Reviewerly/exHarmony huggingface dataset:
Future vocational education teachers upbringing on the basis of K. D. Ushinsky’s pedagogical ideas

Introduction. Contrary to the globalization and technologization trends of many spheres of social life, the education system is characterized by a line of individualization and personification, which is important for all levels of education, and hence for the future teachers’ training process. Theoretical analysis. The article considers the system of value orientations and names the key competences that are formed in the process of preparing vocational education teachers. Empirical analysis. Interpreting the pedagogical ideas of the founder of Russian scientific pedagogy K. D. Ushinsky in relation to the system of vocational education in terms of educating the personality of the future teacher of vocational education, the key value orientations of this process were identified and presented in this article. Among them, there are ideal, professional, creative, and social value orientations. The readiness formation levels (theoretical, practical and personal) of the future vocational education teacher to implement educational activities are given. Conclusion. Complex development of the key value orientations and all readiness levels of the future teacher to implement professional and pedagogical activities constitute the future vocational education teachers upbringing.
Online Coverage Path Planning Scheme for a Size-Variable Robot

Coverage Path Planning (CPP) is an essential feature of robots deployed for applications such as lawn mowing, cleaning, painting, and exploration. However, most of the state-of-the-art CPP methods are proposed for fixed-morphology robots, and the coverage performance is limited by physical constraints such as the inaccessibility of narrow spaces. Apart from area coverage, productivity depends on coverage time and energy usage. A robot capable of varying its footprint size could be a solution for improving productivity in these aspects. In addition to that, the environments, where robots are deployed for coverage, are often subjected to changes causing uncertainties. Therefore, this paper proposes an online CPP scheme for a size-variable robot to improve coverage productivity. The navigation planning of the proposed Size-Variable CPP (VSCPP) scheme has been implemented by adapting a Glasius bio-inspired neural network that guides a robot in an efficient path for coverage while coping with dynamic changes. The size variation required for a situation is determined by analyzing a set of occupancy grid maps corresponding to the size steps of the robot. According to the results, the proposed VSCPP can ascertain coverage while coping with dynamic changes in an environment. The reduction of the coverage time due to the size variability is significant compared to a robot with no VSCPP scheme.
Implementation of Literacy Programs and Reading Corners to Increase Students' Interest in Reading at SDN 2 Pamotan

This literacy program and reading corner aims to (1) Implement a literacy program and reading corner at SDN 2 Pamotan (2) Increase students' interest in reading at SDN 2 Pamotan (3) Have a literacy program and reading corner on an ongoing basis which are continued by the school. The method used in implementing the program starts from creating a reading corner in each class, then there is assistance with teachers in schools regarding the literacy schedule to be implemented, making instruments, 15 minutes of reading before class starts, journaling for 5 minutes, reading in front of the class , and there is an achievement book. The output of the program has been achieved, that is, at SDN 2 Pamotan a literacy program and reading angle have been implemented, students' interest in reading has increased after the program was implemented, and the school has agreed to continue the program. In the program implemented, it makes students more fond of reading, this is evidenced by the results of the questionnaire that 36% of students stated that they strongly agreed, and 64% of students stated that they agreed, then 0% of students who fill in less agree and disagree. Then the literacy program and reading corner are feasible to be continued by the school, this can be seen from the results of the questionnaire which shows an average of 100% in the strongly agree category, so that Pamotan 2 Elementary School teachers strongly agree to continue the literacy program and reading corner at school.
Identification Methods for Information Structures in Different Types of Scientific Discourse

Currently, with the accelerated development of science and technology, there is an increasing number of scientific and technological exchanges between China and the international community.The exchange of scientific and technological achievements is crucial for the sharing of technological experience, as well as for the development and progress of science and technology itself.Therefore, there is a need for scientific and technological papers to express and explain the scientific and technological achievements of different countries in writing to promote technological information exchange and cooperation.Taking English as an example, as one of the internationally recognized languages, about 85% of international scientific and technological literature is in English, and some key information indexes in the international scientific and technological papers are mostly in English.Therefore, it is necessary to grasp the forms of scientific and technological achievements exchange, master the information structure and recognition methods of different types of scientific and technological discourse, and write English scientific and technological papers well to promote scientific and technological exchange.This article introduces the methods for identifying information structures in different types of scientific discourse, in order to better interpret the methods for identifying information structures in scientific discourse and provide some ideas and references for better writing English scientific papers.
Research on OBE Flexible Education Technology for Embedded Hardware Development

Through the analysis of embedded hardware development technology, achievement-oriented education concept and flexible education technology, the learning system of embedded hardware development OBE flexible education technology is proposed.Using OBE combined with flexible teaching mode, it fundamentally solves the difficulty of development and learning, and provides technical support for artificial intelligence development and learning of embedded hardware.Improve students' interest in embedded hardware development, solve the needs of the society for basic embedded hardware development talent training, and provide assistance for entering the era of embedded hardware artificial intelligence.
Towards the next generation intelligent transportation system: A vehicle detection and counting framework for undisciplined traffic conditions

Modern development in deep learning and computer vision techniques, intelligent transportation system (ITS) has emerged as a useful tool for building a traffic infrastructure in smart cities. Previously, several computer vision techniques have been proposed for vehicle recognition, which were limited in handling undisciplined, dense and laneless traffic conditions. Moreover, these frameworks did not incorporate many of the local vehicle configurations common in South Asian countries such as Pakistan, Bangladesh, and India. Considering the limitations of previous frameworks, this paper presents efficient vehicle detection and counting model for undisciplined conditions including dense and laneless traffic, occulusion cases and diverse range of local vehicles. A dataset of more than 2400 images of vehicles has been collected comprising of six new categories of local vehicles, and considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Transfer learning based technique has been used, using faster R-CNN model with Inception V2 as underlying architecture. The experimental results show a precision of 86.14% in terms of mAP. The work finds its application in South Asian contexts as more smart cities are formed in this region. The proposed framework will enable traffic monitoring with higher reliability, accuracy and granularity, contributing in having next-generation ITS.
Tracing the Impact of the Digital Virtual Ludic on Immersive Theatre: A Case of Theatre Gamification

Immersive theatre, a theatrical form emerging at the beginning of the 21st century, invites spectators to become immersed in interactive theatre performances. The use of the term immersive indicates a strong influence from digital media, particularly from virtual worlds (VWs). Immersive theatre and VWs appear to share characteristics. A systematic comparative approach tracing the presence of characteristics shared by immersive theatre and VWs (i.e., virtuality, worldliness, information intensity), among others, still unique to VWs (i.e., agency, ergodicity), reveals that immersive theatre has assimilated some VWs characteristics while still being in the process of negotiating others. The paradigm of pervasive games is brought into the conversation to claim immersive theatre as a partially successful case of theatre gamification, revising theatrical and dramatic conventions, towards what could be called a digitally and ludically inspired neo-dramatic. New intermedial forms of expression could benefit from the adoption of a game/play frame.
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in https://sites.google.com/view/fsnet/home <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of unsupervised monocular depth for robotic deployment. We notice that PoseNet is not generalizable and by nature monodepth2 only predict depths up to a scale. We believe that we should not expect PoseNet, a ResNet on a concatenation of two images, to produce more reliable poses than the localization module in a robot. So we try our best to completely avoid using PoseNet. This creates much unstability in training, but we managed to fix it in FSNet with multichannel output and self-distillation. We also believe the network should try to directly predict accurate depth with a correct scale at any cases. So our method could produce meaningful results on static frames or scenes with little/no VO points (same as the network’s direct prediction). There are images without VO points in our multi-frame experiment, but our method is robust enough to fix this problem. In future research, we will include multi-frame depth predictions for more accurate depth prediction.
Geological characteristics, geochronology and metallogenic dynamics of Chiqionggou Sn-Be deposit in Yidun, West Sichuan

义敦岛弧是青藏高原东缘特提斯成矿域的重要组成部分,其新生代构造岩浆作用强烈,并伴随Ag-Pb-Zn-Cu-Mo等多金属成矿作用。近年来,该区稀有金属地质调查工作取得重要进展,在义敦乡城弧新发现了赤琼沟Sn-Be矿床,以伟晶岩型Be-Rb-La-Cs-Li矿化和矽卡岩型Sn-W矿化共生为特色,两类矿体围绕区内哈嘎拉花岗岩体产出。这一发现对义敦岛弧稀有金属矿勘查和区域成矿规律研究具有重要意义。本文在矿区地质调查基础上,对哈嘎拉花岗岩开展了岩相学、年代学和地球化学研究,系统总结对比了义敦岛弧各构造单元成矿地质条件,阐明了乡城弧白垩纪花岗质岩浆演化与稀有金属成矿的关系,探讨了矿床成因及成岩成矿动力学背景。研究结果表明,哈嘎拉花岗岩LA-ICP-MS锆石U-Pb年龄为91.5~90.2Ma;岩石地球化学显示过铝质、高钾钙碱性-钾玄质岩浆系列,富Rb、U、Nd、Hf,亏损Ba、Sr、P、Ti等,负Eu异常,稀土四分组效应明显,为典型的高分异花岗岩;<i>ε</i><sub>Hf</sub>(<i>t</i>)分别为-3.9~0.5与-4.7~-1.3,对应二阶段模式年龄(<i>t</i><sub>DM2</sub>)为1093~1372Ma、1207~1418Ma。研究认为,赤琼沟Sn-Be矿床形成于晚白垩世伸展构造背景下,成矿物质主要来源于中元古代地壳部分熔融形成的花岗质岩浆。该时期软流圈上涌交代岩石圈地幔,岛弧带内不同构造位置均发生了强烈的中酸性岩浆活动,重熔源区物质差异及封闭系统不同程度的岩浆结晶分异作用,形成了近南北向的昌台弧-乡城弧-格咱弧花岗岩带和明显的矿化分带。其中,乡城弧哈嘎拉地区的花岗岩经历了较高的分异演化程度,Sn-Be-Rb-Cs等稀有金属在岩浆演化晚期高度富集,岩体及其接触带附近具有重要伟晶岩型稀有金属矿体及矽卡岩型锡钨矿体找矿潜力。
Strategies for improving the quality of computer-assisted translation based on internet

The application of AI in the field of translation has led to the birth of many machine translation products, which has completely changed the traditional mode of artificial language translation.However, while machine translation has certain speed advantages, it also has fatal defects, and it is still difficult to achieve ideal translation results.With its unique logic, culture, flexibility and other advantages, human translation can just make up for what machine translation cannot?The combination strategy of machine translation and human translation has become a problem that language translators must consider.
The distinction between standard language and convergence

Convergence is a linguistic issue that is closely tied to numerous other concerns, including nationalism, literary history, sociology, psychology, etc.The concept of convergence has existed throughout the history of all nations; so, it is not a new occurrence.However, at some point in the history of the world, particularly in the late 19th and early 20th centuries, this phenomenon has become more apparent.Standard language is a type of writing or speech that is used by highly educated people in the society, and it is accepted as the dominant language model in the society.In the media, the press (audio and video), lectures and school texts, as well as in formal dialogues and interviews, criteria are utilized.Due of the high social prestige of the standard species, other social groupings attempt to emulate and conform to it.
Who’s responsible for the lack of action?

There are regular discussions among academics as to who should be the prime mover in infrastructure reform. Some point to the publishers to finally change their business model. Others claim that researchers need to vote with their feet and change how they publish. Again others find that libraries should just stop subscribing to journals and use the saved money for a modern publishing system. Finally and most recently, people have been urging funding agencies to use their power to attach...
Malicious URL Detection Using Logistic Regression

Abstract: Over the past few years, there has been a considerable expansion in both the amount and variety of web services. Online services like social networking, online gaming, and banking have quickly advanced along with people’s reliance on them for routine chores. As a result, a lot of information is added to the Web every day. These web services open up new ways for people to engage, but they also give thieves new chances. URLs serve as jumping off points for all types of web attacks, making it possible for any user with bad intent to submit a malicious URL and steal the identity of a legitimate individual. Malicious URL detection is a crucial task in ensuring the security of internet users. This study describes a novel logistic regression technique for identifying malicious URLs.. The proposed method leverages a dataset consisting of various features extracted from URLs and their associated labels indicating whether they are malicious or not. To extract features, we consider both structural and content based characteristics of URLs. Structural features include domain length, path length, and presence of special characters, while content based features involve examining the lexical composition of the URL, such as the presence of suspicious keywords or uncommon words. Using a labelled dataset, the logistic regression model is trained using the retrieved features. The likelihood that a given URL is malicious is then predicted using the trained model. Results from experiments show how effective is the suggested strategy. When identifying fraudulent or benign URLs, the logistic regression model performs with high accuracy.
Development of cadets’ cognitive interest: Reflexive approaches and modes

Introduction. Development of cognitive interest of cadets is one of the pedagogical aspects of great importance. Cognitive interest determines the effectiveness of mastering professional and humanitarian disciplines and contributes to the formation of cadets’ personality, aimed at their further self-improvement. Theoretical analysis. The study of modern sources allowed to come to the conclusion that the cognitive interest of cadets, being a quality of the personality, has a direct connection with such a phenomenon as reflexion. Empirical analysis. The article presents a description of experimental work on the use of reflexive approaches and modes. The main distinctive feature of these approaches and modes of training is the possibility of reflection, that is comprehension of the received knowledge in the area of a foreign language and legal disciplines, abilities of their application in practice and skills of independent acquisition of new knowledge. Novelty consists in development of tasks for tutorials in view of the principles of reflective approach. Conclusion. The results presented confirm the assumption that the use of these approaches and modes helps cadets learn a large volume of educational material easier, reveal creativity, create a positive atmosphere in the classroom, immerse themselves in the learning process and independently identify connections between individual phenomena.
Social-Enhanced Explainable Recommendation With Knowledge Graph

Recommendation systems are of crucial importance due to their wide applications. Knowledge graph (KG) enabled recommendation schemes have attracted great attention due to their superior performance and interpretability. However, the rich social information is not exploited for those systems, which limits the recommendation performance . In this paper, a novel explainable recommendation scheme is proposed by exploiting our designed social enhanced knowledge graph attention network (SKGAN). The hidden relations among users and items are learned and used for recommendation with the collaborative KG (CKG) and the user social graph (USG). Moreover, the high-order semantic information in both CKG and USG are obtained by using the graph convolution networks (GCNs) and the node level attention algorithm. Furthermore, a graph level user-specific attention algorithm is proposed to capture the user personalized preference between CKG and USG. Extensive experiment results demonstrate that normalized discounted cumulative gain (NDCG), precision, recall and hits ratio (HR) achieved with our proposed recommendation system are the best among those obtained with the state-of-the-art benchmark recommendation systems.
Two Views of ℙ <sup>3</sup>

The reconstruction of a 3-dimensional scene from (noisy) camera images is a routine task in computer vision. This problem encompasses a number of interesting mathematical questions with deep and old roots in projective geometry, which makes them amenable to tools from algebraic geometry. In this article I will address two foundational questions that revolve around the existence of a reconstruction from two camera images. The results involve real and semialgebraic geometry, as well as classical algebraic geometry and invariant theory. This article is a written account of my talk at ISSAC 2023.
Study of AI-Driven Fashion Recommender Systems

Abstract The rising diversity, volume, and pace of fashion manufacturing pose a considerable challenge in the fashion industry, making it difficult for customers to pick which product to purchase. In addition, fashion is an inherently subjective, cultural notion and an ensemble of clothing items that maintains a coherent style. In most of the domains in which Recommender Systems are developed (e.g., movies, e-commerce, etc.), the similarity evaluation is considered for recommendation. Instead, in the Fashion domain, compatibility is a critical factor. In addition, raw visual features belonging to product representations that contribute to most of the algorithm’s performances in the Fashion domain are distinguishable from the metadata of the products in other domains. This literature review summarizes various Artificial Intelligence (AI) techniques that have lately been used in recommender systems for the fashion industry. AI enables higher-quality recommendations than earlier approaches. This has ushered in a new age for recommender systems, allowing for deeper insights into user-item relationships and representations and the discovery patterns in demographical, textual, virtual, and contextual data. This work seeks to give a deeper understanding of the fashion recommender system domain by performing a comprehensive literature study of research on this topic in the past 10 years, focusing on image-based fashion recommender systems taking AI improvements into account. The nuanced conceptions of this domain and their relevance have been developed to justify fashion domain-specific characteristics.
A Case Study on Media Literacy Education Targeting Middle School Students Using Fact-checking Method

미디어 리터러시는 디지털 기술로 인한 미디어 환경의 변화와 일상생활에서 미디어의 영향력이 커짐에 따라 미래사회의 필수적 역량으로 주목받고 있다. 최근 미디어 이용 시간이 많은 중학생을 대상으로 미디어 리터러시 교육의 필요성이 강조되고 있다. 본 연구는 미디어 리터러시 교육의 필수요소인 팩트체크를 활용한 카드 뉴스 제작 교육사례를 중심으로 차세대 미디어 리터러시 교육의 방향을 제시하는 데 목적이 있다. 미디어 리터러시 교육과 팩트체크을 연계해 미디어 접근 능력, 증거 수집 및 분석 능력, 비판적 이해 능력을 목표로 교육 프로그램을 구성하였고 충청지역 중학생 50명을 대상으로 하였다. 미디어 리터러시 인식을 조사하고 그중 5명을 선정하여 포커스 그룹 면접을 실시하여 자료를 수집하고 분석하였다. 팩트체크 카드 뉴스 제작에 기반한 미디어 리터러시 교육을 통해 학생들의 정보수집 능력과 비판적 분석 능력이 향상되었고, 미디어 언어를 통해 자신 생각을 자유롭게 표현하고 교류하는 능동적인 참여자로 변화되는 것을 볼 수 있었다. 향후 연구를 통해 미디어 리터러시 교육의 장기적인 효과를 조사하고 학생들의 미디어 리터러시 능력을 향상시키기 위한 정규 교육 프로그램이 개발되기를 기대한다.As the media environment changes due to digital technology and the influence of media in everyday life grows, media literacy is drawing attention as an essential competency in the future society. Recently, the need for media literacy education has been emphasized for middle school students who spend a lot of time using media. This study aims to present the direction of next-generation media literacy education, focusing on the case of media education using fact-check, an essential element of media literacy education. By linking media literacy education with fact-checking, an educational program was organized with the aim of media access, evidence collection and analysis, and critical understanding, and it was targeted at 50 middle school students in Chungcheong-do. We investigated media literacy perceptions, selected 5 of them, and conducted focus group interviews to collect and analyze data. Through media literacy education based on fact-check card news production, students' information collection and critical analysis skills have improved, and they have become active participants who freely express and exchange their thoughts through media languages. Future research is expected to investigate the long-term effects of media literacy education and develop regular education programs that can improve students' media literacy skills.