Reviewerly/exHarmony huggingface dataset:
Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network

Given the explosive growth of the amount of medical image data being produced and transferred over networks every day, employing lossy compression and other irreversible image operations is inevitable. As expected, irreversible image coding may decrease image fidelity by introducing undesired artifacts, which may lead to an invalid diagnosis. The purpose of this study is to propose a no-reference model of assessing the quality of a degraded medical image resulting from irreversible coding, based on pattern recognition with the use of a convolutional neural network (CNN). This deep neural network consists of six convolutional layers followed by two fully connected ones for the final image classification. Such network geometry is a common choice for image classification problems nowadays. We aim to construct a model that is specialized for medical images and could serve as a predictor of image quality for algorithm performance analysis. This technique uses a CNN to classify shapes of randomly chosen grayscale intensities. The shapes and grayscale shadings were chosen with the intention to mimic structures and edges appearing in a medical image. Using the accuracy of a classifier, we attempt to quantitatively measure how the information content in an image deteriorates after applying irreversible operations and how this loss of information affects the ability/inability of the neural network to recognize the shapes. The technique may be used to study the performance of irreversible image coding techniques. Two irreversible operations are employed for image degradation: compression and interpolation. We show the difference of image quality resulting from JPEG and JPEG2000 compression algorithms followed by scaling using several interpolation techniques. The main result of this work is the development of a model to quantitatively measure image quality based on pattern recognition using a deep neural network. The presented model of quantitative assessment of medical image quality may be helpful in determining the thresholds for irreversible image post-processing algorithms parameters (i.e. quality factor in JPEG) in order to avoid misdiagnosis. Further investigation of this problem will involve a connection of the introduced method with specific pathologies and various medical image modalities.
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes

The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries. Most recent methods solve this problem based on edge/keypoint detection or semantic segmentation. However, these approaches have shown limited attention on the geometry of the dominant planes and the intersection between them, which has significant impact on room layout. In this work, we propose to incorporate geometric reasoning to deep learning for layout estimation. Our approach learns to infer the depth maps of the dominant planes in the scene by predicting the pixel-level surface parameters, and the layout can be generated by the intersection of the depth maps. Moreover, we present a new dataset with pixel-level depth annotation of dominant planes. It is larger than the existing datasets and contains both cuboid and non-cuboid rooms. Experimental results show that our approach produces considerable performance gains on both 2D and 3D datasets.
An integrated framework for automatic ontology learning from unstructured repair text data for effective fault detection and isolation in automotive domain

A real-life hierarchical classification system is developed to automatically extract a domain ontology from repair data collected during the warranty period of an original equipment manufacturer (OEM), e.g. automotive. Given the complex nature of products, failures may occur impacting consumers and to limit their adverse effect it is critical to react through effective fault detection and isolation. The key fault signals, such as failed parts, their associated symptoms, and the repair actions are latent in repair data. The overwhelming size of real-life data makes it impossible to read and curate a domain ontology manually in a reasonable time. In our work, a hierarchical classification system firstly classifies key phrases into technical and non-technical classes. Secondly, the technical phrases are classified into part, symptom, or action classes. Our system is deployed as a prototype and its performance is validated using real-life data. The system achieved the average F1 score of 0.82 and the new ontology is used in fault detection and isolation in seven different fault models.
Image Aesthetics Assessment Based on Multi-stream CNN Architecture and Saliency Features

In this paper, we explore how higher-level perceptual information based on visual attention can be used for aesthetic assessment of images. We assume that visually dominant subjects in a photograph influence stronger aesthetic interest. In other words, visual attention may be a key to predicting photographic aesthetics. Our proposed aesthetic assessment method, which is based on multi-stream and multi-task convolutional neural networks (CNNs), extracts global features and saliency features from an input image. These provide higher-level visual information such as the quality of the photo subject and the subject–background relationship. Results from our experiments support the effectiveness of our approach.
Digital instructional practices to promote pedagogical content knowledge during COVID-19

This report documents the adaptations made by three teacher educators in an early childhood teacher preparation programme in New York City when classes were transitioned to digital environments due to COVID-19. Through qualitative methods that included reflection, observation notes, and systematic discussion, four themes emerged of promising digital practices that model effective teaching and promote pedagogical content knowledge. These practices include the use of breakout groups, an interactive whiteboard, an interactive agenda, and community-building activities. The authors hope to support teacher educators globally by illustrating specific practices that facilitate student engagement and model pedagogical practices for pre-service teachers.
Sandstorm image enhancement based on YUV space

Dust weather is increasingly frequent, seriously affecting the quality of outdoor surveillance video images. In dusty weather, due to the presence of a large amount of dust particles in the air, these particles selectively absorb and scatter light, resulting in blurred details of the captured image, low contrast and clarity, and serious color shift problems. In order to solve the above problems, this paper proposes a dust image enhancement method based on YUV space. The method firstly transfers the RGB dust image to the YUV space, and for the first time innovatively uses the color components U, V to correct the color shift in the YUV space, Then, the improved MSR(Multi-scale Retinex) algorithm is used to process the Y component to reduce the influence of scattered light in the air to improve the image clarity. Next, the Y component is stretched linearly to display the Y component on the display screen. Finally, the Y component is enhanced again with CLAHE(contrast-limited histogram equalization), improving the overall contrast and enhancing the detail of the image. The experimental results show that, compared with the contrast algorithm, the proposed algorithm has natural color, clear and bright details, good overall visual effect and real-time performance.
An IoT-Based Anti-Counterfeiting System Using Visual Features on QR Code

This article presents an Internet-of-Things (IoT) anti-counterfeiting system that uses visual features combined with the quick response (QR) code. The visual features guarantee the authenticity of a product with the QR code for tracking and tracing. Two visual features, i.e., natural texture features and printed micro features are exploited in the proposed system. The natural texture features use the texture of fiber paper to achieve physical unclonable function (PUF), while the micro features are artificially generated for improved industrial manufacturability and reliability. Features are generated and registered in the production phase when the QR code is printed. In the anti-counterfeiting verification phase, the feature obtained through the feature extraction algorithm is compared with the record to calculate similarity, which indicates the verification result. Such an approach is fully compatible with the QR code-based logistic process without any additional manufacturing cost. A user-friendly application has been developed on a mobile platform that facilitates easy-to-use and affordable devices for verification, such as a mobile phone or a handheld code reader. The experimental results show 99.6% and 99.9% accuracy of anti-counterfeiting verification for texture features and micro features, respectively. The system with corresponding algorithms and software has been demonstrated in real-life products.
Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.
Double Insurance: Incentivized Federated Learning with Differential Privacy in Mobile Crowdsensing

Exploiting the computing capability of mobile devices with specialized engines (e.g., Neural Engine in iPhone), an attractive paradigm of federated learning that combines the mobile crowdsensing (MCS) has been deeply investigated recently (e.g., Google AI and Nvidia), where the training task is offloaded to the mobile crowd. However, this new paradigm still has numerous problems. Since executing the training task is costly for individual workers, the first problem is how to attract more participants. Following the incentive requirement, the second is how to preserve the workers' bid privacy since the reported costs are usually sensitive. Finally, the third problem is to guarantee the privacy protection on locally training models in the federated learning which involve the private information of local data. In this paper, we propose an incentivized federated learning with differential privacy in MCS system, namely, SHIELD, to solve the three significant problems. In fact, SHIELD satisfies the truthfulness and individual rationality while preserving the differential privacy of workers' bids and locally training models. Furthermore, for accuracy, the excess empirical risk of 1 SHIELD is proved to be upper bounded by O((ln(Kn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> ))1/2/Kn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> + ln(Kn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> )/K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where a special case for totally distributed scenario leads to a much sharper bound O( log(n)/n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) than the latest result O(ln(mn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> )/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Finally, comparing with the state-of-art apmin proaches, SHIELD illustrates superior performance by numerous experiments in classification and regression tasks.
Engineering first-order quantum phase transitions for weak signal detection

The quantum critical detector (QCD), recently introduced for weak signal amplification [L.-P. Yang and Z. Jacob, Opt. Express 27, 10482 (2019)], functions by exploiting high sensitivity near the phase transition point of first-order quantum phase transitions (QPTs). We contrast the behavior of the first-order and the second-order quantum phase transitions in the detector. We find that the giant sensitivity, which can be utilized for quantum amplification, only exists in the first-order QPTs. We define two new magnetic order parameters to quantitatively characterize the first-order QPT of the interacting spins in the detector. We also introduce the Husimi Q-functions as a powerful tool to show the fundamental change in the ground-state wave function of the detector during the QPTs, especially the intrinsic dynamical change within the detector during a quantum critical amplification. We explicitly show the high figures of merit of the QCD via the quantum gain and the signal-to-quantum noise ratio. Specifically, we predict the existence of a universal first-order QPT in the interacting-spin system resulting from two competing ferromagnetic orders. Our results motivate new designs of weak signal detectors by engineering first-order QPTs, which are of fundamental significance in the search for new particles, quantum metrology, and information science.
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with near-perfect accuracy.
Higher-Order Total Directional Variation: Imaging Applications

We introduce a class of higher-order anisotropic total variation regularizers, which are defined for possibly inhomogeneous, smooth elliptic anisotropies, that extends the total generalized variation regularizer and its variants. We propose a primal-dual hybrid gradient approach to approximating numerically the associated gradient flow. This choice of regularizers allows us to preserve and enhance intrinsic anisotropic features in images. This is illustrated on various examples from different imaging applications: image denoising, wavelet-based image zooming, and reconstruction of surfaces from scattered height measurements.
Efficient two-electron ansatz for benchmarking quantum chemistry on a quantum computer

Quantum chemistry provides key applications for near-term quantum computing, but these are greatly complicated by the presence of noise. In this work we present an efficient ansatz for the computation of two-electron atoms and molecules within a hybrid quantum-classical algorithm. The ansatz exploits the fundamental structure of the two-electron system, and treating the nonlocal and local degrees of freedom on the quantum and classical computers, respectively. Here the nonlocal degrees of freedom scale linearly with respect to basis-set size, giving a linear ansatz with only $\mathcal{O}(1)$ circuit preparations required for reduced state tomography. We implement this benchmark with error mitigation on two publicly available quantum computers, calculating accurate dissociation curves for 4- and 6- qubit calculations of ${\rm H}_\textrm{2}^{}$ and ${\rm H}_\textrm{3}^+$.
Multiple Class Novelty Detection Under Data Distribution Shift

The novelty detection models learn a decision boundary around multiple categories of a given dataset. This helps such models in detecting any novel classes encountered during testing. However, in many cases, the test data distribution can be different from that of the training data. For such cases, the novelty detection models risk detecting a known class as novel due to the dataset distribution shift. This scenario is often ignored while working with novelty detection. To this end, we consider the problem of multiple class novelty detection under dataset distribution shift to improve the novelty detection performance. Firstly, we discuss the problem setting in detail and show how it affects the performance of current novelty detection methods. Secondly, we show that one could improve those novelty detection methods with a simple integration of domain adversarial loss. Finally, we propose a method which brings together the techniques from novelty detection and domain adaptation to improve generalization of multiple class novelty detection on different domains. We evaluate the proposed method on digits and object recognition datasets and show that it provides improvements over the baseline methods.
Selective-candidate framework with similarity selection rule for evolutionary optimization

Achieving better exploitation and exploration capabilities (EEC) have always been an important yet challenging issue in the design of evolutionary optimization algorithm (EOA). The difficulties lie in obtaining a good balance in EEC, which is determined cooperatively by operations and parameters in an EOA. When deficiencies in exploitation or exploration are observed, most existing works consider a piecemeal approach, either by designing new operations or by altering the parameters. Unfortunately, when different situations are encountered, these proposals may fail to be the winner. To address these problems, this paper proposes an explicit EEC control method named selective-candidate framework with similarity selection rule (SCSS). M (M > 1) candidates are first generated from each current solution with independent operations and parameters to enrich the search. Then, a similarity selection rule is designed to determine the final candidate by considering the fitness ranking of the current solution and its Euclidian distance to each of these M candidates. Superior current solutions will prefer the closest candidates for efficient local exploitation while inferior ones will favor the farthest for exploration purpose. In this way, the rule could synthesize exploitation and exploration, making the evolution more effective. When applied to three classic, four state-of-the-art and four up-to-date EOAs from branches of differential evolution, evolution strategy and particle swarm optimization, significant enhancement in performance is achieved.
Shape sensing of multilayered composite and sandwich beams based on Refined Zigzag Theory and inverse finite element method

An accurate and effective reconstruction method, which is commonly known as "shape sensing", is presented for real-time reconstructing the deformation field of multilayered composite and sandwich laminated beam using the measured surface strains is presented. The proposed strategy uses the inverse finite element (iFEM) as a general framework and the Refine Zigzag Theory (RZT) as improved theory. Compared to the traditional iFEM, an additional kinematic variable is introduced to improve reconstruction accuracy of the axial displacement through the depth of the beam. Initially, the displacement fields, strain fields and equilibrium relations are presented. Then, the deformed displacement fields are described by the isogeometric analysis method, where the order of kinematic variables can be determined based on the equilibrium relations and the criteria for element interpolations. Next, transformation relationships are established between measured surface strains and section strains. And the number of strain sensor is further reduced through the boundary conditions. Finally, a least-square function is employed to establish the strain–displacement model. A cantilevered beam is used as a case to compare the reconstructed displacements with the theoretical displacement solutions. The numerical results demonstrate the superior accuracy capability and potential applicability of presented approach for performing accurate shape prediction of the RZT beam.
<i>Euclid</i>: Nonparametric point spread function field recovery through interpolation on a graph Laplacian

Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error, and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy. Aims. The aims of this paper are twofold. Firstly, we took steps toward a nonparametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Secondly, we studied the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in an Euclid scenario. Methods. We extended the recently proposed resolved components analysis approach, which performs super-resolution on a field of under-sampled observations of a spatially varying, image-valued function. We added a spatial interpolation component to the method, making it a true 2-dimensional PSF model. We compared our approach to PSFEx, then quantified the impact of PSF recovery errors on galaxy shape measurements through image simulations. Results. Our approach yields an improvement over PSFEx in terms of the PSF model and on observed galaxy shape errors, though it is at present far from reaching the required Euclid accuracy. We also find that the usual formalism used for the propagation of PSF model errors to weak lensing quantities no longer holds in the case of an Euclid-like PSF. In particular, different shape measurement approaches can react differently to the same PSF modeling errors.