Reviewerly/exHarmony huggingface dataset:
Determinants of future preference for e-learning and its implications: A study of Malaysian business students

To mitigate adverse consequences of campus closure caused by the Covid-19 pandemic, many higher education institutions shifted from traditional to one hundred percent e learning overnight. This study aims to identify salient determinants of business undergraduates' future preferences for e-learning and discuss its implications. 251 business undergraduates from a private university in Malaysia participated in an online survey conducted in July 2020, during campus closure. Data were analysed using multiple regression analysis to empirically identify salient determinants of future preference for e learning. Multiple regression results inferred that the two most significant determinants of business undergraduates' future preference for e-learning were the disadvantages of e learning, followed by learning outcomes. Limitations of this study include: (1) the survey was carried out in July 2020 when all courses were conducted using one hundred percent e-learning for the first time;(2) some salient determinants may not be captured because literature on e-learning during Covid-19 is evolving rapidly;and (3) the findings are not generalisable to other contexts. E-learning, whether in the form of one hundred percent e-learning or hybrid learning, is expected to be the new norm post-pandemic. As such, a better understanding of the determinants of business undergraduates' future preference for e-learning will enable stakeholders to overcome barriers to e-learning and improve learning outcomes.
Learning to Synthesize 7 T MRI from 3 T MRI with Few Data by Deformable Augmentation

High-quality magnetic resonance imaging (MRI), which is generally acquired by ultra-high field (7-Tesla, 7 T) MRI scanners, may lead to improved performance for brain disease diagnosis, such as Alzheimer’s disease (AD). However, 7 T MRI has not been widely used due to higher cost and longer scanning time. To overcome this, we proposed to utilize the generative adversarial networks (GAN)-based techniques to synthesize the 7 T scans from 3 T scans, for which, the most challenge is that we do not have enough data to learn a reliable mapping from 3 T to 7 T. To address this, we further proposed the Unlimited Data Augmentation (UDA) strategy to increase the learning samples via the deformable registration, which can produce enough paired 3 T and 7 T MR images to learning this mapping. Based on this mapping, we synthesize a 7 T MR scan for each subject in Alzheimer’s Disease Neuroimaging Initiative (ADNI), and conduct some experiments to evaluate their effect in two tasks of AD diagnosis, including AD identification and mild cognitive impairment (MCI) conversion prediction. Experimental results demonstrate that our UDA strategy is effective to learn a reliable mapping to high-quality MR images, and the synthetic 7 T scans are possible to increase the performance of AD diagnosis.
PoseTED: A Novel Regression-Based Technique for Recognizing Multiple Pose Instances

Pose estimation for multiple people can be viewed as a hierarchical set predicting challenge. Algorithms are needed to classify all persons according to their physical components appropriately. Pose estimation methods are divided into two categories: (1) heatmap-based, (2) regression-based. Heatmap-based techniques are susceptible to various heuristic designs and are not end-to-end trainable, while regression-based methods involve fewer intermediary non-differentiable stages. This paper presents a novel regression-based multi-instance human pose recognition network called PoseTED. It utilizes the well-known object detector YOLOv4 for person detection, and the spatial transformer network (STN) used as a cropping filter. After that, we used a CNN-based backbone that extracts deep features and positional encoding with an encoder-decoder transformer applied for keypoint detection, solving the heuristic design problem before regression-based techniques and increasing overall performance. A prediction-based feed-forward network (FFN) is used to predict several key locations’ posture as a group and display the body components as an output. Two available public datasets are tested in this experiment. Experimental results are shown on the COCO and MPII datasets, with an average precision (AP) of 73.7% on the COCO val. dataset, 72.7% on the COCO test dev. dataset, and 89.7% on the MPII datasets, respectively. These results are comparable to the state-of-the-art methods.
Font Style that Fits an Image – Font Generation Based on Image Context

When fonts are used on documents, they are intentionally selected by designers. For example, when designing a book cover, the typography of the text is an important factor in the overall feel of the book. In addition, it needs to be an appropriate font for the rest of the book cover. Thus, we propose a method of generating a book title image based on its context within a book cover. We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover. The proposed network uses a combination of a multi-input encoder-decoder, a text skeleton prediction network, a perception network, and an adversarial discriminator. We demonstrate that the proposed method can effectively produce desirable and appropriate book cover text through quantitative and qualitative results. The code can be found at https://github.com/Taylister/FontFits.
How to Bake Quantum into Your Pet Petri Nets and Have Your Net Theory Too

Petri nets have found widespread use among many application domains, not least due to their human-friendly graphical syntax for the composition of interacting distributed and asynchronous processes and services, based in partial-order dependencies and concurrent executions. Petri nets also come with abstract semantics, and mathematical methods for compositional synthesis, structural checks and behavioural analysis. These have led to the use of various kinds of nets for real-time, distributed and parallel programming languages, software and services systems, with a view to their interfaces and interaction protocols. These affordances make Petri nets invaluable for distributed software architecture approaches focused on components, their mutual dependencies and environment-facing interactions. Quantum computing – and in particular quantum software engineering – is in its infancy and could benefit from the accumulated insights of software architecture research and of net theory, its methods, and its applications.In this paper, we establish a connection between Petri nets and quantum systems, such that net theory and the component architecture of nets may help in the synthesis and analysis of abstract software models and their interface protocols in hybrid classical-and-quantum programming languages and services systems. We leverage some insights from net formalisms for software specification for a versatile recipe to bake quantum into extant Petri net flavours, and prove universality and compositionality of Petri nets for quantum programming.
NoStop: A Novel Configuration Optimization Scheme for Spark Streaming

An increasing number of big data applications in various domains generate datasets continuously, which must be processed for various purposes in a timely manner. As one of the most popular streaming data processing systems, Spark Streaming applies a batch-based mechanism, which receives real-time input data streams and divides the data into multiple batches before passing them to Spark processing engine. As such, inappropriate system configurations including batch interval and executor count may lead to unstable states, hence undermining the capability and efficiency of real-time computing. Hence, determining suitable configurations is crucial to the performance of such systems. Many machine learning- and search-based algorithms have been proposed to provide configuration recommendations for streaming applications where input data streams are fed at a constant speed, which, however, is extremely rare in practice. Most real-life streaming applications process data streams arriving at a time-varying rate and hence require real-time system monitoring and continuous configuration adjustment, which still remains largely unexplored. We propose a novel streaming optimization scheme based on Simultaneous Perturbation Stochastic Approximation (SPSA), referred to as NoStop, which dynamically tunes system configurations to optimize real-time system performance with negligible overhead and proved convergence. The performance superiority of NoStop is illustrated by real-life experiments in comparison with Bayesian Optimization and Spark Back Pressure solutions. Extensive experimental results show that NoStop is able to keep track of the changing pattern of input data in real time and provide optimal configuration settings to achieve the best system performance. This optimization scheme could also be applied to other streaming data processing engines with tunable parameters.
Efficient scheme for WCE image compression based on strategic chroma subsampling and encoding

The data generated in wireless capsule endoscopy during the examination of the gastrointestinal tract is huge and demands large storage and processing time. One of the essential components of the capsule is the compression module that needs to be low power and a low complexity one. This paper aims to bring significant improvement in pre-processing, chroma subsampling and encoding stages of image compression. Among various chrominance based color spaces, YEF being a best alternative for the RGB endoscopic image is analyzed in terms of statistical measures. New chroma subsampling patterns are tested, which has shown improved data reduction. In the encoder part, the combination of proposed predictive coder and the modified Golomb Rice coder generated better compressed bit stream. The cubic spline interpolation is the method used for image reconstruction during decompression. The endoscopic images from databases namely Gastrolab and WEO Clinical Endoscopy Atlas are utilized in the experimentation. The results show that the proposed near-lossless compression scheme performs better than the competing techniques in terms of compression rate, peak signal to noise ratio and structural similarity index.
Gender-based Feature Disentangling for Kinship Verification

Kinship verification can benefit a wide variety of applications, e.g., exploring social relations, finding the lost children and old people, constructing a family tree, and so on. Previous researches have made promising results in this research, but the gender discrepancy between parent and child is generally neglected. For example, father and daughter, or mother and son, may have different facial features due to gender differences. In view of this, we propose a gender-invariant kinship verification model where the facial feature is divided into two components. i.e., gender-dependent feature and identity-dependent feature. The learning of gender-dependent feature is supervised by the gender prediction task. This identity-dependent feature is required to be uncorrelated to the gender-dependent feature and preserve information that is useful for kinship verification. We factorize facial features through a Residual Factorization Module (RFM) and reduce the correlation between two components through the Decorrelated Adversarial Learning (DAL). The whole network is trained in an end-to-end and multi-task manner. Experimental results on the popular benchmark KinFaceW-II demonstrate that our gender invariant features can effectively reduce the effects of gender differences and show excellent generalization ability on different kinship relations.
Embedded System Based Smart Wheelchair For Physically Challenged People

The degradation of mobility is an important concern that affects the independent living ability of physically Challenged people. Therefore, mobility assistive devices require improvements to uplift the living standards of the people. This paper sets out a design of a smart wheelchair using an embedded system. This paper sets out a design and development of a smart wheelchair using an embedded system. The proposed design of the wheelchair can be controlled through bluetooth which enables the user to control the wheelchair with less effort. Touch commands are deployed to the system in addition to the virtual joystick-controlling interface to enhance the interaction with the user. This aids the disabled in carrying out daily activities independently within indoor environments. Experiments are conducted to verify the functionality of the developed smart wheelchair.
An Approximate Likelihood Ratio Detector for QTMS Radar and Noise Radar

The most important task of any radar is to detect targets. From this perspective, they are machines for distinguishing between two hypotheses: target absent and target present. The test statistic—or detector function—used by the radar is clearly of primary importance. In this paper, we explore the properties of a detector function for quantum two-mode squeezing radar and noise radar. This detector is derived from a second-order approximation of the likelihood ratio, and is attractive because it has a simple mathematical form. In order to analyze the performance of this detector function, we derive an expression for the receiver operating characteristic curve and verify it via simulations.
Development of Speech Dialogue Systems for Social AI in Cooperative Game Environments

There is increasing interest in developing more human-like artificial intelligence (AI) capable of natural social interaction. Previous research has suggested ideas about what it means to be "life-like" AI, and some studies have attempted to test these hypotheses using game environments. In this paper, we introduce the development of the Speech Dialogue System for a "Social AI", which communicates and interacts autonomously with a human player in cooperative game environments (in this case a social survival game called "Don&#x2019;t Starve Together"). Based on our hypothesis that the AI should contain specific components to be perceived as more human-like, we conducted a series of pilot tests to develop the Social AI using a data-driven approach. After finishing the pilot tests, we identified six components to add or revise, based on participant interactions and feedback. These components mainly include features of the Speech Dialogue System that pertain to the interplay of AI behavior with contextual factors of the social environment ("the game state"). In future work, we intend to improve the Social AI based on these findings. The research here highlights the use of cooperative game environments for data-driven development of speech dialogue systems for artificial agents.
GSnet: combine Ghostnet and Shufflenetv2 to get better performance

In computer vision technology based on deep learning, the backbone network is very important, and its performance can usually affect vision-related tasks such as target detection and target segmentation. This article proposes a Ghostnet improvement strategy combined with Shufflenetv2 named GSnet. This paper uses Shufflenetv2's concatenate and shuffle operations to further improve Ghostnet. And the attention module in Ghostnet has been improved and optimized. This paper uses long-distance non-local features to further improve SA attention and embed it in Ghostnet. Experiments are carried out with the data set of cifar100, and after experimental verification, this method reduces the amount of parameters and calculations on the basis of the original method and improves the accuracy of TOP1 by 1.26%. Code is available at https://github.com/Yipzcc later.
Video game prototype to improve Mental Health problems

Video game technology is booming throughout Peru. These have become one of the most extensive recreational activities among children, youth and adults. Many fall for its potentially harmful effects on behavior and mental health. There are many studies that support the possible benefits of video games, as inputs for treatments. A new generation of the indicated conditions are called “serious games” or games for health purposes. Miscellaneous: learning disabilities, attention deficit hyperactivity disorder, Behavioral disorders, autism spectrum disorder, social phobia, addiction, etc. The elaboration of this article will be based on the SUM methodology, with this methodology we can see that by using it we will have a better interaction with the user so that they have an easily accessible benefit in their hands. The obj ective is the investigation of Mental Health problems and improve the learning of users through the tools of video games. The conclusion of our article is to give a better education to our users with the strategies and methodologies implemented within the article.
Efficient Designs of Quantum Adder/Subtractor Using Universal Reversible Gate on IBM Q

Reversible arithmetic and logic unit (ALU) is a necessary part of quantum computing. In this work, we present improved designs of reversible half and full addition and subtraction circuits. The proposed designs are based on a universal one type gate (G gate library). The G gate library can generate all possible permutations of the symmetric group. The presented designs are multi-function circuits that are capable of performing additional logical operations. We achieve a reduction in the quantum cost, gate count, number of constant inputs, and delay with zero garbage, compared to relevant results obtained by others. The experimental results using IBM Quantum Experience (IBM Q) illustrate the success probability of the proposed designs.
Contextual Information Fusion for Small Object Detection

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on contextual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network model. The experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow contextual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images.
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly. We establish theoretically proven convergence guarantees in the stochastic convex settings, based on primal-dual methods. We evaluate the regularized effect of our new optimizers on three large-scale real-world ad click datasets with state-of-the-art deep learning models. The experimental results reveal that compared with the original optimizers with the post-processing procedure which uses the magnitude pruning method, the performance of the models can be significantly improved on the same sparsity level. Furthermore, in comparison to the cases without magnitude pruning, our methods can achieve extremely high sparsity with significantly better or highly competitive performance.
Super-Resolution AFM Imaging Based on Enhanced Convolutional Neural Network

Atomic force microscope (AFM) is a powerful nanoscale instrument, which can obtain the true surface morphology of samples. There are strict requirements for the detailed features in AFM images, so it is necessary to mine the deep information in the images. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-quality images. For most cases, the original images of AFM are with low resolution. In order to get the more detailed texture and feature information as much as possible, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. By learning the mapping relationship between low-resolution images and high-resolution images from the image database, the high-resolution image is finally obtained. Aiming at the problem of long scanning time and too small training database of AFM image, adaptive histogram equalization is used to expand and enhance the training set of AFM images. Compared with the traditional super-resolution methods, the subjective and objective evaluation of the reconstructed image verifies the feasibility of the proposed algorithm.
Classifying Emails into Spam or Ham Using ML Algorithms

Spam is commonly used to conduct fraudulent activities. Users should be cautious enough not to open spam emails and not to respond to them. Otherwise, this could lead to cyber criminals gaining control to access our devices. Emails are one of the most secure ways of communication. But due to its rapid growth the chance of it getting affected by spam emails increases. To prevent this we need to come up with an efficient system for email spam detection. In this paper, we try to implement machine learning algorithms using Scikit-learn in Colaboratory and we aim to find the best algorithm. From Kaggle, a publicly accessible email dataset is taken. In order to compute the output of these algorithms, the dataset is being introduced to classification models like Decision Tree, SVM, Naive Bayes, and Random Forest. This paper can play a significant role by eliminating junk emails, Viruses etc.