Reviewerly/exHarmony huggingface dataset:
Solar Temperature Variations Computed from SORCE SIM Irradiances Observed During 2003 – 2020

Abstract NASA’s Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) instrument produced about 17 years of daily average Spectral Solar Irradiance ( SSI ) data for wavelengths 240 – 2416 nm. We choose a day of minimal solar activity, August 24, 2008 (2008-08-24), during the 2008 – 2009 minimum between Cycles 23 and 24, and compute the brightness temperature ( $T_{o}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>o</mml:mi> </mml:msub> </mml:math> ) from that day’s solar spectral irradiance ( $\mathit{SSI}_{o}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>SSI</mml:mi> <mml:mi>o</mml:mi> </mml:msub> </mml:math> ). We consider small variations of $T$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> and SSI about these reference values, and derive linear and quadratic analytic approximations by Taylor expansion about the reference-day values. To determine the approximation accuracy, we compare to the exact brightness temperatures $T$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> computed from the Planck spectrum, by solving analytically for $T$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> , or equivalent root finding in Wolfram Mathematica . We find that the linear analytic approximation overestimates, while the quadratic underestimates, the exact result. This motivates the search for statistical “fit” models “in between” the two analytic models, with minimum root-mean-square-error, RMSE. We make this search using open-source statistical $R$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>R</mml:mi> </mml:math> software, determine coefficients for linear and quadratic fit models, and compare statistical with analytic RMSEs. When only linear analytic and fit models are compared, the fit model is superior at ultraviolet, visible, and near-infrared wavelengths. This again holds true when comparing only quadratic models. Quadratic is superior to linear for both analytic and statistical models, and statistical fits give the smallest RMSEs. Lastly, we use linear analytic and fit models to find an interpolating function in wavelength, useful when the SIM results need adjustment to another choice of wavelengths, to compare or extend to any other instrument. Advantages of the quadratic $T$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> over the exact $T$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> include ease of interpretation, and computational speed.
Generators and Relations for Real Stabilizer Operators

Real stabilizer operators, which are also known as real Clifford operators, are generated, through composition and tensor product, by the Hadamard gate, the Pauli Z gate, and the controlled-Z gate. We introduce a normal form for real stabilizer circuits and show that every real stabilizer operator admits a unique normal form. Moreover, we give a finite set of relations that suffice to rewrite any real stabilizer circuit to its normal form.
Attribute Subset Selection for Image Recognition. Random Forest Under Assessment

This contribution presents an approach to take advantage of correlation and consistency measures in attribute subset selection for image recognition. The proposal has been evaluated on ten multiple class problems with up to sixty attributes, ten labels and around six thousands of samples. We focus on Random Forest due to its good performance. Two performance measures such as Cohen’s kappa and Area Under receiver operating characteristic Curve (AUC) are reported. They have been averaged on thirty runs with different seeds. Additionally, the computational cost is presented. The results are very satisfactory and show the approach to be more accurate than its competitors. As an important conclusion we may state that it is worth applying directly the new proposal without devoting time to score the two supporting methods which are the basement of the approach; the overhead, over the slowest initial case and, according to the empirical results, probably the best in predictive terms, is around a ten per cent measured in seconds or in number of additional attributes.
Multi-Templates Based Robust Tracking for Robot Person-Following Tasks

While the robotics techniques have not developed to full automation, robot following is common and crucial in robotic applications to reduce the need for dedicated teleoperation. To achieve this task, the target must first be robustly and consistently perceived. In this paper, a robust visual tracking approach is proposed. The approach adopts a scene analysis module (SAM) to identify the real target and similar distractors, leveraging statistical characteristics of cross-correlation responses. Positive templates are collected based on the tracking confidence constructed by the SAM, and negative templates are gathered by the recognized distractors. Based on the collected templates, response fusion is performed. As a result, the responses of the target are enhanced and the false responses are suppressed, leading to robust tracking results. The proposed approach is validated on an outdoor robot-person following dataset and a collection of public person tracking datasets. The results show that our approach achieved state-of-the-art tracking performance in terms of both the robustness and AUC score.
MCDALNet: Multi-scale Contextual Dual Attention Learning Network for Medical Image Segmentation

Medical image segmentation has been widely studied, and many methods have been proposed. Among the existing methods, U-Net and its variants have achieved a promising performance. However, these methods miss certain areas because they only generate fixed-scale receptive fields in each layer of the encoder and cannot establish rich contextual dependencies on the fusion features in the decoder. To solve these problems, this paper proposes a multi-scale contextual dual attention learning network (named MCDALNet) to capture multi-scale information and the dependencies of spatial and channel features. MCDALNet contains two components: an encoder with three multi-scale contextual learning (MCL) modules and a decoder with three dual attention modules. The MCL module extracts multi-scale context information from low-level features through the split-transform-merge-residual architecture. The dual attention module consists of a position attention sub-module and a channel attention submodule, which improve the feature representation and help the medical image segmentation. The position attention submodule captures spatial dependencies by learning similar spatial features, and the channel attention sub-module captures channel dependencies by learning relevant features on the channel maps. Experiment results show that our approach achieves significant improvement in medical image segmentation and outperforms the representative deep learning models on public datasets.
Image Scene Analysis Based on Improved FCN Model

Image scene analysis is to analyze image scene content through image semantic segmentation, which can identify the categories and positions of different objects in an image. However, due to the loss of spatial detail information, the accuracy of image scene analysis is often affected, resulting in rough edges of FCN, inconsistent class labels of target regions and missing small targets. To address these problems, this paper increases the receptive field, conducts multi-scale fusion and changes the weight of different sensitive channels, so as to improve the feature discrimination and maintain or restore spatial detail information. Furthermore, the deep neural network FCN is used to build the base model of semantic segmentation. The ASPP, data augmentation, SENet, decoder and global pooling are added to the baseline to optimize the model structure and improve the effect of semantic segmentation. Finally, the more accurate results of scene analysis are obtained.
Detection and Identification of Mobile Network Signals

In this paper, we present a method to detect and identify mobile network signals. The method relies on features extracted from the power spectral density of the signals. The performance of the proposed method is evaluated using over-the-air measurements acquired in various cellular bands, in different geographical areas, and in rural and urban settings. Results verify the high detection and identification accuracy of the method.
Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration

In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.
Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network

Liver volumetry is an important tool in clinical practice. The calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git .
4 A Comparative Analysis of Machine Learning Techniques for Odia Character Recognition

Character recognition is a challenging area in Machine Learning, Pattern Recognition or Image Processing. The accuracy to recognize handwritten character by human is far better compared to machine recognition. To develop an interface which can differentiate characters written by human yet requires intensive research. Though number of researches have presented in this area, still research is going on to achieve human like accuracy. Both handwritten and printed character recognition are categorized into two types, online and offline. A good number of researches have done work in the area of optical character recognition in different languages but for the Odia language, development is negligible. Odia (formerly it was Oriya), one of the 22 scheduled language recognized by the constitution of India and it is the official language of the state of Odisha (Orissa), more than 40 million people speak Odia. Due to the roundish shape of Odia character, large number of modified and compound characters and similarity between different characters makes this language very hard to create a satisfactory classifier. In the present survey undertaken we have discussed what are challenges be for Odia language and the machine learning techniques used in the recognition of Odia character recognition. This chapter describes complete process of character recognition i. e. pre-processing, extraction and selection of feature set and character recognition elaborately with comparison analysis and the metrics used to evaluate machine learning algorithms.
A Preliminary Study of the Impact of Blockchain Technology on the Application Level of Insurance Industry

“Blockchain”, a kind of technology that may affect insurance industry in future, is considered to be a breakthrough technology that will bring a huge influence on different fields. Therefore, it gets more and more attention from the academic circle and industries. The purpose of this study is to explore the influence of blockchain on insurance industry and view the development possibility of domestic insurance companies in future through blockchain. This study will define blockchain by citing literature and analyze the current status of blockchain and its opportunities to develop in future in insurance industry through the in-depth interview with the experts in insurance industry who have knowledge of blockchain. This study finds from the interview that the application of blockchain in insurance industry will change the existing system greatly, while it is just not mature currently.
An Improvement of Genetic Algorithm with Rao Algorithm for Optimization Problems

This paper proposes an improvement of genetic algorithm for optimization problems. In this study, the Rao algorithm was applied in crossover and mutation operators instead of traditional crossover and mutation. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), JDE self-adaptive algorithm, and intersection mutation differential evolution (IMDE) algorithm. The computation results illustrated that the proposed algorithm can produce optimal solutions for three of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function and the discontinuous step function.
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement

Low-light image enhancement is a challenging low-level computer vision task because after enhancing the brightness of the image, amplified noise, color distortion, detail loss, blurred edges, shadow blocks and halo artifacts occur. In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation. In the first stage, the brightness of the low-light image is enhanced in HSV space using a U-Net based enhancement network, which uses the information of H and S channels to help recover the details in V channel. In the second stage, Channel Attention (CA) mechanism is integrated into the skip connection of a U-Net in order to restore the brightness-enhanced image from several kinds of degradation in RGB space, especially the halo and shadow blocks. Extensive experiments performance demonstrate that our method achieves excellent effect on brightness enhancement as well as denoising, detail preservation and halo artifact elimination.
Natural language processing and machine learning to assist radiation oncology incident learning

Abstract Purpose To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi‐automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR‐RT taxonomy. Methods Over 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert‐generated labels were used to train and evaluate over 500 multi‐output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best‐performing model after tuning was identified for each data element and tested on unseen data. Results The MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process‐step, problem‐type and contributing factors respectively. Conclusions We developed NLP‐ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop‐down menu. This semi‐automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.
Binary signed-digit integers and the Stern diatomic sequence

Stern's diatomic sequence is a well-studied and simply defined sequence with many fascinating characteristics. The binary signed-digit representation of integers is an alternative representation of integers with much use in efficient computation, coding theory and cryptography. We link these two ideas here, showing that the number of i-bit binary signed-digit representations of an integer n with $$n<2^i$$ is the $$(2^i-n)^{\text {th}}$$ element in Stern's diatomic sequence. This correspondence makes the vast range of results known for Stern's diatomic sequence available for consideration in the study of binary signed-digit integers.
Six-Channel Image Representation for Cross-Domain Object Detection

Most deep learning models are data-driven and the excellent performance is highly dependent on the abundant and diverse datasets. However, it is very hard to obtain and label the datasets of some specific scenes or applications. If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models. To address this issue, some image-to-image translation techniques have been employed to generate some fake data of some specific scenes to train the models. With the advent of Generative Adversarial Networks (GANs), we could realize unsupervised image-to-image translation in both directions from a source to a target domain and from the target to the source domain. In this study, we report a new approach to making use of the generated images. We propose to concatenate the original 3-channel images and their corresponding GAN-generated fake images to form 6-channel representations of the dataset, hoping to address the domain shift problem while exploiting the success of available detection models. The idea of augmented data representation may inspire further study on object detection and other applications.
Benchmark of public intent recognition services

The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several intent recognition services are currently available as an API, or we choose from many open-source alternatives. However, there is no comparison of intent recognition services and open-source algorithms. Many factors make the selection of the right approach to the intent recognition challenging in practice. In this paper, we suggest criteria to choose the best intent recognition algorithm for an application. We present a dataset for evaluation. Finally, we compare selected public NLU services with selected open-source algorithms for intent recognition.
Feedback Vertex Set on Hamiltonian Graphs

We study the computational complexity of Feedback Vertex Set on subclasses of Hamiltonian graphs. In particular, we consider Hamiltonian graphs that are regular or are planar and regular. Moreover, we study the less known class of p-Hamiltonian-ordered graphs, which are graphs that admit for any p-tuple of vertices a Hamiltonian cycle visiting them in the order given by the tuple. We prove that Feedback Vertex Set remains \({\text {NP}}\)-hard in these restricted cases, even if a Hamiltonian cycle is additionally given as part of the input.
RESEARCH ON STOCHASTIC FUZZY DIFFERENTIAL EQUATIONS IN MULTIPLE BLURRED IMAGE REPAIR MODELS

This paper aims to study the processing and repairing methods of blurred images, promote the development of partial differential equations in the field of image processing, and expand the application of stochastic fuzzy differential equations in the field of image processing and repair. This study starts with a typical blurred image repair method. First, a comparative analysis of several common blurred image repair methods including Wiener filtering restoration, inverse filtering restoration, and Lucy–Richardson (L-R) filtering restoration is performed. Second, based on the linear partial differential equation learning model (LPDE), the concept of fuzzy integral is introduced, and an improved stochastic fuzzy partial differential equation learning model (SFCPDE) is proposed. The effect of the learning model before and after improvement on blurred color image processing is compared and analyzed. Finally, based on the total variation (TV) blurred image repair algorithm, an improved TV blurred image repair algorithm is proposed. The comparison and analysis of the repair effects of several blurred image repair algorithms are performed. The results show that there are obvious differences in the repairing methods with or without noise. Inverse filtering works best when there is no noise. L-R filtering has the disadvantage of amplifying noise. Compared with LDPE, the training speed of SFCPDE is significantly improved, and the training error is less than LDPE. The SFCPDE learning model performs better in the processing of blurred color images. After 10 iterations, the improved TV algorithm is significantly better than the TV algorithm and the CDD algorithm in repairing blurred images. The PSNR value of the TV algorithm and the curvature-driven diffusion (CDD) algorithm after 10 iterations corresponds to about 60% of the PSNR value of the improved algorithm. The algorithms and models of stochastic fuzzy partial differential equations proposed in this paper have great application potential in the processing and repair of multiple blurred images.