Reviewerly/exHarmony huggingface dataset:
PEER INSTRUCTION METHOD IN IMPROVING STUDENTS SPEAKING ABILITY

This research based on the students’ problem regarding to English teaching especially speaking ability. It was caused by the teaching method of the teacher in classroom was not interesting for students. The objective of the research was whether or not the use of Peer Instruction method was able to improve the speaking ability of the eleventh grade students at SMA Negeri 1 Watang Pulu. The research applied quasi-experimental design with two group namely experimental class and control class. The population of this research was the eleventh grade students at SMA Negeri 1 Watang Pulu in academic year 2016/2017. The sample was XI.IPA 1 as experimental class and XI.IPA 2 as control class. The result of the data analysis showed that the students’ speaking ability improved, it was showed by the mean score of pre-test was 41.13 and the post-test was 57.65. It showed that the speaking ability of the eleventh grade students at SMA Negeri 1 Watang Pulu improved through Peer Instruction method. In conclusion, teaching speaking through Peer Instruction method was effective to improve the eleventh grade students’ speaking ability at SMA Negeri 1 Watang Pulu of academic year 2016/2017.
Accuracy Evaluation of 3D Pose Estimation with MediaPipe Pose for Physical Exercises

Abstract With the recent increase in interest in machine learning and computer vision, camera-based pose estimation has emerged as a promising new technology. One of the most popular libraries for camera-based pose estimation is MediaPipe Pose due to its computational efficiency, ease of use, and the fact that it is open-source. However, little work has been performed to establish how accurate the library is and whether it is suitable for usage in, for example, physical therapy. This paper aims to provide an initial assessment of this. We find that the pose estimation is highly dependent on the camera’s viewing angle as well as the performed exercise. While high accuracy can be achieved under optimal conditions, the accuracy quickly decreases when the conditions are less favourable.
REGARDING THE ANALYSIS OF THE SOURCES OF FINANCING THE PROFESSIONAL EDUCATION SYSTEM

In this article, the analysis of the sources of financing of professional education is studied, the opinions of foreign economists regarding the financing of education are stated. Also, discussions were conducted and conclusions were drawn regarding the relevance of financing professional education today, its impact on the country's economy.
Uav intelligent recognition and tracking method based on deep learning

In the era of artificial intelligence, the traditional industry has undergone great changes under the guidance of modern technology theory. Enterprises or institutions in various fields have begun to develop and innovate UAV equipment with the application value of The Times with the help of technologies such as deep learning or machine vision to meet the increasing business processing needs. Since the unmanned aerial vehicle will transmit the captured images to the ground, and the encoding and decoding during transmission will affect the real-time performance of the overall system, scientific researchers have proposed an intelligent recognition and tracking method for unmanned aerial vehicles, in order to improve the work efficiency and quality of the system equipment. On the basis of understanding the current research status of UAV and target recognition technology, this paper mainly studies the UAV target recognition algorithm with deep learning as the core according to the intelligent UAV platform design scheme. The final result shows that the deep learning recognition algorithm can further improve the efficiency of UAV intelligent recognition and tracking.
Time Series Anomaly Detection with Reconstruction-Based State-Space Models

Recent advances in digitization have led to the availability of multivariate time series data in various domains, enabling real-time monitoring of operations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and model uncertainty is estimated from normal samples. Specifically, a long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the state space. Bidirectional transitions of states are simultaneously modeled by leveraging backward and forward temporal information. Regularization of the state space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level. Empirical studies on synthetic and real-world datasets demonstrate the superior performance of the proposed method in anomaly detection tasks.
A Multi-Task Instruction with Chain of Thought Prompting Generative Framework for Few-Shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) is the task of identifying new named entities using only a small number of labeled examples. Prompt-based learning has been successful in few-shot NER by using prompts to guide the labeling process and increase efficiency. However, previous prompt-based methods for few-shot NER have limitations such as high computational complexity and insufficient few-shot capability. To address these concerns, we propose a multi-task instruction framework called CotNER for Few-shot NER, which utilizes a chain-of-thought prompting generative approach. We introduce two auxiliary tasks, entity extraction and entity recognition, and integrate reasoning processes through chain-of-thought prompting. Our approach outperforms previous methods on various benchmarks, as demonstrated by extensive experiments.
Animal Rescue Team (ART) Application

"ART" is an innovative mobile phone software created to simplify the adoption and animal rescue procedure, ultimately altering the lives of both animals in need and caring people or families looking for companionship. This app uses technology to provide a seamless and effective platform for responsible pet adoption by bridging the gap between animal shelters, rescue groups, and future adopters. This entire project would involve the development of the app using Android Studio and Firebase
PENERAPAN KEBIJAKAN PENERIMAAN PESERTA DIDIK BARU DENGAN SISTEM ZONASI PADA TINGKAT SEKOLAH MENENGAH ATAS DI KOTA BATAM

This study aims to analyze the implementation of school zoning policies in high school PPDB (New Student Acceptance) in Batam city, evaluate their impact on equal distribution of access to education, and identify challenges faced in its implementation. The research method used is a qualitative method with a descriptive approach. The data used in this study were collected from interviews with related parties and collected data from various reliable sources. The results of the study show that the implementation of the PPDB zoning system in Batam City has had a positive impact in achieving equal access to education by prioritizing students from each school zone. However, this research also identified several challenges, such as adaptation to various local conditions and the importance of active community involvement in implementing this policy. This research contributes to understanding the implementation of the PPDB policy with the zoning system in Batam City and provides recommendations for policy improvement and further development in order to achieve equal and fair access to education for all students in Batam city.
PENERAPAAN LAYANAN PENCETAKAN ULANG KTP PADA APLIKASI KEPENDUDUKAN SECARA ELEKTRONIK (LAKSE) DI KOTA BATAM

In conducting public services, there are often problems such as long queues and long waiting times. Many people complained about the offline service due to time constraints and the tight schedules they have. In order to simplify and accelerate the process of making population documents, the Population and Civil Registration Office (Disdukcapil) together with the Communication and Informatics Office (Kominfo) of Batam City created the Electronic Population Service Application (LAKSE). The purpose of this research is to find out the implementation of Electronic Population Service Application (LAKSE) which focuses on ID card reprinting in Batam City. The type of research method used is descriptive qualitative. The result of the research shows that the service of making Identity Card (KTP) in Batam City is still done manually, and for now the LAKSE application only allows society to do reprint service of KTP that is lost or damaged and this application has not been widely spread in society and also experienced technical issues such as lack of understanding in operating it and error problems in the LAKSE application which caused delays in the process of services provided. &#x0D; Keywords: Services, KTP, Application, LAKSE.
The Concept of Cossack Fantasy in Literary Studies Discourse

The article is devoted to a general overview of the concept of fantasy and its typology, as well as to the results of research into the position of Cossack fantasy in the literary discourse. During the work on the article, materials devoted to the following main areas were used: fantasy in academic discourse; types of fantasy classifications; essential elements of a fantasy narrative; examples of Cossack fantasy in scientific research. The article uses a comparative research method. The author of the scientific study introduces the most common definitions of fantasy and the main types of classification of this type of literature. To confirm the originality of fantasy, as well as other vectors of intellectual activity that build their complex structure around the Cossack heritage, we proposed the concept of "Cossackopeia" to describe this complex and sometimes fragmentary phenomenon. In the article, we paid more attention to the artistic and literary vector of Cossackopeia. Based on the ideas of John Clute about taproot texts, Brian Atterbery about the "fuzzy set" of fantasy texts, Fara Mendelsohn and her thoughts about how the fantastic element penetrates the narrative, as well as Robert Boyer and Kenneth Zagorsky and their spatiotemporal system of events, we formed a definition of "Cossack fantasy". This allows us to say that the Cossack fantasy, using the Western European canon, has every opportunity to expand it to create original pseudo-historical worlds and even form a narrative projection of space-time far from Earth. Cossack fantasy is unjustifiably in the shadow of scientific research; therefore, it deserves a detailed analysis of its genre and typological features. In the future, the article's author sees the development of the metaconcept of Cossackopoeia and the establishment of the Cossack fantasy in this artistic phenomenon.
Promoting Social Innovation through the Development of Vocational Education

Promoting social innovation through vocational education is becoming increasingly vital in today’s rapidly changing world. Vocational education plays an important role in equipping individuals with the skills, knowledge, and mindset necessary to create innovative solutions for social issues. Various strategies and approaches that can be employed within vocational education to foster social innovation, such as online learning and college-enterprise cooperation. While the benefits and impacts of integrating these strategies into vocational education were demonstrated. Furthermore, a series of methods on innovation of online learning have been proposed to enhance the quality of vocational education. Overall, this paper underlines the significance of vocational education as a catalyst for social innovation and emphasizes the need to prioritize its integration into educational systems worldwide.
EFL Students’ Grammatical Errors in Writing Descriptive Text

The aim of this study is to find out grammatical most frequently made by the student in writing descriptive text. This study was quantitative and qualitative method. The population of this study is Students' English Department of Muhammadiyah University. The writer chose 23 students as the sample. In this study, collect the data used observation, writing test, and documentation. The result shows there were 328 items of error in the students’ writing. The percentage of errors made by students in writing descriptive text are noun phrase 49,7%, verb phrase36,3%, adjective phrase 8,8%and adverb phrase5,2%. The writer finds that the grammar errors that most of the students made were in noun phrases, with a cumulative total of 163 errors or 49.7%. From the research findings, it is suggested learners have to use these errors as a means to study more about correct the use of grammar in descriptive texting, especially in the use of noun phrases, verb phrases, and adjectives. For teachers to work harder on noun phrases and verb phrases because they are the most frequent failures that are made by students in their writing.
THE ROLE OF INNOVATIVE LEARNING TECHNOLOGIES IN FORMING THE CREATIVE PERSONALITY OF STUDENTS

It is determined and characterized the main components of the educational innovation. Thegeneral laws of the course of innovative processes are presented. Innovative learning technologies shape the moral, intellectual, physical, aesthetic development of
The policy analysis profession in Korea

The role of policy analysis becomes critical for policymaking and implementation in the policy process. This chapter provides a brief review of the history of the policy analysis profession in Korea, the realities of the policy analysis profession and requirements for future policy analysts in terms of professional qualifications. In Korea, policy analysis professions first emerged as they played a critical role in formulating modernised country systems in the early 1960s as a central government formulates the national development agenda. Policy analysis began to be recognised as an independent, arguably most critical element of the policy process after 1980. Now, based on their expertise in policy analysis and evaluation, policy analysts either directly participate in formulating new policies and projects, implementing established policies and projects, and monitoring their performance, or providing professional advice on the adequacy and validity of the outcomes of each process. At the individual level, those who wish to become policy analysts in the future are expected to possess substantial knowledge about both policy process and the field of policy they specialise in, analytical techniques, communication skills, and ethics.
Local or Global: The Variation in the Encoding of Style Across Sentiment and Formality

Research on textual style transfer has observed that the concept of style can vary across domains. This research examines the encoding of style across the sentiment and formality domains and observes that formality appears to be more globally encoded, and sentiment more locally encoded. The work also shows how the encoding of a style can inform the appropriate choice of method to compute content preservation during textual style transfer.
A Solution for Building a V-Museum Based on Virtual Reality Application

Digital transformation is a trend of technology studied and developed in every field recent years. Digitizing and moving real-world heritages into a dataset and visualizing them in a digital environment has been widely explored and improved by using a VR (Virtual Reality) technology. The application of VR and its implementation are being utilized in high-tech areas such as medical training, archaeology, digital heritage, digital museum, entertainment, etc. This paper conducts a depth research in 3D models processing and propose a solution for building a VR application of the real museum to preserve and exhibit the cultural heritage of Vietnam. Our proposed method includes the following steps: research on the different methods of converting the real world objects into the 3D world of VR; developing an immersive and interactive museum environment. This research establishes a framework and workflow to create a VR software, laying the foundation for further research and development for VR environment simulations in other fields.
Pie-UNet: A Novel Parallel Interaction Encoder for Medical Image Segmentation

Most of the initial medical image segmentation methods based on deep learning adopt a full convolutional structure, while the fixed size of the convolutional window limits the modeling of long-range dependencies. ViT has powerful global modelling capabilities, but low-level feature detail is poorly represented. To address the above problems, we propose a novel encoder structure and design a new U-shaped network for medical image segmentation, called Pie-UNet. Firstly, facing the problem of lack of localization in ViT and lack of global perception in CNN, we complement each other by encoding global and local information separately and implementing both in a parallel interaction manner; meanwhile, we propose a network with local structure-aware ViT, called Rwin Transformer, to enhance the local detail representation of ViT itself; in addition, to further refine the local representation, we construct a focal modulator based on large kernels; finally, we propose a pre-fusion approach to optimize the information interaction between heterogeneous structures. The experimental results demonstrate that our proposed Pie-UNet can achieve optimal and accurate segmentation results compared with several existing medical image segmentation methods.
Olympiad movement as an alternative way to enter higher education institutions in the linguistic direction

There are intellectual competitions in the domestic education system aimed at intensifying the search for gifted applicants in various areas. The All-Russian Olympiad of Schoolchildren and the University Olympiads, approved by the Russian Council of Olympiads of Schoolchildren as recommended for participation under the patronage of the Ministry of Science and Higher Education, provide significant benefits for graduates in admission. The article describes projects that are included in the list of the Ministry, as well as analyzes the reasons for the formation of the Olympiad movement as an alternative way to continue the individual educational track. As part of the study, scientific works devoted to the organization and conduct of intellectual competitions, their educational potential, and connections with the career guidance work of universities were studied. An analysis of regulatory documents regulating the expert activities of the Russian Council of Schoolchildren's Olympiads was carried out. Conclusions were made about the creative orientation of schoolchildren's olympiads, as well as about the importance of this alternative system for assessing the knowledge and achievements of applicants. School subject Olympiads not only develop giftedness among the strongest students, but also, thanks to their mass character, allow inciting interest in scientific and technical creativity and research activities from school, allowing universities to form a portrait of a modern specialist at the level of pre-university training.
Improving Clinical Decision Making with a Two-Stage Recommender System

Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this paper, we introduce a 2-Stage Recommendation framework for clinical decision-making, which leverages a publicly accessible dataset of electronic health records. In the first stage, a deep neural network-based model is employed to extract a set of candidate items, such as diagnoses, medications, and prescriptions, from a patient's electronic health records. Subsequently, the second stage utilizes a deep learning model to rank and pinpoint the most relevant items for healthcare providers. Both retriever and ranker are based on pre-trained transformer models that are stacked together as a pipeline. To validate our model, we compared its performance against several baseline models using different evaluation metrics. The results reveal that our proposed model attains a performance gain of approximately 12.3% macro-average F1 compared to the second best performing baseline. Qualitative analysis across various dimensions also confirms the model's high performance. Furthermore, we discuss challenges like data availability, privacy concerns, and shed light on future exploration in this domain.
BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing

Collocated clothing synthesis using generative networks has become an emerging topic in the field of fashion intelligence, as it has significant potential economic value to increase revenue in the fashion industry. In previous studies, several works have attempted to synthesize visually-collocated clothing based on a given clothing item using generative adversarial networks (GANs) with promising results. These works, however, can only accomplish the synthesis of one collocated clothing item each time. Nevertheless, users may require different clothing items to meet their multiple choices due to their personal tastes and different dressing scenarios. To address this limitation, we introduce a novel batch clothing generation framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BC-GAN</i> , which is able to synthesize multiple visually-collocated clothing images simultaneously. In particular, to further improve the fashion compatibility of synthetic results, BC-GAN proposes a new fashion compatibility discriminator in a contrastive learning perspective by fully exploiting the collocation relationship among all clothing items. Our model was examined in a large-scale dataset with compatible outfits constructed by ourselves. Extensive experiment results confirmed the effectiveness of our proposed BC-GAN in comparison to state-of-the-art methods in terms of diversity, visual authenticity, and fashion compatibility.
UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.