Reviewerly/exHarmony huggingface dataset:
Study on Trends and Predictions of Convergence in Cybersecurity Technology Using Machine Learning

&lt;p&gt;The indiscriminate convergence of technologies makes prediction difficult and can cause many difficulties in technology investment. This makes it difficult to choose capital investment and can induce excessive investment in inefficient technologies. Therefore, analyzing the trend of convergence technology and predicting a highly influential convergence area in the future can induce effective investment, and lead the highly influential technology to achieve great technological development. The purpose of this paper is to analyze technologies that are expected to have high influence in the future through prediction of major fusion areas and to present fusion areas that can be used as indicators of investment. The proposed mechanism selected four prominent journals in the security area and collected metadata to generate a dataset in terms of technological excellence and a dataset in terms of commercialization through patent metadata collection. Thereafter, a process of extracting a main keyword according to a topic from a metadata set by applying a Latent Dirichlet Allocation (LDA) is performed. The extracted topics and keywords are not related to topics and keywords of other years. Therefore, a dynamic topic model (DTM) is applied to analyze the trend of the extracted topics and perform prediction. DTM analyzes the topics in the fusion area classified by LDA and the trend of changing topics linked by year for each topic keyword. Finally, the association of the fusion region is analyzed to derive a fusion region with high influence. These results are believed to be used as an indicator of effective technology investment by providing a high impact area in the convergence area of cybersecurity.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Performance Test Application Center For Research And Community Service With The Pieces Method At STKIP Dharma Bakti Lubuk Alung Foundation

The Center for Research and Community Service is a unit that manages the fields of research and community service. So far, the Center for Research and Community Service (P3M) at the Dhar-ma Bakti Lubuk Alung Foundation STKIP manages data on research activities and community service using Microsoft Access, which is entered into the database. Therefore, a web-based research and community service center application for the Dharma Bakti Lubuk Alung Foundation was built using the PHP programming language and database processing using MySQL. The application development method used is the waterfall method. GTMe-trix will be used as software testing to evaluate the performance of this application. The method used in this study is a quantitative method with the PIECES analysis model. The application performance evaluation Center for Research and Community Service received a grade A, indicating that the application was running "very well" based on the results obtained from all pages tested using GTMetrix. The results of the analysis of the level of satisfaction of users of the Center for Research and Community Service applications obtained a value of 4.4, which means that the level of user satisfaction can be categorized as "very satisfied."
Traffic Sign Recognition System for Autonomous Vehicles using Deep Learning

The architecture of a fully autonomous car must be incorporated with a traffic sign recognition system. The Traffic Sign Recognition (TSR) consists of two components: detection and classification. The proposed study, which focuses on identifying these signals, is based on LISA dataset, which is the largest publicly accessible collection of images of traffic signs in the world. The performance of both aggregate channel features-based and integral channel feature-based detection approaches has reached its quality. In the proposed study, the performance of Convolutional Neural Networks (CNN), aggregate channel characteristics, and integral channel features are evaluated. The proposed study investigates the detection performance of CNN by tuning the convolutional layers, max-pool layers, and linear layers. The effectiveness of the proposed detection model is tested using the PASCAL measure, a typical statistic for this system.
Automated Answer Script Evaluation Using Deep Learning

Recent advances in deep learning are replacing conventional systems in many aspects of life. Deep Learning provides a cost-effective and consistent alternative to human marking. An automated script analyzer aids in overcoming the difficulties associated with manual evaluation. The increasing competition in the education field made a heavy load on evaluators which is time-consuming. Finally, irritation results in a difference in paper evaluation from student to student, which is not a reliable and efficient way of evaluation. So to decrease manual power, increase technology for coping with long time-consuming processes, and make equally distributed evaluations according to students writing and standard of answers, we came up with the idea of automated scripting used for automated evaluation of scripts which is an easy and effective way of correction. The system considers factors like grammatical errors, synonyms, stop words, etc for analysis and gives scoring.
Forecasting the Solar Power with Differentiation of Data Figures using Neural Networks

The industrial sector relies heavily on solar power forecasting because it is dependable and readily available. High solar energy penetration is essential for the financial sustainability of power systems. The fact that solar energy is actually renewable is the most significant benefit of all the features of solar panels. Forecast models incorporate information from numerical weather prediction (NWP) factors such as cloud cover, sunshine, radiation, precipitation, etc. However, there is NTP Uncertainty in the assumption of the atmosphere’s starting state. Advanced predictions are made in the meantime by machine learning algorithms. We need to estimate and compare the accuracy rates between Ann algorithm and Rnn algorithm for the outgoing solar power in this study using the dataset from the previous month.In the upcoming month, the crucial machine learning algorithm will be created. The most extensively used “multi-layer Neural Network” model of ANN and RNN should anticipate and compare electricity costs. Our research aims to accurately forecast solar power. The technique of forecasting the solar energy collected from solar panels for future use is attained by machine learning algorithms. The prediction and comparison will be carried out using the Artificial Neural Network and Recurrent Neural Network concepts in Machine Learning.
Public Behavior and Awareness of Face-Mask Litter as Microplastics Source to Human Exposure During the Covid-19 Pandemic

The increased use of masks during the COVID-19 pandemic has enhanced the potential environmental impacts of their disposal, including the release of microplastics (MP). Masks are typically made of plastic materials, which can contribute to plastic pollution if not properly disposed. A survey should be conducted to assess the public awareness of MP emissions and mask disposal, which could inform targeted educational campaigns to promote sustainable practices. This study used an online survey to gather data from a sample of the general public aged 20 years or older who had used masks during the COVID-19 pandemic. The survey included questions on mask usage, disposal, and awareness of the presence of MP in masks. Demographic data were collected to ensure that the results were generalizable to the population of interest. The survey found that, although awareness of the risks of MP was high, the respondents’ understanding of them was low, particularly among younger individuals. The survey also revealed that more than half of the respondents did not know whether their masks contained plastic materials and that there was a lack of awareness regarding the potential environmental and health impacts of improper mask disposal. These results revealed that it is crucial to raise public awareness of the environmental and health impacts of improper mask disposal and the presence of MP in masks.
COMPUTER VISION AIDED HOTSPOT CREATION IN VIRTUAL ENVIRONMENTS

Hotspot creation is one of the most important modules within virtual environments which helps show the navigators of these environments some information about semantic elements within it and facilitate the navigation between the virtual spaces.In this paper, a system for automatic hotspot proposals and creation in virtual environments is proposed.The system uses computer vision modules to automatically propose hotspot locations in addition to identifying and creating these hotspots with candidate labels.Two main modules used in the system are object detection and scene segmentation.The scene segmentation helps give candidate hotspot areas and provides an overall understanding of the semantics of the virtual environment.The object detection module also uses pretrained deep networks for automatic hotspot creation over these objects.The system helps speed up the hotspot creation process and offers a tool for virtual environment users and creators.
AN EVALUATION OF AUGMENTED REALITY-BASED USER INTERFACES IN THE DESIGN PROCESS

The aim of this study is to evaluate the user interfaces that reflect different interaction layers in the context of Augmented Reality technology.Depending on the physical characteristics of human interaction with the computer, these layers were examined under four sections: Graphical User Interface (GUI), Tangible User Interface (TUI), Natural User Interface (NUI) and Spatial User Interface (SUI).In this context, a proposed Augmented Reality application interface has been developed to bridge the physical and digital environment.The use of AR-based applications in the design process provided a basis for evaluating the user interface in these interaction layers.In future studies, the interface and experience offered by this application have the potential to be supported by more comprehensive functions and a collaborative working environment.
Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

The field of sentiment analysis of Arabic scripts remains a major challenge due to the features, characteristics, and complexity of the Arabic language. Few studies on Arabic Sentiment Analysis (ASA) have been conducted to the best of our knowledge when compared to English or other Latin languages. In addition, for ASA, very few studies have been conducted regarding the comments of the users (customers) of the apps available on the Google Play Store within various mobile application reviews. Most of the current studies have been conducted on datasets collected from Twitter user comments. In this paper, we propose a new approach to the analysis of sentiment in Arabic script based on the comments dataset of users of some mobile applications available on the Google Play Store. The proposed approach involves improving algorithms such as the Levenshtein distance (LD) algorithm for data preprocessing and then combining it with the K-Nearest Neighbor (K-NN) algorithm. Through the proposed approach, the results of the experiment were shown, investigating the impact of utilizing the K-NN and LD algorithms for ASA on mobile applications effectively. The experiments were carried out. The K-NN with LD algorithm has gained a better level of evaluation compared to the K-NN algorithm. K-NN with LD algorithm has achieved the highest accuracy, recall, precision, and F-score, which were 83.11% in accuracy, 66.30% in recall, 85.10% in precision, and 74.53% in f-score evaluation measure when =3.
Deepfake: Creation and Detection using Deep Learning

Abstract: The aim of this study is to develop customizedphoto-realistic talking head models, which refers to the creation of systems capable of generating believable video sequences that mimic the speech expressions and facial movements of a specific person. The authors propose a system that can produce talking head models using just a few photographs, a technique known as "few-shot learning," and with minimal training time. This system is capable of generating a plausible outcome using just one photograph, and additional photographs enhance the level of personalization. The authors present a system that can perform few-shot learning by conducting meta-learning on a vast collection of videos, which allows it to address the neural talking head models of new and unseen individuals as adversarial training problems with high-capacity generators and discriminators. The system can personalize both the generator and the discriminator's parameters based on each person, enabling training to be performed quickly with only a few images, despite the need to fine-tune millions of parameters.
A Hybrid Model for Using Cloud Computing and Blockchain Technologies to Protect Hospital Records

Cloud computing technology is one of the hottest trends in information technology (IT) today. It is an important alternative to ensure high data processing and exchange. Thus, this technology can be used in the health sector to exchange patient medical records and health-related information. However, there are fears of the leakage of confidential patient data. This research studied two main contexts, the technological and organizational contexts, to find out the concerns about the use of cloud computing technology in the health sector. The results indicated the desire of hospitals to use cloud computing because of its enormous benefits in the health sector, with concern about data security with cloud computing. For this reason, this paper is proposed. In addition, a model for protecting and storing the patient's medical record using two technologies: cloud computing and blockchain. The model guarantees confidentiality, high protection, and privacy of patient information, and provides all beneficiaries of cloud computing with all the needed information when wanted.
BLOCKCHAIN TECHNOLOGY IN THE DIGITAL MARKETING SECTOR

Similar to the internet, blockchain technology could be the next digital revolution. Blockchains offer tremendous potential, which is why numerous businesses have begun implementing this technology. With the internet enabling the transmission of information, blockchain enables the communication of value. Blockchain is not only associated with cryptocurrencies; thanks to this technology, businesses may increase brand value. As blockchain is a developing technology, there are still numerous possible applications. This has prompted us to investigate the viability of implementing blockchain in the marketing industry. This study examines the application of blockchain technology in the digital marketing industry. This study employs a qualitative approach and descriptive methodologies. The survey results indicate that blockchain is very effective in digital marketing since it may increase consumer trust. This is because blockchain technology guarantees the honesty and integrity of data. In addition, blockchain technology eliminates intermediaries, making the marketing process more straightforward, cheaper, faster, and more accessible.
Twitter sentimental analysis using machine learning

This research paper aims to explore the effectiveness of machine learning algorithms in analyzing sentiment on Twitter.The study utilizes a dataset of tweets collected from various sources, which were then preprocessed to remove noise and irrelevant data [4,5] .To categorize the tweets as positive, negative, or neutral, a number of machine learning techniques were used, such as logistic regression and Naive Bayesian [1] .The efficiency of these algorithms is also assessed in the study using a number of criteria, including accuracy, precision, recall, and F1 score.The results indicate that machine learning algorithms are effective in analyzing sentiment on Twitter, with Naive Bayes providing the best performance [18] .The results of this study have significant ramifications for companies and organizations looking to track consumer opinion of their goods or services [7] .This paper examines the problem of analyzing sentiment in Twitter by examining the tweets' expressed sentiments-whether they be favourable, negative, or neutral.Natural language processing methods will be used to analyze the messages that are tweeted.
Urban Localization using Robust Filtering at Multiple Linearization Points

Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao-Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.
PYRS: A PYTHON PACKAGE TO PROCESS REMOTELY SENSED DATA FOR GEOMATICS EDUCATION

Abstract. PyRS is a Python package developed for processing remotely sensed data. It provides a user-friendly interface to handle various remote sensing tasks, including data reading, radiometric correction, image enhancement, image denoising, image segmentation, land cover classification, change detection, construction of remote sensing indices, quantitative inversion. PyRS includes a wide range of state-of-the-art algorithms and tools for processing optical data. PyRS is an open-source project that is continually updated and maintained by a group of developers. It is designed to be modular and extensible, allowing users to add new algorithms and functionality to the package. PyRS is widely used in the remote sensing community and has been applied to various applications, such as land cover mapping, crop monitoring, and disaster response. PyRS is a comprehensive and powerful Python package for processing remotely sensed data. Its user-friendly interface, broad range of functionalities, and open-source nature make it an attractive tool for researchers and practitioners in the remote sensing field. PyRS encapsulates the current mainstream remote sensing algorithms, which can greatly improve the scientific research efficiency of researchers in the field of remote sensing. The form of Python packages and the characteristics of open source make it more flexible and transparent than professional remote sensing processing software. PyRS will contribute to the progress and development of geomatics education.
Peningkatan Kualitas Pembelajaran melalui Media Pembelajaran LCD Proyektor bagi Guru dan Siswa KB Bina Cendikia Desa Sidorejo Kecamatan Jabung Kabupaten Malang

The use of technology continues to penetrate various aspects of life, one of which is the utilization of technology in the field of education at the Playgroup Level (KB). At KB Bina Cendikia in Jabung Subdistrict, Malang has not used technology for learning media such as LCD projectors; therefore, there is necessary to teach media by utilizing this technology to increase learning activities. This community service activity (PPM) aims to provide learning media in the form of an LCD projector and its equipment and to train KB Bina Cendikia teachers to operate and maintain it. The PPM activities stages include surveys, coordination, procurement, socialization, and handovers. The results of the PPM activity showed that the thematic learning process at KB Bina Cendikia using LCD projector media could make students happier. It demonstrated the students' enthusiasm for participating in the lessons, and there was also an increase in learning activities. KB teacher Bina Cendikia also responded positively to using LCD projectors in thematic learning.
Research on the Impact of the "Three Powers" Decentralization Reform Policy on the Transformation of Scientific and Technological Achievements in Universities: A Case Study of Hubei Province, China

The evaluation of the actual situation of the transformation of scientific and technological achievements in Chinese universities by delegating the "three powers" to universities is of great significance for the formulation and improvement of policies for the transformation of scientific and technological achievements in universities. The article is based on the composite control method and selects multiple provinces in China that have not implemented the "Three Powers Decentralization" policy in university reform pilot projects to simulate the situation where universities in Hubei Province in China have not implemented the policy. The comparison is made with the current "Three Powers Decentralization" policy to highlight the implementation effect of the "Three Powers Decentralization" policy in universities in Hubei Province in China. Research has found that the "three decentralization" reform has a significant promoting effect on the transformation of scientific and technological achievements in universities in Hubei Province. The implementation of the "three decentralization" reform in universities in Hubei Province has a certain degree of policy spillover effect, and has a significant promoting effect on the transformation level of scientific and technological achievements in the entire region. The transformation reform of scientific and technological achievements in universities under the principle of "delegating three powers" has a significant positive promoting effect on the output of scientific and technological achievements in universities in Hubei Province. Based on the research findings, corresponding policy recommendations have been provided for optimizing the transformation plan of scientific and technological achievements in various regions through the exploration of the reform plan in Hubei Province.