Reviewerly/exHarmony huggingface dataset:
Average Error Based Adaptive Regularization Control For The Gradient Constancy Variational Stereo Model

The study of the stereo vision problems is most crucial and challenging task in image processing and computer vision. The Stereo Vision problems address the investigation of the correspondence between the two images of same scene (stereo pairs) captured from two different views. Generally, these problems are inverse and ill posed. To deal with such problems the energy-based regularization techniques are considered as an efficient and most successful approaches. The adaptive finite element method is used here as discretization method for the partial differential equation obtained from the optimization of the designed energy functional. Such type of the regularization generally depends on the smoothness parameters, and their suitable choice. The choice of the smoothing parameter in an adaptive way and specifically their choice as a scalar function over the whole computational domain is an interesting idea. In this work, a variational model based on the gradient constancy assumption is proposed, moreover a post optimization method (mesh refinement strategy) is designed which is based on a priori estimate called average absolute disparity error estimate. The post optimization is based on an adaptive intelligent algorithm which is efficient in identifying the less regular regions of the computed disparity image and reduces the value of the parameter to refine the grid. Consequently, the smoothness appears in the solution which is the main goal.
Sentiment Analysis on Disputed Territory Discrepancies Using Machine Learning-based Text Mining Approach

Conducting a comparative study of real-time sentiment analysis poses a significant challenge due to the continuous variation in individuals' accents and the difficulty in quantifying them. In computer science, specifically supervised machine learning classifiers, researchers face the obstacle of lacking direct observation. This study explores the performance of well-known supervised machine learning classifiers such as SVM, K-SVM, and Multinomial Naïve Bayes. We utilize a comprehensive corpus of real-time Twitter tweets and news blogs related to disputed territories. These classifiers are trained on the Kaggle dataset for real-time sentiment analysis to achieve the highest accuracy and subsequently tested on real-time data. Notably, the kernel support vector machine performs better in real-time data, as evidenced by the substantial proportion of positive sentiments detected. Furthermore, our study offers a pathway for young scholars to assess the real-time sentiment correlation between corresponding countries, enabling predictions of potential conflicts that may lead to significant casualties
Sentiment Analysis of Omicron Tweets by using Machine Learning Models

The COVID-19 epidemic has been affecting a lot of individuals worldwide since 2019. It is emerging as an infectious disease that set off a disaster with far-reaching effects on things like education, economics, and health. During the coronavirus outbreak, new COVID-19 mutations such the Beta, Delta, and Omicron variants emerged, terrifying and alarmed the population. Around 6 million people reportedly died as a result of COVID-19 variations, according to World Meter. The SARS-CoV-2 omicron strain was initially identified in South Africa on November 24, 2021, and it has since spread to more than 57 nations. In this essay, we examine how people feel and act toward the omicron variation. On Omicron, we proposed an approach for determining sentiment analysis for tweets from Twitter. The analysis of Twitter data's sentiment has a lot of potential. In the intended methodology, we extract the best characteristics from the Omicron tweets using NLP techniques in Python, resulting in a dataset that can be used to train the Models. The produced dataset was employed by four ML Classifiers, including “Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)”, to accurately categorise users' emotional behavior into three categories: neutral, negative, and positive. The Class Neutral receives the best score and the Class Negative receives the lowest score based on the accuracy of the forecast level.
Research in Collaborative Tagging Applications: Choosing the Right Dataset

Collaborative tagging is an interesting approach that provides the flexibility to add description(s) to a resource according to the user’s own perception about that resource. These applications are the hottest in the areas of Social Bookmarking, Media Content Sharing and E-Commerce. Being favorite among users, these applications accumulate users’ interactions in the form of embedded datasets very quickly. These datasets are very important for further improving these applications and subsequently facilitating the user in better performing his/her activities. We feel there is a need to study these datasets to help researchers test their proposed algorithms on the right dataset and make valuable assessment and informed decisions. In this paper, we have identified measures for evaluating collaborative tagging applications’ datasets suitability for research experiments. The appropriateness of the identified measures is tested through experiments. Based on the results, recommendations are made on the suitability of the available datasets and how future dataset should look like. Researchers working not only in tagging but also in other disciplines can utilize these datasets to test their proposed algorithms without developing their own. This article provides measures which we dig out by reviewing existing available datasets. These measures are significant in selection of suitable and appropriate dataset(s), as selection of inappropriate dataset leads to errors in the results researchers are expecting. This work will prove extremely relevant and beneficial to all researchers who wish to use datasets of collaborative tagging applications for their research experiments.
Questgator: A Platform for Content Aggregation and Text Classification

The Web has witnessed a surge in content over recent years. Content is revolutionizing the way people conduct business, communicate, and make informed decisions. However, the vast amount of data used for communication today is oftenunstructured and challenging to comprehend. Content aggregators provide a solution to this problem by collecting data from various sources and organizing it into a structured format in one place. This research proposed the content aggregator "Questgator" that extracts content for example news, scholarships, jobs, books, video content, and research papers. In this paper Naive Bayes theorem is used for text classiﬁcation. Moreover, paper also provides comparison with other platforms to show the eﬃciency of proposed content aggregator.
Prediction of Product Rating based on Polarized Reviews using Supervised Machine Learning

E-commerce websites facilitate customers to leave their experiences in the form of textual reviews for a variety of products. Recently, online reviews have played significant influencing role in customers’ decision for purchasing. The reviews have information and first hand experience about products’ quality for customers. Free-text sections are frequently found on online review web pages in addition to star-level reviewing options. But on many web pages, we find only the former option. Therefore, there is a need to convert the text-written reviews to star-level on the basis of the information they contain. Automatic conversion of online text-based reviews has recently been emerged as an active field of research in machine learning and deep learning. This paper presents a supervised machine and deep learning based solution to transform text-based reviews to star-level numerical representation by exploiting polarization detected on the basis of lexical analysis. Experiments were conducted on famous Amazon dataset under different choices of regression and classification techniques. Experimental results have indicated that the use of polarized reviews can significantly improve the rating prediction.
Traffic Light Detection: A cost effective approach

In last couple of decades, the technological advancements in image and video processing has brought great revolution in our life. Some of the key areas where these advancements have played a key role are: autonomous vehicles, drone technology, crowd monitoring, traffic monitoring, object tracking etc. Nowadays a lot of work is under process for improving capabilities of autonomous vehicles and driver assisted systems. Our focus in this paper is related to automated traffic light detection system with improved capabilities in terms of time complexity and accuracy. The time complexity is directly related to image or video quality with regard to resolution of video and the accuracy is often compromised because of identification of similar objects. The similar objects often appear in video frames when each frame of video is analyzed completely. In order to solve the problem of real time detection of traffic lights in a high-resolution video having 30 frames per second with a resolution of 1280 × 720, we propose an algorithm that systematically searches in middle 70% region of each frame. The proposed algorithm optimizes the search space by dividing middle region into 3. There are three methods for searching and registering a traffic light is proposed in this paper. The basic concept is at single instance a traffic light can exist on one of these three regions. These trategies help in reducing computation complexity tremendously. The Hough Circle Transform technique of image processing is exploited to accurately detect red and green circles of light in the traffic light. Efficacy of the proposed technique in terms of improved time and accuracy is demonstrated on a real dataset collected from Nexar (dashcam mobile solution provider), it encompasses different illumination conditions: day, evening, night, cludy weather and rain etc.
Origins and Consequences of Misunderstood/Slipped Requirements on Large Scale Software Projects

Requirement engineering is the first and foremost phase of a software development life cycle. Making mistakes during this process has enormous negative impact on all underlying activities of software development. Defects discovered when a system is deployed cost fifty to two hundred times more than defects discovered during software elicitation phase. In this research paper causes of missed requirements have been identified by literature review and supported by interviews and surveys from leading software industry professionals. In total, sixteen factors of missed requirements have been identified and divided into three categories namely; User factors, Analyst factors, and Common Factors. Understanding of these factors will aid in developing software with complete requirements and thus achieving customer satisfaction level and to avoid wastage of effort and poor quality software.
Application of Blockchain in E-banking Transactions

E-banking has now become a must need of every person. Cyber-attacks has been increasing daily and customer data and money are in danger. So, there must be a securable way of making transaction. In this SLR, we proposed blockchain to solve this issue because it is a fast, cheap, and secured way of making a transaction. Forty -one research paper were finalized and had been studied to conduct this SLR. To the best of our knowledge, there is no such SLR available which covers all the aspects of E-banking and blockchain. In this article, we have reviewed different article and showed the importance of using blockchain to secure transaction. Issues and challenges of E-banking sector has also been shown and how can they be removed by blockchain. Furthermore, we had shown already proposed models of blockchain in E-banking and shown which blockchain features can be implemented to secure transaction. Lastly, we have also shown issues and threats of e-banking.
A Systematic literature Review- “Effects of Hybrid-Agile approaches and methods on the quality of software.”

ABSTRACT Software developers have a reputation of embracing new advancements and practices based on their novelties or empirical indications of their potential. We are conducting an SLR, this paper describes the findings of SLR aimed at determining what combinations of agile practices, and methodologies are utilized in hybrid approaches, as well as how the usage of hybrid and agile approaches affects quality of software. In the last 5 years there are more than 3800 original scientific published were considered in the review, from which 14 papers are yielded on the topic. There are six distinct agile methodologies that have been observed effecting the quality of software, as well as Twenty-one agile practices that have an effect on twenty-five different quality attributes of software.
Data privacy issue in Federated Learning Resolution using Block Chain

Reliable and timely traffic patterns have become an increasingly critical aspect of intelligent transport networks for traffic control. However, current predictive models of traffic flow focused on centralized machine learning need to capture raw data for model education that entails significant privacy risks. Federated learning (FL) that exchanges model changes without raw data sharing have recently been launched as an innovative way to resolve these concerns to the security of personal data. The current federal learning system is based on a central coordinator model dealing with serious security problems, including a single failure point. The Literature review is presented in this study will focus on the integration of blockchain in federated learning. Federated learning (FL) based on blockchain is thus suggested in the paper to support decentralized, efficient, and stable federated learning. The model includes three primary components; customers, blockchain technology, and machine learning engineers. In addition, different methods are communicated to protect model privacy on the blockchain further, and the advantages of the new learning system federated built on the blockchain are discussed.
Malware Injection Attacks in Resource Virtualization of Cloud Computing Environment

Cloud computing is a procedure which gives us a large, reliable and scalable infrastructure and service. Cloud computing have some features of distributed system, according to these features cloud computing also uses the features of networking. In this technique, there is large number of data sets on the internet. These data sets can be stored and accessed by a remote server. Some common and basic examples of cloud computing are Amazon, Google’s application etc. This technique provides the user to build an online application and to access that from any remote server hosted on internet. We know cloud computing system usually operate on an open network (public) so there comes security issues. The critical part of cloud computing technology is Virtualization. Virtualization refers to creating new system on the machine that is not actual but acts and performs like the actual ones. Cloud Computing and Virtualization give different services by working together. -This paper describes the malware injection attack on virtual systems working in cloud computing environment and their solutions as well. Cloud computing based on sharing technique therefore, the security is the leading crisis of this system. In this paper we discussed the diverse types of malware injection attacks and their possible solutions. Virtual machine emulators have turn out to be conventional in the examination of malicious code. This paper presents notorious attacks for the most extensively used virtual machine emulators. This paper also demonstrates newly exposed attacks on Bochs, Hydra, QEMU, and Xen (other virtual machine emulators), and describes how to protect from them.
Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywordsâ€” Docker, Node-Bench method, Node.js, Podman, VirtualBox.
A teacher action recognition model based on pre-trained language and video model

Nowadays, with the development of deep learning technology, intelligent analysis of classroom teaching action has become a focus in the field of smart education. Analyzing teacher action might reveal their teaching philosophy, which is important for carrying out intelligent teaching evaluations. The frameworks used in the current studies for teacher action recognition based on deep learning are still largely conventional, and their accuracy is not very good, especially for small-scale datasets. In this paper we propose a fusion framework that combines pre-trained language and video models with prompts, to transfer these models to the teacher action recognition task. Through experiments, our model outperforms the conventional methods on the teacher action recognition task, and achieve higher performance than baseline models on public small-scale action recognition datasets.
Promoting Scientific Inquiry in Physics Students through the Use of Simulators

The objective of this research is to evaluate the impact of using the simulators on students’ achievement of competencies, such as: Inquire, through scientific methods to build knowledge. The methodology used consisted of qualitative research whose data collection was carried out through an experiment with interactive Physics Educational Technology (PhET) simulators. The experiment was carried out through a simple random sampling of 25 students from a population of 100 students of physics subjects. With this sample, four learning sessions were developed using the PhET interactive simulator, which were implemented in a way that helped the development of skills according to learning indicators oriented to the achievement of the competence, Inquire, through scientific methods to build knowledge. A pretest and a posttest were applied. The results of the study show the benefits of the use of simulators in the teaching-learning processes, contributing to the achievement of the competence inquire, through scientific methods to build knowledge, developing capacities, such as: Problematize situations to inquire, design strategies to inquire, generate and record data or information, analyze data and information, and evaluate and communicate the process and results of their inquiry. In conclusion, 100% of the students develop inquiry competencies through the development of capabilities.
Estimating Answer Strategies using Online Handwritten Data: A Study using Geometry Problems

Emphasis has been placed on logical reasoning skills as a necessary ability and quality for the next generation. Developing logical reasoning skills is one of the objectives of the geometry curriculum in secondary education. Understanding an individual's strategy for finding a solution is necessary to assess his/her logical reasoning abilities. This study is the first to estimate answer strategies using online handwritten data automatically. Using geometry problems as our subject, we: 1) detect symbols written in a diagram; and 2) classify the answer strategies using XGBoost with the features combining uni-grams, bi-grams, and 1-skip-grams of symbols in their written order. Our experimental evaluation with 38 university students for a single geometry problem confirmed that answer strategies were successfully classified into three categories with an accuracy of 0.71, which shows the feasibility of the automatic detection of answer strategies.
Design and Application of AR-based 3D Courseware for Elderly Education

The traditional teaching mode of elderly education is mainly teacher-led, and most of the movement skills courses lack multimedia teaching materials to accompany the courses. Focusing on elderly education, this paper designs a 3D yoga courseware based on AR technology and applies it to the teaching process of elderly education. Through class use and survey analysis, from which the effectiveness of the application of AR-based 3D courseware and expanded teaching models in movement classes for elderly education were explored. The use of this courseware can effectively support teachers in their teaching activities and can effectively help participants to improve their learning efficiency, consolidate their learning effect and enrich the teaching mode of elderly education
"Analysis of the Course Design of\"Financial Analysis\"Based on Big Data Technology"

Taking the "Financial Analysis" course of Major in Financial Management of Modern College of Northwest University as the object, explore the combination of big data technology and course content to set up a teaching platform. The curriculum is designed from the aspects of teaching mode, content reconstruction, curriculum realization process, assessment content, and technical level to solve the problem of teaching difficulty of teachers and students. Fundamentally change the course teaching process. Through the simulation teaching system (X-Verse) and Python code dual-mode operation, embedded data mining algorithm tools, building block mode and other ways to reduce the difficulty of business teachers and students. At the same time, it is close to the real application environment of the enterprise to enrich the teaching content, improve the teaching efficiency, and strengthen the practical operation. Finally, the curriculum teaching concept of "student-centered learning" is realized.