Reviewerly/exHarmony huggingface dataset:
Value of open data: A geoscience perspective

Abstract We are living in a data‐centric society, with governments and businesses increasingly looking at what they can do to gain insight and improve the flow of data. Encouraging the release of data as ‘open data’ is one measure that would remove barriers to access, increase use and facilitate downstream data innovation. Using examples from firstly the non‐geoscience and then geoscience sectors, this paper outlines three factors that can lead to a successful open data programme. These are (1) having a clear strategy with a well‐articulated vision; (2) ensuring that data are not only free but also technically accessible and delivered under an open licence; and (3) continued investment in the programme to ensure its long‐term success. However, not all data can or should be open, and organizations and governments must be careful that their interventions do not have unintended consequences that might reduce incentives to collect, maintain and share data. A primary concern is the financial sustainability of a dataset, but this also extends to other risks that would prevent the data being widely shared such as the inclusion of personal data or third‐party intellectual property. In these cases, use of a data‐sharing risk assessment framework, and the application of the FAIR principles of findable, accessible, interoperable and reusable can be used to increase data sharing and maximize the benefits that can be realized from geoscience data.
Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Electronic nicotine delivery system (ENDS) brands, such as JUUL, used social media as a key component of their marketing strategy, which led to massive sales growth from 2015 to 2018. During this time, ENDS use rapidly increased among youths and young adults, with flavored products being particularly popular among these groups.The aim of our study is to develop a named entity recognition (NER) model to identify potential emerging vaping brands and flavors from Instagram post text. NER is a natural language processing task for identifying specific types of words (entities) in text based on the characteristics of the entity and surrounding words.NER models were trained on a labeled data set of 2272 Instagram posts coded for ENDS brands and flavors. We compared three types of NER models-conditional random fields, a residual convolutional neural network, and a fine-tuned distilled bidirectional encoder representations from transformers (FTDB) network-to identify brands and flavors in Instagram posts with key model outcomes of precision, recall, and F1 scores. We used data from Nielsen scanner sales and Wikipedia to create benchmark dictionaries to determine whether brands from established ENDS brand and flavor lists were mentioned in the Instagram posts in our sample. To prevent overfitting, we performed 5-fold cross-validation and reported the mean and SD of the model validation metrics across the folds.For brands, the residual convolutional neural network exhibited the highest mean precision (0.797, SD 0.084), and the FTDB exhibited the highest mean recall (0.869, SD 0.103). For flavors, the FTDB exhibited both the highest mean precision (0.860, SD 0.055) and recall (0.801, SD 0.091). All NER models outperformed the benchmark brand and flavor dictionary look-ups on mean precision, recall, and F1. Comparing between the benchmark brand lists, the larger Wikipedia list outperformed the Nielsen list in both precision and recall.Our findings suggest that NER models correctly identified ENDS brands and flavors in Instagram posts at rates competitive with, or better than, others in the published literature. Brands identified during manual annotation showed little overlap with those in Nielsen scanner data, suggesting that NER models may capture emerging brands with limited sales and distribution. NER models address the challenges of manual brand identification and can be used to support future infodemiology and infoveillance studies. Brands identified on social media should be cross-validated with Nielsen and other data sources to differentiate emerging brands that have become established from those with limited sales and distribution.
A Novel Subpixel Circle Detection Method Based on the Blurred Edge Model

Circle detection is a critical issue in computer vision and image processing. Whether in natural images or industrial images, the accuracy of circle detection has a significant impact on advanced vision applications. Conventional methods, such as circle Hough transform, random circle detection, and EDCircles, only reach pixel-level edge accuracy. This article proposes a subpixel circle detection method based on subpixel edges with accuracy of one-tenth of one pixel. All candidate circles are first detected by EDCircles. The circle scoring formula based on polarity, radius, and contour is then proposed to sort the detected circles, and the circle with the highest score is selected as the target. The 2-D subpixel calculation problem is transformed into the 1-D fitting problem, and the subpixel edge region is selected according to the gradient direction of the circular edge. To reduce the error of the step model and the real edge, the blurred edge model is proposed to fit the region. Subsequently, the parameters of the edge model are transformed into subpixel coordinates. To solve the problem that the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> -loss function is not robust to outliers, the Huber loss function is finally applied to the circle fitting, and the gradient descent method is adopted to calculate the circle parameters. Experiments on natural and industrial images show that the proposed method has good performance on robustness and accuracy.
Variational Synthesis Network for Generating Micro Computed Tomography from Cone Beam Computed Tomography

The cross-modality generation is usually a challenging task, especially for the medical image with a huge distribution difference. Micro Computed Tomography (Micro-CT) has high resolution and sharp contrast, which is regarded as the gold standard of tooth structure in dentistry. But Micro-CT has to scan a single isolated tooth in vitro, it cannot provide any information before surgery. Cone Beam Computed Tomography (CBCT) is commonly used in clinical treatment, but its low resolution and blurred border often lead to the deficiency of accurate tooth structure for surgery, such as the pulp and crown. The modality conversion from CBCT to Micro-CT is very difficult due to the huge difference between two modality distributions. Most GAN-based models cannot cope with such distribution differences between modalities. Therefore, we propose a novel cross-modality method for generating Micro-CT from CBCT. First, we propose a variation-based distance to reflect the difference between the two modality distributions. Second, integrating the variation-based distance, we design an end-to-end neural network named Variational Synthesis network (VSnet) to generate the Micro-CT from CBCT. Finally, we compare our method with plentiful state-of-the-art image generation methods. The results show that our proposed method is superior to other existing methods on the Micro-CT generation task, with image quality index SSIM of 0.912 $(\pm 0.061)$, PSNR of 22.451 $(\pm 4.810)$, and NRMSE of 0.261 $(\pm 0.142)$. In terms of the generation of the tooth structure, we also achieve superior results in clinic application. For the Micro-CT generated by VSnet, it is easy to achieve precise segmentations of tooth pulp and crown through simple thresholding, which facilitates the diagnosis in dental clinic.
An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.
Deep SR-HDR: Joint Learning of Super-Resolution and High Dynamic Range Imaging for Dynamic Scenes

The visual quality of a single image captured by a digital camera usually suffers from limited spatial resolution and low dynamic range (LDR) due to sensor constraints. To address these problems, recent works have independently applied convolutional neural networks (CNNs) to super-resolution (SR) and high dynamic range (HDR) imaging and made significant improvements in visual quality. However, directly connecting SR and HDR networks is an inefficient way to enhance image quality, because these two tasks share most of the same processing steps. To this end, we propose a deep neural network for the joint task of SR and HDR imaging, termed Deep SR-HDR, which reconstructs a high-resolution (HR) HDR image from a set of differently exposed low-resolution (LR) LDR images of a dynamic scene. Specifically, we merge the shared processing steps, including feature extraction and alignment of these two tasks. In particular, to handle large-scale complex motions, we design a multi-scale deformable module (MSDM) that estimates the sampling location offsets in a coarse-to-fine manner and then flexibly integrates useful information to compensate for the missing content in the motion regions. Then, we divide the fusion stage into two branches for HDR generation and high-frequency information extraction. With the cooperation and interactions of these modules, the proposed network reconstructs high-quality HR HDR images. Extensive qualitative and quantitative experimental results demonstrate the superiority and high efficiency of the proposed network.
Bayesian Deep Neural Networks for Supervised Learning of Single-View Depth

Uncertainty quantification is essential for robotic perception, as overconfident or point estimators can lead to collisions and damages to the environment and the robot. In this letter, we evaluate scalable approaches to uncertainty quantification in single-view supervised depth learning, specifically MC dropout and deep ensembles. For MC dropout, in particular, we explore the effect of the dropout at different levels in the architecture. We show that adding dropout in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> layers of the encoder brings better results than other variations found in the literature. This configuration performs similarly to deep ensembles with a much lower memory footprint, which is relevant for applications. Finally, we explore the use of depth uncertainty for pseudo-RGBD ICP and demonstrate its potential to estimate accurate two-view relative motion with the real scale.
A Quantified Analysis of Bad News for Story Sifting Interfaces

Bad News is an award winning game, simulation, and performance art piece which depends on story sifting and live acting performed simultaneously and in real time by two human performers taking on two roles: a Wizard and an Actor. This paper offers a quantified analysis of eighty-one playthroughs of Bad News, identifying common patterns that were frequently used by the story sifting Wizard to unearth narratively salient content buried in the simulation to reveal to the player. This analysis informs the design of an in-development WizActor interface; a tool intended to reduce the cognitive overhead of story sifting such that future pieces of computationally assisted performance might only depend on one person to fulfill both roles.
Facilitating Health Information Exchange in Medical Emergencies

Nowadays an increasing attention is paid towards the goal of Health Information Exchange (HIE), especially when it comes to the exchange of health data among different healthcare institutions. A very important aspect though still remains the ability to exchange this data with the citizens as well, in a way that gives the control over their data. To this end, this paper proposes two secure HIE protocols, to facilitate on (i) the secure backup of health data to trusted storage cloud providers, and (ii) on the exchange of health data between individual citizens and trusted healthcare professionals during emergency situations. An evaluation scenario is being followed along with these protocols, resulting in their overall efficiency and applicability, with limitations including the continuous changes of interoperability and security requirements, which are equally addressed in this manuscript.
Distant-Supervised Slot-Filling for E-Commerce Queries

Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.). These characteristics can then be used by a search engine to return results that better match the query’s product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating representative labeled data, especially in big-data driven platforms like e-commerce is expensive and time consuming, given the volume and velocity of the data. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical queries and their subsequent transaction logs, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering both how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.
TransMixNet: An Attention Based Double-Branch Model for White Blood Cell Classification and Its Training with the Fuzzified Training Data

White blood cells (WBCs) play a critical part in the human immune system, protecting human from bacteria and viruses. In general, WBCs can be divided into five categories: basophil, monocyte, eosinophil, neutrophil, and lymphocyte. The proportion of different kinds of WBCs is closely related to human health, thus analyzing the numbers and percentages of WBCs is usually included in the blood routine examination. Therefore, accurate identification of different types of WBCs has become an important step in the statistical analysis. However, the insufficiency and imbalance of WBC samples in medical computer vision domains is still a challenge to classify WBCs intelligently and accurately. This paper presents an attention based double-branch network, called TransMixNet, to build the classification model for WBCs recognition. During the model construction, we adopt transfer learning and data augmentation strategies to overcome the problems of insufficient and unbalanced samples. In addition, mixup strategy is used to model the domain relationship between different types of training samples to improve the generalization performance of the model. The evaluation experiment results show that our model is promising and applicable to clinical applications.
Enhanced Flipping Technique to Reduce Variability in Image Steganography

Steganography algorithms hide the secret message in the cover image and provide imperceptibility to the attacker.The Least Significant Bit (LSB) algorithm is the preferred data hiding method.In this method, the least significant bit of the cover image pixel is substituted with the secret data bit.However, this method provides high variability if k -LSB bits of the cover image is replaced with secret data bit.To reduce the variability, various optimization algorithms are de ployed in image steganography.These algorithms search the optimal pixels in the cover image, and data hiding is performed using the k-bits LSB method.Several iterations of optimization algorithms have increased the time complexity upon achieving this goa l.Therefore, in this paper, a data hiding approach is designed based on the flipping approach that reduces variability and provides lesser time complexity.In the proposed method, initially, data hiding is performed using the k-bit LSB method in the cover image, and stego image is obtained.After that, the absolute difference between the cover and stego image is determined and compared with the threshold value.If the absolute difference is higher than the threshold value, then the adjacent bit of the k-bit LSB method is flipped.This process reduces the variability because flipping the adjacent bit will make the pixel value of the stego image closer to the cover image.The simulation evaluates using various performance metrics upon testing on standard dataset images.The simulation results show that the proposed method provides lesser variability, good visual quality, lesser time complexity than Genetic and Bayesian Optimization algorithms and the existing flip method.
A Glove CNN-Bilstm Sentiment Classification

Reviewing products online has become an increasingly popular way for consumers to voice their opinions and feelings about a product or service. Analyzing this Big data of online reviews would help to discern and extract useful facts and information that could provide a competitive and economic advantage to merchants and other organizations that are interested. Text classification organizes documents according to a variety of predefined categories. In other to solve the aforementioned problems, we employed Glove embeddings for our review sentiment analysis. We further integrate this embedding layer into a deep convolutional neural network (CNN)-bidirectional LSTM model. We further train our model on the IMDB and movie review dataset to extract the polarity as positive or negative and subsequently compare our model with other state-of- the-art models. The aforementioned experiments validate the efficacy and superiority of our proposed approach.
Enhancement of X-ray images using various Image Processing Approaches

X-ray imaging are frequently utilized to study inside objects of the human body. X-ray images have less contrast, noise, and brightness than normal images. As a result, improvement is required. Histogram Equalization, Contrast Enhancement, Edge Detection, Denoising, Deblurring, and other enhancement methods are among them. A new image enhancement technique for X-ray pictures is proposed here, which employs various image processing techniques. Contrast-stretching transformation, Gaussian filter, Logarithmic image processing, standard logic, Edge detection method, and other techniques are used in this method. The proposed technology detects edges and produces higher-quality images, allowing doctors, physicians, and patients to appropriately diagnose illnesses. Images with better brightness and contrast, for example, are produced using the proposed method. In addition, our proposed solution takes less time to compute.
SARN: A Lightweight Stacked Attention Residual Network for Low-Light Image Enhancement

Low-light Image suffers from low contrast and brightness. If we increase the brightness of the image, the noise hidden in the dark regions will be amplified, and color and detail information may be lost after brightness enhancement. In this paper, we propose a lightweight Stacked Attention Residual Network (denoted as SARN) for low-light image enhancement. We insert Channel Attention Module (SE Module) into the residual block and its shortcut to construct the Attention Residual Block (ARB) for noise removal, and then stack ARBs as the backbone of our SARN. We insert Bottleneck Attention Module (BAM Module) into the bottlenecks to specially deal with the severe noise in real-world images. We extract the shallow features of the low-light images first, and then fuse the extracted shallow features with the high-level output features of the backbone through global skip connection to preserve the color information. Extensive ablation and comparative experiments demonstrate that our method outperforms many other state-of-the-art methods with much less time cost.
Compositional Closure—Its Origin Lies Not in Mathematics but Rather in Nature Itself

Compositional closure, spurious negative correlations in data sets of a fixed sum (e.g., fractions and percent), is often encountered in geostatistical analyses, particularly in mineralogy, petrology, and geochemistry. Techniques to minimize the effects of closure (e.g., log-ratio transformations) can provide consistent geostatistical results. However, such approaches do not remove these effects because closure does not result from mathematical operations but is an inherent property of the physical systems under study. The natural world causes physical closure; mathematics simply describes that closure and cannot alter it by manipulations. Here, we examine the distinct types of geologic systems and samples to determine in which situations closure (physical and mathematical) does or does not ensue and the reasons therefor. We parse compositional systems based on (1) types of components under study, immutable (e.g., elements) or reactive (minerals), and (2) whether the system is open or closed to component transfer. Further, open systems can be (1) displacive in which addition of a component physically crowds out others, or (2) accommodative in which addition or subtraction of components does not affect the others. Only displacive systems are subject to compositional closure. Accommodative systems, even with components expressed as percent or fractions, are not closed physically or, therefore, mathematically.