Reviewerly/exHarmony huggingface dataset:
DeepDensity: Convolutional neural network based estimation of local fringe pattern density

Fringe pattern based measurement techniques are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe density map can significantly facilitate almost all stages of fringe pattern analysis process. Example includes: (1) using density map as a determinant for the selection of the proper window size in windowed Fourier transform method, (2) guiding the decomposition process in empirical mode decomposition, (3) improving the phase unwrapping accuracy by providing additional reliability indicators, (4) guiding phase estimation process in regularized phase tracking. For these reasons, the accurate and robust estimation of local fringe density map is of high importance and can boost fringe pattern analysis on different stages of processing path, resulting in increased capacity of the full-field noncontact/noninvasive optical measurement system. In this paper, we propose a new, accurate, robust, and fast numerical solution for local fringe density map estimation called DeepDensity. DeepDensity is based on the convolutional neural network and deep learning, making it significantly outperform other conventional solutions to this problem. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepDensity.
Towards a Federated Fuzzy Learning System

The abundant availability of data allows the construction of predictive systems that support decision makers in business and society. A problem arises if an organization does not have a large enough data set by itself to construct a system of adequate quality. In this case, data across organizations has to be used, which introduces risks of data sharing. To overcome these risks, federated learning is getting increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. So far, only crisp systems have been used in this context. The use of a fuzzy inference system can bring advantages to deal with vagueness and uncertainty in predictive systems. Therefore, in this paper we explore the (hopefully) happy marriage of federated learning and fuzzy inference mechanisms. We show that it is indeed possible to build a fuzzy inference model in a federated learning setting, resulting in a Federated Fuzzy Learning System (F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LS). We also show that this combination brings advantages to decision making that cannot be achieved with either mechanism in isolation.
Hyperspectral image restoration via superpixel segmentation of smooth band

Hyperspectral images (HSIs) are inevitably degraded in the acquisition process by mixed noise including Gaussian noise, impulse noise, stripes, and so on. Recently, many low-rank regularization based HSI restoration methods have been proposed to powerfully remove the mixed noise. However, most of them use the square patch based denoising strategy, which destroyed the boundary information of the objects in the HSI. In this paper, we adopt superpixel segmentation to group the pixels of HSI with adjacent position, similar color, texture and luminance into a homogeneous region, whose shape is adaptive. Several homogeneous regions cover the full HSI. This is better than simply dividing the HSI into square patches. By taking advantage of both the low-rank property and the spectral smooth of the HSI, this approach can efficiently remove the mixed noise with few time. Several experiments verify the performance of the proposed approach for HSI restoration.
Bayesian Topology Learning and noise removal from network data

Abstract Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.
Analysis of security and privacy challenges for DNA-genomics applications and databases

DNA technology is rapidly moving towards digitization. Scientists use software tools and applications for sequencing, synthesizing, analyzing and sharing of DNA and genomic data, operate lab equipment and store genetic information in shared datastores. Using cutting-edge computing methods and techniques, researchers have decoded human genome, created organisms with new capabilities, automated drug development and transformed food safety. Such software applications are typically developed to progress scientific understanding and as such cyber security is never a concern for these applications. However, with the increasing commercialisation of DNA technologies, coupled with the sensitivity of DNA data, there is a need to adopt a security-by-design approach. In this paper we investigate bio-cyber security threats to genomic-DNA data and software applications making use of such data to advance scientific research. Specifically, we adopt an empirical approach to analyse and identify vulnerabilities within genomic-DNA databases and bioinformatics software applications that can lead to cyber-attacks affecting the confidentiality, integrity and availability of such sensitive data. We present a detailed analysis of these threats and highlight potential protection mechanisms to help researchers pursue these research directions.
Using self-organizing maps in airborne geophysical data for mapping mafic dyke swarms in NE Brazil

Mapping widespread dyke swarms shed light on the tectonic processes that culminate in continental fragmentation, especially in the early phases of the crustal extension and magma emplacement . Airborne magnetic data are an effective geophysical tool to clarify the extent of dyke swarms in continental-scale areas due to the expressive magnetic contrast between magmatic bodies and host rocks. However, many geological features display similar magnetic patterns, making the qualitative interpretation of magnetic anomaly maps quite subjective and ambiguous. To improve and optimize the predictive mapping of dyke swarms, this research presents a multivariate analysis of airborne geophysical surveys , applying a Self-Organizing Maps (SOM) approach using two magnetic and three gamma-spectrometric variables. This semiautomatic technique has allowed an integrated spatial analysis and the classification of the lithological units based on their magnetic and gamma-spectrometric signatures. We applied the SOM method to investigate a set of mafic dyke swarms that intruded in the Neoproterozoic Borborema Province (BP), Parnaíba Basin (PB) and the São Francisco Craton (SFC) in NE Brazil. These dykes are part of a large magmatic event associated with the continental breakup , which formed the Equatorial Atlantic Ocean in the Early Cretaceous , denominated EQUAMP. First, the SOM hyperparameters were defined by running the algorithm in a representative area in the central part of the BP. This training test area worked as a SOM template through which all data from the area were processed. The SOM analysis identified seven different populations, according to responses found in the five geophysical input variables. Two of these populations were associated with the mafic dykes, reducing the ambiguity of the magnetic anomaly interpretation. These populations represent high SOM quantization error, which means that these groups are the most anomalous data, evidenced in airborne magnetic data. These results were checked during fieldwork , revealing that dyke swarms occur more widely than were previously known, throughout the BP intruding the SFC, and showing some occurrences embedded in the Paleozoic sedimentary infill of the eastern border of the PB. • An unsupervised data mining approach for mapping mafic dykes. • Prediction of dyke swarms using self-organizing maps. • Integrated spatial analysis and classification of airborne geophysical data. • Optimization of geophysical and geological mapping.
Uncertainty quantification by ensemble learning for computational optical form measurements

Abstract Uncertainty quantification by ensemble learning is explored in terms of an application known from the field of computational optical form measurements. The application requires solving a large-scale, nonlinear inverse problem. Ensemble learning is used to extend the scope of a recently developed deep learning approach for this problem in order to provide an uncertainty quantification of the solution to the inverse problem predicted by the deep learning method. By systematically inserting out-of-distribution errors as well as noisy data, the reliability of the developed uncertainty quantification is explored. Results are encouraging and the proposed application exemplifies the ability of ensemble methods to make trustworthy predictions on the basis of high-dimensional data in a real-world context.
A Context-Based Image Contrast Enhancement Using Energy Equalization With Clipping Limit

In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image's spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme's qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.
Popularity Bias in False-positive Metrics for Recommender Systems Evaluation

We investigate the impact of popularity bias in false-positive metrics in the offline evaluation of recommender systems. Unlike their true-positive complements, false-positive metrics reward systems that minimize recommendations disliked by users. Our analysis is, to the best of our knowledge, the first to show that false-positive metrics tend to penalise popular items, the opposite behavior of true-positive metrics—causing a disagreement trend between both types of metrics in the presence of popularity biases. We present a theoretical analysis of the metrics that identifies the reason that the metrics disagree and determines rare situations where the metrics might agree—the key to the situation lies in the relationship between popularity and relevance distributions, in terms of their agreement and steepness —two fundamental concepts we formalize. We then examine three well-known datasets using multiple popular true- and false-positive metrics on 16 recommendation algorithms. Specific datasets are chosen to allow us to estimate both biased and unbiased metric values. The results of the empirical study confirm and illustrate our analytical findings. With the conditions of the disagreement of the two types of metrics established, we then determine under which circumstances true-positive or false-positive metrics should be used by researchers of offline evaluation in recommender systems. 1
Dissipative quantum repeater

By implementing a quantum repeater protocol, our aim in this paper is the production of entanglement between two two-level atoms locating far from each other. To make our model close to experimental realizations, the atomic and field sources of dissipations are also taken into account. We consider eight of such atoms (1, 2, ..., 8) sequentially located in a line which begins (ends) with atom 1 (8). We suppose that, initially the four atomic pairs (i; i + 1), i = 1; 3; 5; 7 are mutually prepared in maximally entangled states. Clearly, the atoms 1; 8, the furthest atoms which we want to entangle them are never entangled, initially. To achieve the purpose of paper, at first we perform the interaction between the atoms (2; 3) as well as (6; 7) which results in the entanglement creation between (1; 4) and (5; 8), separately. In the mentioned interactions we take into account spontaneous emission rate for atoms and field decay rate from the cavities as two important and unavoidable dissipation sources. In the continuation, we transfer the entanglement to the objective pair (1; 8) by two methods: i ) Bell state measurement (BSM), and ii ) cavity quantum electrodynamics (QED). The successfulness of our protocol is shown via the evaluation of concurrence as the well-established measure of entanglement between the two (far apart) qubits (1; 8). We also observe that, if one chooses the cavity and the atom such that holds, the effect of dissipations is effectively removed from the entanglement dynamics in our model. In this condition, the time evolutions of concurrence and success probability are regularly periodic. Also, concurrence and success probability reach to their maximum values in a large time interval by decreasing the detuning in the presence of dissipation.
Estimating Distances in Action Space in Augmented Reality

Augmented reality ( AR ) is important for training complex tasks, such as navigation, assembly, and medical procedures. The effectiveness of such training may depend on accurate spatial localization of AR objects in the environment. This article presents two experiments that test egocentric distance perception in augmented reality within and at the boundaries of action space (up to 35 m) in comparison with distance perception in a matched real-world ( RW ) environment. Using the Microsoft HoloLens, in Experiment 1, participants in two different RW settings judged egocentric distances (ranging from 10 to 35 m) to an AR avatar or a real person using a visual matching measure. Distances to augmented targets were underestimated compared to real targets in the two indoor, RW contexts. Experiment 2 aimed to generalize the results to an absolute distance measure using verbal reports in one of the indoor environments. Similar to Experiment 1, distances to augmented targets were underestimated compared to real targets. We discuss these findings with respect to the importance of methodologies that directly compare performance in real and mediated environments, as well as the inherent differences present in mediated environments that are “matched” to the real world.
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition

Deep learning approaches deliver state-of-the-art performance in recognition of spatiotemporal human motion data. However, one of the main challenges in these recognition tasks is limited available training data. Insufficient training data results in over-fitting and data augmentation is one approach to address this challenge. Existing data augmentation strategies based on scaling, shifting and interpolating offer limited generalizability and typically require detailed inspection of the dataset as well as hundreds of GPU hours for hyperparameter optimization. In this paper, we present a novel automatic data augmentation model, the Imaginative Generative Adversarial Network (GAN), that approximates the distribution of the input data and samples new data from this distribution. It is automatic in that it requires no data inspection and little hyperparameter tuning and therefore it is a low-cost and low-effort approach to generate synthetic data. We demonstrate our approach on small-scale skeleton-based datasets with a comprehensive experimental analysis. Our results show that the augmentation strategy is fast to train and can improve classification accuracy for both conventional neural networks and state-of-the-art methods.
Semantic Segmentation of Remote Sensing Image Based on Regional Self-Attention Mechanism

In remote sensing images (RSIs), accurate semantic segmentation faces more challenges because of small targets, unbalanced categories, and complex scenes. Restricted by local receptive field of convolution layers, the traditional semantic segmentation models cannot use global information of RSIs. According to the characteristics of RSIs, we propose an RSANet based on regional self-attention mechanism. Our model is no longer limited by the locality of convolution, but transfers the information flow in the whole image. It can mine out the relationship between pixels in the surrounding areas, which is more logical for understanding images content. Moreover, compared with the traditional self-attention mechanism, RSANet can effectively reduce the noise of feature maps and the interference of redundant features. Our model can get better semantic segmentation results than other current models on the DroneDeploy data set and the Chreos semantic segmentation data set. The experiments show that our RSANet achieves 2% higher mean intersection over union (mIoU) than the baseline model, especially in terms of fineness, edge integrity, and classification accuracy.
A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.
MPC-Friendly Symmetric Cryptography from Alternating Moduli: Candidates, Protocols, and Applications

We study new candidates for symmetric cryptographic primitives that leverage alternation between linear functions over $$\mathbb {Z}_2$$ and $$\mathbb {Z}_3$$ to support fast protocols for secure multiparty computation (MPC). This continues the study of weak pseudorandom functions of this kind initiated by Boneh et al. (TCC 2018) and Cheon et al. (PKC 2021). We make the following contributions.
Abnormal Behavior Recognition Based on Key Points of Human Skeleton

Human action recognition is one of the most popular fields of computer vision. However, the traditional manual feature-based method, with large background interference, can hardly establish an accurate human model and the deep learning-based method runs slowly with huge amount of parameters. In this paper, we propose a new method which combination of the two. First, we extract time series human 3D skeleton key points by Yolo v4 and apply Meanshift target tracking algorithm; then convert key points into spatial RGB and put them into multi-layer convolution neural network for recognition. This method has a high recognition rate and fast recognition speed in a variety of environment such as enclosed environment and public scene. It can quickly identify holding guns, armed attacks, throwing, climbing, approaching and other abnormal behavior.
Lexicon-based sentiment analysis using Twitter data

COVID-19 is a kind of virus of the Corona family originated from Wuhan, China, and spread over more than 215 countries in the world, more than 2.3 lakhs people died, and more than 32 lakhs are affected globally till date and numbers are continuously increasing. Because of this global pandemic, citizens of the country are in a panic situation. Sentiment Analysis (SA) is a prominent field to analyze data available on social media. This research work explores SA using the Lexicon-based approach to analyze the sentiment of six different countries: India, the USA, Spain, Italy, France, and the UK. Data from March 15 to April 15, 2020 extracted from Twitter and used to identify sentiment as Negative, Neutral, or Positive using Lexicon-based and Valence Aware Dictionary for Sentiment Reasoning (VADER)-based approaches. Empirical results show that negativity exists in almost all the countries because of COVID-19. Out of six countries considered for the SA, the UK has the highest negativity of 23.03%, followed by France with 22.71%, the USA with 22.01%, and India is having negativity of 18.39% using Simple Lexicon-based approach. At the same time, it is 35.92% in France, 35.68% in the UK, and 35.38% in the USA, while India has the least negativity of 31.03% based on the VADER-based approach. Both approaches are almost producing negativity in the same order with slight variations. Furthermore, a comparative detail analysis of India has also been done based on Twitter data. The data collected before and after lockdown using a simple Lexicon-based approach, and it has been observed that negativity is increasing after lockdown and slightly decreased during lockdown 2.0. Overall implication of this research work is that however negativity exists but people are more positive toward panic situation because of COVID-19 and also fighting against COVID-19 with restrictions like lockdown, home isolation, quarantine, limited access of resources, etc.
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence

We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on PatchMatch. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF-Pascal and SPair-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory.