Reviewerly/exHarmony huggingface dataset:
The Effect of Transformation Leadership on Small and Medium Enterprises Performance: The Mediation Roles of Employee Creativity and Organization Innovation

Today, small and medium enterprises (SMEs) become a vital asset for almost all countries in the world and represent a dynamic sector of the global economy.In addition, effective leadership is essential to improve the organizational performance in situations where organizations face many challenges in the market.In the United Arab Emirates (UAE), SMEs are encouraged by the government to rely on innovation and creativity for achieving the economic growth.However, despite the excess of innovation and creativity reviews in SME context worldwide, there is lack of evidences in the case of UAE.Hence, the aim of this study is to examine the significance of relationships between transformational leadership, organization innovation, employee creativity, and SME business performance.Quantitative methods have been deployed to examine these relationships.The respondents are the executive managers of SMEs working in various sector like information and communication, manufacturing, education, financial and insurance, professional scientific and technical.The sample of 441 SMEs was specified for the data analysis based on SEM methods.The results revealed that all identified relationships are significant.The creativity of employees seems to be a key source of innovation particularly in SME, but the financial resources is not enough to achieve economic growth in the market and sustainable profitability.Accordingly, SMEs should focus on leaders who have certain traits, such as idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration to raise profitability and business growth in UAE market.
The Impact of Transformation Leadership on the Performance of Small and Medium Enterprises in United Arab Emirates

In today's business world, Small and Medium Enterprises (SMEs) are the source of development in the national economy.The globalization and the tough competition in the domestic and international markets motivate SMEs to adopt transformational leadership in their mission because of the significant traits of those leaders such as idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration.In recent decades, the United Arab Emirates (UAE) focused on SMEs to achieve economic growth.Transformational leaders have the ability to foster the performance of SMEs.But adopting transformational leadership is not an easy task and require in-depth analysis to recognize the necessary factors that easy the adoption of transformational leadership by SMEs.Hence, the aim of this study is to examine the impact of transformational leadership on business performance of SMEs.Quantitative methods have been used to measure and evaluate this relationship.The respondents are the executive managers of SMEs working in various sector like information and communication, manufacturing, education, financial and insurance, professional scientific and technical.The sample of 441 SMEs was specified for the data analysis.The empirical results revealed that transformational leadership has a significant effect on SME performance.Thereby, SMEs should emphasize on the reality that transformational leaders are the pillar of success in a country rely on innovation and technology to develop all sectors and faces of life.Moreover, SMEs should focus on those leaders who have the personal characteristics such as idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration to foster the organizational performance in the domestic market.
Selection of the Best Newspaper Forecasting Method Using Holt-Winters and Long Short-Term Memory Method

As a result of digitalization, demand for newspapers has become more volatile and difficult to predict.This research aims to determine the best forecasting method with the smallest error value to reduce the company's rate of newspaper returns and losses.The holt-winter and long-short term memory approaches are used to analyze the data.In addition, the results of this research's forecasts were compared to the previous research that used the ARIMA method and trend line analysis method.The forecasting method will be chosen based on the lowest Mean Absolute Percentage Error (MAPE) criteria.According to this research and the previous research, the MAPE value for the trend line analysis method is 2.94%, 3.52% for the ARIMA method, 3.87% for long short-term memory; meanwhile, the holt-winter has the lowest error rate, with a MAPE value of 2%.In conclusion, the best forecasting method for forecasting newspaper demand at PT. XYZ is the holt-winter method.
Formation and development of patriotism among future managers: philosophical-methodological and praxeological aspects

The article reveals the philosophical, methodological and praxeological aspects of the formation and development of patriotism among students studying in the specialty “State and municipal management”. The research methodology, based on the general scientific methods of analysis and synthesis, induction and deduction, includes: 1) a semiotic method that allows us to consider patriotic traditions not only as a symbolic, but also as a communicative phenomenon; 2) comparative method; 3) situational analysis. Patriotism is considered by the authors as a moral and socio-ethical category, which is a dialectical combination of intellectual-rational and mental-spiritual components. Based on this understanding, the authors are conducting practical work on the formation and education of patriotism among future managers within the walls of the Orenburg branch of the RANEPA. This work includes: 1) the formation of historical memory through the familiarization of students with the history of the Fatherland within the walls of the university and participation in extracurricular patriotic events; 2) familiarization with the richest Russian cultural heritage; 3) fostering love for the small motherland, one's native land; 4) industrial practice of future managers as the most realistic way to introduce them to professional activities.
N°353 – Movement disorder analysis: An aid in diagnosing a rare case of Beta Mannosidosis

The automotive augmented reality head-up-display (AR-HUD) system relies on multiple free-form surfaces to project the virtual image into human eyes. A wide range of views and the extensive display area result in extraordinarily complex distortions. Providing a way to correct such distortions is a major leap forward. Methods widely used for calibration, such as lookup tables and interpolation, require considerable memory capacity, complicated calibration procedures, etc. Additionally, when attempting to meet the high-accuracy requirements for such systems, computational memory grow rapidly. In this context, we propose a fully connected neural network (HUDNet) for an automotive AR-HUD to correct dynamic distortion. Here, we propose a parallel prediction framework for the center and the edge of the image, and separate the distortion information from the disparity information in order to balance the unstable central field of view error. Ultimately, we introduce the transfer learning method to improve accuracy and its effect on accuracy is assessed. The present study provides a reference for us to apply deep learning methods to predict dynamic image distortion of various complex multi-freeform surface reflection systems in the future.
A Formalisation of Digital Forensic String Search Specifications

Computer forensic tools are tools concerned with the identification, recovery, preservation and analysisof evidence in digital media involved in criminal investigations and illegal cases. String searchtools are a special class of such forensic tools, which are used to search for specific queries in theinvestigated medium. However, one main issue we find in literature mainly is that computer forensictools, including string search tools, are generally specified in an informal and oftentimes ambiguousand inconstant manner. We adopt in this short paper formal specification approaches, in particularEvent-B, in order to define clearly the behaviour and properties of string search tools, a class of computerforensic tools, and investigate whether there are any requirements that cannot be achieved. Ourresults show that some basic and optional requirements as stated in literature related to string searchtools could be not be consistent and some are unnecessarily restrictive.
Patch-wise Features for Blur Image Classification

Images captured through smartphone cameras often suffer from degradation, blur being one of the major ones, posing a challenge in processing these images for downstream tasks. In this paper we propose low-compute lightweight patch-wise features for image quality assessment. Using our method we can discriminate between blur vs sharp image degradation. To this end, we train a decision-tree-based XGBoost model on various intuitive image features like gray level variance, first and second order gradients, texture features like local binary patterns. Experiments conducted on an open dataset show that the proposed low compute method results in 90.1% mean accuracy on the validation set, which is comparable to the accuracy of a compute-intensive VGG16 network with 94% mean accuracy fine-tuned to this task. To demonstrate the generalizability of our proposed features and model we test the model on BHBID dataset and an internal dataset where we attain accuracy of 98% and 91%, respectively. The proposed method is 10x faster than the VGG16 based model on CPU and scales linearly to the input image size making it suitable to be implemented on low compute edge devices.
A Discourse on Guessing and Reasoning

Computer Science studies the possibility of mechanizing reasoning, an activity of the human brain often confused with thinking, but more or less distinguished from guessing. This paper is devoted to considering, at not too much length, the links between thinking, guessing and reasoning.
Neural Machine Translation from English to Marathi Using Various Techniques

Machine translation (MT) is a term used to describe computerized systems that generate translations from one linguistic communication to another, either with or without the need of humans. Text can be used to evaluate knowledge and converting that information to visuals can help in communication and information acquisition. There have been limited attempts to analyze the performance of state-of-the-art NMT algorithms on Indian languages, with a significant number of attempts in translating English to Hindi, Tamil and Bangla. The paper explores alternative strategies for dealing with low-resource hassle in neural machine translation (NMT), with a particular focus on the English-Marathi NMT pair. To provide high-quality translations, NMT algorithms involve a large number of parallel corpora. In order to tackle the low-resource dilemma, NMT models have been trained, along with transformers and attention models, as well as try hands-on sequence-to-sequence models. The data has been trained for sentence limit of 50 words and then fine-tune the default parameters of these NMT models to obtain the most optimum results and translations.
Classification of 6 Core Competence Potential Classes and Influencing Factors in the 2015 Revised Curriculum

Objectives This study tried to analyze the factors affecting the potential class by classifying the potential class according to high school students' self-management ability, knowledge information processing ability, creative thinking ability, aesthetic ability, community ability, and communication ability.&#x0D; Methods Research data were high school samples from 2020 to 2021, corresponding to the 4th and 5th years, among the surveys of the Busan Education Longitudinal Study (BELS) collected through questionnaires supervised by the Education Policy Research Institute of the Busan Metropolitan Office of Education. A total of 3,046 people (1,603 male students and 1,415 female students) participated in the survey. Latent profile analysis was performed to classify the latent class, and multinomial logistic regression analysis was performed to analyze the influencing factors for each class.&#x0D; Results First, when comprehensively considering the number of latent class cases, the 5 class classification model was judged to be the most appropriate model. Second, gender did not have a significant effect on class classification in most cases, but it was more likely to be included in the ‘middle and lower competency group’ than the ‘overall high competency group’ and ‘community competency imbalanced group’. Third, the higher the learning activity time, the higher the possibility of being included in the ‘higher middle competency group’, ‘overall high competency group’, and ‘unbalanced community competency group’ than the ‘overall low competency group’. The possibility of being included in the ‘lower-middle competency group’ was higher than the ‘community imbalanced competency group’. The better the teacher relationship, the higher the possibility of being included in the ‘overall high competency group’ than the ‘overall low competency group’. It was confirmed that they were more likely to be included in the ‘overall high competency group’ rather than the upper middle competency group.&#x0D; Conclusions intervention of variables (learning activity time, parents, teachers, peer relationships) that affect the classification of latent classes according to the level of core competency development is required. In order to help broken families who have lost the ability to support and protect their children, society and institutions must establish an active support environment and support various activities (student autonomy, after-school activities, etc.) at school to help students secure time for learning activities. do. Considering that students who tend to be excessively obsessed with peer relationships can have a negative impact on their competency development, it suggests that proper education on healthy peer relationships is necessary.
Evaluating the Impact of the COVID-19–Related Public Health Restrictions on Access to Digital Sexually Transmitted and Blood-Borne Infection Testing in British Columbia, Canada: An Interrupted Time Series Analysis

Background Evidence of long-term impacts of COVID-19–related public health restrictions on digital sexually transmitted and blood-borne infection (STBBI) testing utilization is limited. We assessed these impacts on GetCheckedOnline (a digital testing resource for STBBIs) relative to all STBBI tests in British Columbia (BC). Methods Interrupted time series analyses were conducted using GetCheckedOnline program data comparing monthly test episodes (STBBI tests per requisition) among BC residents, stratified by BC region, and testers' sociodemographic and sexual risk profiles, for the prepandemic (March 2018–February 2020) and pandemic periods (March 2020–October 2021). Trends in GetCheckedOnline testing per 100 STBBI tests in BC regions with GetCheckedOnline were analyzed. Each outcome was modeled using segmented generalized least squared regression. Results Overall, 17,215 and 22,646 test episodes were conducted in the prepandemic and pandemic periods. Monthly GetCheckedOnline test episodes reduced immediately after restrictions. By October 2021 (end of the pandemic period), monthly GetCheckedOnline testing increased by 21.24 test episodes per million BC residents (95% confidence interval, −11.88 to 54.84), and GetCheckedOnline tests per 100 tests in corresponding BC regions increased by 1.10 (95% confidence interval, 0.02 to 2.17) above baseline trends. After initial increases among users at higher STBBI risk (symptomatic testers/testers reporting sexual contacts with STBBIs), testing decreased below baseline trends later in the pandemic, whereas monthly GetCheckedOnline testing increased among people 40 years or older, men who have sex with men, racialized minorities, and first-time testers via GetCheckedOnline. Conclusions Sustained increases in utilization of digital STBBI testing during the pandemic suggest fundamental changes in STBBI testing in BC, highlighting the need for accessible and appropriate digital testing, especially for those most affected by STBBIs.
STOCK PRICE PREDICTION USING BI LSTM NEURAL NETWORKS

Stock Market is gaining a lot of popularity recently.Even youngsters are showing interest in stock market investment.But there is a risk element involved as stock market is not easily predictable by everyone especially beginners.This paper is an effort to help such beginners in stock market investment by developing an efficient neural network model based on Bi-LSTM to predict future closing prices of stocks and integrating it with an interactive web application.This paper not only predicts the future closing prices, but also allows users to search for all the stocks that are listed on Bombay stock exchange, analyze their past and current performance, add them to portfolio, mimic buying and selling of shares etc.Stocks data for this entire activity is fetched from AlphaVantage API in real-time and all of the user activity is stored in Firestore database.The application is safe and secure with the Firebase user authentication.This application also has an AI powered voice assistant which helps in performing many tasks on the web application.
Building and Implementation of a Big Data Mining Model for Businesses

"Today's businesses are facing a number of difficulties in statistics, analysis, and processing of their data sources to make appropriate business decisions. The reason is that the data source of the enterprise is stored discretely in many file types with different structures and is not unified. In this paper, we design and build a model of a centralised storage system, using big data mining to provide business data analysis functions according to their business requirements. To test and evaluate the effectiveness of the proposed model, we use the input data which is the actual business data set of an accessory business. The results when the model is applied show that the source data sets are organised, stored, and analyzed with many different criteria, and are displayed on the charts in an obvious and detailed way. Furthermore, we also compare the proposed model with some existing models. The results show that the proposed model is easy to use for end-users. It has high scalability and fault tolerance, and a faster processing speed compared to traditional models."
An empirical study of pre-trained language models in simple knowledge graph question answering

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT ( https://chat.openai.com/ ), which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA ( https://github.com/aannonymouuss/PLMs-in-Practical-KBQA ).
Automatic Fall Detection and Heartbeat Monitoring for Elderly

Abstract: Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or aboveand two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.
Bone osteosarcoma tumor classification

Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy.
An Automated Framework for Detecting Change in the Source Code and Test Case Change Recommendation

Improvements and acceleration in software development have contributed towards high-quality services in all domains and all fields of industry, causing increasing demands for high-quality software developments. The industry is adopting human resources with high skills, advanced methodologies, and technologies to match the high-quality software development demands to accelerate the development life cycle. In the software development life cycle, one of the biggest challenges is the change management between the version of the source codes. Various reasons, such as changing the requirements or adapting available updates or technological upgrades, can cause the source code's version. The change management affects the correctness of the software service's release and the number of test cases. It is often observed that the development life cycle is delayed due to a lack of proper version control and due to repetitive testing iterations. Hence the demand for better version control-driven test case reduction methods cannot be ignored. The parallel research attempts propose several version control mechanisms. Nevertheless, most version controls are criticized for not contributing toward the test case generation of reduction. Henceforth, this work proposes a novel probabilistic rule-based test case reduction method to simplify the software development's testing and version control mechanism. Software developers highly adopt the refactoring process for making efficient changes such as code structure and functionality or applying changes in the requirements. This work demonstrates very high accuracy for change detection and management. This results in higher accuracy for test case reductions. The outcome of this work is to reduce the development time for the software to make the software development industry a better and more efficient world.