Reviewerly/exHarmony huggingface dataset:
Enhancing virtual ontology based access over tabular data with Morph-CSV

Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-the-fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with the GTFS-Madrid benchmark; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of Morph-CSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.
Institutional, Economic, and Socio-Economic Determinants of the Entrepreneurial Activity of Nations

This empirical study analyses the effects of institutional, economic, and socio-economic determinants on total entrepreneurial activity in the contexts of developed and developing countries. It fills a gap in the literature, regarding the lack of empirical studies about the relationships among entrepreneurial activity, corruption, commercial freedom, economic growth, innovativeness, inward foreign direct investment, unemployment, households, and non-profit institutions serving households (NPISHs)’ final consumption expenditure, age dependency ratio, education index, and life expectancy at birth. The empirical application uses annual panel data for the 2003–2018 period, with a total sample of 21 countries, analysed in a two-stage empirical application, including preliminary analysis and a quantile regression model. New empirical evidence is provided, revealing a significantly positive role played by commercial freedom, innovativeness, inward foreign direct investment, households, and NPISHs’ final consumption expenditure and education on entrepreneurial activity. Corruption, unemployment, age dependency ratio, and life expectancy at birth have a significantly negative influence on entrepreneurial activity. In terms of implications, greater government control is recommended, in order to foster the quality of nations’ institutional environment. Additionally, suggested is the launch of new incentives to stimulate research and development activities aimed at registering international patents with a global impact, sourced from new ventures and transnational collaboration.
Causal discovery in machine learning: Theories and applications

<p style='text-indent:20px;'>Determining the cause of a particular event has been a case of study for several researchers over the years. Finding out why an event happens (its cause) means that, for example, if we remove the cause from the equation, we can stop the effect from happening or if we replicate it, we can create the subsequent effect. Causality can be seen as a mean of predicting the future, based on information about past events, and with that, prevent or alter future outcomes. This temporal notion of past and future is often one of the critical points in discovering the causes of a given event. The purpose of this survey is to present a cross-sectional view of causal discovery domain, with an emphasis in the machine learning/data mining area.
SIR-GN: A Fast Structural Iterative Representation Learning Approach For Graph Nodes

Graph representation learning methods have attracted an increasing amount of attention in recent years. These methods focus on learning a numerical representation of the nodes in a graph. Learning these representations is a powerful instrument for tasks such as graph mining, visualization, and hashing. They are of particular interest because they facilitate the direct use of standard machine learning models on graphs. Graph representation learning methods can be divided into two main categories: methods preserving the connectivity information of the nodes and methods preserving nodes’ structural information. Connectivity-based methods focus on encoding relationships between nodes, with connected nodes being closer together in the resulting latent space. While methods preserving structure generate a latent space where nodes serving a similar structural function in the network are encoded close to each other, independently of them being connected or even close to each other in the graph. While there are a lot of works that focus on preserving node connectivity, only a few works focus on preserving nodes’ structure. Properly encoding nodes’ structural information is fundamental for many real-world applications as it has been demonstrated that this information can be leveraged to successfully solve many tasks where connectivity-based methods usually fail. A typical example is the task of node classification, i.e., the assignment or prediction of a particular label for a node. Current limitations of structural representation methods are their scalability, representation meaning, and no formal proof that guaranteed the preservation of structural properties. We propose a new graph representation learning method, called Structural Iterative Representation learning approach for Graph Nodes ( SIR-GN ). In this work, we propose two variations ( SIR-GN: GMM and SIR-GN: K-Means ) and show how our best variation SIR-GN: K-Means : (1) theoretically guarantees the preservation of graph structural similarities, (2) provides a clear meaning about its representation and a way to interpret it with a specifically designed attribution procedure, and (3) is scalable and fast to compute. In addition, from our experiment, we show that SIR-GN: K-Means is often better or, in the worst-case comparable than the existing structural graph representation learning methods present in the literature. Also, we empirically show its superior scalability and computational performance when compared to other existing approaches.
FARF: A Fair and Adaptive Random Forests Classifier

As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyper-parameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding

Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms.In this paper, we propose an unsupervised semantic parsing method -Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammarconstrained decoding.Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterance 1 and meaning representation.During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.Experimental results show that SSD is a promising approach and can achieve competitive unsupervised semantic parsing performance on multiple datasets.
Assessing how visual search entropy and engagement predict performance in a multiple-objects tracking air traffic control task

Behavioral performance metrics employed to assess the usability of visual displays are increasingly coupled with eye tracking measures to provide additional insights into the decision-making processes supported by visual displays. Eye tracking metrics can be coupled with users' neural data to investigate how human cognition interplays with emotions during visuo-spatial tasks. To contribute to these efforts, we present results of a study in a realistic air traffic control (ATC) setting with animated ATC displays, where ATC experts and novices were presented with an aircraft movement detection task. We find that higher stationary gaze entropy – which indicates a larger spatial distribution of visual gaze on the display – and expertise result in better response accuracy, and that stationary entropy positively predicts response time even after controlling for animation type and expertise. As a secondary contribution, we found that a single component comprised of engagement, measured by EEG and self-reported judgments, spatial abilities, and gaze entropy predicts task accuracy, but not completion time. We also provide MATLAB open source code for calculating the EEG measures utilized in the study. Our findings suggest designing spatial information displays that adapt their content according to users' affective and cognitive states, especially for emotionally laden usage contexts.
Machine learning models to predict diffuse solar radiation based on diffuse fraction and diffusion coefficient models for humid-subtropical climatic zone of India

In the present work, twelve machine learning (ML) models are developed for assessment of monthly average diffuse solar radiation (DSR) with solitary input forecaster as clearness index. Two categories of ML models were demarcated (i.e. diffusion coefficient and diffuse fraction) with six models for each group. The correctness of models was examined as a function of some frequently used statistical pointers. A comparision was also done between developed ML models and some well-recognised models available from previous works. The results show that ML models perform very well in comparision to models available in the literature. The top-performing models in category 1 are the k-nearest neighbours (KNN) model for both training and testing data. In category 2, for training data random forest (RF) model perform well while for testing data support vector regression (SVR) models perform well. The performance can be slightly improved by using two or more input parameters such as temperature difference, relative humidity and relative sunshine along with clearness index as input. Thus, ML models can be used to estimate DSR in the humid-subtropical climate of India.
Multiple classifier for concatenate-designed neural network

This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet, with the purpose to alleviate the pressure on the final classifier. We give the design of the classifiers, which collects the features produced between the network sets, and present the constituent layers and the activation function for the classifiers, to calculate the classification score of each classifier. We use the L2 normalization method to obtain the classifier score instead of the Softmax normalization. We also determine the conditions that can enhance convergence. As a result, the proposed classifiers are able to improve the accuracy in the experimental cases significantly, and show that the method not only has better performance than the original models, but also produces faster convergence. Moreover, our classifiers are general and can be applied to all classification related concatenate-designed network models.
Coupling multifunction drones with AI in the fight against the coronavirus pandemic

Abstract When COVID-19 was declared as a pandemic by the World Health Organization on 11 March 2020, national governments and health authorities across the world begun considering different preventive measures to fight against the coronavirus outbreak. Researchers and tech companies worldwide have been striving to utilize advanced technologies to aid in the fight against the Covid-19 outbreak. This paper aims to couple multifunction drone with AI to deliver wireless services that will help the fight against the Coronavirus pandemic. The proposed drone-eye-system with its thermal imaging cameras and an AI framework utilizes a Convolutional Neural Network (CNN) with its Modified Artificial Neural Network (MANN) for face mask detection of people wearing masks in public. The system can perform basic diagnostic functions such as elevated body temperatures for helping minimize the risk of spreading the infection through close contact. The AI framework evolve an optimized elevation angle $$\uptheta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>θ</mml:mi> </mml:math> and altitude $${\mathrm{h}}_{\mathrm{t}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>h</mml:mi> <mml:mi>t</mml:mi> </mml:msub> </mml:math> to enhance wireless connectivity between a drone and a ground station, which in turn leads to better throughput and power consumption. The proposed framework has been developed using the MATLAB toolbox and shows promising results with an accuracy of face mask detection of 82.63%, with an F1-score of 0.98, and an enhanced by 10% link budget parameters.
A Cooperative Coevolution Wingsuit Flying Search Algorithm with Spherical Evolution

Abstract The algorithm wingsuit flying search (WFS) mimics the procedure of landing the vehicle. The outstanding feature of WFS is parameterless and of rapid convergence. However, WFS also has its shortcomings, sometimes it will inevitably be trapped into local optima, thereby yield inferior solutions owing to its relatively weak exploration ability. Spherical evolution (SE) adopts a novel spherical search pattern that takes aim at splendid search ability. Cooperative coevolution is a useful parallel structure for reconciling algorithmic performance. Considering the complementary strengths of both algorithms, we herein propose a new hybrid algorithm that is comprised of SE and WFS using cooperative coevolution. During the search for optimal solutions in WFS, we replaced the original search matrix and introduced the spherical mechanism of SE, in parallel with coevolution to enhance the competitiveness of the population. The two distinct search dynamics were combined in a parallel and coevolutionary way, thereby getting a good search performance. The resultant hybrid algorithm, CCWFSSE, was tested on the CEC2017 benchmark set and 22 CEC 2011 real-world problems. The experimental data obtained can verify that CCWFSSE outperforms other algorithms in aspects of effectiveness and robustness.
Diversity Forests: Using Split Sampling to Enable Innovative Complex Split Procedures in Random Forests

The diversity forest algorithm is an alternative candidate node split sampling scheme that makes innovative complex split procedures in random forests possible. While conventional univariable, binary splitting suffices for obtaining strong predictive performance, new complex split procedures can help tackling practically important issues. For example, interactions between features can be exploited effectively by bivariable splitting. With diversity forests, each split is selected from a candidate split set that is sampled in the following way: for l=1,⋯,nsplits : (1) sample one split problem; (2) sample a single or few splits from the split problem sampled in (1) and add this or these splits to the candidate split set. The split problems are specifically structured collections of splits that depend on the respective split procedure considered. This sampling scheme makes innovative complex split procedures computationally tangible while avoiding overfitting. Important general properties of the diversity forest algorithm are evaluated empirically using univariable, binary splitting. Based on 220 data sets with binary outcomes, diversity forests are compared with conventional random forests and random forests using extremely randomized trees. It is seen that the split sampling scheme of diversity forests does not impair the predictive performance of random forests and that the performance is quite robust with regard to the specified nsplits value. The recently developed interaction forests are the first diversity forest method that uses a complex split procedure. Interaction forests allow modeling and detecting interactions between features effectively. Further potential complex split procedures are discussed as an outlook.The online version contains supplementary material available at 10.1007/s42979-021-00920-1.
What’s in Score for Website Users: A Data-Driven Long-Term Study on Risk-Based Authentication Characteristics

Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
LiDAR-guided stereo matching with a spatial consistency constraint

The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When the percentage of LiDAR points in the simulated datasets was 0.16%, the matching accuracy of our method achieved a subpixel level, while that of the original stereo matching algorithm was 3.4 pixels. The experimental results show that LGSM is suitable for indoor, street, aerial, and satellite image datasets and provides good transferability across semi-global matching and AD-Census. Furthermore, the qualitative and quantitative evaluations demonstrate that LGSM is superior to two state-of-the-art optimizing cost volume methods, especially in reducing mismatches in difficult matching areas and refining the boundaries of objects.
Integrating Creativity and Reading: A Functional Approach

Reading has been studied for decades by a variety of cognitive disciplines, yet no theories exist which sufficiently describe and explain how people accomplish the complete task of reading real-world texts. In particular, a type of knowledge intensive reading known as creative reading has been largely ignored by the past research. We argue that creative reading is an aspect of practically all reading experiences; as a result, any theory which overlooks this will be insufficient. We have built on results from psychology, artificial intelligence, and education in order to produce a functional theory of the complete reading process. The overall framework describes the set of tasks necessary for reading to be performed. Within this framework, we have developed a theory of creative reading. The theory is implemented in the ISAAC (Integrated Story Analysis And Creativity) system, a reading system which reads science fiction stories.
Feasibility of Ground-Based Sky-Camera HDR Imagery to Determine Solar Irradiance and Sky Radiance over Different Geometries and Sky Conditions

We propose a methodological approach to provide the accurate and calibrated measurements of sky radiance and broadband solar irradiance using the High Dynamic Range (HDR) images of a sky-camera. This approach is based on a detailed instrumental characterization of a SONA sky-camera in terms of image acquisition and processing, as well as geometric and radiometric calibrations. As a result, a 1 min time resolution database of geometrically and radiometrically calibrated HDR images has been created and has been available since February 2020, with daily updates. An extensive validation of our radiometric retrievals has been performed in all sky conditions. Our results show a very good agreement with the independent measurements of the AERONET almucantar for sky radiance and pyranometers for broadband retrievals. The SONA sky radiance shows a difference of an RMBD &lt; 10% while the broadband diffuse radiation shows differences of 2% and 5% over a horizontal plane and arbitrarily oriented surfaces, respectively. These results support the developed methodology and allow us to glimpse the great potential of sky-cameras to carry out accurate measurements of sky radiance and solar radiation components. Thus, the remote sensing techniques described here will undoubtedly be of great help for solar and atmospheric research.