Reviewerly/exHarmony huggingface dataset:
A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of oriented primary edge structure (HOPES) for the co-registration of SAR and optical images, aiming to describe the shape structure of patches more firm. In order to extract the primary edge structure, we develop the novel multi-scale sigmoid Gabor (MSG) detector and a primary edge fusion algorithm. Based on the HOPES, we propose the co-registration method. To obtain stable and uniform keypoints, the non-maximum suppressed SAR-Harris (NMS-SAR-Harris) and deviding grids methods are used. NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching. We use two one-look simulated SAR images to demonstrate that the signal-to-noise ratio (SNR) of MSG is more than 10 dB higher than other state-of-the-stage detectors; the binary edge maps and F-score show that MSG has more accurate positioning performance. Compared with the other state-of-the-stage co-registration methods, the image co-registration results obtained on seven pairs of test images show that, the correct match rate (CMR) and the root mean squared error (RMSE) improve by more than 25% and 15% on average, respectively. It is experimentally demonstrated that the HOPES is robust against speckle noise and NRD, which can effectively improve the matching success rate and accuracy.
A polarization-based image restoration method for both haze and underwater scattering environment

Abstract Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance ( I best ) and minimum variance ( I worst ) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of I best and I worst . Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.
Multiparameter simultaneous optimal estimation with an SU(2) coding unitary evolution

In a ubiquitous SU(2) dynamics, achieving the simultaneous optimal estimation of multiple parameters is significant but difficult. Using quantum control to optimize this SU(2) coding unitary evolution is one of solutions. We propose a method, characterized by the nested cross products of the coefficient vector $\mathbf{X}$ of SU(2) generators and its partial derivative ${\ensuremath{\partial}}_{\ensuremath{\ell}}\mathbf{X}$, to investigate the control-enhanced quantum multiparameter estimation. Our work reveals that quantum control is not always functional in improving the estimation precision, which depends on the characterization of an SU(2) dynamics with respect to the objective parameter. This characterization is quantified by the angle ${\ensuremath{\alpha}}_{\ensuremath{\ell}}$ between $\mathbf{X}$ and ${\ensuremath{\partial}}_{\ensuremath{\ell}}\mathbf{X}$. For an SU(2) dynamics featured by ${\ensuremath{\alpha}}_{\ensuremath{\ell}}=\ensuremath{\pi}/2$, the promotion of the estimation precision can get the most benefits from the controls. When ${\ensuremath{\alpha}}_{\ensuremath{\ell}}$ gradually closes to 0 or $\ensuremath{\pi}$, the precision promotion contributed to by quantum control correspondingly becomes inconspicuous. Until a dynamics with ${\ensuremath{\alpha}}_{\ensuremath{\ell}}=0$ or $\ensuremath{\pi}$, quantum control completely loses its advantage. In addition, we find a set of conditions restricting the simultaneous optimal estimation of all the parameters, but fortunately, which can be removed by using a maximally entangled two-qubit state as the probe state and adding an ancillary channel into the configuration. Lastly, a spin-$1/2$ system is taken as an example to verify the above-mentioned conclusions. Our proposal sufficiently exhibits the hallmark of control-enhancement in fulfilling the multiparameter estimation mission, and it is applicable to an arbitrary SU(2) parametrization process.
A Feasible Quantum Sealed-Bid Auction Scheme Without an Auctioneer

In this article, we first define a primitive problem of secure multiparty computations, i.e., secure multiparty disjunction (SMD), and present a novel quantum protocol for SMD that can ensure information-theoretical security, i.e., unconditional security.Furthermore, based on the quantum SMD protocol, we design a quantum sealed-bid auction (QSA) scheme without an auctioneer.In the proposed QSA scheme, all bidders can jointly find the winning bidder without the help of an auctioneer while it can perfectly protect the privacy of nonwinning bidders.The proposed quantum SMD protocol and quantum QSA scheme take Bell states as quantum resources and only perform single-particle Pauli operators and two-particle Bell measurements.Finally, we simulate the related quantum protocols in Qiskit and verify the correctness and the feasibility of the proposed protocols.
Comparative Analysis between Selection Sort and Merge Sort Algorithms

Sorting and merging are two problems that commonly arise in Computer Science especially in data processing tasks. To solve these problems, several algorithms have been developed. Similarly, existing merge and sorting algorithms have been improved to provide more efficient and accurate results. In this paper, selection and merging algorithms were developed on an octa-core processing machine using System.nanoTime methods in Java in order to compare their running times. The results obtained show that Merge Sort performs far better than selection sort with careful implementations by taking advantage of multiple processing cores in the test machine and some concurrency utility in Java. It was concluded that implementing algorithms using a machine with multiple numbers of cores in their Central Processing Unit (CPU) will result in a significant improvement in the performance of both algorithms.
Fundamental limitations on the device-independent quantum conference key agreement

We provide several general upper bounds on the rate of a key secure against a quantum adversary in the device-independent conference key agreement (DI-CKA) scenario. They include bounds by reduced entanglement measures and those based on multipartite secrecy monotones such as a multipartite squashed entanglement-based measure, which we refer to as reduced c-squashed entanglement. We compare the latter bound with the known lower bound for the protocol of conference key distillation based on the parity Clauser-Horne-Shimony-Holt game. We also show that the gap between the DI-CKA rate and the device-dependent rate is inherited from the bipartite gap between device-independent and device-dependent key rates, giving examples that exhibit the strict gap.
CT Segmentation of Dinosaur Fossils by Deep Learning

Recently, deep learning has reached significant advancements in various image-related tasks, particularly in medical sciences. Deep neural networks have been used to facilitate diagnosing medical images generated from various observation techniques including CT (computed tomography) scans. As a non-destructive 3D imaging technique, CT scan has also been widely used in paleontological research, which provides the solid foundation for taxon identification, comparative anatomy, functional morphology, etc. However, the labeling and segmentation of CT images are often laborious, prone to error, and subject to researchers own judgements. It is essential to set a benchmark in CT imaging processing of fossils and reduce the time cost from manual processing. Since fossils from the same localities usually share similar sedimentary environments, we constructed a dataset comprising CT slices of protoceratopsian dinosaurs from the Gobi Desert, Mongolia. Here we tested the fossil segmentation performances of U-net, a classic deep neural network for image segmentation, and constructed a modified DeepLab v3+ network, which included MobileNet v1 as feature extractor and practiced an atrous convolutional method that can capture features from various scales. The results show that deep neural network can efficiently segment protoceratopsian dinosaur fossils, which can save significant time from current manual segmentation. But further test on a dataset generated by other vertebrate fossils, even from similar localities, is largely limited.
Lower Bounds for Oblivious Near-Neighbor Search

We prove an Ω(d lg n/(lg lg n) 2 ) lower bound on the dynamic cell-probe complexity of statistically oblivious approximate-near-neighbor search (ANN) over the ddimensional Hamming cube.For the natural setting of d = Θ(lg n), our result implies an Ω(lg 2 n) lower bound, which is a quadratic improvement over the highest (nonoblivious) cell-probe lower bound for ANN.This is the first super-logarithmic unconditional lower bound for ANN against general (non black-box) data structures.We also show that any oblivious static data structure for decomposable search problems (like ANN) can be obliviously dynamized with O(lg n) overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).
Machine learning-based prediction of critical illness in children visiting the emergency department

Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have attempted to augment this process with machine learning models, showing advantages in predicting critical conditions and hospitalization outcomes. The aim of this study was to utilize nationwide registry data to develop a machine learning-based classification model to predict the clinical course of pediatric ED visits.This cross-sectional observational study used data from the National Emergency Department Information System on emergency visits of children under 15 years of age from January 1, 2016, to December 31, 2017. The primary and secondary outcomes were to identify critically ill children and predict hospitalization from triage data, respectively. We developed and tested a random forest model with the under sampled dataset and validated the model using the entire dataset. We compared the model's performance with that of the conventional triage system.A total of 2,621,710 children were eligible for the analysis and included 12,951 (0.5%) critical outcomes and 303,808 (11.6%) hospitalizations. After validation, the area under the receiver operating characteristic curve was 0.991 (95% confidence interval [CI] 0.991-0.992) for critical outcomes and 0.943 (95% CI 0.943-0.944) for hospitalization, which were higher than those of the conventional triage system.The machine learning-based model using structured triage data from a nationwide database can effectively predict critical illness and hospitalizations among children visiting the ED.
Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.
Evaluation of Classification for Project Features with Machine Learning Algorithms

Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects.
Beyond TPC-DS, a benchmark for Big Data OLAP systems (BDOLAP-Bench)

Online Analytical Processing (OLAP) systems with Big Data support allow storing tables of up to tens of billions of rows or terabytes of data.At the same time, these tools allow the execution of analytical queries with interactive response times, thus making them suitable for the implementation of Business Intelligence applications.However, since there can be significant differences in query and data loading performance between current Big Data OLAP tools, it is worthwhile to evaluate and compare them using a benchmark.But we identified that none of the existing approaches are really suitable for this type of system.To address this, in this research we propose a new benchmark specifically designed for Big Data OLAP systems and based on the widely adopted TPC-DS benchmark.To overcome TPC-DS inadequacy, we propose (i) a set of transformations to support the implementation of its sales data mart on any current Big Data OLAP system, (ii) a choice of 16 genuine OLAP queries, and (iii) an improved data maintenance performance metric.Moreover, we validated our benchmark through its implementation on four representative systems.
Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study

Coronary heart disease (CHD) has become the leading cause of death and one of the most serious epidemic diseases worldwide. CHD is characterized by urgency, danger and severity, and dynamic treatment strategies for CHD patients are needed. We aimed to build and validate an AI model for dynamic treatment recommendations for CHD patients with the goal of improving patient outcomes and learning best practices from clinicians to help clinical decision support for treating CHD patients.We formed the treatment strategy as a sequential decision problem, and applied an AI supervised reinforcement learning-long short-term memory (SRL-LSTM) framework that combined supervised learning (SL) and reinforcement learning (RL) with an LSTM network to track patients' states to learn a recommendation model that took a patient's diagnosis and evolving health status as input and provided a treatment recommendation in the form of whether to take specific drugs. The experiments were conducted by leveraging a real-world intensive care unit (ICU) database with 13,762 admitted patients diagnosed with CHD. We compared the performance of the applied SRL-LSTM model and several state-of-the-art SL and RL models in reducing the estimated in-hospital mortality and the Jaccard similarity with clinicians' decisions. We used a random forest algorithm to calculate the feature importance of both the clinician policy and the AI policy to illustrate the interpretability of the AI model.Our experimental study demonstrated that the AI model could help reduce the estimated in-hospital mortality through its RL function and learn the best practice from clinicians through its SL function. The similarity between the clinician policy and the AI policy regarding the surviving patients was high, while for the expired patients, it was much lower. The dynamic treatment strategies made by the AI model were clinically interpretable and relied on sensible clinical features extracted according to monitoring indexes and risk factors for CHD patients.We proposed a pipeline for constructing an AI model to learn dynamic treatment strategies for CHD patients that could improve patient outcomes and mimic the best practices of clinicians. And a lot of further studies and efforts are needed to make it practical.
Parallel quantum chemistry on noisy intermediate-scale quantum computers

A parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The evaluation of a density-matrix functional as the essential part of our approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.
What Circulates on Partisan WhatsApp in India? Insights from an Unusual Dataset

In countries ranging from the Philippines to Brazil, political actors have embraced WhatsApp. In India, WhatsApp groups backed by political parties are suspected of conveying misinformation and/or of circulating hateful content pointed towards minority groups, potentially leading to offline violence. They are also seen as one of the reasons for the dominance of the ruling party (the BJP). Yet, despite this narrative, we so far know littleabout the content shared on these partisan groups nor about the way in which (mis-)informationcirculates on them. In this manuscript, we describe the visual content of 533 closed threads maintained by party workers across the state of Uttar Pradesh, collected over aperiod of 9 months. Manual coding of around 36,000 images allows us to estimate the amount of misinformation/hateful content on one hand, and partisan content on the other. Additional matching of this data with other sources and analyses based on computer vision techniques inturn allows us to evaluate the extent to which the content posted on WhatsApp threads may serve the interests of the ruling party. Analyses suggest that partisan threads contain relatively few hateful or misinformed posts; more surprisingly maybe, most content cannot easily be classified as “partisan”. While much content appears to be religion-related, which may serve an indirect partisan role, the largest share of the content is more easily classifiable as phatic or entertainment related.
Histogram Based Data Cryptographic Technique with High Level Security

Histogram shifting plays a major role in reversible data hiding technique. By this shifting method the distortion is reduced and the embedding capacity may be increased. This proposed work uses, shifting and embedding function. The pixel elements of the original image are divided into two disjoint groups. The first group is used to carry the secret data and the second group adds some additional information which ensures the reversibility of data. The parameter such as PSNR, embedding capacity and bit rate are used for comparisons of various images
Current Status of Urdu on Twitter

Language is the medium of communication and interaction in words or sign format. Simple and easy access to mobile held devices and popularity of social media has revolutionized the way people used to communicate with each other. The information generated in the past two years is many times greater than the information generated since data storage technology inceptions. The Urdu language has emerged since the 6th century from other languages like Arabic, Hindi, Persian, Prakrit, and Sanskrit and it is the national language of Pakistan. Even after being used widely in all public, official and media use of this language is limited to speaking and reading. This study will be conducted during Pakistan general elections 2018, which is an advantage of analyzing Urdu as the whole nation is engaged in social media promoting their political parties and the amount of information generated during this time will be large. However, the challenge of identification of tweet corpse difficult remains due to the intensive use of Roman Urdu.