Reviewerly/exHarmony huggingface dataset:
HUBUNGAN PEMBELAJARAN DARING DENGAN PRESTASI BELAJAR SISWA PADA MASA COVID 19 DI SMANEGERI 7 SINGKAWANG

The learning process at SMA Negeri 7 Singkawang during the pandemic period July 2020 to June 2021 carried out online learning. During online learning there was a decline in student achievement caused by gadgets, expensive quotas and inadequate internet network factors. The purpose of this study was to determine the relationship between online learning and student achievement during the covid 19 period. This study is a quantitative study using a descriptive correlative design with a cross sectional approach, the population in this study was grade 12 students and the number of samples was 145 respondents with random sampling technique. Data analysis using Spearman Rank test. The results showed that more than half of the respondents (56.8%) considered online learning to be ineffective and more than half of the respondents (70.3%) had good learning achievement. The results of the Spearman Rank test obtained a value (p value = 0.000 &lt; 0.005 and r = 0.378) so that Ha is accepted, then there is a weak relationship between online learning and student achievement at SMA Negeri 7 Singkawang. Online learning becomes ineffective because it is caused by several factors, namely slow internet network, expensive internet quota prices, student behavior is difficult to monitor so they become inactive in learning. It is hoped that the school and family will work together to improve student learning achievement to be more effective.
SCIENTIFIC-PEDAGOGICAL BASIS OF IMPROVING THE THEORETICAL BASIS OF USING TESTOLOGY IN THE EDUCATIONAL SYSTEM

In this article, it is proposed to take into account many factors when assessing students' educational achievements, as well as to take into account the socio-pedagogical environment and conditions. Today, the need to modernize pedagogical dimensions in education and the introduction of international programs is increasing in education. The main purpose of international assessment programs is to express opinions about the status of students' application of the knowledge they study at school in everyday life.
E-Learning and Organizational Support: Investigation of Quality and Behavior

Technology plays an important part in enhancing the educational experience, with e-learning emerging as the most common approach utilized. The assessment of technology’s effectiveness in educational settings requires the fulfillment of user satisfaction, particularly from the perspective of the student. The objective of this research is to investigate models that can effectively facilitate user pleasure, as indicated by the problems identified. The study was conducted on a sample of students who demonstrated a high level of self-directed learning ability in the context of e-Learning. A total of 378 students participated in the collection of their data by using a quantitative questionnaire. Research is conducted by the utilization of model testing and analysis grounded in research hypotheses. The study’s findings validate the significance of organizational support from the university in regard to its influence on students’ opinion of usefulness, e-learning quality, and user satisfaction. However, it is important to note that there exists a negative correlation between the quality of e-learning and satisfaction among students, indicating that an inadequate e-learning experience fails to produce favorable outcomes. The analysis of the importance-performance map reveals that e-learning is perceived as suboptimally constructed inside the university context. The research findings highlight the significance of user satisfaction facilitated by organizational support, specifically within the context of a university. The readiness of the university has become important in order to cultivate an enhanced system of learning. The successful integration of technology within a university setting is dependent upon the level of readiness of the institution’s infrastructure.
They are our daughters; Agatha, Jiah Anna, Rosa, Sohwa Therese and our son Kunjoo DaegonAndrea. They are very good at their behaviors (務玆稼穡)! (Tcheonzamun 641st-656th)

French Missionary Dallet (1874) wrote in his book that ‘The thousand character essay’ was utilized as text for instructing Chinese children for Chinese characters. This education was carried out from the ancient China (Dallet, 1874). The thousand character essay is called as ‘Tcheonzamun’ in Korea. Korean people firmly believed that Tcheonzamun was created by Chinese people (Han, 1583). But several researchers studied that Tcheonzamun poem was well interpreted through Korean grammar (Park et al., 2021a). These researchers found that Tcheonzamun poem was well translated through Korean pronunciation of Chinese characters (Park et al., 2021b). This is the work of translation of Tcheonzamun poem. Several researchers suggested that a Tcheonzamun poem is consisted of 16 Chinese characters in Tcheonzamun (Park et al., 2021a; Park et al., 2021b). There are two methods for the translation. The first one is through the meaning of Chinese characters in Tcheonzamun (Park et al., 2021a). The second method is through Korean pronunciation of Chinese character in Tcheonzamun (Park et al., 2021b). In this study, the translation was done through the meaning of Chinese character in Tcheonzamun. And the pronunciation of Chinese character in this study is taken from the somewhat old textbook of Tcheonzamun (Han, 1583). This study is for the translation of Tcheonzamun poem. This translation is carried out through the meaning of Chinese character in Tcheonzamun. And the range of this study is (Tcheonzamun 641st-656th). The title of this study is ‘They are our daughters; Jieun Agatha, Jiah Anna, Rosa, Sohwa Therese and our son Kunjoo DaegonAndrea. They are very good at their behaviors (務玆稼穡)! (Tcheonzamun 641st-656th)’. The theme of this Tcheonzamun poem is as follows. 649-652 治(Ti) 本(Bon) 於(Eo) 農(Long). This third line shows the secret of the life (治本於農), the hope for our life! And her husband says to his wife as follows. My dear wife! I will speak loudly this joyfulness! I will say to everybody again and again! Yes, this is our great pleasure (治本於農)!.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KESULITAN BELAJAR MATEMATIKA PADA MATERI STATISTIKA SISWA SMK KB

This research is useful to see the results of the binary regression model and provide an overview of the factors that influence student learning difficulties in the statistics material at SMK KB. This study uses a type of quantitative research with survey methods. The population was taken from data from class XII students of Gema Bhakti 1 Jasinga Vocational School with the samples used, namely class XII A and class XII B. The collection of research data was obtained using questionnaires, interviews, and observations. The questionnaire contains independent variables (X) in the form of factors that influence learning difficulties in statistical material with independent variables: (X1) independent learning, (X2) learning motivation, (X3) family environment, (X4) school environment, (X5 ) learning facilities and for the variable (Y) the average test scores in statistics. The data analysis technique is a binary logistic regression method. After the analysis showed that the learning motivation factor (X2) in statistical material had a significant effect on students' learning difficulties at SMK KB. Whereas (X1) learning independence, (X3) family environment, (X4) school environment, and (X5) learning facilities in the learning difficulties of students of Gema Bhakti 1 Jasinga Vocational School on statistics material has no significant effect
Web Application Development Skin Lesion Classification Using Transfer Learning InceptionResNet-v2

The development of machine learning continues from various domains where automation systems are needed. Advanced learning models, such as Convolutional Neural Networks (CNNs) in deep learning, can classify and identify objects even beyond human capabilities. One application is the classification of medical images skin cancer. Automatic diagnosis of skin cancer images is still challenging for CNNs. The use of transfer learning on classification has been leveraged for mobile, accurate, and fast automatic diagnosis. However, such models are imperfect in the categorization of skin lesions. Therefore, this study developed a web application for multiclass classification of 7 classes of disease through Streamlit and HuggingFace, with datasets from HAM10000 using TF Lite-conversion InceptionResNetV2. TF Lite-converted and the model’s classification reports were analyzed. The results on EarlyStopping overall accuracy were 87.56%, top-2 95.05%, and top-3 97.46%. Moreover, latency and classification duration were measured on Streamlit Share and HuggingFace Spaces. The findings are Streamlit has a faster average latency (1.17 ms) than HuggingFace (1.49 ms). The latency standard deviation on HuggingFace less consitent (0.49 ms) than Streamlit (0.10 ms). The HuggingFace classification average duration and standard deviation is 116 ms and 5 ms, while Streamlit is better at 97 ms and 2 ms respectively.
Facial Recognition Databases: Recent Developments and Review of Methods

Facial recognition is one of the most important biometrics that many researchers are increasingly studying, as it is used in various applications such as surveillance, security, law enforcement, information, person identification, smart cards, access control, etc. There is a fundamental relationship between the development of facial recognition algorithms and the possibility of the existence of databases of different faces that influence the appearance of the face in a constrained manner. Standard datasets of images of appropriate size for a subject should be accessible to the public to compare the performance and assessment of identification or verification of a facial recognition system. This paper aims to present a review of the most popular 2D unmasked and masked face datasets available in the current century that are accessible for free download or can be certified with an acceptable effort, where these databases are suitable for training and testing 2D face recognition approaches. Also, this review discussed the evaluation metrics for face recognition and their two types of tasks (identification and verification).
Research on Strategies for School-based Training of Digital Literacy for University Teachers

Accompanied by the leapfrog development of digital technology, intelligent recording and broadcasting, electronic whiteboard applications, and virtual simulation teaching one after another, digital technology is changing the way of operation of education and teaching, the era of digitization of teaching has come in full swing, and the level of digital literacy of college and university teachers has become the focus of attention in the field of education at home and abroad. Enhancing the level of digital literacy of college teachers will effectively improve the transformation of knowledge results. At present, the level of digital literacy of college teachers is not optimistic, and there are problems such as the lack of teachers' ability to use digital technology, the lack of digital learning and innovation, and the low level of media literacy. College teachers, as disseminators of cultural knowledge, developers of intellectual resources, and pioneers of educational innovation, shouldering the important mission of establishing morality and educating people, should actively face the challenges of teaching digitization. This paper argues that school-based training, as a convenient training mode carried out by the school as a carrier, led by school leaders and school administrators, and carried out by the school as the main body, is the best way to improve the level of teachers' digital literacy regularly. By establishing the core status of school-based training, this paper effectively designs a school-based training framework for digital literacy in colleges and universities, which provides reference and lessons for college and university teachers to improve the level of digital literacy and the corresponding level of education and teaching.
A Systematic Literature Review on Software- refactoring Techniques, Challenges, and Practices

Software-refactoring improves the quality and reduces the complexity during the whole life cycle of the software system. The objective of this work is to elicit the existing challenges and refactoring techniques and practices to counter those challenges. This study presents a systematic literature review based upon the scientific literature published since January 2000. After rigorously applying the SLR protocol defined by Barbara Kitchenham, we identify 36 articles discussing software- refactoring challenges, techniques, and practices. We point out complexity, understandability, and maintainability as three challenges of traditional software engineering that are resolved by software-refactoring. This SLR identifies assertions, program slicing, graph transformation, software metrics, and metamodeling, as five commonly used refactoring techniques. Moreover, eight main challenges of software-refactoring, namely determining refactoring and code location, selecting refactoring type and technique, managing refactoring dependency, consistency maintenance, refactoring ordering and increase in testing cost, are also highlighted. The findings of this work suggest that software-refactoring techniques can resolve challenges of traditional software engineering.
SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations

Most learning-based approaches to category-level 6D pose estimation are design around normalized object coordinate space (NOCS). While being successful, NOCS-based methods become inaccurate and less robust when handling objects of a category containing significant intra-category shape variations. This is because the object coordinates induced by global and rigid alignment of objects are semantically incoherent, making the coordinate regression hard to learn and generalize. We propose Semantically-aware Object Coordinate Space (SOCS) built by warping-and-aligning the objects guided by a sparse set of keypoints with semantically meaningful correspondence. SOCS is semantically coherent: Any point on the surface of a object can be mapped to a semantically meaningful location in SOCS, allowing for accurate pose and size estimation under large shape variations. To learn effective coordinate regression to SOCS, we propose a novel multi-scale coordinatebased attention network. Evaluations demonstrate that our method is easy to train, well-generalizing for large intracategory shape variations and robust to inter-object occlusions. Code is provided at: https://github.com/wanboyan/SOCS.
CLNeRF: Continual Learning Meets NeRF

Novel view synthesis aims to render unseen views given a set of calibrated images. In practical applications, the coverage, appearance or geometry of the scene may change over time, with new images continuously being captured. Efficiently incorporating such continuous change is an open challenge. Standard NeRF benchmarks only involve scene coverage expansion. To study other practical scene changes, we propose a new dataset, World Across Time (WAT), consisting of scenes that change in appearance and geometry over time. We also propose a simple yet effective method, CLNeRF, which introduces continual learning (CL) to Neural Radiance Fields (NeRFs). CLNeRF combines generative replay and the Instant Neural Graphics Primitives (NGP) architecture to effectively prevent catastrophic forgetting and efficiently update the model when new data arrives. We also add trainable appearance and geometry embeddings to NGP, allowing a single compact model to handle complex scene changes. Without the need to store historical images, CLNeRF trained sequentially over multiple scans of a changing scene performs on-par with the upper bound model trained on all scans at once. Compared to other CL baselines CLNeRF performs much better across standard benchmarks and WAT. The source code, a demo, and the WAT dataset are available at https://github.com/IntelLabs/CLNeRF.
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction

Despite recent advances in 3D human mesh reconstruction, domain gap between training and test data is still a major challenge. Several prior works tackle the domain gap problem via test-time adaptation that fine-tunes a network relying on 2D evidence (e.g., 2D human keypoints) from test images. However, the high reliance on 2D evidence during adaptation causes two major issues. First, 2D evidence induces depth ambiguity, preventing the learning of accurate 3D human geometry. Second, 2D evidence is noisy or partially non-existent during test time, and such imperfect 2D evidence leads to erroneous adaptation. To overcome the above issues, we introduce CycleAdapt, which cyclically adapts two networks: a human mesh reconstruction network (HMRNet) and a human motion denoising network (MDNet), given a test video. In our framework, to alleviate high reliance on 2D evidence, we fully supervise HMRNet with generated 3D supervision targets by MDNet. Our cyclic adaptation scheme progressively elaborates the 3D supervision targets, which compensate for imperfect 2D evidence. As a result, our CycleAdapt achieves state-of-the-art performance compared to previous test-time adaptation methods. The codes are available in here.
Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction

Generating realistic human 3D reconstructions using image or video data is essential for various communication and entertainment applications. While existing methods achieved impressive results for body and facial regions, realistic hair modeling still remains challenging due to its high mechanical complexity. This work proposes an approach capable of accurate hair geometry reconstruction at a strand level from a monocular video or multi-view images captured in uncontrolled lighting conditions. Our method has two stages, with the first stage performing joint reconstruction of coarse hair and bust shapes and hair orientation using implicit volumetric representations. The second stage then estimates a strand-level hair reconstruction by reconciling in a single optimization process the coarse volumetric constraints with hair strand and hairstyle priors learned from the synthetic data. To further increase the reconstruction fidelity, we incorporate image-based losses into the fitting process using a new differentiable renderer. The combined system, named Neural Haircut, achieves high realism and personalization of the reconstructed hairstyles. For video results, please refer to our project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> .
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions

Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are given. LLP aims to train an instance-level classifier by using the label proportions of the bag. In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed. We also propose a confidence interval loss designed based on statistical theory to use the augmented bags effectively. To the best of our knowledge, this is the first attempt to propose bag-level data augmentation for LLP. The advantage of MixBag is that it can be applied to instance-level data augmentation techniques and any LLP method that uses the proportion loss. Experimental results demonstrate this advantage and the effectiveness of our method.
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval

Large-scale noisy web image-text datasets have been proven to be efficient for learning robust vision-language models. However, to transfer them to the task of video retrieval, models still need to be fine-tuned on hand-curated paired text-video data to adapt to the diverse styles of video descriptions. To address this problem without the need for hand-annotated pairs, we propose a new setting, text-video retrieval with uncurated & unpaired data, that uses only text queries together with uncurated web videos during training without any paired text-video data. To this end, we propose an approach, In-Style, that learns the style of the text queries and transfers it to uncurated web videos. Moreover, to improve generalization, we show that one model can be trained with multiple text styles. To this end, we introduce a multi-style contrastive training procedure, that improves the generalizability over several datasets simultaneously. We evaluate our model on retrieval performance over multiple datasets to demonstrate the advantages of our style transfer framework on the new task of uncurated & unpaired text-video retrieval and improve state-of-the-art performance on zero-shot text-video retrieval. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
V-FUSE: Volumetric Depth Map Fusion with Long-Range Constraints

We introduce a learning-based depth map fusion framework that accepts a set of depth and confidence maps generated by a Multi-View Stereo (MVS) algorithm as input and improves them. This is accomplished by integrating volumetric visibility constraints that encode long-range surface relationships across different views into an end-to-end trainable architecture. We also introduce a depth search window estimation sub-network trained jointly with the larger fusion sub-network to reduce the depth hypothesis search space along each ray. Our method learns to model depth consensus and violations of visibility constraints directly from the data; effectively removing the necessity of fine-tuning fusion parameters. Extensive experiments on MVS datasets show substantial improvements in the accuracy of the output fused depth and confidence maps. Our code is available at https://github.com/nburgdorfer/V-FUSE
DISeR: Designing Imaging Systems with Reinforcement Learning

Imaging systems consist of cameras to encode visual information about the world and perception models to interpret this encoding. Cameras contain (1) illumination sources, (2) optical elements, and (3) sensors, while perception models use (4) algorithms. Directly searching over all combinations of these four building blocks to design an imaging system is challenging due to the size of the search space. Moreover, cameras and perception models are often designed independently, leading to sub-optimal task performance. In this paper, we formulate these four building blocks of imaging systems as a context-free grammar (CFG), which can be automatically searched over with a learned camera designer to jointly optimize the imaging system with task-specific perception models. By transforming the CFG to a state-action space, we then show how the camera designer can be implemented with reinforcement learning to intelligently search over the combinatorial space of possible imaging system configurations. We demonstrate our approach on two tasks, depth estimation and camera rig design for autonomous vehicles, showing that our method yields rigs that outperform industry-wide standards. We believe that our proposed approach is an important step towards automating imaging system design. Our project page is https://tzofi.github.io/diser.