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International Joint Conference on Neural Networks (IJCNN)

作者： David Guijo-Rubio Pedro A. Gutiérrez Anthony Bagnall César Hervás-Martínez Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain School of Computing Sciences University of East Anglia Norwich United Kingdom

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Nominal time series classification has been widely developed over the last years. However, to the best of our knowledge, ordinal classification of time series is an unexplored field, and this paper proposes a first approach in the context of the shapelet transform (ST). For those time series dataset where there is a natural order between the labels and the number of classes is higher than 2, nominal classifiers are not capable of achieving the best results, because the models impose the same cost of misclassification to all the errors, regardless the difference between the predicted and the ground-truth. In this sense, we consider four different evaluation metrics to do so, three of them of an ordinal nature. The first one is the widely known Information Gain (IG), proved to be very competitive for ST methods, whereas the remaining three measures try to boost the order information by refining the quality measure. These three measures are a reformulation of the Fisher score, the Spearman's correlation coefficient (ρ), and finally, the Pearson's correlation coefficient (R 2 ). An empirical evaluation is carried out, considering 7 ordinal datasets from the UEA & UCR time series classification repository, 4 classifiers (2 of them of nominal nature, whereas the other 2 are of ordinal nature) and 2 performance measures (correct classification rate, CCR, and average mean absolute error, AMAE). The results show that, for both performance metrics, the ST quality metric based on R 2 is able to obtain the best results, specially for AMAE, for which the differences are statistically significant in favour of R 2 .

关键词： Time series analysis Shape measurement Time measurement Correlation Computer science numerical analysis

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Tree-Shaped Ensemble of Multi-Label Classifiers using Grammar-Guided Genetic Programming

Tree-Shaped Ensemble of Multi-Label Classifiers using Gramma...

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Congress on Evolutionary Computation

作者： Jose M. Moyano Eva L. Gibaja Krzysztof J. Cios Sebastián Ventura Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Dept. of Computer Science Virginia Commonwealth University Richmond VA USA

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Multi-label classification paradigm has had a growing interest because of the emergence of a large number of classification problems where each of the instances of the data can be associated with several output labels simultaneously. Several ensemble methods were proposed to solve the multilabel classification problem. However, most of them simply create diversity in the ensemble by following a random procedure and give the same importance to all members. In this paper, we propose a Grammar-Guided Genetic Programming algorithm to build ensembles of multi-label classifiers. Given a pool of multilabel classifiers, each of them modeling dependencies among a subset of k labels, they are combined into a tree-shaped ensemble. At each node of the tree, predictions of its children nodes are combined, while each leaf represents a classifier from the pool. We propose two configurations for the method: using a fixed value of k for all classifiers in the pool, or using a variable value of k for each classifier, thus being able to capture relationships among groups of labels of different size in the ensemble. The experiments performed over sixteen multi-label dataset and using five evaluation metrics demonstrated that our method performs significantly better than the state-of-the-art ensembles of multilabel classifiers.

关键词： Prediction algorithms Grammar Vegetation Genetic programming Sociology Statistics

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A Learnable Prior Improves Inverse Tumor Growth Modeling

arXiv

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arXiv 2024年

作者： Weidner, Jonas Ezhov, Ivan Balcerak, Michal Metz, Marie-Christin Litvinov, Sergey Kaltenbach, Sebastian Feiner, Leonhard Lux, Laurin Kofler, Florian Lipkova, Jana Latz, Jonas Rueckert, Daniel Menze, Bjoern Wiestler, Benedikt Dept. of Computer Science The Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Dept. of Neuroradiology School of Medicine and Health Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University MA United States Dept. of Quantitative Biomedicine University of Zurich Zurich Switzerland Helmholtz AI Helmholtz Zentrum Munich Germany Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Department of Pathology University of California IrvineCA United States Department of Mathematics University of Manchester United Kingdom Department of Computing Biomedical Image Analysis Group Imperial College London United Kingdom Klinikum rechts der Isar Technical University Munich Germany

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Brain solute transport is more rapid in periarterial than perivenous spaces

Research Square

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Research Square 2021年

作者： Vinje, Vegard Bakker, Erik Nicolaas Theodorus Petrus Rognes, Marie E. Department of Scientific Computing and Numerical Analysis Simula Research Laboratory Martin Linges vei 25 Fornebu 1364 Norway Amsterdam University Medical Center Dept. of Biomedical Engineering and Physics Meibergdreef 9 Amsterdam1105 AZ Netherlands University of Bergen Department of Mathematics Bergen Norway

Background: Perivascular fluid flow, of cerebrospinal or interstitial fluid in spaces surrounding brain blood vessels, is recognized as a key component underlying brain transport and clearance. An important open question is how and to what extent differences in vessel type or geometry affect perivascular fluid flow and transport. Methods: Using computational modelling in both idealized and image-based geometries, we study and compare fluid flow and solute transport in pial (surface) periarterial and perivenous spaces. Results: Our findings demonstrate that differences in geometry between arterial and venous pial perivascular spaces (PVSs) lead to higher net CSF flow, more rapid tracer transport and earlier arrival times of injected tracers in periarterial spaces compared to perivenous spaces. Conclusions: These findings can explain the experimentally observed rapid appearance of tracers around arteries, and the delayed appearance around veins without the need of a circulation through the parenchyma, but rather by direct transport along the PVSs. © 2021, CC BY.

关键词： Blood vessels

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Set-valued prediction in hierarchical classification with constrained representation complexity

arXiv

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arXiv 2022年

作者： Mortier, Thomas Hüllermeier, Eyke Dembczynski, Krzysztof Waegeman, Willem Dept. of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Ghent Belgium Institute of Informatics LMU Munich Akademiestr. 7 Munich Germany Institute of Computing Science Poznań University of Technology Piotrowo 2 Poznan Poland Yahoo! Research Broadway NY770 United States

Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical multiclass classification problems, where valid sets (typically) correspond to internal nodes of the hierarchy. We argue that this is a very strong restriction, and we propose a relaxation by introducing the notion of representation complexity for a predicted set. In combination with probabilistic classifiers, this leads to a challenging inference problem for which specific combinatorial optimization algorithms are needed. We propose three methods and evaluate them on benchmark datasets: a naïve approach that is based on matrix-vector multiplication, a reformulation as a knapsack problem with conflict graph, and a recursive tree search method. Experimental results demonstrate that the last method is computationally more efficient than the other two approaches, due to a hierarchical factorization of the conditional class distribution. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Co-Located Human-Human Interaction analysis using Nonverbal Cues: A Survey

arXiv

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arXiv 2022年

作者： Beyan, Cigdem Vinciarelli, Alessandro Del Bue, Alessio Dept. of Management Information and Production Engineering University of Bergamo Via A. Einstein 2 Dalmine24044 Italy School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Enrico Melen 83 Genoa16152 Italy

Automated co-located human-human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, personality traits), social roles/relations, and interaction dynamics (e.g., group cohesion, engagement, rapport). Our target is to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings (free-standing conversations, meetings, dyads, and crowds). We also present a comprehensive summary of the related datasets and outline future research directions which are regarding the implementation of artificial intelligence, dataset curation, and privacy-preserving interaction analysis. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively;multimodal features are prominently performing better;deep learning architectures showed improved performance in overall, but there exist many phenomena whose detection has never been implemented through deep models. We also identified several limitations such as the lack of scalable benchmarks, annotation reliability tests, cross-dataset experiments, and explainability analysis. © 2022, CC BY-NC-ND.

关键词： Support vector machines

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Using Others' Tests to Identify Breaking Updates

Using Others' Tests to Identify Breaking Updates

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International Mining Software Repositories, MSR

作者： Suhaib Mujahid Rabe Abdalkareem Emad Shihab Shane McIntosh Dept. of Computer Science and Software Engineering Data-driven Analysis of Software (DAS) Lab Concordia University Montreal Quebec Canada Software Analysis and Intelligence Lab (SAIL) School of Computing Queen's University Kingston Ontario Canada Dept. of Electrical and Computer Engineering Software Repository Excavation and Build Eng. Labs McGill University Montreal Quebec Canada

The reuse of third-party packages has become a common practice in contemporary software development. Software dependencies are constantly evolving with newly added features and patches that fix bugs in older versions. However, updating dependencies could introduce new bugs or break backward compatibility. In this work, we propose a technique to detect breakage-inducing versions of third-party dependencies. The key insight behind our approach is to leverage the automated test suites of other projects that depend upon the same dependency to test newly released versions. We conjecture that this crowd-based approach will help to detect breakage-inducing versions because it broadens the set of realistic usage scenarios to which a package version has been exposed. To evaluate our conjecture, we perform an empirical study of 391,553 npm packages. We use the dependency network from these packages to identify candidate tests of third-party packages. Moreover, to evaluate our proposed technique, we mine the history of this dependency network to identify ten breakage-inducing versions. We find that our proposed technique can detect six of the ten studied breakage-inducing versions. Our findings can help developers to make more informed decisions when they update their dependencies.

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Urban Intelligence: Toward the Digital Twin of Matera and Catania

Urban Intelligence: Toward the Digital Twin of Matera and Ca...

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Blockchain for Renewables Integration (BLORIN), Workshop on

作者： Giordana Castelli Amedeo Cesta Mario Ciampi Riccardo De Benedictis Giuseppe De Pietro Matteo Diez Giovanni Felici Roberto Malvezzi Barbara Masini Riccardo Pellegrini Andreas Scalas Giuseppe Stecca Lucanos Strambini Gabriella Tognola Paolo Ravazzani Emilio Fortunato Campana Engineering ICT and Technology for Energy and Transportation Dept. (DIITET) National Research Council (CNR) Rome Italy Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR) Rome Italy Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy Institute of Marine Engineering (INM) National Research Council (CNR) Rome Italy Institute for Systems Analysis and Computer Science (IASI) National Research Council (CNR) Rome Italy Engineering ICT and Technology for Energy and National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) National Research Council (CNR) Bologna Italy Institute for Applied Mathematics and Information Technologies (IMATI) National Research Council (CNR) Genoa Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” (IASI) National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) (CNR) Pisa Italy

ISBN: (纸本)9781665472500

The Urban Intelligence (UI) paradigm conceived by CNR consists of an ecosystem of digital technologies joined within a Digital Twin (DT) of the city aimed at improving the city governance towards goals addressed also by the UN Agenda 2030, such as urban environment, sustainability and resilience, wellbeing and quality of life, local development, and social inclusion. In particular, UI provides a set of candidate policies in complex scenarios, and supports policy makers and stakeholders in designing shared, evidence-based, and integrated solutions. UI is being applied for the first time to two Italian cities, Matera and Catania, paving the way for a deeper scientific framing of the paradigm, as well as for the technological development and testing of the core UI ecosystem in real-life situations. The paper introduces the UI key-concepts and components, illustrates the ongoing experimentations in these pilot cities related to the development of two DTs on parts of the urban areas, and presents some initial results.

关键词： Renewable energy sources Urban areas Ecosystems Lakes Digital twins Sensors Stakeholders

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Chaos suppression in a gompertz-like discrete system of fractional order

arXiv

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arXiv 2019年

作者： Danca, Marius-F Fečkan, Michal Dept. of Mathematics and Computer Science Avram Iancu University of Cluj-Napoca Romania Romanian Institute od Science and Technology Cluj-Napoca400487 Romania Dept. of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Slovak Republic Mathematical Institute Slovak Academy of Sciences Slovakia

In this paper we introduce the fractional-order variant of a Gompertz-like discrete system. The chaotic behavior is suppressed with an impulsive control algorithm. The numerical integration and the Lyapunov exponent are obtained by means of the discrete fractional calculus. To verify numerically the obtained results, beside the Lyapunov exponent, the tools offered by the 0-1 test are used. Copyright © 2019, The Authors. All rights reserved.

关键词： Calculations

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Chaos control in the fractional order logistic map via impulses

arXiv

引用

arXiv 2019年

作者： Danca, Marius F. Fečkan, Michal Kuznetsov, Nikolay Dept. of Mathematics and Computer Science Avram Iancu University Cluj-Napoca400380 Romania Romanian Institute for Science and Technology Cluj-Napoca400487 Romania Dept. of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Slovakia Mathematical Institute Slovak Academy of Sciences Slovakia Department of Applied Cybernetics Saint-Petersburg State University Russia Dept. of Mathematical Information Technology University of Jyväskylä Finland

In this paper the chaos control in the discrete logistic map of fractional order is obtained with an impulsive control algorithm. The underlying discrete initial value problem of fractional order is considered in terms of Caputo delta fractional difference. Every ∆ steps, the state variable is instantly modified with the same impulse value, chosen from a bifurcation diagram versus impulse. It is shown that the solution of the impulsive control is bounded. The numerical results are verified via time series, histograms, and the 0-1 test. Several examples are considered. Copyright © 2019, The Authors. All rights reserved.

关键词： Chaos control

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