Reviewerly/exHarmony huggingface dataset:
Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network

Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, the traditional GA only focuses on restraining the infinite growth of the optimal species without reducing the similarity among the remaining excellent individuals when using the exclusion operator. Additionally, when performing arithmetic crossover or Cauchy mutation, a functional operator that conforms to the law of evolution is not constructed to generate proportional coefficients, which seriously restricted the exploitation of the hidden potential in genetic algorithms. To solve the above problems, this paper adopts three new methods to explore the performance enhancement of genetic algorithms (EGA). First, the mean Hamming distance (H-Mean) metric is designed to measure the spatial dispersion of individuals to alleviate selection pressure. Second, arithmetic crossover with transformation of the sigmoid-based function is developed to dynamically adjust the exchange proportion of offspring. Third, an adaptive scale coefficient is introduced into the Gauss-Cauchy mutation, which can regulate the mutation step size in real time and search accuracy for individuals in the population. Finally, the EGA solver is employed to deeply mine the optimal initial parameters of wavelet neural network (EGAWNN). Moreover, the paper provides the algorithm performance test, convergence analysis and significance test. The experimental results reveal that the EGAWNN model outperforms other relevant wavelet-based forecasting models, where the RMSE in test sets based on EGAWNN is 305.72 smaller than other seven algorithms.
Measurement-based estimator scheme for continuous quantum error correction

Canonical discrete quantum error correction (DQEC) schemes use projective von Neumann measurements on stabilizers to discretize the error syndromes into a finite set, and fast unitary gates are applied to recover the corrupted information. Quantum error correction (QEC) based on continuous measurement, known as continuous quantum error correction (CQEC), in principle, can be executed faster than DQEC and can also be resource efficient. However, CQEC requires meticulous filtering of noisy continuous measurement data to reliably extract error syndromes on the basis of which errors could be detected. In this paper, we show that by constructing a measurement-based estimator (MBE) of the logical qubit to be protected, which is driven by the noisy continuous measurement currents of the stabilizers, it is possible to accurately track the errors occurring on the physical qubits in real time. We use this MBE to develop a continuous quantum error correction (MBE-CQEC) scheme that can protect the logical qubit to a high degree, surpassing the performance of DQEC, and also allows QEC to be conducted either immediately or in delayed time with instantaneous feedbacks.
Machine-Learning-Inspired Workflow for Camera Calibration

The performance of modern digital cameras approaches physical limits and enables high-precision measurements in optical metrology and in computer vision. All camera-assisted geometrical measurements are fundamentally limited by the quality of camera calibration. Unfortunately, this procedure is often effectively considered a nuisance: calibration data are collected in a non-systematic way and lack quality specifications; imaging models are selected in an ad hoc fashion without proper justification; and calibration results are evaluated, interpreted, and reported inconsistently. We outline an (arguably more) systematic and metrologically sound approach to calibrating cameras and characterizing the calibration outcomes that is inspired by typical machine learning workflows and practical requirements of camera-based measurements. Combining standard calibration tools and the technique of active targets with phase-shifted cosine patterns, we demonstrate that the imaging geometry of a typical industrial camera can be characterized with sub-mm uncertainty up to distances of a few meters even with simple parametric models, while the quality of data and resulting parameters can be known and controlled at all stages.
Investigation of the Ni-rich regolith in Bavanat region, Fars province, Iran: Constraints from mineralogy, geochemistry and Ni isotopes

A nickel-rich regolith outcrops discontinuously in varying thickness between the Sedimentary Zagros Orogenic (SZO) and Metamorphic Sanandaj-Sirjan (MSS) belts in Fars province, southwest Iran. It is the only known Ni-bearing regolith in the country. The laterites occur as residual and transported regolith in the Bavanat region. Profiles of the residual regolith from bottom to top include protolith, hard and soft saprolite, and oxide horizon. The parent rock is a highly serpentinized harzburgite containing relicts of olivine and orthopyroxene. From the hard saprolite to the oxide horizon, the proportions of hematite, goethite, talc, dolomite, and quartz increase, while chlorite, olivine, serpentine, and pyroxene decrease. Hematite and goethite are the dominant mineral phases in the oxide horizon. Part of the profile of transported regolith, especially oxide horizon has moved downslope due to landslide, slip, creep, and likely faulting. The Ni concentration varies gradually along the laterite profiles, with a maximum grade of 1.2 wt% in the oxide horizon. SEM-EDS data show that Ni released from serpentine group minerals in the parent rock was concentrated in the hematite of the oxide horizon. The isotopic fractionation of Ni along the soil profiles is controlled by the circulation of meteoric water and the intensity of weathering with formation of secondary minerals. • Nickel laterites derived from the serpentinized harzburgite in the Bavanat region. • Residual soils show a complete succession of different horizons of the laterites. • Nickel was concentrated in iron oxides and secondary silicates of the oxide horizon. • Topography controls the Ni isotopic signatures in the laterite profiles.
The Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP methods of tourism. The fuzzy-AHP method will rank tourism trends based on data from social media. Social media is one of the channels with the largest source of data input in determining tourism development. The development uses social media interactions based on the facilities visited, including reviews, stories, likes, forums, blogs, and feedback. This experimental analysis aims to prioritize facilities that are the trend of tourism. The priority ranking uses the fuzzy-AHP method for the process of determining weight criteria and the ranking process. The highest-ranking is on the Parks/Picnic Spots attraction and make it a priority to develop.
Case Study on Application of Artificial Intelligence to Oral English Teaching in Vocational Colleges

The intelligent education represented by artificial intelligence technology provides a breakthrough and a new revolution in English teaching as it carries out accurate teaching, constructs intelligent learning network space and solves the dilemma of "spending more time but achieving little effect". With the increasing demand for students in higher vocational colleges to speak English, the FIF oral English platform is tested to carry out an experimental study. Based on a comparative analysis of experimental and controlled classes, the effectiveness of AI is studied in promoting English listening and speaking teaching to boost the deep integration of AI and English teaching in higher vocational Colleges.
Quantum private comparison with six-particle maximally entangled states

A novel quantum private comparison (QPC) protocol with six-particle maximally entangled states is put forward, which can guarantee that the true content of secret messages from two users cannot be extracted by an illegal party. The entanglement correlation among different particles in one six-particle maximally entangled state is taken to realize the private comparison of secret messages from two users. Neither quantum entanglement swapping nor unitary operations are adopted. Third party (TP) only needs Bell basis measurements while two users only need Bell basis measurements and [Formula: see text] basis measurements. The proposed protocol has no security loophole for the outside attack and the participant attack.
National-Scale Cobalt Geochemical Mapping of Exposed Crust in China

Geochemical background is the basis of anomaly evaluation in mineral exploration and environmental investigation. However, the background value obtained from secondary media is inevitably affected by weathering, transportation, and sorting, which leads to secondary depletion or enrichment of chemical elements. This problem can be avoided by the geochemical mapping of the exposed crust. In this paper, more than 38,000 samples of rocks data were collected, and an original method was presented for the first time to produce the cobalt geochemical map of exposed crust across the whole of China. Using a geological map grid of 1:200,000 as the basic calculation unit, the area and content of rock types in each unit were calculated, and then the grid was assigned using the area-weighted average method. Based on this, an geochemical contour map was drawn in ArcGIS. The results show that the median of the exposed crust is 9.74 mg/kg, which is lower than that of the whole crust and soils. This value is explained by the vertical heterogeneity of the crust and the secondary enrichment, respectively. Co anomalies and anomalous centers of exposed crust are distributed in southwest, north, and northwest China, where they are primarily related to Permian Emeishan Large Igneous Province (ELIP), Pacific plate, and the big mantle wedge (BMW), Archean metamorphic basic rocks, and ophiolite belts.
Multi-Scale Graphical Representation of Cell Environment

We present a multi-scale graphical network that can capture the relevant representations of individual cell morphology, topological structure of cell communities in a tissue image, as well as whole slide level attributes. This helps to effectively merge the disease relevant cell morphology to the overall topological context within the sample, within one unified deep framework. From the explainability point of view, instead of empirical design, the graphs are designed with biomedical considerations in mind in order to have translational validity. We also provide a clinically interpretable visualisation of the cells and their micro- and macro-environment by leveraging label noise reduction. We demonstrate the efficacy of our methodology on myeloproliferative neoplasms (MPN), a haematopoietic stem cell disorder as an exemplar test case. The proposed method achieves an encouraging performance in the robust separation of different MPN subtypes in this exciting new dataset as part of this work.
Integrating Medical Code Descriptions and Building Text Classification Models for Diagnostic Decision Support

Accurate diagnosis plays an important role in the clinical decision-making process. To reduce diagnostic errors, the Diagnostic Decision Support System (DDSS) is designed to assist clinicians with the diagnostic decision-making. Recently, DDSS is developed by diagnosis classification models which employ deep learning networks training on real-world patient-visit data which are often in the form of medical codes. We realize the importance of medical code descriptions for diagnosis classification and believe that incorporating medical code descriptions can improve diagnostic accuracy. Incorporating medical code descriptions by mapping medical codes to textual descriptions, we build diagnosis classification models using 3 text classification methods (TextCNN, TextRCNN and CharCNN) on Ambulatory Health Care (AHC) data with 629,827 patient-visits in the United States, to provide diagnostic decision support under the certain situation when patients' data are recorded in the form of medical codes. The experiments demonstrate the improved accuracy performance and time efficiency of our models compared the baseline models (XGBoost and MLP) taking only medical codes as the input.
EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation

Multi-turn dialogue generation is an essential and challenging subtask of text generation in the question answering system. Existing methods focused on extracting latent topic-level relevance or utilizing relevant external background knowledge. However, they are prone to ignore the fact that relying too much on latent aspects will lose subjective key information. Furthermore, there is not so much relevant external knowledge that can be used for referencing or a graph that has complete entity links. Dependency tree is a special structure that can be extracted from sentences, it covers the explicit key information of sentences. Therefore, in this paper, we proposed the EAGS model, which combines the subjective pivotal information from the explicit dependency tree with sentence implicit semantic information. The EAGS model is a knowledge graph enabled multi-turn dialogue generation model, and it doesn't need extra external knowledge, it can not only extract and build a dependency knowledge graph from existing sentences, but also prompt the node representation, which is shared with Bi-GRU each time step word embedding in node semantic level. We store the specific domain subgraphs built by the EAGS, which can be retrieved as external knowledge graph in the future multi-turn dialogue generation task. We design a multi-task training approach to enhance semantics and structure local feature extraction, and balance with the global features. Finally, we conduct experiments on Ubuntu large-scale English multi-turn dialogue community dataset and English Daily dialogue dataset. Experiment results show that our EAGS model performs well on both automatic evaluation and human evaluation compared with the existing baseline models.
Flying Washer: Development of High-Pressure Washing Aerial Robot Employing Multirotor Platform with Add-On Thrusters

In this study, we propose a multirotor aerial robot for high-pressure washing tasks at high altitudes. The aerial robot consists of a multirotor platform, an add-on planar translational driving system (ATD), a visual sensing system, and a high-pressure washing system. The ATD consists of three ducted fans, which can generate force in all directions on the horizontal plane. The ATD also allows the multirotor to suppress the reaction force generated by the nozzle of a high-pressure washing system and inject water accurately. In this study, we propose a method to precisely inject water by installing an ATD in the multirotor and using its driving force to suppress the reaction force and move the multirotor while keeping its posture horizontal. The semi-autonomous system was designed to allow the operator to maneuver the multirotor while maintaining a constant distance from the wall by the sensor feedback with onboard LiDAR or stereo camera. In the experiment, we succeeded in performing the high-pressure washing task in a real environment and verified that the reaction force generated from the nozzle was actually suppressed during the task.
Compound Batch Normalization for Long-tailed Image Classification

Significant progress has been made in learning image classification neural networks under long-tail data distribution using robust training algorithms such as data re-sampling, re-weighting, and margin adjustment. Those methods, however, ignore the impact of data imbalance on feature normalization. The dominance of majority classes (head classes) in estimating statistics and affine parameters causes internal covariate shifts within less-frequent categories to be overlooked. To alleviate this challenge, we propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. In addition, a moving average-based expectation maximization (EM) algorithm is employed to estimate the statistical parameters of multiple Gaussian distributions. However, the EM algorithm is sensitive to initialization and can easily become stuck in local minima where the multiple Gaussian components continue to focus on majority classes. To tackle this issue, we developed a dual-path learning framework that employs class-aware split feature normalization to diversify the estimated Gaussian distributions, allowing the Gaussian components to fit with training samples of less-frequent classes more comprehensively. Extensive experiments on commonly used datasets demonstrated that the proposed method outperforms existing methods on long-tailed image classification.
Nighttime Image Dehazing Based on Point Light Sources

Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications.
Hybrid Machine Learning–Statistical Method for Anomaly Detection in Flight Data

This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving the safety and operation of their fleet. Traditionally, this is performed by checking exceedances in pre-set limits to the flight data parameters. However, this method highlights single events during a flight, making this analysis laborious. The process also fails to establish trends or reflect potential unknown hazards. This research took advantage of machine learning techniques to recognize patterns in large datasets by implementing the local outlier factor (LOF). In order to minimize human input, a statistical approach was adopted to establish the threshold value above which the flights are considered to be anomalous and interpret the scores. This paper shows that LOF quantifies the degree of outlier-ness of an outlier rather than binary categorizing a point into inlier or outlier, as in the case of clustering algorithms. Thus, with LOF, for the first time, we demonstrated that in the aviation industry, anomalous flights could not only be identified but also be given an anomaly score to compare two anomalous flights in an unsupervised manner. Furthermore, LOF helps to track anomalous behavior in time during the flight. This is insightful when a flight is abnormal, only for some seconds or short duration. For the first time, we attempted to detect flight parameters responsible for anomalous behavior or at least give direction to human experts looking for the cause of abnormal behavior. This was all analyzed with real-life flight data in an unsupervised manner in contrast to simulated data.
An Untrained Neural Network Prior for Light Field Compression

Deep generative models have proven to be effective priors for solving a variety of image processing problems. However, the learning of realistic image priors, based on a large number of parameters, requires a large amount of training data. It has been shown recently, with the so-called deep image prior (DIP), that randomly initialized neural networks can act as good image priors without learning. In this paper, we propose a deep generative model for light fields, which is compact and which does not require any training data other than the light field itself. To show the potential of the proposed generative model, we develop a complete light field compression scheme with quantization-aware learning and entropy coding of the quantized weights. Experimental results show that the proposed method yields very competitive results compared with state-of-the-art light field compression methods, both in terms of PSNR and MS-SSIM metrics.
Analysis of shared research data in Spanish scientific papers about COVID‐19: A first approach

Abstract During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta‐research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID‐19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.