Reviewerly/exHarmony huggingface dataset:
Simulation of Resistivity in Silver and Copper Nanowire Composite Filament using Composite Filament Simulation 3D to predict the Resistivity of the Nanowire

Aim: The aim of this research work is to determine and compare the electrical resistivity of silver and copper nanowire by using the Novel Composite Filament Simulation Tool. Materials and Methods: This study was carried out at the Simulation laboratory, Saveetha School of Engineering, Chennai. The sample size calculation was done using clinical.com by keeping alpha error-threshold by 0.05, enrollment ratio as 0:1, 95% confidence interval, power at 80%. Group 1 was silver nanowire (N=20) and group 2 was copper nanowire (N=20). The total sample size was 40. Results: Comparison of electrical resistivity is done by independent sample test using SPSS software. There is a statistical indifference between silver and copper nanowire. Copper nanowire (55%) showed higher resistivity in comparison to silver nanowire (45%). There appears to be a statistically insignificant difference (p=0.269, p&gt;0.05) by using an independent sample T test. Conclusion: Silver nanowire appears to give better electrical conductivity than in copper nanowire.
Analysis and Comparison for Innovative Prediction Technique of Breast Cancer Tumor using Decision Tree Algorithm over Support Vector Machine Algorithm with Improved Accuracy

Aim: The major goal of this research is to improve the accuracy of the Decision Tree (DT) and Support vector machine (SVM) algorithms and compare their efficiency in detecting breast cancer tumors. Materials and Methods: This work depends on the data obtained from the UCI Machine Learning Repository and used to acquire the data sets for the research of Innovative breast cancer prediction using machine learning algorithms. The sample size of breast cancer prediction involves two groups: Decision tree (N=20) and Support vector machine (N=20) according to clincalc.com by keeping 0.05 alpha error-threshold, 95% confidence interval, enrollment ratio as 0:1, and 80% G power. The accuracy, sensitivity, and precision are calculated using MATLAB software. Result: The accuracy of the DT is 83.83% (p&lt;0.001) while the accuracy rate of the Support vector machine is 97.50%. The Decision tree outcomes have a sensitivity and precision rate of 87.46% (p&lt;0.001) and 84.13% (p&lt;0.001) respectively, whereas the Support vector machine sensitivity and the precision rate are 95.83% and 100% respectively. Conclusion: Support vector machine algorithm performed significantly better with improved accuracy of 97.50% for breast cancer prediction.
Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Linear Contrast Enhancement Technique over Histogram Equalization Technique

Aim: The aim of this study is to compare and analyze linear contrast enhancement algorithm for lung image enhancement over novel histogram equalization technique. Material and Methods: In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for linear contrast filter and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both linear contrast filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also PSNR values of each image were extracted. Then through SPSS software comparison and analysis has been made. Result: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than linear contrast filter. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and linear contrast filter. The novel histogram equalization technique showed higher results of PSNR (65.9197dB) with (p=0.04) in comparison with linear contrast filter (36.1190dB). Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in linear contrast enhancement technique.
Designing Real-Time Frame Modification Functions Using Hand Gesture Recognition

Computer vision experts have been fascinated by gesture recognition and its integration with new technologies for a long time. Gesture recognition and its application area range from the medical industry, gaming, and virtual reality to automobiles and the consumer electronics market. With the use of gesture detection and tracking technologies, devices can be accessed, and functions can be performed from a distance without any additional hardware. Many breakthroughs have been achieved in hand gesture recognition algorithms, yet functionalities designed without high computing power for training data requirements, the difficulty of usage, and real-time processing with additional hardware remain difficult for real-world applications. The study focuses on developing a hand gesture detection setup with functionalities for real-time frames where the user interacts with a bare hand. The functions interact with and augment real-time video frames in a virtual world using computer vision algorithms and gesture recognition techniques. The paper presents five functions performed using hand gestures on real-time video captured by a camera. The functions are Color Mode Change, drawing in real-time with Virtual Pen, ROI Selection, and cropping, saving the desired frame and Window closing as per the user's control.
Security Index of Major Crops

Following the founding of New China, especially after the reform and opening-up, China has made remarkable achievements in grain production, with total grain production increasing by 486%.
Image and Video Dataset for Visually Impaired using Intel Realsense Camera

The Dataset consists of two videos, one recorded with blindfold on and the other without blindfold recorded using a 1080p Intel RealSense depth camera. It contains the videos, images extracted using ffmpeg and processed video which is made of a video with skipped frames created using ffmpeg. The scope of the dataset is for machine vision purposes to allow for tasks such as instance segmentation. A hat fixed on the head of a blindfolded person is used to record walking activities. For the generation of this dataset, we have recorded videos and extracted images with a blindfold to emulate the slower and more careful gait of a visually impaired person.
3D Scenes Semantic Understanding: New Approach Based on Image Processing for Time Learning Reducing

Recently, the 3D scenes semantic understanding axis has attracted many researchers’ attention interested in the computer vision field; it become a major factor for several applications development such as robotics, autonomous-driving, and smart city applications. The research's main purpose related to the scene Understanding tasks is to furnish the computers with needed techniques to simulate the human mind's ability to fully perceive their environments and to analyze real-time scene information. Moreover, if computers can express this understanding in natural human language, then they can effectively communicate with humans and their environment, which paves the way for the investment of these systems in many vital areas such as industrial, medical, and astronomical fields. To overcome this challenge; various related works have been explored, among which we find the axis of detection of the visual relationships in the workspace; which appeared as an important research area aimed at detecting objects in the scene; categorizing the relations connecting each objects pair, then expressing them in natural language. In our research, we worked with the visual relationship detection method based on extracting visual, semantic, and spatial features of the image using the faster RCNN, transforming them into a weighted digraph using GNN, and merging them for the lower-dimensional feature vectors’ final extraction. We propose a new image processing-based approach to decrease the learning time and increase the relationship detection task efficiency. Given the available equipment's limited effectiveness; we had to work with only 25% of the VG dataset.
Color Medical Image Encryption Based on Chaotic System and DNA

The discipline of information security known as deoxyribonucleic acid (DNA) cryptography is one of the newest and most promising areas. In this context, we suggest a new color medical imaging algorithm that combines the biogenetic principle of DNA with a four-dimensional, extremely chaotic system. This program encrypts the image blocks using the hyper-chaotic system's chaotic output, then determines the encoding, decoding, and DNA calculation of each image block to increase key space and resist image processing attacks. Simulation results exhibit the strength and efficiency of the suggested algorithm against different types of image processing attacks compared to other encryption algorithms.
KARAKTERISTIK TOKO PEMPEK BERDASARKAN FASILITAS DI KOTA PALEMBANG

This research aims to identify the characteristics of pempek stores based on facilities. The research was conducted in six different stores by grade. Grades include Grade I until IV. Pempek stores are made based on the grade of facilities. Facilities include place, atmosphere and comfort. Grade I has open room, facilities and infrastructure. Grade II has open space room, adequate infrastructure facilities. Grade III has open and closed room, more complete facilities and infrastructure. Grade V has a closed space and facilities are quite complete while grade VI has the most complete closed room, facilities and infrastructure compared to other grades. So that grade VI has the most complete character.&#x0D; &#x0D;
PENGEMBANGAN MEDIA PEMBELAJARAN PENYAKIT LANGKA BERBASIS MULTIMEDIA INTERAKTIF DENGAN METODE LUTHER

The types of rare diseases are currently unknown to many people, especially the general public, due to a lack of information and outreach. Although this disease is very rare, there is a need for early education on its symptoms as a form of anticipation and information and tolerance for sufferers of this disease with the aim of increasing knowledge and awareness about rare diseases. The main problem in this study is the lack of information media for health education about rare diseases, so we need an information media that can present rare disease knowledge that is not only easy to understand but also creative in order to be able to attract people's attention and make learning more fun by creating a interactive multimedia-based learning system. This application is to support understanding of rare diseases, especially for ordinary people who suffer from rare diseases or are around people with rare diseases. The method chosen is the Luther method, which is a multimedia development method based on six stages, namely Concept, Design, Material Collection, Assembly, Testing, and Distribution.
Privacy preserving using extended euclidean – algorithm applied to RSA

RSA cryptography is a strong encryption method widely used in online transactions. Using the extended Euclidean algorithm is an important and efficient technique for finding the secret key in RSA cryptography. This study provides an implementation of the extended Euclidean algorithm to find secret keys based on RSA cryptography and hopes that it can be of help to experts in the field of information security.
Descriptive statistical study of the most important possible causes of obstetric and postpartum hemorrhage in Thi-Qar province

The aim of the study is to describe and analyze the most important causes that lead to postpartum hemorrhage. This research was carried out at The Bent al-Huda Hospital for Maternity and Children's Hospital in Nasiriyah, where the study included (63) cases of women with postpartum hemorrhage from the reviews of the hospital for the period from February 2020 to December 2020 as shown in the hospital records, and collected information for the study through a simple questionnaire, including information: age of women, place of residence, place of birth, type of birth and causes of hemorrhage. Age groups between (40-44) accounted for (22?), (35-39), which are the highest age groups at risk of postpartum hemorrhage. The most frequent deliveries take place in the hospital, because natural or cesarean deliveries increase in this percentage. The results of the current study also show that there is no difference between natural childbirth and caesarean section with the occurrence of postpartum hemorrhage , that the condition of non-constriction of the uterus (inertia of uterus) constitutes (30%) and is considered the highest percentage among the causes of hemorrhage after birth, and then the causes such as the remnants of part of the placenta and the frequency of caesarean section by (16%), the use of accelerated medicines for childbirth and by percentage (14%) while the other causes came in low rates of cases postpartum hemorrhage.&#x0D;
BLENDED LEARNING IN HIGHER EDUCATION: AN APPROACH, A MODEL, AND TWO THEORETICAL FRAMEWORKS

In the last 2 years Higher Education Institutions around the world were forced to make the transition to online and now evaluate and recalculate mistakes, successes and opportunities to continue or adapt teaching to Blended Learning methodology, since going back to what was in the past no longer seems to be a viable path. This paper, through a literature review, is a first approach within the concept and the theoretical frameworks that make Blended Learning a possibility and a strong ally to promote meaningful Teaching and Learning in Higher Education. Subsequently, it is planned to carry out a systematic bibliographic analysis in order to go a step further and identify the main challenges and opportunities that Blended Learning brings to Teaching and Learning in Higher Education in the (post) pandemic context.
Analysis of student responses toward activities using e-learning in higher education

The aim of this study is to investigate how students respond to learning activities using e-learning. Researchers analyzed students' responses based on four aspects of activity, including attendance discipline, assignment collection accuracy, enthusiastic use of e-learning teaching materials, and participation in group discussions. The research method used in this study is a quantitative descriptive method and usability testing method. The data analysis technique used is descriptive percentage. The sample for this study consisted of 100 students from the informatics education study program, including the batches of 2019, 2020, and 2021. The results showed that the average response rate of students towards e-learning activities was 67.19% for the year 2019, 81.32% for the year 2020, and 78.59% for the year 2021. Based on the findings, it can be concluded that students from all three batches responded positively to the e-learning activities.
Innovating Teaching Methods to Improve The Quality of Human Resources: Necessity and Challenges

On current, with the strong development of science and technology and the trend of globalization, the training of high-quality human resources, and the ability to create and adapt quickly to the new working environment are conditions that exist in each nation. As one of the key components of the teaching and learning process, the method is a special activity and must always be changed to fit the practical, demanding requirements of age and suit. Employer requirements. These changes set for teachers as well as learners to always explore, and discover new teaching and learning methods that are appropriate, which is also one of the important bases to improve the quality of human resources on the current.
Data stream mining for predicting the thermal power consumption of the Mars Express spacecraft

The Mars Express (MEX) spacecraft, operated by the European Space Agency (ESA), has been orbiting Mars for the past 18 years. During this period, it has provided unprecedented scientific data about the red planet, but it has also aged, and its batteries have degraded. Thus, MEX needs careful and accurate power modeling to continue its significant contribution without breaking, twisting, deforming, or failure of any equipment. The power consumed by the autonomous thermal subsystem, that keeps all equipment within its operating temperature in a difficult environment, is the only unknown variable in the spacecraft’s power budget. In this pilot study, we address the task of predicting the thermal power consumption (TPC) of MEX on all of its 33 thermal power lines, learning predictive models from the stream of its telemetry data, which is a task of multi-target regression on data streams. To analyze such data streams and to model the MEX power consumption, we consider both local and global approaches, i.e., predicting each target by a separate model and predicting all targets at once by a single model, respectively. Our evaluation of the considered approaches investigates their performance in predicting the MEX power consumption, the influence of the time resolution of the measurements of TPC on this performance, and the success of the methods in detecting and adapting to change.
The model of formation of cognitive independence of students when using electronic textbooks in teaching

Main problem: the article is devoted to the description of the model of formation of cognitive independence of students, based on the use of electronic textbooks in teaching. The most important pedagogical, methodological aspects that the proposed model should reflect are described in detail. The problem under study depends entirely on the construction and implementation of a model of the pedagogical process based on the use of electronic textbooks, as well as on the construction and implementation of a model for the formation of cognitive independence of students. Based on the considered models, the strategy of the teacher's activity in teaching students through the use of electronic textbooks is determined, which most favorably and effectively affects the formation of cognitive independence in students. Purpose: to describe the technologies of constructing a model for the formation of cognitive independence of students through the use of electronic textbooks and its practical application in the educational process on the basis of the proposed strategy of the teacher's activity. Methods: the methods of analysis, synthesis and deduction were used in the article. The analysis of John Dewey's didactic concept and the requirements and principles of constructing a model of the pedagogical process formed on its basis was carried out on the basis of a descriptive and analytical method. Results and their significance: the practical result of the models proposed by the author is a set of organizational and pedagogical conditions, the observance of which contributes to the more intensive development of students' cognitive independence in the learning process through the use of electronic textbooks. The model proposed by the author will contribute to a more effective formation of students' cognitive independence in the process of acquiring knowledge through the use of electronic textbooks.
Real-Time Phishing Website Detection using Machine Learning and Updating Phishing Probability with User Feedback

Phishing attacks remain a significant threat to internet users worldwide. Cybercriminals often send out phishing links through various channels such as emails, social media platforms, or text messages, to trick users into disclosing their sensitive infor-mation such as passwords, usernames, or credit card details. This stolen information is then used to perpetrate various types of fraud or sold on the dark web for profit. To combat this problem, various machine learning-based solutions have been developed for detect-ing phishing websites. However, these solutions vary in their effec-tiveness, with some focusing on URL-based algorithms while oth-ers focus on website content. This paper proposes a machine learn-ing-based approach to real-time phishing website detection, with a focus on the website's URL, domain page, and content. The pro-posed framework will be implemented as a browser plug-in, which can identify phishing risks as users visit websites. The framework integrates several techniques, including blacklist interception, whitelist filtering, and machine learning prediction, to improve ac-curacy, reduce false alarm rates, and minimize computation times. The proposed approach also incorporates user feedback to update the phishing probability over time, thereby increasing the accuracy of detecting phishing websites. This feedback loop involves users reporting suspected phishing websites to the system, which then updates the phishing probability calculation with new information to improve its accuracy. The significance of this research lies in its ability to provide real-time phishing detection capabilities, which can help protect internet users from falling victim to phishing at-tacks. Furthermore, the use of machine learning-based algorithms and user feedback ensures that the system is continuously updated to remain effective against new and emerging threats.
Design , Implementing, and Testing a Novel Chaotic Random Generator CRN

Nowadays, the importance of implementing a secure communication system that protects the privacy and confidential information from cyber-attacks and eavesdroppers is widely recognized. This requires developing solutions that not only address security issues but should not affect the speed of our system also. To achieve secure communication, most security systems rely on randomness generators, which play a vital role in the encryption process, with more randomness equaling greater security. In this paper, we propose a methodology for designing and implementing a novel random generator by using Lorenz chaotic signals. The proposed chaotic random generator CRN has been tested using NIST's Statistical Test Suite for Random and Pseudorandom Generators for Cryptographic Applications and results have been discussed.
A Tale of Two Languages in Blogging: Code-Switching Analysis in Pakistani Blogosphere

Computer-Mediated Communication (CMC) have brought about a new medium for information sharing and communication and weblogs are becoming more and more popular in the virtual sphere. The process of code- switching can be traced in this fast evolving medium of communication. This paper aims at investigating the existence of code-switching patterns by examining the categories and frequency of Urdu code-switches in Pakistani English weblogs authored by Pakistani bloggers. The quantitative and qualitative approaches were used in this study. The blog entries of 10 Pakistani bloggers were analyzed by using the descriptive research paradigm. The findings illustrated that the linguistic choices of Pakistani bloggers as bilingual internet users are living in between two worlds, two cultures, and two languages which they employ in this mode of communication to fully express themselves. The findings showed the linguistic features that are particular to the context of CMC. The study concluded that the presence of code-switching in CMC have to be considered and treated as a unique and different entity from spoken form or written form of code-switching to capture its fundamental attributes.