Reviewerly/exHarmony huggingface dataset:
Content-Based Superpixel Segmentation and Matching Using Its Region Feature Descriptors

Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted superpixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.
An Improved High Capacity and Efficient Data Hiding Scheme for 3D Videos Without Distortion Drift

Compared to traditional images and 2D videos, 3D videos are more likely to cause distortion drift problems in data hiding. In this paper, an improved scheme for high capacity and efficient data hiding in 3D videos based on Multi-view coding (MVC) standard is proposed, which avoids the problems of distortion drift. To improve the visual quality of data hiding, two selection modes are provided to limit the distortion drift. By modifying the selected coefficients of 4 × 4 quantized discrete cosine transforms (QDCT) in macroblocks and hiding data into b4-frames, the proposed scheme will effectively prevent distortion drift which caused by intra prediction and inter prediction. Moreover, the proposed algorithm maintains great randomness by using two random sequences. Several benchmark 3D video sequences of different resolutions and diverse contents are used for experimental evaluation. It is experimentally proved that the algorithm has greater embedding capacity and higher efficient than the previous methods.
Proposal of Knowledge Graph and Completion Method for Solving Social Issues: An Attempt to Improve the Bicycle Parking Environment in the University

Societies in Japan face many social issues today. To address these issues, the government is exploring ways to solve problems using data. The purpose of this study is to provide support using existing data for grasping the current situation, taking up the problem of bicycle parking environment in a university as an example of social issues. Specifically, we tried to design a unique schema and build knowledge graph for integration of various on-campus data. Then we applied and modified the knowledge graph completion methods to improve efficiency and accuracy. For the knowledge graph constructed in this study, the number of bicycles was estimated by the conventional method and the proposed method, and the transition of the sum of absolute value errors was compared. The proposed method exceeded the existing method in efficiency and accuracy. In addition, approximately 650 data aggregated at any date and place were able to be estimated about 54 units correctly, compared to the conventional method. Finally, we improved efficiency and accuracy of knowledge graph completion methods.
Background Subtraction by Difference Clustering

Previous approaches to background subtraction typically considered the problem as a classification of pixels over time. We frame the problem as clustering the difference vectors between pixels in the current frame and in the background image set, and present a novel background subtraction method called Difference Clustering. This not only saves computational time, but also achieves high Pr and Fm values for accuracy. In particular, the difference between the current frame and the background image set is extracted using the quartile method for clustering. Compared to traditional k-means model to generate k clusters, our quartile method needs only 2 clusters. Moreover, traditional k-means clustering models need to update the means until convergence, which is time-consuming. In contrast, our quartile method finds the final means directly to reduce the numbers of iterations and computational time, resulting in a real-time algorithm. Experiments on several videos from standard benchmarks demonstrate that our proposed approach achieves promising results compared to several previous methods.
Can Cryptocurrencies Become a Commonly Accepted Means of Exchange?

This chapter studies the challenges a cryptocurrency faces to become a common means of exchange. In particular, the paper discusses the scalability constraint that limits the number of transactions a cryptocurrency may be able to verity per unit of time, the network effect in goods that function as money that increases the cost of new currencies to gain market share, and the implications of the fixed monetary rule present in most cryptocurrencies that departs from an elastic optimal monetary policy. Potential solutions for each case are also discussed.
TRANSFERABLE SKILLS FOR PHD GRADUATES

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 5134-5141ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1335Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
The Open Algorithm Based on Numbers (ABN) as an Educational Innovation for Teaching and Learning in Ecuador

To prevent difficulties in learning mathematics, effective and innovative teaching methodologies should be used.The present study presents the results of a relational and comparative study between the application of the open number-based algorithm (ABN) and the closed numberbased algorithm (CNB) method.The research was of a descriptive cross-sectional quantitative type.Descriptive and comparative, quasi-experimental cross-sectional design study.The sample was 320 students of the first cycle of the National University of Education (UNAE).A multiple choice questionnaire with geometry exercises and a Likert type scale was applied for the data collection.The results were analyzed through an analysis of variance (ANOVA).We worked with two variables, the informal geometric reasoning variable and the formal geometric reasoning variable.We applied a correction of the degrees of freedom with Welch's robust test.The results indicated that the ABN-oriented group (n=151) achieved better results than the CNB-oriented group (n=92).The results were relevant, because the skills demonstrated in the results, show that the ABN method reinforces the capacity to give solution to the exercises, contributing to the development of the numerical sense.Concluding that it can be described as a flexible or innovative model in the field of mathematics teaching.
Some new discrete biorthogonal wavelets constructed with Laguerre polynomials

In this study, we present a new family of discrete wavelets which are constructed with the help of Laguerre polynomials and the Daubechies biorthogonal wavelets construction method. Our aim is to propose the discrete version of some previously constructed continuous Laguerre wavelets and also to present a method of discrete wavelets construction by several iterations. With this scheme, we use two different sets of finite impulse response filters for the signal decomposition and their duals for reconstruction. The quadruplet finite impulse response filters respect the anti-aliasing and the perfect reconstruction conditions, and at the same time, they resemble as much as possible the continuous Laguerre wavelets when using the cascade algorithm. We use the mean squared error, the maximum deviation, and the standard deviation to quantify the similarity between the continuous Laguerre wavelets and the constructed discrete Laguerre wavelets. The results show that, they are both the same wavelets due to the small nature of these parameters. Our method is important because, it can permit the determination of the finite impulse response filter coefficients corresponding to many other continuous wavelets.
Identity Attributes Metric Modelling based on Mathematical Distance Metrics Models

Internet has brought a lot of security challenges on the interaction, activities, and transactions that occur online.These include pervasion of privacy of individuals, organizations, and other online actors.Relationships in real life get affected by online mischievous actors with intent to misrepresent or ruin the characters of innocent people, leading to damaged relationships.Proliferation of cybercrime has threatened the value and benefits of internet.Identity theft by fraudsters with intent to steal assets in real space or online has escalated.This study has developed a metrics model based on distance metrics in order to quantify the credential identity attributes used in online services and activities.This is to help address the digital identity challenges, bring confidence to online activities and ownership of assets.The application forms and identity tokens used in the various sectors to identify online users were used as the sources of the identity attributes in this paper.The corpus toolkits were used to mine and extract the identity attributes from the various forms of identity tokens.Term weighting schemes were used to compute the term weight of the identity attributes.Other methods used included Shannon Entropy and the Term Frequency-Inverse Document Frequency scheme (TF*IDF).Standardization of data using data normalization method has been applied.The results show that using the Cosine Similarity Measure, we can identify the identity attributes in any given identity token used to identify individuals and entities.This will help to attach the legitimate ownership to the digital identity attributes.The developed model can be used to uniquely identify an online identity claimant and help address the security challenge in identity management systems.The proposed model can also identify the key identity attributes that could be used to identify an entity in real or cyber spaces.
ChoseAmobile: A Web-based Recommendation System for Mobile Phone Products

Information and communication technologies brought-in tools and technologies in the field of electronic commerce that introduced new modes of shopping. With the emergence of e-commerce platforms, the user base is shifting from the traditional mode of shopping towards online e-commerce sites. A large number of e-commerce sites share multiple brands and products that make it difficult for the user to decide which site is presenting genuine specifications as well as the best price offerings. In this work, we present a recommendation system choseAmobile that uses specific metrics and product reviews as side information and provides suitable recommendations to the user. The system continuously monitors and analyzes the data sources to see and incorporate the updates in order to present up to date and accurate results. The experimentation results show that the system is proved to be feasible and effective, thus resulting in better recommendations for mobile phone selection, especially for novice users.
The Effect of Hotel Employee’s Informal Learning on Proactive Career Behavior : Mediating Effects of Learning Agility and Career Adaptability

The objective of this study is to suggest the direction of career development that positively affects individuals and organizations through understanding the relationship between informal learning, learning agility, career adaptability, and proactive career behavior of hotel workers. Furthermore, we want to check whether learning agility and career adaptation play a mediating role in the relationship between informal learning and proactive career behavior. To verify the study, a mobile survey was conducted for 2 weeks from May 2 to May 16, 2020, with 350 domestic hotel employees. A total of 300 participants participated in the questionnaire, and the final 297 copies of the questionnaire responses were used for analysis, excluding unfaithful responses. As a result of analysis, first, informal learning had a positive effect on proactive career behavior, learning agility, and career adaptability. Second, learning agility and career adaptation have a mediating effect in relation to informal learning and proactive career behavior.
Key Success Factors of PRINCE2 Project Management Method in Software Development Project

Project management uses many tools, techniques, and methodologies in project development. PRINCE2 methodology set documentation which must be tailored to suit the occasion project. Since software projects may often fail due to lack of well-structured project management method, it is necessary to identify the key success factors of PRINCE2 method in software development project. The purpose of this research study is to identify key success factors of PRINCE2 project management method in software development project. The researcher undertakes a case study in an organisation where software project is implemented in PRINCE2 project management method. Data has been collected from questionnaires and face-to-face interviews. Documents review, and observation techniques were used as secondary data collection sources. The researcher has conducted template analysis with interview data and excels analysis with the questionnaires data. The researcher has then triangulated the data to get authentic and accurate findings. 20 factors have been identified as key success factors in this research study such as defined roles & responsibilities, scope management, manage by stages, well planning, top management support, time management, risk management, monitor project progress, change management, communication management, quality management, project team competency, managing product delivery, cost management, learn from experience, prioritizing task, assign work to right person, benefit realisation and being agile.
Quantum Secret Sharing Scheme with Credible Authentication based on Quantum Walk

Based on the teleportation by quantum walk, a quantum secret sharing scheme with credible authentication is proposed.Using the Hash function and quantum local operation, combined with the two-step quantum walks circuit on the line, the identity authentication and the teleportation of the secret information in distribution phase are realized.Participants collaborate honestly to recover secret information based on particle measurement results, preventing untrusted agents and external attacks from obtaining useful information.Due to the application of quantum walk, the sender does not need to prepare the necessary entangled state in advance, simply encodes the information to be sent in the coin state, and applies the conditional shift operator between the coin space and the position space to produce the entangled state necessary for quantum teleportation.Security analysis shows that the protocol can effectively resist intercept/resend attacks, entanglement attacks, participant attacks, and impersonation attacks.In addition, the quantum walk circuit used has been implemented in many different physical systems and experiments, so this quantum secret sharing scheme may be achievable in the future.
EOLGD: an edge feature descriptor-based method for long-wave infrared and visible image registration

Image registration is an essential prerequisite for multisource image fusion. To further improve the registration accuracy for long-wave infrared and visible images, a feature point descriptor construction method—edge-oriented log-Gabor descriptor is proposed. Initially, grayscale edge images are obtained by a structured random forests edge detector, which is more suitable for extracting features using multiscale and multioriented log-Gabor filters. Then, edge features are used for selecting orientation-stabilized feature points from speeded up robust feature points, and descriptors are constructed with a two-stage Gaussian weighting scheme. Finally, feature point pairs are matched by computing the difference of descriptors. Experimental results show that the proposed approach achieves better performance compared with state-of-the-art methods.
The Effect of Story Telling Activity Using Pop-Up Book on the Social Caring Character

This study aimed to determine if the pre and post story telling activity of the social caring character could improve the character when the stories of the social caring character were read to kindergarten children.The story telling activity focused on the caring behaviour.The stories of social caring character were read to children using pop-up story book for experiment group (N= 40).The control group (N= 40) learned the caring character with no stories, but with conversing.The performance a quasi-experimental design, with nonequivalent control group design was used to data analyze.The result indicated that story telling activity of social caring character to treatment group significantly improved social caring character.A paired sample ttest score was significant (0.000<0.05) on the social caring aspect.The conclusion was story telling activity of social caring character could influence the behavior of children's social caring character.
Variable Definition and Independent Components

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.
The DARE Project: Exploring Creative Multimedia Students’ Acceptance Towards Augmented Reality-enhanced Learning Environments

This paper presents a study that looked into the use of AR Technology with Authentic learning elements to develop AR-enhanced learning materials that can be used in a classroom.
Application of hierarchical clustering analysis for vocal feature extraction

Since Mel-Frequency Cepstral Coefficient (MFCC) was firstly proposed in 1980s, it became the mostly-used speech feature in the field of automatic speech. MFCC employed Mel filters to transform the vocal frequency into Mel frequency, and considered a logarithmic relationship between practical frequency and Mel frequency. However, most of MFCCs used 26 blocks of uniform Fbanks without consideration of the dynamic characteristics of cepstrum coefficients. To address this issue, this paper proposes a hierarchical clustering approach to analyze the inverse discrete cosine transform cepstrum coefficient (IDCT CC), and uses cosine similarity to measure the distribution of blocks of the IDCT CC in the frequency domain. Our method extracts a 14-dimension vocal feature vector, and calls this feature as C-vector. In the experiment, we investigated the performance of C-vector for the speaker identification (SI) tasks, and employ Gaussian mixture model (GMM) to train SI models based on the C-vector. The results showed that the identification accuracy of C-vector was better than MFCC.
Design of Image Extraction Hardware for Hand Gesture Vision Recognition

In this paper, we propose a system that can detect the shape of a hand at high speed using an FPGA. The hand-shape detection system is designed using Verilog HDL, a hardware language that can process in parallel instead of sequentially running C++ because real-time processing is important. There are several methods for hand gesture recognition, but the image processing method is used. Since the human eye is sensitive to brightness, the YCbCr color model was selected among various color expression methods to obtain a result that is less affected by lighting. For the CbCr elements, only the components corresponding to the skin color are filtered out from the input image by utilizing the restriction conditions. In order to increase the speed of object recognition, a median filter that removes noise present in the input image is used, and this filter is designed to allow comparison of values and extraction of intermediate values at the same time to reduce the amount of computation. For parallel processing, it is designed to locate the centerline of the hand during scanning and sorting the stored data. The line with the highest count is selected as the center line of the hand, and the size of the hand is determined based on the count, and the hand and arm parts are separated. The designed hardware circuit satisfied the target operating frequency and the number of gates.
REGISTRATION OF MMS LiDAR POINTS AND PANORAMIC IMAGE SEQUENCE USING RELATIVE ORIENTATION MODEL

Abstract. We propose using the relative orientation model (ROM) of panoramic to register the MMS LiDAR points and panoramic image sequence, which has the wide applicability. The feature points, extracted and matched from panoramic image pairs, are used to solve the relative position and attitude parameters in the ROM, then, combining the absolute position and attitude parameters of the initial panoramic image, the MMS LiDAR points and panoramic image sequence are registered. First, we propose the position/attitude ROM (PA-ROM) and attitude ROM (A-ROM) of panoramic images respectively, which are apply to the position/attitude parameters both unknown and only the attitude parameters unknown. Second, we automatically extract and match feature points from panoramic image pairs using the SURF algorithm, as these mismatching points will affect the registration accuracy, the RANSAC algorithm and ROM were used to choose the best matching points automatically. Finally, we select the feature points manually from MMS LiDAR points and panoramic image sequence as the checkpoints, and then compare the registration accuracy of continuous/discontinuous panoramic image pairs. The results show that MMS LiDAR points and panoramic image sequence are registered accurately based on ROM (7.36 and 3.75 pixels in dataset I and II), what's more, our registration method just tackle the image pairs (uninvolved LiDAR points), so it is suitable for more road scenes.
Implementasi Metode Demonstrasi Berbantuan Google Classroom Untuk Meningkatkan Minat Belajar Mahasiswa Kelas A Prodi PGSD

This study aims to increase the interest in learning of class A students in semester 5 of the 2019/2020 academic year at the PGSD study program at STAHN Mpu Kuturan Singaraja through the implementation of the Google ClassroomÂ  assisted demonstration method. The research subjects of class A semester 5 students were 20 people. While the object of this research is student interest in learning by implementing the Google Classroom assisted demonstration method. Data on the results of learning implementation are collected by observation method. The data obtained were analyzed using descriptive statistical analysis. Based on the results of the data analysis, it can be concluded that the implementation of the Google ClassroomÂ  assisted demonstration method can increase the interest in learning of Class A students in semester 5 of 2019/2020 academic year at the PGSD study program at STAHN Mpu Kuturan Singaraja. This result is evident from the increase in the average of the first cycle of 14.75 to 18.52 in the second cycle. Classical completeness of students also increased from 65% in the first cycle to 95% in the second cycle. Â  Keywords: demonstration method, Google Classroom,Â interest in learning