Reviewerly/exHarmony huggingface dataset:
A Study on the Influence of the Cultivation of Students' English Learning Ability on the Learning of Other Courses

Cultivating students' autonomous learning ability is a key task in college English teaching. However, to achieve this, teachers need to start from many aspects, among which the creation of information technology environment has a positive impact on students' autonomous learning. Teachers should attach great importance to this, flexibly apply information technology in normal teaching, and actively optimize classroom teaching design. The curriculum standard also emphasizes to stimulate students' interest in learning and guide them to turn their interest into stable learning motivation. Formative evaluation focuses on students' learning process, students and their learning itself, and the incentive, guidance, diagnosis and correction functions of process evaluation. It can timely and effectively detect and implement judgments on students' English study process, so as to effectively stimulate students' motivation to learn English in a targeted and timely manner, and encourage students to do better in English study. In order to further study the factors that affect English majors' oral autonomous learning ability, this paper, based on a large number of studies, establishes five main influencing factors and puts forward relevant hypothesis models. This study then uses stepwise regression analysis to verify the rationality of the hypothetical model.
A Study on the Differences of Teacher-student Relationship in Chinese and American University Classrooms

The different cultural backgrounds of Chinese and American countries make the education of the two countries show great differences, and these differences permeate all aspects of education. A good view of teachers and students and a harmonious relationship between teachers and students affect the transmission of educational information between teachers and students, which is related to students' mental health and all-round development. By comparing the differences between the characteristics of teacher-student relationship between Chinese and American, this paper aims to help us to absorb the essence of teacher-student relationship in American schools, make up for our own shortcomings and make our school education work better. Try to find out some factors that can be used for reference to promote the healthy growth of Chinese students and further improve the quality of Chinese education.
Strategies on the English-Chinese Translation of Petroleum Engineering English

Based on Eugene Nida’s functional equivalence theory, this article delves into the language features of petroleum engineering English from the three dimensions of vocabulary, syntax and discourse. With practical translation examples for illustration, several translation strategies are developed at each level respectively, such as contextual domestication, ground supplement, unity in form and spirit, transfer from dynamic to static at lexical level; flexibility in form, adjustment of word order and division at syntactical level; recognition and reconstitution in discourse, which have certain referential value for the E-C Translation of petroleum engineering English and the international connectivity of petroleum.
Analysis of the Training Model of Music Education in Vocational Colleges

The vocational colleges in Inner Mongolia mainly cultivate practical talents with professional techniques and skills according to the needs of social jobs. However, the expansion policy of colleges and universities in recent years and the increasing number of people who can enjoy higher education resources have led to the lack of social competitiveness of students under the traditional training mode in Inner Mongolia vocational colleges, so there is a need to explore how vocational colleges can build a new talent training mode in the direction of music education under the unstable situation of funding, teachers and equipment factors. This paper comprehensively analyzes the specific problems of music education in current vocational colleges in Inner Mongolia, and proposes the reform methods of its music education cultivation mode according to the relevant requirements and later goals of vocational education.
On the Strategies of Cultivating Higher Vocational Students' Sense of Self efficacy in English Learning

This paper introduces the current situation of students' autonomous learning ability in higher vocational colleges, analyzes the influence mechanism of self-efficacy on students' autonomous learning process in higher vocational colleges, and then puts forward how teachers can help cultivate students' self-efficacy, so that students' autonomous learning ability can be further cultivated and improved.
A Systematic Literature Review of Extended Reality Exercise Games for the Elderly

In recent years, with the rise of the ageing population worldwide, the health of the elderly has attracted increasing attention. This study explored existing extended reality (XR) game applications aiming at physical exercise for the elderly. Through the review of 1847 papers from the Scopus database, 17 articles were included. Based on these papers, we explored the existing contributions of exercise XR games for the elderly, the development opportunities and challenges of such games, and their special considerations in adapting to the characteristics and requirements of the target user. The results were organized into several perspectives: publication information and keywords, immersive technologies and game concepts, teamwork and social games, evaluation, opportunities and challenges, and adapting designs. We found the elderly interested in and accepted using XR games. The reported research results proved positive effects on such games’ physical and mental health. XR exercise games for the elderly should considerately adapt to the elder’s cognition, behaviour, and demand. Although problems existed, such as simulator sickness, safety risks, device problems, and cost, there were opportunities and space for research and future developments. Researchers and developers could refer to this paper for XR exercise games for the elderly and create or enhance future XR applications by learning from existing work.
Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews

We present a concept for quantifying evaluative phrases to later compare rating texts numerically instead of just relying on stars or grades. We achieve this by combining deep learning models in an aspect-based sentiment analysis pipeline along with sentiment weighting, polarity, and correlation analyses that combine deep learning results with metadata. The results provide new insights for the medical field. Our application domain, physician reviews, shows that there are millions of review texts on the Internet that cannot yet be comprehensively analyzed because previous studies have focused on explicit aspects from other domains (e.g., products). We identify, extract, and classify implicit and explicit aspect phrases equally from German-language review texts. To do so, we annotated aspect phrases representing reviews on numerous aspects of a physician, medical practice, or practice staff. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and physicians’ professions (e.g., surgeon, ophthalmologist). As a result, we have individual numerical scores that contain a variety of information based on deep learning algorithms that extract textual (evaluative) information and metadata from the Web.
On the Use of WebAssembly for Rendering and Segmenting Medical Images

Rendering medical images is a critical step in a variety of medical applications, from diagnosis to therapy. There is a growing need for advanced viewers that can display the fusion of multiple layers, such as contours, annotations, doses, or segmentation masks, on the top of image slices extracted from volumes. Such viewers obviously necessitate complex software components. But desktop viewers are often developed using technologies that are different from those used for Web viewers, which results in a lack of code reuse and shared expertise between development teams. Furthermore, the rise of artificial intelligence in radiology calls for Web viewers that integrate deep learning models and that can be used outside of a clinical environment, for instance to evaluate algorithms or to train skilled workers. In this paper, we show how the emerging WebAssembly standard can be used to tackle these challenges by sharing the same code base between heavyweight viewers and zero-footprint viewers. Moreover, we introduce a fully functional Web viewer that is entirely developed using WebAssembly and that can be used in research projects or in teleradiology applications. Finally, we demonstrate that deep convolutional neural networks for image segmentation can be executed entirely inside a Web browser thanks to WebAssembly, without any dedicated computing infrastructure. The source code associated with this paper is released as free and open-source software.
On the Impact of the Vocabulary for Domain-Adaptive Pretraining of Clinical Language Models

Pretrained language models tailored to the target domain may improve predictive performance on downstream tasks. Such domain-specific language models are typically developed by pretraining on in-domain data, either from scratch or by continuing to pretrain an existing generic language model. Here, we focus on the latter situation and study the impact of the vocabulary for domain-adaptive pretraining of clinical language models. In particular, we investigate the impact of (i) adapting the vocabulary to the target domain, (ii) using different vocabulary sizes, and (iii) creating initial representations for clinical terms not present in the general-domain vocabulary based on subword averaging. The results confirm the benefits of adapting the vocabulary of the language model to the target domain; however, the choice of vocabulary size is not particularly sensitive with respect to downstream performance, while the benefits of subword averaging is reduced after a modest amount of domain-adaptive pretraining.
Multi-Source Domain Fusion Cross-Domain Pedestrian Recognition Based on High-Quality Intermediate Domains

Pedestrian detection has received considerable attention over the last few years because the technology can be combined with pedestrian tracking and re-identification in areas such as vehicle-assisted driving and intelligent video surveillance. However, existing pedestrian detection techniques have achieved excellent results. Problems such as domain gaps lead to poor generalization performance, thus limiting its application and practical value. This paper proposes a High-Quality Integration Domain framework for pedestrian recognition. First, the source domains are produced as super-resolution training data. The HCycleGAN model uses super-resolution algorithms and a generative framework to generate high-quality intermediate domains. Second, a multi-source domain fusion scheme based on the NPIQE module is proposed to improve the generated framework’s quality and reduce the dataset’s overfitting. It fuses images by three aspects: similarity, blurriness and unsupervised image quality score values. Finally, we use an anchor-free Center and Scale Prediction model for pedestrian detection. The experimental dataset contains two common pedestrian detection datasets, Caltech and CityPersons. Cross-domain experimental results show that the framework can improve the cross-domain detection from CityPersons to Caltech by 6% and from Caltech to CityPersons by 5%. The training of CityPersons to Caltech achieves almost the same detection accuracy as that of the Caltech original domain. In conclusion, the framework in this paper is effective for cross-domain pedestrian detection and can provide ideas and inspiration for future practical applications.
Improved breast ultrasound tumor classification using dual-input CNN with GAP-guided attention loss

Ultrasonography is a widely used medical imaging technique for detecting breast cancer. While manual diagnostic methods are subject to variability and time-consuming, computer-aided diagnostic (CAD) methods have proven to be more efficient. However, current CAD approaches neglect the impact of noise and artifacts on the accuracy of image analysis. To enhance the precision of breast ultrasound image analysis for identifying tissues, organs and lesions, we propose a novel approach for improved tumor classification through a dual-input model and global average pooling (GAP)-guided attention loss function. Our approach leverages a convolutional neural network with transformer architecture and modifies the single-input model for dual-input. This technique employs a fusion module and GAP operation-guided attention loss function simultaneously to supervise the extraction of effective features from the target region and mitigate the effect of information loss or redundancy on misclassification. Our proposed method has three key features: (i) ResNet and MobileViT are combined to enhance local and global information extraction. In addition, a dual-input channel is designed to include both attention images and original breast ultrasound images, mitigating the impact of noise and artifacts in ultrasound images. (ii) A fusion module and GAP operation-guided attention loss function are proposed to improve the fusion of dual-channel feature information, as well as supervise and constrain the weight of the attention mechanism on the fused focus region. (iii) Using the collected uterine fibroid ultrasound dataset to train ResNet18 and load the pre-trained weights, our experiments on the BUSI and BUSC public datasets demonstrate that the proposed method outperforms some state-of-the-art methods. The code will be publicly released at https://github.com/425877/Improved-Breast-Ultrasound-Tumor-Classification.
The generalization ability of logistic regression with Markov sampling

&lt;abstract&gt;&lt;p&gt;In the case of non-independent and identically distributed samples, we propose a new ueMC algorithm based on uniformly ergodic Markov samples, and study the generalization ability, the learning rate and convergence of the algorithm. We develop the ueMC algorithm to generate samples from given datasets, and present the numerical results for benchmark datasets. The numerical simulation shows that the logistic regression model with Markov sampling has better generalization ability on large training samples, and its performance is also better than that of classical machine learning algorithms, such as random forest and Adaboost.&lt;/p&gt;&lt;/abstract&gt;
Book Review of Winning Online Instruction: A Q&amp;A for Higher Education Faculty

Hillman, D., Schudy, R., &amp; Temkin, A. (2022). Winning online instruction: A Q&amp;A for higher education faculty. Routledge. 196 pages. ISBN 978-0-367-75165-4 (pbk). ISBN 978-1-00-316128-8 (ebk). https://doi.org/10.4324/9781003161288.
Guided Rotational Graph Embeddings for Error Detection in Noisy Knowledge Graphs

Knowledge graphs (KGs) use triples to describe real-world facts. They have seen widespread use in intelligent analysis and applications. However, the automatic construction process of KGs unavoidably introduces possible noises and errors. Furthermore, KG-based tasks and applications assume that the knowledge in the KG is entirely correct, which leads to potential deviations. Error detection is critical in KGs, where errors are rare but significant. Various error detection methodologies, primarily path ranking (PR) and representation learning, have been proposed to address this issue. In this paper, we introduced the Enhanced Path Ranking Guided Embedding (EPRGE), which is an improved version of an existing model, the Path Ranking Guided Embedding (PRGE) that uses path-ranking confidence scores to guide TransE embeddings. To improve PRGE, we use a rotational-based embedding model (RotatE) instead of TransE, which uses a self-adversarial negative sampling technique to train the model efficiently and effectively. EPRGE, unlike PRGE, avoids generating meaningless false triples during training by employing the self-adversarial negative sampling method. We compare various methods on two benchmark datasets, demonstrating the potential of our approach and providing enhanced insights on graph embeddings when dealing with noisy KGs.
Adaptive Learning from Peers for Distributed Actor-Critic Algorithms

Training distributed reinforcement learning models over a network of users (or agents) has great potential for many applications in distributed devices such as face recognition, health tracking, recommender systems, and smart homes. Cooperation among networked agents by sharing and aggregating their model parameters can benefit considerably the learning performance. However, agents may have different objectives and unplanned cooperation may lead to undesired outcomes. Therefore, it is important to ensure that cooperation in distributed learning is beneficial especially when agents receive information from unidentifiable peers. In this paper, we consider the problem of training distributed reinforcement learning models and we focus on distributed actor-critic algorithms because they are used successfully in many application domains. We propose an efficient adaptive cooperation strategy with linear time complexity to capture the similarities among agents and assign adaptive weights for aggregating the parameters from neighboring agents. Essentially, a larger weight is assigned to a neighboring agent that performs a similar task or shares a similar objective. The approach has significant advantages in situations when different agents are assigned different tasks and in the presence of adversarial agents. Empirical results are provided to validate the proposed approach and demonstrate its effectiveness in improving the learning performance in single-task, multi-task, and adversarial scenarios.
Introduction to the Extended Reality as a Teaching Resource in Higher Education

Traditional lectures, in which learners play a passive role, have not lived up to the expectations of students and the educational demands of the knowledge society in which we live. Technology has gradually been included in all facets of life, to the point where it has become the core of life. It has been found that this trend has also had a significant impact on the education sector in recent years. Technologies such as virtual and augmented reality offer a series of benefits and potentialities for teaching practice at any educational stage and in any subject. The following paper aims to provide an overview of the benefits of using these methodologies in the classroom, including ensuring that learning is meaningful and contextualised, as it facilitates the understanding of abstract content and enriches educational mathematics. It helps to prevent risks, overcome spatial and temporal obstacles and eliminate barriers to learning. Moreover, they are motivating and innovative resources for students, increasing their interest in learning. For this reason, their implementation in university classrooms should be advocated, regardless of the discipline, as they represent a significant improvement in the quality of teaching.
Step-Wise Model Aggregation for Securing Federated Learning

Federated learning (FL) is a distributed machine learning technique that enables remote devices to share their local models without sharing their data. While this system benefits security, it still has many vulnerabilities. In this work, we propose a new aggregation system that mitigates some of these vulnerabilities. Our aggregation framework is based on: Connecting with each client individually, calculating clients’ model changes that will affect the global model, and finally preventing aggregation of any client model until the accepted range of distances with other clients is calculated and the range of this client is within it. This approach aims to mitigate against Causative, Byzantine, and Membership Inference attacks. It has achieved an accuracy of over 90 percent for detecting malicious agents and removing them.
Classification of Scenes Using Specially Designed Convolutional Neural Networks for Detecting Robotic Environments

This article addresses the task of classifying scenes in typical indoor environments navigated by robots, using pre-trained convolutional neural network - CNN models with ImageNET and PLACES 365 datasets. The implemented models are the CNN VGG16 and RESNET50 architectures, which underwent various manipulations such as freezing and modification of intermediate layers. The performance of these networks in scene recognition tasks was analyzed, where a “scene” refers to an image that captures a partial or complete view of an indoor environment, displaying objects and their spatial relationships, and is similar to the scenes that mobile robots must perceive in their navigation task. To achieve this objective, a set of custom images JUNIO20V1, FEBR20V3, JUNIO20V3 and others, as well as 2 state-of-the-art image sets 15-Scenes and Sports, were subjected to various manipulations such as rotations, perspectives, sectioning, and lighting changes, creating a diverse image bank for both training and testing, allowing the analysis of the performance of the different modified CNN structures. The obtained results demonstrate the strengths of the different CNN structures in scene recognition tasks for application in indoor environments where mobile robots move.
Extracting Knowledge from Testaments - An Ontology Learning Approach

The extraction of ontologies from textual data is not a simple task. It is necessary to have specific expertise and knowledge about the ontology application domain to carry it out, and knowing how to use properly a set of sophisticated methods and techniques, requiring the use of advanced ontology learning tools. For some years, ontologies have been instruments of great importance in the process of designing and developing knowledge systems. In this paper, we present and describe the design and development of a semi-automatic system for extracting an ontology of Portuguese testaments from a set of ancient texts of the 18th century, towards the acquisition of the knowledge about the legacies of people of a Portuguese region on that period. This ontology has great interest and relevance for knowing many aspects of that time, namely linguistic, historical, religious, cultural, economic or agricultural, among others.
Operation of a Genetic Algorithm Using an Adjustment Function

In this work we describe in some details the operation of a genetic algorithm (GA), using an adjustment function to compare solutions and determine which is the best. The three basic processes of GAs are: selection of solutions based on their adjustment or adquate to the environment, reproduction for genes crossover, and mutation, which allows random changes to occur in genes. Through these processes, GAs will find better and better solutions to a problem as species evolve to better adjustment their environments. A basic process of a GA begins by randomly generating solutions or “chromosomes” to the problem. Posteriorly, an iterative process is carried out in which, at each step, the good solutions are selected and the crossing between species is carried out. Occasionally we can have mutations on certain solutions. Through the selection of good solutions in the iterative process, the computer will develop better and better solutions. The results of our experiment show that there is a general improvement over the initial population, both in the total adjustment, as well as the medium and maximum.