Reviewerly/exHarmony huggingface dataset:
Analysis on the Application of New Media Technology in Computer Course Teaching

In recent years, with the rapid development of new media technology, its application in modern society has penetrated into various fields, including the field of education.As one of the core courses of computer science and technology majors, computer course teaching is facing many challenges, such as the traditional teaching method is single, and students' interest in learning is not high.New media technology, with its advantages of rich learning resources and strong interactive learning environment, has brought new possibilities for computer course teaching.New media technologies include social media, mobile Internet, virtual reality, etc., which are characterized by interactivity, immediacy and diversity.By making the use of new media technology, students can provide rich learning resources for computer course teaching.For example, online learning platform can provide rich teaching videos, case analysis, programming practices, etc., so that students can learn anytime and anywhere.Virtual laboratory can simulate the real experimental environment, allowing students to carry out experiments through virtual operation and improve their practical ability.Teaching games can increase students' participation and interest through gamification, and encourage students to actively participate in course learning.This paper explores the application of new media technology in computer course teaching, and hopes that on this basis, the teaching mode of computer course can be continuously optimized.
Face Recognition with Knowledge Distillation on Arcface

This paper studied the model lightening and speed improvement of face recognition. To this end, we propose a method of training with MobileNetV2 through knowledge distillation as a student model and based on ArcFace and ResNet50 as a teacher model. ArcFace is face recognition model using Additive Angular Margin Loss. The main objective was to compare the performance of the student model against that of the teacher model in terms of similarity results. The findings indicated that the student model outperformed the teacher model in terms of similarity results. These results suggest that the MobileNetV2-based student model can achieve comparable face recognition performance to the more complex ResNet50-based teacher model, while being computationally more efficient.
Enhanced Ternary Fibonacci Codes

Extending previous work on non-binary Fibonacci codes, a new ternary variant is proposed sharing the main features like robustness against errors and ease of encoding and decoding, while improving the compression efficiency relative to other ternary codes. The improvement is based on an increased density of the codes and also shown empirically on large textual examples. A motivation for d-ary codes, with $$d>2$$ , may be the emergence of future technologies that enable the representation of more than just two values in an atomic storage unit.
Verified Verifying: SMT-LIB for Strings in Isabelle

The prevalence of string solvers in formal program analysis has led to an increasing demand for more effective and dependable solving techniques. However, solving the satisfiability problem of string constraints, which is a generally undecidable problem, requires a deep understanding of the structure of the constraints. To address this challenge, the community has relied on SMT solvers to tackle the quantifier-free first-order logic fragment of string constraints, usually stated in SMT-LIB format. In 2020, the SMT-LIB Initiative issued the first official standard for string constraints. However, SMT-LIB states the semantics in a semi-formal manner, lacking a level of formality that is desirable for validating SMT solvers. In response, we formalize the SMT-LIB theory of strings using Isabelle, an interactive theorem prover known for its ability to formalize and verify mathematical and logical theorems. We demonstrate the usefulness of having a formally defined theory by deriving, to the best of our knowledge, the first automated verified model verification method for SMT-LIB string constraints and highlight potential future applications.
State-Transition-Aware Anomaly Detection Under Concept Drifts

Detecting temporal abnormal patterns over streaming data is challenging due to volatile data properties and the lack of real-time labels. The abnormal patterns are usually hidden in the temporal context, which cannot be detected by evaluating single points. Furthermore, the normal state evolves over time due to concept drifts. A single model does not fit all data over time. Autoencoders are recently applied for unsupervised anomaly detection. However, they are trained on a single normal state and usually become invalid after distributional drifts in the data stream. This paper uses an Autoencoder-based approach STAD for anomaly detection under concept drifts. In particular, we propose a state-transition-aware model to map different data distributions in each period of the data stream into states, thereby addressing the model adaptation problem in an interpretable way. Our experiments evaluate the proposed method on synthetic and real-world datasets. While delivering comparable anomaly detection performance as the state-of-the-art approaches, STAD works more efficiently and provides extra interpretability.
Research on Physical Education Teaching in Universities and Quality Education for College Students

Humanistic quality education, as an important measure for colleges and universities in China to cultivate outstanding social talents, is also one of the important contents of physical education reform in colleges and universities in the new era.Integrating humanistic quality education into current physical education teaching in universities not only conforms to the development requirements of modern society and the times, but also further promotes the need for quality education.It plays an important role in improving the comprehensive literacy of college students and cultivating more outstanding social talents.To enhance the physical fitness of college students, improve their comprehensive quality, and promote their healthy development, building an excellent team of physical education teachers is the key, establishing a good teacher-student relationship is the guarantee, and improving the quality of college students through physical education teaching is an important way.
Research on Higher Education Management Curriculum Based on Digital Twin Technology

The innovative mode of practical teaching is a key link in achieving the cultivation of innovative "intelligent" talents in engineering.However, the specific application of artificial intelligence technology in the construction of innovative practical teaching has always been a challenge for the academic and industry sectors.Based on the perspective of school enterprise cooperation, with digital twin technology as the core, assisted by various artificial intelligence technologies such as augmented reality and synchronous modeling curriculum management in higher education institutions is an academic field that has just begun systematic research in China, and it is also a very promising field.This article reviews some research achievements in this field in Europe and America in recent years, and explores the concept, scope, significance, function, managers of curriculum management, as well as management in curriculum reform.The aim is to provide some ideas and references for future in-depth research in this field in China.
The Effect of Electronic Human Resources Management on Employee Satisfaction of Telecommunications Companies in Jordan

The purpose of this study was to investigate the effect of E-HRM on employee satisfaction. The focus was on telecommunications companies operating in Jordan. Aspects of e-HR practice is (e-recruitment, e-training, e-performance evaluation, e-reward system). Data were collected primarily through self-assessment questionnaires created in Google Forms and distributed via email to a sample of targeted executives. AMOSv24 was used to test the research hypothesis. Survey results show that E-HRM has a positive impact on employee satisfaction. Based on the results obtained, the researchers recommend that managers and decision makers of telecommunications companies in Jordan invest in electronic personnel systems. This allows personnel management to be fully electronic, realizing economic savings and attracting talent.
A Study on the Actor Recommendation Algorithm for Animal Characters

This study designed a virtual casting system that can be incorporated into the film industry. Through CNN deep learning and PCA region extraction we obtain face standard data for each animal and store similarity and region vector values in DB. We also leverage two similarity measurements to enable users to accurately provide the desired information. When the user chooses an animal of a character to be implemented in due diligence the recommender system utilizes the existing analyzed DB to provide standardized animal images. This allows users to recommend and cast actors who are most similar to the image of an implemented animal. This work is meaningful in showing that the results of existing studies can be utilized industrially.
Development of a system for measuring the efficiency of teaching and scientific activities in higher education: Experience of St. Petersburg State University

The system of higher education has not yet developed verified and reasonable criteria for evaluating the effi ciency of the teaching staff. The mentioned problem affects not only the field of management theory in higher education, but also has a quite visible practical implementation. Th e current state of affairs and prospects for the development of Russian higher education largely depends on the extent to which the system of performance indicators will create impetus for the professional growth of university teachers. The correctness and validity of the practical methods used in the implementation of these indicators is a factor that determines the social reputation and professional authority of scientific and pedagogical staff in higher education institutions. In the modern Russian system of higher education, there is a search for the most reasonable, objective and fair criteria for evaluating the activities of the teaching staff. The main of these methods presents visible innovations in the employment relations based on a system of effective contracts. At the same time, the large-scale changes that this system brought to the sphere of employment relations could not objectively enter into everyday life bypassing problems and contradictions. The objectives of this article are to summarize and show the positive experience in application of the system of indicators for evaluating the efficiency of scientific and pedagogical staff accumulated at St. Petersburg State University. The experience allowed us to evaluate the most significant and important elements of this system, which, being tested in practice, have shown their effectiveness as motivation for the professional growth of the teaching and scientific staff and, at the same time, confirmed their importan ce as a mean of achieving the goals of the St. Petersburg State University’s development program. The research relevance of this article is to point out the practical foundations available at St. Petersburg State University for the development of universal provisions on the creation and implementation of an effective system of incentives regulating the work of scientific and pedagogical staff.
Automatic Evaluation of Herding Behavior in Towed Fishing Gear Using End-to-End Training of CNN and Attention-Based Networks

This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences captured by a remotely controlled camera and classified by an expert in fishing technology. The sequences depict a scene in front of a fishing trawl where the conventional herding mechanism has been replaced by directed laser light. The goal is to detect the presence of a fish in the sequence and classify whether or not the fish reacts to the lasers. A two-stream CNN model, a CNN-transformer hybrid, and a pure transformer model were trained end-to-end to achieve 63%, 54%, and 60% 10-fold classification accuracy on the three-class task when compared to the human expert. Inspection of the activation maps learned by the three networks raises questions about the attributes of the sequences the models may be learning, specifically whether changes in viewpoint introduced by human camera operators that affect the position of laser lines in the video frames may interfere with the classification. This underlines the importance of careful experimental design when capturing scientific data for automatic end-to-end evaluation and the usefulness of inspecting the trained models.
Implement the Book List

Our first requirement is to implement a book list. From the perspective of the acceptance tests, all we have to do is to make sure that the page contains a list of books – we don't need to worry about what technology will be used to implement the page. And it doesn't matter if the page is dynamically generated or just static HTML, as long as there is a list of books on the page.
Contrastive text summarization: a survey

Abstract In our data-flooded age, an enormous amount of redundant, but also disparate textual data is collected on a daily basis on a wide variety of topics. Much of this information refers to documents related to the same theme, that is, different versions of the same document, or different documents discussing the same topic. Being aware of such differences turns out to be an important aspect for those who want to perform a comparative task. However, as documents increase in size and volume, keeping up-to-date, detecting, and summarizing relevant changes between different documents or versions of it becomes unfeasible. This motivates the rise of the contrastive or comparative summarization task, which attempts to summarize the text of different documents related to the same topic in a way that highlights the relevant differences between them. Our research aims to provide a systematic literature review on contrastive or comparative summarization, highlighting the different methods, data sets, metrics, and applications. Overall, we found that contrastive summarization is most commonly used in controversial news articles, controversial opinions or sentiments on a topic, and reviews of a product. Despite the great interest in the topic, we note that standard data sets, as well as a competitive task dedicated to this topic, are yet to come to be proposed, eventually impeding the emergence of new methods. Moreover, the great breakthrough of using deep learning-based language models for abstract summaries in contrastive summarization is still missing.
When correct spelling hardly matters: Teenagers’ production and perception of spelling error corrections in Dutch social media writing

Abstract The present paper examines teenagers’ production and perception of spelling error corrections (e.g., *zij for zei ) in online messaging. It discusses both a quantitative and qualitative analysis of spelling corrections in a large corpus of private online conversations between Flemish adolescents and the results of an online survey with a similar target group. Our study reveals that teenagers hardly correct their own spelling errors and those of their peers in informal social media writing. Several factors play a role in whether or not they rectify an error, such as the type of error and their socio-demographic profile. In general, adolescents tend to have a negative attitude towards correcting other people’s spelling mistakes. Consequently, teenagers often perform this face-threatening act (FTA) to tease or irritate their interlocutor or by way of payback for another FTA. Strikingly, even in non-conflictual contexts, errors are generally pointed out quite bluntly, though in some cases, both the error-maker and the interlocutor engage in damage control when the error has been acknowledged by the former. By conducting this research, we can achieve a better understanding of the sociopragmatic mechanisms underlying error perception and error handling in a social media context that generally embraces nonstandard writing.
Refractive fringe projection profilometry for three-dimensional shape measurement

Light emitted by an object placed in a transparent protective cover will change its original propagation direction before entering a fringe projection profilometry (FPP) system due to the influence of light refraction. This Letter proposes refractive FPP for accurate three-dimensional (3D) shape measurement. The method derives a coordinate mapping between a 3D measurement space and a 2D space formed by an incident ray, a normal of a refractive interface, and an emergent ray. Based on the coordinate mapping, the equation of the emergent ray in the 3D space can be directly determined with the equation of the incident ray and used for 3D measurement without refraction error. For validation, an FPP system through a planar glass was established and used for 3D shape measurements of a ring board, a regular sphere, and two objects with complex surfaces. Experimental results demonstrate the effectiveness and accuracy of the proposed refractive FPP.
Transtornos mentais em profissionais de enfermagem na pré-vacinação da pandemia COVID-19 [Mental disorders in nursing professionals in the pre-vaccination period of the COVID-19 pandemic] [Trastornos mentales en profesionales de enfermería antes de la vacunación de la pandemia COVID-19]

Objetivo: identificar os fatores associados à ocorrência de transtornos mentais em profissionais de enfermagem atuantes em instituições hospitalares no período de pré-vacinação na pandemia COVID-19. Método: estudo multicêntrico de abordagem quantitativa, transversal, descritivo e analítico, desenvolvido com 859 profissionais de enfermagem, atuantes em três instituições hospitalares de ensino brasileiras, que responderam ao formulário de coleta de dados, entre novembro de 2020 e fevereiro de 2021. Resultados: evidenciou-se a ocorrência de 358 (41,9%) profissionais diagnosticados com COVID-19, sendo que 128 (44,14%) relataram a ocorrência de transtornos mentais (p&lt;0,05). Conclusão: diabetes e doença autoimune, assim como a assistência direta a pacientes com COVID-19, estiveram associadas à ocorrência de sintomas de transtornos mentais em profissionais de enfermagem.ABSTRACTObjective: to identify factors associated with the occurrence of mental disorders in nursing professionals working in hospitals in the pre-vaccination period during the COVID-19 pandemic. Method: multicenter study with a quantitative, cross-sectional, descriptive and analytical approach, developed with 859 nursing professionals, working in three Brazilian teaching hospital institutions, who answered the data collection form, between November 2020 and February 2021. Results: the occurrence of 358 (41.9%) professionals diagnosed with COVID-19 was evidenced, with 128 (44.14%) reporting the occurrence of mental disorders (p&lt;0.05). Conclusion: diabetes and autoimmune disease, as well as direct assistance to patients with COVID-19, were associated with the occurrence of symptoms of mental disorders in nursing professionals.RESUMENObjetivo: identificar los factores asociados a la incidencia de trastornos mentales en profesionales de enfermería que trabajan en hospitales en el período previo a la vacunación durante la pandemia de COVID-19. Método: estudio multicéntrico con enfoque cuantitativo, transversal, descriptivo y analítico, desarrollado junto a 859 profesionales de enfermería que trabajan en tres instituciones hospitalarias brasileñas de enseñanza y que respondieron el formulario de recolección de datos, entre noviembre de 2020 y febrero de 2021. Resultados: Se evidenció la incidencia de 358 (41,9%) profesionales diagnosticados con COVID-19, siendo que 128 (44,14%) informaron la aparición de trastornos mentales (p&lt;0,05). Conclusión: la diabetes y las enfermedades autoinmunes, así como la asistencia directa a pacientes con COVID-19, se asociaron con la aparición de síntomas de trastornos mentales en profesionales de enfermería.
Vehicle pedestrian detection algorithm at ferry entrance based on improved YOLOX

This study introduces a number of enhancements to the YOLOX-S target detection network in an effort to address the issues of heavy traffic at the ferry, complex traffic environment, and sluggish detection speed. The conventional residual block in CSPDarknet, which has a significant number of parameters and high equipment requirements, is replaced by the MBConv module in the deep layer and by the Fuse-MBConv module in the shallow layer. This is completed for YOLOXS's backbone feature extraction network, CSPDarknet. The enhanced model's mAP value is 83.39%, 2.7% more than the baseline method. The experimental findings demonstrate that the enhanced method presented in this study is appropriate for the real-time detection of moving objects, such as cars and people, in the vicinity of the ferry entrance
A garbage sorting method using an adaptive deep neural network

Achieving automatic sorting of garbage through a mechanical arm depends on accurate recognition and localization of garbage. In this paper, we propose a garbage sorting method based on an adaptive deep neural network. The method addresses the limitations of YOLOv5 object detection algorithm, such as the fixed number of anchor boxes and the inability of the feature fusion network to adjust according to the target scale. Our proposed method introduces an object detection algorithm based on an adaptive deep neural network. We use the adaptive K-means clustering algorithm to automatically determine the initial clustering center and the number of clusters, extract features of multiple scales using the feature extraction backbone network, and automatically adjust the structure and feature fusion times of the adaptive feature fusion network based on the clustering results of adaptive K-means. We test the proposed algorithm and YOLOv5 object detection algorithm on a self-made garbage classification dataset. The experiments demonstrate that our proposed adaptive deep neural network reduces the model parameters of YOLOv5 by 27.03%, improves the detection speed by 18%, and enhances the detection accuracy by 0.7%. Finally, we transplant the adaptive deep neural network to the garbage sorting platform and use it for real-time garbage sorting.
ORB feature extraction and feature matching based on geometric constraints

This paper studies feature extraction and feature matching in visual odometry. Aiming at the problems that ORB feature extraction does not have illumination invariance and feature distribution is uneven, an adaptive threshold algorithm for feature extraction is added, and a quadtree is used to manage feature points. Aiming at the problem of high time cost of the feature matching algorithm, an outlier removal algorithm based on geometric constraints is proposed, and the constraint set is constructed by using the slope, distance, and descriptor distance between the matching feature point pairs. Tested on the TUM dataset, the feature extraction algorithm can adapt to scenes with different brightness, and the robustness is improved. The time taken by outlier removal algorithm based on geometric constraints is about 10% of RANSAC. After that, combined with RANSAC, the running time of RANSAC can be reduced by 60%. Our algorithm can improve the estimation accuracy and robustness of the system.