Reviewerly/exHarmony huggingface dataset:
The Structural Relationship among Distributed Leadership, Professional Learning Communities, and School Organizational Effectiveness Perceived by School Teachers

The purpose of this study was to the identify the mediating effect of professional learning communities in the relationship between distributed leadership and school organizational effectiveness perceived by elementary and secondary school teachers. The results of the study were as follows. First, there were positive correlations among all of the distributed leadership, professional learning communities and school organizational effectiveness. Second, the direct effect of distributed leadership on school organizational effectiveness(β=.388, p&lt;.01) and the indirect effect through the professional learning communities(β=.443, p&lt;.01) were both significant. Third, it was found that professional learning communities partially mediated the distributed leadership and school organizational effectiveness. Through this study, it was confirmed that distributed leadership had a greater impact on school organizational effectiveness through professional learning communities. Therefore, in order to increase the effectiveness of school organization, various approaches such as creating an environment and preparing a institutional framework for teachers' cooperative activities are needed by activating professional learning communities based on distributed leadership.
AN ERROR ANALYSIS OF SEMANTIC LEVEL IN RIAU NEWSPAPER ARTICLES

Language errors often occur when communicating, both oral communication and written communication. This happens because of the lack of understanding of the communicator in delivering messages to the communicant or the author's lack of understanding in providing information to his readers. In addition, language errors also often occur due to spoken language factors, so that when applied in written language, there tend to be errors. Language errors at the semantic level still often occur in written language, especially articles in the Riau newspaper. These errors can be in the form of errors due to confused pairs, errors due to incorrect choice of words, and errors due to cognate pairs. This study uses a prescriptive content analysis method with the data source from the newspaper Koran Riau. Data is collected by means of documentation with data collection techniques including, collecting error samples, identifying errors, explaining errors, classifying errors, and evaluating or correcting errors. Based on the results and discussion, there are two errors in the language of the semantic level in the Riau Koran newspaper article. First, the fault of a confused partner. Second, errors due to incorrect word choices. However, errors due to cognate pairs were not found.
Digital transformations in culture and art: new opportunities and challenges

Digitalization covers all areas of social activity. For a long time, cultural and artistic life has been incorporating practical elements of the digital world into its arsenal, while traditionally leaving its creative components unchanged. Technologization, informatization, and digitalization have been used at the stage of production or broadcasting of cultural and artistic content (digital culture, cultural technologies) since their introduction. However, the digital world is gradually beginning to claim the role of a direct creator of cultural elements. Such digital transformations in culture have become a relevant subject of research in the innovation cluster of this sphere of human activity. The purpose of the article is to search for new principles of the process of digitalization of the cultural and artistic cluster in the current conditions of development of the socio-cultural space. It is proposed to find the principles by which the digitalization of culture and art is implemented in two key dimensions: creative and organizational. These principles focus on the flexibility, dynamism and synergy of the cultural and artistic space in the context of digitalization. The research methodology is based on general scientific and cultural methods, as well as synergistic methodological principles. The results of the study indicate that cultural and artistic development in the digital environment is gaining qualitatively new opportunities in the technical, organizational, information, communication, and logistical aspects while demonstrating the potential ability to be realized in the creative segment. Thus, digitalization as a powerful global trend in the development of society shapes innovative trends in the cultural and artistic sphere. The author traces the prospects for synergy of the digital segment in the cultural environment with the actualization of innovative principles.
Research on Image Analysis and Processing Technology Based on Big Data Technology

With the increase of information, the traditional image processing technology still has a big shortage in image quality and processing speed. Therefore, how to improve the image quality and processing speed is a research hotspot. To solve the above problems, we adopt the method of combining the generative countermeasure network with the super-resolution reconstruction, and use the open data set to train the model. Through the reasonable selection and optimization of the image processing algorithm, we can achieve the goal of improving the image super-resolution reconstruction effect. In order to improve the slow image processing speed, we set up Spark cluster to integrate computing resources. The experimental results show that the method based on the generation of countermeasure network has a good effect for high resolution image reconstruction, and this method can expand the resolution of low resolution image by four times; The cluster based on the Spark platform shows better performance advantages in processing a large number of image data. When the hardware indicators of the cluster environment and the stand-alone environment are consistent, the cluster based on the Spark platform can increase the running speed of the program by about 10%. When the data volume reaches the tera-byte level or even the petabyte level, the operation speed of the system can still be improved by increasing the overall running memory of the cluster.
Research on Electric Vehicle Driverless Test System Based on Computer Big Data Technology

In this paper, based on computer big data technology and modular design method, a set of high-power, wide-speed electric vehicle powertrain unmanned test platform is developed. In this paper, a set of unmanned driving test platform is designed and implemented by using vehicle accessories, driving motor and sensor. The system is composed of four subsystems: steering control, braking control, speed control and gear control. The results show that the developed test platform can meet the requirements of the vehicle plant for the performance and durability of the power motor system.
Research on oil painting recommendation algorithm based on collaborative filtering

The traditional oil painting search method is to use search engines like Baidu and Google to find useful oil painting information based on the oil painting keywords entered by users, such as oil painting name, author and other information. As the data volume of oil painting works is increasing, it is increasingly difficult for people to find information that they are interested in and useful to themselves. On the one hand, the keywords of oil paintings entered by users into search engines may be inaccurate or too long; On the other hand, because of the differences in the information content of oil paintings, some oil paintings cannot be accurately searched by search engines, resulting in the flooding of oil paintings that users are interested in. With the rapid increase of the amount of data shared on the Internet, it is more important to use human feedback when retrieving information. Collaborative filtering of information is a promising method for retrieving useful information. Since a single system that provides the best performance for each type of object is unlikely, or at least may be too complex in terms of implementation, it is more practical to use multiple systems, each of which handles a specific goal. This design is to design an oil painting recommendation algorithm based on collaborative filtering based on the research of collaborative filtering algorithm. This algorithm implements user based collaborative filtering algorithm and project scoring prediction based collaborative filtering algorithm, and can show users the results of personalized recommendations.
Initiatives and the process of the development of regional students’ cross-cultural communication skills

The article states the problem of preparing students for cross-cultural communication by some means of initiatives. The authors provide a detailed series of definitions and determine the necessity to consider regional specificities during the development of initiatives aimed at improving the students’ cross-cultural communication skills. Thus the following main prospects outline the design of the initiatives: the examination of the quantitative and qualitative analysis, which will enable to identify the students’ preparedness for the project; the review of research works and analytical data reflecting the role and importance of initiatives; the analysis of the mechanisms for initiatives’ implementation, one of which is a professional task. The main results are the determination of the value foundations of the mechanisms for students’ preparation, justification for the selection of the region taken for comparison and analysis, the experience of which is adapted to the conditions of the local region, consolidation of the main areas of students’ cross-cultural training implemented within the project framework, and the design of a predictive model of the initiative. Systematic and structural-functional approaches are used to fulfill the goal set (to expand the right understanding of the prospects for the design of initiatives during the development of cross-cultural communication).
Ensemble Classifier for Hindi Hostile Content Detection

Detection of hostile content from social media posts ( Facebook TM , Twitter TM etc.) is a demanding task in the field of Natural Language Processing (NLP). Daily growing nature of hostile content in different electronic media opened up new challenges in language understanding. It becomes more difficult in regional languages. AI-based solution is required to identify hostile content on a large scale. Though a satisfactory amount of researches has been carried out in the English language, finding hostile content in regional languages is still under progress due to unavailability of suitable datasets and tools. In terms of the number of speakers, Hindi ranks third in the world and first in the Indian Subcontinent. The objective of the article is to design hostile content detection system in Hindi language using coarse-grained (binary) classification and fine-grained (multi-class, multi-label) classification. We noted that different baseline learning method with different pre-trained language models perform differently. Using the Constraint 2021 Hindi Dataset, this research proposes a Bidirectional Encoder Representations from Transformers (BERT) based contextual embedding technique with a concatenation of emoji2vec Embedings to classify social media posts in Hindi Devanagari script as hostile or non-hostile. Additionally, for the fine-grained tasks where hostile posts are sub-categorized as defamation, fake, hate, and offensive, we develop an Ensemble Classifier varying different learning methods and embedding models. With an F1-Score of 0.9721, it is found that our proposed Indic-BERT+emoji model outperforms the baseline model and other existing models for the coarse-grained task. We have also observed that our proposed Ensemble method is giving good results than the existing models and the baseline model for the fine-grained tasks with F1-Score of 0.43, 0.82, 0.58 and 0.62 for defamation, fake, hate, and offensive classes respectively. The code and the data are available in https://github.com/skarifahmed/hostile.
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.
System-Identification-Based Activity Recognition Algorithms With Inertial Sensors

This paper focuses on activity recognition using a single wearable inertial measurement sensor placed on the subject's chest. The ten activities that need to be identified include lying down, standing, sitting, bending and walking, among others. The activity recognition approach is based on using and identifying a transfer function associated with each activity. The appropriate input and output signals for each transfer function are first determined based on the norms of the sensor signals excited by that specific activity. Then the transfer function is identified using training data and a Wiener filter based on the auto-correlation and cross-correlation of the output and input signals. The activity occurring in real-time is recognized by computing and comparing the input-output errors associated with all the transfer functions. The performance of the developed system is evaluated using data from a group of Parkinson's disease subjects, including data obtained in a clinical setting and data obtained through remote home monitoring. On average, the developed system provides better than 90% accuracy in identifying each activity as it occurs. Activity recognition is particularly useful for PD patients in order to monitor their level of activity, characterize their postural instability and recognize high risk-activities in real-time that could lead to falls.
A Smart-Contract-Based-Access Control Framework for Smart Healthcare System

Distributed computing is a business and monetary model permitting the clients to use top of the line registering and stockpiling practically with insignificant foundation on their end. In current medical care frameworks, electronic clinical records (EMRs) are modernized records which contains rundown of insights about patients wellbeing. Electronic clinical records are compelling contrasted with the current traditional stockpiling approaches because of its unified stockpiling of information, it prompts weak link as patients being the genuine proprietor forget about their private and delicate EMRs. Distributed computing utilizing encryption calculation is one of the critical figure the clinical administration framework which permits the clients to get to the wellbeing subtleties of patients in the safeguarded manner. The proposed work is to fabricate an encoded admittance control structure, contrasting the different encryption calculations accessible and utilize the most ideal calculation to get the sharing of EMRs among various elements associated with the shrewd medical services framework. Keywords- Medical Care; Electronic Clinical Records
Book Summarization using NLP

It is crucial to offer an enhanced system for swiftly and effectively extracting information in this modern era where the Internet is a wealth of knowledge. It is quite challenging for humans to manually extract the summary from a lengthy written document. On the Internet, there is a wealth of textual content. As a result, finding relevant papers from the many that are available and learning useful information from them is a challenge. An automatic text summary is crucial for resolving the two issues. The technique of extracting the most significant information from a document or group of related texts and condensing it into a concise version while maintaining its overall meaning is known as text summarizing. Text summarization reduces the length of the original text while maintaining the information and overall meaning. Large texts are particularly challenging for humans to manually summarize. There are two types of text summarizing techniques: extractive and abstractive. Selecting significant sentences, paragraphs, etc. from the original content and concatenating them into a shorter version constitutes an extractive summary method. Sentences' linguistic and statistical characteristics are used to determine their importance. Understanding the original material and retelling it in fewer words are the components of an abstractive summary method. It analyzes and interprets the content using linguistic techniques before identifying fresh ideas and expressions to create a new, concise copy that effectively communicates. Key Word: NLP(Natural Language Processing), LDA(Latent Dirichlet Allocation).
A Study on the Improvement of Industrial Technology Protection Capacity Considering the Generation of Technology Management of SMEs in KOREA : Focusing on the Case of Technology Leakage

This study began with a research problem that the types of technology leakage may vary depending on the characteristics of generation of technology managemnet, such as management conditions and technology innovation activities of SMEs. Considering these points, it is necessary to prepare a plan to revitalize industrial technology protection in consideration of the characteristics of each generation of technology management. To this end, this study examined the characteristics of each generation of technology management, presented an analysis framework suitable for this, and then examined the cases of technology leakage from SMEs. As a result of the analysis, it was confirmed that first and second-generation technology management companies may need industrial technology protection activities, which are most important to recognize and separate major assets and establish a minimum management security system. Second, in the case of third and forth generation technology management companies, it was confirmed that additional industrial technology protection activities related to the use of strategic intellectual property rights may be necessary along with strengthening management of key personnel. These analysis results confirm the need to further consider technology innovation capabilities beyond the SME industrial technology protection policy, which has been developed around management conditions by SMEs such as size and industry.
Estimating knowledge level for job applicants

With the progress of technology and mankind, demand for different job positions has emerged. Reports indicating various new job types in the last decade are continuously published, giving us perspective on where we were a decade ago and where we are now. Most of the jobs are created around new technologies, yet not exclusively as jobs within technology production or usage (e.g., machine learning engineers, data scientists, app developers, etc.), but also as a type of jobs built atop of new technologies (e.g., social media manager, podcast producer, content moderator, etc.). With new job types, there is a gap between qualified employees and employers demands created. Taking into consideration trends that we have seen in the last years, more and more new job types will be created, and we can predict that this gap will become larger as time passes.
Software Engineering Challenges for Quantum Computing

The First Working Seminar on Quantum Software Engineering (WSQSE 2022) took place on December 15 and 16, 2022 in Innsbruck, Austria. An audience of 33 quantum computing and software engineering researchers and practitioners joined the two-day event. The workshop enabled us to foresee short-term and longterms perspectives of Quantum Software Engineering, as well as a set of requirements, issues, and challenges for architecting, programming, and testing Quantum Software Engineering applications. In this report, we provide the summary of the workshop, by reporting on the structure of the event and the main results coming from the sessions and working groups.
Ecological and environmental education and ecological awareness in the municipal library

The results of 3-year joint efforts of Ecology and Earth Sciences Chair and Levoberezhye Municipal Library in ecological education are discussed. Within the core project EcoKnowledge, ecological lessons for schoolchildren and students, ecological quests, actions and exhibitions are held. The project was initiated in 2019. In the recent year, the project «SDGs through the students eyes» has been attracting attention of the visitors of the library’s mass media pages and presenting current information on sustainable development goals.
An Intrusion Detection System Using Machine Learning for Internet of Medical Things

The COVID-19 pandemic has impacted everyday life, the global economy, travel, and commerce. In many cases, the tight measures put in place to stop COVID-19 have caused depression and other diseases. As many medical systems over the world are unable to hospitalize all the patients, some of them may get home healthcare assistance, while the government and healthcare organizations have access to substantial sickness management data. It allows patients to routinely update their health status and have it sent to distant hospitals. In certain cases, the medical authorities may designate quarantine stations and provide supervision equipment and platforms (such as Internet of Medical Things (IoMT) devices) for performing an infection-free treatment, whereas IoMT devices often lack enough protection, making them vulnerable to many threats. In this paper, we present an intrusion detection system (IDS) for IoMTs based on the following gradient boosting machines approaches: XGBoost, LightGBM, and CatBoost. With more than 99% in many evaluation measures, these approaches had a high detection rate and could be an effective solution in preventing attacks on IoMT devices.
Introduction of machine learning for astronomy (hands-on workshop)

This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to process gravitational wave data.