Reviewerly/exHarmony huggingface dataset:
The Using of Film Media to Analyze Intrinsic Element in Literature in High School

This study examined the use of film media on Indonesian Language. The problem revealed was how is student learning outcomes, and what is the outcomes difference between learning to analyze the intrinsic elements of literary works using film media and using conventional learning approaches in class XI students of SMA N 1 Tumbang Titi. This type of research is experimental research. The design used was Post-test Only Control Group Design. Data analysis was done by normality test, homogeneity test, and t-test (Paired Simple t-Test). Data collection techniques in the form of tests. Based on the results of data analysis, it can be concluded that student learning outcomes analysis the intrinsic elements of literary works after being given conventional learning is 54.38 while student learning outcomes analyze the intrinsic elements of literary works after using film media is 71, 67. Besides, after analyzing the data statistically, the results show that there are significant differences between the learning outcomes of the material analyzing the intrinsic elements of literary work between those who use film media and conventional learning. Indonesian language learning material becomes the intrinsic elements of literature in class XI students of SMA N 1 Tumbang Titi using film media can also improve student learning outcomes and contribute to the scale of effectiveness of 32,64. Thus, learning with film media can be used by teachers in the field of learning Indonesian in analyzing intrinsic elements of literary works.Keywords: Utilization of Film Media, Intrinsic Elements of Literary Work
Flushgeist: Cache Leaks from Beyond the Flush

Flushing the cache, using instructions like clflush and wbinvd, is commonly proposed as a countermeasure against access-based cache attacks. In this report, we show that several Intel caches, specifically the L1 caches in some pre-Skylake processors and the L2 caches in some post-Broadwell processors, leak information even after being flushed through clflush and wbinvd instructions. That is, security-critical assumptions about the behavior of clflush and wbinvd instructions are incorrect, and countermeasures that rely on them should be revised.
Research on image mosaic of low altitude UAV based on harris corner detection

Low-altitude UAV image acquisition has the characteristics of high resolution, but its field of view is narrow. The shortcomings of narrow field of view can be solved by image mosaic of low-altitude UAV. This study combines Harris corner detection algorithm, adaptive non-maximal suppression algorithm, then the transformation matrix is calculated by using RANSAC algorithm, removing the mismatched feature point pairs, finally, a weighted average image fusion algorithm based on gradual-in and gradual-out is used to fused image, the image mosaic of UAV with 80% overlap rate and 130.6 meters low altitude was completed. The experimental results show that after using this algorithm, the meaningless corners are removed, the local representative corners are retained, and the detection time of corners is reduced by 30.2%. This algorithm improves the stitching speed.
The Use of Digital and Information Technologies in Order to Increase The Effectiveness of Mathematical Education

The relevance of the problem under study is due to the need to increase the effectiveness of mathematical education, which is inextricably linked with the availability of publicly available information resources necessary for the implementation of mathematical education curricula, including in electronic format. Subject-pedagogical ICT competency is necessary for the future mathematics teacher to carry out pedagogical activities for the implementation of secondary and vocational education programs using the educational information environment and digital technologies. The relationship between mathematics and information technology is based on their content. Modern information technology allows you to effectively solve mathematical problems. This is especially true in the process of teaching geometry and mathematical sections related to geometric constructions. The ability to visually represent geometric objects is a key feature of computer mathematics systems. Their use in the process of teaching geometry as a means of supporting the research activities of students helps to increase the level of assimilation of material in complex sections of mathematics. The article proposes a methodology for the formation of subject-pedagogical ICT competence of a mathematics teacher, based on the implementation of integrated tasks focused on the practical application of computer modeling to deepen knowledge in higher mathematics, as well as elementary mathematics and its teaching methods. A feature of this technique is the use of digital technology in the implementation of curricula for teaching mathematics based on the integration of teaching mathematics and information technology using computer mathematics systems.
Comparative Analysis of NFS and iSCSI Protocol Performance on OpenStack Cinder Technology

Cloud computing is a term used nowadays. Cloud computing usage is already spread all over the world and used by many companies. Because of its high usage, a need for cloud storage now emerges. Cloud storage not only uses resources more effectively but also makes it much easier to utilize a virtual machine. Several alternative protocols can be implemented for cloud storage, and each protocol has its advantages. This research aims to determine the most suitable alternative protocol to implement OpenStack cinder in utilizing cloud storage. Cloud computing implementation is done in a computer laboratory at Petra Christian University using the private cloud. A NAS Synology DS416J was used as the storage provider. The application built by using the OpenStack cloud framework that provides Infrastructure as a Service (IaaS). OpenStack cinder is one of the OpenStack projects that offer cloud storage with persistent storage. OpenStack Cinder itself can be implemented using fiber channel, NFS, and iSCSI protocols, but this research primarily focuses on two protocols, namely NFS and iSCSI. NFS and iSCSI have their respective advantages, so testing is needed to determine the most suitable protocol to be implemented on OpenStack cinder. After implementation, testing was carried out by measuring the performance of the NFS and iSCSI protocols when applied on OpenStack cinder using IO-zone. Based on the results, it can be seen where NFS has the advantage when writing files with a small record size, whereas iSCSI has the advantage when writing files with a large record size; however, in reading activity, there is no noticeable difference between the NFS and iSCSI protocols. By taking into account the results of testing and analysis of the systems that have been made, the conclusion is that the iSCSI protocol is better to be implemented on OpenStack cinder than NFS.
QIK: A System for Large-Scale Image Retrieval on Everyday Scenes With Common Objects

In this paper, we propose a system for large-scale image retrieval on everyday scenes with common objects by leveraging advances in deep learning and natural language processing (NLP). Unlike recent state-of-the-art approaches that extract image features from a convolutional neural network (CNN), our system exploits the predictions made by deep neural networks for image understanding tasks. Our system aims to capture the relationships between objects in an everyday scene rather than just the individual objects in the scene. It works as follows: For each image in the database, it generates most probable captions and detects objects in the image using state-of-the-art deep learning models. The captions are parsed and represented by tree structures using NLP techniques. These are stored and indexed in a database system. When a user poses a query image, its caption is generated using deep learning and parsed into its corresponding tree structures. Then an optimized tree-pattern query is constructed and executed on the database to retrieve a set of candidate images. Finally, these candidate images are ranked using the tree-edit distance metric computed on the tree structures. A query based on only objects detected in the query image can also be formulated and executed. In this case, the ranking scheme uses the probabilities of the detected objects. We evaluated the performance of our system on the Microsoft COCO dataset containing everyday scenes (with common objects) and observed that our system can outperform state-of-the-art techniques in terms of mean average precision for large-scale image retrieval.
Build receptive pyramid for efficient color image compression artifact reduction

A traditional image pyramid is an effective way to extract multiscale features. However, for image restoration, downsampling and upsampling lose details in the original resolution and result in an over smooth result. To overcome this defect, we propose a receptive pyramid (RP) that replaces downsampling by dilated convolution to extract multiscale features on the original resolution and make a full resolution correction. We present an RP-based convolution neural network named receptive pyramid convolutional network (RPCN) for efficient color image compression artifact reduction (CAR). Specifically, we propose residual connected convolution blocks as the baseline of RPCN. RPs work in convolution blocks to extract the hierarchical multiscale features for local feature correction. Moreover, the global feature fusion and vector correction are also introduced to further exploit the hierarchical features from the baseline. Benefiting from the RP, our RPCN achieves state-of-the-art performance with a much smaller model and much faster running speed for the CAR task.
Face Recognition for Attendance System Using Neural Networks Technique

Face recognition have come from considering various aspects of this specialized perception problem such as apply for help checking attendant. In order to solve this problem, many systems have been completely changed due to this evolve to achieve more accurate results. This research aims to develop the facing attendant system to be more effective and the mechanic of the system, which students can easily verify. The cloud storage was used for Attendance System. The experiment of this research is to find the way to recognize the face by using the technique of Neural Networks, which can correctly recognize up to 95%. This model can apply with school and university.
Speckle-based pose estimation for 3D measurement of the featureless environment by two cameras

In this research, we propose the 3D measurement system combining structured light and speckle based pose estimation by introducing two different setting cameras. The proposed system consists of two lasers, spot laser and line laser, and two cameras, with and without lens, which can obtain both focused and defocused images at once. Local shapes are measured using focused images by a structured light method. 3D positions of points projected by laser are calculated by triangulation. Pose changes are estimated from speckle information using defocused images. Displacements of speckle patterns are detected as optical ow by Phase Only Correlation (POC) method. Pose changes are estimated from speckle displacements by solving equations derived from the physical nature of speckle. The target shape as a whole is reconstructed by integrating local shapes into common coordinates using estimated pose changes. In the experiment, the texture-less at board was measured with motion. From the experimental results, it is confirmed that the shape of the board was reconstructed correctly by the proposed 3D measurement system.
Real-time Data Visualization of Intelligent Networked Vehicles

In recent years, with the advent of the Internet of Things era of "Internet of Everything", the amount of data generated by human society has exploded. Visualization provides an intuitive, vivid, and effective way to visualize complex data in a visual and interactive way, making it possible to display data information very efficiently. In the field of transportation, data information is closely related to visual elements such as maps and statistical charts. Visualization technology can be well used. Therefore, combining visualization technology with traffic data processing will greatly promote the development of the intelligent transportation industry. This paper first outlines the three directions of visualization technology research: scientific visualization, information visualization, visual analysis, and introduces the method of intelligent networked automotive visualization analysis. Several mainstream visualization components and visualization tools were then elaborated: ECharts, HighCharts, D3, and DataV. Finally, the design and implementation of a real-time data visualization system based on hierarchical structure is analysed in detail.
Self-supervised depth completion with attention-based loss

Deep completion which predicts dense depth from sparse depth has important applications in the fields of robotics, autonomous driving and virtual reality. It compensates for the shortcomings of low accuracy in monocular depth estimation. However, the previous deep completion works evenly processed each depth pixel and ignored the statistical properties of the depth value distribution. In this paper, we propose a self-supervised framework that can generate accurate dense depth from RGB images and sparse depth without the need for dense depth labels. We propose a novel attention-based loss that takes into account the statistical properties of the depth value distribution. We evaluate our approach on the KITTI Dataset. The experimental results show that our method achieves state-of-the-art performance. At the same time, ablation study proves that our method can effectively improve the accuracy of the results.
VR Technology and Its Impact on Academic Performance

The relevance of the study is due to the fact that the potential of VR technologies is only just beginning to emerge. Virtual reality technology continues to gain popularity in the educational technology market. The article analyzes the virtual reality industry, which allows you to highlight the main segments of the technology. The key features of an effective education system are identified. The author concludes that the ultimate goal of all changes is the acceleration of talent and training for the digital economy.
Applying Pair Programming Practice in the Improvement of Software Design Skills, in an Undergraduate Course

Teaching design patterns has proved to be a complicated topic in software engineering courses. Practitioners require mastering this technique in order to develop higher-quality programs. As part of the experiment, we used pair programming practice to solve this problem in an undergraduate classroom activity, aiming that finding the best pair configuration based on previous programming skills and character affinity, would lead us to better results. The experiment was divided into five rounds, increasing the complexity every each exercise. Afterward, we compared the score obtained by the students in the mid-term exam with last semester scores, resulting in an improvement of 0.37 points with the previous semester, which was 12.3% of the highest score (3 points).
Managing Company Competitiveness in the Digital Economy

The 21st Century Management Concept modifies management processes and mechanisms. Obsolescence of technologies and the formation of the new digital environment leads to a decrease in the level of competitiveness of companies. The application of new approaches to managing the competitiveness of companies in the digital economy is the main tool. The theoretical study of the issue is missing today. New tasks to manage the company’s competitiveness need to be formed. When writing a scientific article, the following tasks were solved: to the triangle of company competitiveness management was considered; to the company’s competitiveness management processes are identified; to the company’s competitiveness management system has been formed; to the model of the company’s competitiveness management in a digital economy is modeled. The solution of the tasks carried out on the basis of the application of methodological tools. The research methods were: the method of staging installations, the method of theorizing the conceptual apparatus, the method of comparative characteristics, the method of abstracted application, the method of formalization, the method of implication settings, the method of descriptive data, the method of structuring the phenomenon under study.
An Inexact Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration

In this paper, we apply an inexact dual alternating direction method of multipliers (dADMM) to an image decomposition model. In this model, an image is divided into two meaningful components, i.e., a cartoon part and a texture part. The dADMM presents not only the cartoon part and the texture part of an image but also the restored image (cartoon [Formula: see text] part). We also summarize the global convergence for the algorithm. Numerical experiments demonstrate the efficiency and robustness of the inexact dADMM. Furthermore, we can obtain relatively higher signal-to-noise ratio (SNR) comparing to other algorithms.
Research on Path Planning of Robot Based on Artificial Intelligence Algorithm

Abstract the Task of Robot Path Planning Based on Artificial Intelligence Algorithm is to Determine an Optimal Strategy, with the Goal of Maximizing the Return. When the Robot is in a New State, It Needs to Select an Action from the Action Set and Execute It. in Order to Improve the Quality of the Algorithm, It is Necessary to Apply the Search Strategy to the Action Selection. in the Path Planning System of the Robot, One of the Most Critical Links is the Realization of Directional Reference Vehicle Scheduling. Generally, Artificial Intelligence Algorithm is the Most Widely Used in Robot Path Planning. in This Paper, an Artificial Intelligence Algorithm is Used to Optimize the Vehicle Scheduling Problem. the Path Variable of the Problem Can Be Defined Correspondingly, Which Originates from the Planning Node. the Sequence of Sending Points Reached by Completing Other Sending Requirements is Called the Legitimate Sub Path, While the Corresponding Traversal Path Does Not Include the Sequence of Several Legitimate Sub Paths of Repeated Planning Points. the Algorithm Can Significantly Improve the Efficiency of Multi-Robot System, Reduce the Number of Explorations and Speed Up the Process of Convergence.
A Machine Vision Framework for Autonomous Inspection of Drilled Holes in CFRP Panels

This paper presents a fully autonomous framework for the inspection of drilled holes in planar carbon fiber composite panels used in the aerospace and automotive industries. The proposed framework can automatically recognize a part and extract the geometrical information from an existing library of DXF files. It then determines the location and orientation of the part with respect to the motion platform without a need for explicit programming of the part coordinate system. Visual servoing and optimal motion planning techniques are used to autonomously move the end-effector's camera to each hole to capture high resolution images. Image processing techniques are used to determine the geometrical errors and delamination factors for each hole. All of the proposed computer vision modules have been implemented in Python and OpenCV, which are open source and thus readily available to the industry. Experimental results prove that the proposed framework can efficiently and autonomously inspect drilled holes in composite panels with minimal programming required of the end-user.
NLDNet++: A Physics Based Single Image Dehazing Network

Deep learning methods for image dehazing achieve impressive results. Yet, the task of collecting ground truth hazy/dehazed image pairs to train the network is cumbersome. We propose to use Non-Local Image Dehazing (NLD), an existing physics based technique, to provide the dehazed image required to training a network. Upon close inspection, we find that NLD suffers from several shortcomings and propose novel extensions to improve it. The new method, termed NLD++, consists of 1) denoising the input image as pre-processing step to avoid noise amplification, 2) introducing a constrained optimization that respects physical constraints. NLD++ produces superior results to NLD at the expense of increased computational cost. To offset that, we propose NLDNet++, a fully convolutional network that is trained on pairs of hazy images and images dehazed by NLD++. This eliminates the need of existing deep learning methods that require hazy/dehazed image pairs that are difficult to obtain. We evaluate the performance of NLDNet++ on standard data sets and find it to compare favorably with existing methods.
An Experimental Evaluation of Global Navigation Satellite System/Inertial Navigation System-Verification Strategies for Vehicular Applications

An effective way to detect the presence of a spoofing attack is to verify Global Navigation Satellite System (GNSS) data with measurements from other sensors, such as inertial navigation systems (INSs). In this article, uncoupled GNSS/INS-verification approaches are experimentally evaluated in an automotive context. The approaches are uncoupled in the sense that GNSS and INS sensors are operated independently without an exchange of information. The techniques considered are the magnitude verification (MAV) technique, where the acceleration and angular velocity magnitudes are compared, and the horizontal components (HoCs)-verification strategy, which requires the vertical alignment of GNSS and INS sensor frames. The analysis shows the effectiveness of these types of procedures and discusses their limitations; for example, in low-dynamic conditions, when low accelerations and angular velocities are recorded. Possible enhancements are also discussed.
Machine Learning and Semantic Orientation Ensemble Methods for Egyptian Telecom Tweets Sentiment Analysis

The vast amount of data currently available online attracted many parties to analyze sentiments expressed in these data extracting valuable knowledge. Many approaches have been proposed to classify the posted content utilizing a single classifier. However, it has been proven that ensemble learning and combining multiple classifiers may enhance classification performance. The aim of this study is to improve the Egyptian sentiment classification by combining different classification algorithms. First, we investigated the benefit of combining multiple SO classifiers using different subsets from SATALex Egyptian lexicon. Second, we investigated the benefit of combining three classification algorithms; Naïve Bayes, Maximum Entropy and Support Vector Machines, adopted as base-classifiers. The experimental results show that combining classifiers can effectively improve the accuracy of Egyptian dataset sentiment classification. However, building these ensembles require more time for processing than the individual classifiers. The time needed depends on the number of classifiers used and the combination method used to combine these classifiers. Thus, the more classifiers used, the more time needed.
Study on the Mixed Teaching Mode of the Program Language Courses in the New Situation

Abstract With the spread of new coronavirus, new forms of online teaching are widely used in colleges and universities in China. Teachers are the main parts relying on the existing online teaching platform, which can make up for the defect of single online platform, simulate off-line teaching better, complement the advantage of off-line teaching, and improve the quality of online teaching effectively by using the new hybrid teaching mode of program language courses with other network tools software.