Reviewerly/exHarmony huggingface dataset:
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixing augmentation strategies have advanced to generate the mixup mask that can enrich the saliency information, which is a supervisory signal. However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. We develop an efficient pairing algorithm that pursues to minimize the conflict of salient regions of paired images and achieve rich saliency in mixup images. Moreover, GuidedMixup controls the mixup ratio for each pixel to better preserve the salient region by interpolating two paired images smoothly. The experiments on several datasets demonstrate that GuidedMixup provides a good trade-off between augmentation overhead and generalization performance on classification datasets. In addition, our method shows good performance in experiments with corrupted or reduced datasets.
Interpretation of digitalization indicators of the Russian system of general education

The article analyzes digitalization indicators of the Russian general education system provided by the Federal State Statistics Service (Rosstat) and the Unified Interdepartmental Information and Statistical System (EMISS), as well as data from a secondary analysis of the results of the sociological survey “Teenagers 360°” (within the framework of the strategic program “Teenagers Russia” of the Commissioner for Children’s Rights under the President of the Russian Federation) and other sources. The following indicators are considered as indicators: the indicator of the use of special software tools available to students in educational organizations; the number of students using e-learning and distance learning technologies; the number of students in organizations engaged in educational activities under additional general educational programs for children; the proportion of children attending additional educational classes; the purposes of using the Internet by children. Their dynamics over the past few years is traced. The interpretation of the digitalization impact on education is presented at the quantitative and qualitative levels. There are analyzed the opinions of specialists in the field of social and pedagogical sciences, teachers and physicians directly related to education and childhood on the digitalization impact on health, mental abilities, assimilation of knowledge and acquisition of craft and work skills. It is concluded that the excessive introduction of digital technologies in all sectors of domestic general education, including additional school education, can interfere with acquisition and systematization of basic knowledge and not contribute to upbringing of the younger generation in a traditional and moral way. Digital educational innovations are perhaps useful as additional educational tools, but only with a quality and serious content.
Optimization of Energy Efficiency According to Freud’s Disk Theory Depending on Propel Pitch Angles

Propeller systems are used extensively in many areas, especially in ships, airplanes, air conditioning and wind turbines. Unmanned aerial vehicles (UAVs), which are produced and used intensively nowadays, are also one of the most important areas where propeller systems are used. Thanks to the propeller system, UAVs can hover in the air and move. It is of great importance that the propellers are designed in a stable, efficient and reliable manner. In this study, drone propellers are optimized in terms of energy efficiency by considering Freud’s Disc Theory. For the optimization process, a new mathematical model, which is used for the first time in the literature, is proposed, and the efficiency of the proposed model is analysed by comparing it with the simulation algorithm and real propeller values. As a result of the analysis study, it has been determined that an improvement of 11.84% compared to the real propeller data and 11.39% compared to the simulation results has been achieved in terms of energy efficiency. It is estimated that high efficiency can be achieved by applying the proposed solution approach to other propeller systems.
Drone Simulation, Mapping and Navigation via ROS

In recent years, simulation environments with robust physics engines, high-quality graphics and user-friendly interfaces allow researchers to validate their work in simulation environments before real-test environments. In this study, a ROS-based mapping and navigation of a real drone model in three-dimensional simulation environment Gazebo is described. The mapping process is carried out using the Gmapping algorithm. The purpose of this study is to evaluate the drone’s mapping, location and navigation in an unknown environment.
Graph TopoFilter: A Method for Noisy Labels Detection for Graph-Structured Classes

Detection of incorrectly conducted failure repairs is not a trivial task for companies manufacturing big volumes of goods. Extensive data sets of service calls are periodically updated and subject matters experts would not be efficient in manual annotating of the data. Symptoms described in free text form might be caused by different components - not necessarily by the most obvious. Classes are imbalanced due to different time to failure of particular components and thus actions taken for some rare failures might be noted as incorrect ones. The presented problem is similar to the problem of learning in a presence of noisy labels, which are caused by human errors, variation of annotator to annotator perception, faults made by annotating algorithms or by other reasons. There are multiple techniques to prevent neural networks from overfitting to the noisy data, but to our best knowledge none of them considers relationships between classes, which is crucial in engineering systems built from multiple components connected in a specific way. A novel approach of selecting clean data samples in an unsupervised manner is presented in this paper. It is based on a topological approach exploring the deep representation of the features in the hidden space, enriched with knowledge graphs reflecting the structure of the classes. We present the case study of the algorithm utilized for service calls data set for home appliances.
SPMD-Based Neural Network Simulation with Golang

This paper describes the design and implementation of parallel neural networks (PNNs) with the novel programming language Golang. We follow in our approach the classical Single-Program Multiple-Data (SPMD) model where a PNN is composed of several sequential neural networks, which are trained with a proportional share of the training dataset. We used for this purpose the MNIST dataset, which contains binary images of handwritten digits. Our analysis focusses on different activation functions and optimizations in the form of stochastic gradients and initialization of weights and biases. We conduct a thorough performance analysis, where network configurations and different performance factors are analyzed and interpreted. Golang and its inherent parallelization support proved very well for parallel neural network simulation by considerable decreased processing times compared to sequential variants.
Model of Perspective Distortions for a Vision Measuring System of Large-Diameter Bent Pipes

The measuring system described in the article was designed for measuring large and heavy bent pipes with diameters up to 1.2 m. Currently, measurements of large-diameter bent pipes are taken using either simple and inaccurate protractors or measuring systems requiring a 3D model of the pipe created in graphics software, an optical scanner and markers. This paper presents methods of modeling distortions for measuring system based on one camera that is easy to install in an industrial plant. Those models allow to perform measurement quickly at any position of the pipe on a large industrial measurement table. The paper describes the mathematical models of perspective projections of single- and double- bent pipes, as well as the method of bending angle determination and detection of straight sections of the bent pipe. As part of the research, measurement accuracy of the designed system model were described and confirmed.
Multiple feature fusion algorithm for human fall detection in intelligent monitor video

Human fall detection is main abnormal event in intelligent power distribution network monitor scenario. Due to diverse body postures and human actions before human fall, it is difficult to detect human fall action effectively. To solve this problem, we studied five human fall-related features and proposed multiple feature fusion algorithm to detect human fall action in intelligent monitor video. Experiment results and field test both demonstrated its effectiveness of the proposed algorithm.
A correntropy-based local additive bias-field-corrected image fitting model for image segmentation

Segmenting images with intensity inhomogeneity is a difficult problem in the field of image processing. In order to deal with it, this paper presents a novel local additive bias-field-corrected image fitting model using correntropy criterion. Firstly, a local additive bias-field-corrected fitting image model in the level set formulation is built by using information of bias field and reflection ratio simultaneously. Secondly, an energy function is introduced by minimizing the difference between the fitting image and original input image in a neighborhood, which makes it effective in segmenting images with intensity inhomogeneity. Thirdly, by incorporating the correntropy criterion into the new energy, the proposed method can reduce the impact of noise on segmentation results. Finally, the local energy is integrated with respect to the neighborhood center and a global result of image segmentation in the whole domain is obtained. Experiments show that our method is robust to different kinds of noises, and the computational efficiency is better than the existing bias field correction model.
Datafication Engineering

Datafication engineering is designing, developing, and deploying artificial intelligence (AI) and machine learning (ML) systems. It involves a range of tasks, including data collection and preparation, model selection, deployment, hyperparameter tuning, model training and evaluation, and deployment and monitoring of the models.
Sentiment Analysis

Sentiment analysis is a process used in natural language processing (NLP) to analyze and extract information related to individual opinions and emotions and determine the information’s polarity, such as positive, negative, or neutral. The analysis process involves several steps: data collection, pre-processing, feature extraction, classification, and evaluation.
Spectral Graph Filtering for Noisy Signals Using the Kalman filter

Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.
Terrorism Financing, the United Kingdom, and the Financial Action Task Force: A Series of Omissions or Missed Opportunities?

In its 2018 and Fourth Mutual Evaluation Report of the United Kingdom’s (UK) compliance with its Recommendations, the Financial Action Task Force (FATF) rated the UK’s laundering (AML) and counter-terrorism financing (CTF) regime as the best in world. In order to achieve this rating, the UK has adopted an aggressive and, at times, holistic strategy towards tackling money laundering and terrorism financing. For the purpose of this paper, the UK’s response was identified by reviewing related legislation and policy developments since the UK’s 2007 Mutual Evaluation Review. The legislative measures included the introduction of a comprehensive AML legislative framework that exceeds the international standards. The UK, unlike many other nation states, has an established approach towards implementing a robust CTF regime, which is identified by the FATF as an example of international best practice. HM Government (HMG) stated, ‘the findings of the Mutual Evaluation Report (MER) showed that the UK has the strongest overall … CTF regime of over 60 countries assessed’. However, the rating has been questioned by the Royal United Services Institute (RUSI) who stated that ‘the UK remains central to global money laundering schemes … [thus it] brings into question the relevance of this evaluation’. HMG dismissed these criticisms and referred to the findings of the 2018 Mutual Evaluation Report, which does not consider, among others, the ramifications arising out of the length of time to report and review. The use of Distributed Ledger Technology (DLT) has the potential of overseeing financial transactions for AML/CTF compliance purposes, which by alleviating the need for manual oversight would limit costs and time owing to the innate qualities of the ledger automatically updating its records, thereby facilitating organisations in meeting their regulatory obligations more efficiently as well as working towards a more sustainable regulatory model. The use of technology overall can be beneficial for gathering intelligence through open sources, including social media as evidenced by the work carried out by the Centre for Strategic Counterterrorism Communications in the USA. This chapter presents evidence that there are inherent weaknesses within the UK’s CTF strategy, related legislation and challenges the findings of the UK’s Fourth MER and considers the ways in which technology can be used to tackle terrorism financing.
A Security Enhanced Image Encryption and Compression Using Residue Number System and Discrete Cosine Transform

In today’s world, to transfer information, internet is the main media. This information may be text, audio, image, graphics, video, etc. There is more insecurity among users of internet against unauthorized eavesdroppers. While sending information like image and video, bandwidth is also the main constraint in any channel of communication. To introduce the security, and high channel capacity, a method is proposed which uses cryptography with compression. The need for compressing these images is to save the bandwidth and storage purposes. This paper proposes a high speed and secured encryption method to transfer any image data over a network. This is done by using Reside Number System (RNS) for encryption. Since the length of the data increases during encryption process of RNS, the compression method of Discrete Cosine Transform (DCT) algorithm is implemented. This process of encryption and compression combined together will yield many advantages in security as well as bandwidth for the communication.
A Critical Review on the Policy Discourse of Youth Volunteering

This paper address volunteering of youth as an important socio-cultural trend which have been generally taken for granted and not interrogated. The volunteering had been regarded as a charity which came from altruism and intimacy in community. But in the neoliberal post-welfare society, the policy discourse of youth volunteering have been changed by the state. Now, Young people have to participate voluntary activity in order to enter university or to get a job in intense competition. The field and scope of volunteering of youth have been expanded and the policy of volunteering have influenced on the life of young people. To understand the changing of volunteering policy discourse, researcher have investigated the historical and sociological contexts of policy discourses in the case of USA, British, China and Korea and have tried to interpretate it’s meanings. The main points that have constructed the policy discourses of youth volunteering, ‘service learning’, ‘citizenship’, and ‘service work’ have been discussed. Lastly, it has been discussed the possibility of alternative perspectives to learning to become active citizenship through youth volunteering.
Research on the Construction of Knowledge Graph of Traditional Chinese Pattern Literature

This paper aims to show the development and application of pattern design application research in the past 30 years, and enable designers to clearly identify and understand the research hotspots and new trends in the field of pattern design applications.VOSviewer was used to systematically analyze 2313 pattern design studies published in CNKI core journals from 1992 to 2021 in terms of publication volume, annual output, authors, research institutions and keyword maps.Coupled analysis methods and multiscale dissimilarity matrices show collaboration between authors and the distribution of research topics.The cultural inheritance of traditional patterns, shape grammar, cultural and creative design, and women's wear design will become new trends in the future; The lack of close cooperation in institutional research, the lack of many prolific authors, and the lack of obvious innovation in research methods and research theory are the main limitations of research at this stage.
Detecting and Classifying the Vehicles in Traffic Signal Using Transfer Learning

In existing methods, there are different machine learning techniques carried out for detecting and classifying the vehicles. But in real world scenario, their validation performance is not that good. Also, the vehicles are not detected accurately, and detection rate is slow in real time implementation. To overcome this issue, we used a deep learning technique to speed up the process of detecting the vehicles and classifications. Moreover, different deep learning techniques are used for the vehicle’s detection and classifications. Some of the two major techniques include one stage object detector like YOLO V4, SSD, Detect Net, Retina Net, and 2 stage object detectors like RCNN, Fast RCNN, and Faster RCNN. Among all these YOLO V4 is a recent super-fast model and is best for real time applications for object detections. In this paper, we implemented the transfer learning techniques as YOLO V4 for detecting and classifies the huge numbers of vehicles at traffic signals. Using this technique, we achieved 80.01% accuracy.
Global context-aware feature modulation networks for unified multi-scale super-resolution

Compared with training multiple single-scale models to achieve different scale image super-resolution (SR), using the internal correlation of multiple scales to aggregate multi-scale learning into a single model can effectively reduce the network parameters and improve efficiency. Correspondingly, it is important to study how to take the advantage of inter-scale correlation at a fixed model size for the performance improvement of multi-scale SR. We combine the capture of richer context dependencies and convolutional response modulation to improve the discriminative ability of the model, and then propose a global context-aware feature modulation network, which has a stronger representational capability for multi-scale SR. Specifically, we first model the global context based on the self-attention mechanism, which can effectively extract rich global information from convolutional features. Then, we propose to modulate the feature responses using global contextual dependencies in spatial and channel dimensions respectively. Further, we construct the global context-aware feature modulation block, which can be stacked to form a deep feature modulation network and applied to the unified multi-scale SR architecture to learn inter-scale correlation. Extensive experiments demonstrate that the proposed global context-aware modulation is effective, which can improve the performance of multi-scale SR task both quantitatively and qualitatively.
Market Аnalysis of Secondary Vocational Education Organizations in the Kirov Region

This article analyzes the market of educational organizations of secondary vocational education of the Kirov region. Currently, secondary vocational education is being systematically modernized. After the adoption of the Law on education in 2013, which establishes the legal, organizational and economic foundations of education in the country, the general rules for the functioning of the education system and the implementation of educational activities, the system of secondary vocational education is undergoing structural and strategic changes. At the moment, the situation has developed in such a way that educational organizations exist in conditions of market competition, which has its own specifics. The purpose of the study is to conduct an analysis of the market of the organizations of the Kirov region, to determine the level of competition and the occupied market share of each organizations. In the course of the study, statistics were collected on the number of full-time students of vocational education organizations in the Kirov region, the level of competition in the educational services market, market dynamics, analysis of the competitive environment were calculated, organizations were divided by sectors and groups of competitors were identified by their market share. The specifics of the market of organizations in the region are revealed, conclusions are drawn for each group of competitors by the occupied market share. The results of the study can be used to model relations in the educational services market and for further research in this area during the consideration of the organizations of the Volga Federal District.
CNDAS-WF: Cloud Native Data Analysis System Based On Workflow Engine

With the development of modern big data technology, data size in daily life is expanding rapidly and data relationship is more complex. However, the requirements of data analysis for different resources continuous to surging. Therefore, how to handle a large number of data analysis tasks with complex dependencies efficiently become the challenge. In this paper, we design and implement a cloud native data analysis system based on workflow engine. The system arranges the data analysis tasks, which deployed by containers, with dependency through the workflow engine based on cloud native technology. Flexibility of container cloud makes data analysis procedure effective and efficient. In addition, we designed a workflow engine and an operation and maintenance subsystem for overall system platform anomaly detection. Finally, we verify the effectiveness and efficiency of the system through scientific workflow data. The cloud native data analysis system based on workflow engine has passed all tests and has been applied in small and medium-sized enterprises.