Reviewerly/exHarmony huggingface dataset:
Biometric and blockchain-based vehicular technology

With new discoveries being made every day in the field of technology and new inventions rising among the society, those tasks that once required manual labors have now been hugely replaced with softwares and automation systems that simplifies the tasks with the click of a button. With a huge population across the world and with emerging trends in the industrial sector, the human economy has not only observed huge changes in growth and development but also rising sophisticated demands to meet every day needs of the common man. With India becoming a trillion economy, a huge rise in population and growth in various sectors, have led to drastic changes in the environment. Today, travel has become a necessity for many people in the workforce. With public transports, hugely meeting the demands of a large population, people today prefer to use their own forms of transport for commuting. This practice no only increases the amount of carbon-footprint and consumption of resources such as fuel but also requires a huge demand in infrastructure to store and park vehicles. According to a recent survey made by World Health Organization (WHO), lack of infrastructure for parking the vehicles has resulted in increased waiting time, fuel consumption and high amounts of air pollution. With parking systems mostly being man-handled, today with the help of technologies, we have been able to automate this process of allotting a parking space for vehicles with the help of software and apps. This system gains access to the user information and allots a parking space manually by analyzing user data and the vehicle requirements. But the question of security of user-data is one question that remains unanswered. Since user data is vulnerable to the breach of security attacks and manhandling, we need a solution that can not only satisfy the storage requirements but also meet the security requirements. One such solution that we will be considering is a hybrid technology of blockchain and biometrics that can provide two layers of protection to the user level data, offering storage in the cloud at the same time. Blockchain revolves around distributed computing and architecture that takes care of integrity and authentication of data., whereas biometrics plays the role of authenticating and analyzing the user information for the purpose of verification and control of data. Deploying and studying the use of this hybrid technology can help in providing a secure environment for user data in vehicle transportation systems.
A Comparison of Regularization Methods for Near-Light-Source Perspective Shape-from-Shading

3D shape reconstruction from images is an active topic in computer vision. Shape-from-Shading is an important approach which requires the surface properties and light source position to infer the 3D shape. A L2 regularizer is typically used to penalize the irradiance equation. In this article, anisotropic diffusion (AD) is introduced as a regularizer to solve the image irradiance equation. The method is then compared with L1 and L2 regularization methods, where all of the three techniques are formulated using gradient descent. Results shows that with AD, edges can be better preserved. AD shows lower depth error and higher correlation when compared with L1 and L2 regularization methods.
Learning to Jointly Segment the Liver, Lesions and Vessels from Partially Annotated Datasets

The segmentation of the liver, lesions, and vessels from pre-operative CT scans is of major importance in hepatic surgery planning. However, large databases with reference segmentations for these regions of interest remain unavailable, a challenge often encountered in medical image segmentation. In this work, we propose the FuSe loss, a novel loss function for multi-task learning on datasets with partial annotations. By employing the nnU-Net’s 3D self-configuring pipeline to calibrate and train a deep network for the joint segmentation of the liver, lesions, and vessels, we show how the FuSe loss allows to learn from the differently annotated IRCAD and LiTS datasets, improving the overall baseline segmentation performance. With the FuSe loss, the dice scores reached up to 95.9%, 70.6% and 60.0% for the liver, lesions, and vessels respectively.
Interpreting Deep Representations of Phonetic Features via Neuro-Based Concept Detector: Application to Speech Disorders Due to Head and Neck Cancer

The popularity of Deep Neural Networks (DNNs) is growing significantly, and so is the interest in gaining a better understanding of their functioning. In this work, it is even more interesting to reveal the behavior of these black-boxes since we are involved in a clinical context. To this end, we propose a general analytic framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neuro-based Concept Detector (NCD)</i> , for interpreting deep representations of a DNN. Based on the activation patterns of the hidden neurons, this framework highlights the capacity of neurons to detect a specific concept related to the final task. The key strength of our framework is that it provides an interpretability tool for any type of DNN performing a classification task regardless of the application field. In this paper, we evaluate this framework on a Convolutional Neural Network (CNN) trained for the task of French phone classification. This choice was guided by the final objective of a long-term research project, which aims to identify the linguistic units best contributing to the maintenance or loss of intelligibility in the context of speech disorders. Through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NCD</i> , we demonstrate the emergence of phonetic features in the classification layers of the CNN-based model, while applied on healthy speech, a concept with a great interest in the field of clinical phonetics. Indeed, we further show that these interesting findings shed light on the characteristics of speech disorders in terms of altered phonetic features and provide relevant information for clinical practice, notably, patients' rehabilitation and follow-up.
Identifikasi Citra Cacat Las Menggunakan Metode Gray Level Co-Occurance Matrix (GLCM) dan K-NN

Abstract— The welding process is a very common thing done in industrial activities. The welding results must certainly be assessed so that the quality of the welding results is guaranteed. The quality of the welding results can be seen from the absence of defects on the surface of welding area. This research aims to determine the quality of welding results. The methods used to identify welding defects in penetrant test results are the Grey Level Co-Occurance Matrix (GLCM) and K-Nearest Neighbor methods. From 80 test data, total accuracy reached 85% which is divided into 4 classifications namely good, porosity, transverse and longitudinal. Test results for the good class obtained the highest accuracy of 90% of the 20 good test images compared to the accuracy of the other 3 classes
Undesigned Data in Discovery Processes and Design of Their Interpretation

In this study, we stress the importance of treating undesigned data to discover knowledge. The category of undesigned data is clarified by revisiting knowledge discovery in databases (KDD), process models, and hypotheses in each of the models. We propose the treatment of undesigned data by referring to mathematical logic. We also show that the process model of KDD can be refined by referring to the process models of fields other than KDD.
On the Kana Notations of Group Shan(山) in Mako-Inkyo : Focusing on Grade 3th and 4th

In this paper, we studied the principle of left and right phonetic notation of Mako- Inkyo compared with Go’on and Kan’on. The results are summarized as follows. 1) The second opened rhyme Yuan(元) appears as -on, -an/-at, -en types in Go’on, but described as -on/-ot, -en/-et types in Mako-Inkyo. 2) The third rounded rhyme of Yuan(元) appears as -(w)on/-(w)ot, -an/-at, en types in Go’on. However, Velar･Guttural are written in -wan/-wat type, and Labial is described mainly as -on/-ot type in Mako-Inkyo. 3) The second opened rhyme Xian(仙) appears mainly as -en/-et type in Go’on･Kan’on･Mako-Inkyo. But [ベン面] of the initial of Ming(明) should be changed to [メイ] on Kan’on in Mako-Inkyo. 4) The third rounded rhyme of Xian(仙) appears mainly as -(w)en/-(w)et type, and -wan, -wen, -on types are mixed in Go’on, but is written in -(w)en/-(w)et type in Mako-Inkyo 5) The fourth rounded rhyme of Xian(先) appears -(w)en/-(w)et type in Go’on･Kan’on･Mako-Inkyo. But some characters ending in [チ] will have to be corrected with [ツ] on Go’on in Mako-Inkyo. 6) The Final-t of Go’on only as [チ] and Kan’on only as [ツ] in MakoInkyo. It can also be seen in Sino-Japanese dictionaries.
Exploring Intra-Group Income Convergence for the Central Asian Countries

The main objective of this study is to explore income convergence for the Central Asian countries whereas income convergence refers to the state when all nations in a region converge to the identical level of steady state in terms of per capita. This study tested the income convergence through beta convergence and sigma convergence over period from 2003 to 2019. The beta convergence indicates that the poor economies grow more rapidly than the rich nations and catch up with them in terms of per capita income, whereas sigma convergence means that the gaps between the per capita incomes of the nations in the region decrease over the passage of time. The beta convergence is determined through panel unit root tests, whereas sigma convergence is determined through the coefficient of variation. This study finds beta convergence as well as sigma convergence henceforth; income convergence is confirmed in Central Asian countries. It can be concluded that economic integration is in favor of Central Asia, so it is recommended that these nations must ensure economic cooperation with each other through easing trade restrictions and lessening import taxes on imports. Moreover, it will also be helpful to ensure the free mobility of the labor force among Central Asian countries, as it will further help them to ensure economic integration and helps in reducing the income inequality in the region.
Development Of Alumni Portal Application Based Android

Alumni are graduates from a school, academy, high school, college, or university. Alumni are one of the important assets that must be embraced, coordinated, and developed as early as possible. The existence of alumni is very influential in improving the quality of an institution. So far, the Information Systems Study Program of UIN North Sumatra has not provided a means that can be used to collect data and track tracer study alumni so that the Information System of UIN North Sumatra has difficulties and obstacles in collecting and tracking data. tracer study alumni. The purpose of this research is to build an android-based application that can be used by alumni in filling out tracer study data online and can also assist the study program in tracking tracer study alumni easily and quickly through an android smartphone. The application development method used in this study is the Extreme Programming (XP) method which consists of planning, design, coding, and testing stages. In making a visual design of the application to be built, the author uses UML (Unified Modeling Language) tools consisting of use case diagrams, sequence diagrams, activity diagrams, and class diagrams. The application is built using the Kodular framework and the Airtable database. With this application, it is hoped that it will make it easier for alumni to fill in data and help the information system study program track alumni data easily and quickly through an Android smartphone.
Understanding MCDM Preference Relations Index Method and Its Application

Custom table (PSI) method. Priority selection coding was developed by Mania &amp; Butt (2010) to solve MCDM problems. As proposed, it is not necessary to assign comparative importance between attributes. Choosing a desirable machine is an important concern for the manufacturing company. The selection process has some important selection properties and the task of this process is to select the preferred machine from among the multiple candidate machines. The problem of machine selection is a problem that is determined by many characters. This paper solves the problem of machine selection, which generates an optional selection code in a Flexible production cell. A case study is used to demonstrate that the proposed method is effective and feasible. This article explores the use of the MCDM System of Choice (PSI) method to address various decision-making issues commonly encountered during the design phase of a production system life cycle. To demonstrate the feasibility, applicability, and accuracy of the PSI method in solving the decision-making problem at the design stage of the product life cycle, five examples are cited from the literature and compared with the results obtained by past researchers. Research has concluded that the BSI method is logical and highly relevant to material selection issues. The choice of marketing area is an important factor in sales activities. Many factors are required in choosing a marketing area, including rental prices, area, audience, and distance. For a decision to select this marketing area to be effective, the hotel needs a decision support system that can provide the best possible service to guests and deliver the right decision in implementing alternatives, as there are responsibilities and responsibilities of many departments
Natural Language Processing with Graph and Machine Learning Algorithms-based Large-scale Text Document Summarization and Its Applications

Analyzing text and image data is always time-consuming, and with the rapid growth in the amount of data, important meanings of the information may be lost. Natural language processing (NLP) and graph-based methods can be used to summarize large documents, The system proposed in this chapter is an integrated approach for text summarization using images, table labels, etc. The first step is text processing, which extracts important text information. The second step involves using graph-based algorithms to extract the most important sentences from the document. Techniques such as node ranking aim to extract the meaningful information from the document. A machine learning (ML) model is used to train the system and provide an accurate document summary. NLP-based text summarization opens up various applications areas, such as e-learning, meeting summarization, e-news systems, social network data analysis, rapid decision support in business analysis, and many more.
Two-Phase Image Denoising Using Hough Transform

Advancement in applications requires more efficient image denoising techniques as it is still an unexplored area for the researchers. The crucial step in image denoising process is detection of noisy pixel, where over and under detection may affect the desire outcomes. Random impulse noise is difficult to remove because it appears randomly on the image, and most filters fail to identify all damaged pixels. To remove random impulse noise, we proposed two-step sequential algorithm, in the first step, ROAD-TGM filter ensure accurate noise estimation by avoiding over and under selection of noisy pixels. The second step performs image restoration using Hough transform method. Hough transform technique is used to extract features form images and by using these features, we restore the damaged pixel by the mean of undamaged pixel. In comparison with other well-known methods, the results of proposed method indicate that our restored images show a substantial change.
Pengaruh Video Pembelajaran Terhadap Hasil Belajar PILH Siswa di Kelas V Sekolah Dasar

This research was quantitative with experimental methods in pre-experimental designs to one-group pretest-posttest design. Based on the pre-test and post-test results, the average value was obtained as follows. The average pre-test value was 40,238, and the average post-test value was 83,476. The data analysis used in hypothesis testing was the t-test (pretest-posttest) test of two different means of paired data. The value of the t-count was 18,884 &gt; t-table 1,725. It showed that H0 was rejected and Ha was accepted, so it could be concluded that there was an effect of the implementation of learning videos on student learning outcomes in PLH class V SDN 15 Simpang Teritip.
Exploring the Factors Affecting Teachers Perception of Teaching and Learning for Learner-Centered Instruction in Teacher Education

Objectives The purpose of this study is to examine the impact of teaching and learning environment support, learning evaluation support, and student guidance support in teacher education for learner-centered classes, and to examine the perception of instructors' programs for competency development. Methods For this purpose an online survey was conducted on the needs of instructional design support, teaching and learning support, learning evaluation support, student guidance support, and instructor competency development at A University, a four-year general university located in Busan. For the collected data, multiple regression was conducted to examine the degree to which teaching and learning environment support, learning evaluation support, and student guidance support variables affect teaching design support. Results The results of this study are as follows. First, among the demands for instructional design support, teaching and learning environment support, learning evaluation support, and student guidance support, the demand for instructional design was the highest. Second, as a result of analyzing the impact of teaching and learning environment support, learning evaluation support, and student guidance support on teaching design, it was found that teaching and learning environment support had the greatest influence. Third, looking at the instructor's program requirement for the development of instructor competency, special lectures and workshops on teaching methods were the highest. Conclusions The conclusions derived from the analysis results are as follows. First, it can be seen that the demand of instructors increases according to the importance of instructional design and social necessity for effective instruction and difficulty in instructional design of prospective and incumbent teachers in teacher training institutions. Second, it can be seen that instructors recognize the importance of teaching and learning methods for learner-centered classes, and in particular, there is a high demand for special lectures and workshops on teaching methods.
International Research Trends on the Use of Augmented Reality in Chemical Education: Focusing on Bibliometric Analysis and Content Analysis

Objectives This study aimed to figure out the trends of international research on chemistry education using augmented reality(AR) and derive implication for domestic chemistry education. Methods We selected a total of 39 research papers on the application of AR in chemistry education published abroad from 2012 to 2021 in the Web of Science(WoS) of Clarivate Analysis. The biblometric analysis was performed using the VOSviewer program based on the bibliometric information and the content analysis was performed on the extracted papers. Results As a results of the study, the number of published papers has steadily increased. The bibliometric analysis results showed that ‘augmented reality’ and ‘computer-based learning’ were mainly used as the keywords, and ‘student’, ‘augmented reality’, and ‘technology’ were mainly used in the abstract. In addition, recent papers have focused on ‘molecular modeling’ of organic chemistry. Cai and Wu were the most co-cited authors in the field, and among the extracted papers, the most cited paper was the study of Wojciechowski &amp; Cellary(2013). Also, the most published and co-cited journal was Journal of Chemical Education, and the most cited journal was Computers &amp; Education. The content analysis results showed that the largest portion of the research topic was chemistry(general), and most of the research environments was laboratories. Quantitative research was the most common research method, and questionnaires and surveys were preferred as the data collection methods. As for the data analysis method, mean and standard deviation in descriptive statistical analysis, t-test in inferential statistical analysis, and content analysis in qualitative analysis were mainly performed. As research variables, attitude, achievement, and usability were reviewed the most. For the research participants, convenience sampling was the most common sampling method, 30 to 100 people was the most sampling size, and college students were the most sampling group. Conclusions Based on the results of this analysis, we suggested that future research on chemical education using AR in Korea needs to be systematically conducted by finding various research topics, expanding research methods and analyzing various aspects for in-depth approach, and diversifying research groups.
Retracted: P094 Sleep quality assessment among Chronic Obstructive Pulmonary Disease patients in a tertiary teaching hospital

Abstract This article has been retracted. Please see: https://doi.org/10.1093/sleepadvances/zpad009
Unanswered Questions in the Electroweak Theory (Before and After the Higgs-Boson Discovery) [Slides]

the TeV energy scale. Indirect constraints from global analyses of electroweak measurements suggest that the mass of the standard-model Higgs boson is less than 200 GeV. Once its mass is assumed, the properties of the Higgs boson follow from the electroweak theory, and these inform the search for the Higgs boson. Alternative mechanisms for electroweak symmetry breaking are reviewed, and the importance of electroweak symmetry breaking is illuminated by considering a world without a specific mechanism to hide the electroweak symmetry. For all its triumphs, the electroweak theory has many shortcomings.
Research on application of wavelet analysis in signal processing of extremely low frequency magnetic targets

Based on the measured signals of extremely low frequency underwater magnetic targets, this paper extracts the magnetic anomaly data of sensors at 50Hz for denoising processing, and uses the denoised magnetic anomaly data as the basic data. Random noise is added to the basic data as a simulated noise signal. Based on Stein's unbiased likelihood estimation threshold method, two wavelet family Symlets, Daubechie and soft and hard threshold selection methods commonly used in denoising literature are used to denoise the measured underwater magnetic target signal data. The different de-noising results are calculated and compared. The de-noising signal-to-noise ratio of the data is used as the evaluation index, and the de-noising effect and reliability of different combined methods are objectively analyzed. Research shows that the hard-thresholding of sym7 as the generating wave function has the lowest signal-to-noise ratio and poor denoising effect. The soft threshold processing using db9 as the generating wave function has the highest signal-to-noise ratio, which can achieve the best denoising effect.
Research and application of key technologies of cloud edge collaboration

With the rapid development of 5g large-scale commercial and edge computing, cloud edge collaboration technology is widely used in various industries. This paper studies and analyzes the key technologies of cloud edge collaboration in realizing resources, data, applications and services at different levels of IAAs, PAAS and SaaS, and introduces the application scenarios of cloud edge collaboration in many fields such as industrial Internet, smart transportation, smart city and medical imaging.