Reviewerly/exHarmony huggingface dataset:
Financial ticket intelligent recognition system based on deep learning

Facing the rapid growth in the issuance of financial tickets (or bills, invoices etc.), traditional manual invoice reimbursement and financial accounting system are imposing an increasing burden on financial accountants and consuming excessive manpower. To solve this problem, we proposes an iterative self-learning Framework of Financial Ticket intelligent Recognition System (FFTRS), which can support the fast iterative updating and extensibility of the algorithm model, which are the fundamental requirements for a practical financial accounting system. In addition, we designed a simple yet efficient Financial Ticket Faster Detection network (FTFDNet) and an intelligent data warehouse of financial ticket are designed to strengthen its efficiency and performance. At present, the system can recognize 194 kinds of financial tickets and has an automatic iterative optimization mechanism, which means, with the increase of application time, the types of tickets supported by the system will continue to increase, and the accuracy of recognition will continue to improve. Experimental results show that the average recognition accuracy of the system is 97.07%, and the average running time for a single ticket is 175.67ms. The practical value of the system has been tested in a commercial application, which makes a beneficial attempt for the deep learning technology in financial accounting work.
Exploration and Practice of online teaching mode of operating system course

Abstract In the spring of 2020, the Ministry of Education proposed to suspend classes but keep learning and carry out online teaching. In this paper, after analyzing the advantages and disadvantages of online teaching, combined with the ongoing operation system, some ideas of operating system online teaching reform are put forward from three aspects of leading to learn, live to learn in class and after class superintendent. Course evaluation and network teaching platform evaluation pay attention to the usual inspection and evaluation of learning process, which makes the evaluation more reasonable and fair.
ALTO Multi-Domain Extension: Challenges, Existing Efforts and Next Steps

The emerging multi-domain applications, such as flexible interdomainrouting, distributed, federated machine learning and multi-domaincollaborative dataset transfer, can benefit substantially from gettinginformation from networks. The ALTO base protocol [RFC7285] provides anorthbound interface for applications to retrieve the networkinformation. In particular, it specifies the communication between anALTO client and an ALTO server, where the ALTO is implicitly assumedto be able to answer any query from the ALTO client. However, it doesnot specify the cases when the network information are originated frommultiple domains (i.e., administrative entities or geographicallypartitions). This document summarizes the discussion on the ALTOweekly meeting since IETF 108 on how to extend ALTO to support multi-domain applications. It identifies the key challenges for retrievingnetwork information from multiple networks, reviews the existingefforts in the work group, and discusses the next steps to fullyaddress the challenges.
ANALISIS PELAKSANAAN PEMBELAJARAN BERHITUNG DI TK ASSISI MEDAN

This study aims to know how the implementation of learning to count in TK ASSISI Medan. This research is a type of qualitative descriptive research to see the implementation of numeracy learning in TK ASSISI Medan. Retrieval The data source in this study was purposive sampling, namely 3 classes of TK B ASSISI Medan, namely TK B1, TK B3, and TK B5. Data collection techniques used were non-participant observation and documentation. Based on the results of the study showed that seen from the Zoltan P Dienes theory that the implementation of numeracy learning in TK ASSISI Medan still did not involve children in learning, games were still rarely performed and the media and teaching aids were also still underused.
Application Analysis of Computer Music Production in Composition Teaching

Abstract Computer music composition is the music created by using computer related software and matched digital sound generator and sound effect processor. It also includes the participation of computers in all activities in the music field in a broad sense. Although the development of computer music technology is not long, but the impact and scope of influence is amazing. It can be said that computer music production is everywhere in our music life. [1]
Face Detection Opencv Based ATM Security System

The Aim of this paper is to bolster security of the traditional cash dispenser machine (ATM) model. So a replacement is proposed that enhances the general expertise, usability and convenience of the group action at the ATM. ATM are wide used these days by the people however its exhausting to hold their ATM card all over. The user might forget their ATM PIN number. This paper is developed for identification and authentication of ATM users so creating face as key. Options like face recognition, image steganography and mobile application management are used for sweetening of privacy of users and security of accounts. Face recognition technology helps the machine to spot each and every user unambiguously. Image steganography is the technique used for the image of user by embedding it into an another image and keep. Its one among the ways utilized to guard the image of users from malicious attacks and hackers. The mobile application helps the particular account holders to supply permission to others to do the ATM transactions. The mobile application contains options like secret key and amount withdrawal to offer confirmation. It helps folks to access another person’s ATM account in emergency with their authentication. This utterly eliminates all the possibilities of fraud thanks to larceny and duplicity of the ATM cards. Moreover, the experiment is that the method in ATM security framework to enhance security and innovative ATM group action.
Collaborative Method for Incremental Learning on Classification and Generation

Although well-trained deep neural networks have shown remarkable performance on numerous tasks, they rapidly forget what they have learned as soon as they begin to learn with additional data with the previous data stop being provided. In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks. As one of its component, we also introduce a generative model, incGAN, which can generate images with increased variety compared with the training data. Under challenging environment of data deficiency, ICLAS incrementally trains classification and the generation networks. Since ICLAS trains both networks, our algorithm can perform multiple times of incremental class learning. The experiments on MNIST dataset demonstrate the advantages of our algorithm.
TIME TRANSFORMATION OF THE BASIC CONCEPTS OF THE COMPETENCE APPROACH IN THE DOCUMENTS OF THE EDUCATIONAL PROCESS IN PHYSICS

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Assessment of staff resources of the regional system of general education (on the example of Primorsky Krai)

It is recognized worldwide that the teaching staff is the determining factor in the quality of school education. The article uses the example of Primorsky Krai as an example to discuss a relevant for Russia problem of staff resources in the system of school education and to analyze the factors that can affect its solution. It is important to solve the personnel problems of regional education systems by relying on the experience of regions and leading countries taking into account the context. The goals of the national project „Education” cannot be achieved without solving the personnel problem in the industry. On the example of a specific region, the problem of providing schools with teaching staff was analyzed, key indicators of the quality of staff resources in the regional education system and the factors affecting it have been identified. The research used statistical data, studied the laws and other regulations, official reports (open data) and data from monitoring conducted by the authorities in accordance with the legislation on education, as well as materials from surveys of students studying under teacher education programs.
MLAS: Metric Learning on Attributed Sequences

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learning mainly focus on learning the Mahalanobis distance metric on data attributes. Recent research on metric learning has been extended to sequential data, where we only have structural information in the sequences, but no attribute is available. However, real-world applications often involve attributed sequence data (e.g., clickstreams), where each instance consists of not only a set of attributes (e.g., user session context) but also a sequence of categorical items (e.g., user actions). In this paper, we study the problem of metric learning on attributed sequences. Unlike previous work on metric learning, we now need to go beyond the Mahalanobis distance metric in the attribute feature space while also incorporating the structural information in sequences. We propose a deep learning framework, called MLAS (Metric Learning on Attributed Sequences), to learn a distance metric that effectively measures dissimilarities between attributed sequences. Empirical results on real-world datasets demonstrate that the proposed MLAS framework significantly improves the performance of metric learning compared to state-of-the-art methods on attributed sequences.
Performance analysis of one-dimensional naiïve bayes as a data imputation method for car insurance problems

Machine learning methods are very widely used in helping human work. Not all data is as we expected. Some data have missing values. Data that has a missing value must be handled first at the pre-processing stage, one of which is by the imputation of the missing value. This study is comparing the imputation method of missing value uses mode and One-Dimensional Na1ve Bayes Classifier (1DNBC) to determine the performance analysis by using Support Vector Machine (SVM) for the prediction of car insurance participation. A better method is seen from the accuracy. Based on the simulation is obtained the same results for imputation using mode and One-Dimensional Na1ve Bayes are 1.00, which when examined further turns out to be the imputation of each missing value with the mode and prediction of imputation with One-Dimensional Na1ve Bayes are the same.
FEATURES OF CREATING AN INFORMATIONAL REAL ESTATE MODEL

The article discusses some features of creating an information model of a real estate object, as well as ways to create a model and the relevance of its application.
Compliance of New Scientific Platforms with Formal Requirements for Scientific Publications

As a result of the development of the electronic environment, new actors appear in the scientific communication system, including repositories, mega-journals, and open peer review platforms, all of which operate in accordance with the open science paradigm. At the same time, the results of the work of scientists are still being assessed by traditional databases and scientometric services based mostly on the citation counts, which is often irrelevant for the new projects due to non-compliance with formal criteria of such services. This article discusses the requirements of the Web of Science, Russian Science Citation Index, Scopus, and the Higher Attestation Commission (VAK) for the sources of scientific information, and analyzes 25 projects for compliance with these requirements. It was revealed that some of the new projects do not meet the criteria due to the lack of traditional markers of the scientific publication. Based on the results obtained, a number of recommendations were formulated that will allow to adjust the requirements to reflect the new realities.
Analysis on the Operational Effectiveness of Certain Landmine Based on the AHP-ADC Combination Model of Computing

Considering the characteristics of landmine, in order to evaluate the operational effectiveness of it with advantage, the analysis on the operational effectiveness based on the combination model of computing AHP and ADC is proposed. On the basis of the characteristic of certain landmine, the evaluation system of operational effectiveness is built with AHP-ADC for calculating the weights of impact factors. AHP-ADC combination model of computing is built based on the ADC model and takes the optimal tactical model as calculating example. The result shows that the analysis on the operational effectiveness of AHP-ADC model is both logical and practical which can give an index to it actually. Besides, it is found that reliability and maintainability have a great impact on the operational effectiveness of landmine. And attention should be paid to both reliability and maintainability for the future usage, management and development planning.
Information Value of the Database “Vocational Education in Sverdlovskaya Oblast (1920s-1950s)”

The article describes the conceptual model and the structure of the database on the history of vocational education in Sverdlovskaya Oblast in the 1920s-1950s. The database systematizes and generalizes the main indicators of the development dynamics of vocational education in the region on the basis of a wide range of mass sources. These indicators are organizational, industrial, economical, socio-demographic and behavioral ones. Particular attention is paid to describe the database in terms of its ability to systematize, analyze and group information on the history of work training in the region that is the number of students, the employment of graduates, the dynamics of the educational institutions network, etc. The research novelty is the original structure of the database as an information product for multipurpose use which, first of all, provides for the effective systematization of primary information from various types of mass sources and the creation of a model of historical data on the vocational training system in Sverdlovskaya Oblast.&amp;nbsp;The authors attempt to generalize their practice to digitize, systematize, analyze and synthesize archival vocational education data and formulate the principles to create and use the local historical database within the digital humanities context: complexity, task-orientation, integration, etc.
Saharan Dust and Solar Energy Generation in Europe: Case Study of June 2019

&amp;lt;p&amp;gt;Aeolian dust, the most dominant atmospheric aerosol by mass, decreases the solar energy reaching the Earth surface by absorbing and scattering the solar radiation. This energy loss increases mainly with the dust concentration in the atmosphere, which is controlled by the emission, transport and removal of the dust particles. All these processes can vary significantly depending on the convection treatment in the model simulations, thereby affect the solar energy forecast.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study investigates the dust impacts on solar energy generation within convection-resolving simulations using the next-generation atmospheric modeling system ICON-ART (ICOsahedral Nonhydrostatic with Aerosols and Reactive Trace gases). The simulation set-up includes a global domain with 40 km horizontal resolution with three nests down to 5 km horizontal resolution over North Africa and Europe. The innermost nest resolves convection while other domains are based on parameterized convection. This set-up is used to simulate the period 22-27 June 2019, which is associated with a Saharan dust outbreak in clear sky conditions over North Africa and large parts of Europe.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Compared to the global simulation, the convection-resolving simulation leads to significantly higher dust optical depth in North Africa. This is related to the elevated coarse mode concentrations due to higher vertical velocities in the convection-resolving simulation. However, dust optical depth over Europe only slightly changes as a large portion of coarse mode particles do not reached Europe due to their large sedimentation velocities. The results are compared with AERONET, ceilometer and radiation measurements.&amp;lt;/p&amp;gt;
Precision-Recall Curve (PRC) Classification Trees

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision-recall curve (AUPRC) for variable selection in the classification context. Our algorithm, named as the "Precision-Recall Curve classification tree", or simply the "PRC classification tree" modifies two crucial stages in tree building. The first stage is to maximize the area under the precision-recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously has shown good performance in imbalanced data. The combination of them, the PRC-ROC tree, also shows great promise in identifying the minority class.
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound

We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not alter the resulting classifier. We use this technique to derive a variety of novel or improved data-dependent generalization bounds for several learning algorithms. In particular, we prove a new margin bound for SVM, removing a log factor. The new bound is provably optimal. This resolves a long-standing open question about the PAC margin bounds achievable by SVM.
Real-Time Decentralized knowledge Transfer at the Edge

The proliferation of edge networks creates islands of learning agents working on local streams of data. Transferring knowledge between these agents in real-time without exposing private data allows for collaboration to decrease learning time and increase model confidence. Incorporating knowledge from data that a local model did not see creates an ability to debias a local model or add to classification abilities on data never before seen. Transferring knowledge in a selective decentralized approach enables models to retain their local insights, allowing for local flavors of a machine learning model. This approach suits the decentralized architecture of edge networks, as a local edge node will serve a community of learning agents that will likely encounter similar data. We propose a method based on knowledge distillation for pairwise knowledge transfer pipelines from models trained on non-i.i.d. data and compare it to other popular knowledge transfer methods. Additionally, we test different scenarios of knowledge transfer network construction and show the practicality of our approach. Our experiments show knowledge transfer using our model outperforms standard methods in a real-time transfer scenario.
Data Protection of Multimedia Content for E-Learning Using Discrete Wavelet Transform

The issues of assessment of images watermarking using discrete wavelet transform in the e-learning system is considered in the article.A digital watermark is primarily hidden data that is embedded inside a digital image, video, audio, or any other digital container.If the container with the embedded watermark was stolen, then the watermark will be present in it, which will help to track the fact of illegal distribution of the protected digital document or confirm the rights of the original owner.An attacker may try to remove a watermark to illegally assign a digital document.Therefore, one of the tasks when implementing a watermark is to ensure its resistance to removal.Embedding is performed in the low-frequency domain of the wavelet spectrum.The experimental results are showing the possibility of using the considered image watermarking method for solving data protection of multimedia content problems are presented.The application of the implemented method under consideration for use in conjunction with modern compression formats is justified.
DEVELOPMENT OF TEACHING MATERIALS FOR ILLUSTRATED STORY ORIENTED PROBLEM BASED LEARNING ASSISTED ANDROID TO IMPROVE SCIENCE LEARNING OUTCOMES

This study aims to develop developing teaching materials oriented Problem Based Learning (PBL) android-assisted android to improve science learning outcomes in class IV Elementary School. The approach used is research and development (R&amp;D). This research method is mixed methods design. The subjects in this study were students in grade IV SDN 1 and 2 Manyargading, SDN 1 and 2 Bandungrejo and SDN Teluk Kulon Jepara Regency. Data collection techniques using questionnaires, observations, interviews, and documentation. PBL-oriented comics-based teaching materials supported by android media are developed in reference to the stages of development by Borg and Gall. The results showed that PBL-oriented comic teaching materials assisted by android media are feasible to be applied in learning to improve science learning outcomes. This is based on the results of the validity test, the effectiveness and practicality of teaching materials. Validity test results show that the assessment of material experts, media, language and practitioners shows good categories. In the effectiveness test, it is known that student learning outcomes have increased from an average pretest of 51.2 to 76.8 in the posttest. In extensive trials it is known that student learning outcomes of the experimental class have an average value (86.5) better than the control class (73.2). Different test results showed the value of p = 0,000 (p &lt;0.05), meaning that there were significant differences in the learning outcomes of the experimental and control class students. Thus it was said that PBL oriented android-based teaching materials were effectively used to improve science learning outcomes. In practicality test, it is known that android-based PBL-oriented teaching materials get a good response from students and teachers.
AN EXPERIMENTAL RULE-BASED PARSER FOR RUSSIAN EMPLOYING THE NLP RESOURCES OF THE ETAP SYSTEM

This paper presents a rule-based dependency parser for Russian based on bottom-up approach. Its rules are partially rewritten ETAP syntagms, organized into groups that constitute a single pipeline. We demonstrate that such an organization enhances the performance of our parser relative to the ETAP system’s and enables it to successfully process long phrases (more specifically, heavy nominal and prepositional phrases at the current experimental stage of our work).