Reviewerly/exHarmony huggingface dataset:
RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image

Remote sensing image super-resolution (SR) plays an important role by supplementing the lack of original high-resolution (HR) images in the study scenarios of large spatial areas or long time series. However, due to the lack of imagery information in low-resolution (LR) images, single-image super-resolution (SISR) is an inherently ill-posed problem. Especially, it is difficult to reconstruct the fine textures of HR images at large upscaling factors (e.g., four times). In this work, based on Google Earth HR images, we explore the potential of the reference-based super-resolution (RefSR) method on remote sensing images, utilizing rich texture information from HR reference (Ref) images to reconstruct the details in LR images. This method can use existing HR images to help reconstruct the LR images of long time series or a specific time. We build a reference-based remote sensing SR data set (RRSSRD). Furthermore, by adopting the generative adversarial network (GAN), we propose a novel end-to-end reference-based remote sensing GAN (RRSGAN) for SR. RRSGAN can extract the Ref features and align them to the LR features. Eventually, the texture information in the Ref features can be transferred to the reconstructed HR images. In contrast to the existing RefSR methods, we propose a gradient-assisted feature alignment method that adopts the deformable convolutions to align the Ref and LR features and a relevance attention module (RAM) to improve the robustness of the model in different scenarios (e.g., land cover changes and cloud coverage). The experimental results demonstrate that RRSGAN is robust and outperforms the state-of-the-art SISR and RefSR methods in both quantitative evaluation and visual results, which indicates the great potential of the RefSR method for remote sensing tasks. Our code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dongrunmin/RRSGAN</uri> .
A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical rigor: Mathematical setup, application and extension of a formula based on the subsolar point and atan2 function

A conceptually and mathematically concise formula for computing the solar azimuth angle has been used by a subgroup of scientists, but for lack of documentation and publication, it has not been well circulated. This paper introduces this formula which is based on the idea of a unit vector, S, originating from the observer’s location and pointing toward the center of the Sun. The vector is completely determined by the coordinates of the subsolar point and of the observer. The x- and y-components of the vector determine the solar azimuth angle, and their use along with the function atan2, which is available in a number of programming/scripting languages, including Fortran and Python, renders any circumstantial treatment absolutely unnecessary. The z-component of the vector, at the same time, determines the solar zenith angle. The use of the unit vector also facilitates a figure that can be used as a full 3D graphic depiction of the daily and annual cycles of the Sun’s position for any given location, and this figure can be called “wreath of analemmas”.
Obfuscation of images via differential privacy: From facial images to general images

Due to the pervasiveness of image capturing devices in every-day life, images of individuals are routinely captured. Although this has enabled many benefits, it also infringes on personal privacy. A promising direction in research on obfuscation of facial images has been the work in the k-same family of methods which employ the concept of k-anonymity from database privacy. However, there are a number of deficiencies of k-anonymity that carry over to the k-same methods, detracting from their usefulness in practice. In this paper, we first outline several of these deficiencies and discuss their implications in the context of facial obfuscation. We then develop a framework through which we obtain a formal differentially private guarantee for the obfuscation of facial images in generative machine learning models. Our approach provides a provable privacy guarantee that is not susceptible to the outlined deficiencies of k-same obfuscation and produces photo-realistic obfuscated output. In addition, we demonstrate through experimental comparisons that our approach can achieve comparable utility to k-same obfuscation in terms of preservation of useful features in the images. Furthermore, we propose a method to achieve differential privacy for any image (i.e., without restriction to facial images) through the direct modification of pixel intensities. Although the addition of noise to pixel intensities does not provide the high visual quality obtained via generative machine learning models, it offers greater versatility by eliminating the need for a trained model. We demonstrate that our proposed use of the exponential mechanism in this context is able to provide superior visual quality to pixel-space obfuscation using the Laplace mechanism.
Generalized Momentum-Based Methods: A Hamiltonian Perspective

We take a Hamiltonian-based perspective to generalize Nesterov's accelerated gradient descent and Polyak's heavy ball method to a broad class of momentum methods in the setting of (possibly) constrained minimization in Euclidean and non-Euclidean normed vector spaces. Our perspective leads to a generic and unifying nonasymptotic analysis of convergence of these methods in both the function value (in the setting of convex optimization) and in the norm of the gradient (in the setting of unconstrained, possibly nonconvex, optimization). Our approach relies upon a time-varying Hamiltonian that produces generalized momentum methods as its equations of motion. The convergence analysis for these methods is intuitive and is based on the conserved quantities of the time-dependent Hamiltonian.
Computers as Social Actors? Examining How Users Perceive and Interact with Virtual Influencers on Social Media

With the rapid development of s-commerce and CGI technology, the concept of “virtual influencer” has emerged and is rapidly growing in popularity in social media and network service (e.g., Instagram). This paper investigated the effects of the number and types of social actors in the contents of Instagram on users' reactions. To identify this, we collected the users' comments from the contents of the virtual influencers' accounts on Instagram and conducted sentiment analysis. The results showed that users tend to use more social words and 3rd-person pronouns in contents with multiple social actors including the virtual influencer than in contents with a virtual influencer. Moreover, when a virtual influencer is presented in the contents of social media with humans, users tend to use more positive words and anxious words than in the contents without humans. Based on these outcomes, several interesting issues are discussed below.
Measuring the cognitive load of software developers: An extended Systematic Mapping Study

Cognitive load in software engineering refers to the mental effort users spend while reading software artifacts. The cognitive load can vary according to tasks and across developers. Researchers have measured developers’ cognitive load for different purposes, such as understanding its impact on productivity and software quality. Thus, researchers and practitioners can use cognitive load measures for solving many aspects of software engineering problems. However, a lack of a classification of dimensions on cognitive load measures in software engineering makes it difficult for researchers and practitioners to obtain research trends to advance scientific knowledge or apply it in software projects. This article aims to classify different aspects of cognitive load measures in software engineering and identify challenges for further research. We conducted a Systematic Mapping Study (SMS), which started with 4,175 articles gathered from 11 search engines and then narrowed down to 63 primary studies. Our main findings are: (1) 43% (27/63) of the primary studies focused on applying a combination of sensors; (2) 81% (51/63) of the selected works were validation studies; (3) 83% (52/63) of the primary studies analyzed cognitive load while developers performed programming tasks. Moreover, we created a classification scheme based on the answers to our research questions. despite the production of a significant amount of studies on cognitive load in software engineering, there are still many challenges to be solved in this particular field for effectively measuring the cognitive load in software engineering. Therefore, this work provided directions for future studies on cognitive load measurement in software engineering.
Medical Prescription Recognition using Machine Learning

Admittedly, because of how busy doctors are nowadays, they tend to scribble unreadable prescribed medicines which leads to the problem of misinterpreting medicine names. Patients are sometimes curious to know information about their prescribed medicines before purchasing them. Recently, developers have been searching for a method to address this problem efficiently but, no technique leads to full recognition of medicine names due to the bad handwriting of doctors and its variety so that leads us to machine learning where the system will learn various types of handwritings for the same medicine to be able to recognize new handwritings. This paper proposed a system that presents a solution for both the pharmacist and the patient through a mobile application that recognizes handwritten medicine names and returns a readable digital text of the medicine and its dose. The System identifies the medicines' names and the doses for the collected data set with some, pre-processing techniques like image subtraction, noise reduction, and image resizing. After that, the pre-processed images will undergo some processing as it will be classified and feature extracted through Convolutional Neural Network and finally Optical Character Recognition technique applied on the medicines with low accuracy in the post-processing phase to identify their names by comparing the result with the dataset containing all the medicines. This will help in diminishing the instances of distortion of medication names assisting pharmacists in limiting their doubts. The proposed system tested on different real cases, and accuracy has reached 70% using (CNN) model.
Accelerated manifold embedding for multi-view semi-supervised classification

Multi-view semi-supervised learning has gained much attention since a great number of unlabeled multi-view data are easy to obtain while few labeled data are available. Accordingly, how to utilize the relationship between labeled and unlabeled data is of significance in multi-view semi-supervised learning. In this paper, we propose an auto-weighted manifold embedding model to address multi-view semi-supervised classification problems, where only a small percentage of labeled data points are used for model training. In the proposed model, data points close to each other in the feature space will be assigned to similar classes in the label space through manifold embedding. Accordingly, the class information of labeled data will be employed in the prediction process for unlabeled data. Moreover, an optimal weight for each view of multi-view data is learned automatically, which enables an encouraging fusion quality for multi-view manifold embedding. To further speed up the calculation process and reduce the computational complexity of the proposed model, an effective accelerated auto-weighted manifold embedding scheme is developed. Besides, theoretical analyses are then provided to indicate a tight approximation bound for the primary manifold embedding method, while the accelerated method is designed by an order decrease of magnitude of computational complexity. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed models over compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, the experimental results are suggestive of positive robustness and promising generalization capacity of the proposed methods.
Modulation Classification Based on Fourth-Order Cumulants of Superposed Signal in NOMA Systems

In this paper, we study the automatic modulation classification in a non-orthogonal multiple access system. To mitigate the effect of interference, a likelihood-based algorithm and a fourth-order cumulant-based algorithm are proposed. Different from the maximum likelihood classifier for a single signal without interference, a likelihood function of the far and near users' signals is derived. Then, a marginal probability for the far user is obtained by using the Bayesian formula. Hence, the modulation type can be determined by maximizing the marginal probability. The high computational complexity of the likelihood-based algorithm renders it impractical; accordingly, it serves as a theoretical performance bound. On the other hand, we construct a feature vector through the estimated fourth-order cumulants of the received signal including the superposed signal and noise. For each modulation pair, using the mean and covariance matrix of the estimated feature vector, its probability density function can be obtained. Then, the key is to calculate the mean and covariance matrix of the estimated feature vector. To solve this problem, the moments of the superposed signal are derived. Therefore, modulation classification can be performed by maximizing the probability density function. Extensive simulations verify that the two proposed algorithms perform well under a wide range of signal-to-noise ratios and observation lengths.
Specifics of using image visualization within education of the upcoming chemistry teachers with augmented reality technology

Abstract It is beneficial to study chemical disciplines, applying the Augmented Reality for the upcoming chemistry teachers, as far as the visualisation of the demonstration material in the 3D helps students understand various processes and phenomena, the structure of chemical compounds and the mechanisms of their correlation in a better way. The object of the project is the development of program and printed tools, designed to visualize the education process within chemistry disciplines according to the augmented reality technology. The authors have developed applications, designed for visualisation of the study material with AR. When a mobile phone is pointed on a marker, the image “comes to life”, its three-dimensional model appears; it can be manipulated in some way (inversion, enlargement, viewing from different sides) to understand its structure, operating principle etc. better. Applying augmented reality objects gives the teacher an opportunity to explain big amount of theory quickly and effectively, and the students – to memorise it effectively, develops creativity and boosts motivation for study. Applying AR while training the upcoming chemistry teachers gives the ability to prepare them for applying this technology during the education process in the general secondary education establishments.
Lightweight Deep Learning Model for Detection of Copy-Move Image Forgery with Post-Processed Attacks

As digital image forgery can be alarmingly detrimental, therefore, an insight into detection and classification of tampered digital images is of paramount importance. Without undermining the significance of other image forgery types, copy-move can be regarded as one of the most commonly used forgeries due to its ease of implementation. To counter the rapidly complicating forgery methods due to easily accessible technologically advanced tools, passive image forensic methods have also undergone massive evolution. Presently, deep learning based techniques are regarded as state-of-the-art for image processing/image forgery detection and classification due to their enhanced accuracy and automatic feature extraction capabilities. But the existing deep learning based techniques are time and resource-intensive as well. To cater for these solutions with complexities as stated, this research focuses on experimentation using two state-of-the-art deep learning models; SmallerVGGNet (inspired from VGGNet) and MobileNetV2. These two models are time and resource friendly deep learning frameworks for digital image forgery detection on embedded devices. After rigorous analysis, the study considers a suitably modified version of MobileNetV2 to be more effective on copy-move forgery detection which also caters for inconsistencies executed post-forgery including visual-appearance related such as brightness change, blurring and noise adding and geometric transformations such as cropping and rotation. The experimental results demonstrate that the proposed MobileNetV2 based model shows 84% True Positive Rate (TPR) and 14.35% False Positive Rate (FPR) for the detection of digital image forgery post-processed with the said multiple attacks.
Actor-critic reinforcement learning to estimate the optimal operating conditions of the hydrocracking process

Determining the optimal operating conditions for hydrocracking units is imperative due to the changing nature of production requirements. However, it is expensive to optimize the hydrocracking process with mathematical models because hydrocracking units have a limited capacity for quick response and customization. This study proposes an actor-critic reinforcement learning optimization strategy using a DNN surrogate model, which was developed from a validated mathematical model with a marginal error of less than 2%. The surrogate model interacted with the A2C algorithm and the optimal operating conditions were determined with an accuracy of 97.86% and 98.5%. To demonstrate the reliability, case studies were executed; the strategy was found to be consistent, with an average efficiency of 98%. The proposed approach offers the advantages of quick response time, low computational burden and customizability for online implementation, which are essential for practical optimization problems. It can be extended beyond hydrocracking to other chemical industries.
Multi-criteria recommender system based on social relationships and criteria preferences

Multi-criteria recommender systems have garnered considerable interests from researchers and practitioners. In this paper, we study the optimization of the accuracy and scalability of multi-criteria recommendation systems using social relationships and criteria preferences information. We firstly construct a hybrid social recommendation algorithm to investigate the advantages of social relationships, and extend the application scope of the algorithm by an implicit social relationship inference technique. Then the nonlinear aggregate functions are adopted to uncover the relationship between criteria and the overall rating. Besides, we cluster users and train the aggregate function for each user group with a much smaller sample size, which is useful for improving the training efficiency. Finally, we validate the proposed approaches on TripAdvisor multi-criteria rating data sets with different sparsity. The proposed social recommendation model outperforms traditional approaches for both active and cold start users in predicting criteria ratings. Multi-criteria ratings enhance accuracy on the condition that criteria ratings can be accurately predicted. Our results also confirm the benefits from nonlinear aggregate functions and cluster analysis, especially when the data set is extremely sparse.
Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs.
Joint extraction of entities and overlapping relations using source-target entity labeling

Joint extraction of entities and overlapping relations has attracted considerable attention in recent research. Existing relation extraction methods rely on a training set that is labeled by the distant supervision method for supervised relation extraction. However, the drawbacks of these methods are that large-scale unlabeled data cannot be used and the quality of labeled data cannot be guaranteed. Moreover, owing to the relatively complex overlapping relations, it is difficult to perform joint entity-relation extraction accurately. In this study, we propose an end-to-end neural network model (BERT-JEORE) for the joint extraction of entities and overlapping relations. First, we use the BERT-based parameter-sharing layer to capture the joint features of entities and overlapping relations. Then, we implement the source-target BERT model to assign entity labels to each token in a sentence, thereby expanding the amount of labeled data and improving their quality. Finally, we design a three-step overlapping relations extraction model and use it to predict the relations between all entity pairs. Experiments conducted on two public datasets show that BERT-JEORE achieves the best current performance and outperforms the baseline models by a significant margin. Further analysis shows that our model can effectively capture different types of overlapping relational triplets in a sentence.
Health Management of Dry-Type Transformer Based on Broad Learning System

This article presents a novel health management method of the dry-type transformer to diagnose the early unhealthy behavior and evaluate the transformer's health condition by health score. The health condition diagnosis implemented by a proposed dynamic-weighted-feed-back broad learning system (BLS) (DW-FB-BLS) method, which helps to determine the BLS network structure effectively, and adjusts the weight of features in the online application to avoid reduction of accuracy caused by concept drift. Then, a rational score rule is set to evaluate the health condition of the dry-type transformer by health score, which allows intuitive presentation and preservation of transformer's health condition over a long period. Finally, the effectiveness and validity of the proposed method are verified based on the real field data of dry-type transformer. Satisfactory results for unhealthy behavior diagnosis and health evaluation are obtained, it shows that health management of this article can reflect the real health condition of dry-type transformer appropriately.