Reviewerly/exHarmony huggingface dataset:
Application of big data technology in electromechanical operation and maintenance intelligent platform

Abstract Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.
OCR / HTR technologies and Armenian Heritage Preservation

OCR (Optical Character Recognition) and HTR (Handwritten Text Recognition) are now ready for Armenian language. This technology may offer a greater valorization for documents by enabling improved accessibility, using by instance keywords search, and consists in a new challenge for Digital Libraries. Our presentation intends to propose a view on what is possible today, by introducing a state-of-the-art of the challenges raised by text recognition for Armenian. A focus will be drawn on the technology developed by Calfa for handwritten archives, ancient manuscripts and old printed books. We will present our feedback on three of our ongoing projects: processing catalogs of manuscripts (Mekhitarist, Venice), printed newspapers of Fundamental Scientific Library of NASRA, and handwritten correspondences (Mekhitarist, Venice). Methodology applied by Calfa leads to an accuracy higher than 95% for handwritten documents and higher than 99,5% for printed documents.
Modeling and Optimization of Rice Wine Fermentation Process Based on Hierarchical Adaptive Network-based Fuzzy Inference System

<b><sc>Abstract.</sc></b> Rice wine fermentation process is a nonlinear, multi-input multi-output, complex biochemical reaction process. Modeling the rice wine fermentation process can realize the optimization control of the process and improve the quality of rice wine. Among various modeling methods, fuzzy system provides a reasonable framework for modeling by decomposing nonlinear systems into a set of local linear models. Multi-Output Adaptive Network-based Fuzzy Inference System (MOANFIS) is one of its branch methods, but it faces the problem of fuzzy rule explosion, which makes the model complex and reduces its interpretability. In this paper, a hierarchical structure of ANFIS, namely H-ANFIS, is proposed. Firstly, the number of fuzzy rules is reduced by using the Dropout method to discard nodes with small weights. Then, the Extreme Learning Machine (ELM) algorithm is used to expand the output dimension of ANFIS and solve the model parameters, creating the ANFIS-ELM module. Based on this module, a two-layer H-ANFIS model is proposed, where the output of the first layer is used as the augmented input feature of the second layer, expanding the input information of the second layer. The model achieves a good balance between interpretability, robustness, and accuracy.
Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction

In this paper, the Direction of Arrival -DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP).The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade.The outputs of the neural network models determine the direction of arrival of the incoming signals.Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise.Both considered neural network models were tested with noisy samples.The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm.The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm.Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.
Perceived usefulness and ease of using virtual reality during physiotherapy–A cross-sectional survey from physiotherapists perspective

&lt;b&gt;Background:&lt;/b&gt; Virtual reality (VR) is a cutting-edge technology that consists of a sophisticated user-computer interface that allows for real-time visualization and interaction by using both visual and auditory sensorial channel. The implementation of inclusive innovation using VR in health services should be considered and hence, this study is conducted to investigate the perceived usefulness and ease of using VR during physiotherapy among Malaysian physiotherapists.&lt;br /&gt; &lt;b&gt;Materials and methods:&lt;/b&gt; A cross-sectional a survey through social media, with an adapted questionnaire about the perceived usefulness and simplicity of adopting VR during physiotherapy is distributed to physiotherapists in Malaysia. In total, 102 physiotherapists participated in this study. Frequency and percentage are used to analyze the data of questionnaire items.&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br /&gt; &lt;b&gt;Results:&lt;/b&gt; Overall 62% agree that using VR would enable to accomplish tasks more quickly, 63% finds it improve job performance, 57% believes that VR would increase my productivity. 59% enhance the effectiveness on the job. 64% easier to do my job, 61% VR useful in job. Regarding perceived ease of use 57% accepts learning to operate VR would be easy for me, 55% find it easy to get VR to do what physiotherapist want it to do, 53% agrees that interaction with VR would be clear and understandable, 54% agree that it is flexible to interact with, 54% agree to be become skillful using VR and lastly 55% agree it is easy to use.&lt;br /&gt; &lt;b&gt;Conclusions:&lt;/b&gt; The majority of Malaysian physiotherapists find VR slightly useful for inclusive innovation in physiotherapy, improving work efficiency and performance. However, some remain neutral regarding its overall usefulness. They also find VR neither easy nor difficult to use in terms of inclusivity.
Development of Lab Safety Leadership Measurement Tool

The purpose of this study is to develop a laboratory safety leadership measurement tool. In this study, safety leadership was defined as a series of actions in which laboratory managers establish safety plans based on safety communication with research workers based on their interest and belief in safety, and all laboratory members establish the importance of safety. This measurement tool was finally developed through the stages of literature review, factor and item development, and validity analysis. A survey was conducted to confirm the validity of the measurement tool, and a total of 404 copies were analyzed. As a result of the validity verification, the safety leadership measurement tool consisted of 14 questions of four factors, and the four factors were safety communication, safety interest, safety belief and planning, safety behavior, and participation. For each question, a 6-point Likert scale was used.
Research on Face Recognition System Based on Deep Convolutional Machine Learning Model

This paper proposes a face recognition system based on a deep convolutional neural network algorithm. Firstly, according to the distribution law of pose face, the nonlinear manifold space of pose face is divided into different manifold layers and local subspaces. At the same time, this paper defines the low-level feature construction method for the pose face in the local subspace to realize the face sample expansion with pose change. Then this paper obtains a self-learning deep convolutional neural network through network structure initialization, global and local adaptive expansion of the network structure. In this way, the deep nonlinear feature extraction and recognition of pose-changing faces is realized. The experimental simulation shows that the recognition accuracy rate of the algorithm on Clubfeet, AR and ORL face databases reaches 98.89%, 99.67% and 100% respectively. The algorithm has a fast convergence rate.
Forecasting IT Industry trends using a Fuzzy Decision Support System

The Information Technology (IT) industry plays a vital role in the global economy and its significance will only continue to grow as digitalization accelerates. During times of increased entropy resulting from crises or military conflicts, the stable functioning of the IT industry is of paramount importance. This study explores the possibility of using a fuzzy Decision Support System (DSS) to forecast and predict how the IT industry in a specific country will operate.For the purpose of demonstration, we use self-developed FuzzyKIDE fuzzy DSS software platform, which employs fuzzy logic to provide a reasonable assessment even when the information is imprecise. We propose a DSS structure that utilizes a combined model of semantic networks and fuzzy implication rules. To demonstrate its effectiveness, we analyze Ukraine's IT industry at various time points and compare the results with historical data.
Pre-trained Language Models in Multi-Goal Conversational Recommender Systems

In this paper, we examine pre-trained language models used in Multi-Goal Conversational Recommender Systems (MG-CRS), comparing and analyzing their performances of various pre-trained language models. Specifically, we investigate the impact of the sizes of language models on the performance of MG-CRS. The three types of language models of BERT, GPT2, and BART, and compare their accuracy in two tasks of 'type prediction and topic prediction on the MG-CRS dataset, DuRecDial 2.0. Experimental results show that all models demonstrate excellent performance in the type prediction task, but provide significant in performance depending on models or their sizes in the topic prediction task. Based on these findings, the study provides directions for improving the performance of MG-CRS.
Text Mining of Articles on Children's Right to Play

This study aimed to identify the trends and content flow of media articles on children's right to play in South Korea and to analyze the keywords that have been highlighted. This was done through text mining of the articles to explore the words used in the media, identify the flow through topics, and examine the networks formed by the texts. In doing so, this study examined how the public views children's right to play, provided practical implications for respecting the right to play and conducted a comparative discussion with previous research. For the purpose of this study, 992 articles reported from 2008 to 2022 were selected and reviewed for words, and a total of 31,222 words in 5,241 categories were used for analysis. The frequency of occurrence of each word in the articles was counted, topic modeling and TF-IDF index were calculated in TEXTOM 6.0, and words that appeared more than five times were entered into the UCINET6 program to examine the centrality of the language network and the ego network. The results of the study are as follows. First, the words used in the articles on children's right to play were 'child', 'play', 'playground', 'Seoul', 'right to play', 'school', 'project', and 'parent'. Second, the topic modeling analysis adopted a model consisting of four topics: 'Movement to ensure the right to play and learn', 'Discussion to promote the right to play', 'Building a foundation for play through public cooperation', and 'Policy implementation in local governments'. Third, 'play', 'playground', 'right to play', 'Seoul', 'school', and 'project' were identified as keywords. This study found that articles on children's right to play tend to be reported in connection with education and policy, emphasize cooperation in the public sector to respect the right, and reveal policy implementation in local governments. By exploring the keywords that run through the articles and uncovering the connections between them, the focus of society's view of the right to play was identified. Also, this study discussed follow-up research on children's right to play in South Korea and the practical implications of respecting the right.
A Study on the Effect of Work-Life Balance on Organizational Effectiveness of Coast Guard Officer Working on Vessel: Focusing on the effect of controlling Job Stress

Korea's sovereign sea is about 4.5 times the size of the land. In order to play such a vast security area, the coast guard operate ships. Coast guard officers must overcome their fragile working conditions, including the psychological atrophy of being at sea, the dangers of working at sea, the narrow work place of vessel, high noise, motion sickness, shift work, and isolation from family and society for a certain period of time. In this study, we examined how the work-life balance of coast guard officials working in vessel affects organizational effectiveness, and furthermore, we empirically analyzed what regulatory effects job stress has. The results of the study are as follows. First, it was found that work growth balance and work-family balance, which are the components of work-life balance of coast guard officials working in vessels, had a positive(+) effect on job satisfaction and organizational commitment, which are sub-factors of organizational effectiveness. Second, it is the result of analyzing whether the ultimate goal of this study, the work-life balance of coast guard officials working in vessels, has a moderating effect of job stress in affecting organizational effectiveness perception. First, as a result of analyzing the moderating effect of job stress in the relationship between job satisfaction and organizational commitment, job stress in the relationship between job satisfaction and organizational commitment was found to have a negative(-) moderating effect.
Autoethnography of the application of the PBL learning method to a barista practical class by a department of food service-related professor

This study describes the subjective story of the 'me' experienced with applying the PBL learning method to students majoring in food service-related studies at university and the process of barista practical class experience through autoethnography. Through this process, as a teacher, I reflected on myself for my teaching process and presented an insight into the educational meaning and value. As a personal record of my PBL class experiences, I listed the following: exploring PBL learning methods, applying the PBL teaching model, and confirming the effectiveness of the PBL learning methods. In college courses related to food service, using the PBL teaching method can help secure the necessary expertise and enhance competency while providing suggestions for improvements as follows. First, it is necessary to present learners with scenarios of problems closely related to the industry and develop a model for the industry-academia PBL teaching method. Second, in a PBL class, an instructor should intentionally organize teams and be a facilitator to encourage collaborative learning among students. Third, it is necessary to hold PBL teaching workshops where the collaboration and creativity that characterize the PBL teaching method can be effectively applied and to support teaching and learning in the operation of PBL classes.
A study on the human rights protection activities of the police

In order to dispel concerns about the police's enlargement of power due to the adjustment of investigative powers and to successfully establish an local police system, it is necessary to be reborn as a human rights-friendly police force. Through in-depth interviews with police officers, this study sought to find out the current status and problems of human rights protection activities, focusing on the organization and work in charge, and to obtain implications for establishing human rights-friendly police activity strategies. For the purpose of the study, in-depth interviews were conducted with 15 police officers in charge of human rights and investigation departments. As a result of the in-depth interview, various organizational problems such as the organization and personnel in charge of police human rights, problems of human rights education, wrong investigation practices, and a performance-oriented culture were identified. Based on the interview results, it was proposed to establish a human rights department and redefine the rights and roles of the Human Rights Commission. In addition, strategies for human rights-friendly police activities were presented, such as concrete plans for human rights education contents and methods, improvement of organizational culture, human rights measures for the socially underprivileged, and strengthening of internal human rights in the police.
On the multiple feature copying in Korean

This paper aims to give a formal explanation of the multiple feature copying in Korean. The study argues that the postsyntactic operation of feature copying occurs more than three times in Korean. This study also proposes that feature copying takes place once in each phase, following Choi & Harley (2019). For the analysis, three different exponents of the verb root √EXIST in two types of √EXIST constructions, one in the affirmative and the other in the negative context, along with the distributive plural marker /tul/, are examined. The distributive characteristics are presented in terms of multiple spell-out within Minimalist Syntax and late insertion of vocabulary items within Distributed Morphology.
A case study of preservice English teachers’ online learning experience amid the COVID-19 pandemic

This qualitative case study explored the online learning experience of five preservice English teachers in Korea during the COVID-19 crisis. It specifically examined their perceptions of emergency remote instruction in a practice-oriented teacher education course on language materials design. Data were collected from the preservice teachers’ reflection papers, materials design project, and researcher’s reflective log, and analyzed qualitatively. The findings demonstrated that preservice teachers had a high perception of their semester-long experience in the class as they did not encounter much difficulty from the online instructional mode and could successfully complete the practical component of materials design. The preservice teachers accredited their positive online learning experience to the carefully organized instructional design and the interactive atmosphere of the class, which made possible their active participation with a self-directed attitude toward learning. The collective enthusiasm shared by the class was also perceived as contributing to their positive learning experience.
A Tiltable Airframe Multirotor UAV Designed for Omnidirectional Aerial Manipulation

Aerial manipulators coupled to UAVs can be beneficial for doing tasks in difficult-to-reach areas. Inspection is one of such most commonly required tasks conducted on aging infrastructure including bridges and tunnels. However, while employing an inspection UAV, the manipulator tip’s angle of reach is often mechanically limited to only about the location of its attachment on the aircraft. In the proposed system, a design that allows a manipulator tip to reach all directions surrounding the UAV is developed. As the manipulator body, we propose a basic tiltable airframe design and employ auxiliary actuators to maintain the rotors’ axis. The thrust direction of the rotors remains upright in the proposed design, which allows the rotor thrust to hold the maximum payload of the UAV even while tilted. We examine its efficiency and usefulness through experimental demonstration.
Review of Small Target Detection based on Deep Learning

With a large number of applications of target detection in daily life, the performance requirements of target detection are constantly improving. Many challenges about target detection have been put forward constantly, such as imbalanced samples, fewer pixels, and occlusion of the detected target, all of which bring difficulties for the model to correctly identify the target. Small target detection has always been a difficult point and research hotspot. In recent years, many algorithms for small target detection have been proposed, such as data enhancement, feature fusion, attention mechanism, and super-resolution network structure. According to the characteristics of different network structures, the training strategy can be appropriately adjusted and then applied to different environments, which can greatly improve the detection accuracy of small targets. This paper will introduce the data sets and related small target detection algorithms proposed in recent years, and classify, analyze, and compare the corresponding training strategies.
An Underwater Target Ranging System based on Binocular Vision

In order to accurately measure the distance and size information of underwater targets, an underwater target ranging and size measurement system based on binocular vision is proposed. Underwater binocular calibration method is used to obtain the underwater parameters of binocular camera, and the underwater images are calibrated to make them coplanar and aligned. The improved CLAHE algorithm is used to process underwater images and improve the image contrast. The parallax map is obtained by SGBM stereo matching algorithm, and the distance and size of the target are measured and calculated according to the parallax map. The results of underwater experiment show that the measurement accuracy of the system is high for the distance and size of the close-range underwater target, and the measurement error conforms to the requirements of underwater experiment.