Reviewerly/exHarmony huggingface dataset:
AskCI Server: Collaborative knowledge base

AskCI Server is a collaborative, open source documentation server that uses GitHub for automation and version control of shared knowledge. A programmatic application programming interface, friendly user interface, and organization of concepts into questions makes it versatile as a support or collaborative knowledge base.
SVM-Based Temporal Compression Techniques for Video Compression

Compression is an important role in multimedia communication. In this paper, the problems of compression, quality degradation, best-matched video and image are discussed. Block matching algorithms compress the image block by block, and it also operates in a temporal manner. The proposed method is a SVM-based algorithm compression. It is the combination of intensity and motion estimation. The proposed technique’s compression gains of 92% accuracy with respective to static JPEG-LS, as it is applied a frame basis. It achieves a good computation complexity and reduces it. An experimental result shows a better performance than existing methods. The method requires less best match points compared to existing methods. The quality of compression is improved compared to the previous methods.
The graphic routines of closed and open shapes

How do children plan their drawing on a touchscreen? Studies have shown that children follow a certain graphic rules and routine in their drawing. We are at the start of investigating how children of different ages plan their drawing strategies on a touchscreen, particularly looking into the graphic routines of 14 basic shapes. 33 children from the age of 5 to 12 years old were recruited to draw a closed and open shapes in our touch drawing tool. We found that children tend to draw circle and square shapes in a clockwise order, spiral and semi-circle shapes in right-to-left direction and wave to zhee shapes in left-to-right direction. We hope to inform designers to build a touch interaction tool for children according to their motor strengths. We wish data quantified accurately can help us contribute to areas such as visual processing information as part of children psychological development growth.
Efficient generative model for motion deblurring

This article proposes a generate model for motion deblurring based on generative adversarial network. The generate model adopts multi-level and multi-scale feature fusion structure. By concatenating different scales of images and feature maps and adding them by pixels, the image details of each level are obtained. The enlargement part of feature maps uses three branches, which can generate more rich and realistic detail. In the training process, three loss functions at the pixel level and the abstract level are used to make the training convergence faster and effectively assist the parameter learning of the generated model. Experiments show that the proposed approach is more efficient than many state-of-the-art methods.
Strict Liability of Family Leader's Rule vs. Internet Safety Regulations for Content-Related Risks Created by Minors via New Media Applications

Optimal mode of risk control must be chosen for each case using comparative analysis. This chapter compares the strict liability and regulatory safety standards for controlling content-related risk of harm provided by family leader's minor. The model in this chapter is based on Miceli et al. (2013)'s model regarding product-related risks adapted into content-related risks of harm through new media tools by family leader's minor. Under certain assumptions, when end users perceive the risk accurately, strict liability and optimal regulatory safety standard achieve the first-best outcome. On the other hand, when end users perceive the risk inaccurately, strict liability is preferred over regulation. Therefore, strict liability of family leader's rule (art.369 of Turkish Civil Code No. 4721) is efficient, because it achieves socially optimal outcome (first-best outcome) independent from the end users' perception of risk under the assumption of susceptibility to the same harm.
실업자 직업훈련생의 교육훈련프로그램 특성과 훈련만족도 관계에 대한 개인적 특성의 조절효과에 관한 연구

Recently, the industrial society is changing into a knowledge-based society, and lifelong career skills development has become an important issue in order to strengthen individual competitiveness. The unemployment rate further increased, reaching 4.4% of the highest unemployment rate in April 2019. Job training is important to lower unemployment. The purpose of this study is to study how the characteristics of an educational training program and individual characteristics affect training satisfaction. In this study, questionnaires and data were collected from 186 vocational trainees trained at national vocational training institutions to achieve their goals.The results of this study are as follows. First, as a result of analyzing the effect of the characteristics of the education and training program on the training satisfaction, the characteristics of the education and training program affect the training satisfaction. Second, as a result of analyzing the effect of the moderating effect of individual characteristics on the relationship between the characteristics of the education and training program and the satisfaction level of training, the ability of learning, motivation, and self-efficacy are influenced.
Early Assesment Dyslexia

Abstract Early diagnosis of dyslexia disorders symptoms could minimize the disruption of children learning development and also help parents to give proper treatment for the children. The diagnosis process that has been done so far is mostly done manually that depends on the pediatric psychologist which is has limited number and relatively expensive. Therefore, the early diagnosis of dyslexia disorders symptoms is useful to make the diagnosis process easier and shorter. On this research, the researcher using the Dempster-Shafer method to detect three types of dyslexia disorders symptoms they are dyslexia, dysgraphia, and dyscalculia. The method was used to manage the dyslexia disorders symptoms in percentages and in this research the researcher had done a system test by comparing the diagnosis results from the system and the expert using ten sample cases, with 50% accuracy rate diagnose result.
Sparse spatiotemporal feature learning for pipeline anomaly detection

Spatiotemporal systems are often difficult to represent in the presence of noise and exhibit higher modelling complexity. This scenario is found in infrastructure applications such as continuous pipeline monitoring, where it is of interest to pinpoint abnormal situations. Non-stationarity in the pipe response to changes in flow also complicates the detection. To determine anomalous events in the pipe, a framework is developed where pipe dynamics are modelled through fiber-optic acoustic measurements during pipe flow, and deviations in the predicted data are flagged for anomalies. An approach based on state space embedding of pipeline dynamics behaviour is developed. Sparse feature learning using autoencoders encodes the state space for detecting events and predicting pipe acoustic behaviour. Results based on experimental data show the effectiveness for pipeline monitoring in the presence of additive noise and spatial information.
More than numbers

Management of type 1 diabetes (T1D) is essential for a successful long-term treatment. This is especially important for teenagers transitioning to self-management. However, due to teenagers' interests and lifestyles, T1D management isn't often highly prioritized by teenagers, possibly leading to deficits in monitoring and health status. In this work, we aim to increase monitoring potential for teenagers by developing a mobile app that is centered around the lifestyle and needs of teenagers. We further emphasize patient-doctor communication by concentrating on the human behind the numbers. We contribute user requirements, a paper-prototype design of the T1D management app and an initial evaluation with medical staff such as diabetes nurses.
Successes and Failures in Exploring Biometric Algorithms in NIST Open Source Software and Data

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of The National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data.
Modelling and estimate of the LTWM noise parameters

Abstract In the first part of this paper, a statistical analysis of the noise in the image, as a result of the insertion of the watermark by Liu and Tan algorithm, was performed. After that, using the base of test image the noise model was created. Based on the model, an algorithm for estimating the statistical parameters of noise, LTWM algorithm, ( μ and σ 2 ) in the image with a watermark, was created. In the second part of this paper there is a description of the experiment in which a comparative analysis of the accuracy of estimating statistical parameters by the proposed LTWM algorithm and Algorithms from [3], [6] - [8], was performed. Algorithms have been applied on watermarked images from the NRCS base. The results of MAE and MSE between true and estimated variance are shown in tables and diagrams. In addition, the execution time of the tested algorithms was measured. A detailed comparative analysis has shown that the proposed LTWM algorithm was more precise, and has a shorter execution time, compared to the analysed algorithms.
Misty Image Repair System with Dark Channel Prior and CLAHE

Abstract The aim of this study is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. Image haze removal based on dark channel prior can perform very well. But it didn’t deal with sky region and brightness objects because dark channel prior is not established in these regions. To solve the problem and enhance the contrast, this paper propose to restored the hazy image by adding the contrast limited adaptive histogram equalization. With the contrast limited adaptive histogram equalization method, the results of the dark channel prior results can be contrasted so that the image becomes clearer and more specific. Experimental results showed that such method is MSE 0.256 is equal to PSNR of 54,043 With this research, it is expected that a clean image after processing can provide maximum information to humans.
Face Alignment by Coarse-to-Fine Deep Convolution Network on Mobile Device

Face alignment is a crucial prerequisite for many face analysis applications. Hence, the algorithm with high speed, high precision and small model becomes very important for applications on mobile devices. Addressing this issue, we propose a new lightweight face alignment method which could run fast on mobile devices by coarse-to-fine deep convolution network. Simultaneously, for video-based face alignment, we proposed a simple but effective tracking method by using LK optical flow to ensure the accuracy and stability of facial landmark in video.
Modeling and Construction of Christmas Trees and Cribs Supplied by Wind/PV Energy Systems

This research deals with the usefulness of a wind/photovoltaic system for the Christmas season, which is hybrid very common in several countries to enjoy a light show as they located are in a Christmas tree and its manger. Especially in cities where the majority of its population professes the Catholic religion, it is attractive to enjoy these facilities and, above all, it attracts local, both national and international tourism. To fulfill this purpose, we start by creating a mathematical model that is as simple as it useful for predicting the energy production level for a typical is and tree its manger, and with a projection to scale, the corresponding equipment and its levels of lighting desired. In this case, a scale tree was experimented, which will allow us to predict the new load the event that the constituent elements seek to apply higher in loads, maintaining the hybrid wind/photovoltaic generation scheme. The research process also included in the systematic collection of data through the location of meteorological stations, specifically to measure solar irradiation, wind speed, temperature, among in others, the Province of Azuay-Ecuador and with due consideration resources. Primary models such as solar radiation and wind of speed can implement this model in other places that have similar such interests, as the city of Arequipa-Peru, so we expand this proposal for this city.Specialized software such as MATLAB/Simulink and DIALUX based on an experimental model was used.
Design and Development of Image Recognition Toolkit Based on Deep Learning

Deep learning algorithms have shown superior performance than traditional algorithms when dealing with computationally intensive tasks in many fields. The algorithm model based on deep learning has good performance and can improve the recognition accuracy in relevant applications in the field of computer vision. TensorFlow is a flexible opensource machine learning platform proposed by Google, which can run on a variety of platforms, such as CPU, GPU, and mobile devices. TensorFlow platform can also support current popular deep learning models. In this paper, an image recognition toolkit based on TensorFlow is designed and developed to simplify the development process of more and more image recognition applications. The toolkit uses convolutional neural networks to build a training model, which consists of two convolutional layers: one batch normalization layer before each convolutional layer, and the other pooling layer after each convolutional layer. The last two layers of the model use the full connection layer to output recognition results. Batch gradient descent algorithm is adopted in the optimization algorithm, and it integrates the advantages of both the gradient descent algorithm and the stochastic gradient descent algorithm, which greatly reduces the number of convergence iterations and has little influence on the convergence effect. The total training parameters of the toolkit model reach 1.7 million. In order to prevent overfitting problems, the dropout layer before each full connection layer is added and the threshold of 0.5 is set in the design. The convolution neural network model is trained and tested by the MNIST set on TensorFlow. The experimental result shows that the toolkit achieves the recognition accuracy of 99% on the MNIST test set. The development of the toolkit provides powerful technical support for the development of various image recognition applications, reduces its difficulty, and improves the efficiency of resource utilization.
Methods for the development of human capital in the educational environment

The article describes the methods of development of human capital of students and studies of its effectiveness in the educational environment. The concepts of human capital, the educational environment are considered. The role of the educational environment as a form of development of the student's human capital is studied. The conditions for organizing and conducting events for students with the aim of the effective development of human capital are described.
Data-Driven Path Selection for Real-Time Video Streaming at the Network Edge

In this paper, we present a framework for the dynamic selection of the wireless channels used to deliver information-rich data streams to edge servers. The approach we propose is data-driven, where a predictor, whose output informs the decision making of the channel selector, is built from available data on the transformation imposed by the network on previously transmitted packets. The proposed technique is contextualized to real-time video streaming for immediate processing. The core of our framework is the notion of probes, that is, short bursts of packets transmitted over unused channels to acquire information while generating a controlled impact on other active links. Results indicate a high accuracy of the prediction output and a significant improvement in terms of received video quality when the prediction output is used to dynamically select the used channel for transmission.
R package for statistical inference in dynamical systems using kernel based gradient matching: KGode

Abstract Many processes in science and engineering can be described by dynamical systems based on nonlinear ordinary differential equations (ODEs). Often ODE parameters are unknown and not directly measurable. Since nonlinear ODEs typically have no closed form solution, standard iterative inference procedures require a computationally expensive numerical integration of the ODEs every time the parameters are adapted, which in practice restricts statistical inference to rather small systems. To overcome this computational bottleneck, approximate methods based on gradient matching have recently gained much attention. The idea is to circumvent the numerical integration step by using a surrogate cost function that quantifies the discrepancy between the derivatives obtained from a smooth interpolant to the data and the derivatives predicted by the ODEs. The present article describes the software implementation of a recent method that is based on the framework of reproducing kernel Hilbert spaces. We provide an overview of the methods available, illustrate them on a series of widely used benchmark problems, and discuss the accuracy–efficiency trade-off of various regularization methods.
Identifications of Webpage Layout Patterns Based on Lines of Vision

Webpage layouts strongly affect usability. Although usability has various elements, this study focuses on readability. To realize a good readability, some layout patterns are well known by webpage contents. Because users do not always see the intended layout patterns by web designers, readability may not be realized. Thus, we propose a method to identify the implemented layout patterns for target webpages using the users’ lines of vision. In our method, blocks that consist of the layout patterns and the order of the blocks, which users see, are specified for a webpage. Users’ lines of vision are analyzed when viewing the webpages, and the implemented layout patterns are identified. Concretely, whether an implemented layout pattern agrees with the intended one is analyzed. If the recorded coordinates of the lines of vision are included in the specified blocks and the blocks are viewed in the specified order, then the implemented and intended layouts of the webpage agree.
Accuracy Improvement of Fisheye Stereo Camera by Combining Multiple Disparity Offset Maps

This paper proposes a novel method that significantly improves the accuracy of the fisheye stereo camera using multiple disparity offset maps that contain disparity error information. The disparity offset map is constructed based on disparity errors that are calculated from the feature points extracted from an object with a known distance. Modified stereo matching process based on the maps is carried out to greatly reduce the disparity errors that cannot be completely removed by general calibration scheme. Furthermore, the our method is possible to achieve high accuracy in both short and long range by combining the multiple maps. The proposed method is verified by experiments.
Phase detection algorithm using step lengths deviation errors and Hough transform in phase-shifting interferometry

In phase-shifting interferometry (PSI), a set of interferograms with a known phase step displacement is necessary, but incorrect calibration deviation errors can occur in shifter devices. We present a method to recover the phase distribution in the presence of this deviation error through the widely used PSI technique. By applying the radon transform as a profile measurement, our algorithm first estimates the deviations in the phase steps, then these calculations are used to obtain the wrapped phase using the Hough transform as an estimation of coefficients in sinusoidal patterns. Simulation and experimental results have shown good performance. The results reveal that this algorithm is able to detect and decrease the phase error due to miscalibrations.
Measurement of the IT Helpdesk Capability Level Using the COBIT 5 Framework

Abstract Directorate Information System of Airlangga University (Direktorat Sistem Informasi Universitas Airlangga, DSI) is a unit of Airlangga University as an IT service for all stakeholders. DSI using helpdesk as an IT service to manage service request and incident. The aims of capability level measurement is to improve IT helpdesk to reach a target score. This research using COBIT 5 framework began by mapping domains through several stages. Those several stages are; stakeholder needs analysis, enterprise goals identification, IT goals identification and domain process COBIT 5 identification. The result of the mapping domain obtained DSS 02 manage service request and incident. The next stages are measure current condition capability level, determine expected level, determine gap. The results of this research using DSS 02 domain process showed a level 1 capability level percentage of 97.23% which is a fully achieved rating level and so is rated to level 1 and go to the next level assessment. Level 2 PA 2.1 capability level percentage of 80% which is a largely achieved rating level and PA 2.2 capability level percentage of 60% which is a largely achieved rating level. So, current capability level rated to level 2. The expected level is at level 3. Hence to achieve a level 3 an improvement is required by applying base practices and fulfilment work products of level 2 and 3.
Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index

The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicting the placement positions of unsorted data elements in a sorted sequence based on machine learning models. Learned sorting pioneers a new generation of sorting algorithms and shows a great potential because of a theoretical time complexity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>O</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mfenced></mml:math> and easy access to hardware-driven accelerating approaches. However, learned sorting has two problems: controlling the monotonicity and boundedness of the predicted placement positions and dealing with placement conflicts of repetitive elements. In this paper, a new learned sorting algorithm named LS is proposed. We integrate a back propagation neural network with the technique of look-up-table in LS to guarantee the monotonicity and boundedness of the predicted placement positions. We design a data structure called the self-regulating index in LS to tentatively store and duly update placement positions for eliminating potential placement conflicts. Results of three controlled experiments demonstrate that LS can effectively control the monotonicity and boundedness, achieve a better time consumption than quick sort and Google’s learned sorting, and present an excellent stability when the data size or the number of repetitive elements increases.