Reviewerly/exHarmony huggingface dataset:
Individual, Group Dynamics, and Organizational Processes in the Workplace: Factors for Better Performance and Organizational Success

Introduction/Main Objectives: Research in individual, group dynamics, and organizational processes for organizational behavior can offer valuable insights into how people behave in the workplace. These insights can assist organizations in enhancing their effectiveness, promoting teamwork, and cultivating a culture that is more diverse and inclusive. Background Problems: Organizational behavior is complex and multifaceted, and there are many different factors that can influence good work performance. Novelty: In the area of organizational behavior, there is a growing interest in the intersection of technology and human behavior, also the need to understand team performance and collaboration. There is a lot of need to explore literature studies in various time spans regarding these aspects to be able to find models related to new systems and technologies. Research Methods: Literature study, a research method that involves collecting and analyzing existing research and academic literature on this topic. The data in this study are from previous studies in the form of textbooks, research papers, and articles. Research was conducted with 6 steps of literature study. Finding/Results: There are 7 main factors and 18 sub factors related to individual aspects. There are 6 main factors and 18 sub factors related to group aspects. And there are 3 main factors and 9 sub factors related to organizational processes, where the three main factors above affect good work performances. Research results can be further tested in specific organizational environments for the development of knowledge and to explain empirical and theoretical gaps.
A SURVEY OF RECENT STUDIES INVESTIGATING THE POTENTIAL OF DEEP LEARNING ALGORITHMS FOR IDENTIFYING AND CATEGORIZING BREAST CANCER

This paper provides a summary of recent investigations on the use of algorithms based on deep learning for detecting and classifying breast cancer, with the goal of improving timely diagnosis and treatment outcomes. Machine learning has an accuracy of 91% in identifying cancer, whereas human specialists have an accuracy of just 79%. In this study, we analyze and contrast the two most current machine learning algorithms for detecting and classifying breast cancer: RetinaNet and YOLO (You Only Look Once). This study adds support to the theory that machine learning-based technologies may be able to make more precise diagnoses of cancer than human doctors. When compared to other breast cancer screening and categorization systems using prominent public datasets, RetinaNet and YOLO fared the best.
Ontological Modeling in Humanities

The article analyzes the possibilities of using transdisciplinary ontology to represent information about the work of Ivan Franko (based on the program of Ukrainian literature for 10–11 grades, standard level). The object of research is to study the characteristics of the transdisciplinarity phenomenon in scientific and educational dimensions. The subject of research is transdisciplinary ontology as a tool to solve scientific problems. The aim of the study is to create a transdisciplinary ontology based on the work of Ivan Franko in the program of Ukrainian literature for 10–11 grades (standard level). The scientific significance of the study comprises the multidimensionality and multilevel nature of transdisciplinary research, which is determined by the study of scientific problems through the analysis of diverse projections in different scientific fields, which forms a holistic transdisciplinary paradigm. The practical significance of the study is determined by the consideration of important features of transdisciplinary ontology and means of its expression on the material of a fragment of the curriculum in Ukrainian literature for 10–11 grades; by the implementation of visualization of the formed transdisciplinary ontology with the help of cognitive IT platform “POLYEDR” (KIT “POLYEDR”); by the opportunities to use different forms of transdisciplinary ontology visualization.
UNIVERSITY OMNICANAL: NEW STRATEGIES TO IMPROVE THE TECHNICAL SUPPORT INSTITUTIONAL

The TWENTY-first century has given rise to considerable technological advances within educational levels, political and economic, however, with the arrival of the pandemic COVID-19, these features have shown a facet of compulsory developthe human. One of the relevant aspects found in the condition of omnicanalidad that has emerged as an instrument of great importance in these times. Therefore, the research has been proposed as the aim of clarifying the influence that has tenidor new strategies in the support institutional in their work with students and teachers in the midst of the pandemic COVID-19 through a model omnicanal The work was addressed from the qualitative approach, we used the method etnometodológico, and the tool of the in-depth interview, which prompted respondents to reflect on events that occurred due to the pandemic COVID-19. The findings showed the development of the category University omnicanal: New strategies to improve the technical support institutional. The document shows us that these strategies are accurate
Innovative Generation of Transcripts and Validation Using Public Blockchain: Ethereum

University certificates are the biggest asset to prove our excellence and our worth to others. They are essential to help students succeed in higher education or gain employment. The methodology for creating, verifying, and managing certificates in the current analog system is unreliable and slow. Verification of the digital certificate requires institutions to reach out to the issuing authority to verify certificates. To ensure the security and authenticity of such certificates, a system must maintain records of transcripts and make them available online. Ideally, if such a system were proposed and developed, authorities and students would easily be able to confirm the authenticity of certificates. In the proposed system, a permission blockchain-based system for secure verification of academic certificates. Using a hash-based storage system to ensure the authenticity and security of digital contents stored on the platform, allows universities to upload student certificates and verify certificates from other member universities. We use the Raft ordering service to handle numerous distributed orderers and Paxos service. Deploying the system in a cloud environment enables the user to access it from anywhere in the world.
Teachers' Training Impact on Teachers' Professional Development at Primary School Level

Researchers investigated how primary school teachers benefited from their training. A total of 150 public school teachers in primary schools (75 men, 75 women) participated in the study. Using the survey method, the researcher adopted a simple random sampling technique. The researche employed a self-developed questionnaire with a five-point scale to compile the results. The questionnaire consisted of six main aspects. The validity of the research instrument was ensured in light of experts' opinions and pilot testing. The reliability was also ensured properly by applying Cronbach Alpha and its value was found .83. The data collected from respondents was organized properly. The data was analyzed through inferential statistics, and an independent-sample t-test was applied. The data was tabulated, presented and interpreted in a mean score, standard deviation and in the form of t- value to see the significant difference in mean score. It was concluded that teachers' training enhanced theteachers' professional competencies and recommended that teachers' professional training may be conducted continuously.
Cryptocurrency Price Prediction Using LSTM, ARIMA, and Linear Regression

Cryptocurrencies have emerged as a popular investment option, characterized by their high volatility and potential for significant price fluctuations.The ability to accurately predict cryptocurrency prices is crucial for investors to make informed decisions.In this research paper, we conduct a comprehensive comparative analysis of three widely used forecasting models -Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Linear Regression -for cryptocurrency price prediction.We evaluate and compare the performance of these models using historical cryptocurrency data, considering various evaluation metrics and scenarios.
Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19, and incidentally test positive for the virus. Because COVID-19-related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who were admitted for other indications.We compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from the electronic health records (EHR), including structured EHR data elements, provider notes, or a combination of both data types.We conducted a retrospective data analysis utilizing clinician chart review-based validation at a large academic medical center. We reviewed and analyzed the charts of 586 hospitalized individuals who tested positive for SARS-CoV-2 during January 2022. We used LASSO regression and Random Forests to fit classification algorithms that incorporated structured EHR data elements, provider notes, or a combination of structured data and provider notes. We used natural language processing to incorporate data from provider notes. The performance of each model was evaluated based on Area Under the Receiver Operator Characteristic (AUROC) and an associated decision rule based on sensitivity and positive predictive value. We also identified top words and clinical indicators of COVID-19-specific hospitalization and assessed the impact of different phenotyping strategies on estimated hospital outcome metrics.Based on a chart review, 38% of 586 patients were determined to be hospitalized for reasons other than COVID-19 despite having tested positive for SARS-CoV-2. A computable phenotype that used provider notes had significantly better discrimination than one that used structured EHR data elements (AUROC: 0.894 vs 0.841, P < .001), and performed similarly to a model that combined provider notes with structured data elements (AUROC: 0.894 vs 0.893). Assessments of hospital outcome metrics significantly differed based on whether the population included all hospitalized patients who tested positive for SARS-CoV-2 versus those who were determined to have been hospitalized due to COVID-19.These findings highlight the importance of cause-specific phenotyping for COVID-19 hospitalizations. More generally, this work demonstrates the utility of natural language processing approaches to derive information related to patient hospitalizations in cases where there may be multiple conditions that could serve as the primary indication for hospitalization.
Research on the Training Path of Young Innovative Talents

It is of great significance to vigorously cultivate innovative talents in the context of the knowledge economy.The standards for innovative talents include both internal and external aspects.To cultivate innovative talents, it is necessary to respect the basic laws of student growth, form a talent cultivation model that emphasizes both doctoral and vocational education and take the path of internationalization providing an optimized growth environment for talent cultivation.The article analyzes the current situation of cultivating future oriented technological innovation talents in universities and finds that there are problems such as unclear understanding and insufficient educational resources.It proposes implementation paths for cultivating innovative talents oriented towards future technology, such as adhering to the student-centered concept, emphasizing value guidance and knowledge impartation, innovating academic assessment mechanisms, and constructing research-oriented courses, providing reference for universities to explore innovative talent cultivation oriented towards future technology.
The Automatization of Social Media Communication

This research paper compiles and evaluates several studies that have a particular interest in social media analysis and its applications. Studies on evaluating the impact of movies and television shows on social media platforms, using semantic knowledge graphs to analyze Covid-19 news articles and identify fake news on social media, forecasting social media data using machine learning, and the evolution of the power of central nodes in a Twitter social network are all covered in this article. The significance of sentiment analysis and opinion mining in social media, analysis of social media-based profiles, and mining serendipitous drug use from social media using machine learning are all topics covered in the paper. The paper also emphasizes the application of social media text analysis for a high-frequency-link DC transformer based on switched capacitors for medium-voltage DC power distribution applications, and urban region mining services. The research paper offers details on the state of social media analysis right now and some potential uses for it.
Manifesting Creativity in Virtual World Building: A Case Study in Gather

The environmental design field has established methodologies for designing indoor and outdoor environments and developed best practices for designing spaces to support human creativity. Thoring et al. have systematized current knowledge around the design of spaces in the physical world to facilitate creative processes into a series of ten propositions [47]. However, the COVID-19 pandemic has led to a surge in the use of online spatial platforms to support virtual co-working. How to design virtual workplaces to support various creative processes is an emergent and open question. Thus, we present an initial exploration of the opportunities and constraints we confronted while attempting to manifest Thoring et al.’s ten propositions for creative facilitation in Gather [9], a 2D virtual world platform.
PENGEMBANGAN MEDIA PEMBELAJARAN MAGIC BOX BERBASIS SAVI (SOMATIC, AUDITORIAL, VISUAL DAN INTELEKTUAL)

This research stems from the problem of the need for more learning media teachers provide because, in general, a teacher still uses a conventional learning system and does not thoroughly understand the material that students can understand. This study aimed to determine the development and feasibility of SAVI-based magic box media for SD Kertasana Serang, Banten fourth-grade students. This research uses the research and development method (Research and Development). This research and development step uses six steps: problem analysis, data collection, product design, design validation, design revision, and product trials. The results of this study are the feasibility of Magic Box learning media in SAVI learning as a means of visual learning styles of students in class IV SD Negeri Kertasana obtained through validation of material experts, linguists, and media experts. The feasibility level obtained from two material experts is 92.5%, from two language experts is 78.3%, and from two media experts is 89.5%. The three experts obtained an average value of 86.76% which was included in the "Very Feasible" category. Based on the explanation, it can be concluded that the SAVI-based Magic Box learning media is suitable for students IV SD Negeri Kertasana Serang, Banten.
The Impact of Reading Habits on School Success: Perspectives of Student and Teacher Librarians

The study aims to identify the impact of reading habits on the academic success of students who completed the 9th year of schooling in the 2019–2020 school year, in the school named Agrupamento de Escolas de Alpendorada, school of North of Portugal and to what extent the School Library (SL) contributed to the construction of these reading habits, from the perspectives of the students and librarian teachers involved. This article is divided into two parts—the first, which presents the theoretical framework of SL as a promoter of reading, the action of the librarian teacher, also looking at reading habits as a factor of school success, and the second, dedicated to the presentation and analysis of the data collected in the questionnaires applied to students and librarian teachers and in the observation of the final evaluations of the students in the previous academic year, in the statistical data related to the home requisition of books and in the evaluation reports of the last three years of the SL. The data allowed us to conclude that students with more and better academic success are simultaneously those who cultivate better reading habits and those who most value and take advantage of SL’s action.
AN EXPERIMENTAL SET-UP OF A GRAPHICAL USER INTERFACE FOR IMAGE ENHANCEMENT USING CNN

MATLAB GUI provides a friendly graphical user interface.We know that deep learning is very in demand in today's life.It is used when talking about a large number of data, deep learning is used to learn features of a data set, or we can say that an image.So for this process of convolution, neural networks play an important role.CNN is used for speech recognition, face recognition, image classification, etc.In this research, an experimental set-up has been done for lowlight image enhancement based on deep learning, and also enhancement has been done based on intelligent retrieval with the help of GUI in MATLAB.The authors also stressed minimizing the complexity as computational time has been measured, including all parameters.It is also known as Deep Learning.Enhancement, in terms of strengthening, is the same.Deep learning and CNN are the same; it is a subclass of intelligent retrieval.When we talk about a massive number of facts, and we have to classify them, so the system is very complex at that time, so we need to reduce that complexity.If we talk about GPU, it is very expensive.It is used for both software and hardware design; when we need to train data with a large amount of data, system complexity automatically increases.
Assessing the Effectiveness of Cybersecurity Measures in the United States Telecommunications Industry: A Comprehensive Analysis

This research study assessed the effectiveness of cybersecurity measures in the United States telecommunications industry through a comprehensive analysis. Descriptive survey design was employed to establish a theoretical foundation and draw insights from existing scholarly articles, industry reports, and relevant cybersecurity frameworks. The analysis focused on historical cyber threat data and incidents within the telecommunications sector to identify prevalent threats and vulnerabilities. The literature review provided valuable insights into the challenges faced by the industry and the evolving landscape of cybersecurity. Quantitative data was collected through surveys administered to telecommunication companies operating in the United States. The surveys covered various aspects of cybersecurity practices, protocols, and incidents. The data collected from the surveys was analyzed to evaluate the effectiveness of existing cybersecurity measures in mitigating cyber threats. Additionally, qualitative insights and perspectives were gathered through interviews with key stakeholders, including cybersecurity experts, telecommunication industry professionals, and regulatory authorities. The findings of this study contribute to a better understanding of the state of cybersecurity in the United States telecommunications industry. The literature-based approach allowed for a comprehensive assessment of the industry's cybersecurity posture based on existing knowledge and research. The research outcomes provide valuable insights for telecommunication companies, policymakers, and regulatory authorities. By addressing the identified gaps and weaknesses, policymakers and regulatory authorities can implement targeted interventions to enhance cybersecurity practices. Telecommunication companies can leverage the research findings to strengthen their cybersecurity protocols and protect critical infrastructure. The research study demonstrates the importance of continuous evaluation and improvement of cybersecurity measures in the telecommunications industry. It highlights the need for ongoing collaboration between industry stakeholders, policymakers, and regulatory bodies to address the evolving cyber threat landscape effectively. Keywords: Cybersecurity, Measures, United States, Telecommunications Industry, Comprehensive Analysis.
The Development of Interactive 3D Animal Non-Player Characters: A study of Research framework and Implementation of Framework at Actual Research Site

This is a study about the research framework and the implementation of the framework toward fieldwork, most importantly data collection and visual literature preparation for VR Longhouse: The walkthrough. The research team getting ready in the pre-research stage with the sharing and discussion of the research framework. From the very beginning stage until the finalized version. As the process of understanding the framework took place for 3 months, the research team also tried to study the references which be able to support the field work after the pre-research stage. After getting the framework the team went for the preliminary trial stage to test the usability of the framework at 2 dummy sites before embarking to Sarawak for the real fieldwork at the research site in Saribas. Testing and follow-up with modification. The process can confirm a final framework for the fieldwork in Sarawak. The team implemented the framework on the research site. The team managed to collect the visual data to support the VR longhouse: The walkthrough project. The entire process is to determine the usability of the research frame on the actual research project.
Exploration of Blended Teaching Mode from the Perspective of Deep Learning

The deep integration of education and technology has revolutionized teaching models, and blended learning is one of the important means of future classroom teaching.However, superficialism, shallow learning, and sham learning have become the current state of blended learning.This article starts from the perspective of deep learning, deeply analyzes the high compatibility between deep learning and blended teaching, and proposes blended teaching design strategies based on actual course cases, in order to have a positive effect on blended teaching mode.
Automatic exposure control method for 3D shape measurement of specular surface based on digital fringe projection

Abstract Digital fringe projection (DFP) is widely applied in three-dimensional (3D) shape measurements. However, its performance is severely disturbed while measuring complex surfaces with an extensive range of reflectivity. In this paper, to enhance the underexposed regions and reduce the saturated regions, an automatic and effective method is proposed for the multi-exposure determination of the DFP system. During the whole process, only one unsaturated image must be captured preliminarily, which could establish an adaptive strategy based on the intensity value distribution function. Raw absolute phase maps captured with different exposure times are synthesized at the pixel level for phase retrieval and 3D reconstruction. To evaluate the effectiveness of our method, a high dynamic range method and a global optimal exposure method are introduced for comparison. Verification experiments demonstrate that our method performs better for the 3D reconstruction results of complex surfaces with a large reflectivity range.