Reviewerly/exHarmony huggingface dataset:
Construction and Application of Collaborative Editing System for the Nuclear Power Large Comprehensive Report

Abstract The nuclear power large comprehensive reports need a lot of editing work and participants, the process is opaque, the format is not standardized and there are no effective collaborative tools. Because of overall process improving requirements, report review needs of format verification, optimization process, centralized storage of documents, and specification of document content, our company is in urgent need of new technical means. Collaborative editing is a new technology that is fundamental and critical in multidimensional collaboration of organizations, tasks, resources, processes, and format specifications. This thesis is based on the promotion of collaborative editing of large comprehensive reports, in-depth study of related technologies including the timeliness of collaborative editing, unified document format standards, unified and effective process control, unified document storage and version management from tools, processes and platforms, and proposes a construction plan to solve these existing problems, and finally provides a collaborative editing platform for each major. Construction and application of collaborative editing system for the nuclear power large comprehensive report realizes workflow and standardization, greatly improves editing efficiency and quality of large comprehensive reports, reduces manpower input, human errors, saves management costs of various professions and lately helps HPR1000 successfully pass qualification verification.
Performance of Secure Data Deduplication Framework in Cloud Services

Data Deduplication makes it convenient to utilize the available disk space efficiently and proves as a solution for preventing data redundancy. Deduplication process allows only a unique set of data to be stored in the storage and all other copies are pointed towards connecting pointers that point towards unique data. In this work we propose a novel deduplication technique that focuses on preserving client privacy. The suggested method uses an effective deduplication algorithm which splits the input file into small units which get encrypted by the client using a secure hash function along with a chunk algorithm. In this technique the user constructs one index tree with hash values of the small units and encrypts them using asymmetric search encryption technique. The index tree assists the CSP in searching index structure and also restores the requested units. According to the suggested method, it enables the CSP to run deduplication without requiring the client's plain texts or decryption key.
Las TIC en la enseñanza de la química: Una revisión sistemática

The objective is to analyze the teaching -in basic education- of chemistry through the use of ICT, for which a systematic review of the scientific literature published between 2015 and 2021 in the Scielo, Redalyc, Dialnet databases was developed. and Scopus, finding 100 articles based on 04 search equations related to the keywords ICT, CHEMICAL TEACHING, BASIC EDUCATION; After applying the inclusion and exclusion criteria, 35 articles were selected. As a result, we have that the application of ICT in the teaching of chemistry favors student learning while developing digital skills in the student. It is concluded that the use of ICT has changed the different spheres of education, technifying the traditional teaching methodology for an interactive methodology.
CREATIVE CONTESTS AT THE ALL-RUSSIAN OLYMPIAD OF SCHOOLCHILDREN IN ENGLISH

Introduction. Subject Olympiads for schoolchildren are intellectual contests of increased complexity, participation in which allows both selecting the best minds for studying at a higher school, and developing creative abilities in all schoolchildren participating in competitive trials. Objective. The study aims to identify the features of creative Olympiad tasks and propose their categorization. Materials and methods. The methods employed in the study include the method of content analysis of normative documents of the Central Subject-Methodological Commission, including tasks of previous years, and analysis of our empirical experience of preparing schoolchildren for the Olympiad from the perspective of a coach of the Moscow city team. Results of the study. The research has considered the specific features of various contests held at the English Olympiad, and indicated the typical difficulties that Olympiad participants encounter due to the creative nature of the tasks. Conclusions. Being a national educational project, the All-Russian Olympiad of schoolchildren updates the eff orts of schools, and public and special additional education centers aimed at developing giftedness and supporting talents.
Cost-Aware Calibration of Classifiers

Most classification techniques in machine learning are able to produce probability predictions in addition to class predictions. However, these predicted probabilities are often not well-calibrated, in that they deviate from the actual outcome rates (i.e., the proportion of data instances that actually belong to a certain class). A lack of calibration can jeopardize downstream decision tasks that rely on accurate probability predictions. Although several post-hoc calibration methods have been proposed, they generally do not consider the potentially asymmetric costs associated with over- vs. under-prediction. In this research note, we formally define the problem of cost-aware calibration and propose a metric to quantify the cost of miscalibration for a given classifier. Next, we propose three approaches to achieve cost-aware calibration, two of which are cost-aware adaptations of existing calibration algorithms and the third one (named MetaCal) is a Bayes optimal learning algorithm inspired by prior work on cost-aware classification. We carry out systematic empirical evaluations of the proposed approaches and find that MetaCal is able to consistently and significantly outperform alternative approaches on multiple public datasets. Finally, we generalize the definition, metric, as well as solution algorithms of cost-aware calibration to account for nonlinear cost structures that may arise in real-world decision tasks.
Arithmetic

We learned in Chapter 7 that variables can be “converted” from one numeric data type to another, which is referred to as casting, or from a string value to a numeric value, which is called parsing. Parsing uses methods from a wrapper class to convert the string data to a numeric data type value. These are important concepts and widely used in all programming languages by professional developers. As we develop our C# skills throughout the chapters, we will use these concepts, so it is worthwhile constantly reminding ourselves of the differences between casting and parsing and how they are used within C# code.
The SAT-Based Automatic Searching and Experimental Verification for Differential Characteristics with Application to Midori64

In this paper, we show that it is inaccurate to apply the hypothesis of independent round keys to search for differential characteristics of a block cipher with a simple key schedule. Therefore, the derived differential characteristics may be valid. We develop a SAT-based algorithm to verify the validity of differential characteristics. Furthermore, we take the key schedule into account and thus put forward an algorithm to directly find the valid differential characteristics. All experiments are performed on Midori64 and we find some interesting results.
Global Ranking of Losses to Photovoltaic Power

Solar power is growing quickly and especially helpful in achieving decarbonization goals. With more installed solar generation capacity, understanding losses becomes increasingly important for optimizing solar development and planning. This paper will attempt to quantify and attribute solar losses globally, focusing on soiling, snow, and temperature as individual losses and how they relate to each other. This will be done by comparing simulated solar power output under a variety of different scenarios with and without the effects of soiling.
Remote Sensing Warship Detection Method Based on Improved Mask R-CNN

Aiming at the phenomenon that the remote sensing warship target has a large aspect ratio and an arbitrary placement angle, which causes the detection frame to contain redundant information and occlude each other, this paper was proposed an improved Mask R-CNN rotating frame detection algorithm. Firstly, a multi-angle rotation factor suitable for warship detection is added to the region proposal network and the IOU calculation is fine-tuned so that the warship detection results in a rotation frame that fits the actual target more closely. Secondly, due to the complex imaging background of remote sensing images and the difficulty of detection, the depth-separation deformable convolution is used to replace the standard convolution and the feature pyramid structure is optimized. Through a series of experiments, it is shown that the remote sensing warship detection model proposed by the algorithm in this paper achieves a good balance between detection accuracy and detection speed, and has a high practicality.
Robot competition management method considering infectious disease countermeasures in WRS2020

Robot competitions are expected to promote innovation of robot development and its utilization, and now in the "with-corona" era, the competition management should consider infection control measures against COVID-19. In this paper, a case study on the Word Robot Summit Disaster Robotics Category is reported, where field layout and additional devices, e.g. virus shield, are examined.
Adaptive Reasoning: An Affect Related Feedback Approach for Enhanced E-Learning

Recognition of affective states to enhance e-learning platforms has been a topic of machine learning research. Compared to other input modalities, facial expressions have the potential to reveal nonverbal cues about a learner’s learning affect. However, most studies were limited in their analysis of learning affects exhibited by a learner with the possibility of providing appropriate feedback to teachers and learners. This work proposes an adaptive reasoning mechanism that considers the estimated affective states and learning affect in generating feedback with reasoning incorporated. This work utilizes a Convolutional Neural Network- Bidirectional Long-Short Term Memory (CNN-BiLSTM) cascade framework for affective states analysis through processing a live/stored observation of a learner in the form of a temporal signal. Using the proposed ensemble, four affective states were estimated, namely boredom, confusion, frustration, and engagement. Dataset for Affective States in E-Environment (DAiSEE) was used to train, validate, and test the baseline model, which reported an accuracy of 86% on 4305 test samples. In the next stage, mappings between estimated affective states and learning affects (i.e. positive, negative and neutral) were established based on an adaptive mapping mechanism, to consolidate the mapping between affective states and learning affects. Live testing and survey feedback were then used to further validate, adapt and amend the feedback process. Incorporating and interpreting the estimated affective states and learning affect is imperative in providing information to both teachers and learners, and hence potentially improve the existing e-learning platforms.
Empirical analysis of online learning behavior during the post-COVID-19 era

Due to the COVID-19 pandemic, schools all over the world were closed and thereby students had to be instructed from distance. Consequently, the use of online learning environments for online distance learning increased massively. In this study, descriptive analysis and correlation analysis were carried out according to the learning data of online learning of Altitude Training course for ethnic traditional sports major and sports training major in the School of Physical Education of Yunnan Normal University. From this, the influence of online learning behavior investment is obtained.
Live Support by Chatbots with Artificial Intelligence: A Future Research Agenda

This research uses a Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol to investigate the utility of artificially intelligent (AI) conversational chatbots in service business settings. The findings shed light on key theoretical underpinnings focused on human-computer interactions and clarify the benefits and costs of using responsive chatbot technologies. This contribution implies that, for the time being, works are still in progress for interactive, anthropomorphic chatbots to mimic human customer services agents' verbal, vocal and visual cues, when they respond to online queries. In conclusion it puts forward plausible research avenues in this promising area of study.
Research on the influence of artificial intelligence customer service on consumers' purchase process under the background of Internet

This paper aims to understand the inner state of users using intelligent customer service and their psychological expectations for intelligent customer service under the era boom of the development of mobile Internet and big data, and analyze the purchase process of consumers in three stages: before purchase, during purchase and after purchase. The purpose is to promote the "personification" process of intelligent customer service in the field of online shopping, Provide consumers with a better service experience for intelligent customer service during shopping. Through questionnaire survey, statistical analysis and other methods, this paper analyzes the online shopping consumers' attitude towards intelligent customer service, the impact of intelligent customer service on consumers' different purchase stages, and explores the influencing factors to improve consumers' shopping experience from the perspective of intelligent customer service. The results show that before purchase, consumers pay more attention to the information provided by intelligent customer service, pay more attention to the personalized recommendation of intelligent customer service, and pay more attention to the understanding and solution of intelligent customer service after purchase. Finally, according to the findings, this study puts forward targeted suggestions to the platform, merchants and users.
Deep Learning Based Application in Identifying Originality of the Hand Written Document using Convolution Neural Network

Handwriting recognition is one of the foremost areas of exploration in pattern identification and recognition. as of now we have a system recognizes that converts the handwritten character into the printed text of many languages like English, Kannada, Tamil, Bengali, Latin, Devanagari, etc., and also the system which converts handwritten, printed copy, image or other documents into digitized format. The objective of the research is to identify the originality of handwritten documents using deep learning methods. During pandemics much of the offline work is shifted to online work, some of them are education, banking, etc. The originality of handwritten copies is checked for fraud copies to genuine verification from the original owner copies. The research includes a collection of sample written documents of around 1000 characters which consist of all the possible characters and numbers called training data, later it is cross verified by the testing data with a new input document. The framework includes proposing a CNN model and feature extraction using neural networks and proves the originality of the written copy against the test written copy. The steps involved are pre-processing, followed by segmentation, in turn, feature extraction, recognition, and comparison of each word, stroke, height, and slant of the alphabet to verify with test input. The data set will be pre-processed, CNN model extracts the features of each character, and generates a threshold value, which is compared with the testing data threshold values, if the result returned is more than 90% document will be considered to be accepted as original handwritten of the individual and if the threshold value comparison fails and less than 90% matching, the script/document is rej ected, it is categorized to fraud document.
Changed Tendencies in Research Issues on Motivation for Foreign Languages Learning (Review for 2021)

The COVID-19 pandemic has changed social and educational environment, which affected the educational process participants and the research directions. The article shows the changes in the direction of studies dealing with the issues of university students` foreign languages learning motivation, published in 2021. Researching is still aimed at studying the online environment and its potential. Its high potential to develop the motivation is stated, but the lack of participants` experience of its using prevents its full acceptance so far. The problems of maintaining personal interaction between a teacher and students; overcoming stress and anxiety; adaptation to the virtual learning environment and their impact on the motivation are of research interest. Another part of the research works is traditionally devoted to the components of educational motivation, strategies for their managing, etc. The results of the analysis will help to understand how well the problem has been studied and determine the direction of future research.
Vehicle Detection and Counting Framework in Aerial Images Based on SoC-FPGA

The use of smart cameras on highways has increased to monitor traffic variables such as density, speed, and flow of vehicles and to prevent car accidents. Many current systems can become expensive due to the high demand for resources since large amounts of data must be processed. Other systems only store the video and perform the processing offline. In this work, it is presented the development of a system that performs the processing using a heterogeneous architecture based on FPGA to achieve a real-time implementation due to hardware acceleration and low power consumption. The implementation of the model obtained with SSD MobilenetV2 in the Ultra96V2 achieved a rate of 23 FPS only in detection and 18 FPS adding vehicle counting, tracking, and display on a monitor.
Trans-GAN network for image super-resolution reconstruction

In order to solve the problems of network training difficulties and artifacts in the generated image super-resolution reconstruction algorithm based on convolutional neural network, this paper proposes a Trans-GAN super-resolution reconstruction algorithm based on generative adversarial network. By introducing Transformer model, the algorithm proposed a new Trans-GAN network, and improved residual module, using VGG19 network as the discriminator model framework, using adaptive pooling to replace the full connection layer to prevent overfitting, introducing per-pixel mean square error loss, perceived loss, The antagonism loss and total variation loss constitute the total objective loss function of the generator to solve the problem of insufficient feature extraction ability in the super-resolution algorithm based on convolutional neural network. Experimental results show that the proposed network can obtain better detail texture and image quality, and the PSNR of 4-fold image hyperfraction reconstruction is improved by 0.57 on average on five public datasets of Set5, Set14, BSD100, Urban100 and DIV2K.
Analysis of Sales and Cash Receipt Accounting Information Systems To Improve Internal Control Over Revenue At PT. Indomarco Prismatama Sidoarjo Branch Office

The accounting system for sales and cash receipts operated at PT Indomarco Prismatama is used to improve internal control over revenue at PT Indomarco Prismatama in Sidoarjo branch offices. This accounting system is carried out to minimize errors and fraud in the process of selling and receiving cash, because there are executors in the field who deliberately cheat in carrying out their duties, but there are also executors who make mistakes but they do not intentionally or have no intention. The error is due to a procedure that was not executed. Therefore, the existence of an accounting system will be able to support the performance of employees in carrying out their duties properly.
On Representing the Degree Sequences of Sublogarithmic-Degree Wheeler Graphs

We show how to store a searchable partial-sums data structure with constant query time for a static sequence S of n positive integers in $$o \left( \frac{\log n}{(\log \log n)^2} \right) $$ , in $$n H_k (S) + o (n)$$ bits for $$k \in o \left( \frac{\log n}{(\log \log n)^2} \right) $$ . It follows that if a Wheeler graph on n vertices has maximum degree in $$o \left( \frac{\log n}{(\log \log n)^2} \right) $$ , then we can store its in- and out-degree sequences $$D_\textsf{in}$$ and $$D_\textsf{out}$$ in $$n H_k (D_\textsf{in}) + o (n)$$ and $$n H_k (D_\textsf{out}) + o (n)$$ bits, for $$k \in o \left( \frac{\log n}{(\log \log n)^2} \right) $$ , such that querying them for pattern matching in the graph takes constant time.
Application

An Android app consists of components like activities, services, broadcast receivers, and content providers. See Figure 2-1. Activities are for interacting with device users, services are for program parts that run without a dedicated user interface, broadcast receivers listen for standardized messages from other apps and components, and content providers allow other apps and components to access a certain amount and kind of data provided by a component.
DEVELOPING A TWO-AXIS HYBRID SYSTEM FOR TRACKING THE MOVEMENT OF THE SUN

A hybrid system for tracking the Sun movement is developed which selects the an¬g¬le of inclination of the system at any time of the day, at which the largest amount of ene¬r¬gy is obtained. An algorithm for the operation of the hybrid system is developed, the pri¬n¬c¬i¬p¬le sche¬m¬e of the control system is constructed.
Development of a Remote-Controlled Wheeled Mobile Robot System with a Mechanical Safety Brake by Using Eye Movements

Recently, the demand for remote-controlled robots is increasing due to Covid-19, etc. In remote-controlled robots, safety is very important. We have developed a velocity-based mechanical safety brake. In this paper, we introduce a remote-controlled robot system with the safety brake by using only eye movements. Since the robot is controlled by eye movements, many people such as amyotrophic lateral sclerosis (ALS) patients can use this robot system. If the safety brake detects an unexpected driveshaft’s angular velocity, it switches all of the robot’s motors off, and then stop the driveshaft by gradually reducing the driveshaft’s velocity. The safety brake is composed of only passive mechanical components such as springs. First, we describe the remote-controlled robot system. Second, we explain the safety brake. Finally, we present experimental results to verify the effectiveness of the safety brake in the remote-controlled robot system.
Training system for collaborative tasks with avatars using transmissive HMD and vibrating glove

To perform a certain task smoothly, it is necessary for them to become proficient of a task through the training and practice. If training can be executed in a virtual space, it will be possible to train anytime and anywhere. In the training of tasks, avatars in the virtual space have been used. In the case of the virtual space, it is expected to promptly improve proficiency, reduce the cost and psychological burden comparing conventional physical training. However, the virtual collaborative tasks have not been studied, yet. In the training of collaborative tasks, the use of avatars in the virtual space are needed. It will be possible to train the work even by one operatorif avatars join the training in the case of collaborative tasks. In this paper, a collaborative task between a physical operator and an avatar in the virtual space using HMD is proposed. A physical operator wears a VR glove in MR, and the effectiveness of repeated practice training is evaluated.