Reviewerly/exHarmony huggingface dataset:
Social awareness education for childlen with asd 5-6 years old in steam oriented preschool

Education of social cognition through experiential activities with an interdisciplinary approach, which combine with practical lessons for children with autism spectrum disorder (ASD) 5- 6 years old integrates into issues related to science, technology, engineering, art and math in specific contexts, creating conditions for children with ASD 5-6 years old to accurately understand other people's emotions, empathize, care, create interaction, behave positively with people around, bring value to the community. This study focuses on presenting 4 main components of a theoretical model of social cognitive education for children with autism spectrum disorders aged 5-6 years old in STEAM-oriented preschools with a combination of construction from social emotional learning theory, theory of STEAM education and theory of children with ASD according to ICD 10, ICD 11, including: Social emotions; Sharing cooperation; Social adjustment; Communicate behavior.
Some solutions to enhance the quality of inclusive education for children with disabilities through STEAM education application

Inclusive education is an education method in which children with disabilities (CWD) are educated with non-disabled peers in the same high school system right in the community where their families and children live. This is a progressive education model that comes from the view that every child has the right and opportunity to learn according to the needs and abilities of each child. In this educational environment, children with disabilities are equal with other children in all physical, cultural, and social aspects; Children with disabilities will be provided with all conditions to meet their needs and maximize the potential available to them. Therefore, children are fully developed in personality, participate and affirm their contribution to society. STEAM education with the orientation of integrating science - technology - engineering - art - math elements is considered as an effective solution suitable to the diverse learning characteristics of children with disabilities in an inclusive classroom. contribute to improving the quality of education for children with disabilities today.
LEAN EDUCATION: STRATEGIES OF ECONOMIC BEHAVIOR UNDER SANCTIONS

Introduction. The paper presents the results of a study on the current problems facing educational organizations due to the introduction of Western sanctions, and the ways to solve them productively through the implementation of the concept of lean education. New challenges to educational organizations in the current context are described, possible avenues to modernize the Russian education system are proposed, including a possible solution to the problematic situation. The issues of fostering lean culture and lean thinking in teachers and students are touched upon. Materials and methods. The methodological framework for the research is the concept of lean production. The study substantiates the need and the possibility of implementing this concept in the educational sphere, and demonstrates the potential of Lean technologies to improve the efficiency and quality of educational activity. An attempt is made to raise topical research issues of the implementation of the lean production principles in education, which are insufficiently represented in the scientific literature. Research results. The examination of the initial experience of rational economic behavior of educational organizations under sanctions involves an analysis of examples, methods, and prospects for the development of the concept of lean education. Various aspects of functioning of the educational organizations under sanctions are touched upon, which force them to search and find effective means of solving current and potential problems. The study shows that in today’s context, educational organizations should pay more attention to the issues of economy, rational use of resources, import substitution, and, at the same time, contribute to shaping a lean personality type, which is a strategic direction for ensuring economic independence, sovereignty, and competitiveness of Russia and its education system. Conclusions. It is emphasized that the conceptualization of the lean education model and its practical implementation in the national education can and should be a relevant response to Western sanctions. The practicality of creating a lean culture and lean thinking as one of the key factors of sustainable socio-economic development of the country is argued.
Some theoretical issues on training organization skills experience activities in teaching subjects for student in Pedagogical Schools of Primary Education

According to the 2018 general education program, at the primary level, the organization of experiential activities in teaching subjects plays an important role in developing students' qualities and capacities. However, in fact, the organization of experiential activities in teaching subjects in primary schools still has many shortcomings. In order for the organization of experiential activities in teaching subjects to be effective, primary school teachers must have good skills in organizing experiential activities. Because of that, the central task of the university of pedagogy is to train students' skills in organizing experiential activities in teaching subjects in order to help teachers better understand the nature of organizing activities. Experience in teaching subjects contributes to improving pedagogical capacity. This article has outlined some core theoretical issues about training skills in organizing experiential activities in teaching for students of primary education at pedagogical schools, including the issue of organizing experiential activities. in teaching, skills to organize experiential activities in teaching, and practice skills in organizing experiential activities in teaching as a scientific basis to propose activities to practice this skill to meet requirements current educational reform.
Shaping Cryptocurrency Gatekeepers with a Regulatory 'Trial and Error'

In the past fifteen years, cryptocurrencies have grown from a whitepaper released on a mailing list to an ecosystem supporting millions of transactions a day using a whole host of technology never imagined before. However, governments have been slow to keep up. We present an analysis of regulatory developments from within the EU and by the Financial Action Task Force (FATF). These regulatory responses focus on intermediaries such as cryptocurrency exchanges, and wallet providers. We trace the AML/CFT policy recommendations made by the FATF, and examine the EU’s Fifth Anti-Money Laundering Directive and upcoming Markets in Crypto-Assets (MiCA) regulation. Here we find a natural tension: the pace of regulation is slower than the pace of technology, so the scope is non-comprehensive, yet current trials to accelerate this pace are leading to subtle conflicting errors. These findings present a deeper understanding of the ongoing regulatory dilemma, its essence, and suggest directions for the future of cryptocurrency regulation.
A Training-Free Neural Architecture Search Algorithm based on Search Economics

Motivated by the observation that most neural architecture search (NAS) methods are time-consuming because a “training process” is required to evaluate each searched neural architecture, this paper presents an efficient NAS algorithm based on a promising metaheuristic algorithm named search economics (SE) and a new training-free estimator to evaluate the searched neural architectures for not only obtaining a good neural architecture but also accelerating the computation time. The basic idea of the proposed NAS algorithm is to use the so-called expected value of each region in the search space to guide the search so that it will focus on searching high potential regions instead of solutions with high objective values in particular regions. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art non-training-free and training-free NAS methods. Experimental results show that the proposed algorithm is capable of finding a result that is similar to or better than those found by most non-training-free NAS algorithms compared in this study but taking only a tiny portion of the computation time.
Designing formative assessment toolkit in teaching grade 6 Natural Science at Secondary schools

Formative assessment is an important activity in the teaching process. In particular, in order to carry out process assessment, it is necessary to build a toolkit for assessing competencies in general and specific competencies through subject teaching in particular. It is considered a necessary and meaningful task to ensure success when implementing the 2018 General Education Program. The article mentions issues related to formative assessment such as purpose, content, timing, methods and tools, requirements, and principles of assessment. On the basis of the proposed natural science competency framework, a toolkit for assessing this capacity development process has been designed based on a 6-step process in teaching grade 6 Natural Science in secondary schools.
Developing creativity for children in preschool

Creativity is considered an important quality that needs to be developed in humans. It is essential to create environments that allow creativity to develop from a young age. The article discusses how to understand the creativity of preschool children, factors affecting the development of children’s creativity, activities that can support children to develop their creativity, and preschool teachers’ role in the development of creativity in children. As a result, providing recommendations for preschool teachers’ training to meet the requirements of the current creative education for children.
GPS/IMU fusion localization based on attention-LSTM networks and sliding windows

Global Positioning System (GPS)/Inertial Measurement Unit (IMU) fusion improves positioning accuracy. Existing fusion algorithms are mainly based on the Bayesian filtering framework. Still, the algorithm performs poorly in applying historical states, defining the state transition model, and correcting IMU computation errors during GPS interruption. This paper proposes GPS/IMU fusion localization based on Attention-based Long Short Term Memory (Attention-LSTM) Networks and sliding windows to solve these problems. we use Attention-LSTM networks to fuse GPS and IMU information to build a nonlinear model that fits the current noisy environment by training the model. In addition, the sliding window size can determine the number of historical state information utilized. We experimentally demonstrate that the method's positioning accuracy is improved under interrupted and uninterrupted GPS conditions.
A method of photoelectric ship image detection based on improved SSD

There are inevitable problems in the imaging mechanism of visible and infrared images, which bring more difficulties to the detection task. Firstly, this paper introduces the principle of SSD one-stage detection model. Aiming at the characteristics and problems of the model, residual network is used to improve it, and the prediction feature layer is reselected and sorted. Based on the photoelectric equipment, the video data of marine ship targets are collected and the data set is constructed. Through the comparison of the training results of various models, it is found that the detection accuracy of small-sized targets is low. In order to solve this problem, First, reduce the size of the anchor , and the feature extraction network is modified. The experimental results show that the average detection accuracy of the improved model increases by 7%, and the single category accuracy of small targets increases to 87%.
VioNets: efficient multi-modal fusion method based on bidirectional gate recurrent unit and cross-attention graph convolutional network for video violence detection

Recent research on video violence detection has made great progress with the development of multi-modal fusion techniques. However, existing approaches still pose huge challenges in real-time violence detection due to fusion features being fixed and not able to be further fine tuned. We aim to address this challenge by exploring the multi-modal fusion method for analyzing multiple modal violent information, i.e., audio, optical flows, and RGB images. We propose a unified network called VioNets, which contains both a cross-attention graph convolutional network (GCN) module and a bidirectional gate recurrent unit (Bi-GRU) module for fusing different modalities of information. First, the cross-attention GCN module is utilized to extract the cross-modal spatial–temporal features. The Bi-GRU module is then applied to accurately capture both past and future context features for each time step of the single-modal features. As a result, the model retains important single-modal information in the extracted features while using the cross-modal features to improve the detection accuracy. Experiments conducted on the XD-Violence dataset show that the proposed method achieves an average precision of 80.59% and an inference time of 0.16 s with 1.82 M parameters.
Unknown radar signal recognition based on deep metric learning

In view of the fact that the current radar signal modulation recognition method based on depth learning needs enough samples to achieve good recognition effect, this paper designs a depth metric learning recognition method based on Cosine Softmax (CS Softmax), which has strong intra class aggregation and inter class separation, and can recognize unknown signals. First, the Wigner Ville distribution (WVD), Pseudo Wigner Ville distribution (PWVD) and Smooth Pseudo Wigner Ville distribution (SPWVD) of known signals are taken as the input, the vectors obtained from the network output are learned into the embedded space to identify radar signals, and several unknown signals are combined with the initial training set in the verification phase. The simulation results show that under the signal to noise ratio of 10<i>dB</i>, the recognition accuracy of the known signal of this method can reach about 94%, can distinguish the unknown signal from the known signal, and can distinguish the unknown signal separately, with an accuracy of about 85%.
Swin-transformer based target detection with enhanced maritime SAR images data

In the complex remote sensing image detection, there are still many challenges in maritime ship detection. This paper combines the latest swin-transformer model with satellite remote sensing big data, uses the SSDD dataset, researches for maritime ship target detection, realizes the target detection support for SSDD dataset, at the same time, this paper proposes an improved version of augmix method Multiple-augmix, based on the principle of data enhancement for SAR image dataset, is proposed to achieve target detection and data enhancement for SSDD dataset and make comparison experiments. The detection accuracy of swin-transformer can reach 86.5% for the original SSDD dataset, and 96.2% for the SSDD dataset enhanced with Multiple-augmix method, which is 10 percentage points higher than that before enhancement, and at the same time, by conducting experiments with Fasterrcnn, RetinaNet and Focos algorithms and making comparisons, the experiments prove that This framework can achieve high detection accuracy in remote sensing image detection, and the Multiple-augmix method proposed in this paper is very effective in improving the detection accuracy of SSDD dataset. In addition, we verify the effectiveness, stability and accuracy of the above algorithm for SAR image target detection in complex remote sensing scenarios.
Contrast preserving decolorization method under gradient domain global mapping

In recent years, many decolorization methods have been proposed in various fields. In the traditional decolorization methods, people often adopt strict color order to reduce the dimension of the original color image, but the gray image resulting from this method often has low contrast. However, there are still many obvious problems such as low contrast, lack of detail and lack of layering in the current proposed de-color methods. In this paper, by studying the projection amplification of global mapping, a bimodal mapping method is designed by using gradient correlation as the standard. Under the premise of enhancing the gray contrast of the color image, the detail features of the original color image can be better preserved. At the same time, the new method takes into account the computational load of the algorithm, reduces the complexity and time cost of the algorithm, and improves the real-time performance.
A comparative analysis of mathematical transformations for signal processing

A signal is a physical quantity that represents information and is also a carrier of information. As signals are indispensable in every aspect of life, signal processing has always been a hot topic of research. With the continuous improvement of signal processing technology, it is used in a wide range of fields, such as digital filters, speech signal processing, image signal processing, biomedical aspects, etc. This paper first introduces the types of signals and basic concepts and some modern signal processing techniques which are commonly appeared. Afterwards, the principles and characteristics of the Fourier Transform, the Wavelet Transform and the Hilbert-Huang Transform are described. Furthermore, the advantages and disadvantages of the three signal processing methods and their respective fields of application are derived by means of comparative analysis. At the same time, the connections and unique features that exist between them are explained. Finally, the paper gives a conclusion and an outlook.
Color transfer based on cartoon decomposition and efficient illuminant estimation

The traditional color transfer method does not consider the influence of the light source of the reference image well, which is easy to cause problems such as poor visual effect and fuzzy texture details in the resulting image. To solve these problems, a color transfer method combining cartoon decomposition and illuminant estimation is proposed in this paper. Firstly, considering that the resulting image is easily affected by the brightness of the reference image illuminant, an efficient illuminant estimation for color constancy using grey pixels is introduced to alleviate the influence of the reference image on the resulting image. Secondly, to better preserve the texture details of the source image, the cartoon decomposition is used to decompose the source and reference images into a texture part containing the main details and a cartoon part containing the main contour structure. Finally, a full color gamut mapping technique is executed on the cartoon portion of the source and reference images to complete the color transfer. The experimental results show that the proposed method can not only effectively maintain the texture details of the source image but also achieve good results in terms of objective evaluation indexes, compared with the traditional color migration method, while achieving better visual effects.
Image hiding via compressed sensing and frequency domain transform

In this paper, a transform domain information hiding algorithm with visual security based on compressed sensing (CS) is proposed. To increase the security, an improved coupled map lattices is used for keystream and measurement matrix generation. To save the storage space, the semi-tensor product is applied to the measurement matrix. Using the image transform domain property, the compressed and encrypted image to be hidden is embedded into the high-frequency part of the carrier image through mathematical operations, and then the carrier image is inverted to finally form a visually secure image. Simulation results and performance analysis show that the designed information hiding scheme has better hiding effect and high security.
Image compression and encryption algorithm via deep compressed sensing

In this paper, we propose an image encryption algorithm via deep compressed sensing. First, a convolutional network is used to sample the image and produce the results of the sampling. Second, the matrix STP calculation is conducted on the sampled results utilizing a logistic chaos system, SHA-256, and a chaotic sequence matrix produced from a normal image in order to produce the final encrypted image. During the decryption procedure, the decryption measurements are obtained by performing the STP inverse operation on the encrypted image and the chaotic sequence matrix, and the measurements are then transmitted to the reconstructed image network to get the final decrypted image. Experiments demonstrate that the method provides a high level of image reconstruction quality, security, and runtime simplicity.
Insulator defect detection based on improved you-only-look-once v4 in complex scenarios

As power grid structure becomes increasingly complex, the impact of the operation status of key substation equipment on the safe and stable operation of the power grid is gradually increasing. Aiming at the problems of dense targets and occlusion recognition difficulties in the complex background of substations, an improved you-only-look-once (YOLO) v4 insulator defect detection algorithm is proposed. A large number of insulator and defect datasets were collected from the Internet. We use MobileNet-v2 to replace the backbone network of YOLOv4, and in view of the problem that the accuracy of YOLOv4 target detection model is not high for small targets or dense targets, convolutional block attention module (CBAM) attention mechanism is added to the backbone network to enhance the feature extraction ability, and ultra-lightweight subspace attention module (ULSAM) is added to the detection head to learn the cross-channel information. Experiments based on InDataset show that the insulator defect detection algorithm based on deep learning proposed achieves a high detection accuracy and detection speed; the mean average precision of the improved YOLOv4 model is 87.48%, and the recall rate of insulator defects reaches 79.84%, which meets the requirements of robustness and accuracy of insulator defect detection in real scenarios.
Complementary pseudolabel based on global-and-channel information for unsupervised person reidentification

Unsupervised person reidentification (re-ID) is designed to deal with the problem that in industrial application scenarios, the consistent features of the same person cannot be fully mined due to the lack of annotated information in the collected image resources. Researchers focus on using pseudolabels to label images with the same clustering attributes, but traditional clustering methods are prone to generating noisy pseudolabels, which greatly reduce the accuracy of unsupervised person re-ID. We propose a method to refine pseudolabels by generating discriminative information based on channel partitioning. In the design process, the feature map is divided from the channel level, generating a proximity matrix by calculating the distance between the channel feature and the global feature, and select the most negative sample as the verification label to optimize the pseudolabel of the global feature. As a bidirectional guide, the global pseudolabel can be used to further smooth each channel label, keeping the consistency of channel features and global features in pseudolabel selection. This unique method of learning local features can link global information and channel information. Experimental results demonstrate the superior performance of our proposal on representative person re-ID datasets.