Reviewerly/exHarmony huggingface dataset:
Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7

Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years.
A Method of Camera Calibration Based on Kriging Interpolation

The basis of visual measurement is camera calibration. In the traditional calibration method, the radial and tangential distortion models usually are adopted. Because of the randomness of lens distortion, the above fixed form distortion model cannot accurately express the distortion distribution. To solve the above problems, a calibration method with a new distortion model is presented in the paper. First, an exact linear model is obtained, using only the corner coordinates of the image center region; then, using this model, the projection deviation of all corner points in the pixel plane can be obtained, that is, the point cloud of projection deviation distribution; finally, the Kriging interpolation method is used to obtain a continuous projection deviation distribution function which can accurately express lens distortion in the pixel plane. Using this function and the corresponding linear model, all two-dimensional image points can be accurately projected into three-dimensional space. To compare with the traditional method, the mean error of projection and measurement error are calculated in the experiment, and the experimental results show that the calibration method is more accurate and more suitable for measuring requirements.
Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods

The diagnosis of oral squamous cell carcinoma is based on a histopathological examination, which is still the most reliable way of identifying oral cancer despite its high subjectivity. However, due to the heterogeneous structure and textures of oral cancer, as well as the presence of any inflammatory tissue reaction, histopathological classification can be difficult. For that reason, an automatic classification of histopathology images with the help of artificial intelligence-assisted technologies can not only improve objective diagnostic results for the clinician but also provide extensive texture analysis to get a correct diagnosis. In this paper various deep learning methods are compared in order to get an AI-based model for multiclass grading of OSCC with the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{AUC}_{\mathbf{micro}}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\text{AUC}}_{\text{macro}}$</tex> values.
Spatial‐temporal slowfast graph convolutional network for skeleton‐based action recognition

In skeleton-based action recognition, the graph convolutional network (GCN) has achieved great success. Modelling skeleton data in a suitable spatial-temporal way and designing the adjacency matrix are crucial aspects for GCN-based methods to capture joint relationships. In this study, we propose the spatial-temporal slowfast graph convolutional network (STSF-GCN) and design the adjacency matrices for the skeleton data graphs in STSF-GCN. STSF-GCN contains two pathways: (1) the fast pathway is in a high frame rate, and joints of adjacent frames are unified to build ‘small’ spatial-temporal graphs. A new spatial-temporal adjacency matrix is proposed for these ‘small’ spatial-temporal graphs. Ablation studies verify the effectiveness of the proposed adjacency matrix. (2) The slow pathway is in a low frame rate, and joints from all frames are unified to build one ‘big’ spatial-temporal graph. The adjacency matrix for the ‘big’ spatial-temporal graph is obtained by computing self-attention coefficients of each joint. Finally, outputs from two pathways are fused to predict the action category. STSF-GCN can efficiently capture both long-range and short-range spatial-temporal joint relationships. On three datasets for skeleton-based action recognition, STSF-GCN can achieve state-of-the-art performance with much less computational cost.
Towards Green Automated Machine Learning: Status Quo and Future Directions

Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticised for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool wrt. their "greenness", i.e. sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community into a more sustainable AutoML research direction. Additionally, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML.
Data-Driven Fuzzy Transform

The Fuzzy transform is applied mainly to 1-D signals and 2-D data organized as a regular grid (e.g., 2-D images), thus, limiting its potential application to arbitrary data in terms of dimensionality and structure. This article defines and analyzes the properties of the data-driven F-transform, with a focus on the construction of the class of data-driven membership functions, which are multiscale, local, linearly independent, intrinsic, and robust to data discretization. Data-driven membership functions are defined by applying a 1-D filter to the Laplace–Beltrami operator, which encodes the geometric and topological properties of the input data. Then, we address the efficient computation of the data-driven F-transform through a polynomial or a rational polynomial approximation of the input filter. In this way, the computation of the data-driven F-transform is independent of the evaluation of the membership functions at any point of the input domain and reduces to the solution of a small set of sparse and symmetric linear systems. Finally, the data-driven F-transform is efficiently evaluated on large and arbitrary data, in terms of dimensionality, structure, and size.
FAIR Data Austria - Aligning the Implementation of FAIR Tools and Services

This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for long-term archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria.
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots

Obtaining affective response is a key step in building empathetic dialogue systems.This task has been studied a lot in generation-based chatbots, but the related research in retrievalbased chatbots is still in the early stage.Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality.To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework.The framework replaces the reranking mechanism with a new discriminate-andrewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module.This can not only guarantee the quality of the response, but also satisfy the given affect label.In addition, another challenge for this line of research is the lack of an off-theshelf affective response dataset.To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus.Experimental results show that our proposed framework is effective and outperforms competitive baselines.
Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.
Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients' records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) "confirmed cases", 10 (3.05%) "probable cases", 9 (2.74%) "suspected cases", and 91 (27.74%) "no sufficient evidence". The identification of "probable cases" and "suspected cases" was more challenging than "confirmed cases" where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients' records. "No sufficient evidence" records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.
A Space-Time Fully Decoupled Wavelet Integral Collocation Method with High-Order Accuracy for a Class of Nonlinear Wave Equations

A space-time fully decoupled wavelet integral collocation method (WICM) with high-order accuracy is proposed for the solution of a class of nonlinear wave equations. With this method, wave equations with various nonlinearities are first transformed into a system of ordinary differential equations (ODEs) with respect to the highest-order spatial derivative values at spatial nodes, in which all the matrices in the resulting nonlinear ODEs are constants over time. As a result, these matrices generated in the spatial discretization do not need to be updated in the time integration, such that a fully decoupling between spatial and temporal discretization can be achieved. A linear multi-step method based on the same wavelet approximation used in the spatial discretization is then employed to solve such a semi-discretization system. By numerically solving several widely considered benchmark problems, including the Klein/sine–Gordon equation and the generalized Benjamin–Bona–Mahony–Burgers equation, we demonstrate that the proposed wavelet algorithm possesses much better accuracy and a faster convergence rate than many existing numerical methods. Most interestingly, the space-associated convergence rate of the present WICM is always about order 6 for different equations with various nonlinearities, which is in the same order with direct approximation of a function in terms of the proposed wavelet approximation scheme. This fact implies that the accuracy of the proposed method is almost independent of the equation order and nonlinearity.
The Practicality of Adopting Blockchain-Based Distributed Identity Management in Organisations: A Meta-Synthesis

Blockchain has become an irresistible disruptive technology with the potential to innovate businesses. Ignoring it may in itself result in a competitive disadvantage for organisations. Except for its original financial application of cryptocurrency, more applications are being proposed, the most common being supply chain management and e-voting systems. However, less focus is made on information and cybersecurity applications of blockchain, especially from the enterprise perspective. This paper addresses this knowledge gap by exploring blockchain as a use case for identity management in the context of an organisation. The paper gives a comprehensive background aiming at understanding the topic, including understanding whether claims made around it, especially blockchain’s potential to address identity management challenges, are based on facts or just a result of hype. Meta-synthesis was used as a research methodology to summarise the 69 papers selected qualitatively from reputed academic sources. The general trend shows theoretical evidence supporting some of the claims made but not necessarily friendly to the enterprise context. The study reveals a promising but immature state of blockchain, consequently questioning whether adopting blockchain-based distributed identity management in organisations is fully practical. A research model called TOE-BDIDM is proposed to guide further investigation.
Simulating strongly interacting Hubbard chains with the variational Hamiltonian ansatz on a quantum computer

Hybrid quantum-classical algorithms have been proposed to circumvent noise limitations in quantum computers. Such algorithms delegate only a calculation of the expectation value to the quantum computer. Among them, the variational quantum eigensolver has been implemented to study molecules and condensed matter systems on small size quantum computers. Condensed matter systems described by the Hubbard model exhibit a rich phase diagram alongside exotic states of matter. In this paper we try to answer the question: How much of the underlying physics of a 1D Hubbard chain is described by a problem-inspired variational Hamiltonian ansatz in a broad range of parameter values? We start by probing how much the solution increases fidelity with increasing ansatz complexity. Our findings suggest that even low fidelity solutions capture energy and number of doubly occupied sites well, while spin-spin correlations are not well captured even when the solution is of high fidelity. Our powerful simulation platform allows us to incorporate a realistic noise model and shows a successful implementation of noise-mitigation strategies---postselection and the Richardson extrapolation. Finally, we compare our results with an experimental realization of the algorithm on IBM Quantum's ibmq_quito device.
A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method

Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis. In this study, a new model-based clustering approach is proposed for the estimation of the number of clusters. In the proposed method, the number of components in each variable is determined by using univariate Gaussian mixture models. The number of alternative cluster centres and mixture models was determined according to the number of components in heterogeneous variables. In this study, appropriate Gaussian mixture models were determined with the help of "mixture model soft computing method" for the first time. Vector arrays showing the number and addresses of clusters in appropriate Gaussian mixture models were created, and according to the parameter estimations of these models that fit the arrays, the best model was obtained through information criteria. The clustering success achieved with the proposed mixture model soft computing method was compared with the results of Gaussian mixture model clustering methods namely, mclust, clustvarsel, varselLCM, selvarMix and vscc model selection methods in R package. All respective methods analyse and determine the number of clustering for the data sets, synthetic-1, synthetic-2, Iris, and Landsat Satellite Image data sets, respectively and evaluate the correct classification rate. The results revealed that the proposed method shows better results for the determination of number of clustering as well as correct classification rate. The novelty of the study is that a new model-based dimension reduction method is proposed for the estimation of the number of clusters. A deterministic clustering approach is proposed for clustering and classification success on reduced data.
Systematic Review of the Development of Spatial Intelligence through Augmented Reality in STEM Knowledge Areas

This study presents a systematic review (SR) of the literature on the use of Augmented Reality (AR) for the development of spatial skills of secondary and higher education students in the teaching of subjects related to the area of Science, Technology, Engineering and Mathematics (STEM). Numerous researchers have confirmed that students tend to have difficulties in acquiring learning content with a high visual load, which is why AR is being used to improve teaching–learning processes. This paper aims to present information of interest to researchers, teachers and software developers about the advantages and limitations of AR in the development of spatial intelligence, the integration and development of AR applications, as well as the predominant spatial skills and assessment methods. In this regard, an initial search was carried out which returned 242 articles. After reviewing and assessing their eligibility, a total of 17 papers published since 2002 were selected. The findings reveal an increase in the number of investigations over the last few years. Some of the most notable findings are the improvement of spatial skills of students through the inclusion of AR in educational environments, the need for teacher training, the lack of personalization in the applications developed and the scarcity of augmented materials in the form of Open Educational Resources (OER).
Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

In software engineering, defect prediction is significantly important and challenging. The main task is to predict the defect proneness of the modules. It helps developers find bugs effectively and prioritize their testing efforts. At present, a lot of valuable researches have been done on this topic. However, few studies take into account the impact of time factors on the prediction results. Therefore, in this paper, we propose an improved Elman neural network model to enhance the adaptability of the defect prediction model to the time-varying characteristics. Specifically, we optimized the initial weights and thresholds of the Elman neural network by incorporating adaptive step size in the Cuckoo Search (CS) algorithm. We evaluated the proposed model on 7 projects collected from public PROMISE repositories. The results suggest that the contribution of the improved CS algorithm to Elman neural network model is prominent, and the prediction performance of our method is better than that of 5 baselines in terms of F-measure and Cliff’s Delta values. The F-measure values are generally increased with a maximum growth rate of 49.5% for the POI project.
Adversarial Machine Learning on Social Network: A Survey

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.