Reviewerly/exHarmony huggingface dataset:
A study on the application of a fun outdoor play program where children become the main characters

본연구의 목적은 영유아들이 중심이 되고, 놀이가 중심이 되는 어린이집의 바깥놀이 활성화를 위한 방안을 모색하고자 한다. 본 연구의 대상은 충남 홍성군에 위치한 충청남도청어린이집 영아 97명과 유아 126명을 포함한 영유아 213명과 영아반교사 19명과 유아반 교사 9명을 포함한 교사 28명을 대상으로 참여적 실행연구를 하였다. 연구방법은 참여관찰, 현지자료 조사, 기타자료조사 등을 수집하여 이를 바탕으로 심층적 분석과 고찰이 이루어졌다. 본 연구의 결과는 첫째, 영유아 중심의 바깥놀이 활성화를 저해하는 요인을 분석하여 영유아 중심의 바깥놀이를 활성화하는 프로그램을 구성하였다. 둘째, 구성한 프로그램 적용을 통해 긍정적이고 주도적인 영유아의 변화와 영유아 중심의 바깥놀이 중요성 및 바깥놀이를 연구하는 등 교사의 변화가 나타났다. 따라서 본 연구의 결과를 바탕으로 자유롭게 바깥놀이에 참여하는 과정을 통해 영유아 주도의 바깥놀이가 활성화되고, 교사의 바깥놀이에 대한 적극적인 태도와 상호작용을 통해 영유아가 바깥놀이에 대한 인식에 긍정적 영향을 미치게 되어 어린이집의 바깥놀이 활성화에 기여할 수 있음을 논의하였다.The purpose of this study is to find ways to revitalize outdoor play in daycare centers where infants and toddlers are centered and play is centered. The subjects of this study were 213 infants and toddlers, including 97 infants and 126 toddlers at the Chungcheongnam-do Provincial Government Daycare Center in Hongseong-gun, Chungcheongnam-do, and 28 teachers, including 20 infant class teachers and 8 toddler class teachers. As for the research method, participatory observation, field data survey, and other data survey were collected, and in-depth analysis and consideration were conducted based on this. The results of this study first constructed a program to activate infant-centered outdoor play by analyzing the factors that hinder the activation of infant-centered outdoor play. Second, through the application of the organized program, positive and leading changes in infants and toddlers, the importance of playing outside centered on infants, and changes in teachers appeared. Therefore, based on the results of this study, it was discussed that infant-led outdoor play can be activated through the process of freely participating in outdoor play, and infants and toddlers can have a positive effect on their perception of outdoor play.
Evaluating the Appropriateness of English Textbook Used for Teaching English for Specific Purposes

One of the steps in creating a course that delivers validity and relevance for all course design activities is analyzing the needs of the students. Need analysis helps teachers to identify students’ needs in learning English based on their daily needs and future needs. This study intends to evaluate the English textbook appropriateness for students and the school’s needs in ESP in the office administration program at SMK Setiabudhi Semarang. Hutchison and Waters (1987) proposed the theory that was employed in this research. The theory elaborates on the concept of need analysis in ESP that should contribute to the target and learning needs. Target needs are linked to what the students need to accomplish in the target situation, while learning needs refer to what the students need to do to learn effectively. This study applied a descriptive qualitative research approach to analyze the information gained via student surveys, teacher interviews, and document observations. The study's findings demonstrate that the English textbook used was deemed appropriate for meeting both the students and the school’s needs. The degree of appropriateness revealed that, on average, the textbook satisfied 77% of school needs and 87% of student needs. The result is expected to give a contribution to both teachers and students to evaluate their English materials in the textbook used in learning ESP.
Overview of DocILE 2023: Document Information Localization and Extraction

This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets.
IMPROVING THE STUDENTS’ READING FLUENCY OF RECOUNT TEXT THROUGH MULTIMEDIA RUNNING TEXT AT THE TENTH GRADE OF SMA NEGERI 1 GUNUNGSITOLI IN 2022/2023

The method used in the research was one qualitative research is classroom action research. The purpose of this research is to improve the students reading fluency of recount text through multimedia running text at the tenth grade of SMA Negeri 1 Gunungsitoli in 2022/2023. Based on the result of the data in cycle I showed that there are 17 students in fair level (47%), there are 12 students in fairly good level (33%), there are 2 students in good level (5,60%), and there are 5 students in very good level (14,00%) and the average of students’ value is 62,6. Cycle II, there are 19 students in very good level (53%), there are 17 students in excellent level (47%) and get the average of students value is 87,36. It means that the students’ ability in reading fluency of recount text through multimedia running text in cycle II is success. Based on the result of the study, it can be concluded the multimedia running text improves the students’ reading fluency of the tenth grade of SMA Negeri 1 Gunungsitoli.
Students Perception on Vocabulary Mastery through Song Lyric Gap Fill

Mastering a lot of vocabulary will make it easier for someone to read, write, listen and speak. Learners need to adjust their learning style, strategy as well as the use of media that could help students develop their vocabulary mastery early. This study aims to get information on the effect of using song lyric gap fill to improve student vocabulary mastery and student perception of mastering vocabulary using song lyric gap-fill method. This study design is descriptive quantitative whose data collecting method used test and questionnaire as instrument. The test would be conducted by having students answer pre-test and post-test. The questionnaire was being done when the student had received treatment using song lyric gap fill. The result obtained shows that song lyric gap-fill method that was used in this research would increase the average student’s score by 40.8%. The statistical test results show that there is an effect of using the Song Lyric Gap Fill method in increasing vocabularies in 8-grade junior high school students. And the results of student perception of using song lyric gap fill on the vocabulary learning showed that the highest achievement (80%) that students increased new vocabulary with help of song lyric gapfill. While 67% students agreed if they had difficulties in learning parts of speech.
CLOTHING FASHION IMAGE GENERATION FROM TEXT USING ARTIFICIAL INTELLIGENCE

Development of dynamic, intensely engaging, and fascinating images has greatly benefited from the recent exponential advancements in image synthesis techniques. The architecture proposed in this research allows users to enter text regarding a particular dress, and the model then create images of fashionable apparel based on that content. The model suggested can let people become their own fashion designers by utilizing the strength of Deep Learning and Artificial intelligence to create a variety of fashionable outfits for themselves. DALL-E model is utilized to engender realistic images based on text description. DALL-E is an artificial intelligence model that generates realistic images from a description in natural language. While there are alternative text-to-image systems, the DALL-E produces far more coherent visuals. The world and the relationships between objects appear to be well understood by this technology. DALL-E uses GPT-3 model and dataset of textimage pairs for image synthesis. Image is encoded into size of 32×32 grid using VQ-VAE. Then image and text are combined together in the form of single stream for training of DALL-E. Deep Fashion dataset is used for training of DALL-E, which is simply more realistic dataset and contains High definition images that further enable accurate generation. After training DALL-E produce more accurate results and provides higher inception score than preceding models.
Data-Driven Learning in an EFL class: a study of Ecuadorian learners’ perceptions

Previous research has reported an increased interest in the application of Data-Driven Learning (DDL), which was derived from corpus linguistics, to aid English as a foreign or second language learning. DDL emerged as a learner-centered approach that emphasizes the discovery of linguistic patterns by analyzing natural occurrences of language. Many studies have investigated the effect of DDL on several linguistic components and have reported positive outcomes. Consequently, its application has germane importance in an Ecuadorian context as it has not been widely explored. This study seeks to document the experience of 14 EFL students from an Ecuadorian public university and examine their perceptions towards the implementation of DDL activities. The intervention included two stages: assisted guidance for students to retrieve information on their own and corpus-based materials developed by the teacher. Students' perceptions were gathered via a questionnaire, a reflection, and a follow-up interview. The findings indicate that students perceived DDL as a practical and innovative approach to support grammar discovery in an EFL class. Nevertheless, students encountered some challenges associated with the use of DDL, for example, the limited number of searches, an issue that can be resolved by a complete registration in the COCA web. This article concludes with pedagogical implications and recommendations for future DDL applications and research in Ecuadorian EFL classes.
Indirect Education: Exploring Indirectness in Teaching and Research

Book Review: Saeverot, H. (2022). Indirect Education: Exploring Indirectness in Teaching and Research. Routledge, Taylor &amp; Francis Group. 140 pages. https://doi.org/10.4324/9781003193463
Literacy voluntary teaching for low socio-economic early graders in Sumbawa Regency of Indonesia

&lt;span lang="EN-US"&gt;The research reported on a literacy intervention program for 150 early grade learners from low socio-economic background by involving 50 volunteers in 50 different village spots in Sumbawa Regency of Indonesia. The research was emancipatory because it seeks to empower the community to address the local problems of literacy learning loss experienced by the needy early graders. The data were collected through literacy assessment, observation, interviews, and documentation. The findings showed that 90% of the early graders improved their literacy mostly with two and one level progress. Factors contributing to the success were good preparation of volunteers, engaging teaching approaches, relevant materials that suited student levels, and continuous assessment. The last key finding was that the volunteers strongly embodied teaching voluntarism as a soul with six features (affection towards others, action of giving, availability, positive inner feelings, heroism, and divinity). The recommendation from these findings is that multi-stakeholders, including the government, provide support to enhance the program sustainability. This research contributes a model of voluntary teaching for low socio-economic early graders that can be adapted in other locations in Indonesia and overseas.&lt;/span&gt;
A Local-observation-based Safe Autonomous Navigation Approach in an Unknown Environment

Control Lyapunov functions (CLFs) and control barrier functions (CBFs), serving as constraints in quadratic programming (QP), have been demonstrated to provide safe navigation using a specified map. However, the practical implementation of CBF-CLF-QP for autonomous navigation in unknown environments faces the challenge of formulating the convex CBFs using sensory data. This paper presents a pipeline to construct the local control barrier functions based on observed point cloud data and forming a convex optimization problem. We use iterative plane fitting and cuboid bounding boxes to estimate the CBFs, which can separate safe and unsafe regions and ensure that the optimization problem is convex. The proposed method achieves mapless safe autonomous navigation in 3-dimensional space, which has the potential to enhance the level of autonomy for unmanned aerial vehicles (UAVs). The effectiveness of the proposed method is validated via numerical simulations.
Flow-Guided Transformer for Video Colorization

Video colorization aims to add color to black-and-white films. However, propagating color information to the whole video clip accurately is a challenging task. In this paper, we propose Flow-Guided Transformer for Video Colorization (FGTVC), consisting of a Global Motion Aggregation (GMA) module, Residual modules, Flow-Guided Attention blocks (FGAB) based on encoder and decoder, to exploit the information from the neighbor patch with high similarity for each video patch colorization. Specifically, we employ Transformer to capture the long-distance dependencies between frames and learn non-local self-similarity in the frame. To overcome the shortcomings of previous optical flow-based methods, FGAB enjoys the guidance of optical flow to sample elements from spatio-temporal adjacent frames when calculating self-attention. Experiments show that the proposed FGTVC has an outstanding performance than the state-of-the-art methods. In addition, comprehensive findings demonstrate the superiority of our framework in real-world video colorization tasks.
Power Generation Prediction Model of Distributed Photovoltaic System Based on Adaptive Extended Kalman Filter

Low-polluting renewable energy generation has expanded both in height and breadth over the past few years. Photovoltaic power generation is another widely used renewable energy generation technology after wind power generation. How-ever, due to the influence of multiple external factors, photovoltaic power generation has the disadvantages of uncertainty and intermittent of power generation, which increase the difficulty of grid connection and lead to the serious phenomenon of power abandonment. In order to solve these problems, this paper proposes a photovoltaic power generation prediction model to provide a reference for power distribution, grid connection decisions, and photovoltaic builders. Firstly, the solar insolation, which mainly affects the distributed photovoltaic generation is normalized, and the finite element calculation method is used to divide the solar radiation intensity in the study area, and the average radiation intensity in the cell is obtained to eliminate the influence of irrelevant environmental factors. Secondly, a new photovoltaic array arrangement method is proposed that has the advantages of a small footprint, long maximum radiation time, and the highest power generation. Finally, in order to obtain an accurate PV power generation prediction value, this paper devotes the adaptive extended Kalman filter algorithm to calculate the future PV power generation of the local place according to the existing power generation. The experimental results show that the prediction accuracy of the new filtering algorithm is higher than that of the prediction method based on the traditional extended Kalman filtering algorithm.
A Cross modal Image and Text Retrieval Method for OpenKylin

With the rapid development of computer vision and natural language processing, there is an increasing demand for cross-modal retrieval (CMR) from users. The domestic operating system, openKylin, plays a key role in promoting domestic operating systems. It enriches the operating system functions with secure and trusted operation technology as the core. It is of great practical significance to integrate the field of computer vision into the domestic operating system. A graph retrieval method based on OpenKylin is proposed to enhance the functionality of searching graphs using text. This paper begins with a review of relevant domestic and international research on this technology. It then provides details on the design and implementation process, and concludes by demonstrating the final implementation results. The actual operational results demonstrate that the method is characterized by both high performance and high accuracy.
DoG Accuracy Via Equivariance: Get The Interpolation Right

We study the influence of image interpolation algorithms on local feature detectors operating on a scale pyramid, focusing on the Difference-of-Gaussian, as used in SIFT. We show that commonly used implementations, such as in OpenCV and Kornia, are neither rotational nor scale equivariant. We present a simple solution and demonstrate its positive influence on the downstream image matching tasks. The implementation of the method has been accepted in standard libraries OpenCV [1] and Kornia [2].
Inference Acceleration of Deep Learning Classifiers Based on RNN

This paper proposes a hybrid strategy for accelerating image classification inference based on the Modern Continuous Hopfield Neural Network (MHNN). To implement this strategy, the fully connected layers of convolutional neural networks (CNNs) are replaced by the MHNN. The proposed hybrid architecture achieves promising results for image classification tasks, as demonstrated through experiments on multiple benchmark datasets, including ImageNet, and different CNN architectures. It offers a remarkable speedup in inference time (ranging from 1.12x to 1.6x) and significant compression in terms of the number of neural network parameters (ranging from 1.32x to 49.37x), while maintaining high accuracy. Furthermore, the proposed CNN-MHNN model achieves an accuracy of 99.18% on the Noisy MNIST dataset, outperforming state-of-the-art models with a 0.75% improvement for the Added White Gaussian Noise version.
Deep Robust Image Restoration Using the Moore-Penrose Blur Inverse

This paper proposes a deep learning model for robust image restoration when the degradation is not precisely known. We show how the Moore-Penrose pseudo-inverse of a blur convolution operator can be approximated by a Wiener filter’s impulse response. The image restoration problem is then cast as the learning of a residual on the frequencies where the blurring filter is zero which, when added to the Wiener restoration, will satisfy the image formation model. A Dynamic Filter Network removes artifacts introduced by inaccurate blur estimations and other image formation model inconsistencies. The experiments conducted on synthetic and real image datasets assert the performance and robustness of the proposed method and show its superiority to existing ones.
Blind Quality Assessment of Light Field Image Based on Spatio-Angular Textural Variation

Light Field Image Quality Assessment (LF-IQA) is vitally important to facilitate the development of immersive technologies. However, current state-of-the-art LF-IQA metrics still struggle to handle Light Field Image (LFI) with massive data in an efficient manner. To cope with this challenge, we propose a simple yet effective Blind LF-IQA metric based on Spatio-Angular Textural Variation, named SATV-BLiF. Given a distorted LFI, we first apply Local Binary Pattern (LBP) operator to measure the textural variation in the spatial and angular domains respectively. Then the generated spatial and angular textural matrices are merged and further transformed into statistical textural histogram features. Finally, Support Vector Regression (SVR) is employed to construct a nonlinear mapping function between the statistical textural histogram features and the perceptual quality score of the distorted LFI. Experimental results on three representative light field databases show that the proposed metric achieves state-of-the-art quality evaluation performance, while having much lower complexity than the existing No-Reference (NR) LF-IQA metrics. The code of the proposed SATV-BLiF metric is available at https://github.com/ZhengyuZhang96/SATV-BLiF.
Event-Based Camera Simulation Using Monte Carlo Path Tracing with Adaptive Denoising

This paper presents an algorithm to obtain an event-based video from noisy frames given by physics-based Monte Carlo path tracing over a synthetic 3D scene. Given the nature of dynamic vision sensor (DVS), rendering event-based video can be viewed as a process of detecting the changes from noisy brightness values. We extend a denoising method based on a weighted local regression (WLR) to detect the brightness changes rather than applying denoising to every pixel. Specifically, we derive a threshold to determine the likelihood of event occurrence and reduce the number of times to perform the regression. Our method is robust to noisy video frames obtained from a few path-traced samples. Despite its efficiency, our method performs comparably to or even better than an approach that exhaustively denoises every frame. Visit our project page for more information: https: //github.com/0V/ESIM-AD.git.
3D Facial Expression Generator Based on Transformer VAE

We present a generative model for the 3D facial expression mesh sequences, from onset to the termination of a desired expression. We tailor a Transformer VAE architecture: The encoder compresses a sequence of facial landmarks into an expression-aware regularized latent space, while the decoder generates a new sequence from the sampled latent variable, conditioned on a desired expression. After a landmark-guided mesh deformation, a given 3D neutral face is driven to an animated mesh sequence with the expected expression. The generated sequences are consistent, of quality, and exhibit a good level of diversity, improving over state-of-the-art methods. We validate our model by conducting extensive experiments on two representative datasets. The supplementary video and code are available on a GitHub page (https://github.com/ZOUKaifeng/FacialExpressionGeneration).
Modality Meets Long-Term Tracker: A Siamese Dual Fusion Framework for Tracking UAV

Tracking an Unmanned Aerial Vehicle (UAV) to obtain its locations and trajectory is a crucial task to avoid the unlawful use of UAVs. However, most existing UAV tracking methods fail when facing cluster environments, out-of-view, and occlusions because of their insufficient representation of global context information capacity. To mitigate these issues, we propose a new tracker, namely SiamFusion, to innovate a dual fusion procedure that leverages the advantages in both the feature and decision levels. In particular, we propose a novel feature fusion module named Modality-Fusion to utilize multi-modal information, enhancing the perception of the target. From the decision level, we further develop a local-global converter based on a multi-modal fusion decision-making mechanism to reduce the accumulation during tracking, which significantly increases the robustness of the tracking process. Extensive experiments demonstrate the superiority of the proposed SiamFusion, which achieves the best performance on Anti-UAV in terms of accuracy and speed. In particular, we exceed the state-of-the-art tracking algorithm in the tracking accuracy by 4.2% at a similar frame rate. Our source codes, pre-trained models, and online demos will be released upon acceptance.