Reviewerly/exHarmony huggingface dataset:
Pedagogy Research Based on Artificial Intelligence Technology and Digital Economy Product R&amp;D-Research on education information system based on virtual reality technology, taking Hunan Medical University of China as an example

We have studied the research and development of digital economy products by using artificial intelligence technology.Its core is to use virtual reality technology to solve the problem of functional design of educational information system.At the same time, we have put forward an innovative computer model and combined the application program of computer client to integrate the entertainment function into the overall educational information system.This strategy can solve the problem of students' enthusiasm for learning.At the same time, we analyzed Hunan Medical University in China and conducted research.Through the research results, we completed the market economics prospect analysis of the education information system.The core of the digital economy product is market oriented.Therefore, the research team conducted a sociological experiment.The research team currently analyzes and reports the engineering model design and digital economy product development, as well as the experimental results and data, and discusses them at the end.
THE IMPLEMENTATION OF VIDEO TO IMPROVE STUDENTS ABILITY IN WRITING PROCEDURE TEXT AT XI CLASS SMA SWASTA DARUL IMAN IN ACADEMIC YEAR 2021/2022

This study deals with the use video to improve students’ procedure text writing class XI SMA Swasta Darul Iman Academic Year 2021/2022. Its objective is to find out whether the use video has a significance effect in writing procedure text. Population used in this study of class XI and samples used in this study were 64 students. The method used in this study is an experimental method with a one group as pre-test and post-test design. This study was carried on using quantitative method. The data was analysed by using t-test. The researcher found the pre-test mean = 55,39 , deviation standard = 3,26 and experienced an increase in the post-test mean = 80,31 deviation standart = 3,66.From this analysis, writer found that guided video has significane in teaching writing procedure text. It can be proved by the result of the analysis showed that “” observed about 40,85 was higher than “t-table” about 1,998 with the level significance is 0,05% it implies that the teacher of English should use video in teaching writing procedure text so that the teaching and learning process can be successful.
THE EFFECTIVENESS EXPLICIT INSTRUCTION METHOD TO IMPROVE STUDENTS’ ABILITY IN WRITING PROSEDURE TEXTS AT CLASS XI SMA NEGERI 2 LAWE BULAN ACADEMIC YEAR 2021/2022

This study deals with the effect of explicit instruction method in ability to make procedure text at class XI in SMA Negeri 2 Lawe Bulan Academic Year 2021/2022. Its objective is to find out whether the use explicit instruction has a significance effect in make procedure text. Population and samples used in this study were all students of class X total 36 students. The method used in this study is an experimental method with a one group as pre-test and post-test design. This study was carried on using quantitative method. The data was analysed by using t-test. The researcher found the pretest mean = 43,05 , deviation standard = 4,48 and experienced an increase in the posttest mean = 80,83, deviation standard = 4,55. From this analysis, writer found that guided explicit instruction method has significance in teaching writing procedure text. It can be proved by the result of the analysis showed that “ ” observed about 35,64 was higher than “ttable” about 2,030 with the level significance is 0,05% it implies that the teacher of English should use explicit instruction method in teaching make procedure text so that the teaching and learning process can be successful.
Quantum coherence and nonlocality of two qubits in the presence of a common dephasing environment

We study the coherence and quantum correlations for a system consisting of two interacting qubits under the effect of a common environment, where the system of qubits has the dephasing coupling with its surrounding. We study the dynamical behaviour of the quantumness measures when the influence of the qubits’ interaction on the dephasing is analyzed. We display the effect of the qubit-qubit interaction and qubits-environment interaction on the measures considering the case of symmetry and asymmetry interaction between the qubits and environment. Finally, we explicitly illustrate the relation between the coherence and quantum correlations and show that quantum coherence and correlations can be preserved during the dynamics.
Spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution

In recent days, capsule endoscopy is the most preferred method for diagnosing and assessing small intestinal ailments. But, the low capsule endoscopy image resolution is a significant drawback for both subjective and automated diagnostics. Many studies have proved that high-resolution images improve lesion detection rates, and it is expected to accomplish the same for capsule endoscopy. A U-Net framework is proposed to enhance the resolution of the capsule endoscopy images with a spatial attention gate incorporated in skip connection to capture the relation between the feature maps of different resolutions. A comparative assessment of the state-of-the-art super-resolution deep learning frameworks developed to learn a mapping from low-to-high-resolution images is presented in this paper. To analyze and verify the proposed with various methodologies, quantitative evaluations for the resolution factors 4x, 8x, and 10x are conducted. The evaluation results show that the super-resolution performance of the proposed framework is better compared to existing super-resolution frameworks.
Implementasi Literasi Digital Di Kalangan Mahasiswa Pendidikan Biologi ITS NU Pasuruan

This study aims to determine the effectiveness of digital literacy implementation among students. The research method used is a survey method by distributing questionnaires online through the google form platform. Based on the results of research conducted on 31 female students of the ITS NU Pasuruan biology education study program class 22d-A, it was found that 74.2% or 23 of the 31 respondents used gadgets every day, while 19.4% or 6 of the 31 respondents used gadgets every day but rarely used gadgets. And the remaining 6.5% or 2 out of 31 only use gadgets several times a week. So it can be concluded that almost all biology education students in class 22d-A stated that they use gadgets every day, with a percentage of 74.2% or 23 out of 31 respondents. All 22d-A ITSNU Pasuruan biology education study program students have access and understanding of sophisticated technology, but not all of them have an interest or preference for digital literacy. The current learning process also utilizes technology according to current needs. It is imperative that individuals are able to improve their digital literacy skills. This is because digital technology is also very supportive of a person's process in developing themselves and seeking as much knowledge as possible without any time and space limitations.
The Application of Modern Apprenticeship System in the Training of High-end Skilled Clothing Talents

Vocational skill education is an important way to cultivate technical and skilled talents.The training of skilled talents is the need to improve students' employment competitiveness and realize their self-worth.The modern apprenticeship system is a talent training mode of deep cooperation between schools and enterprises.Its basic feature is to combine school teaching and enterprise production, take "work-study combination" as the main feature, give full play to the main role of enterprises in professional teaching, take the real production tasks of enterprises as the guidance, work and study alternation, double post education, and cultivate high-quality workers and skilled talents with solid theoretical foundation and strong practical ability.This article takes the clothing specialty as an example to introduce the application of modern apprenticeship education in the training of high-end skilled talents in the clothing specialty.
Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and severity of associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states and actions. Second, we learn finite state controllers (FSCs) that predict NSE severity for a given trajectory and generalize well to unseen data. Finally, we develop a constrained MDP model that uses information from the underlying MDP and the learned FSC for planning while avoiding NSEs. Our empirical evaluation demonstrates the effectiveness of our approach in learning and mitigating Markovian and non-Markovian NSEs.
Isometric Manifold Learning Using Hierarchical Flow

We propose the Hierarchical Flow (HF) model constrained by isometric regularizations for manifold learning that combines manifold learning goals such as dimensionality reduction, inference, sampling, projection and density estimation into one unified framework. Our proposed HF model is regularized to not only produce embeddings preserving the geometric structure of the manifold, but also project samples onto the manifold in a manner conforming to the rigorous definition of projection. Theoretical guarantees are provided for our HF model to satisfy the two desired properties. In order to detect the real dimensionality of the manifold, we also propose a two-stage dimensionality reduction algorithm, which is a time-efficient algorithm thanks to the hierarchical architecture design of our HF model. Experimental results justify our theoretical analysis, demonstrate the superiority of our dimensionality reduction algorithm in cost of training time, and verify the effect of the aforementioned properties in improving performances on downstream tasks such as anomaly detection.
Reshaping State-Space Search: From Dominance to Contrastive Analysis

State-space search is paramount for intelligent decision making when long-term thinking is needed. We introduce dominance and contrastive analysis methods, which enable reasoning about the relative advantages among different courses of action. This re-shapes how agents reason and leads to new families of state-space search algorithms.
CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data

Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging situations such as occlusion, degraded weather, and sensor failure, objects can go unobserved and unlabeled. In this paper, we present CLUE-AD, a general-purpose method for detecting and labeling unobserved entities by leveraging the object continuity assumption within the context of a scene. This method is dataset-agnostic, supporting any existing and future AD datasets. Using a real-world dataset representing complex urban driving scenes, we demonstrate the applicability of CLUE-AD for detecting unobserved entities and augmenting the scene data with new labels.
ContraFeat: Contrasting Deep Features for Semantic Discovery

StyleGAN has shown strong potential for disentangled semantic control, thanks to its special design of multi-layer intermediate latent variables. However, existing semantic discovery methods on StyleGAN rely on manual selection of modified latent layers to obtain satisfactory manipulation results, which is tedious and demanding. In this paper, we propose a model that automates this process and achieves state-of-the-art semantic discovery performance. The model consists of an attention-equipped navigator module and losses contrasting deep-feature changes. We propose two model variants, with one contrasting samples in a binary manner, and another one contrasting samples with learned prototype variation patterns. The proposed losses are computed with pretrained deep features, based on our assumption that the features implicitly possess the desired semantic variation structure including consistency and orthogonality. Additionally, we design two metrics to quantitatively evaluate the performance of semantic discovery methods on FFHQ dataset, and also show that disentangled representations can be derived via a simple training process. Experimentally, we show that our models achieve state-of-the-art semantic discovery results without relying on layer-wise manual selection, and these discovered semantics can be used to manipulate real-world images.
DetAIL: A Tool to Automatically Detect and Analyze Drift in Language

Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional software is made dependable by following rigorous practice like static analysis, testing, debugging, verifying, and repairing throughout the development and maintenance life-cycle. Similarly for machine learning systems, we need to keep these models up to date so that their performance is not compromised. For this, current systems rely on scheduled re-training of these models as new data kicks in. In this work, we propose DetAIL, a tool to measure the data drift that takes place when new data kicks in so that one can adaptively re-train the models whenever re-training is actually required irrespective of schedules. In addition to that, we generate various explanations at sentence level and dataset level to capture why a given payload text has drifted.
Geometric Inductive Biases for Identifiable Unsupervised Learning of Disentangled Representations

The model identifiability is a considerable issue in the unsupervised learning of disentangled representations. The PCA inductive biases revealed recently for unsupervised disentangling in VAE-based models are shown to improve local alignment of latent dimensions with principal components of the data. In this paper, in additional to the PCA inductive biases, we propose novel geometric inductive biases from the manifold perspective for unsupervised disentangling, which induce the model to capture the global geometric properties of the data manifold with guaranteed model identifiability. We also propose a Geometric Disentangling Regularized AutoEncoder (GDRAE) that combines the PCA and the proposed geometric inductive biases in one unified framework. The experimental results show the usefulness of the geometric inductive biases in unsupervised disentangling and the effectiveness of our GDRAE in capturing the geometric inductive biases.
Methodology for using digital educational tools

The article discusses the process of designing a modern lesson using electronic educational resources and digital tools, the role of teaching aids in the educational process. Analyzed didactic model of V.P. Smirnov, which is used in both traditional and distance learning. The process of designing a lesson through this model is looked at. In pedagogical work, when summing up the final results of training, mental skills are systematized and classified using the taxonomy of the American psychologist B.S. Bloom. In accordance with the taxonomy, the learning outcomes make it possible to understand how well the student has mastered the content of the discipline. The studies of Robert Gagne were studied, who developed a simple 9-step algorithm that can make the learning process as efficient as possible. Each level of education corresponds to a specific task, for the solution of which a variety of techniques, methods, teaching aids are offered. These stages of events are known to pedagogy even before Gagne, he designed and assembled them into his system. Stimulating internal learning processes with digital tools can dramatically improve performance. The teacher is asked to be careful about the choice of digital tools for implementation in their professional activities, to use well-established teaching methods and tools, to introduce new resources gradually.
Quantum education: how to teach a subject that nobody fully understands

Quantum technologies are expected to be among the most transformational technologies of the twenty-first century, changing how we sense the world around us, approach security, and process critical information. Transitioning the industry from quantum research labs to the commercial environment requires a sizable workforce skilled in supporting Quantum 2.0. To achieve the goal of the entire quantum ecosystem, society at all levels needs to be aware of this emerging field and then be inspired, attracted, educated, and trained with the new quantum skills and competencies. This poses a challenge as quantum science is a difficult and counterintuitive subject. How is a subject such as quantum mechanics that, as the famous quantum scientist Richard Feynman once said, “nobody fully understands” to be taught? In this presentation, we will share our experiences and results of EdQuantum, an NSF-funded project whose goal is to develop a curriculum to train future quantum technicians. The proposed curriculum intends to provide an essential first step in quantum education at the associate’s level. The curriculum relies heavily on a visual, hands-on approach that is based on commercially available quantum educational hardware. The curriculum strives to bring complex quantum science to a level understandable to individuals without a solid scientific background through algebra-based theory and simple experiments. As such, it may also be used to raise awareness and inspire high school students to seek careers as future quantum scientists.
flexCAT: Computerized Adaptive Test Development Platform

Computerized Adaptive Testing (CAT) is a new testing mode that utilizes the adaptive measurement concept of "tailored to fit." Compared with traditional paper-and-pencil testing, CAT has the advantages of improving measurement accuracy, reducing test length, and ensuring test security. Therefore, it is highly regarded by researchers and practitioners both domestically and internationally. However, the platform construction of CAT involves complex statistical measurement theory and tedious numerical calculations, which hinder the application and promotion of CAT in practice. This article mainly introduces the development platform of computerized adaptive testing - flexCAT. Users can quickly build their own CAT system using the convenient human-computer interactive interface provided by the flexCAT platform. This article will introduce the first web-based computerized adaptive testing development platform in China - flexCAT, from the perspectives of its advantages, basic theory, module functions, etc. The aim is to provide free adaptive testing platform development services for research and application personnel in the fields of education, psychology, and further promote the development of psychological and educational measurement theory and technology in China. The URL for the flexCAT platform is: http://www.psychometrics-studio.cn/app/cat_demo/index.html?Id=false&amp;Block=false.
EXARL-PARS: Parallel Augmented Random Search Using Reinforcement Learning at Scale for Applications in Power Systems

With recent advances in deep learning and large-scale computing, learning-based controls have become increasingly attractive for complex physical systems. Consequently, developing generalized learning-based control software that takes into account the next generation of computing architectures is paramount. Specifically, for the case of complex control, we present the Easily eXtendable Architecture for Reinforcement Learning (EXARL), which aims to support various scientific applications seeking to leverage reinforcement learning (RL) on exascale computing architectures. We demonstrate the efficacy and performance of the EXARL library for the scientific use case of designing a complex control policy to stabilize a power system after experiencing a fault. We use a parallel augmented random search method developed within EXARL and present its preliminary validation and performance stabilization of a fault for the IEEE 39-bus system.
flexCDMs: A Web-based Platform for Cognitive Diagnostic Data Analysis

Cognitive diagnosis is an important component of modern measurement theory and has received widespread attention from researchers in the fields of education and psychological measurement. Existing cognitive diagnosis analysis tools rely on professional software packages (such as R packages), which creates significant challenges for users, especially those who are not familiar with computer programming. To remove this technical barrier, our team has developed a web-based, user-friendly platform, named flexCDMs, for cognitive diagnosis data analysis. This article describes the features of the platform, the functional modules, the implemented cognitive diagnosis models (CDMs) and algorithms, and illustrates the operations of the platform. This platform can be used to analyze data based on various cognitive diagnosis models, carry out Q-matrix theory, model-data fit tests, parameter estimation, quality analysis of cognitive diagnostic tests, differential item functioning (DIF) detection, and Q-matrix modification. It produces various charts and graphs to report results. It is a powerful, yet easy to use cognitive diagnosis data analysis tool. The website for the flexCDMs platform is: http://111.230.233.68:1001/?Id=false&amp;Block
Deep Learning-Based Instance Segmentation of Aircraft in Aerial Images using Detectron2

The project aims to leverage the advanced features of the Detectron2 framework for object detection and instance segmentation in computer vision. The goal is to achieve state-of-the-art accuracy and robustness in detecting and segmenting objects in images, especially in scenarios with small or complex shapes and cluttered backgrounds. To accomplish this, the project uses the latest deep learning models based on the Faster R-CNN and Mask R-CNN architectures, which employ the region proposal network (RPN) and feature pyramid network (FPN) modules, respectively. The models are trained on large-scale datasets such as COCO and fine-tuned on specific domains to improve their performance on target objects. The models are also augmented with additional techniques such as data augmentation, multi-scale training, and hyperparameter optimization. The project further enhances the models by incorporating state-of-the-art techniques such as Cascade R-CNN, which uses a series of R-CNN models with increasing IoU thresholds to improve object detection accuracy. The project also explores using weighted feature fusion and self-attention mechanisms to capture object context better and improve segmentation accuracy. To evaluate the effectiveness of the proposed approach, the project conducts extensive experiments on various datasets, including COCO, YOLO, and custom datasets. The experimental results show that the proposed approach using Detectron2 and deep learning models achieves state-of-the-art performance in object detection and instance segmentation tasks, outperforming other state-of-the-art methods. Overall, the project demonstrates the effectiveness of using the latest deep learning models and advanced techniques to improve object detection and instance segmentation in computer vision. The proposed approach provides a valuable contribution to the field of computer vision and paves the way for future research in this area.