Reviewerly/exHarmony huggingface dataset:
VaFLE: Value Flag Length Encoding for Images in a Multithreaded Environment

The Run Length Encoding (RLE) algorithm substitutes long runs of identical symbols with the value of that symbol followed by the binary representation of the frequency of occurrences of that value. This lossless technique is effective for encoding images where many consecutive pixels have similar intensity values. One of the major problems of RLE for encoding runs of bits is that the encoded runs have their lengths represented as a fixed number of bits in order to simplify decoding. The number of bits assigned is equal to the number required to encode the maximum length run, which results in the addition of padding bits on runs whose lengths do not require as many bits for representation as the maximum length run. Due to this, the encoded output sometimes exceeds the size of the original input, especially for input data where in the runs can have a wide range of sizes. In this paper, we propose VaFLE, a general-purpose lossless data compression algorithm, where the number of bits allocated for representing the length of a given run is a function of the length of the run itself. The total size of an encoded run is independent of the maximum run length of the input data. In order to exploit the inherent data parallelism of RLE, VaFLE was also implemented in a multithreaded OpenMP environment. Our algorithm guarantees better compression rates of upto 3X more than standard RLE. The parallelized algorithm attains a speedup as high as 5X in grayscale and 4X in color images compared to the RLE approach.
An Emotional Distance Calculation for Recommending Musical Films in Kansei Space

For each movie, every person recognizes every event with Kansei which is unique to the individual. In this method, we focus on the point that people like about musical movies and then create a Kansei space for musical films using the idea of mathematical models of meaning. In this method, we calculate an emotional distance in Kansei space and provide the best recommendation for users with new user experiences.
An Improved Two-Stage Multi-person Pose Estimation Model

Generally, multi-person pose estimation plays a crucial role in behavior recognition in images and videos. Previously, pose estimation of a single person is popular and achieves high prediction accuracy with the development of deep learning. However, pose estimation of multi-person remains to be a huge challenge and cannot achieve the same effect as that of a single person. It mainly results from the rare, missing or incorrect location detection and overlap of pose, which are usually caused by incomplete person identification. Therefore, we propose an improved two-stage multi-person pose estimation model (ITMPE) to further improve the performance of multi-person pose estimation. The first stage, Mask R-CNN is used for person identification. The second stage, processed images or videos with identified people only are fed into OpenPose model for multi-person pose estimation. The comparative experiments show that our proposed model achieves a significant improvement than original model. Our proposed model reduces the MSE, MAE by around 27.38%, 21.57% and increases R2, Mean values by 49.80% and 96.91% on average, respectively. The improvement in person identification and misclassification are shown in our comparison images. More people are captured and given the pose estimation, which directly affect the performance of behavior recognition.
Autonomous Vehicle Path Planning Considering Dwarf or Negative Obstacles.

Autonomous vehicle path planning involves finding a collision-free and kinematic-feasible path from the start to the goal. Evidently, collision-free and kinematic-feasible are the two most important parts of the path planning process. In this paper, we propose a collision checking method that considers dwarf or negative obstacles, which can improve the traversabil-ity and stability under the promise of ensuring collision-free, and we use a kind of motion primitives which considering non-holonomic constraints for path planning to generate a kinematic-feasible path. Specifically, some existing approaches of collision checking deal with lofty and dwarf obstacles, or wide and narrow, negative obstacles in the same manner that require the entire vehicle body to steer awayfrom them. However, some dwarf obstacles are shorter than the chassis of the vehicle while some negative obstacles are narrower than the vehicle track. In those cases, overstriding maneuvers will prove much more efficient and reasonable. Moreover, dealing with these obstacles in the same wayas lofty obstacles can dramatically reduce the flexibility and disturb the stability of path planning under clustered environments, especially when the vehicle moves at higher speed. This paper proposes a double layer collision checking (DLCC) method that deals with lofty and dwarf obstacles, or wide and narrow, negative obstacles separately, effectively increasing the flexibility under the complex scenarios where dwarf and narrow negative obstacles exist.
Natural Scene Chinese Character Text Detection Method Based on Improved CTPN

Abstract Text detection in natural scenes, due to differences in size, font, line direction, lighting conditions, text weakness and image background complexity, plays an important role in the research field and remains a challenging and important topic. We have improved the CTPN text detection network and changed the Side-refinement detection box to determine the scaling mechanism. And based on the experiment, change the LSTM network to the GRU neural network. In the dataset of Chinese character text game in natural scene released by Meituan, it reached 0.78 F1-Measure, which reached 0.89 and 0.61 F1-Measure on the ICDAR 2013 and 2015 data sets respectively. Compared to the 0.88 and 0.61 F1-Measure in the CTPN article, there is a big improvement.
Multi-objective Optimization Based on NSGA-II Algorithm for 3DP Process

The forming direction and layer thickness of the model in 3D inkjet printing technology are the key factors that affect the surface accuracy, processing time and processing cost of the part. In order to reduce the processing time and improve the surface precision, a hierarchical optimization method based on improved fast non-dominated sorting genetic algorithm is proposed. Two objective functions of surface precision and processing time are established, and iterative solution is realized by selection, crossover and mutation. Experiments show that this method can effectively solve the optimization problem of layering direction in 3D printing process.
Scaling Function Based Analysis of Symlet and Coiflet Transform for CT Lung Images

The main aim of the work is to develop image compression algorithms with high quality and compression ratio. The objective also includes finding out anbest algorithm for medical image compression techniques. The objective is also alert towards the choice of the developed image compression algorithm, which do not modify the characterization behavior of the image. In this paper, image compression algorithm based on discrete symlet and coifletwavelet transform is implemented for decomposing the image. The selection of different levels are discussed dependence on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum moments for compression are also chosen based on the results.
Chaos Aided Audio Secure Communication over SC-FDMA System

The tremendous growth of LTE in wireless communication requires a fast, reliable system with the high-performance approach. In modern life, using OFDMA and SC- FDMA as the multiple access technique, the impact of long- term evolution (LTE) has increased. Lack of awareness of recent trends and technology makes a significant impact on the risk of security. This article is to establish a secure communication channel for audio transmitting and receiving devices. There are many algorithms available for encrypting the data. One of the encryption algorithm is a chaotic algorithm which is more secure, fast and reliable. This approach can be used for applications, where high data security is required such as military applications. This article proposes a robust authentication mechanism by using a chaotic audio encryption algorithm in the SC-FDMA system to improve data security. Transmission and reception of encrypted audio data over the SCFDMA system will be tested using MATLAB. The performance analysis is based on the SNR, PSNR, MSE, Entropy, Histogram and Bit Error Rate (BER) signals.
Making DIA Accessible to Non-Experts: Designing a Visual Programming Language for Document Image Analysis

The research field of Document Image Analysis (DIA) produces state-of-the-art methods that are intended to analyze digitized documents, a substantial portion of which lie in the realm of humanities. However, applying the methods, more often than not, requires some programming knowledge. This results in low accessibility and, consequently, many of the methods, if not most, are not used by scholars in the humanities. There is a distinct need to bridge this gap and make DIA methods more accessible to end users. In this context, we discuss the design of a Visual Programming Language (VPL) paradigm for an integrated DIA application environment targeting end-users that are not capable of programming. We elucidate in detail the various design considerations that went into the VPL's development and provide a description of our prototype VPL environment, named, AMAP and, finally, discuss future work.
Motion Blur Detection Using Convolutional Neural Network

In this paper, we identify movement obscure from a solitary, hazy picture. We propose a profound learning way to deal with and anticipate the likelihood dissemination of movement obscure at the fix level by utilizing a Convolutional Neural Network (CNN). The design we follow will moved toward the issue by cutting 100 pictures into 30 × 3 0 fixes and connected our movement obscure calculation to them (with an irregular rate of half). At that point named the hazy and non-foggy patches with 1 s (0 for still, 1 for hazy), and stacked the adjusted pictures as our preparation information. In this Paper, we aim to estimate blurred motion from a single blurry image and propose an in-depth learning approach to predict probabilistic patch level movement blur distribution using a Convolutional Neural Network (CNN).
Formation of key competencies of urges on study-seminars of Ukraine's history

The article is devoted to the actual theme of the formation of key competencies of students. The list of key competencies, which according to the program «History of Ukraine» is to be mastered by graduates of comprehensive educational institutions, is analyzed. In order to achieve this goal, the author convincingly proves the expediency of organizing lessons-seminars with high school students on the example of the subject «History of Ukraine». Examines the seminar as a way of organizing school education and the form of subject-subject interaction of participants in the educational process. The analysis of the peculiarities of the organization and the methods of conducting shows the great possibilities of its application in the history lessons of Ukraine in the upper classes. The author emphasizes that such varieties of seminars as a question and answer seminar, a seminarunfinished conversation, a seminar-collective reading, a seminar-discussion, a seminar-conference, a seminar-»brainstorming» meet the demands of modern education for interactive lessons. The format of seminars allows you to use interactive methods and exercises: «Let’s imagine a situation», «Problem issue», «Deferred guess», «Associative bush», «Cryptogram solving». According to the author, conducting lessons-seminars stimulates students to become independent in gaining additional knowledge, encourages the formation of critical thinking, teamwork, and other necessary competencies. Examples from the practice of carrying out seminars on the history of Ukraine among high school students are shown, which demonstrate the possibilities of forming key competencies. In the prospects of scientific research of the author of the article – the study of the specifics of control and evaluation of student achievements in seminars on the history of Ukraine.
Detection of Natural Features and Objects in Satellite Images by Semantic Segmentation Using Neural Networks

In recent years, Neural Networks have become one of the most research focused areas of Artificial Intelligence. From detecting objects in real time to the classification of images, these Neural Networks are efficient and are achieving maximum possible accuracies based on the given inputs. In this work, we use Neural Networks for detecting features in satellite images. Using image segmentation and object detection techniques, we find objects, like roads, buildings, trees, and other resources, in the satellite images. In this work, Neural Network architecture used for segmentation of the images is ConvNet also called Convolutional Neural Network. U-Net which has a convolutional autoencoder architecture maps the layers to find the features and resources in the given satellite images. U-Nets do per-pixel semantic alignment for finding objects and features which result in segregation of resources. By using these, each feature or resource in the satellite image is segmented in different colors with regards to the distinct features allowing us to estimate the resources.
Optimization analysis for image-based steganography using generative adversarial networks

Steganography is a method of hiding secret information within non-secret information. For the purpose of steganography, a lot of works based on convolutional neural network(CNN) were framed recent years and they showed the improvement of deep learning particularly in the field of hiding information. The major key factors that were kept in the account by those works include enhancing the capacity, invisibility, and security. In this research, a work based on steganography via generative adversarial networks was utilized to increase the invisibility and security, thus extracting that same secret image at the receiver side precisely. The focus of this research was to select the best suitable optimizer for the image based Steganography. Here, Stochastic Gradient Descent (SGD) and Adaptive Momentum (Adam) were compared and from the investigation, it was concluded that Adam optimizer performs better in handling the model to improve the hiding and revealing ability.
Sentinel-2 “low resolution band” optimization using Super-Resolution techniques:Lysimachia Lake pilot area of analysis

This work super-resolves the lowest-resolution 60m/pixel Sentinel-2 B1 and B9 to the highest-resolution 10m/pixel. Two different categories of super-resolution (SR) techniques are utilized, in specific a SR technique which performs information transfer among different bands and the stochastic regularized SR technique Var-norm+BTV. The study area is the Lysimachia Lake, Western Greece. The Sentinel-2 image of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> November 2018 has been selected to test the different techniques.
How Do Some Employees Carry Negatives in the Company and What to Do With It?

The article is devoted to a complex research of the behavior of people, which affects to the colleagues in a team negatively. It is considered such models of behavior as deviant, aggressive and confl ict and also their characteristics. As a result, it was suggested by the authors of this article that this classification is incomplete, in consequence of this fact another model was proposed — “toxic behavior”. The research on the basis of which the conclusion is drawn on expediency of introduction of a new concept is conducted. Distinctive features of toxic behavior are marked out. The methods of limiting infl uence of “toxic behavior” on people around are offered.
Phase correction method for dynamic 3D measurement based on fringe projection

An additional phase shift is produced when the 3D shape of dynamic scene is measured by phase shifting method. The phase distribution measured by traditional method is distorted and the recovered 3D shape is distorted as well. The Gerchberg iteration algorithm and the windowed Fourier assisted phase shifting method were proposed to reduce the distortion. The main ideal of these methods is to accurately estimate the phase shift between two fringe patterns. The accuracy of phase shift estimation is low near the edge where the frequency of fringe changes suddenly. We proposed a method to increase the accuracy of phase shift estimation. The edge of frequency sudden change is found out and then the fringe pattern is divided into different regions. The Gerchberg iteration algorithm is adopted to extend the fringe to outside of every region. The windowed Fourier analysis is adopted to estimate phase of every region and then the phase distribution of whole pattern is obtained. The phase shift between two fringe patterns is obtained by subtracting the estimated phase of first pattern from that of second pattern. Finally, the accurate phase distribution is obtained from the fringe patterns and the estimated phase shifts by least square method. The comparative experiments were carried out to evaluate the proposed method. The experimental results show that the accuracy of measured phase by proposed method is higher than that by windowed Fourier assisted phase shifting method.
COMBINATION OF CRYPTOGRAPHIC ALGORITHM AND STEGANOGRAPHY TO HIDDEN MESSAGE IN IMAGE

Along with the increasing use of the internet in Indonesia, the threat to messages in image media has increased. The confidential data that will be sent requires security so that it can be read by the recipient of the message. For this reason, it is necessary to design a security system for this picture's media message by encrypting and decrypting it then then hiding the message. The algorithm used by combining various cryptographic algorithms and steganography techniques using the LSB method. The test results show the security of the message on the image media, especially in protecting the copyright rights of the image. The original original image measuring 242 kb in the .jpg format will increase in value after adding secret data with a size of 536 kb using the .png format. This system successfully displays secret messages in the image and does not change the cover of the image.
Law and Economics—Five Core Principles in the Cloud

By its very nature, the study of cloud computing transformations is an interdisciplinary endeavor, drawing on complex technical and legal issues that include a variety of other social disciplines. Consequently, structuring a coherent theoretical framework requires careful scrutiny that goes beyond the capacity of any single discipline. Therefore, in order to gain a holistic understanding of these complex issues and to resolve adequately some of these problems, this chapter focuses on the contributions and synergies across the disciplines of law and economics.
Forecast Solar Energy Using Artificial Neural Networks

Energy harvested from the natural resources such as solar energy is highly intermittent. However, its future values can be predicted with reasonable accuracy. In this paper, a solar energy forecasting model based on Artificial Neural Network is proposed. Intensity of solar radiations is predicted 24 h ahead based on pressure, dew point, temperature, relative humidity, wind speed, zenith angle and historical values of solar intensity. The dataset of 4 months is used from National Solar Radiation Database. Simulations were performed to analyse the impact of each input variable on results. Results indicate the efficiency of the model both in terms of accuracy and computational costs.
The Semi-Supervised Structure from Motion with Ground Plane Constraint

Depth estimation of monocular views is an important task. We find that view synthesis methods often perform well in unsupervised deep recovery learning. Inspired by it, we propose a framework of depth recovery algorithm based on view synthesis. However, because view synthesis is based on the inherent constraints of the image pixels themselves, it is greatly affected by the “burst” conditions such as occlusion and illumination. In computer vision's domain, the algorithm of road segmentation and recognition from monocular images has been able to perform fairly well. Combining with the task of segmentation, we constrain depth estimation based on the premise that the spatial position of road pixels is on the ground. The experimental results show that with this rigid constraint in three-dimensional space, the task of depth estimation can perform better. The whole algorithm only needs the camera internal parameters to be known, and does not need road information, pixel depth information and other labeling, nor additional image information such as GPS, map, etc. The input raw data is used as the sequence of monocular data or video images, which greatly reduces the input of human resources and increases the level of automation of the system.
A Case Study on the Work Experience Program Development in the Field of Human Ecology

The purpose of this study was to develop a work experience program which enables university students to experience real working situations in the field of Human Ecology. We have developed a work experience program based on students’ needs: preferred career, preferred work experience program, and career mentor they want to meet. After that, a one-day job experience program was conducted. At the same time, the program was evaluated through a participant satisfaction survey. The results of the evaluation show implications for planning an improved work experience program that considers participants in the field of Human Ecology. First, to increase the satisfaction of participants, a program focusing on a specific grade is needed. Second, work experience programs need to be designed in such a way that industry practitioners and students can freely discuss issues rather than engage only in unilateral lectures. Third, a work experience program is required to experience and share with others in a variety of different fields towards the integration of academic disciplines. This work experience program is a student-centered program that closely examines the needs of students, and reflects them.
A Novel Image Dehazing Algorithm Based on Area Weight of NAM Blocks

In this paper, we propose a single image dehazing algorithm based on the atmospheric scattering model and the area weight of the Non-symmetry and Anti-packing Representation Model (NAM) blocks. The image is segmented using the NAM method, and the area weight of the sub-block is used to calculate the atmospheric light. The boundary constraint and the context regularization are then used to estimate the scene transmission. Finally, the image is defogged according to the atmospheric scattering model formula. The experimental results show that our proposed method can achieve image defogging in most cases and achieve better defogging effect.
GAMIFICATION IN EDUCATION: BADGES AS INDICATORS OF THE FORMATION OF EDUCATIONAL RESULTS

The article presents an analysis of gamification tools based on the use of the ClassDojo service, proof of the need for a systematic approach to badging for the organization of professional reflection in terms of the formation of students' educational results.