Reviewerly/exHarmony huggingface dataset:
Improvement and Orientation of Method of Data Arrays Sorting by Confluence on Architecture of Graphic Processor Unit

The method of confluence sort is improved by the spatial parallel separation process of sorting that is driven to the simultaneous receipt of elements of growing and handing down arrays. The basic development stages of flowchart graph of parallel sorting of data arrays with using of improved method of confluence are defined and considered. The concrete flowchart graph of the parallel data arrays sorting with the use of the improved method of confluence sort and referenced to architecture of graphic processor unit is developed. Development of software means for the parallel sorting of data arrays with the use of the improved method of confluence on the basis of complex approach that embraces: research, improvement and development of methods and algorithms of the parallel data arrays sorting; flowchart graphs of algorithms of the parallel sorting; architecture of graphic processor unit GPU and program model CUDA is proposed. It is shown that realization of the parallel data arrays sorting with the use of the improved method of confluence provides considerable reduction of sorting time in comparison with the use of only central processing unit.
Effect of Viewing Angle and Distance on Motion Sickness in 3D game

3D games have become popular in recent years. However, it is easy to produce a symptom when playing games, called 3D discomfort or motion sickness. The current explanations mostly focus on the authenticity of the game or the differences in the physiological level of the viewer. There is a rare discussion about whether the game causes human visual and brain discomfort which are caused by the 3D game production process. As many people feel uncomfortable with 3D games, the players gradually lose enthusiasm for the game. This study aims to understand motion sickness through the experiment and confirm that the field of view and viewing distance causes motion sickness through data collection. The experiment in this study has open and closed scenarios for psychological and physical tests. In the experiment, the players determine the most comfortable and uncomfortable vision and distance. The responses using a questionnaire are collected and analyzed in the online community. Based on the data, we test whether motion sickness occurs in different scenarios. The result helps prevent motion sickness in the game and allows players to enjoy the game. The result also provides basic data to prevent motion sickness in the game.
An Accelerated VLSI Approach for K-Means Clustering Algorithm for the application of Microarray Data Analysis

This paper proposed a parallel and pipelined architecture for faster execution of k-means clustering algorithm for microarray data analysis.This is verified by calculating the computational time of algorithm when run on XILINX ISE 14.4 and MATLAB.The computational time taken by MATLAB simulation it is 14.2390ms while for VLSI simulation is found to be 0.0754ms hence VLSI simulation is 188 times faster than MATLAB Simulation.Also, computational time of proposed work which is 0.0754 ms, is compared with the previous work time which is 0.5891 ms and it is found that proposed work is 7 times faster than previous work.
PENINGKATKAN AKTIVITAS BELAJAR BAHASA INDONESIA MELALUI MODEL DISCOVERY LEARNING PADA SISWA KELAS VII.1 SMPN 3 KOTO BARU

This study aims to improve Indonesian language learning activities through the application of discovery learning models to class VII.1 students of SMPN 3 Koto Baru. Classroom action research was carried out for two cycles, with each cycle holding two meetings. Each meeting is held by applying the discovery learning model. As a data collection tool is the result of observation of student learning activities in cycle 1 and cycle 2. The results of research obtained on student learning activities at the initial observation of 68% categories (less active), increased 80% categories (active) in the first cycle, and increased again 85% (active) category in cycle II. Learning activities of students learning Indonesian experience an improvement from the first cycle to the second cycle, this shows that the discovery learning model can be used as an alternative learning to improve student learning activities.
Method of tire pattern image retrieval based on wavelet transform and Siamese network

Image retrieval of tire tread pattern plays a vital role in traffic accidents and criminal investigation. We propose a tire pattern retrieval method based on wavelet transform and Siamese network, which combine traditional techniques with deep learning. Firstly, we use the wavelet transform to extract low-level features of tire patterns. Secondly, we design a lightweight network called TireNet to extract high-level features of tire patterns. The TireNet based on MobileNet V1 replace the single convolutional neural network of the original Siamese network. Finally, the low-level and high-level features are fused to generate a one-dimensional vector, and calculated the similarity by Euclidean distance. To verify the proposed method in this article, we construct a tire pattern dataset named TireDataset. The experimental results show that the Siamese network is feasible in the task of learning the similarity of tire patterns, and TireNet outperforms existing methods in terms of speed and accuracy trade-off.
Application of multi-sensor unmanned aerial system for identification of hydrothermal alteration zones

&amp;lt;p&amp;gt;In this study, we proposed a method to utilize a multi-sensor Unmanned Aerial System (UAS) for exploration of hydrothermal alteration zones. This study selected an area (10m &amp;amp;#215; 20m) composed mainly of the andesite and located on the coast, with wide outcrops and well-developed structural and mineralization elements. Multi-sensor (visible, multispectral, thermal, magnetic) data were acquired in the study area using UAS, and were studied using machine learning techniques. For utilizing the machine learning techniques, we applied the stratified random method to sample 1000 training data in the hydrothermal zone and 1000 training data in the non-hydrothermal zone identified through the field survey. The 2000 training data sets created for supervised learning were first classified into 1500 for training and 500 for testing. Then, 1500 for training were classified into 1200 for training and 300 for validation. The training and validation data for machine learning were generated in five sets to enable cross-validation. Five types of machine learning techniques were applied to the training data sets: k-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). As a result of integrated analysis of multi-sensor data using five types of machine learning techniques, RF and SVM techniques showed high classification accuracy of about 90%. Moreover, performing integrated analysis using multi-sensor data showed relatively higher classification accuracy in all five machine learning techniques than analyzing magnetic sensing data or single optical sensing data only.&amp;lt;/p&amp;gt;
Securing images using chaos and multidimensional Playfair cipher

Image is utilized in diverse fields due to its vividness, intuitiveness, and copiousness. Existing methods for image encryption have limitations to support for 24-bit pixel images, greater cipher image size, low resistance against differential attacks, the high correlation between cipher images obtained by using a 1-bit modification of the plain image, restriction on the size of the secret image and overhead of sharing secret key among participants for every secret image. The proposed encryption method uses hyper-chaotic Chen system based triple permutation, XORing, and multidimensional Playfair encryption that overcome the limitations in the existing methods. The statistical analysis is performed in respect of the parameters: histogram analysis, correlation coefficient, randomness tests, information entropy, encryption quality, diffusion characteristics, Mean Absolute Error, key and search space analysis, and computational complexity. The results of the analysis manifest that the proposed method has got potency to be an efficient image encryption method.
A Study of the Occupational Consciousness of Newly Registered Students at KOSEN (NIT) of Merchant Ships

A survey of newly registered students at KOSEN (National Institute of Technology, NIT) of merchant ships from the academic year 2016 to 2018 was conducted to determine their occupational consciousness. The results showed that 89.4% of the respondents had a job that they were aspiring for their future, with 78.0% aspiring to be seafarers. This is an indication that newly registered students have clear targets before entering the KOSEN (NIT) of merchant ships. The survey respondents with high self-efficacy regarding career decision-making had a clear idea of the occupation they intend making a career out of. The results also showed that the self-efficacy score in relation to career decision-making of the respondents from the KOSEN (NIT) of merchant ships is higher than that of newly registered students of fisheries university and fisheries high schools.
Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning

Recently, deep models have been successfully adopted in several applications, especially where low-level representations are needed. However, sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-knowledge guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate that our approach leads to either superior overall performance or faster convergence (i.e., both effective and efficient).
Application of Hybrid Filtering in Video Stabilization

The motion estimation algorithm based on feature matching and Kalman filtering (KF) has been used for video stabilization in recent years. However, the video obtained by this method is still not stable due to the high-frequency jitter. Therefore, a hybrid scheme based on improved Kalman prediction filtering (KPF) and low-pass filtering (LPF) is proposed in this paper. Firstly, the results of feature points matching and tracking are used to obtain the global motion estimation. Afterward the value of estimation is sent to the LPF and KPF to process. Finally, we compensate the adjacent video frames with the smooth active camera scanning motion. The method can effectively filter out jitter, and get smoother active camera scanning motion. The variance of motion parameters average reduces by 67.15% after filtering. Experimental results show that the quality of compensated video is obviously improved by this method.
Computational Physics Methods and Algorithms

Computational Physics plays a major role in solving the physical problems numerically, which cannot be achieved using analytical methods due to the time constraint and complexity of the underlying physical systems. Computational physics combines the theoretical and experimental aspects of conventional scientific study. The advancement in the field of computing and numerical analysis helped computational physics to solve the problems associated with molecular modeling, protein folding, atmospheric science more effectively. In this paper, we review some of the important methods and equations used in Computational Physics in order to solve mathematical problems numerically. Integration, Root finding, Ordinary differential equations, Matrix eigenvalue problems, system of linear equations and Partial differential equations along with some of the well-known methods are briefly discussed. Challenges and applications associated with Computational Physics are also reviewed.
MOOCs and the Online Data Mining for Efficient Practical Framework of Catering Management for Foreign-related Tourist Hotels with Intelligent Tools

MOOCs and the online data mining is used for efficient practical framework to cater the management for foreign-related tourist hotels integrated with intelligent tools are studied in this paper. The novelties are organized from three aspects. First, original super-multi-objective optimization problem is transformed into a two-objective optimization problem, that is, minimizing the basic convergence evaluation function, hence, the core system is implemented to validate performance. Second, uncertainty methods are generally used to express preference information when there are uncertain factors in the decision-making process. It is then used to deal with the complex scenarios. Third, the experiment results have shown the effectiveness of the model.
Automatic Intelligent Korean Character Semantic Recognition and Analysis Framework based on Machine Learning

Automatic intelligent korean character semantic recognition and analysis framework based on machine learning is implemented in this manuscript. In the research process of this article, algorithm uses the idea of genetic system programming paradigm to derive characters in the initial system, while the initial L system operates a set of actual related parameter strings represented by letters to expand the grammar. There are core innovations as follows. (1) The random optimization algorithm is only required to know the unbiased estimation of the gradient of the objective function, especially for machine learning problem of finite samples. Hence, it is extended into higher complexity. (2) The random optimization method only needs to calculate the gradient of the objective function, it is therefore used to enhance the overall efficiency. The experiment compared with the latest methods have proven the performance.
Eye-tracking Data for Weakly Supervised Object Detection

We propose a weakly supervised object detection network based on eye-tracking data. A large number of training samples cannot be used due to the following problems: (1) the labels of training samples in object detection are not all pixel-level and (2) the cost of labeling is too high. Thus, we introduce a framework whose input combines images with only image-level labels and eye-tracking data. Based on the position given by the eye-tracking data, the framework has effective performance even in the case of incomplete sample annotation. Thus, we use an eye-tracker to collect the data on the most interesting area in the sample images and present the data in the fixations way. Then, the bounding boxes produced by the fixations data and the original image-level label become the input data of the object detection network. In this way, eye-tracking data helps us selecting the bounding boxes and providing detailed location information. Experiment results verify that the framework is effective with the support of eye-tracking data.
The Optimal Inverter DC/AC Value Selection Method Based on Big Data Technology

It is a commonly used strategy to reduce the levelized cost of energy by improving the ratio of the photovoltaic panel installation DC capacity to inverter AC capacity. However, selection of DC/AC ratio value generally relies on experience, lacking a scientific method to estimate the optimal value under different circumstances. This article analyzes and explores the historical data of photovoltaic power station using data mining technology to establish a photovoltaic power generation prediction model, and then, calculates the return on investment based on the power generation predicted by the model. The calculation result shows that the return on investment generally increases with the increase of R, and then decreases with the increase of R after reaching a maximum value. Therefore, the optimal DC/AC ratio in different regions can be found.
Text/Language-Independent Unknown Speaker Rejection Method using LSP Codes

Scam phone calls pretending relatives have become a major social problem. However, current countermeasure against such scam phone calls stays only enlightenment activities although there is a limited number of technical measures in telephones. In this study, we have proposed a text-independent unknown speaker rejection method which recognizes whether a known speaker or an unknown speaker on receiving a call. In this method, we used a line spectral pair (LSP) spectrogram as the feature and a convolutional neural network (CNN) as the identification engine. From the evaluated results, the proposed method recognized known/unknown speakers with the false acceptance rate (FAR) of 13.1% in Japanese speech and 25.2% in German speech, respectively.
Research on Simulation Method for Reliability Prediction of Pyrotechnical System

Aiming at the problem of errors in the reliability prediction calculation of pyrotechnical systems based on the Monte Carlo simulation method, this paper studies the three aspects of series and parallel systems, the number of components, and the parameters of the Monte Carlo simulation method to improve the accuracy of the reliability prediction simulation calculation method. The results show that the order of the reliability calculation errors from small to large is parallel system <; hybrid system <; series system, the reliability calculation error can be evaluated by the series system, and the calculation error obtained is the maximum error of the system. When calculating the reliability by Monte Carlo simulation method, the system needs to be simplified in series and parallel, and the number of random sampling elements in the system should be minimized to reduce the calculation error of the Monte Carlo simulation method. The number of samples has a significant impact on the Monte Carlo simulation method. To improve the calculation accuracy of reliability, but due to the limitation of computer memory and calculation time, it is recommended to take a sample size of n = 10000000, the repeated calculation times of the Monte Carlo simulation method can stabilize the calculation results, and it is recommended to choose the number N = 1000. The control of the statistical value of the sampling individual components has a significant effect on reducing the calculation error of the Monte Carlo simulation method. It is recommended that the absolute error of random number sampling be limited to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> .
Quality of Service Evaluation of a Learning Management System using ISO/IEC 25011: A Prioritization Strategy

Online education has gone through great changes after the massification of the Internet, mobile devices, and the positioning of the Learning Management System (LMS) platforms. However, the service quality of these platforms still presents difficulties in some contexts, particularly in an entrepreneurship. The objective of this study is to apply ISO/IEC 25011 to establish priorities for improving an LMS using the Analytic Hierarchy Process (AHP). For this work, a systematic mapping of the literature and a case study of a local entrepreneurship were carried out. The quality of service model was established on relevant LMS services, and the sub-characteristics selected were used as criteria on AHP; to be able to provide information for the decision making of product improvement. The application of AHP into the prioritization process allowed to stablish a ranking of service and sub-characteristics in a systematic way.
Degrees as kinds vs. degrees as numbers: Evidence from equatives

Within formal semantics, there are two views with regard to the ontological status of degrees: the ‘degree-as-number’ view (e.g., Seuren, 1973; Hellan, 1981; von Stechow, 1984) and the ‘degree-as-kind’ view (e.g., Anderson and Morzycki, 2015). Based on (i) empirical distinctions between comparatives and equatives and (ii) Stevens’s (1946) theory on the four levels of measurements, I argue that both views are motivated and needed in accounting for measurement- and comparison-related meanings in natural language. Specifically, I argue that since the semantics of comparatives potentially involves measurable differences, comparatives need to be analyzed based on scales with units, on which degrees are like (real) numbers. In contrast, since equatives are typically used to convey the non-existence of differences, equatives can be based on scales without units, on which degrees can be considered kinds.
An Adaptive Digital Watermarking Scheme Based on Chaos Sequence and DCT Transform

The article proposes a digital watermarking algorithm based on DCT transform. The method can adaptively select the watermark embedding position. According to human visual properties, it also reasonably allocates the watermark embedding strength in the different position. In addition, the combination of chaotic sequence and scrambling technology enhances the attacked ability. Verfied by the experiment, the method basically meets the demand of invisibility and robustness, and especially has stronger ability to resist JPEG compression attack.
Pixelwise Traffic Junction Segmentation for Urban Scene Understanding

Self-driving vehicles require detailed information of the surrounding environment to drive autonomously in complex urban scenarios, especially traffic junction crossing. Currently, most self-driving and driver assistance systems depend strongly on GPS and backend high definition map for information about traffic junctions. In this work, we would like to look into the possibility of identifying traffic junctions using onboard cameras by formulating it as a segmentation task. To tackle this, we first analyzed how a junction should be defined in image space, and then used it to extend the Cityscapes dataset with a new Junction class label. We took the extended dataset and trained segmentation models to segment out traffic junction within an image. The models were able to achieve an overall mean Intersection-over-Union mIoU of 73.8% for multi-class semantic segmentation and Intersectionover-Union IoU of 58.7% for Junction. This has the potential to improve self-driving vehicles that depend strongly on a high definition map by providing an alternative source of information for navigation. Finally, we introduced an algorithm operating in sensor space to determine how strong the vehicle should decelerate in order to stop prior to the traffic junction based on the segmentation results.
Intelligent Analysis of Data Streams

The huge amount of data generated on a daily basis in many areas of our lives predetermines data science to be one of the most significant current IT research areas. Thanks to the recent technological trends, such as internet of things (IoT), the data streams constitute a majority of currently created data. This chapter aims to provide one possible view on the recent trends in stream mining. Our focus is on two frequent data mining tasks – namely the prediction and the optimization. We have several years of experience in predictive modeling and we would like to offer here a summarization of the selected outcomes of our research work. The domain in which we have verified our methods, is the power engineering area. Due to population growth and technological advancement, there has been a huge increase of global energy demand in recent years. The ultimate goal is to manage the energy supply in the most efficient way. To propose smart solutions, the first step is to have a clear idea about its future consumption and production. Then we can proceed to efficient control of the smart grid, by involving recent trends in optimization and utilizing machine learning approaches. Hence the second part of the chapter is devoted to our endeavors in solving tasks of smart grid optimization. The common denominator of the described approaches is the effort to cover various types of knowledge entering these procedures.