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arXiv 2024年

作者： Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Paluzo-Hidalgo, Eduardo The Department of Applied Math I School of Engineering University of Sevilla Seville Spain The Department of Computer Science and Artificial Intelligence School of Engineering University of Sevilla Seville Spain The Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain

In this paper, we present SIMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output. The SIMAP layer is an enhanced version of Simplicial-Map Neural Networks (SMNNs), an explainable neural network based on support sets and simplicial maps (functions used in topology to transform shapes while preserving their structural connectivity). The novelty of the methodology proposed in this paper is two-fold: Firstly, SIMAP layers work in combination with other deep learning architectures as an interpretable layer substituting classic dense final layers. Secondly, unlike SMNNs, the support set is based on a fixed maximal simplex, the barycentric subdivision being efficiently computed with a matrix-based multiplication algorithm. © 2024, CC BY.

关键词： Deep learning

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Giving Shape to Words: Visual Knowledge Discovery for Textual Contents in Legal Scenarios

Giving Shape to Words: Visual Knowledge Discovery for Textua...

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International Conference on Information Visualisation (IV)

作者： Nicola Lettieri Alfonso Guarino Delfina Malandrino Rocco Zaccagnino Salvatore Del Piano National Institute for Public Policy Analysis Rome Italy Department of Law Economics Management and Quantitative Methods University of Sannio Benevento BN Italy Computer Science Department University of Salerno Fisciano SA Italy

Visual Knowledge Discovery (VKD), the emerging research field exploring the integration between Artificial Intelligence and Visual Analytics is impacting a growing number of contexts. This paper dwells on the potential intersections between the VKD perspective and the legal world, a scenario for many reasons drawn by the idea of combining empirical insights offered by computation with intuitive, visually-enhanced forms of data mining. We will focus on criminal justice, taking the cue from an ongoing experimental research project that investigates novel applications of computational social science methods - complex network analysis - to analyze criminal organizations for both scientific and investigative purposes. The work will describe the VKD solutions devised to allow public prosecutors to visually browse the content of phone calls and environmental tapping gathered during preliminary inquiries. We will discuss how given visualizations - graph-based visualizations in the first place - can enhance text mining techniques helping to capture in-a-glance communication flows, discussions' topics and both structural and functional features of criminal networks at hand.

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Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

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computers, Materials & Continua 2021年第5期67卷 2029-2047页

作者： Mohammed A.Aleid Khaled A.Z.Alyamani Mohieddine Rahmouni Theyazn H.H.Aldhyani Nizar Alsharif Mohammed Y.Alzahrani College of Education King Faisal UniversityAl-Ahsa31982Saudi Arabia Community College in Abqaiq King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Economics and Quantitative Methods ESSECTUniversity of TunisTunisTunisia Department of Computer Engineering and Science Al-Baha UniversityAl-BahahSaudi Arabia

This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 *** other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new *** focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)*** to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and *** health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age *** average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic *** results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.

关键词： COVID-19 health habits safety behaviors anxiety support vector machine regression regression trees

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Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression

arXiv

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arXiv 2024年

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Chen, Xiangwei Sun, Zexu Wu, Fei Zhang, Kun College of Computer Science and Technology Zhejiang University China Department of Quantitative Theory and Methods Emory University Atlanta United States Gaoling School of Artificial Intelligence Renmin University of China China Carnegie Mellon University United States

In causal inference, encouragement designs (EDs) are widely used to analyze causal effects, when randomized controlled trials (RCTs) are impractical or compliance to treatment cannot be perfectly enforced. Unlike RCTs, which directly allocate treatments, EDs randomly assign encouragement policies that positively motivate individuals to engage in a specific treatment. These random encouragements act as instrumental variables (IVs), facilitating the identification of causal effects through leveraging exogenous perturbations in discrete treatment scenarios. However, real-world applications of encouragement designs often face challenges such as incomplete randomization, limited experimental data, and significantly fewer encouragements compared to treatments, hindering precise causal effect estimation. To address this, this paper introduces novel theories and algorithms for identifying the Conditional Average Treatment Effect (CATE) using variations in encouragement. Further, by leveraging both observational and encouragement data, we propose a generalized IV estimator, named Encouragement-based Counterfactual Regression (EnCounteR), to effectively estimate the causal effects. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of EnCounteR over existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Regression analysis

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The use of information and communication technologies in training ecology students

The use of information and communication technologies in tra...

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2020 International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters, ICSF 2020

作者： Barna, Iryna Hrytsak, Ludmyla Henseruk, Halyna Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Geoecology and Methods of Teaching Ecology Ternopil46027 Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Science and Teaching Methods Ternopil46027 Ukraine

Increasing demand for experts capable of high-quality assessment of the impact of a particular planned activity on the environment involves a more effective use of information and communication technologies (ICT) in the educational process of training ecology students. The authors present a methodology for students to study the educational material of the "Environmental Impact Assessment" course by using ICT. The methodology and algorithm for using the Padlet interactive whiteboard, Mindomo mind maps, Easel infographics, Google Drive and Google Docs in lectures and practical lessons are described. Particular attention is paid to the preparation of a group project using ICT. Tested in the Ternopil Volodymyr Hnatiuk National Pedagogical University, the methodology allows to implement a number of tasks such as: promotion of intensification and effectiveness of teaching;acquisition of practical skills to quickly find the necessary information on various online resources for ecology students;simulation of the real procedure of environmental impact assessment in the classroom;development of the ability to professionally communicate with experts in other fields of knowledge or activity, etc. Examination of the results of using ICT to study the "Environmental Impact Assessment" course has shown a significant increase in informational literacy among ecology students and their individual professional growth. © The Authors, published by EDP sciences, 2020.

关键词： Students

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Hybrid Machine Learning Classification for Tipple Negative Breast Cancer

Hybrid Machine Learning Classification for Tipple Negative B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Amira M. Abozaid Ahmed B. Zaky Biomedical Engineering Department Cairo University Giza Egypt Computer Science and Information Technology Programs (CSIT) Egypt Japan University of Science and Technology Molecular Biology division Teaching assistant at Faculty of Science Alexandria University Faculty of Engineering at Shubra Benha University Egypt

This research introduces an innovative approach to dissecting Triple-Negative Breast Cancer (TNBC) by integrating count-based RNA analysis and machine learning techniques. Through the strategic amalgamation of K-means clustering, Convolutional Neural Network (CNN), and Support Vector Machine (SVM), this study transcends conventional classification methods, revealing detailed molecular insights within the complex TNBC landscape. The methodology initiates with count-based RNA analysis for dimensionality reduction, refining molecular classifications through K-means clustering. The incorporation of CNN allows for nuanced feature extraction, capturing intricate genomic relationships, while SVM enhances subtype predictions with precise classification. The addition of Gene Ontology (GO) and pathway analyses enriches our understanding by unraveling the functional implications of identified gene clusters. This synergistic methodology not only advances our comprehension of TNBC subtypes but also establishes a groundwork for targeted therapeutic strategies. The fusion of count-based RNA analysis and machine learning enables a comprehensive and nuanced exploration of TNBC complexity, offering valuable insights for personalized oncology approaches in the era of precision medicine

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Algorithm for globally identifiable reparametrizations of ODEs

arXiv

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arXiv 2024年

作者： Falkensteiner, Sebastian Ovchinnikov, Alexey Sendra, J. Rafael Max Planck Institute for Mathematics in The Sciences Leipzig Germany Department of Mathematics CUNY Queens College Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center New York United States Department of Quantitative Methods CUNEF-University Spain

Structural global parameter identifiability indicates whether one can determine a parameter’s value in an ODE model from given inputs and outputs. If a given model has parameters for which there is exactly one value, such parameters are called globally identifiable. Given an ODE model involving not globally identifiable parameters, first we transform the system into one with locally identifiable parameters. As a main contribution of this paper, then we present a procedure for replacing, if possible, the ODE model with an equivalent one that has globally identifiable parameters. We first derive this as an algorithm for one-dimensional ODE models and then reuse this approach for higher-dimensional *** Codes 93C15, 93B25, 93B30, 34A55, 14E08, 14M20, 14Q20, 12H05, 92B05 © 2024, CC BY-NC-SA.

关键词： Observability

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A Mixed Approach for Pallet Building Problem with Practical Constraints 22nd

A Mixed Approach for Pallet Building Problem with Practical ...

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22nd International Conference on Enterprise Information Systems, ICEIS 2020

作者： Iori, Manuel Locatelli, Marco Moreira, Mayron C. O. Silveira, Tiago Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy Department of Engineering and Architecture University of Parma Parma Italy Department of Computer Science Federal University of Lavras Lavras Brazil

ISBN: (纸本)9783030754174

We study a pallet building problem that originates from a case study in a company that produces robotized systems for freight transportation and logistics. We generalize the problem by including the concept of family of items, which allows us to consider specific constraints such as visibility and contiguity. We solve the problem with an algorithm based on a two-step strategy: an Extreme Points heuristic is used to group items into horizontal layers and an exact method is invoked to stack layers one over the other to form pallets. The performance of the algorithm is assessed through extensive computational tests on real-world instances. The results show that the exact model considerably increases the solution quality, creating very compact packings with a limited computational effort. © 2021, Springer Nature Switzerland AG.

关键词： Pallets

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Enhancing Online Learning in LCM Moodle through Interactive Adaptive Learning

Enhancing Online Learning in LCM Moodle through Interactive ...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Valerii Habrusiev Hryhorii Tereshchuk Valerii Panchenko Serhii Martyniuk Oleksii Sysoiev Halyna Henseruk Department of Computer Science and Teaching Methods Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Department of Theory and Practice of Translation Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopi Ukraine Licensing and accreditation department Kyiv International University Kyiv Ukraine

The article considers the practical experience of introducing interactive adaptive learning in the e-learning environment. The basis of the learning environment is the LCM Moodle learning resource management system. One of the ways to individualize and personalize the learning process is to attract interactive and adaptive technologies for teaching students. The LCM Moodle’s interactive module of activity “Lesson’’ is a series of HTML pages linked by transitions. The main difference between “Lesson’’ and other LCM Moodle modules is that there are elements of adaptability. Using this tool, each student’s choice can be accompanied by appropriate comments from the teacher and the ability to go to different pages of the course depending on the correctness/incorrectness and completeness of the answers. With such planning, “Lesson’’ can provide theoretical material and control tasks to check its mastery for each student automatically, without additional action by the teacher. As a result of completing the tasks, the corresponding score appears in the student’s register of marks. In general, this module of activity is similar to a classroom lesson, when the teacher, presenting new material, from time to time interviews students in order to identify the level of its mastery.

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A Two-Layer Trilateral Teleoperation Architecture for Mentoring in Surgical Training

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IFAC-PapersOnLine 2021年第19期54卷 267-274页

作者： Marco Minelli Filippo Loschi Federica Ferraguti Cristian Secchi Department of Sciences and Methods of Engineering University of Modena and Reggio Emilia Italy Department of Computer Science University of Verona Italy

In this paper we propose a novel trilateral dual-master-single-slave teleoperation control architecture that can be used for the training of novice surgeons in surgical procedures. Starting from the concept of energy tank, we propose a flexible and stable trilateral interconnection over a delayed communication channel between the masters and the slave. Exploiting the flexibility provided by the controller, we design a training strategy for novice surgeons. The proposed architecture is experimentally validated.

关键词： Teleoperation Stability of nonlinear systems Passivity-based control Telepresence Robots manipulators

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