Reviewerly/exHarmony huggingface dataset:
Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys

The mass adoption of automated vehicles in the near future will benefit safety (of occupants and pedestrians), the environment (low emissions), and society (accessibility, on-demand travel). There are, however, still challenges that need to be addressed, with one of the most crucial being motion sickness. In automated vehicles, the interior could be transformed into a living room or a working space, allowing occupants to spend their time with non-driving activities. These changes are likely to provoke, and increase, motion sickness incidence. To that end, this workshop will explore the current state of motion sickness detection and mitigation methods from different angles (e.g., closed-loop detection, multimodal motion cues,etc.) through expert talks and reflections, followed by discussions. The workshop will develop an agenda for motion sickness research in automated vehicles, facilitate new research ideas and fruitful collaborations.
Graph Neural Networks

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
A Secure Message Passing Method using Multi-Layer Adaptive Video Steganography

Steganography is covert communication of a secret message hidden in a medium passed between the intended sender and receiver with no evidence that the work contains a hidden message. The paper proposes a heuristic approach in steganography to secretly embed message in the cover and make use of the Diffie-Hellman key exchange method to be passed on to a secure channel and to be extracted without being detected. Hence the proposed system is a hybrid crypto stego scehme. The study proposes a new and adaptive steganography method that provides multiple layer of security and adaptiveness in passing secret data which emphasizes on undetectability.
http://bulletinnosu.ru/issues/Archive/section.php?ELEMENT_ID=2139

In the theory and practice of higher education today there is no unified approach to the assessment of universal competencies The difficulty lies in the fact that competence cannot be interpreted like a set of subject knowledge and skills. Since universal competencies are not subject-specific and represent a structure that manifests itself at different levels, the objects that need to be evaluated will include many variables of a very different nature. It is extremely difficult to develop tools that will evaluate all these variables, taking into account all their relationships, and provide evidence that exactly what was originally planned to be evaluated is being evaluated. It should be noted that the developers of modern documentation in the context of the implementation of the educational process recommend designing and evaluating competence tools depending on the goals, levels or types of the educational program at different levels of complexity and uncertainty. The purpose of the presented research is to study various approaches to the assessment of competencies, to reveal the structure and possibilities of assessing the universal competencies of future teachers. The research was based on scientific and methodological literature that reveals the essence of the educational process in higher educational institutions, the implementation of the competence approach and the pedagogical foundations of the formation of competencies. The research used methods of theoretical analysis and synthesis, their abstraction and concretization. As a result, a three-component structure of universal competencies of future teachers has been developed, which allows assessing their formation at various stages of the educational process. The analysis and conclusions of the study can be used in scientific and pedagogical activities in the construction of the educational process in higher educational institutions.
Optimizing a Variational Quantum Classifier through the Behavior Analysis of its Components

Quantum computer introduces a novel approach to process information. In quantum information processing, the law of quantum mechanics is applied to solve many practical computational problems. Classification is one such problem that can be resolved efficiently with the gate model quantum computer. There are several types of classifiers available in quantum domain, such as- variational quantum classifier (VQC), Quantum Support Vector Machine (QSVM) with Kernel Approximation, Hybrid Quantum Neural Network (QNN) etc. However, in this study, the mathematical similarities between VQC and classical support vector machine (SVM) and the components of the VQC are analyzed to optimize the performance of the classifier. For the convenience of the study, publicly available datasets, such as- IRIS dataset and Breast cancer dataset, are used in the experiments. IRIS dataset is brought into play for the testing and breast cancer dataset dimension reduced by Principle component analysis (PCA) is for validity test of the optimized VQC. After studying the VQC components in detail, it is found that the optimized VQC outperforms some of the classical machine learning algorithms or sometimes works as similar as classical SVM. The optimized VQC algorithm classifies IRIS dataset with 100% of accuracy and PCA dimension reduced Breast cancer dataset with 90% of accuracy. All of these studies are conducted with the help of Qiskit- an open-source software development kit (SKD) which is developed by IBM. So, the quantum device is considered to be ideal in every experiment.
Light Field View Synthesis Using the Focal Stack and All-in-Focus Image

Light field reconstruction and synthesis algorithms are essential for improving the lower spatial resolution for hand-held plenoptic cameras. Previous light field synthesis algorithms produce blurred regions around depth discontinuities, especially for stereo-based algorithms, where no information is available to fill the occluded areas in the light field image. In this paper, we propose a light field synthesis algorithm that uses the focal stack images and the all-in-focus image to synthesize a 9 x 9 sub-aperture view light field image. Our approach uses depth from defocus to estimate a depth map. Then use the depth map and the all-in-focus image to synthesize the sub-aperture views and their corresponding depth maps by mimicking the apparent shifting of the central image according to the depth values. We handle the occluded regions in the synthesized sub-aperture views by filling them with the information recovered from the focal stack images. We also show that if the depth levels in the image are known, we can synthesize high-accuracy light field image with just five focal stack images. Our algorithmâ€™s accuracy is compared with three state-of-the-art algorithms; one non-learning and two CNN-based approaches, and the results show that our algorithm outperforms all three in terms of PSNR and SSIM metrics.
Consequence of Phishing during the pandemic in Manaus

In results, we sought to identify the ways in which phishing is used during the pandemic. The discussion sought to report the ways in which the phishing attack works and discussed ways to prevent phishing attacks and make people aware of the risks and precautions when browsing the Internet
Strong Phase Modulation of Single Photons with Surface Acoustic Wave Cavities

We demonstrate modulation of the light scattered from a quantum emitter enhanced by surface acoustic wave cavities etched into the surface of GaAs, with the half-wave voltage V π as low as 44 mV.
Person-Denoting Nominals: Interpretations and Structures

Chapter 14 examines several sets of person-denoting nominals that show interesting patterns in terms of how they participate in compounding and how they take arguments and modifiers in the syntax. Using a few key exemplar nominals such as ‘writer’, ‘author’, and ‘passenger’, the chapter argues that the different structures in which the nominals appear relate to whether the denotation of a given use of the nominal is fundamentally dispositional, relating to a long-term property, or fundamentally episodic, relating to a particular event or situation. It illustrates several morphological and syntactic differences which it then accounts for in terms of these semantic properties. The last part of the chapter examines NP-internal syntax and develops an account for structures involving modifiers which are marked with genitive case, and those which lack genitive marking.
GESTÃO DA INFORMAÇÃO EM SITUAÇÃO DE CRISE: REFLEXÕES SOBRE COMUNICAÇÃO NOS ACIDENTES DE MARIANA E BRUMADINHO

The purpose of this article is to reflect on the information management process in crisis situations in the context of the Network Society. Such an analysis will be made from the observation of the communication management procedures carried out in the accidents of the Mariana and Brumadinho dams, in Minas Gerais, disclosed through the media and in official documents. Based on the data and under the logic of the concept of power deriving from communication, the article indicates that the culture of training in information management, investment in analysis and the development of communication protocols for
Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach

Abstract Entropy and extropy are central measures in information theory. In this paper, Bayesian non-parametric estimators to entropy and extropy with possibly right censored data are proposed. The approach uses the beta-Stacy process and the difference operator. Examples are presented to illustrate the performance of the estimators.
Image Steganography for Confidential Communication and Secured Data Storing

Since the Middle Ages, steganography has been utilized to hide messages under a cover material. The purpose of our paper is to use modern steganography techniques to disguise digital messages. A secret message (text or picture) with N * N RGB pixel must be conveyed in a container image with N * N RGB with minimal content alterations. The carrier, often known as the cover image, can be made public. In this study, lift Haar wavelet transform based on AES is useful in training the model which consists of a hidden network and a reveal network, in addition to LSB encoding. Using the proposed assignment algorithm, the text is encrypted and embedded into the cover image. When compared to older systems with good embedding, this technique will provide greater security and robustness. The system will have improved PSNR and MSE values, and load capacity is analyzed in this proposed system.
"Face identification in the laboratory and in virtual worlds": Correction to Bindemann et al. (2022).

[This corrects the article DOI: 10.1016/j.jarmac.2021.07.010.].
Implementation of E2EE Using Python

The need for cyber security is felt now more than ever. In an era of constant mass surveillance, illegal spying and cyber-attacks, it is extremely important to have a secure means of communication which cannot be intercepted. Several new protocols were introduced such as HTTPS to encrypt the connection between the client and the server. This made sure that no third person can read the data being transmitted to and from the client. This model of encryption had one major flaw: the server itself. Every message that was encrypted by the sender was decrypted at the server, encrypted again and sent to the receiver. Thus the server can read all the messages. The users of such chatting services had to trust the owners of the services with their privacy. Even if the owners were not involved in shady data deals, they still had the risk of their servers getting hacked or being pressured by the government to reveal the data of their users. All these issues paved the way for a new type of implementation of encryption known as end-to-end encryption often abbreviated as E2EE. The message to be sent is encrypted by the sender and is sent to the server which relays it to the receiver as it is. Since the keys used to encrypt and decrypt the data are available only to the users, the server cannot read the messages sent through it. This model quickly gained popularity and was implemented by many messaging applications, the most notable being WhatsApp, Signal, Telegram, and Wire. In this project, we are going to implement E2EE using Python. For encryption, we intend to use the AES algorithm. AES stands for advanced encryption standard which was introduced in 2001 by the NITS (U.S.A.). It was developed by Vincent Rijmen and Joan Daemen in response to the earlier broken algorithm DES. AES is a symmetric key encryption algorithm meaning that the same key is used for encryption as well as decryption of the messages. This algorithm has three key lengths—128,192 and 256 bits, whereas a single block size of 128 bits. The version we are going to implement is 128 bits key size.
Harnessing the synergies of independent central banks and human capital for enhanced financial sector development in Africa

Using panel data from 2004 to 2012, we employ a two-step system GMM estimation technique, with robust standard errors, collapsed instruments and illus-trate marginal effects of central bank independence on financial development in Africa. We also examine the moderating roles of human capital, proxied by literacy rates on the CBI-financial development nexus. We find that, in countries with higher literacy rates/human capital, the positive impact of dejure CBI on financial development is enhanced. Higher literacy rates however, worsen the negative impact of de facto CBO on financial devel-opment. Independent central banks can be made more effective in achieving financial development through governments improving literacy rates. The study is the first to empirically examine the impact of central bank independence on financial development using both dejure and de facto CBI measures and literacy rates in explaining this relationship
Appendix ExploreDAS software documentation

PreviousNext You have accessDistributed Acoustic Sensing for Seismic Measurements – What Geophysicists and Engineers Need to KnowAppendix ExploreDAS software documentationhttps://doi.org/10.1190/1.9781560803850.app SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Software applications are a key part of a scientist's and engineer's toolbox allowing them to stand on the shoulders of those who have gone ahead of them and apply their learnings to new problems. I developed this open-source, MATLAB graphical user interface software package, called ExploreDAS, for this course to help you build a more intuitive understanding of the strengths and potential weakness of DAS seismic data acquisition and processing. The software allows you to quickly build a geologic structure, perform forward modeling to obtain seismic data, and create a migrated image with that data. Permalink: https://doi.org/10.1190/1.9781560803850.appFiguresReferencesRelatedDetails Sep 2022Pages: 174ISBN (print):978-1-56080-384-3ISBN (online):978-1-56080-385-0 publication data© 2022 All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transcribed in any form or by any means, electronic or mechanical, including photocopying and recording, without prior written permission of the publisher.Publisher:Society of Exploration Geophysicists HistoryPublished in print: 06 Sep 2022 CITATION INFORMATION (2022), "Appendix ExploreDAS software documentation," Distinguished Instructor Series : 127-144. https://doi.org/10.1190/1.9781560803850.app Plain-Language Summary PDF DownloadLoading ...
A Novel Machine Learning Approach for Detection of Coronary Artery Disease Using Reduced Non-linear and Chaos Features

In this research paper, authors present an automated system in this paper that integrates a ranking technique with Principal Component Analysis (PCA), Generalized Discriminant Analysis (GDA) and a 1-Norm Bidirectional Extreme Learning Machine (1-NBELM) to reliably classify normal and coronary artery disease groups. Twenty chaotic and non-linear attributes were hauling out from the Heart Rate Variability (HRV) signal to detect coronary artery disease groups. The HRV data for this study derived from a typical database of Normal Old (ELY), Young (YNG), and Coronary Artery Disease (CAD) people. Fisher, Wilcoxon and Bhattacharya were used to compute the rankings of attributes. GDA then turned the ranking features into a new feature. The Radial Basis Function (RBF) kernel was used to transfer original features to a high-dimensional feature space in GDA and PCA, and then it was deployed to 1-NBELM, which utilized the sigmoidal or multiquadric non-linear activation. Numerical experiments were performed on the combination of database sets as Young-ELY, Healthy-CAD, and Healthy ELY-CAD subjects. The numerical results show that ROC with GDA and 1-NBELM approach achieved an accuracy of 98.12±0.14, 96.21±0.12 and 99.87±0.28 for Young-CAD, Young-ELY and Healthy ELY-CAD groups with the use of sigmoidal and multiquadric activation function. The Fisher with GDA and 1-NBELM and Bhattacharya with GDA and 1-Norm Extreme Learning Machine (1-NELM) approach achieved an accuracy of 99.98±0.21 for all databases.
Reading Motivation and Reading Comprehension Ability of The Students at Islamic University of Malang

This article aims at knowing whether there is significant correlation between reading motivation and reading comprehension ability of the third semester students of English Language Teaching Department of University of Islam Malang or not. The research design in this study is quantitative research in the form of correlation design. The subject of this study was 33 students. The researcher developed two research instruments; a questionnaire to measure students’ reading motivation and a test of reading to measure reading comprehension ability. The finding indicated that there was a positive correlation between students’ reading motivation and their reading comprehension ability because the coefficient correlation obtained showed on the table was 0.497. It can be concluded that the correlation between those two variables was on moderate correlation. The coefficient correlation obtained from the computation was significant because r observed was higher than the r critical (0.497 &gt; 0.344).
On the Use of Bayesian Artificial Intelligence for Hypothesis Generation in Psychiatry.

In this study, I introduce the use of Bayesian Artificial Intelligence, namely through the probabilistic and structure learning of Bayesian Network models, for hypothesis generation in psychiatry. Bayesian Networks are directed acyclic graphical models that allow researchers to account for complexity in multivariate data sets, as well as identify what is the likely causal direction in detected associations. This in turns leads to more effective designs for confirmatory studies in clinical settings, that go beyond association studies and can provide meaningful impact in clinical practice. As an example, I use three different data sets to highlight several frameworks for hypothesis generation. Bayesian Networks are useful models since the early stages of knowledge generation in psychiatry, and they can be easily adopted by most applied and clinical researchers for use in quantitative studies.
Dense deep stochastic configuration network with hybrid training mechanism

Thanks to the supervised parameter generation strategy and non-iterative training mechanism, deep stochastic configuration network (DSCN) has achieved very efficient modelling efficiency in scenarios with relatively small problem complexity. However, the increasing number of hidden layers and the amount of training data have issued a challenge to the implementation of DSCN. To solve this problem, we propose a Dense DSCN with a Hybrid Training mechanism (HT-DDSCN), which extends the network structure of the DSCN to a dense connection type and combines three typical optimisation techniques and one universal control strategy to optimise the calculation process of the output weights. Extensive experiments on four benchmark regression problems show that HT-DDSCN can significantly improve the generalisation ability and the stability of DSCN.
Comparative Performance Analysis of various Digital Image Edge Detection Techniques against Salt and Pepper Noise

The edge detection is a digital image processing technique to find the boundaries or edges of an image or object through the brightness discontinuity. There are many operators to get boundaries or edges but we need more effective and accurate methods. This paper will provide a comparison of Conventional Sobel, Prewitt, Roberts, Canny and Log Operators Edge Detector Techniques against Salt and Pepper noise With regard to Mean Square Error (MSE), Root Mean Square Error (RMSE), signal to noise ratio (SNR) , peak signal to noise ratio (PSNR) , mean-absolute error (MAE) and Bit error, etc.
Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.