Reviewerly/exHarmony huggingface dataset:
A Robust Video Stabilization Method Based on Feature Point Extraction and Warping

In recent technologies there are various applications which include a camera joined to a moving platform, for example, cars, drones and Unmanned Aerial Vehicles (UAV). The moving platform may suffer from vibrations which may cause unwanted motion in recordings that can cause degradation of performance in various applications like surveillance, tracking and detection of object. Stabilization of video in various applications is an emerging research area nowadays. To remove the unwanted motion from video, the stabilization is necessary to preserve the important content present in the video. In this paper the feature points from recorded videos are detected and then these feature points are extracted and matched. The obtained feature points are smoothed by K means clustering, a mesh grid on every video frame is set up and every grid is warped by matching and comparing the features points, from original video frame with the smoothed and stabilized feature points. The reduced distortions in the video are estimated from various parameters. The efficiency of algorithm is compared in which the robust video stabilization algorithm based on feature extraction and mesh grid warping obtains better improvement in Inter-frame Transform Fidelity (ITF) factor than the traditional video stabilization algorithm.
High-Performance Computing using Apache Ignite Hadoop Accelerator

Recently, IT has popularized the term high performance computing. In STEM High Performance Computing is used not only to model complicated calculations, but also to improve the applications, reduce costs and reduce development times, in order to solve large problems, in a much higher level of performance than a normal computer. In simple terms, the memory computing primarily relies on keeping data in the RAM as storage implies at high speeds. The HPC allows big data to be processed as quickly as possible.
Path Generation and Map Construction for Mobile Robot Navigation

Vision based mobile robot navigation is one of the important because the robots can resolve lots of problems in which human life is dangerous, difficult and costly. A difficult problem is to make a vision-based robot navigate properly to the target by avoiding the obstacles. Path information is very essential for successful navigation of the robot. The main purpose of this paper is to present an approach for mobile robots to generate path and construct maps. This work is focused on providing the path information of an environment using a single 2-D camera. Whenever robot identifies the human in the corridor, it generates the information of the path information and constructs the map is achieved based on the human moment and the human position. On the robot the camera is mounted which acquires images in real time. Oath information is generated for mobile robot using acquired real time images and processing real time images.
An Exploratory Study on The Use of 3d Hologram Visualization in Egypt's Educational Theater

This research paper is an exploratory study on the uses of hologram technology in educational theaters in Egyptian schools. Considering theater as one of the instructional methods adopted by the Egyptian Ministry of Education in the school curricula, and as an accredited school activity within a group of other educational activities. The introduction provides an overview of holographic technology and predicts its future in student learning. It further outlines educational activities that have become heavily dependent on technology in training and application, with the simplistic definition of 3D technology and 3D imaging explained. The literature review presents publications in hologram technology and related publications, explaining the different concepts of holographic, virtual, and augmented reality. The applicability of these concepts is elucidated, especially in theater activity as an educational tool in schools and educational institutions. Besides, the research focuses on the educational theater in Egypt, and the use of technology as a supportive means of achieving the goals of educational activities that may apply to exceptional capabilities and requirements. Modern technology using hologram may provide solutions to basic requirements for the stage, lighting arts, decoration, and theatrical illusions to achieve the goals of theatrical performances for educational purposes. The study used a sample size of 400 individuals represented as those responsible for performing theater activity in several schools in four different governorates in Egypt. The study results showed the impact of holographic technology in Egyptian school theaters and future challenges that may hinder the implementation of holographic technology in educational theatres, making references to previous studies on the topic. In its conclusions, the study addressed the funding gap and other challenges facing the implementation of hologram technology in educational theaters in Egypt
A Access of MBFD, Logo Allocator, MINLP and MWFDVP VM Placement Algorithm on Cloud Datacenter

allotted computing offers the distinct assets oninterest for the prevent customers on pay with regards to utilize via net affiliation. This has advanced the interest for figuring sources precept to the development of power intake inward datacenters. the most important undertaking is the means via which to dependability the blessings by diminishing the power usage associated with SLA. Powerfully merging the advanced contraption, which places the inert hubs to paintings hubs or switches off the unused host tool in datacenters by way of methods for relocating the virtual gadget stay from underloaded or over-burden have device. in this paper we've in examination 4 diverse advanced system arrangement set of concepts for the electricity intake, scope of VM relocation, SLA infringement (SLAV), execution debasement in light of Migration (PDM), SLA infringement Time according to dynamic host (SLATAH) and strength SLA infringement (ESV). to evaluate the association of ideas we've utilized the CloudSim activity toolbox and actual international works of art burden traces of planet lab VMs for our trial.
Using of Problem Tasks in Development of Creative Abilities of Pupils in Teaching Mathematics

This article is written about the result of research on the using of problem tasks in development of creative abilities of pupils in teaching mathematics. The aim of the research is to improve the development methodology of students' creative abilities, through a task-oriented to teaching. The tasks of the research are following from and analytically study those about the practical basis for the development of students' creative abilities using the problematic tasks to teaching mathematics;to improve the methodology for the development of creative abilities of students by teaching them mathematical modeling in solving problems of a non-standard and practical-applied nature;to develop and scientifically substantiate the structural-functional model of the organization of educational and creative activities of students in the process of teaching mathematics; to develop guidelines for improving the methodology, based on the problem task skills development.The article consists of from introduction, literature survey, methodology, discussions,recommendation,and conclusion.
Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Learning Platform

Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XGBoost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.
Step 3: Fundamentals of Machine Learning

This chapter focuses on different algorithms of supervised and unsupervised machine learning (ML) using two key Python packages.
Use of 'A light weight secure image encryption scheme based on chaos and DNA computing' for encrypted audio watermarking

Watermarking is one of the best way to authenticate the ownership or the source of data by embedding copyright information onto the image, audio or video. At the same time to maintain anonymity or source of data from unintended users its need to encrypt before embedding. This paper presents an effective use of encryption algorithm in audio watermarking. The watermark data is initially encrypted with 'A light weight secure image encryption scheme based on chaos and DNA computing'. In the second part, the encrypted data embedded onto an audio using discrete cosine transform (DCT) and discreet wavelet transformation (DWT). The test results are promising and the watermarked audio does not looses its quality.
Communication to the Public of Works Licensed as ‘Free of Broadcasting Rights’ by a Music Platform

Journal Article Communication to the Public of Works Licensed as ‘Free of Broadcasting Rights’ by a Music Platform Get access GRUR International, Volume 69, Issue 8, August 2020, Pages 866–868, https://doi.org/10.1093/grurint/ikaa052 Published: 01 May 2020
Contribution of Technical and Vocational Education in Human Resource Development of Urban Areas: A Case of Islamabad City

Education is a basic human right and is considered a key tool for national development.Pakistani society follows the universal pattern of differentiated general education and technical education.One hundred and twenty respondents were selected through a convenient sampling technique and were interviewed by a well-structured research tool (questionnaire).Ten respondents were pretested to check and examine the workability of the questionnaire.Collected data was analyzed through appropriate statistical techniques by using SPSS software, and recommendations were made for the improvement of technical and vocational education regarding human resources.The study found that the majority of the respondents belonged to those skilled persons who were agreed with private organizations taking an interest in conducting vocational training courses.During conducting data, it was also observed that the majority of respondents were agreed that their institutes are providing skills according to international standards.Near to half of the respondents were found a tendency toward getting technical-vocational education in our country with a high ratio.The indings of this study will contribute more in the literature in the area of education in human resource management and will also open a new avenue for practitioners as well as for researchers.
Variable Selection

Variable selection is an important topic in many types of multivariate modelling: the choice which variables to take into account to a large degree determines the result.
Research and Realization of a Three-level Label System for Railway locomotive Equipment Portrait

Locomotives are important transportation production equipment for railway industry.Carrying out equipment portrait analysis and label system research is an important way to promote the development of big data technologies in railway locomotive system.Firstly, by briefly summarizing the theoretical knowledge of portrait technology and label technology, this paper researches and designs a technical framework suitable for locomotive label system and locomotive equipment portrait.Secondly, based on this technical framework, the three-level label system of locomotives is introduced in detail.Finally, an optimized clustering algorithm for centroid selection is proposed to form a complete and reasonable locomotive label system.The related research of locomotive label system can help to realize the applications of locomotive equipment portrait, improve the railway transportation production efficiency, and strengthen the safety management ability.
Online Health Information Seeking and Information Quality: A Preliminary Contemplation

The healthcare industry has undergone a rapid change as the internet shows latent abilities to assist in information seeking and decision making regarding self-care and health-related issues. Therefore, it is essential to discern issues concerning the quality of the information and the system. Thus, this paper aims to explore the current issues in information quality, system quality and health risks within the context of online health information seeking. First, a literature review is conducted to gather information about the issues in detail. Second, a preliminary study is implemented to clarify the existence of the research problem in the real world. Relying on online survey method, the data were collected from doctors, health professionals, nurses, and consumers. The resultsfound that the main issue in information quality is the consumers' lack of ability to distinguish quality health information, whereas, the lack of system integration is the major problem associated with system quality. 88.6% of the respondents had underlined that health information could be misleading as a vital health risk. This study is a significant addition to the literature, in that it confirms the impacts of information quality, system quality and health risks on consumers’ behavior in health information seeking while using e-health websites.
Research on Achieving Data Security with the Cloud Computing Adoption Framework

offering perpetual statistics safety for petabytes of facts is important for handed on enrolling. A ordinary overview on cloud safety imparts that the safety of customers' records has the maximum lifted need and moreover challenge. We acquire this need in order to accomplish with a manner of thinking this is succesful, adoptable and all around made. consequently, this paper has constructed up a device known as Cloud Computing Adoption Framework (CCAF)which has been modified for verifying cloud information. This paper clears up the chart, device for deduction and regions within the CCAF to make certain statistics safety. CCAF is outlined through using the framework route of action depending on the fundamentals and the execution confirmed up thru the CCAF multi-layered protection. while you do not forget that our records center has 10 petabytes of data, there can be splendid challenge to provide nonstop safety and detach. We use business agency manner Modeling Notation (BPMN) to mirror how information is getting used. using BPMN redirection allows us to assess the picked protection introductions in advance than showed execution. outcomes show that an opportunity to assume duty for security break may have a few spot inside the extent of 50 and 100 twenty 5 hours. The server farms have skilled problems of brisk increase within the data. as an example, in a server broaden that the lead writer used to art work with, properly ordered option of 100 terabytes of statistics modified into elegant this proposes greater safety is needed to guarantee all statistics is particularly ensured inside the squeezing a hundred twenty five hours. This paper has correspondingly tested that CCAF multi-layered protection can ensure statistics coherently and it has 3 layers of safety: 1) firewall and get admission to govern; 2) personality affiliation and obstruction adjusting movement and 3) joined encryption. To avow CCAF, this paper has gotten a deal with on techniques of proper hacking assessments required with attack trying out with 10,000 Trojans and pollutions. The CCAF multi-layered security can rectangular 9,919 illnesses and Trojans which can be beaten like a burst and the staying ones may be disengaged or disconnected. The tests seem paying little admire to the way in which that the diploma of blockading can decrease for steady aggregate of ailments and Trojans, 90 seven.forty three percent of them can be restricted. Our CCAF multi-layered safety has a humdrum of 20 percentage ideal execution over the singlelayered manner of thinking which can simply block7, 438 defilements and Trojans
Machine Learning Algorithms: Diagnosing Breast Cancer

Breast Cancer has become one of the common diseases not only in women but also in few men. According to research, the demise rate of females has increased mainly because of Breast Cancer tumor. One out of every eight women and one out of every thousand men are diagnosed with breast cancer. Breast cancer tumors are mainly classified into two types: Benign tumor which is a non-cancerous tumor and other one is malignant tumor which is a cancerous tumor. In order to know which type of tumor a patient has; the accurate and early diagnosis is a very crucial step. Machine Learning (ML) algorithms have been used to develop and train the model for classification of the type of tumor. For accurate and better classification several classification algorithms in ML have been trained and tested on the dataset that was collected. Already algorithms like Naïve Bayes, Random Forest, K-Nearest Neighbor and SVM showed better accuracy for classification of tumor. When we implemented Multilayer Perceptron (MLP) algorithm it gave us the best accuracy levels among all both during training as well as testing .i.e. 97%. So, the exact classification using this model will help the doctors to diagnose the type of tumor in patients quickly and accurately
Spam, a Digital Pollution and Ways to Eradicate It

Due to the growing popularity of the microblogging and networking sites like twitter, Gmail, Facebook etc., there has been an increase in the number of spammers. Spammers on Twitter seem to be more dangerous than the mail spammers as they exploit the limitation on the characters of Twitter for their own purposes. Spammers have also become creative in framing their content to cleverly escape the classifiers. This survey is thus mainly used to discuss and analyze the recent research that had been put forth regarding the spam detection in social media sites such as Twitter. This survey analyses the papers that tackled various problems faced on Twitter and the problems faced by the methods that have already been presented before. We then compared all the methods present in the papers to see which method or combination of methods could give the best result in detecting spam.
Execution of scientific workflows on IaaS cloud by PBRR algorithm

Job scheduling of scientific workflow applications in IaaS cloud is a challenging task. Optimal resource mapping of jobs to virtual machines is calculated considering schedule constraints such as timeline and cost. Determining the required number of virtual machines to execute the jobs is key in finding the optimal schedule makespan with minimal cost. In this paper, VMPROV algorithm has been proposed to find the required virtual machines. Priority-based round robin (PBRR) algorithm is proposed for finding the job to resource mapping with minimal makespan and cost. Executions of four real-world scientific application jobs by PBRR algorithm are compared with MINMIN, MAXMIN, MCT, and round robin algorithms execution times. The results show that the proposed algorithm PBRR can predict the mapping of tasks to virtual machines in better way compared to the other classic algorithms.
A New Method for Medical Image Denoising using DTCWT and Bilateral Filter

The quality of digital medical images plays vital role in Non-invasive imaging techniques, which are suitable for medical diagnosis and treatment. Removal of noise from a noisy image without losing the diagnostic details in medical image is still a challenging task even though several denoising methods have been proposed since past years. The wavelet thresholding ap-proach has been reported to be a highly successful method for image denoising. However, the main problem experienced in wavelet thresholding is smoothening of edges. In order to retain original texture while denoising medical images, several methods have been reported in literature. In this paper, we proposed, a new method based on combination of dual-tree complex wavelet transform (DTCWT) and bilateral filters for denoising of medical images. The proposed models are experimented on standard medical images, like MRI image of knee contaminated with Rician noise, CT Scan image of brain contaminated with Gaussian noise, Ultrasound image of liver contaminated with Speckle noise. The results have shown that denoised images using the proposed ap-proach have better performance in terms of smoothness and ac-curacy compared with existing methods. To assess quality of denoised images the quality metrics, the standard Signal to Noise Ratio (SNR), Universal Image Quality Index (UQI) Mean square error (MSR), and Structural Similarity Index (SSIM) are em-ployed.
The Effect of Social Learning-Related Variables on Academic Achievement

This study determined the relationship between the social learning-related variables and the skills achievement in Mathematics of the 200 Grade 8 students in a public school in the Philippines.Descriptive research design was used through a self-constructed questionnaire and an assessment test.The data gathered were presented in a tabular mean while the relationship between variables was tested using Pearson r correlation coefficient.The students' social learning-related variables were highly manifested (WAM = 3.44, SD = 1.11).However, only 11.5% of the students have satisfactory performance in Mathematics.Among the demographic profile, only mothers' educational attainment has shown significant relationship with mathematics achievement in terms of estimation (r = 0.184).Among the social learning-related variables, attitude of the students indicated significant relationship with both estimation (r = 0.184) and problem solving (r = 0.196).Peer support also revealed significant relationship with both representation (r = 0.167) and estimation (r = 0.159).The mathematics teacher must find effective ways on how to overcome the students' difficulties and thus develop their mathematical skills.Efforts to thresh out problems in vocabulary development especially solving worded problems may be exerted.A parallel study may be conducted using variables not considered in this research.
Tri-Objective NSGA-II Based Methods for Load Balancing

The rapid rise of virtual machines are affecting the daily lives of people profusely. It is clear that to cater to such huge amounts of requests, servers which can withstand the upper bound of those requests must be maintained. In this paper, we propose a model based on Evolutionary Algorithms which attempts to schedule given tasks to virtual machines in such a manner, so as to minimise the load imbalance among the different machines available. We show that using a greedy approach with certain optimisation functions, a workable solution can be reached which would help reduce this "upper bound" mentioned above. Through it, one can expect the load on any particular machine to not exceed a certain amount and be distributed amongst all virtual machines.