity-oriented Component; Content-oriented Perspective and Critical and Meta-reflective Component) with the existing European theoretical frameworks.
Multiple Metasomatic Events Recorded by a Stratified Lithosphere Beneath the Sw Margin of the São Francisco Craton, Se Brazil

A suite of kimberlite-hosted mantle xenoliths from the Catalão region, southwestern margin of the São Francisco Craton (SFC), Brazil, consists of spinel lherzolites, harzburgite, and garnet-phlogopite wehrlites. The chemical composition of these xenoliths provides evidence for a stratified subcontinental lithospheric mantle (SCLM) where the deeper mantle xenoliths have experienced multiple metasomatic events, but the shallower mantle rocks are products of partial melting process slightly overprinted by metasomatic melts. Samples derived from the deeper mantle (~170 km) underwent ~ 10 % of partial melting and are represented by garnet-phlogopite wehrlites (1066-1101 °C; 5.03-5.23 GPa) and one harzburgite (950 °C; 5.28 GPa). These xenoliths record multiple metasomatic events that were caused by alkaline-rich fluids (K 2 O, H 2 O, CO 2 , F, ± Cl), carbonatite and proto-kimberlite melts. Low-Ti-Cr phlogopite (Phl1) found in garnet-phlogopite wehrlites was formed by metasomatic reactions of alkaline-rich fluids with peridotites. High-Cr and low-Al clinopyroxene present in garnet-phlogopite wehrlites and harzburgite have high Mg#, high Ca/Al, La/Yb N, Zr/Hf ratios, and high Sr contents, coupled with low to intermediate Ti/Eu and Ti/Nb ratios, suggesting that they were formed by carbonatite melt metasomatism. These geochemical signatures together with the presence of magnesite and dolomite inclusions trapped in the olivine of garnet-phlogopite wehrlites and harzburgite confirm carbonate-rich melt as a metasomatic agent. Metasomatic reactions involving garnet-phlogopite wehrlites and a proto-kimberlite melt formed high-Ti-Cr phlogopite (Phl2), which occurs as rims around Phl1, as isolated flakes around garnets, and in the matrix of wehrlites. Conversely, the shallower mantle (~50 km) represented by spinel lherzolites (876-915 °C; 1.5 GPa) underwent a lower degree of partial melting (2-4 %) and record little evidence of metasomatic processes. These samples contain low-Cr high-Al clinopyroxene depleted in incompatible trace elements, with geochemical trace element patterns typical of partial melting processes. Some clinopyroxene crystals, however, show slight enrichment in light rare earth elements (LREE), large ion lithophile elements (LILE), and Ti. Their low Ca/Al, La/Yb N , and Zr/Hf ratios, high Sr contents, high Ti/Eu, Ti/Nb ratios, and strong positive correlation between Ti with the other high field strength elements (HFSE) and LREE, indicate that these clinopyroxene crystals were metasomatized by a subalkaline silicate melt depleted in incompatible elements. The stratification of the SCLM beneath the southwestern margin of the SFC shown by different degrees of partial melting and metasomatic processes occurring within the cratonic lithosphere may suggest refertilization and possible rejuvenation that might weaken and modify the lithosphere, with a substantial impact on its long-lasting tectonic stability.
The generic complexity of the bounded problem of graphs clustering

Generic-case approach to algorithmic problems studies behavior of an algorithm on typical (almost all) inputs and ignores the rest of inputs. In this paper, we study the generic complexity of the bounded problem of graphs clustering. In this problem the structure of objects relations is presented as a graph: vertices correspond to objects, and edges connect similar objects. It is required to divide the set of objects into bounded disjoint groups (clusters) to minimize the number of connections between clusters and the number of missing links within clusters. We have constructed a subproblem of this problem, for which there is no polynomial generic algorithm provided P ≠ NP and P = BPP. To prove the theorem, we use the method of generic amplification, which allows to construct generically hard problems from the problems hard in the classical sense. The main component of this method is the cloning technique, which merges the inputs of a problem together into sufficiently large sets of equivalent inputs. Equivalence is understood in the sense that the problem for them is solved in a similar way.
MAGVI: TOWARDS SALIENCY-DRIVEN VIDEO MAGNIFICATION APPLICATION FOR THE PEOPLE WITH LOW VISION

Interacting with dynamic content such as videos, movies is very challenging for people with low vision.While screen magnifier software enables visually impaired people to pan and zoom the static content, such as text and images.They still lack efficient and intuitive means to pan and zoom over the dynamic content such as videos, movies, or YouTube videos.Therefore, a new approach is needed to enable people with low vision to interact with the dynamic content with ease.This paper presents a concept of the fully integrated application called as MagVi which aims towards making dynamic content accessible.This is composed of two main components; the first component is the video magnification application that uses the video saliency models to automatically identify the salient Region of Interests (ROI) in different videos.The second component of this application allows visually impaired users to view the identified ROI in a mobile application and also to dynamically pan and zoom, change the font size of the text throughout the application.The quantitative and qualitative evaluation with five low vision people revealed that the application was considered effective and efficient to use.Our approach has enabled people with low vision to pan and zoom over the dynamic content from a mobile phone which indicates the potential of making videos accessible to visually impaired people.
Construction Method of Equipment Software Defect Knowledge Base

The method of constructing the embedded software defect knowledge base is studied in this paper. The software defect pattern feature data is classified and a series of reasoning rules are established. Through Failure-based reasoning, relevant and useful knowledge can be obtained from the knowledge base, and new knowledge that is not in the knowledge base can be obtained according to the reasoning rules, so as to realize the automatic analysis and processing ability of defects. It can more effectively help find defects in the software, guide software developers to avoid introducing possible defects into the software, and guide software testers to design test cases.
The Impact of Augmented Reality (AR) Books on the Reading Engagement and Comprehension of Child Readers.

Augmented Reality (AR) books offer new opportunities for interaction and engagement, and additional features provide potential to support children’s reading comprehension, yet there is currently very little research in this area. This project aims to capture and understand children’s (aged 7-10) experiences with AR books, specifically focusing on whether there are benefits to reading engagement and comprehension. A particular focus will be on understanding whether, to what extent, and how, AR books have potential to support and engage struggling readers. This poster describes the protocol for study 1 of the project (pre-registration here https://osf.io/9q678)&#x0D; Opportunity sampling will be used to recruit up to four primary school teachers and 30 children (aged 7-10) from 3-4 classrooms from 1-2 Scottish primary schools. Twelve groups of students from different year groups (with 1-3 children in each group) will be observed participating in an AR reading session. Students will be observed interacting naturally with the AR book (printed book, with tablet affording AR features), with the support of an adult (if required) and will be audio recorded and observed during this time. During the observation written (anonymized) notes will also be made in relation to key points (e.g., usability, enjoyment, engagement, discussion etc).&#x0D; Following this, children will be interviewed by the researcher and asked about their engagement and comprehension of the AR book and their perceptions of the similarities and differences (positive and negative) between AR books and traditional print books. In addition, all class teachers (n = ~4) will be interviewed to explore their perceptions of AR books in terms of supporting reading engagement and comprehension and their perceptions regarding usability of AR books in the classroom.&#x0D; Thematic analysis will be used to identify and report themes related to child readers interaction, experiences and perceptions (engagement and comprehension) of AR books, and teachers’ perceptions of AR books. The six phases outlined in Braun &amp; Clarke (2006) will be applied.&#x0D; Pre-registration has been used for this project as it facilitates a shared understanding of my project, as a PhD researcher, with my supervisors and the wider research community. It ensures I clearly articulate my research questions and related methods prior to conducting the research. Furthermore, it prompts me to consider credibility strategies prior to starting data collection or analysis. Finally, it provides readers with an opportunity to learn about my research at any early stage and understand how research evolves across the different phases.
Effortless and beneficial processing of natural languages using transformers

Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components.
Disaster recovery plan design in energy government agency using NIST SP 800-34 guidelines

The Energy Government Agency has government duties in research, development, and utilization of nuclear science and technology. Government Energy Agency has implemented Information Technology (IT) to support business continuity. The application of IT in Government Energy Agencies and providing benefits also poses various threats and risks that can disrupt the organization’s ICT services continuity. The purpose of this research is to identify, analyze the impact, and assess the impact of the business process so that the appropriate contingency strategy can be determined for the Government Energy Agency’s IT. The application of IT in Government Energy Agencies is certainly used to support business process operations. Therefore, the information system/application owned by the organization must not be damaged or disrupted beyond the specified time tolerance (RTO). With DRP, if a disturbance occurs, the information system/application can be mitigated. In this study, the DRP document preparation used the standard guidelines of NIST SP 800-34 Rev. 1, which determines disaster recovery procedures in the organization. This research is the DRP design in a contingency strategy in the Government Energy Agency case study. It is expected to be a guide in developing a disaster recovery center. The results of this study are three applications that require disaster recovery procedures at mirrored alternative locations.
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis

The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled data. Many self-supervised training strategies have been investigated especially for medical datasets to leverage the information available in the much fewer unlabeled data. One of the fundamental strategies in image-based self-supervision is context prediction. In this approach, a model is trained to reconstruct the contents of an arbitrary missing region of an image based on its surroundings. However, the existing methods adopt a random and blind masking approach by focusing uniformly on all regions of the images. This approach results in a lot of unnecessary network updates that cause the model to forget the rich extracted features. In this work, we develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. To this end, we propose a reinforcement learning-based agent which learns to intelligently mask input images through deep Q-learning. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks. We perform our experiments on two public datasets for diagnosing breast cancer in the ultrasound images and detecting lower-grade glioma with MR images. In our experiments, we show that our novel masking strategy advances the learned features according to the performance on the classification task in terms of accuracy, macro F1, and AUROC.
Design and Development of Foldable Domestic Rescue Capsule

Domestic rescue capsule can provide emergency refuge space for people when urban disasters occur, which is of great significance to its research. Based on the analysis of functional requirements, the overall scheme of the rescue capsule was designed, which included foldable structure, perception and control system and communication system. In terms of structure, the parametric design was carried out in combination with the simulation results of ANSYS, so that the domestic rescue capsule has the functions of impact resistance and high temperature resistance. In terms of perception and control, the domestic rescue capsule can sense the environmental information, including the concentration of O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , CO and SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , as well as temperature and humidity, and the environment can be adjusted automatically. In terms of communication, people inside the capsule can communicate with people outside the capsule through video and voice. The experimental results show that the domestic rescue capsule designed in this paper has good application effect.
MATH IN THE EARLY YEARS: IMMERSIVE VIRTUAL ENVIRONMENT

This paper presents an Immersive Virtual Environment to assist in the teaching-learning process of the basic math operations of the early years.From a problem established in the learning difficulties and the bad results verified in the formal evaluations in the area of mathematics, it is proposed an environment that is established on some presuppositions (abstraction, contextualization, motivation, and cognition) and seeks to be an ally in the construction of this mathematical knowledge.The results obtained through evaluations (SUS and GQM) allowed demonstrating the validation of the built tool and its effectiveness in achieving the proposed objectives.
New approaches in regression data analysis

This article discusses the approaches of regression analysis. A brief description of the following approaches is given: quantile regression, the GAMLSS, regression in symbolic data analysis, regression in functional data analysis. The purpose of the article is to study new approaches in data regression analysis, which will be applied in the future to create the most accurate model for predicting temperature data.