Reviewerly/exHarmony huggingface dataset:
Trends of University Students Towards the Use of YouTube and the Benefits Achieved - Field study on a sample of Al Ain University students

The study focuses on revealing Al Ain University students ’attitudes toward using YouTube and the achieved gratifications. To achieve this goal, three main questions have been asked: To what extent do Al Ain University students use YouTube? What are the motivations for Al Ain University students to use YouTube? And what are Al Ain University students ’attitudes toward using YouTube? The study adopted the available sample method, and the descriptive survey method for You-Tube users. The most important results of the study were as follows: - YouTube ranked fourth in the order of usage rates for the sampled students. - YouTube usage time varied with age. - There is no variation in the duration of use of the YouTube site, according to gender, degree and grade point average (GPA) of the student. -The students’ answers to the total 23 phrases used by the researcher in measuring their attitudes towards using YouTube and the verifications obtained from him determined four factors that affect the continued adoption of YouTube use. The four factors are namely: study and learning, Communication with others, escape from problems, and Leisure and entertainment.
Mini Review: The Last Mile—Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology

The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxicologic pathology would improve the daily workflow and future impact of toxicologic pathologists globally. Now 2 years later, the authors of this review consider whether, in their opinion, there is any evidence that supports that thesis. Specifically, we consider the opportunities and challenges for applying ML (the study of computer algorithms that are able to learn from example data and extrapolate the learned information to unseen data) algorithms in toxicologic pathology and how regulatory bodies are navigating this rapidly evolving field. Although we see similarities with the “Last Mile” metaphor, the weight of evidence suggests that toxicologic pathologists should approach ML with an equal dose of skepticism and enthusiasm. There are increasing opportunities for impact in our field that leave the authors cautiously excited and optimistic. Toxicologic pathologists have the opportunity to critically evaluate ML applications with a “call-to-arms” mentality. Why should we be late adopters? There is ample evidence to encourage engagement, growth, and leadership in this field.
NBDT: Neural-Backed Decision Tree

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at https://github.com/alvinwan/neural-backed-decision-trees.
A Remote Sensing Image Registration Algorithm Based on Multiple Constraints and a Variational Bayesian Framework

Aiming at complicated remote sensing image registration problem, we proposed a multi-constraint registration algorithm based on variational inference. Gaussian mixture model is used to construct the entire Bayesian network, which facilitates the estimation of heteroscedasticity noise in mapping function regression, and distribution of outliers in scene point density estimation. We use a variational parameter proxy to approximate complex posterior distribution. In addition, while maintaining the stability of global structure, similarity of local structure is restricted. In order to limit the cost of image conversion, we added constraints on spatial curvature for image. Accuracy and smoothness of the spatial transformation can be maintained by minimizing the curvature in horizontal and vertical directions. Experimental results show that compared with other point set matching algorithms, this method can achieve better results in robustness and matching accuracy.
Learning From Architectural Redundancy: Enhanced Deep Supervision in Deep Multipath Encoder–Decoder Networks

Deep encoder–decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. A new structure variance loss is introduced to build a bridge between deep encoder–decoders and variance minimization, and provides a new way to minimize the variance by forcing different intermediate decoding outputs (paths) to reach an agreement. We also design a focal weighting strategy to effectively combine multiple losses in a scale-balanced way, so that the supervision information is sufficiently enforced throughout the encoder–decoders. To evaluate the proposed method on the pixel-level estimation task, a novel multipath residual encoder is proposed and extensive experiments are conducted on four challenging density estimation and crowd counting benchmarks. The experimental results demonstrate the superiority of our EDS over other paradigms, and improved estimation performance is reported using our deeply supervised encoder–decoder.
A survey based on knowledge graph in fault diagnosis, analysis and prediction: key technologies and challenges

With the advent of artificial intelligence and the advent of the era of big data, knowledge graph can graphically display the relationship between a large number of structured, semi-structured and unstructured data. A knowledge graph is a graph-based data structure that connects different types of information into a semantic network. The knowledge graph is intelligent and efficient, helping users to quickly find and search for the information they want. Today, knowledge graphs are used in a variety of fields, such as smart search, chat bots, finance and medical. Nowadays, the mobile phone has become an indispensable tool for us, but its software and hardware often have various problems. In order to better meet the needs of users, knowledge graph are used for fault diagnosis, analysis and prediction. In this paper, the knowledge graph is discussed in the field of mobile phone faults. This paper first outlines the application of knowledge graph in other fields and methods used to construct knowledge graph in the field, such as finance, biology and medicine; discusses the data sources for knowledge graph construction; discusses the architecture of knowledge graph and related technologies, including The extraction of entities and relationships; finally, some of the challenges and open issues facing the knowledge graph are discussed.
Asymmetric Quantum Concatenated and Tensor Product Codes With Large <i>Z</i>-Distances

In this paper, we present a new construction of asymmetric quantum codes (AQCs) by combining classical concatenated codes (CCs) with tensor product codes (TPCs), called asymmetric quantum concatenated and tensor product codes (AQCTPCs) which have the following three advantages. First, only the outer codes in AQCTPCs need to satisfy the orthogonal constraint in quantum codes, and any classical linear code can be used for the inner, which makes AQCTPCs very easy to construct. Second, most AQCTPCs are highly degenerate, which means they can correct many more errors than their classical TPC counterparts. Consequently, we construct several families of AQCs with better parameters than known results in the literature. Third, AQCTPCs can be efficiently decoded although they are degenerate, provided that the inner and outer codes are efficiently decodable. In particular, we significantly reduce the inner decoding complexity of TPCs from $\Omega(n_2a^{n_1})(a>1)$ to $O(n_2)$ by considering error degeneracy, where $n_1$ and $n_2$ are the block length of the inner code and the outer code, respectively. Furthermore, we generalize our concatenation scheme by using the generalized CCs and TPCs correspondingly.
Comparative Analysis for Fraud Detection Using Logistic Regression, Random Forest and Support Vector Machine

For easy payment and classless Credit card payment has become very popular these days. From our bank account we can directly pay the amount online. In spite of this easy payment method it has the disadvantage of having frauds. The unauthorized person accessing the bank details of other person is called as Intruder. These intruders can access some unauthorized transactions also. To prevent this we need some strong mechanisms. In this paper we used three different classification algorithms (Logistic Regression, Random Forest and support vector) for fraud detection and will find out the comparison of accuracy for these three algorithms.
DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.
Correction of aberration-induced phase errors in phase measuring deflectometry

Phase measuring deflectometry is a powerful measuring method of complex optical surfaces that captures the reflected fringe images associated with a displaying screen and calculates the normal vectors of the surface under test (SUT) accordingly. The captured images are usually set conjugate to the SUT, which in turn makes the screen defocused. As a result, the blurring effect caused by the defocus and aberrations of the off-axis catadioptric imaging system can severely degrade the phases solved from the blurred images. In order to correct the phase errors, the space-variant point spread functions (PSFs) are modeled using a skew-normal function. The phase bias is estimated by forward convolution between the captured images and the PSF models. Demonstrated with a highly curved aspheric surface, the measurement accuracy can be improved by three times.
Quantum anonymous voting protocol based on single-particle

Abstract Anonymous voting is often one decision-making choice in many democracies. Facing the security threats from quantum computers, classical anonymous voting protocols based on computational complexity hypothesis have innovated to quantum anonymous voting protocols based on quantum mechanics. In this paper, we propose a quantum anonymous voting protocol by using high-dimension single particles. With the help of EA and T , the construction of our protocol makes each legitimate voter cast only one ballot, and all ballots open in a quite random rearrangement. Each voter can anonymously trace his/her vote by a private position index, thus our protocol achieving the precious properties of eligibility, privacy, no-reusability, verifiability and fairness. We also analyze that the protocol can successfully resist attacks from external eavesdroppers and internal participants, ensuring that our voting protocol is secure under ideal conditions. Designing mature quantum anonymous voting protocols is challenging.
Cyber-victimization Trends in Trinidad &amp; Tobago: The Results of An Empirical Research

Cybertechnology has brought benefits to the Caribbean in the form of new regional economic and social growth. In the last years, Caribbean countries have also become attractive targets for cybercrime due to increased economic success and online presence with a low level of cyber resilience. This study examines the online-related activities that affect cybercrime victimization by using the Routine Activity Theory (RAT). The present study seeks to identify activities that contribute to different forms of cybercrime victimization and develop risk models for these crimes, particularly the understudied cyber-dependent crimes of Hacking and Malware. It also aims to explore if there are similarities or differences in factors leading to victimization, which correlate to the classification of crimes as either cyber-dependent or cyber-enabled. The data analysis suggests that there is significant applicability for RAT in explaining Online Harassment victimization, while the usability of the RAT for predicting Malware victimization proved to be minimal, with only two significant variables being identified, with both being associated with Capable Guardianship.
Farmers’ Food Price Volatility and Nigeria’s Growth Enhancement Support Scheme

The prices of food in Nigeria have become considerably higher and more volatile since 2012. The aim of this research was to ascertain factors affecting farmers’ involvement in the growth enhancement support programme (GESS) in the country. We ascertained the effect of the GESS on the handiness of market information and agricultural inputs that impact on price volatility at farm gate level. In number, 600 rural farmers were sampled across the six geopolitical zones of Nigeria. Results obtained from the use of a bivariate probit model show that farmers relied on the GESS for resolving food price volatility by making available the food market information and agricultural inputs that cut down the incidence and degree of panic-compelled price increment in Nigeria. The findings suggested the need to enhance the GESS in line with the agricultural transformation agenda (ATA) by reducing the hindrances mostly connected to the use of mobile phones, and how far the registration and collection centers are.
The effect of CEO characteristics on financial reporting timeliness in Saudi Arabia

The purpose of this paper is to examine the effect of some demographic characteristics of the Chief Executive Officer (CEO) on Financial Reporting Timeliness (FRT) in Saudi Arabia. More particularly, this study aims to test whether or not CEO characteristics, namely, tenure, accounting financial expertise, and sociability are associated with FRT. The sample of this study consists of 119 non-financial firms listed on Tadawul Stock Exchange for a period of four years (2014-2017). We use panel regressions and two proxies of FRT. Our findings report that a long-tenured CEO is associated with timely financial reports when the IFRS transition is simultaneously considered. This result implies that companies with a long-tenured CEO reduce the period taken to prepare and disclose their financial reports in the period of IFRS transition. Our findings show that CEO accounting financial expertise is significantly associated with timely financial reporting. This result implies that companies with a CEO who is an accounting financial expert reduce the period taken to prepare and disclose their financial reports on the capital market website. Our findings also report that CEO sociability is significantly associated with timely financial reporting in all instances. This result suggests that companies with a more sociable CEO reduce the period taken to prepare and disclose their financial reports on the capital market website. This result implies that when the CEO is more sociable via social media, firms tend to be more active in disclosing their annual reports timely. Overall, findings report that CEO characteristics do matter regarding the timeliness of financial reporting. Results are robust to an alternative measure of financial reporting timeliness. Our findings should be of interest to policymakers and regulators in Saudi Arabia in formulating new policies as they need to play a role in ensuring the shorter gap of financial report delay. The findings of this research have also a practical implication for shareholders and boards of directors in selecting a new CEO by taking into consideration their accounting financial expertise and their sociability on social media. Findings of this research contribute to the growing literature by examining the effect of CEO characteristics on timely reporting in Saudi Arabia, an understudied and unique context. The present study also complements the recent literature on the determinants of financial reporting timeliness by providing evidence that the sociability and accounting financial expertise of top leaders improve the financial reporting timeliness.
The 20k Samples-Per-Second Real Time Detection of Acoustic Vibration Based on Displacement Estimation of One-Dimensional Laser Speckle Images

Audio signal acquisition using a laser speckle image is an appealing topic since it provides an accurate and non-contact solution for vibration measurement. However, due to the limitation of camera frame rate and image processing speed, previous research could not achieve real time reconstruction of an audio signal. In this manuscript, we use a one-dimensional laser speckle image to measure the acoustic vibration of sound source and propose a fast and sub-pixel accuracy algorithm to estimate the displacement of captured one-dimensional laser speckle images. Compared with previous research, the proposed method is faster and more accurate in displacement estimation. Owing to this, the frequency bandwidth and the robustness are significantly increased. Experiment results show that the proposed system can achieve 20k samples-per-second sampling rate, and the audio signal can be reconstructed with high quality in real time.
Is SGD a Bayesian sampler? Well, almost

Deep neural networks (DNNs) generalise remarkably well in the overparameterised regime, suggesting a strong inductive bias towards functions with low generalisation error. We empirically investigate this bias by calculating, for a range of architectures and datasets, the probability PSGD(f∣S) that an overparameterised DNN, trained with stochastic gradient descent (SGD) or one of its variants, converges on a function f consistent with a training set S. We also use Gaussian processes to estimate the Bayesian posterior probability PB(f∣S) that the DNN expresses f upon random sampling of its parameters, conditioned on S. Our main findings are that PSGD(f∣S) correlates remarkably well with PB(f∣S) and that PB(f∣S) is strongly biased towards low-error and low complexity functions. These results imply that strong inductive bias in the parameter-function map (which determines PB(f∣S)), rather than a special property of SGD, is the primary explanation for why DNNs generalise so well in the overparameterised regime. While our results suggest that the Bayesian posterior PB(f∣S) is the first order determinant of PSGD(f∣S), there remain second order differences that are sensitive to hyperparameter tuning. A function probability picture, based on PSGD(f∣S) and/or PB(f∣S), can shed light on the way that variations in architecture or hyperparameter settings such as batch size, learning rate, and optimiser choice, affect DNN performance.
Semantic Road Segmentation using Deep Learning

Semantic segmentation is an important task in self-driving cars. The aims of semantic segmentation are to recognize pre-defined objects and its pixel-by-pixel location. The most popular method in semantic segmentation is Deep learning which has considerably improved semantic image segmentation. This work does an overview for semantic segmentation using Deep learning. This works also implement comparisons in term of precision, mean IOU and processing time. Three popular algorithms are PSPNet, FCN and SegNet that are examined carefully. In detail, the aim of this work points out a trade-off between processing time and mean IOU, and also between processing time and precision. Moreover, this paper concentrates on road segmentation for embedded devices, so processing time is significantly important. This work also figures out which method is suitable for embedded devices on road segmentation.
Self-supervised on Graphs: Contrastive, Generative, or Predictive.

Deep learning on graphs has recently achieved remarkable success on a variety of tasks while such success relies heavily on the massive and carefully labeled data. However, precise annotations are generally very expensive and time-consuming. To address this problem, self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks without relying on manual labels. In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of the existing SSL techniques for graph data. Specifically, we divide existing graph SSL methods into three categories: contrastive, generative, and predictive. More importantly, unlike many other surveys that only provide a high-level description of published research, we present an additional mathematical summary of the existing works in a unified framework. Furthermore, to facilitate methodological development and empirical comparisons, we also summarize the commonly used datasets, evaluation metrics, downstream tasks, and open-source implementations of various algorithms. Finally, we discuss the technical challenges and potential future directions for improving graph self-supervised learning.
Risk Attitudes, Investment Behavior, and Linguistic Variation

Juliana Bernhofer⇑, Francesco Costantini and Matija Kovacic⇑ Juliana Bernhofer is an honorary fellow (“cultrice della materia”) at the Ca’ Foscari University of Venice and a researcher at the Free University of Bozen-Bolzano, Faculty of Economics and Management; Francesco Costantini is associate professor in linguisticsat the University of Udine, Department of Humanities and Cultural Heritage Studies. Matija Kovacic is an honorary fellow (“cultore della materia”) at the Ca’Foscari University of Venice, Department of Economics, and a researcher at the European Commission, Joint Research Centre, Ispra (Italy); juliana.bernhofer{at}unive.it matija.kovacic{at}unive.it