scholarly journals A Thorough Exploitation of Distance-Based Meta-Features for Automated Text Classification

2021 ◽  
Author(s):  
Sergio Canuto ◽  
Marcos André Gonçalves ◽  
Thierson Couto Rosa
Keyword(s):  
Feature Space ◽  
Distance Measures ◽  
Computational Time ◽  
Specific Information ◽  
Selection Strategies ◽  
Complex Relationships ◽  
Definition Of ◽  
Context Specific ◽  
High Level ◽  
Automated Text Classification

The definition of a set of informative features capable of representing and discriminating documents is paramount for the task of automatically classifying documents. In this doctoral dissertation, we present the most comprehensive study so far on the role of meta-features (high-level features built from lower-level ones) as an alternative for representing documents. We start by proposing new sets of (meta-)features that exploit distance measures in the original (bag-of-words) feature space to summarize potentially complex relationships between documents. We then (i) analyze the discriminative power of such meta-features with novel multi-objective feature selection strategies; (ii) provide new GPU implementations to reduce computational time; (iii) enrich distance relationships with labeled or context-specific information; (iv) adapt the proposed meta-features for tasks as hard as sentiment analysis. Our experimental results show that our meta-features can achieve remarkable classification results by distance exploitation, being the state-of-the-art in many situations and scenarios.

scholarly journals LINKING INTERACTIVE OPTIMIZATION FOR URBAN PLANNING WITH A SEMANTIC 3D CITY MODEL

2018 ◽  
Vol IV-4 ◽  
pp. 179-186 ◽  
Author(s):  
N. Schüler ◽  
G. Agugiaro ◽  
S. Cajot ◽  
F. Maréchal
Keyword(s):  
Urban Planning ◽  
Data Model ◽  
Active Management ◽  
Specific Information ◽  
Exploration Process ◽  
Interactive Optimization ◽  
Potential Applications ◽  
City Model ◽  
Definition Of ◽  
Context Specific

<p><strong>Abstract.</strong> The cities in which we live are constantly evolving. The active management of this evolution is referred to as urban planning. The according development process could go in many directions resulting in a large number of potential future scenarios of a city. The planning support system URB<sup>io</sup> adopts interactive optimization to assist urban planners in generating and exploring those various scenarios. As a computer-based system it needs to be able to efficiently handle all underlying data of this exploration process, which includes both methodology-specific and context-specific information. This article describes the work carried out to link URB<sup>io</sup> with a semantic city model. Therefore, two key requirements were identified and implemented: (a) the extension of the CityGML data model to cope with many scenarios by the proposition of the Scenario Application Domain Extension (ADE) and (b) the definition of a data model for interactive optimization. Classes and features of the developed data models are motivated, depicted and explained. Their usability is demonstrated by walking through a typical workflow of URB<sup>io</sup> and laying out the induced data flows. The article is concluded with stating further potential applications of both the Scenario ADE and the data model for interactive optimization.</p>

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

scholarly journals Shape Sensing of a Complex Aeronautical Structure with Inverse Finite Element Method

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1388
Author(s):  
Daniele Oboe ◽  
Luca Colombo ◽  
Claudio Sbarufatti ◽  
Marco Giglio
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Boundary Conditions ◽  
Strain Field ◽  
Element Analysis ◽  
Inverse Finite Element Method ◽  
Shape Sensing ◽  
Definition Of ◽  
High Level ◽  
Element Method

The inverse Finite Element Method (iFEM) is receiving more attention for shape sensing due to its independence from the material properties and the external load. However, a proper definition of the model geometry with its boundary conditions is required, together with the acquisition of the structure’s strain field with optimized sensor networks. The iFEM model definition is not trivial in the case of complex structures, in particular, if sensors are not applied on the whole structure allowing just a partial definition of the input strain field. To overcome this issue, this research proposes a simplified iFEM model in which the geometrical complexity is reduced and boundary conditions are tuned with the superimposition of the effects to behave as the real structure. The procedure is assessed for a complex aeronautical structure, where the reference displacement field is first computed in a numerical framework with input strains coming from a direct finite element analysis, confirming the effectiveness of the iFEM based on a simplified geometry. Finally, the model is fed with experimentally acquired strain measurements and the performance of the method is assessed in presence of a high level of uncertainty.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals A fatal toxic shock-like syndrome post COVID-19 infection in a child

2021 ◽  
Vol 47 (1) ◽  
Author(s):  
Houda Ajmi ◽  
Wissem Besghaier ◽  
Wafa Kallala ◽  
Abdelhalim Trabelsi ◽  
Saoussan Abroug
Keyword(s):  
Ejection Fraction ◽  
Pediatric Intensive Care ◽  
Fresh Frozen Plasma ◽  
Multiple Organ ◽  
Clinical Feature ◽  
Fresh Frozen ◽  
Life Threatening ◽  
High Doses ◽  
Definition Of ◽  
High Level

Abstract Background Children affected by Coronavirus disease 2019 (COVID-19) showed various manifestations. Some of them were severe cases presenting with multi-system inflammatory syndrome (MIS-C) causing multiple organ dysfunction. Case presentation We report the case of a 12-year-old girl with recent COVID-19 infection who presented with persistent fever, abdominal pain and other symptoms that meet the definition of MIS-C. She had lymphopenia and a high level of inflammatory markers. She was admitted to pediatric intensive care unit since she rapidly developed refractory catecholamine-resistant shock with multiple organ failure. Echocardiography showed a small pericardial effusion with a normal ejection fraction (Ejection Fraction = 60%) and no valvular or coronary lesions. The child showed no signs of improvement even after receiving intravenous immunoglobulin, fresh frozen plasma, high doses of Vasopressors and corticosteroid. His outcome was fatal. Conclusion Pediatric patients affected by the new COVID-19 related syndrome may show severe life-threatening conditions similar to Kawasaki disease shock syndrome. Hypotension in these patients results from heart failure and the decreased cardiac output. We report a new severe clinical feature of SARS-CoV-2 infection in children in whom hypotension was the result of refractory vasoplegia.

scholarly journals Energy Minimization Scheme for Split Potential Systems Using Exponential Variational Integrators

2021 ◽  
Vol 2 (3) ◽  
pp. 431-441
Author(s):  
Odysseas Kosmas
Keyword(s):  
Numerical Solutions ◽  
Oscillatory Behavior ◽  
Lagrangian Function ◽  
Computational Time ◽  
Variational Integrators ◽  
Weighted Sum ◽  
Lagrange Equations ◽  
Exponential Functions ◽  
Intermediate Time ◽  
Definition Of

In previous works we developed a methodology of deriving variational integrators to provide numerical solutions of systems having oscillatory behavior. These schemes use exponential functions to approximate the intermediate configurations and velocities, which are then placed into the discrete Lagrangian function characterizing the physical system. We afterwards proved that, higher order schemes can be obtained through the corresponding discrete Euler–Lagrange equations and the definition of a weighted sum of “continuous intermediate Lagrangians” each of them evaluated at an intermediate time node. In the present article, we extend these methods so as to include Lagrangians of split potential systems, namely, to address cases when the potential function can be decomposed into several components. Rather than using many intermediate points for the complete Lagrangian, in this work we introduce different numbers of intermediate points, resulting within the context of various reliable quadrature rules, for the various potentials. Finally, we assess the accuracy, convergence and computational time of the proposed technique by testing and comparing them with well known standards.

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

Ubiquitous location tracking for context-specific information delivery on construction sites

2008 ◽  
Vol 17 (6) ◽  
pp. 737-748 ◽  
Author(s):  
Amir H. Behzadan ◽  
Zeeshan Aziz ◽  
Chimay J. Anumba ◽  
Vineet R. Kamat
Keyword(s):  
Information Delivery ◽  
Specific Information ◽  
Location Tracking ◽  
Construction Sites ◽  
Context Specific

The Impact of the Privacy Rule on Cancer Research: Variations in Attitudes and Application of Regulatory Standards

2009 ◽  
Vol 27 (24) ◽  
pp. 4014-4020 ◽  
Author(s):  
Elizabeth Goss ◽  
Michael P. Link ◽  
Suanna S. Bruinooge ◽  
Theodore S. Lawrence ◽  
Joel E. Tepper ◽  
...  
Keyword(s):  
Cancer Research ◽  
Future Research ◽  
Research Committee ◽  
Data Repositories ◽  
Privacy Rule ◽  
Regulatory Standards ◽  
American Society ◽  
Definition Of ◽  
High Level ◽  
The Impact

Purpose The American Society of Clinical Oncology (ASCO) Cancer Research Committee designed a qualitative research project to assess the attitudes of cancer researchers and compliance officials regarding compliance with the US Privacy Rule and to identify potential strategies for eliminating perceived or real barriers to achieving compliance. Methods A team of three interviewers asked 27 individuals (13 investigators and 14 compliance officials) from 13 institutions to describe the anticipated approach of their institutions to Privacy Rule compliance in three hypothetical research studies. Results The interviews revealed that although researchers and compliance officials share the view that patients' cancer diagnoses should enjoy a high level of privacy protection, there are significant tensions between the two groups related to the proper standards for compliance necessary to protect patients. The disagreements are seen most clearly with regard to the appropriate definition of a “future research use” of protected health information in biospecimen and data repositories and the standards for a waiver of authorization for disclosure and use of such data. Conclusion ASCO believes that disagreements related to compliance and the resulting delays in certain projects and abandonment of others might be eased by additional institutional training programs and consultation on Privacy Rule issues during study design. ASCO also proposes the development of best practices documents to guide 1) creation of data repositories, 2) disclosure and use of data from such repositories, and 3) the design of survivorship and genetics studies.

Sign in / Sign up

Export Citation Format

Share Document