Reviewerly/exHarmony huggingface dataset:
A Study on the Development of the Augmented Reality's Maintenance and Repairs Related Safety Manual according to the Risk Analysis of Power Equipment

Purpose: The complexity and safety of power facilities have increased very rapidly, yet efforts are insignificant in ensuring safety according to risks. Hence, the purpose of is, in order to help prevent safety accidents in advance, risk factors are listed through the risk analysis of power facilities, and to develop an augmented reality safety manual according to the improvement of risk scales to ensure the safety by conducting pre-simulation in the field and taking prompt response at the accident site. Method: By enumerating the risk factors simultaneously with the causal analysis for the failure together with the impact of the failure mode of the components forming the power generation facility system for which the design failure mode effect analysis(DFMEA) technique is applied, and by developing the augmented reality (AR) based software’s for maintenance and repairs based on the details of improvement as per the level of risk (RPN), the methodology for maintenance and repairs is presented due to the expansion of the infectious diseases in the era of contactless. Results: The development of augmented reality system(software) for maintenance implements the 2D and 3D facility exploded view and work manual, etc., which enable the actual work processes to be performed across various field environments according to emergency situations of power facilities, and the big data based field data, and through acquisition, it would be possible to establish a precision standard maintenance platform. Fur-thermore, it is expected to have the effect of reducing the costs for accident prevention and maintenance work, and increasing the quality and quantity. Conclusion: Through this study, the safety of electric power facilities and a preventive method in emergency situations are presented. The software development through augmented reality(AR) through the Design Failure Mode Effect Analysis(DFMEA) can help prevent accidents and implement condition based maintenance plans in an integrated system, and hence, a high efficiency can be expected. It is considered that accuracy, reliability, and safety improvement are required in future papers through the addition of risk points and continuous develop-ment of diagnostic algorithm libraries.
Book Review

The aim of this discussion article is twofold. First, we critically review Jong-Bok Kim’s (2013) proposal [Floated numeral classifiers in Korean: A non-derivational, functional account. Lingua 133, 189–212], articulating the strengths and weaknesses of his approach. Second, after identifying some crucial weaknesses of Kim’s proposal, we offer an alternative analysis of floated quantifiers (FQs), while maintaining the same goal set by Kim (2013). In doing so, we point out several challenges Kim’s analysis faces, primarily the adoption of the theme-rheme partition. We demonstrate that the theme-rheme division is orthogonal to the focus property FQs exhibit. Ultimately, we show that our criticism strengthens Kim’s analysis, as opposed to contradicting it.
An Analysis of the Effects of University Freshmen's Extracurricular Programs for the Public Value

Purpose: The purpose of this study is to provide the basic data for the operation of the extracurricular program by examining whether it is effective for university life adjustment and student competency improvement accord-ing to the motivation to participate in the extracurricular program and the participation in the extracurricular program. Method: As for the data collection, a survey was conducted with freshmen in the natural science department of O University O located in in the metropolitan area. 1. To analyze as to whether the motivation for participation in the extracurricular program has an effect on the university life adjustment, the validity of the motivation to participate and the university life adaptation fac-tors was verified, then the inter-individual effect was verified. 2. Group statistics were analyzed to analyze as to whether the participation in the extracurricular program has an effect on the university freshmen's adaptation to the university life. 3. To verify the effect of participation in the extracurricular program and the student competency enhance-ment, the O University’s 5L Core Competency Diagnostic Tool(2021) was used, and the competency evaluation scores for the 4 competencies except for L3 were compared on an ex ante and ex post basis. Results: The motivation to participate in the extracurricular program for Research Question 1 was verified to have an effect on the university freshmen's adaptation to the university life, and consequently, it was apparent that motivation is more important before publicity or program improvement for the participation in extracurric-ular program. As a result of analysis as to whether to participate in the extracurricular program for Research Question 2 and the university life adjustment of freshmen, the participation in the extracurricular program was effective for all questions about university life adaptation. As a result of analyzing the effect of the extracurricular program for Research Question 3, it was apparent that the extracurricular program related to the auditory per-sonality competency L1, future learning competency L2, and job-based competency L3 was effective in enhancing the student competency, and it is considered that the extracurricular program related to the competency L5 needs to be improved for quality control to strengthen the student competency. Conclusion: This study was conducted with freshmen in the natural science department, and hence, there are limitations to generalizing to all university students. In the future studies, if factors such as segmentation by category and program learning efficacy are considered together, it will be possible to present a more specific way to activate the extracurricular program.
Influence of Competitive Activity on the Development of Self-Realization Among School Students: in Case of Kazakhstan

The article examines the problems of social self-realization of modern school students of Kazakhstan in competitive activity.Based on the conducted scientific studies, the authors determine the important role of competitions in the process of school students' socialization.The article presents the most important criteria of adolescents' level of self-realization in competition activity such as selfesteem adequacy, creativity, self-organization, teamwork, and stress resistance, as well as the results of studying the level of development of such paired personality traits as sociability/insularity and friendliness/aggressiveness developing in school students in competitive activities.The authors emphasize the high degree of risk of any contests as the participants experience worry, fear, and stress.In this research paper, it is formulated that when overcoming the fear of competition, is important for a child to freely express his emotions.The article presents a comparative analysis of studies of the level of socialization in school students conducted in the years 2003, 2013, 2019, and 2021.Authors substantiate that competition is a relevant form of developing social personality traits in school teenagers in modern society.It is necessary to create certain pedagogical conditions when preparing and holding a competition and ensure pedagogical assistance and support to minimize the possible risks.It is important for modern pedagogics to study and account for childhood and adolescent fear, as well as psychological and pedagogical risks of carrying out competitions for school students.The purpose of the research work is a comparative assessment of the current state and trends in the development of competitive games in the secondary education system of Kazakhstan based on the world example.
Teaching Reading Using CIRC Method

This research is aimed at describing the use of the CIRC (Cooperative Integrated Reading Comprehension) method to teach reading and finding the strengths and weaknesses of CIRC to teach reading to young learners. This research is applied in qualitative research with a descriptive analysis model. The data collecting is done by observation, interview, and documentation. In analyzing data, it is done by data reduction, data display, and conclusion drawing and verification. The result of this research can be concluded as follows the Implementation of CIRC Method to teach reading are: (1) partner reading, (2) story grammar and related writing (3) word out loud, (4) word meaning, (5) story retell, and (6) spelling. The advantages of CIRC method in teaching reading are first, the students are easy to understand the material. Second, the students have the motivation to learn to read more. Third, the situation of the class is fun. Fourth, students pay more attention and are more focused on learning. Meanwhile, the disadvantage of CIRC method in teaching reading is sometimes the students are confused by the teacher’s explanation and instruction
Demonstration of Einstein–Podolsky–Rosen Steering with Multiple Observers via Sequential Measurements

Quantum correlation is a fundamental property that distinguishes quantum systems from classical ones, and is also a fragile resource under projective measurements. Recently, it has been shown that a subsystem in entangled pairs can share nonlocalities with multiple observers in sequence. Here we present a new steering scenario where both subsystems are accessible by multiple observers. Simulation results show that the two qubits in a singlet state can be simultaneously steered separately by two sequential observers.
Research of autoencoder-based user biometric verification with motion patterns

In the current research, we continue our previous study regarding motion-based user biometric verification, which consumes sensory data. Sensory-based verification systems empower the continuous authentication narrative – as physiological biometric methods mainly based on photo or video input meet a lot of difficulties in implementation. The research aims to analyze how various components of sensor data from an accelerometer affect and contribute to defining the process of unique person motion patterns and understanding how it may express the human behavioral patterns with different activity types. The study used the recurrent long-short-term-memory autoencoder as a baseline model. The choice of model was based on our previous research. The research results have shown that various data components contribute differently to the verification process depending on the type of activity. However, we conclude that a single sensor data source may not be enough for a robust authentication system. The multimodal authentication system should be proposed to utilize and aggregate the input streams from multiple sensors as further research.
[Development of Intelligent Multifunctional Knee Joint Functional Exercise Equipment].

Develop an intelligent equipment that can perform multiple modes of functional exercise on the knee joint, and can accurately measure and control strength, angle, angular velocity, time, frequency and so on.Using geared motors, magnetic powder brakes and synchronous belt wheel sets and other mechanical structure and transmission technology combined with sophisticated mechanical design methods, the knee joint multiple functional exercise methods are cleverly integrated; a highly reliable PLC is used as the control core; the resistance torque out-put by the equipment is accurately controlled by adjusting the size of the exciting current; the angle sensor is used to accurately measure the rotation angle around the axis; and the error of each parameter index and the actual measured value is controlled within 5%.The developed prototype has a compact structure and a simple and convenient method of use. Based on the control of joint parameters such as resistance, the expected goal is achieved.The equipment can carry out diversified, accurate and automatic rehabilitation treatment for knee joint diseases, and has certain social and economic benefits.
Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data

With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user’s experience, and creates economic value. Since it is difficult to explain the implicit features generated by matrix factorization, explainable recommendation system has attracted more and more attention recently. In this paper, we propose an explainable fast recommendation model by combining fine-grained sentiment in review data (FSER, (Fast) Fine-grained Sentiment for Explainable Recommendation). We innovatively construct user-rating matrix, user-aspect sentiment matrix, and item aspect-descriptive word frequency matrix from the review-based data. And the three matrices are reconstructed by matrix factorization method. The reconstructed results of user-aspect sentiment matrix and item aspect-descriptive word frequency matrix can provide explanation for the final recommendation results. Experiments in the Yelp and Public Comment datasets demonstrate that, compared with several classical models, the proposed FSER model is in the optimal recommendation accuracy range and has lower sparseness and higher training efficiency than tensor models or neural network models; furthermore, it can generate explanatory texts and diagrams that have high interpretation quality.
Properties of entanglement in the cluster of N-qubits

In this study, we analyzed the properties of entanglement in the ground state of an [Formula: see text]-qubit cluster. For the first time, we discussed the previously overlooked relation between spin squeezing and concurrence for a multi-qubit system, and critically explained the special value of parameter [Formula: see text] at which the concurrence becomes 0. Additionally, this work also elaborates on the relation between decay probability and concurrence for the multiple numbers of qubits. Results showed that when the number of qubits is high, the decay is sharp as compared to the small number of qubits, which indicates that entanglement dies faster at a higher number of qubits.
FAKE NEWS AND FAKE PROFILE DETECTION

Traditionally we got our day-to-day news from newspapers and trusted mainstream media channels that are required to follow strict codes of practice.Nowadays, online news has become a great source of information for netizens.People's news consumption habits have been modified as a result of the rapid growth and popularity of social media.The internet has introduced a new way of publishing and consuming news articles having very few editorial standards.However, most of this news are specious and deceptive that includes clickbait, propaganda, satire, and misleading headlines.Many news articles surfacing on social media are hoaxes and lookalike actual news, making it difficult for the netizens to distinguish between the two.Fake profiles are the major sources for disseminating such rigged news on social media.Finding these profiles has grown to be a crucial study area that has lately received a lot of attention.Detecting these fake news and fake profiles fast and effectively, an automated tool has become an essential requirement.In our research work, we have used 5 distinct machine learning models on 3 distinct datasets to diversify the results and then selected the best model with the highest accuracy for detecting fake articles and profiles disseminating such rigged news.We have also used TF-IDF for the tokenization of news articles.Distinct ML algorithms specifically Naive Bayes, Decision tree, Passive-aggressive, Support Vector Machine, and Logistic Regression are trained multiple times to achieve maximum accuracy for all the utilized datasets.The accuracy of all machine learning models is compared to find out which one is the most accurate and suitable for an assigned task.Then we put forward a novel model to check the authenticity of news articles and profiles using the same platform.Achieving better accuracy, we recommend our proposed model for verifying the legitimacy of articles and social media handles.
Line Segmentation of Tibetan Ancient Books Based on A* Algorithm

Line segmentation is an important step in image character recognition. However, due to the problems of interline adhesion, overlapping, and skewing of document images, the effect of text line segmentation is not ideal. Therefore, further research on image line segmentation of Tibetan ancient books is urgently needed. The standard A* algorithm is not ideal for line segmentation of Tibetan ancient books, so this paper proposes a block-based A* algorithm for line segmentation of Tibetan ancient books. The method firstly preprocesses the image such as binarization and tilt correction and then performs horizontal projection. The obtained horizontal projection histogram is subjected to smoothing and peak detection to determine the position and number of text lines. Use the peak value to find the core area of the image text, extract the upper vowel and perform line attribution. Then, the image after removing the upper vowel is divided into 7 blocks, and each block is processed separately by the A* algorithm. When the algorithm finds the text line segmentation path, five cost functions are selected to calculate the cost from the starting point to the endpoint of the line segmentation, and the minimum cost path is found for text line segmentation. Finally, merge the segmented text lines, attribute the upper vowels, and rotate the text lines to obtain the final text line segmentation result. Experimental results show that our methos can effectively deal with the problems of text line adhesion and overlap, and achieve a good effect on text line segmentation.
Adversarial example generation using object detection

Deep neural networks perform well in many areas, but research has shown that they are vulnerable to adversarial example attacks. Therefore, adversarial attacks can be an important method to evaluate the robustness of the model before deep neural networks are deployed. However, the attack success rate of adversarial example in the black-box case still needs to be improved. There are numerous algorithms for attacking against neural networks, but most of the attack algorithms are slow, so fast generation of adversarial examples is gradually becoming a research focus in the field of adversarial examples. To address this situation, an adversarial example generation algorithm based on target detection is proposed. First, starting from the black-box scenario, target detection is performed on the original input image in the adversarial example generation, and constraints are added; then, through the improved differential evolution algorithm, the interference degree of each individual in the population to the image is calculated, and the individual with the best attack effect is continuously selected for iteration; finally, the adversarial example is generated based on the individual with the best attack effect, so as to eliminate the overfitting in the adversarial example generation and improve the adversarial This is used to eliminate the overfitting in the generation of the adversarial examples and improve the transferability of the adversarial examples. Experiments on the CIFAR-10 dataset validate the effectiveness of the proposed algorithm. The target detection-based adversarial example generation algorithm has a higher success rate of black-box attacks compared to the original one-pixel attacks.
Cybersecurity in India

research-article Free AccessCybersecurity in India Author: Debdeep Mukhopadhyay IIT Kharagpur, India IIT Kharagpur, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 11November 2022 pp 98–102https://doi.org/10.1145/3554910Published:20 October 2022Publication History 0citation5,487DownloadsMetricsTotal Citations0Total Downloads5,487Last 12 Months5,487Last 6 weeks2,276 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Reliability at multiple stages in a data analysis pipeline

Data-centric methods designed to increase end-to-end reliability of data-driven decision systems.
Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment

Learning a high-performance trade execution model via reinforcement learning (RL) requires interaction with the real dynamic market. However, the massive interactions required by direct RL would result in a significant training overhead. In this paper, we propose a cost-efficient reinforcement learning (RL) approach called Deep Dyna-Double Q-learning (D3Q), which integrates deep reinforcement learning and planning to reduce the training overhead while improving the trading performance. Specifically, D3Q includes a learnable market environment model, which approximates the market impact using real market experience, to enhance policy learning via the learned environment. Meanwhile, we propose a novel state-balanced exploration scheme to solve the exploration bias caused by the non-increasing residual inventory during the trade execution to accelerate model learning. As demonstrated by our extensive experiments, the proposed D3Q framework significantly increases sample efficiency and outperforms state-of-the-art methods on average trading cost as well.
Error compensation for phase retrieval in deflectometry based on deep learning

Abstract Fringe patterns are widely applied in optical metrology, and phase retrieval is an essential process for decoding surface information. In the field of phase measuring deflectometry (PMD), phase errors in the phase retrieval process have more significant effects for PMD is a slope-based technique and is more sensitive to low-frequency errors. The main factors affecting the quality of the captured fringe images include the gamma effect of the liquid crystal display screen, the random noise from the charge-coupled device camera, and the random noise amplified by the defocused fringe patterns. Conventional methods compensated the phase errors of these factors separately with different methods, which are inefficient in handling the errors from coupling factors effectively. In this paper, we propose a deep neural network to compensate for the phase errors resulting from the combination of the factors. Experimental results demonstrate that the proposed network can significantly suppress the errors in phase retrieval with non-ideal fringe images. The phase errors can be reduced in both simulated and authentic data for deflectometry, which verifies the robustness and effectiveness of the proposed method.
Computational Trust

In the world of Internet of Everything, intelligent autonomous systems and cloud-based knowledge management systems, data is gathered from several sources such as application, sensor devices, documents from several sources which contain information used to develop services, etc. These services may be regarded as having a degree of trustworthiness depending on the trustfulness of the sources fused together for obtaining the output of the service. To understand this whole concept of what a trustworthy service is, we need to understand several keywords and concepts which are described next.