Reviewerly/exHarmony huggingface dataset:
The cloud computing systems’ adoption in the higher education sector in Oman in light of the COVID-19 pandemic

&lt;p&gt;&lt;span&gt;The objective of the research was to find out the key factors that influence the acceptance and usage of cloud computing systems in the Omani higher education sector, with special emphasis on the COVID-19 outbreak. For this purpose, a quantitative research approach was conducted where 200 students from several Omani higher education institutions were surveyed, and by using Partial Least Square (PLS) to analysis the collected data. The findings revealed that the intention to use cloud computing in this context is significantly dependent on its perceived ease of use, usefulness, perceived reliability and responsiveness. It is noteworthy that this is one of the early studies that address the subject of cloud computing usage during times of crises, and specifically the COVID-19 outbreak. As such, it provides significant contributions in the area of technology adoption.&lt;/span&gt;&lt;/p&gt;
Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

COVID-19-related quarantine measures resulted in a significant increase of interest in online collaboration tools. This includes virtual reality (VR) or, in more general term, extended reality (XR) solutions. Shared XR allows for activities such as presentations, training of personnel or therapy to take place in a virtual space instead of a real one. To make online XR as accessible as possible, a significant effort has been put into the development of solutions that can run directly in web browsers. One of the most recognized solutions is the A-Frame software framework, created by Mozilla VR team and supporting most of the contemporary XR hardware. In addition, an extension called Networked-Aframe allows multiple users to share virtual environments, created using A-Frame, in real time. In this article, we introduce and experimentally evaluate three components that extend the functionality of A-Frame and Networked-Aframe. The first one extends Networked-Aframe with the ability to monitor and control users in a shared virtual scene. The second one implements six degrees of freedom motion tracking for smartphone-based VR headsets. The third one brings hand gesture support to the Microsoft HoloLens holographic computer. The evaluation was performed in a dedicated local network environment with 5, 10, 15 and 20 client computers. Each computer represented one user in a shared virtual scene. Since the experiments were carried out with and without the introduced components, the results presented here can also be regarded as a performance evaluation of A-Frame and Networked-Aframe themselves.
Towards Interactive Image Segmentation by Dynamic and Iterative Spanning Forest

Interactive image segmentation aims to partition the image into background and foreground objects by taking into account seeds inserted by users. Nowadays, many methods are capable of generating segmentations with few user interactions, especially region-based techniques. However, such methods are highly sensitive to seed displacement and quantity, and delineation errors are often propagated to the final segmentation result. Recently, a novel superpixel segmentation framework, named Dynamic and Iterative Spanning Forest (DISF), was proposed, which achieved top delineation performance while assessing many seed-based state-of-the-art methods’ drawbacks. In this work, we propose interactive DISF (iDISF), an interactive segmentation framework, by modifying each step of DISF to consider user-validated information. DISF uses the Image Foresting Transform (IFT) framework for computing an optimum-path forest rooted in a seed set in the delineation step. To consider path and image gradient variation, we propose three new connectivity functions for the IFT. Finally, we also propose two new seed removal strategies for detecting relevant seeds for subsequent iterations. Results show segmentation improvements for minimal user effort—i.e., a single click—and show theoretical advances that may benefit recent optimum-path-based interactive methods from scribbles.
Automatic Modulation Classification in Real Tx/Rx Environment using Machine Learning and SDR

Software defined radio (SDR) platforms offer a solution for the dynamic access of the radio spectrum. Intelligent communications systems can be built using SDR and they must continuously perform the identification of user parameters, for example, the type of modulation used. In this work, we design a system that performs the automatic classification of modulations' types in a real transmission and reception environment. The proposed system uses machine learning techniques and a low-cost SDR receiver. The results of the experiments demonstrate that the method is promising, obtaining accuracy above 82% using the decision tree classifier.
Semantic and syntactic information for neural machine translation

Abstract Introducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.
SIDGAN: Single Image Dehazing without Paired Supervision

Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for single image dehazing, named SIDGAN. The proposed SIDGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed SIDGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.
Combining Retargeting Quality and Depth Perception Measures for Quality Evaluation of Retargeted Stereopairs

Stereoscopic Image Retargeting (SIR) aims to adapt stereoscopic images and videos to 3D display devices with various aspect ratios by emphasizing the important content while retaining surrounding context with minimal visual distortion. To address the issue of SIR evaluation, this paper presents a new objective quality assessment method for retargeted stereopairs by combining image quality and depth perception measures. Specifically, the image quality measure is conducted between the source and retargeted intermediate views generated by the view synthesis method to characterize the geometric distortion and content loss of the retargeted stereopair, while several depth-aware features are extracted to measure the visual comfort&#x002F;discomfort and depth sensation when human views a 3D scene. Then, the extracted features are integrated into an overall perceptual quality prediction. Experiment results on NBU SIRQA and SIRD databases verify the superiority of our method.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation

Machine Translation Quality Estimation (QE) is a task of predicting the quality of machine translations without relying on any reference. Recently, the predictor-estimator framework trains the predictor as a feature extractor, which leverages the extra parallel corpora without QE labels, achieving promising QE performance. However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly. We propose a novel framework called DirectQE that provides a direct pretraining for QE tasks. In DirectQE, a generator is trained to produce pseudo data that is closer to the real QE data, and a detector is pretrained on these data with novel objectives that are akin to the QE task. Experiments on widely used benchmarks show that DirectQE outperforms existing methods, without using any pretraining models such as BERT. We also give extensive analyses showing how fixing the two gaps contributes to our improvements.
How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features—the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets—that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.
Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition

Image denoising, a classic ill-posed problem, aims to recover a latent image from a noisy measurement. Over the past few decades, a considerable number of denoising methods have been studied extensively. Among these methods, supervised deep convolutional networks have garnered increasing attention, and their superior performance is attributed to their capability to learn realistic image priors from a large amount of paired noisy and clean images. However, if the image to be denoised is significantly different from the training images, it could lead to inferior results, and the networks may even produce hallucinations by using inappropriate image priors to handle an unseen noisy image. Recently, deep image prior (DIP) was proposed, and it overcame this drawback to some extent. The structure of the DIP generator network is capable of capturing the low-level statistics of a natural image using an unsupervised method with no training images other than the image itself. Compared with a supervised denoising model, the unsupervised DIP is more flexible when processing image content that must be denoised. Nevertheless, the denoising performance of DIP is usually inferior to the current supervised learning-based methods using deep convolutional networks, and it is susceptible to the over-fitting problem. To solve these problems, we propose a novel deep generative network with multiple target images and an adaptive termination condition. Specifically, we utilized mainstream denoising methods to generate two clear target images to be used with the original noisy image, enabling better guidance during the convergence process and improving the convergence speed. Moreover, we adopted the noise level estimation (NLE) technique to set a more reasonable adaptive termination condition, which can effectively solve the problem of over-fitting. Extensive experiments demonstrated that, according to the denoising results, the proposed approach significantly outperforms the original DIP method in tests on different databases. Specifically, the average peak signal-to-noise ratio (PSNR) performance of our proposed method on four databases at different noise levels is increased by 1.90 to 4.86 dB compared to the original DIP method. Moreover, our method achieves superior performance against state-of-the-art methods in terms of popular metrics, which include the structural similarity index (SSIM) and feature similarity index measurement (FSIM). Thus, the proposed method lays a good foundation for subsequent image processing tasks, such as target detection and super-resolution.
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation

Abstract We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic simulators, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student- t observations; (ii) Student- t processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1–6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.
Skeleton-aware Multi-scale Heatmap Regression for 2D Hand Pose Estimation

Hand pose estimation plays an essential role in sign language understanding and human-computer interaction. Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The first one presents a segmentation-based approach to detect the hand skeleton and localize the hand bounding box. The second module regresses the 2D joint locations through a multi-scale heatmap regression approach that exploits the predicted hand skeleton as a constraint to guide the model. Moreover, we construct a new dataset that is suitable for both hand detection and pose estimation tasks. It includes the hand bounding boxes, the 2D keypoints, the 3D poses and their corresponding RGB images. We conduct extensive experiments on two datasets to validate our method. Qualitative and quantitative results demonstrate that the proposed method outperforms the state-of-the-art and recovers the pose even in cluttered images and complex poses.
Privacy-Preserving Non-Participatory Surveillance System for COVID-19-Like Pandemics

COVID-19 pandemic has revealed a pressing need for an effective surveillance system to control the spread of infection. However, the existing systems are run by the people's smartphones and without a strong participation from the people, the systems become ineffective. Moreover, these systems can be misused to spy on people and breach their privacy. Due to recent privacy breaches, people became anxious about their privacy, and without privacy reassurance, the people may not accept the systems. In this paper, we propose a non-participatory privacy-preserving surveillance system for COVID-19-like pandemics. The system aims to control the spread of COVID-19 infection without depending on the participation of the people and with privacy preservation. In the proposed system, surveillance cameras of public places take images of visitors and compute embedding vectors that encode the facial features of the images by using deep learning techniques. A searchable encryption scheme is used by public places to encrypt the embedding vectors and send them to a cloud server (service manager). Similarly, hospitals and test centers send the encryptions of the embedding vectors of the images of positively-tested people to the cloud server. Finally, the server does operations on encrypted data to learn whether an infected person visited a public place for contact tracing without being able to learn the images or identify the visitors of the places and the infected persons, and then it alerts these places to take precautionary measures. Our analysis indicates that our system is secure and can preserve privacy. Our experimental results demonstrate that our system has high success rate and low false alarm rate, and needs low computation and communication overhead and acceptable search time.
Dimension reduction of multimodal data by auto-weighted local discriminant analysis

Dimension reduction technology is playing an increasingly important role in machine learning. One of the most important reduction technologies is linear discriminant analysis (LDA), but the main disadvantage of LDA is that it is unable to find the local manifold structure, which means that it may fail to dispose of multimodal data. However, high-dimensional multimodal data are ubiquitous in many rations. In this paper, we propose a new dimensionality reduction method called auto-weighted local discriminant analysis (ALDA). Our method learns the similarity matrix and updates in the subspace simultaneously so that the neighborships can be evaluated in the optimal subspaces instead of in the original space. Furthermore, the new model is built based on the ℓ2,1-norm and automatically assigns a small weight to the pairwise points with large distance, and vice versa; thus, the local structure information can be captured by the ALDA. Additionally, an iterative re-weighted optimization algorithm is provided to efficiently solve the proposed model. Finally, extensive experiments conducted on several benchmark datasets and some synthetic datasets demonstrate the effectiveness of ALDA when comparing with some state-of-the-art dimensionality reduction methods.
E-CARGO and Role-Based Collaboration

Role-Based Collaboration (RBC) has emerged into an investigative methodology from a computational methodology with continuous research effort in the past decade. RBC uses roles as the primary underlying mechanism to facilitate collaboration activities. It consists of a set of concepts, principles, models, and algorithms. RBC imposes challenges and benefits not discovered in traditional methodologies and systems. RBC and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model have been investigated for over 18 years and have established a solid foundation for further research and investigation. Related research has brought and will bring exciting improvements to the development, evaluation, management, and execution of computer-based systems including services, clouds, productions, and administration systems. RBC and E-CARGO grow gradually into a strong fundamental methodology and model for exploring solutions to problems of complex systems including Collective Intelligence, Sensor Networking, Scheduling, Smart Cities, Internet of Things, Intelligent Transportation Systems, Cyber-Physical Systems, Social Networking, and Social Simulation Systems. In this keynote, we examine the requirement of research on collaboration systems and technologies, discuss RBC and its model E-CARGO; review the related research achievements on RBC and E-CARGO in the past years; discuss those problems that have not yet been solved satisfactorily; present the fundamental methods to conduct research related to RBC and E-CRAGO and discover related problems; and analyze their connections with other cuttingedge fields.
Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification

Supervised learning corresponds to minimizing a loss or cost function expressing the differences between model predictions yn and the target values tn given by the training data. In neural networks, this means backpropagating error signals through the transposed weight matrixes from the output layer toward the input layer. For this, error signals in the output layer are typically initialized by the difference yn- tn, which is optimal for several commonly used loss functions like cross-entropy or sum of squared errors. Here I evaluate a more general error initialization method using power functions |yn- tn|q for q>0, corresponding to a new family of loss functions that generalize cross-entropy. Surprisingly, experiments on various learning tasks reveal that a proper choice of q can significantly improve the speed and convergence of backpropagation learning, in particular in deep and recurrent neural networks. The results suggest two main reasons for the observed improvements. First, compared to cross-entropy, the new loss functions provide better fits to the distribution of error signals in the output layer and therefore maximize the model's likelihood more efficiently. Second, the new error initialization procedure may often provide a better gradient-to-loss ratio over a broad range of neural output activity, thereby avoiding flat loss landscapes with vanishing gradients.