Reviewerly/exHarmony huggingface dataset:
Historical Perspectives on the Teaching Research System and Teacher Learning since the Founding of New China

The teaching research system was established after the founding of New China in emulation of the Soviet Union for the purpose of learning, research and teaching management, with the aim of promoting improvement in the caliber of primary and secondary school teaching and guaranteeing the quality of education. The system played an important role in the construction and development of education in New China and ensuring the quality of education. The system also made unique contributions with respect to teacher learning: the routine practice of teaching research which it established fostered habits of daily learning and research in teachers, with the result that this practice permeated teachers’ careers, spurring their professional development. This design also caused teachers’ daily work to be rooted in research, raising the level of professionalism in teaching, and ensuring the basic quality of education. It gave rise to a teaching professional learning community in the Chinese environment, moving teachers’ work out of isolation, and allowing it to unfold within a supportive peer environment. However, in the face of the new era, there is still considerable room for the improvement of teaching research organizations as learning communities.
The Visualization of Cross-media Knowledge Graph of Tang and Song Poetry

The special grammar and abstruse rhetoric of Tang and Song poetry doomed the diversity of poetry research. In order to help scholars explore the information space of poetry, we took into account the breadth and depth of learning, combined with heterogeneous data sources such as poetry, videos, textbooks, knowledge graph and other media sources, and carried out visual modeling. We provide a visual and informative platform to demonstrate the potential power of visualization. It supports comprehensive search capabilities for multiple types of data, online generation of poets' knowledge graphs. Relationship diagram, word cloud diagram, pie chart and other visual charts are displayed on the platform, so that users can complete exploratory visual analysis by dragging, clicking and typing.
Tri Hita Karana In Bali Arts Festival

Abstract Bali as a famous tourist destination in Indonesia has major problems related to the environment and climate change. Garbage until now has been felt by most of the people in Bali but it is still underestimated. A series of regional regulations (Perda) and traditional regulations ( awig-awig ) have attempted to address the issue. Based on this the environmental preservation movement is now increasingly vigorous, while the Balinese Hindu philosophy of life “Tri Hita Karana” in the life of modern Balinese society appears just like a discourse. The aim of this research is to analyze the role of Bali Arts Festival to environmental preservation. This research was conducted using quantitative and qualitative methods at the largest routine arts and cultural festival in Bali known as the “Bali Arts Festival”. The results show that from the exhibitor`s perspective Bali Arts Festival has played a role in preserving the environment through its craft exhibition.
Research on Deep Learning Methods of UUV Maneuvering Target Tracking

This paper studies a single target tracking method based on deep learning. If the target's motion model is known, then we can use appropriate filtering methods to achieve target tracking based on the target's motion state. However, when the target motion is complex and the motion model is unknown, the filtering method is difficult to apply. To this end, we have designed two single target tracking methods based on recurrent neural networks, that is, a single target tracking algorithm based on Long and Short-Term Memory Network (LSTM) and Bidirectional Long and Short-Term Memory Network (Bi-LSTM). LSTM is a variant network based on RNN, and Bi-LSTM is based on LSTM plus One hidden layer, both LSTM and Bi-LSTM networks can process data on time series. Finally, tracking training and testing are performed for UUV under different maneuvering states to verify the effectiveness of the method, and the tracking effects and accuracy of the two models are analyzed.
The effectiveness of using magic book Math in Mathematics learning during the Covid-19 pandemic in Senior High School

Abstract This study aims to find out the effectiveness of using augmented reality-based magic book math at State Senior High School 15 Semarang on student achievement. The type of research used was Quasy Experimental research. The population of this study was X grade students of State Senior High School 15 Semarang. With random sampling technique, two classes were selected, X grade of IPA 4 as the experimental class and X grade of IPA 2 as the control class. Data processing with t comparative test and regression effect test. The results showed that learning was said to be effective which was indicated by: a) the average value of learning achievement in the experimental class was 89.57 which exceeded the Passing Grade 70 limit; b) the effect of student responses can be seen from the value of R square = 0.882 which means 88.2% of student achievement was influenced by student response factors in learning using magic book math based on augmented reality; and c) the average learning achievement of the experimental class was 89.57 while the control class was 70.84, so the experimental class had a significantly better final score average than the control class average.
Application of active learning algorithm in handwriting recognition numbers

Abstract Active learning is very suitable for many problems in natural language processing, where unlabeled data may be abundant, but annotation is slow and expensive. This article aims to illustrate some active learning methods for handwritten digit recognition tasks, such as the least confidence and entropy methods. We investigated the previously used sequence model query selection strategies and used some selection strategies for sample labeling in handwritten digit recognition. We also conduct a large-scale empirical comparison of using multiple corpora, which shows that our proposed method improves the technical level.
Graph Transformer: Learning Better Representations for Graph Neural Networks

Graph classifications are significant tasks for many real-world applications. Recently, Graph Neural Networks (GNNs) have achieved excellent performance on many graph classification tasks. However, most state-of-the-art GNNs face the challenge of the over-smoothing problem and cannot learn latent relations between distant vertices well. To overcome this problem, we develop a novel Graph Transformer (GT) unit to learn latent relations timely. In addition, we propose a mixed network to combine different methods of graph learning. We elucidate that the proposed GT unit can both learn distant latent connections well and form better representations for graphs. Moreover, the proposed Graph Transformer with Mixed Network (GTMN) can learn both local and global information simultaneously. Experiments on standard graph classification benchmarks demonstrate that our proposed approach performs better when compared with other competing methods.
Experience of intensification of personnel departments training of local bodies of the MIA of Russia in the system of advanced training

The article reveals the features of the activities of personnel departments from the point of view of legal specifics and their role in the implementation of the tasks assigned to the internal affairs bodies. The constantly changing regulatory framework governing the activities of human resources departments requires a high level of qualification and the ability to analyze and master it in a multitasking environment. The problems and shortcomings in the level of preparedness of personnel departments for modern conditions of service are shown. The features of their professional development are revealed. The author analyzes the domestic and foreign experience of professional development of law enforcement agencies and police officers, in particular. The analysis of foreign literature allows us to compare the domestic experience of professional development with foreign experience in terms of applied methods and technologies. Intensification is highlighted as a way to increase the effectiveness of training in a short time. The author’s employee training program is presented, revealing the organization, content, technologies and methods that contribute to the intensification of training. The experience of implementing this program and its effectiveness are described. The description of evaluation tools is given, the comparative data of training according to the experimental and traditional program with the use of quantitative and qualitative indicators confirming the effectiveness of experimental work are given.
Solar radiation budget in the atmosphere under broken cloudy sky. An analytical model

This study theoretically investigates the transport of broadband solar radiation in the Earth's cloudy atmosphere (extended and broken) by considering three plane parallel layers: i) thin bottom layer adjacent to the Earth's surface; ii) intermediate layer where clouds are most embedded; and iii) upper layer that is essentially cloud-free with possible occurrence of only thin clouds. These three layers are coupled by upwelling and downwelling radiation exchanges, including multiple retro-diffusion, and both direct beam and diffuse components are considered. The interrelation and relative magnitude of the solar radiation flux cascade across the atmosphere - αP, Ra, Rc, Rg and G/H – was established and explored as a tool for the present simulations. Two distinct radiation transport regimes through the inter-cloud gaps are identified, with the regime transition occurring when (AR*TAN(θ)+1)*CF = 1 where AR, θ and CF are the cloud aspect ratio, solar zenith angle, and cloud fraction, respectively. The variation of the solar radiation fluxes with CF and θ exhibit the transition between the two radiation transport regimes, as evidenced in the flux absorbed in the cloudy layer. The effect of AR upon the solar fluxes also exhibits the two radiation transport regimes, with one regime having linear dependence of the various fluxes on AR and the other one having fluxes stay at constant levels independent of AR. The shape factor SF of cloud top and bottom boundary “surfaces” is introduced to account for the self-irradiation effect for different cloud types. It is found that increased roughness of one or both boundaries increases the cloud absorptance in all cases whereas transmittance and reflectance for upwelling and downwelling can either be diminished or enhanced. With the proposed model the estimated impact of clouds on the enhancement of the shortwave radiation absorption in the atmospheric column was improved leading to results closer to the cloudy-sky absorptance observations reported in the literature. The ratio of the cloud radiative forcing at the surface to the cloud radiative forcing at the top of the atmosphere (Rnet), obtained by means of the present model, lead to Rnet = 1.3, closer to the estimate of 1.5 by Cess et al. than the values reported by other cloud radiative transfer models.
Research on line loss prediction model based on clustering and multi prediction model fusion

Due to the influence of many factors in power grid, the prediction accuracy of line loss rate is not high and it's applicability is poor. To solve this problem, a line loss rate prediction model based on density clustering and ensemble method is proposed. 18 characteristics related to distribution network in 2019 are selected as independent variables, and comprehensive line loss rate in 2019 is taken as dependent variable. The DBSCAN clustering algorithm is used to cluster the data set, and the random forest and LR- Bagging are used to predict each type of data respectively, and a combined line loss rate prediction model is established. The experimental results show that the prediction accuracy and universality of line loss rate can be effectively improved after the processing of clustering algorithm and a variety of integrated prediction methods.
Enabling design: a case of maker workshops as a method for including special educators in creating digital interactions for learners with profound disabilities

Purpose This purpose of this paper is to present findings from three workshops that aimed to enable learners with profound and multiple learning disabilities to have a richer, more meaningful experience of a digital curriculum and to bolster a more meaningful creative exchange than currently offered by off the shelf products. Design/methodology/approach Informed by the maker culture and participatory action research, this workshop method focussed on making prototypes and creating concepts that encourage a dialogic approach for envisioning future technologies. As a process of enabling design, this approach placed participants at the heart of iterative methods that support imaginative ideation and improvisation, rather than the production of marketable products. Findings A thematic analysis of post-workshop discussions revealed that participants felt inspired and supported to envision learner responses to stimuli as novel interactions, without prior knowledge of designing and coding. The collaborative approach provoked the articulation of narratives on learner ability and a reconsideration of “digital” with a contemporary curriculum for learners with profound disabilities. Practical implications Running a maker workshop requires a time and resource commitment from all parties. The emphasis is on easy-to-follow, direct teaching together with low cost electronics and non-digital materials, ensured that these demands were kept to a minimum. However, the gains of participation can only be maintained when support exists both in and out of the workshop environment through the provision of resources and communication channels. Originality/value The originality of the workshops lies in the focus on basic electronics, coding and prototyping as a means to think about digital futures. As a method of enabling technologies to tap into learner ability, this process fostered design dialogues through the social act of making, sharing and learning without the need for prerequisite skills.
Sculpture 3D Modeling Method Based on Image Sequence

This thesis first introduces the basic principles of model-based image sequence coding technology, then discusses in detail the specific steps in various implementation algorithms, and proposes a basic feature point calibration required in three-dimensional motion and structure estimation. This is a simple and effective solution. Aiming at the monocular video image sequence obtained by only one camera, this paper introduces the 3D model of the sculpture building into the pose tracking framework to provide initial depth information. The whole posture tracking framework can be divided into three parts, namely, the construction of the initial sculpture model, the posture tracking between frames, and the robustness processing during continuous tracking. In order to reduce the complexity of calculation, this paper proposes a new three-dimensional mesh model and a moving image restoration algorithm based on this model. At the same time, the influence of the intensity and direction factors in the scene is added, the simulation results are given, and the next step is discussed. The optimization work that needs to be done.
Design of a Built-in-Self-Test Implemented AES Crypto Processor ASIC

This paper presents the design of a (built-in self-test) BIST implemented Advanced Encryption Standard (AES) crypto-processor Application-Specific Integrated Circuit (ASIC). Hardware implementation provides higher speed and better physical security than that of software implementation. One of the burning issues is the testability of a complex VLSI chip. This research addresses this problem by implementing a mixed-mode BIST technique into the chip. The ASIC is designed using Verilog Hardware Description (HDL). It works both in normal mode and test mode. In test mode, the test manager enables an LFSR and initializes it with seed value from the memory and generates the desired number of pseudorandom and deterministic test patterns which are applied to the AES ASIC and outputs are compressed into a signature through the output response analyzer (ORA) which is compared with that of the pre-stored golden signature kept in the memory. If both the signatures match each other then the ASIC is ensured as fault-free otherwise it is faulty. The ASIC is simulated in two platforms: Java and ModelSim to ensure its proper functionality which has been verified using NIST provided input and output.
The VIMMJIPDA: Hybrid state formulation and verification on maritime radar benchmark data

We propose a variation of the Joint Integrated Probabilistic Data Association (JIPDA) method for multitarget tracking that includes multiple models and a visibility state. The method is tested on two radar data sets which were recorded during maritime collision avoidance experiments.
Super-Resolution of Sea Surface Temperature Satellite Images

Availability of high-resolution maps of geophysical fields, devoid of data loss due to clouds, is an urgent requirement for operational forecasting. We develop a Bayesian algorithm for super-resolution (or downscaling) of lower resolution geophysical fields observed by satellites. The key novelty in the present algorithm is the development and use of a Generative Adversarial Network (GAN) to learn the prior probability distribution of the high-resolution geophysical fields from historical data and/or model forecasts. The trained GAN is used to sample from the high-resolution prior and a particle filter along with the low-resolution data (as observation) is used to obtain the posterior high-resolution geophysical field. The resultant algorithm has been named the Particle Filter Generative Adversarial Network super-resolution (PF-GAN-SR) algorithm. The new algorithm is applied to downscale sea surface temperature fields in the northwest Atlantic Ocean. Results show consistent performance across different downscaling ratios. Notably, the high-resolution fields obtained from the new algorithm has better similarity score with the true high-resolution field compared to those from bi-cubic interpolation (commonly used in the geophysical community) and the SR-GAN algorithm (used in the computer vision community).
Dual-Path Sparse Hierarchical Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images aims to label every pixel with the correct semantic category. The core challenge of the current deep convolutional network (ConvNet)-based methods lies in the difficulty of effectively aggregating high-level categorical semantics and low-level local details along the hierarchy of backbone. Most current approaches consider only fusing adjacent feature layers gradually with short-range feature connections, which lack the diversity of feature interactions, such as long-range cross-scale connections. To this end, we propose a novel dual-path sparse hierarchical network that is characterized by rich cross-scale feature interactions. Multiscale features are first sparsely grouped with a predefined interval, which is then aggregated via both long-range and short-range cross-scale connections in a hierarchical manner. Moreover, in order to further enrich the diversity of feature interactions, we also introduce another fusion path in parallel but with different sparsity for feature grouping, forming a dual-path network. In this way, our model is able to effectively aggregate multilevel features by incorporating both long-range and short-range feature interactions in both parallel and hierarchical manner. Meanwhile, the semantic and resolution gap between multilevel features can also be bridged.
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

We investigate how to solve the cross-corpus news recommendation for unseen users in the future.This is a problem where traditional content-based recommendation techniques often fail.Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news).To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus.To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora.The learned translator can be used to generate representations for unseen users in the future.We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics.We also show that our translator is effective among existing transfer strategies.
Proficient Technique for Satellite Image Enhancement Using Hybrid Transformation with FPGA

Visual quality of images is improved by digital techniques for the improvement of photographs. The main purpose of image improvements is to process an image to make the output more desirable for a particular use than the original image. This paper proposes a new approach, which improves the picture of the satellite by the use of the SVD DWT concept, the Gaussian transformation DWT and multiwavelet transformation. This suggested approach would convert and approximate the single-colour value matrix of the low-flowing sub-band into one low-frequency and 15 high-frequency sub-bands, and then recreate the improved picture using the inverse transformation. In terms of technical criteria as PSNR, RMSE and CC, this approach can have higher quality and quantitative performance. This paper introduces strategies for improving hardware images using a programmable door array in real-time (FPGA). The suggested algorithm is implemented successfully with Xilinx ISE, MATLAB and ModelSim on different scale satellite images in Verilog HDL. In this article, these algorithms should be simulated and implemented using Verilog HDL. The Spartan-3E from Xilinx is the unit chosen here.
The Relationship Between Neural Networks And Stock Market Predictions

It is rightly proclaimed that “there are patterns are in everything.” so does apply in symmetry in between the “human brain neurons and it’s simulation in artificial neural network”. ANN is just simply the software implementations of the neuronal structures of our brains. Now the neural network in our ideological brains “made of star dust” is a hugely interconnected network of neurons, just like the interconnection between the galaxies and the universe of observable homo-species, like ANN are the simulation of the observable neural network of our brain. Now, on technical terms neural networks are attempted to mimic and learn about given specific task on the basis of the weighted input and to provide specific output. The Artificial neural network is basically divided into “supervised and unsupervised neural network”. The working model of neural networks is based on the connections. Neural networks learn repeatedly by taking certain input and by application of “activation function “. Learning occurs by repeatedly activating certain neural connections over others, and this reinforces the connections. Neural networks are finding many applications in various industries like beating players in certain games and one of its major applications come in “stock market predictions”, and pattern recognition is also one of its major areas of work. This paper introduces an audit of the neural network and stock market, and its fundamentals, concise history of neural network, about its origin, certain concepts, and its use in stock market world.