Reviewerly/exHarmony huggingface dataset:
Uncertainty-Supervised Super-Resolution Deep Learning Network in Diffusion MRI

The research of uncertainty has shown great potential in the field of medical image processing. However, most of the research in the field of medical image is aimed at quantifying uncertainty. In this paper, we introduce an uncertainty supervised learning method. Specifically, we integrate the dropout variable reference and heterostatic noise model to estimate uncertainty and then guide super-resolution processing. Finally, we evaluate the enhancement effect of uncertainty supervised learning on super-resolution processing under the demonstration of DIQT model.
Hierarchical federated learning with mobile edge computing in the Internet of Vehicles

Federated Learning is a distributed machine learning framework, which can be used in the Internet of Vehicles to train deep learning models without directly accessing the original data of mobile edge vehicle nodes. ECS can access massive data, but it has the characteristics of high latency and high communication overhead. However, mobile edge computing (MEC) platform can directly and efficiently communicate with mobile edge vehicle nodes. Combining the advantages of the two, a three-layer federated learning system of edge car network edge server cloud server is used. This system is supported by the HierFedProx algorithm and aggregates the model output of the edge car to the edge server to improve the model learning efficiency and reduce the global communication frequency. The experimental results show that the system can reduce the training time and improve the accuracy of the model compared with the federated learning without introducing the edge server.
Hermeneutic perception of the text of musical works in the formation of the spiritual experience of the person

Spiritual potential is one of the most important experimental reservoirs of a person. The work deals with the hermeneutic analysis of the spiritual experience in perceiving the text of musical works. The author draws attention to associativity, which constitutes an additional source and establishes the connection between the musical information and the personal experience of the individual. The article highlights the factors of interaction between the semantic contexts of the student and those of the teacher in the process of perceiving a work of art, which requires not only the ability to highlight each semiotic unit of the text, but also to know the principles and ways of combining them within a text, as well as the coherence of each phrase integrated in the context of the work. Within the whole, the meaning of each element of a work of art is revealed. At the Institute of Arts of the ”Boris Grinchenko” University in Kyiv, research is conducted on the development of an important field of pedagogical science - the pedagogy of the spiritual potential of the personality.
Patient Obligation Perceiver Using Eye Gestures

Abstract: We might have encountered some people who suffer from paralysis, had just undergone surgery, or have strange syndromes (such as Locked-In, Quadriplegia, etc.). They might not be able to speak or move their body. People in these circumstances may find it difficult to communicate, which could have an impact on their health. Consequently, a system that can inform the care taker of their basic needs is necessary. As a result, we can develop a method by which the patient can readily communicate his or her needs through eye gazes. To link these movements with an alphabet that is predefined for each purpose, we can apply the idea of Morse code. This alphabet is then used to find the need in the dataset and create a visual and auditory alert for the nurse/care taker.
Maximizing Efficiency and Customer Satisfaction with Modern Helpdesk Ticketing System

Abstract: HelpDesk Ticketing system (HDTS) is an electronic support ticket management system for creating, managing, updating &amp; viewing of tickets logged in to the web portal of ticketing system. Incidents in organizations &amp; institution are difficult to track &amp; unmanageable to work upon when used traditional method. This HelpDesk Ticketing System will track all the tickets logged into the ticketing system while providing users an efficient &amp; simple way to raise the tickets. Also, it becomes easier for engineer to solve the issues incoming in an organization or institution. This tool becomes a centralized way of communication for any incidents. When the user raises the ticket for the incident, is then forwarded to the IT HelpDesk followed by the IT Manager &amp; IT Engineer
Construction and practice of international automobile professional innovation practice platform

In order to achieve the goal of cultivating innovative technical and skilled talents with international vision and international recognition, we have cooperated with overseas universities and relevant enterprises to explore and practice the international innovative technical and skilled personnel training system. The innovation practice platform proposes to explore a "results-oriented" innovation practice platform in accordance with the construction goal of "relying on competitions, combining production and education integration, results-oriented, and continuously improving the construction effect". We have established an "international" teacher team led by engineering practice, and formed a "systematic" institutional mechanism of school-enterprise collaborative education. After several years of research and practice, the quality of talent training has been comprehensively improved.
3D Human Pose Estimation Using Two-Stream Architecture with Joint Training

With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, we propose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solve bone direction. It enables more inter-frame features to be captured and makes the utmost of the feature relationships between data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use of intra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimation network with the bone length estimation network. The proposed method has extensively experimented on the public benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that the proposed method can achieve more accurate 3D human pose estimations.
Graph Convolutional Network-Based Repository Recommendation System

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problems with the repository recommendation system are the insufficient utilization of open-source community information and the fact that the scoring metrics used to calculate the matching degree between developers and repositories are developed manually and rely too much on human experience, leading to poor recommendation results. To address these problems, we design a questionnaire to investigate which repository information developers focus on and propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solve insufficient information utilization in open-source communities, we construct a Developer-Repository network using four types of behavioral data that best reflect developers’ programming preferences and extract features of developers and repositories from the repository content that developers focus on. Then, we design a repository recommendation model based on a multi-layer graph convolutional network to avoid the manual formulation of scoring metrics. This model takes the Developer-Repository network, developer features and repository features as inputs, and recommends the top-k repositories that developers are most likely to be interested in by learning their preferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-source repository recommendation methods, GCNRec achieves higher precision and hit rate.
Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, and social network of developers. The model integrates the above five-dimensional features, and a prediction model is built based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve the quality of rejected PRs, we focus on problems raised in the review process and review comments of similar PRs. We propose a PR revision recommendation model based on the PR review knowledge graph. Entity information and relationships between entities are extracted from text and code information of PRs, historical review comments, and related issues. PR revisions will be recommended to code contributors by graph-based similarity calculation. The experimental results illustrate that the above two models are effective and robust in PR review result prediction and PR revision recommendation.
Apathy, Political Emotion, and the Politics of Space in Thoreau's Antislavery Writing

This essay takes Thoreau's frustrations with the impossibility of perceiving the injustice of slavery as an invitation to discern within his larger corpus a distinct body of work that we can call Thoreau's antislavery writing. Beyond the mere topic of antislavery, I argue in this essay that Thoreau's antislavery writing is defined by a spatial politics that intersects with the politics of emotion: I argue that Thoreau is deeply invested in how the issue of slavery is consistently displaced spatially and emotionally. Through careful close readings of a number of shorter entries from Thoreau's Journal and his major antislavery essays—including "Resistance to Civil Government," "Slavery in Massachusetts," "A Plea for Captain John Brown," and "The Last Days of John Brown"—I show how Thoreau returns throughout his career to a recurring set of images and rhetorical tropes to help conceptualize the immediate importance of abolition, even to those who think themselves too far removed from slavery to do anything about it, or to care. In his writing, Thoreau wants to know why slavery and the abolitionist cause engender such an apathetic response, and why, similarly, slavery appears to be such a distant issue. As I show, the emotional and spatial distance from the issue of slavery that Thoreau notes in his neighbors results from how economics compartmentalizes the political sphere. Political economy and the language of economics make the most pressing political issues always seem remote and insignificant. Thoreau is interested in how political conviction and action can become disjointed: just because you know something is wrong does not mean you are going to do anything about it. This essay, then, starts from a place of frustration. It tracks Thoreau's frustrations, and it pairs those frustrations with a shared set of politically coded images and rhetorical tropes to consider Thoreau not just as an antislavery speaker and figure, but also as a literary writer whose most complex thinking reveals itself to us when we treat him and his writing as such.
Chinese medical event detection based on event frequency distribution ratio and document consistency

Structured information especially medical events extracted from electronic medical records has extremely practical application value and play a basic role in various intelligent diagnosis and treatment systems. Fine-grained Chinese medical event detection is crucial in the process of structuring Chinese Electronic Medical Record (EMR). The current methods for detecting fine-grained Chinese medical events primarily rely on statistical machine learning and deep learning. However, they have two shortcomings: 1) they neglect to take into account the distribution characteristics of these fine-grained medical events. 2) they overlook the consistency in the distribution of medical events within each individual document. Therefore, this paper presents a fine-grained Chinese medical event detection method, which is based on event frequency distribution ratio and document consistency. To start with, a significant number of Chinese EMR texts are used to adapt the Chinese pre-training model BERT to the domain. Second, based on the fundamental features, the Event Frequency - Event Distribution Ratio (EF-DR) is devised to select distinct event information as supplementary features, taking into account the distribution of events within the EMR. Finally, using EMR document consistency within the model improves the outcome of event detection. Our experiments demonstrate that the proposed method significantly outperforms the baseline model.
Negation detection techniques in sentiment analysis: A survey

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning and deep learning. In addition, this paper points out the gaps and future research directions in this area.
An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

One of the distinguishing features of an individual is handwriting and it has been established that everyone has unique handwriting differing from one another. This unique feature evolves with time and is influenced by a variety of factors such as gender, physical and mental health, and age among others. Also, the recent development in using individual peculiar features for forensic investigations in banks and other allied institutions either for signature verification or identification spelled the need to develop a smart system that can predict the age range with offline handwriting recognition. It is on this background that this research employed an optimized deep learning technique comprising of Gravitational Search Algorithm and Convolutional Neural Network (CNN-GSA) for offline handwriting age range prediction. A local database was populated with samples of the signature captured with a digital camera (5 megapixels), the CNN was employed for feature extraction, GSA was utilized to select optimal CNN parameters used for classification while the combined CNNGSA was utilized for an offline handwritten-based age prediction system. The performance evaluation of the approach proposed was done using sensitivity, specificity, precision, false positive rate, recognition accuracy, and processing time for all the variants, while the superiority of the system developed was ascertained by comparing it with the original CNN.
The great challenges and opportunities of the next 20 years

Each section of this chapter is written by a different author focusing on a particular challenge or opportunity for the future of their own choosing. There are various ways in which the sections could have been organised but in the end, we left them in the order that they were originally submitted. Note, however, that Sections 1, 3, 7 and 9 all deal with issues around access and equity, Sections 4, 5, 8, 10 and 11 cover different kinds of learning and enabling technology and Sections 2 and 6 offer two aspects of the same issue, namely ubiquity and information overload.
Theoretical Study on Formative Processes and Pedagogical Theories in the formation of teaching professionals

The article discusses how the dichotomy or the unification of theoretical and practical relationships can influence the education of educators, positively or negatively affecting their professional performance.The general objective of the article is to present how the analysis of pedagogical practices and their foundations, understood as praxis according to the theoretical perspective adopted, can help in understanding the complexity of these relationships.The idea is to investigate how teaching praxis in the face of dilemmas and perspectives can be developed in projects, with the aim of contributing to the application of studies on innovations in teaching practices.To better address the objectives and appreciate this research, it is observed that it is classified as exploratory research.A need for bibliographical research was also detected, in which materials already prepared were used, such as books, scientific articles, magazines, electronic documents and encyclopedias, to seek and allocate knowledge about the theoretical and practical foundations of teacher training, understood as praxis according to the theoretical perspective adopted.These can only be understood from the perspective of their totality.Thus, we sought to gather information with the purpose of answering the following research question: how the analysis of theoretical and practical relationships that involve the process of teacher training, the subject of several contemporary studies, can help in the development of epistemological knowledge that they are learned in the experience of teacher education.
Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit

Abstract Background The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addressed in an effort to close this gap. No models are perfect, and it is crucial to know in which use cases we can trust a model and for which cases it is less reliable. Methods Four different algorithms are trained on the eICU Collaborative Research Database using similar features as the APACHE IV severity-of-disease scoring system to predict hospital mortality in the ICU. The training and testing procedure is repeated 100 times on the same dataset to investigate whether predictions for single patients change with small changes in the models. Features are then analysed separately to investigate potential differences between patients consistently classified correctly and incorrectly. Results A total of 34 056 patients (58.4%) are classified as true negative, 6 527 patients (11.3%) as false positive, 3 984 patients (6.8%) as true positive, and 546 patients (0.9%) as false negatives. The remaining 13 108 patients (22.5%) are inconsistently classified across models and rounds. Histograms and distributions of feature values are compared visually to investigate differences between groups. Conclusions It is impossible to distinguish the groups using single features alone. Considering a combination of features, the difference between the groups is clearer. Incorrectly classified patients have features more similar to patients with the same prediction rather than the same outcome.
DEVELOPMENT OF PEDAGOGICAL COMPETENCE IN FUTURE TEACHERS

This article highlights the issue of pedagogical competence, professional competence, formation of professional qualities and development of future teachers based on self-development and self-assessment. The main goal of modern education is to train ambitious and talented personnel who are mature in all respects, socially flexible to contribute to society, able to work independently on themselves, for a highly developed society. From this point of view, the level of competence of the pedagogue is of great importance in the education of a comprehensively developed personality.
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: Single-blind / Double-blind / Triple-blind / Open / Other (please describe) Single-blind • Conference submission management system: http://www.chairin.cn/login?conf=msee2022 and the IOP Morressier Online Submission system: https://www.morressier.com/call-for-papers/62616fe3e8bc6d0013395335 • Number of submissions received: 454 • Number of submissions sent for review: 454(submitted for publication) • Number of submissions accepted:210 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100):46.2% • Average number of reviews per paper: 3-6 • Total number of reviewers involved:196 • Any additional info on review process: No • Contact person for queries: Ms Kun Zhang, Email: 2792810276@qq.com, Tel: 13554301922 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.
Extraction of STEM Knowledge Relationship in Physical Education Course Textbooks Based on KNN

Using different ways of correlation, the characteristics based on the differences between knowledge points, core predicates, and discourse characters are investigated. The relevant content of sports textbooks is used to train the word2vec relationship model with the similarity between the statistical knowledge points. As a result, the features are obtained based on the noun vector along with in-depth meaning-related information. The extracted features are used to train the sorter method of support vector machine (SVM) and K-nearest neighbor (KNN) for the analysis of the relationship between knowledge points. According to the experimental data, the specific content of the physical education textbook is selected. Compared with the traditional methods, the refined method can effectively improve the F score of the correlation. Finally, the new association extraction method is used to establish the knowledge image of sports discipline. The experimental results show that this method can effectively extract the knowledge points from the physical education curriculum textbooks.