Reviewerly/exHarmony huggingface dataset:
Now Trending … Earthquake Information

Abstract The U.S. Geological Survey Earthquake Hazards Program has overall successfully fulfilled its mission of providing timely earthquake information via web applications and other methods. Imagine a single month of earthquake data delivery, serving 3.6 billion total data requests, including 29 million pageviews by 7.1 million users, 606 million automated data feeds, and 45 million catalog downloads. Yet, some challenges and lapses in delivery have happened at critical times, including during the Ridgecrest earthquakes in 2019. We delve into the evolving demand for real-time information as well as the technologies put in place to support the ever-growing number of users in an increasingly mobile world.
Design of nonstationary wavelets through the positive solution of Bezout’s equation

In this paper, we present a new technique for constructing a nonstationary wavelet. The key idea relies on the following: for each wavelet level, we solve the Bezout’s equation and we propose a positive solution over the interval [–1, 1]. Using the Bernstein’s polynomials we approximate this proposed positive solution with the intention to perform a spectral factorization.
Super-Resolution Reconstruction of Electric Power Inspection Images Based on Very Deep Network Super Resolution

Aiming at the problem of getting low resolution image and blurred image during unmanned aerial vehicle (UAV) inspection, a super-resolution reconstruction algorithm based on very deep network super resolution (VDSR) is proposed. The algorithm model is composed of deep convolution neural network and residual structure, which is improved on the basis of super resolution convolutional neural network (SRCNN). By deepening the network to 20 layers, the receptive field can be expanded, and the residual structure can be combined to obtain better reconstruction effect. The experimental results show that the proposed super-resolution method based on VDSR has richer texture and more realistic visual effect, with 2.95 dB and 0.037 improvement in PSNR and SSIM compared with the super-resolution methods based on Bicubic Interpolation, Sparse Coding and SRCNN. The proposed algorithm further promotes the theoretical research of inspection image super-resolution, and can be effectively applied to the practical application of power inspection.
Exploring Relationship Between User Satisfaction and Impacts of Digital Competence Certification System in Schools

This research developed a measurement instrument to assess user satisfaction and impact of digital competence system in primary and secondary schools. The testing platform was a prototype of a digital competence certification system that has been developed and piloted during 2019 in primary and secondary schools in six European countries involving more than 800 teachers and 6.000 students. In the process of evaluation, 298 teachers’ responses were analysed, and the measurement instrument was evaluated for validity and reliability. Results revealed a strong positive relationship between user satisfaction and impacts the DC system has on teachers and their work. Such a relationship is expected to engage teachers to use the system as well as their students to acquire new competences. Moreover, it is concluded that satisfaction contributes to motivation, user experience and performance within the system.
A Quality Measurement Method For Maintenance of Space TT&amp;C Software Based on Objective Data

Space TT&C software is the brain and central nervous system of the integrated space TT&C system of earth and sky. Its quality directly determines the success or failure of space launch TT&C mission. For each mission of the space TT&C software, application maintenance development needs to be performed on the software system accumulated over the years. The application maintenance process is closely integrated with the mission organization implementation process. According to the attributes of each stage of the application maintenance development process, the quality parameters are calculated based on the actual accumulated objective data, and then the stage quality assessment factors are determined. The proportion of each quality assessment factor is determined according to the degree of influence on the result. As the dual factors of efficiency and quality comprehensively considered, a quantitative measurement method for the maintenance quality of the space TT&C software based on objective data was established. The method can be used to evaluate the preparation status of the applied software before the implementation of the space TT&C mission.
ChRelBAC data access control model for large-scale interactive informational-analytical systems

Information systems providing interactive access for a huge number of users worldwide are exposed to numerous security threats. One of the most significant threats to this sort of systems is the threat of unauthorized access to system resources, leading to the breach of data confidentiality (privacy), data integrity and to the denial of service. The design and implementation of models and algorithms to reduce the risks of realizing such threats and to ensure the prompt response to the incidents is an important problem. In this article we introduce the chain-relational model of access control (ChRelBAC), which was designed and implemented for a large scientometric system. We describe two software tools for supporting the model. Visualization tool presents access control rules in a user-friendly way. Verification tool for the processes of the model integration with the target information system source code identifies the entry points of the system that are not covered by the model. Finally, we discuss the problem of testing the relational model on the real data sets.
Distance preserving multi-modal correlation feature learning with applications to image recognition

Canonical Correlation Analysis (CCA) is a famous feature learning method that learns a pair of correlation projection directions by maximizing correlation coefficients between different modalities. However, CCA ignores nonlinear discriminative geometry structure information hidden in multi-modal data, and the ignored information is crucial to the recognition performance of feature learning. Aimed at this issue, we propose a novel multi-modal correlation feature learning method, i.e. distance preserving supervised correlation analysis (DPSCA). In this method, we construct discriminative distance scatters for capturing distance-based discriminative geometry structures. By maximizing the between-modal correlation and simultaneously constraining the discriminative distance scatters, correlation features learned by DPSCA can effectively preserve the structures, which will be beneficial to enhance the discriminative power of the learned correlation features. Excellent experimental results on real image datasets prove the superiority of the method.
Regulated Evolution Strategies: A Framework of Evolutionary Algorithms with Stability Analysis Result

Evolutionary algorithm (EA) is a generic term for optimization algorithms inspired by biological optimization processes in the natural world. Although EAs are widely applied to complex real‐world problems because they do not require mathematical expression of target problems, theoretical design methods of EAs have not been established. To solve this issue, this paper proposes an approach of designing EAs within an algorithm framework in which mathematical characteristics are derived, and it also presents a concrete framework for that purpose. The presented framework, Regulated Evolution Strategies (RES) provides a stability analysis result that contributes to designing algorithms with expected behavior. The RES framework has a high degree of freedom in designing algorithms, so that it is possible to incorporate various contrivances such as local improvement of samples and reduction of constraint violations in RES‐based algorithms while maintaining the stability analysis result. Numerical experiments prove that the RES framework has a capability for designing high‐performance EAs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Analysis of Intelligent Operation Path of Shanghai Public Sports Venues under Internet + Background—Take PHP Technology System as an Example

The Internet in the information age gives birth to the intelligent way of stadium operation. In the field of Shanghai public stadiums and gymnasiums, the public attribute of the stadiums and gymnasiums endows the fitness groups with the sociality and commonweal, while the economic characteristics of the stadiums and gymnasiums cause the problem of how to operate intelligently under the wave of Shanghai public fitness. This paper uses the methods of literature, induction and logical analysis to sort out the resources sharing materials of all kinds of public sports venues in Shanghai. Based on the application of PHP technology system, it designs the logical framework of the intelligent service operation platform of Shanghai public sports venues, in order to provide reference for the innovation of resources sharing mode of Shanghai public sports venues.
Management of Virtualization Technologies with Complex Event Processing

With the increase of data flow obtained from application virtualization platforms, requirements such as detecting patterns within such data and producing resource management actions have become an emerging need. In this study, a software architecture for a complex event processing module has been proposed and implemented for the analysis of data obtained from application virtualization environments. The prototype of the proposed module is discussed in detail. The performance of the implemented module was evaluated and the results were observed to be promising.
Ability Study of Proximity Measure for Big Data Mining Context on Clustering

Proximity measure is used for data mining such as classification, cluster construction, regression, statistical analysis, analyzed and validated the mine results, and so on. Due to clustering, the proximity measure is based on cluster construction and cluster validation. Nowadays, growing of digital and communication technology is changing the nature of traditional data to big data. When any traditional taxonomy used for big data mining, it suffers various challenges due to big data dimensions. Proximity measure is one of the challenging issues of the clustering under big data criteria such as Volume, Variety, and Velocity. From a theoretically, practically and the existing research perspective, this paper study various proximity measure under Minkowski, L(1), L(2), Inner product, Shannon’s entropy, Combination, Intersection, and Fidelity family and recognized proximity measures for Volume (Dataset size), Variety (Data type), and Velocity (Time complexity) big data criteria. This study also identifies how to use these proximity measures for cluster construction under the Partition, Hierarchical, Density, Grid, Model, Fuzzy, and Graph-based clustering.
Research on FRUC Algorithm Based on Improved U-Net

Frame rate up up-conversion(FRUC), as a video post-processing technology, is of great help in improving video quality. The widely used frame rate improvement technology is based on the motion-compensated frame interpolation (MCFI). Although this method significantly improves video jitter and blur, there will still be problems such as block effects and holes. The paper proposes an improved U-Net frame rate improvement method that combines the U-Net and the Residual Neural Network (ResNet). The ResNet structure can effectively solve the problems of information loss, gradient disappearance and explosion during transmission. Combining these two networks and predicting the interpolation frames of the video sequences, such interpolation frames are closer to the original frames, and the predicted interpolation frames are better and effectively avoid problems such as block effects and holes. Experiments show that the algorithm in this paper is superior to other FRUC algorithms in the PSNR value of the interpolated frame.
Monitoring Temperature-Related Hazards Using Mobile Devices and a Thermal Camera

Marzec, MariuszThe paper proposes an algorithm that allows for automatic detection of thermal hazards using a smartphone-type device and a mobile thermal camera. The algorithm works in two stages. First, it analyses the environment of a disabled person for areas with a high or very high temperature. If they are detected, it signals by voice messages the appearance of such an object in the camera field of view. The second stage is the detection of a situation when a visually impaired person brings their hand closer to a hot object. In this situation, the application automatically detects the hand and the hot object and signals by voice messages a higher hazard level. In order to classify the level of thermal hazards, image thresholding methods were used, which enable to detect objects with temperatures above 40, 55, 70 $$^\circ $$ C. For hand detection in a situation threatening burns, an approach based on sequential thermal image analysis and machine learning was used. Tests were carried out for various algorithm configurations. The accuracy and precision values were as follows: Acc $$=0.89$$ , Prec $$=0.91$$ . The proposed method allows for quick and completely automatic warning of hazards and can be used for popular Android mobile devices.
Identifying Post-Traumatic Stress Symptoms Using Physiological Signals and Data Mining

The number of people diagnosed with anxiety disorders has been increasing in recent years. The correct diagnosis of such disorders is not always a trivial task, sometimes forcing an individual to consult with many clinicians and performing several medical exams. Post-traumatic Stress Disorder (PTSD) is a disorder related to experienced events, which presented a certain degree of threat to an individual. When experiencing situations that refer to past events, an individual may present reactions that trigger physiological changes in the organism such as tachycardia or bradycardia. Many disorders have common symptoms, and identifying these subtleties results in greater diagnosis efficiency and effectiveness. Artificial Intelligence (AI) and Data Mining (DM) techniques have helped specialists in the diagnosis and prevention of diseases and disorders. In our research, we aim at finding new biomarkers to diagnose PTSD analyzing physiological signals with DM techniques. In this paper, we used a dataset from an experiment with civilians that were recently exposed to traumatic events related to violence. Those individuals completed a questionnaire that evaluates the impact of such events through PCL (PTSD Disorder Checklist for DSM-IV) scale. Heart rate and skin conductance signals were collected while viewing emotional and neutral stimuli images. We applied DM techniques and classification algorithms to evaluate and maximize PCL score prediction performance considering those physiological signal data. The best result was obtained with SMO algorithm with its hyperparameters values suggested by an auto-learning procedure, presenting an accuracy of 85.45% (p-value = 0.001), precision of 0.8 (p-value = 0.001), recall of 0.5714 and F-Measure of 0.6667 (p-value = 0.001).
Using Augmented Reality to Train Flow Patterns for Pilot Students - An Explorative Study

Today, just as in the early days of flying, much emphasis is put on the pilot student’s flight training before flying a real commercial aircraft. In the early stages of a pilot student’s education, they must, for example, learn different operating procedures known as flow patterns using very basic tools, such as exhaustive manuals and a so-called paper tiger. In this paper, we present a first design of a virtual and interactive paper tiger using augmented reality (AR), and perform an evaluation of the developed prototype. We evaluated the prototype on twenty-seven pilot students at the Lund University School of Aviation (LUSA), to explore the possibilities and technical advantages that AR can offer, in particular the procedure that is performed before takeoff. The prototype got positive results on perceived workload, and in remembering the flow pattern. The main contribution of this paper is to elucidate knowledge about the value of using AR for training pilot students.
Show me where the action is!

Abstract Reality TV shows have gained popularity, motivating many production houses to bring new variants for us to watch. Compared to traditional TV shows, reality TV shows have spontaneous unscripted footage. Computer vision techniques could partially replace the manual labour needed to record and process this spontaneity. However, automated real-world video recording and editing is a challenging topic. In this paper, we propose a system that utilises state-of-the-art video and audio processing algorithms to, on the one hand, automatically steer cameras, replacing camera operators and on the other hand, detect all audiovisual action cues in the recorded video, to ease the job of the film editor. This publication has hence two main contributions. The first, automating the steering of multiple Pan-Tilt-Zoom PTZ cameras to take aesthetically pleasing medium shots of all the people present. These shots need to comply with the cinematographic rules and are based on the poses acquired by a pose detector. Secondly, when a huge amount of audio-visual data has been collected, it becomes labour intensive for a human editor retrieve the relevant fragments. As a second contribution, we combine state-of-the-art audio and video processing techniques for sound activity detection, action recognition, face recognition, and pose detection to decrease the required manual labour during and after recording. These techniques used during post-processing produce meta-data allowing for footage filtering, decreasing the search space. We extended our system further by producing timelines uniting generated meta-data, allowing the editor to have a quick overview. We evaluated our system on three in-the-wild reality TV recording sessions of 24 hours (× 8 cameras) each taken in real households.
An Hybrid Energy-Aware Cloudlet Virtual Machine Migration for Small and Macro Medium Enterprises

Live Virtual Machine (VM) migration ensures that the hosts allow the VM to move freely without any interference while services were being consumed from the VM. The proposed research reduces Cloudlet virtual machine migration rate when SMMEs are using virtual machines in the cloud so that energy consumption can be reduced in Mobile Cloud Computing. Literature review had identified various VM migration techniques that exists in cloud platform. We formulated the hybrid energy-aware migration which combined both pre-copy and post-copy approaches. Experiments were conducted to evaluate the performance of propose approach using downtime and migration time as performance metrics. These performance metrics are used to access the performance of our proposed approach for better efficiency. The reduction in the rate of cloudlet virtual machine migration implies by direct relationship that the amount of energy consumed for the task at hand has been reduced accordingly. The results from our experiments clearly shows that our hybrid energy-aware cloud migration mechanism helps to reduce significant amount of energy to the percentage of 60% reduction.
Theme Reference: Part 2

In Chapter 4 , "Theme Reference: Part 1," you have learned about some of the most important Twitter Bootstrap components, and you have started creating a reference page for all of them. In this chapter, the second part, you continue with some more components, within the same reference project.
Joint Extraction of Entity and Semantic Relation Using Encoder - Decoder Model Based on Attention Mechanism

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention based neural network model for joint named entity recognition and relation extraction. We explore different strategies in incorporating the alignment information to the encoder-decoder framework, and propose introducing attention mechanism to the alignment-based recurrent neural networks (RNN) models. Such attentions provide additional information to relation extraction and named entity recognition. Our independent models achieve state-of-the-art named entity recognition performance on the benchmark CoNLL04 dataset. Our joint training model further obtains 0.5% F1 absolute gain on named entity recognition and 0.9% F1 absolute improvement on relation extraction over the best models.
User-profiling based E-learning system using Question Adaptation

This paper proposes an adaptive testing system that uses a novel approach to generate the tests based on a student's current knowledge of the concept and later adapt the questions based on the performance of the students. To elaborate, the system consists of a test generation module that relies on input from a knowledge assessment module in order to generate tests. The knowledge assessment module measures a student's understanding using three parameters: Screen Time, External Resources and Notes taken. These parameters are given appropriate weights and their weighted mean is used as a measure to assess the knowledge level of the student. After the student attempts the test, our system uses their performance to determine whether they can continue with learning a new concept or not. If the student fails to clear the required proficiency level of a concept then the system will not allow the student to proceed with learning further. On top of that, the system also uses their performance to adapt the question difficulty based on whether students answered them correctly as well as the time taken to answer and time for which the question has been in the database. The question adaptation module performs this update based on the mentioned parameters after every 100 tests. This kind of question adaptation allows to maximize the quality of the questions being asked to the students over time.
How do LSPs compute MT discounts? Presenting a company’s pipeline and its use

In this paper we present a pipeline developed at Acolad to test a Machine Translation (MT) engine and compute the discount to be applied when its output is used in production. Our pipeline includes three main steps where quality and productivity are measured through automatic metrics, manual evaluation, and by keeping track of editing and temporal effort during a post-editing task. Thanks to this approach, it is possible to evaluate the output quality and compute an engine-specific discount. Our test pipeline tackles the complexity of transforming productivity measurements into discounts by comparing the outcome of each of the above-mentioned steps to an estimate of the average productivity of translation from scratch. The discount is obtained by subtracting the resulting coefficient from the per-word rate. After a description of the pipeline, the paper presents its application on four engines, discussing its results and showing that our method to estimate post-editing effort through manual evaluation seems to capture the actual productivity. The pipeline relies heavily on the work of professional post-editors, with the aim of creating a mutually beneficial cooperation between users and developers.