Reviewerly/exHarmony huggingface dataset:
Estimation of chemical weather indices of sediments using R program

For estimating major elemental geochemistry of sedimentary packages, various Weathering Indices (WIs) are utilized by researchers involved in geochemical evolution of sediments. Through developing a computer program, GeoChem, based on ‘R’ programming environment, we intend to simplify the evaluation and implementation of various Weathering Indices (WIs), including CIW, CPA, PIA, CIX, CIA, WIP, R, ICV, V, MWPI and STI. Additionally, it generates plots of input data used to quantify chemical weathering of clastic sediments in various compositional space diagrams, such as Al2O3 – CaO – Na2O, Al2O3 – K2O – CaO – Na2O, Al2O3 – CaO + Na2O – K2O, Al2O3 – CaO + Na2O + K2O – Fe2O3 + MgO and M – F – W compositional space diagram. Using GeoChem, one can do all the calculations and artwork associated with the finished report without having to use Microsoft Excel, CorelDraw or similar software. Input data source files and output calculations results data files are in CSV format. Data plots are produced in vector as well as in raster formats, providing additional flexibility to the user. Also, we are trying to figure out the associated statistical parameters, their correlation and specially restricting the major elemental geochemistry in a variety of compositional space diagrams to constrain sediment and rock composites, demonstrating chemical weathering profiles and mineralogical composition of the parent rock. By altering different arguments in the code, the ‘R’ programming environment allows the investigator to change the code, depending on the dataset under consideration and the user requirement to generate user-defined, customized plots. To demonstrate the applicability of the tool, its performance was tested, evaluating the major elemental geochemical data of diverse geological settings and sedimentary provenance different in kind. For the same, sediments selected include channel, suspended and overbank sediments of the Peninsular rivers of Ganga basin; Surma group and Barail group of sandstones; lake sediments of Manasbal, Kashmir; Mizoram Foreland Basin, India.
Stability estimates for phase retrieval from discrete linear canonical Gabor transformation measurements

Phase retrieval gives the problem of recovering some signal from phaseless measurements. This paper mainly studies the stability estimation of phase recovery from the measurement of discrete linear canonical Gabor transformation (DLCGT). We first show that we can recover the signal on the connected components of in global phase sense. Then, we give the stability estimates results from the DLCGT measurements and the autocorrelation relation.
Learning to Optimize

With a growing availability of ambient computing power as well as sensor data, networked systems are emerging in all areas of daily life. Coordination and optimization in complex cyber-physical systems demand for decentralized and self-organizing algorithms to cope with problem size and distributed information availability. Self-organization often relies on emergent behavior. Local observations and decisions aggregate to some global behavior without any apparent, explicitly programmed rule. Systematically designing algorithms with emergent behavior suitably for a new orchestration or optimization task is, at best, tedious and error prone. Appropriate design patterns are scarce so far. It is demonstrated that a machine learning approach based on Cartesian Genetic Programming is capable of learning the emergent mechanisms that are necessary for swarm-based optimization. Targeted emergent behavior can be evolved by evolution strategies. The learned swarm behavior is already significantly better than just random search. The encountered pitfalls as well as remaining challenges on the research agenda are discussed in detail. An additional fitness landscape analysis gives insight in obstructions during evolutions and clues for future improvements.
Predicting the Visual Attention of Pathologists Evaluating Whole Slide Images of Cancer

This work presents PathAttFormer, a deep learning model that predicts the visual attention of pathologists viewing whole slide images (WSIs) while evaluating cancer. This model has two main components: (1) a patch-wise attention prediction module using a Swin transformer backbone and (2) a self-attention based attention refinement module to compute pairwise-similarity between patches to predict spatially consistent attention heatmaps. We observed a high level of agreement between model predictions and actual viewing behavior, collected by capturing panning and zooming movements using a digital microscope interface. Visual attention was analyzed in the evaluation of prostate cancer and gastrointestinal neuroendocrine tumors (GI-NETs), which differ greatly in terms of diagnostic paradigms and the demands on attention. Prostate cancer involves examining WSIs stained with Hematoxylin and Eosin (H &E) to identify distinct growth patterns for Gleason grading. In contrast, GI-NETs require a multi-step approach of identifying tumor regions in H &E WSIs and grading by quantifying the number of Ki-67 positive tumor cells highlighted with immunohistochemistry (IHC) in a separate image. We collected attention data from pathologists viewing prostate cancer H &E WSIs from The Cancer Genome Atlas (TCGA) and 21 H &E WSIs of GI-NETs with corresponding Ki-67 IHC WSIs. This is the first work that utilizes the Swin transformer architecture to predict visual attention in histopathology images of GI-NETs, which is generalizable to predicting attention in the evaluation of multiple sequential images in real world diagnostic pathology and IHC applications.
Generalized Probabilistic U-Net for Medical Image Segementation

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net [14] by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic U-Net. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet .
Optimal Seating Assignment in the COVID-19 Era via Quantum Computing

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should allow the resolution of complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving combinatorial optimization problems, specifically assignment problems, by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization (QUBO) paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency.
On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation

Estimating the prediction uncertainty of a deep segmentation network is very useful in multiple learning scenarios. For example, in the semi-supervised learning paradigm, the vast majority of recent methods rely on pseudo-label generation to leverage unlabeled data, whose training is guided by uncertainty estimates. While the commonly-used entropy-based uncertainty has shown to work well in a binary scenario, we demonstrate in this work that this common strategy leads to suboptimal results in a multi-class context, a more realistic and challenging setting. We argue, indeed, that these approaches underperform due to the erroneous uncertainty approximations in the presence of inter-class overlap. Furthermore, we propose an alternative solution to compute the uncertainty in a multi-class setting, based on divergence distances and which account for inter-class overlap. We evaluate the proposed solution on a challenging multi-class segmentation dataset and in two well-known uncertainty-based segmentation methods. The reported results demonstrate that by simply replacing the mechanism used to compute the uncertainty, our proposed solution brings consistent improvements .
Using software visualization to support the teaching of distributed programming

Abstract In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical interface, the execution of a sequence of MapReduce transformations that target an input dataset. Then, the execution of each operation is illustrated on the screen by playing an appropriate graphical animation stage, highlighting aspects related to its distributed nature. The sequence of all animation stages, played back one after the other in a sequential order, results in a visualization of the whole algorithm. The content of the resulting visualization is not simulated or fictitious, but reflects the real behavior of the requested operations, thanks to the adoption of an architecture based on a real instance of a distributed system running on Apache Spark. On the teacher’s side, it is expected that by using MARVEL he/she will spend less time preparing materials and will be able to design a more interactive lesson than with electronic slides or a whiteboard. To test the effectiveness of the proposed approach on the learner side, we also conducted a small scientific experiment with a class of volunteer students who formed a control group. The results are encouraging, showing that the use of software visualization guarantees students a learning experience at least equivalent to that of conventional approaches.
Embedding Gradient-Based Optimization in Image Registration Networks

Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional iterative energy optimization-based registration and network-based registration, and propose Gradient Descent Network for Image Registration (GraDIRN). Our proposed approach trains a DL network that embeds unrolled multi-resolution gradient-based energy optimization in its forward pass, which explicitly enforces image dissimilarity minimization in its update steps. Extensive evaluations were performed on registration tasks using 2D cardiac MR and 3D brain MR images. We demonstrate that our approach achieved state-of-the-art registration performance while using fewer learned parameters, with good data efficiency and domain robustness.
Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation. The differentiability of the rasterizer enables us to train the method end-to-end. One advantage of the proposed method is that by tracking the motion of each vertex, it is able to keep the vertex correspondence of 3D meshes between time frames, which is important for quantitative assessment of the cardiac function on the mesh. We evaluate the proposed method on CMR images acquired from the UK Biobank study. Experimental results show that the proposed method quantitatively and qualitatively outperforms both conventional and learning-based cardiac motion tracking methods.
Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling

Abstract One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing E vent- T riggered K ernel A djustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA’s ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.
Information Gain Sampling for Active Learning in Medical Image Classification

Large, annotated datasets are not widely available in medical image analysis due to the prohibitive time, costs, and challenges associated with labelling large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. This work presents an information-theoretic active learning framework that guides the optimal selection of images from the unlabelled pool to be labeled based on maximizing the expected information gain (EIG) on an evaluation dataset. Experiments are performed on two different medical image classification datasets: multi-class diabetic retinopathy disease scale classification and multi-class skin lesion classification. Results indicate that by adapting EIG to account for class-imbalances, our proposed Adapted Expected Information Gain (AEIG) outperforms several popular baselines including the diversity based CoreSet and uncertainty based maximum entropy sampling. Specifically, AEIG achieves $${\sim }95\%$$ of overall performance with only 19% of the training data, while other active learning approaches require around 25%. We show that, by careful design choices, our model can be integrated into existing deep learning classifiers.
Face image inpainting via latent features reconstruction and mask awareness

To achieve reasonable structural inpainting and satisfactory texture reconstruction for missing regions in images, inspired by the fact that the face image can be decoupled into multi-level latent semantic features, a two-stage face image inpainting framework with the aid of latent feature reconstruction and mask awareness is proposed. In the first stage, a series of reconstructed style features are directly generated and input into the pre-trained StyleGAN generator to obtain the preliminary restoration results. We further design a latent cosine similarity loss to obtain better results in structural reconstruction. In the second stage, we employ a well-designed hierarchical attention mechanism between the encoder and decoder. Comprehensive experiments show that the proposed approach achieves competitive performance compared with state-of-the-art methods on the canonical datasets. A number of ablation studies demonstrate the effectiveness of critical components in our method.
Did you hear our brand is hated? The unexpected upside of hate-acknowledging advertising for polarizing brands

A number of well-known brands are not only loved by many consumers, but also hated by a sizeable portion of the population and are thus termed polarizing brands. Because digital media offers consumers nearly unlimited opportunities to voice their hate, managers can no longer ignore vocal haters. However, the current marketing literature offers few strategies for addressing the challenge of brand hate. This paper introduces the concept of hate-acknowledging advertising (HAA), an ad technique in which polarizing brands openly admit that some segment of the population hates them. Over the course of three studies, the data indicates that, compared with supportive advertising, HAA results in higher perceptions of ad credibility and ultimately higher levels of brand trust. Moreover, the improvements in ad credibility and brand trust drive increased consumer intentions to engage in positive word of mouth on behalf of the polarizing brand.
Fair Cloud Auditing Based on Blockchain for Resource-Constrained IoT Devices

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> air <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> loud <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> uditing proposal by employing the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> lockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">honest</i> cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.
Non-intrusive operator inference for chaotic systems

This work explores the physics-driven machine learning technique Operator Inference (OpInf) for predicting the state of chaotic dynamical systems. OpInf provides a non-intrusive approach to infer approximations of polynomial operators in reduced space without having access to the full order operators appearing in discretized models. Datasets for the physics systems are generated using conventional numerical solvers and then projected to a low-dimensional space via Principal Component Analysis (PCA). In latent space, a least-squares problem is set to fit a quadratic polynomial operator, which is subsequently employed in a time-integration scheme in order to produce extrapolations in the same space. Once solved, the inverse PCA operation is applied to reconstruct the extrapolations in the original space. The quality of the OpInf predictions is assessed via the Normalized Root Mean Squared Error (NRMSE) metric from which the Valid Prediction Time (VPT) is computed. Numerical experiments considering the chaotic systems Lorenz 96 and the Kuramoto-Sivashinsky equation show promising forecasting capabilities of the OpInf reduced order models with VPT ranges that outperform state-of-the-art machine learning (ML) methods such as backpropagation and reservoir computing recurrent neural networks [1], as well as Markov neural operators [2].
Low power design of energy efficient median filter

The demand for cost, power and area-efficient de-noising filter is increasing day by day, because of its remarkable performance in various image processing applications. In this paper, a variable-node shift-based median filter is designed for achieving low power and area utilisation. Here, the proposed design encompasses a Max Tree Extraction (MTE) unit in conjunction with various devices, like comparators, variable-node shift generators, token generators, and noise detector units, which enable low hardware requirement and minimise the power consumption. The proposed model lessens the need of complex operations, like rank generation, rank calculation and median selection. Moreover, the proposed median filter is structured by Xilinx software and the hardware implementation is done by FPGA (Virtex 7 ×C7VX980 FPGA). Additionally, the proposed model is evaluated for different test images at various noise levels. The performance of the proposed median filter design is analysed under peak signal to noise ratio (PSNR), mean square error (MSE), power, delay and area. The experimental results show that the overall performance of the proposed method is superior to the state-of-art technologies.
Secured‐KDS: Secret key distribution and authentication scheme for resource‐constrained devices

In the era of the Internet of things (IoT), billions of electrical devices are connected to the Internet and communicate remotely with one another without any human intervention. IoT devices often do not enforce proper security measures; this raises serious privacy and security concerns. One of the big issues in securing IoT communications is that traditional cryptographical protocols incur high computational costs and are not suitable for resource-constrained devices. In this paper, the authors focus on issues related to secret keys distribution and authentication schemes. Although there exist some lightweight authentication schemes for IoT devices in the literature, they come with some limitations (e.g. preconfiguration of shared secret keys). A new lightweight authentication scheme suitable for IoT devices is proposed. This scheme incurs low resource utilisation and provides a trusted mode of Secret key distribution between IoT devices and a control server. A real-time test-bed environment is deployed to examine whether the authors’ proposed scheme is applicable for resource-constrained devices. The authors’ performance analysis shows that the proposed scheme is efficient and is resistant to possible security attacks such as replay attacks, man-in-the middle attacks, impersonation attacks, modification attacks, and remote access trojan attacks.