Reviewerly/exHarmony huggingface dataset:
Super-Resolution of Solar Active Region Patches Using Generative Adversarial Networks

Monitoring solar active region patches from Helioseismic and Magnetic Imager (HMI) instruments is essential for space weather forecasting. However, recovering small bipolar details in HMI patches requires additional pre-processing steps to obtain better quality. This work uses a generative adversarial network, with transposed convolution and super-pixel convolution up-sampling layers, to generate the higher quality of HMI patches. It trains and validates the network based on binary cross-entropy, mean absolute error and multi-scale dice-coefficient functions. It illustrates the performance of the generative method in two image types (magnetogram and continuum intensity patches) from two instruments (SDO/HMI and SOT/NET). It also compares its performance with state-of-the-art methods. The results demonstrate that the generative method produces high-quality images by increasing polarity contrast and retrieving smaller structures.
Video Tutorials and Quick Response Codes to Assist Mathematical Literacy Students in a Non-classroom Environment

codes, on Grade 10 mathematical literacy students’ ability to complete their homework. To assist them outside of the classroom, an intervention involving video tutorials explaining specific sections of work and how to go about solving problems, was devised. Students could access the relevant tutorials on a mobile device via the scanning of barcodes provided on the worksheets. The effectiveness of the intervention was assessed both quantitatively and qualitatively, through analysis of the participating students’ homework submissions and interviews with the students after the intervention had ended. Feedback from students via focus group interviews and questionnaires revealed that they found the tutorials helpful. This would indicate that the intervention was potentially beneficial. Keywords: Quick Response codes, video tutorials, homework.
An Algorithm for Color Correction in Marine Biological Images Based on U-Net Network

With the rapid development of the economy, marine resources have attracted much attention. Marine biodiversity observation is especially important to work. However, due to the complex underwater environment, the captured marine biology has a color distortion and blur, which greatly hinders the marine biological observation work. The clean underwater image is taken at a precise measurement distance without the influence of light wavelength attenuation and scattering of impurities in the water, and all uncertain reasons. At present, it is still challenging to get clean and high-quality underwater images in complex marine environmental conditions through hardware equipment and external conditions. Not only is there huge difficulty and instability in operation, but also a huge loss in cost. Acquiring poor-quality underwater images has led to limited marine biological recognition technology, and indirectly affected the development of marine biological diversity research. Therefore, this paper proposes an algorithm for correcting the color in marine biological images based on the U-Net network. The first step is to build a deep convolutional neural network combined with the U-Net network, and make the network continuously learn the color difference between the input image and the output image; the second step is to use the image structure similarity algorithm to make the output image and real image undergo iterative learning and gradually approach the real image. After many experiments, it is proved that the algorithm proposed in this paper has a good effect on correcting the color of marine biological images.
Software Product Line Testing—A Proposal of Distance-Based Approach

Quality is very important in any software product, so that the customer trusts the efficiency of the product. The quality or validity of a product is measured by its testing. The test measures the quality of a product as well as shows the validity of the product. For example, testing and measuring the validity of a product in a single software system is not easy. Similarly, the software product line, which produces many different products of the same family at once, makes it even more difficult to check their validity and quality. Several techniques and models have been proposed to test the validity of the product line products and to ascertain the quality. In the same direction, we also propose a method for testing the products of software product line. The proposed method is based on the distance between the features of the product (desired and actual). Used the proposed technique on product line with less features and found that the method presented gives fast and better results than traditional methods.
Design of Deep Learning Algorithm Composition System Based on Score Recognition

After algorithmic composition has become a hot research direction in the field of computer music creation, in recent years, with the rise and application upsurge of artificial intelligence, deep learning and other technologies, the research and exploration of related theories and technologies have been deepened, and the multi-dimensional integration of technology and music has entered a new historical period. Artificial intelligence composition will become the main research branch of algorithmic composition in the future. The purpose of this paper is to realize a deep learning algorithm composition system based on score recognition.
Ensuring the Security and Load Balancing in the Cloud Environment by BPA-SAB Method

Cloud storage is the latest standard, with the benefits of pay-per-use and on-demand applications in the future. The load balance in a distributed setting for the cloud is difficult due to factors such as stability, portability, security, and interoperability. To achieve this the proposed research aims for intrusion detection with a higher data protection quality and load balancing performance by developing a novel hybrid-based Binomial Protection-based Authentication with a Stochastic Agent-based Load Balancing (BPA-SAB) technique. The proposed BPA-SAB technique only permits users with proper licenses. The intruders have been discovered through the user behavior. The load balancing mechanism is handled by the SAB technique. The performance metrics of intrusion detection, computational time are better when compared with the existing work Secure and Energy-Aware Load Balancing.
Hierarchical Multi-scale Architecture Search for Self-supervised Monocular Depth Estimation

Self-supervised learning has shown great promising in monocular depth estimation task, using images as the only source of supervision. Most existing methods design the network architecture in a handcrafted manner, which is usually time-consuming and not specific to depth estimation task. To reduce the human efforts in network design, we propose a hierarchical multi-scale Neural Architecture Search framework for self-supervised monocular depth estimation, named Auto-Depth. Specially, our method consists of a two-level search space: cell level and network level. In the cell level, we explore the possible hierarchical connections within one single cell to enhance the ability of multi-scale information representation. In the network level, we search the depth and width of network to balance the accuracy and computational complexity. Furthermore, the two-level path binarization is adopted to reduce the computational cost in searching stage, which only activate one operation path among several candidate operations. Extensive experiments on the KITTI and Make3D datasets show that our searched network yields better performance than previous state-of-the-art monocular depth estimation methods.
The Art of Video Mapping and its Applicationsspatial Augmented Realityapplied Researchal-Zaytoonah University Buildings

The study examines the latest uses of audio-visual technology.This technology has found its way into advertising via the application of projection mapping technology. or projection technology, and the indicators point to its success in attaining advertising goals. The study will examine the technology's evolution and future possibilities, and impact on the marketing process in general.It also tries to define the productive aspect of this technology in terms of the tools and talents required to enhance the campus's structures. As a result of simultaneous digital effects and animations on various surfaces, equipment, and objects, [PJM] is a cutting-edge approach.This technology should be studied in graphics and multimedia studies since it is closely related to many design processes and approaches. The study recommends this because ideal circumstances exist for this technology to spread, grow, and improve in the commercial, cultural, and entertainment industries, where it will be needed in the near future.
The Application of Statistical Analysis Method in the Comprehensive Evaluation of Enterprise Employees’ Promotion

Nowadays, the competition of social enterprises is not only the competition of technology and capital, but also the competition of talents. If social enterprises want to survive for a long time, they must know how to choose talents, how to match talents with suitable positions, and how to keep talents in the enterprise. It is the vision of every company to train employees into the talents needed by the company and let employees and the company grow together. At the same time, statistics, especially statistical analysis methods, have become more and more common in the analysis of employee promotion. This phenomenon is worthy of attention and research. In view of this, this article studies the application of statistical analysis methods in the comprehensive evaluation of enterprise employee promotion. This article first analyzes the importance of corporate talents, conducts an in-depth study of statistical analysis methods, and then implements comprehensive evaluation of employee promotion with 165 employees of a domestic company with promotion space, and finally does a review of a certain employee of the company. Whether it conforms to the promotion principle and the analysis of the promotion comprehensive evaluation score.
A Novel RGBD Image Superpixel Segmentation Intergrated Depth Map Quality

Superpixel segmentation is to gather adjacent pixels into sub-regions with certain semantics, which can greatly reduce the computational complexity of subsequent image processing. The existing SLIC-D algorithm of RGBD image is a superpixel segmentation method that integrates depth information based on SLIC algorithm. However, for some images with complex background, it cannot stick to the weak edge of the image, and even has the defect of wrong segmentation. Therefore, this paper proposes a novel RGBD image superpixel segmentation intergrated depth map quality. Firstly, RGB map is enhanced by bilateral filtering to suppress and smooth noise, joint bilateral filtering is used to fill the depth map hole. Secondly, Holistically-Nested Edge Detection method based on convolutional neural network is used to obtain the RGB contour map, the edge consistency map is obtained by Hadamard product operation with depth gradient map. Finally, superpixel segmentation is performed by clustering based on LAB color space, spatial location and high-quality depth information similarity. Through the verification on NYU dataset, the advantages of this algorithm in edge fit degree are fully demonstrated.
Single Frame Phase-Shift Fringe Projection Profilometry

In order to avoid the problem of large filtering error of the fundamental frequency in the Fourier transform profilometry (FTP) and shorten the measurement time needed in the phase-shifting profilometry (PSP), a method with specific projection fringe is proposed to measure the three-dimensional contour of an object. Only one composite grating in which two sinusoidal fringe patterns with a [[EQUATION]] phase difference are modulated by two other orthogonal sinusoidal fringe patterns with different frequencies respectively is projected on the object. While the deformed composite pattern is captured, the two sinusoidal deformed patterns with a [[EQUATION]] phase difference are demodulated from the deformed composite pattern. The other two required deformed fringe patterns can be obtained by performing the symmetrical transformation with sub-pixel precision on the demodulated fringe patterns. According to the captured fringe pattern and the calculated fringe patterns, the fast reconstruction of the three-dimensional contour of the objects could be achieved. The three-dimensional information about the object can be obtained using only one deformed composite pattern. Both simulation and experimental results show that this method retains the advantages of the traditional four-step phase shift method with high measurement accuracy, and effectively solves the problem of long acquisition time of the phase shift method. The proposed method has broad application prospects in real-time 3D measurement field due to its single-shot feature.
Understanding Angular Projects and Tools

In this chapter, I explain the structure of an Angular project and the tools that are used for development. By the end of the chapter, you will understand how the parts of a project fit together and have a foundation on which to apply the more advanced features that are described in the chapters that follow.
Contact Force-Velocity Control for a Planar Aerial Manipulator

This paper addresses the challenge of controlling contact force and velocity between the end-point of a manipulator mounted on an aerial robot and an external surface. This has numerous applications involving tactile interactions such as painting of walls, cleaning window panes on skyscrapers, maintenance of bridges, solar farms etc. First, this paper presents a static analysis of a planar UAV with a two-link manipulator to determine conditions that the configuration variables (degrees-of-freedom) need to satisfy to generate a desired normal contact force. Following this, a hybrid force/velocity control strategy is presented to track (i) a desired normal contact force with a vertical wall and (ii) a desired velocity of the point of contact along the wall. Finally, to demonstrate the effectiveness of this controller, numerical simulation results of this controller with a dynamic model of a planar two link aerial manipulator are presented.
Roles of Quasi-variables in the Process of Discovering Mathematical Propositions

The purpose of this paper is to clarify roles of quasi-variables by focusing on the process of discovering mathematical propositions. For this purpose, the author analyzed the assignment reports of third-year undergraduate students. As a result, the author found that "looking back" is important in the generalization-oriented inquiry process, but it is not enough. It is important to "re-examine" the found matter and its form of expression from the perspective of a new concept. In the process of "looking back" and "re-examine", it was confirmed from the description of the metacognitive part of the students that the use of quasi-variables clarified the object of consideration and made it easier to clarify which numbers contributed to the generalization and expansion in what sense.
Cryptography

With security as a central objective, SCION naturally makes extensive use of cryptography. Different SCION components require different types of crypto-graphic primitives: symmetric and asymmetric. For example, while the control plane mainly relies on asymmetric cryptography in the form of signatures to enable any entity to verify the authenticity of the signed data and achieve non-repudiation, only symmetric primitives such as message authentication codes (MACs) are sufficiently efficient for data-plane packet processing.
Interactive Large-Scale Graph Visualization and Analysis for Social Networks

Dramatic popularity and prevalence of online social networking services have involved an increase of members publicly who articulate mutual “friendship” relations. Therefore, people have shown a great need to exchange information, share data, and adopt innovations in social networking applications. Data exchange has become the backbone of the economy on a global scale. In this paper, we investigate the Interactive Large-Scale Graph Visualization and Analysis of Social Networks that can process large-scale data. We have adopted a concrete large-scale corporate social networking site to realize this work; however, the approach can be easily generalized to other Social Networks. The problem is first stated as clearly as possible, and the social network is formulated as a joint optimization problem of graph selection. Then we have proposed some low-complexity search algorithms. The simulation results prove that our approach achieves good performance with a much lower computational complexity.
Learning Document Graphs with Attention for Image Manipulation Detection

Detecting manipulations in images is becoming increasingly important for combating misinformation and forgery. While recent advances in computer vision have lead to improved methods for detecting spliced images, most state-of-the-art methods fail when applied to images containing mostly text, such as images of documents. We propose a deep-learning method for detecting manipulations in images of documents which leverages the unique structured nature of these images in comparison with those of natural scenes. Specifically, we re-frame the classic image splice detection problem as a node classification problem, in which Optical Character Recognition (OCR) bounding boxes form nodes and edges are added according to a text-specific distance heuristic. We propose a Variational Autoencoder (VAE)-based embedding algorithm, which when combined with a graph neural network with attention, outperforms both a state-of-the-art image splice detection method and a document-specific method.
Catch-Up or Divergence? Clientele Effects and the Distribution of Bank Technology Adoption

We study how consumers trade off digital versus in-person banking services and how consumer demand impacts bank technology choices. We use the COVID-19 pandemic as a laboratory, where pandemic risk raised the cost of in-person bank services. After the cost of in-person banking rises, customers, particularly those from tech-oriented demographics, sharply transition from in-person to digital services and re-allocate from low IT to high IT banks. Banks - particularly high-IT ones - appear to respond to shifts in customer demand for digital services by upgrading technology and closing branches. In turn, the digital divide also increases in the banking market, with tech-savvy banks gaining market share.
A Split-Then-Join Lightweight Hybrid Majority Vote Classifier

Classification of human activities using smallest dataset is achievable with tree-oriented (C4.5, Random Forest, Bagging) algorithms. However, the KNN and Gaussian Naïve Bayes (GNB) achieve higher accuracy only with largest dataset. Of interest KNN is challenged with minor feature problem, where two similar features are predictable far from each other because of limited number of classification features. In this paper the split-then-join combiner strategy is employed to split classification features into first and secondary (KNN and GNB) classifier based on integral conditionality function. Therefore, top K prediction voting list of both classifier are joined for final voting. We simulated our combined algorithm and compared it with other classification algorithms (Support Vector Machine, C4.5, K NN, and Naïve Bayes, Random Forest) using R programming language with Caret, Rweka and e1071 libraries using 3 selected datasets with 27 combined human activities. The result of the study indicates that our combined classifier is effective and reliable than its predecessor Naïve Bayes and KNN. The results of study shows that our proposed algorithm is compatible with C4.5, Boosted Trees and Random Forest and other ensemble algorithms with accuracy and precision reaching 100% in most of 27 human activities.
Sentimental Analysis of online study of College and School going Students

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Game Planning Class as an International Cultural Content for the Education of UN SDGs

In this study, an attempt was made to plan as serious educational game as an international cultural content by adding SDGs educational content to the game planning class, which Korea has a high global status. In addition, industry-academic cooperation was formed for SDGs educational content design and practical expertise. As a result of the survey conducted after class, students showed very high learning satisfaction. The narrative responses indicated that they had a will to practice the SDGs and that they were able to awaken a sense of professional ethics through the educational effect of the game. And they had both interest and difficult in the effort to properly fuse the conflicting contents of education and fun. They also showed a very positive response to the special lecture on SDGs education content and the process of creating a plan with working-level staff. Since the results of the study were for specific class cases, it may be difficult to generalize them. Nevertheless, it is thought to have value as a material that can be used in classes for similar majors and liberal arts projects that want to complement both content and functional expertise.
Intersection of Justice, Crime Scene Investigation and Forensic Evidence in Pakistani Court

The criminal justice system in any jurisdiction demands that the charges against the accused should be proved beyond any doubt. The forensic science helps in restructuring and reconstruction of all the events happened at the crime scene. Although those events are not eye witnessed by the investigators, but certain elements help the forensic expert and crime scene investigator to link the ends of the broken pieces of evidence and reconstruct a story. This article underpins the fact that the evidence must be observed in its context and background. The forensic science encompasses various areas of scientific study which enable the law enforcement agencies to prove the legal cases. The article suggests that the crime scene investigation needs proper, accurate and attentive operation, documentation of crime scene, and collection of evidence. During the collection of evidence certain precautions must be kept in mind while dealing with the hazardous and fragile material.