Reviewerly/exHarmony huggingface dataset:
A Practical Study on Off-campus Practice Mode for Early Education Major Groups

With the improvement of people's living standards and the gradual liberalization of the maternity policy, infant care and education are developing in a refined and personalized direction.For students of related majors in vocational colleges, the society has higher and higher requirements for their professional skills.Therefore, the improvement of students' practical skills can no longer rely solely on the last year's internship.In the teaching process, we must expose students to the real working environment as early as possible to improve their theoretical and practical abilities.This paper takes the "school-enterprise dual-phase three-stage" talent training model of grade 19 students majoring in child health and management in Shandong Vocational College of Industry as an example to discuss the role of the practical training model.
The Implementation of "Blended Teaching Mode" in Higher Vocational English Teaching

With the continuous progress of the times and the rapid development of various industries, people have new requirements for the development of various fields in the new era.As the progress of scientific and technological information ushered in the Internet era, the education industry, as an evergreen industry in China, should also keep innovating in teaching methods.This paper analyzes the blended teaching mode in vocational English teaching, mainly expounds the significance of the blended teaching mode, and puts forward the essential elements of the blended teaching mode in higher vocational English teaching, and puts forward the implementation strategy of the mode.
Research on the teaching of universities’ program design course by project decomposition and progressive method

In order to improve the computer knowledge level of college students and strengthen their practical programming ability, a teaching model based on project decomposition and progression is constructed.In view of the problems existing in the teaching process of computer programming courses in colleges and universities, based on the analysis of the teaching content and modular construction of computer language courses, this paper puts forward a course teaching method aiming at cultivating the ability of computational thinking and programming.Adopting the progressive design idea of dividing the project into multiple tasks along with the progress of teaching content, introducing the teaching mode driven by task and project decomposition into the process of classroom teaching and practice teaching, designing a teaching mode of completing project modules progressively according to the teaching content and completing the project at the end of teaching.The practice proves that the teaching effect is better.
Swarm Intelligence Based Algorithm for Feature Selection in High-Dimensional Datasets

In this era of ever-expanding data complexity, feature selection plays a paramount role in-to reducinge the issues associated with high dimensionality. The purpose of feature selec-tion is to discover a minimal optimal subset of features from the set of all available fea-tures with high accuracy. For datasets with numerous features, it is ubiquitous to determine the smallest possible subset of features to elevate performance accuracy. Prudently select-ed feature subset provides outstanding results, as compared to complete set of features, thereby making it convenient for the analysts to explore patterns and relationships among data. This research examines a temperature variable’s inclusion to the existing Ant Colony Optimization algorithm that demonstrates a significant increase in classification perfor-mance. The objective of this research is to evaluate the performance of hybrid Ant Colony Optimization- Simulated Annealing (ACO-SA) algorithm with other algorithms on several benchmark datasets using a Decision tree classifier.
Machine Learning Methods to Recognize the Critical Flood Conditioning Elements for Flood-Prone Region Mapping

Floods are one of nature's most devastating disasters, yet predicting them is incredibly difficult.Flood prediction model advancements have resulted in risk mitigation, policy suggestions, a decrease in human deaths, and a decrease in flood-related damage to property.By replicating the intricate numerical methods of actual flood dynamics, machine learning (ML) methods are made major contributions to the improvement of a prediction model in the last 2 centuries.As a result, better efficiency and cost-effective options have emerged.Because of its multiple advantages and potential, ML has grown in popularity with hydrologists.By integrating new ML algorithms and hybridizing current ones, researchers expect to produce more efficient and accurate forecasting models.The primary goal of this research is to demonstrate the current state of machine learning models in flood forecasting and to offer an understanding of the best models.To provide a full overview of the many ML methods used in the domain, this work examines the research where ML systems were directly compared through a qualitative analysis of resilience, precision, efficiency, and performance.In the framework of a thorough examination and discussion, the comparison results of ML algorithms give a detailed understanding of the various methodologies.As a result, this research presents the most likely long-and short-term flood forecast methods.The importance of recent advancements in improving the accuracy of flood projections is also examined.As per the authors, integration, data segmentation, algorithm aggregation, and prototype modification are some of the most effective strategies for developing ML methods.
Quantum Filtering in Robotics, Information, Feynman Path Integrals, Levy Noise, Haar Measure on Groups, Gravity and Robots, Canonical Quantum Gravity, Langevin Equation, Antenna Current in a Field Dependent Medium

Quantum Filtering in Robotics, Information, Feynman Path Integrals, Levy Noise, Haar Measure on Groups, Gravity and Robots, Canonical Quantum Gravity, Langevin Equation, Antenna Current in a Field Dependent Medium - 1
Map Matching Based Leader-Follower Path Retracing in Infrastructure-Free Environments

This chapter revisits the fleet maneuvering problem for multi-UGV systems without localization infrastructures. The approaches introduced in the previous two chapters both use vision-based techniques to achieve relative localization between vehicles. Vision-based technologies, however, are subject to environmental interference and motion blurring. In view of this, this chapter introduces a new relative localization technology, map matching. This technology estimates the transformation between individual maps of vehicles such that all the vehicles can be localized in a common coordinate frame. Based on the localization results, fleet planning and control algorithms are developed to achieve leader-follower path retracing in such environments. Real UGV experiments have been conducted to demonstrate the effectiveness of the proposed approach.
Transparency Prediction of Fraud Violations as an Anti-corruption Culture: Experiment of Decision Tree

Several prominent reports have highlighted the unsatisfactory level of anti-corruption transparency for the private sector in Indonesia. Hence, the anti-corruption vision is still an aspect that deserves to be campaigned for to form an advanced and just civilization. This study aims to obtain a pattern of knowledge in predicting the level of transparency of disclosure of fraud violations based on a data mining approach. The classification function algorithm in this study is a decision tree which is then compared with other classification function algorithms, naive Bayes, and k-nn. The sample in this study is 141 companies combined in the construction, mining, and banking sectors, which are on the IDX for the 2019 period. As a result, the decision tree algorithm provides the second-best performance in predicting the level of corporate transparency, namely an accuracy of 70.92% and an AUC level of 0.740. Then in terms of different tests, the decision tree algorithm is in the same cluster as the algorithm with the best performance because the t-test results show no significant difference. Based on the pattern generated by the decision tree algorithm, the elements of opportunity, pressure, and arrogance are considered key factors in predicting the level of transparency of disclosure of fraud violations. One of them can be interpreted that a company that is supervised by a minimum of four independent commissioners means company tends to be predicted to be more daring in disclosing anti-corruption information in its annual report to the wider public. This study also recommends that every authorized institution in Indonesia can apply a data mining algorithm approach in utilizing the advantages of each agency's internal data volume to map anti-corruption cultural socialization strategies in private sector companies.
Robust Background Identification for Video Editing

Extracting background features for estimating the camera path is a key step in many video editing and enhancement applications. Existing approaches often fail on highly dynamic videos that are shot by moving cameras and contain severe foreground occlusion. In this talk, we present practical methods that can reliably identify background features in complex video, leading to accurate camera path estimation and background layering. Our methods significantly outperform other alternatives, and can be directly used to improve common video editing applications such as stabilization, compositing and background reconstruction. We also show a novel application benefited from the robust background identification and separation, temporal coherent video generation.
Getting to Know Your Data

An R Companion for The Fundamentals of Social Research offers students the opportunity to delve into the world of R using real data sets and statistical analysis techniques directly from Paul M. Kellstedt, Guy D. Whitten, and Steven A. Tuch's new textbook. Workbook sections parallel chapters in the main text, giving students a chance to apply the lessons and techniques learned in each chapter in a statistical software setting. Detailed chapters teach students to reproduce results presented in the textbook, allowing them to become comfortable performing statistical analyses for evaluating causal claims through repeated practice. Step-by-step instructions for using R are provided, along with command lines and screenshots to demonstrate proper use of the software. Instructions for producing the figures and tables in the main text are integrated throughout the workbook. End-of-chapter exercises encourage students to formulate and evaluate their own hypotheses.
Sequence Models

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Simple and Visual Algorithm for Factorizing Integer Numbers

An iterative algorithm for decomposing an integer composite number C into prime factors X1 and X2 is proposed in which the properties of Vieta’s theorem are used for the reduced quadratic equations X2+B·X·C = 0, when the first approximation in iterative computation is taken equal to the square root of the composite number C, then is С, and B is equal to the rounded up to a larger integer from the number С, that is, B = C. In this case, the calculations are carried out by linearly increasing the approximations by one.
A LEARNING BASED CLASSIFICATION MODEL FOR VEGETABLES RECOGNITION AND RECIPES RECOMMENDATIONS

This paper presents the building of a classification model using Transfer learning, deployment of the model on the webpage, and recipes Recommendation using web scrapping. First, the vegetable classification model was built using transfer learning on the Pre-trained model MobilenetV2, which was originally trained on the Image Net dataset with 1.4 Million images and 1000 classes of web images. We used this as our base model to train our vegetable dataset and classify the images of 26 different vegetables. Second, the Model was deployed on a webpage using Tensor flow. JS. The model takes an image as input, classifies the image, and gives the vegetable name as output. Images can be taken from the webcam as well as from the storage. Third, based on model outputs, the web scraping script is used to fetch links for top-rated recipes from the internet.
Passive Automata Learning: DFAs and NFAs

It is a natural question to find a DFA or NFA for which a given set of words should be accepted and another given set should not be accepted. In this short note we investigate how to find a smallest automaton for both types by means of SMT solving, and compare the results.
Exploring University Students’ Perceptions of Metaverse-Based Classroom in Foreign Language Pedagogy Course

With the metaverse emerging as the next leap in the evolution of the Internet in the post-pandemic era, a wealth of opportunities are expected to emerge in the educational sector. As a pioneer study, this research project is aimed at exploring pedagogical values of the metaverse-based classroom in a university setting. The three research questions for this study are: 1) What are the levels of students’ overall satisfaction in metaverse-based classrooms? 2) What is the user experience of the metaverse platform? and 3) What are the strengths/weaknesses of the metaverse-based classroom as perceived by the students? Twenty university students took a foreign language pedagogy course on Gather.town in spring of 2022 for 11 weeks. Student questionnaire and focus-group interviews were conducted and data analysis revealed two major findings as follows: Firstly, both students’ overall satisfaction of the metaverse-based classroom and user experience of the metaverse platform were positive, excepting only instability problems caused by poor internet connections. Secondly, more perceived strengths than weaknesses were found of the newly adopted program: ‘a realistic classroom setting’ and ‘easy communication in multiple ways’ were mentioned as strengths while the main weakness was ‘newness.’ Pedagogical implications are discussed for teachers interested in adopting the metaverse platform for their future classrooms.
Empowering transgender population in India through community libraries

The paper attempts to identify the dilemmas faced by the transgender community in India with respect to their social life and information needs. Through a study of literature, the possibilities of introducing community libraries or Library-cum- Resource Centres (LRC) dedicated towards the betterment of transgender community in India have been explored. READ (Rural Education and Development) - a successful community library model has been discussed in the paper which could be adapted and modified to create such initiatives across the transgender populated areas of India. The limitations and implications are also discussed in the paper.
Disciplinary nature of 『the politics of education』 revisited: In the middle of its identity and boundary spanning

본 연구는 새로운 교육 패러다임의 전환이 필요하다는 우려와 기대가 교차하는 시점에서 오늘날 교육정치학이 경험하고 있는 현실에 대한 이해를 바탕으로 학문적 성격 탐구의 확장을 시도하면서 이에 부합하는 미래지향적인 교육정치학의 학문적 성격을 탐색하고자 한다. 코로나19 팬데믹과 디지털 전환은 교육정치학 연구에 새로운 도전으로 등장하고 있다. 이러한 상황에서 교육정치학은 학문적 성격의 검토를 통해 정체성을 점검하고 학문의 경계 확장을 모색해야 할 지점에 서 있다. 이런 점에서 교육과 정의에 관한 탐구와 정치적 합리성으로 대변되는 논리의 탐구는 교육정치학의 성격을 내재적으로 접근하던 전통적인 방식의 확장을 가져다 줄 것이다. 이러한 논구를 통해 미래지향적인 교육정치학의 학문적 성격은 융합학으로서의 교육정치학, 국가의 동학으로서의 교육정치학, 현장기반 미래학으로서의 교육정치학으로 확립될 필요가 있다. 이에 따라 교육정치학의 탐구 영역은 개인적 행위 양식의 차원, 조직의 행위 양식 차원 그리고 제도적 수준에서 국가, 정부, 시도의회, 사법제도, 시장제도, 시민사회와 결사체 등을 대상으로 다양하게 연구할 수 있다. 교육정치학의 연구접근에 있어서는 교육문제와 그 해결 과정에서 분명하고 명료한 언어를 사용하여 사회구조를 분석하고, 행위자와 구조의 상호작용을 이해하는 통합적 추론 논리와 방법을 개발할 필요가 있다.At the time of anxiety and expectation for new paradigm shift are crossing, this study intends to understand the reality which the politics of education faces, attempts to expand inquiry on its disciplinary features, and explores future-oriented nature. Corona 19 pandemic and digital transformation have emerged as new challenges in the politics of education research. Under these current situations, the politics of education stood at the point of examining its identity and seeking its boundary spanning through the revisit to its disciplinary nature. It is expected that inquiries on the relations of both education and social justice, and the logic of political rationality would contribute to expanding discussions about the nature of the politics of education beyond its traditional approach to the nature. This study suggests the future oriented disciplinary natures of the politics of education as interdisciplinary science, the nation’s dynamic academics, and field-based futurology. Therefore, the areas of inquiries would be listed in variety as apparent behaviors and modes at the individual, organizational levels, the nation, government, national assembly, local legislative councils, market system, civil society and interest groups at the institutional level. For the research approach, social structure needs to be analyzed using clear and definitive language in education problem and problem solving processes, and it is required to develop the logic of integrative inferences to understand the interactions between actors, structure, and its practical methods.
A Study on Path Planning Using Bi-Directional PQ-RRT* Algorithm and Trajectory Tracking Technique Using Incremental Backstepping Control

An autonomous flight system is essential for effective mission performance of UAVs, which are increasingly being applied in civil and military missions. Thus, in this study, an effective approach for implementing guidance and flight control systems is proposed. Based on the rapidly-exploring random tree (RRT) algorithm, the bidirectional potential quick (PQ)-RRT* is proposed to implement the path planner of the guidance system. The proposed bidirectional PQ-RRT* algorithm has a combination of three different types of improvement methods based on RRT*: potential field guided sampling (P-RRT*), modified RRT*(Q-RRT*), and bi-directional searching tree methods (Bi-RRT*). The convergence path was efficiently optimized using the line-of-sight path optimization algorithm. Then, a flyable trajectory was generated by a 7th order spline generator with waypoints from the path planner. Finally, incremental backstepping control was adopted to ensure trajectory-tracking performance within the overall operational flight envelope. To validate a series of processes, a simulation was performed to examine the practical realization. Based on the results, the bidirectional PQ-RRT* and line-of-sight path optimization algorithms were validated to provide an effective solution. In addition, the proposed flight control system exhibited excellent trajectory-tracking performance.
Early Detection of Breast Cancer Using CNN

High death rate in women is caused mainly due to breast cancer. Image processing, as well as data mining and machine learning approaches, has all aided in the development of autonomous cancer detection systems. One employed machine learning classification algorithms to distinguish between benign and malignant tumors. Digital mammography is found to be the successful tool for detecting cancer in women who have no symptoms and diagnosing cancer in women who have symptoms such as discomfort in a lump or nipple discharge. As a result, one can use convolutional neural networks (CNN) in the suggested system, and OpenCV was performed on a data set from the UCI repository (Laurance in Breast Cancer Cases Rise 80% since Seventies; BREAST CANCER, The Independent, London, 2006). The accuracy, precision, sensitivity, specificity, and false positive rate of each algorithm are analyzed and evaluated.
Segue: How to Write Programs

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Automatic detection of potential customers by opinion mining and intelligent agents

Customer acquisition is an issue that continues to receive attention from companies worldwide.Various marketing campaigns using psychological methodologies have been designed to address this issue.However, once a campaign is launched, it is highly complicated to detect which sets of customers are most likely to purchase an offered product.This fact is key since it allows companies to focus their efforts on specific clients and discard others.Several selection techniques have been implemented, but most of them are usually very demanding in terms of time and human resources for the companies.Artificial Intelligence techniques appear to help to simplify the process.Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase.Toward this goal, this paper presents a novel purchase propensity detection ML system based on Sentiment Analysis techniques able to consider customer comments regarding the offered products.The tourist domain was selected for the case study, where the obtained product was successfully embedded in an initial prototype.
Gizarte zientziak ikasteko baliabide berritzaileak: egungo euskarazko appen eskaintzaren analisia eta kasu azterketa

Mobile learning and apps have acquired importance as teaching resources. In this study, we located a series of apps in Basque language that are currently available on digital platforms and that can be useful for working on social sciences. Using an analysis template, we studied their general characteristics, their communicative model and teaching processes, as well as their validity in meeting the objectives of social science teaching. The results suggest that, although the analyzed apps have several shortcomings, they can be a potential resource to be integrated into educational contexts.