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- poster
Iterative Design of Two Art-Inspired Experimental Scenarios for Collecting Expressive Movement Data of Individuals and Groups
- Antonio Camurri,
- Cora Gasparotti,
- Eleonora Ceccaldi,
- Andrea Cera,
- Benoît Bardy,
- Marta Bieńkiewicz,
- Stefan Janaqi,
- Gualtiero Volpe,
- Giorgio Gnecco,
- Nicola Ferrari
AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces•June 2024, Article No.: 92, pp 1-3• https://doi.org/10.1145/3656650.3656750This poster presents an art-inspired iterative design approach applied to the definition of two experimental scenarios for movement data collection. Scenarios are inspired by warm-up exercises dancers perform to broaden group consciousness. Research ...
- Article
Matrix Completion for the Prediction of Yearly Country and Industry-Level CO Emissions
Machine Learning, Optimization, and Data Science•September 2022, pp 14-19• https://doi.org/10.1007/978-3-031-25599-1_2AbstractIn the recent past, yearly CO emissions at the international level were studied from different points of view, due to their importance with respect to concerns about climate change. Nevertheless, related data (available at country-industry level ...
- research-article
Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory
Minds and Machines, Volume 32, Issue 1•Mar 2022, pp 13-42 • https://doi.org/10.1007/s11023-022-09592-zAbstractThe idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We ...
- Article
Deeper Insights into Neural Nets with Random Weights
AI 2021: Advances in Artificial Intelligence•February 2022, pp 129-140• https://doi.org/10.1007/978-3-030-97546-3_11AbstractIn this work, the “effective dimension” of the output of the hidden layer of a one-hidden-layer neural network with random inner weights of its computational units is investigated. To do this, a polynomial approximation of the sigmoidal activation ...
- Article
On Principal Component Analysis of the Convex Combination of Two Data Matrices and Its Application to Acoustic Metamaterial Filters
Machine Learning, Optimization, and Data Science•October 2021, pp 119-123• https://doi.org/10.1007/978-3-030-95467-3_9AbstractIn this short paper, a matrix perturbation bound on the eigenvalues found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data ...
- short-paper
A Computational Method to Automatically Detect the Perceived Origin of Full-Body Human Movement and its Propagation
ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction•October 2020, pp 449-453• https://doi.org/10.1145/3395035.3425971The work reports ongoing research about a computational method, based on cooperative games on graphs, aimed at detecting the perceived origin of full-body human movement and its propagation. Compared with previous works, a larger set of movement ...
- research-article
Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials
Journal of Optimization Theory and Applications, Volume 187, Issue 3•Dec 2020, pp 630-653 • https://doi.org/10.1007/s10957-019-01614-8AbstractRecently, an increasing research effort has been dedicated to analyze the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has ...
- research-article
Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 24, Issue 21•Nov 2020, pp 15937-15949 • https://doi.org/10.1007/s00500-020-05317-5AbstractThis paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers ...
- Article
Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning?
Machine Learning, Optimization, and Data Science•July 2020, pp 55-59• https://doi.org/10.1007/978-3-030-64583-0_6AbstractIn this short paper, a theoretical analysis of Occam’s razor formulation through statistical learning theory is presented, showing that pathological situations exist for which regularization may slow down supervised learning instead of making it ...
- Article
Machine Learning Application to Family Business Status Classification
Machine Learning, Optimization, and Data Science•July 2020, pp 25-36• https://doi.org/10.1007/978-3-030-64583-0_3AbstractAccording to a recent trend of research, there is a growing interest in applications of machine learning techniques to business analytics. In this work, both supervised and unsupervised machine learning techniques are applied to the analysis of a ...
- Article
Optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel Data Model
Machine Learning, Optimization, and Data Science•September 2019, pp 531-542• https://doi.org/10.1007/978-3-030-37599-7_44AbstractWe investigate a modification of the classical fixed effects panel data model (a linear regression model able to represent unobserved heterogeneity in the data), in which one has the additional possibility of controlling the conditional variance ...
- research-article
Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems
Neurocomputing, Volume 306, Issue C•Sep 2018, pp 141-159 • https://doi.org/10.1016/j.neucom.2018.04.016Highlights- Theoretical analysis of symmetry/antisymmetry properties of optimal solutions to kernelbased machine learning problems.
AbstractA particularly interesting instance of supervised learning with kernels is when each training example is associated with two objects, as in pairwise classification (Brunner et al., 2012), and in supervised learning of preference ...
- research-article
Neural approximations in discounted infinite-horizon stochastic optimal control problems
Engineering Applications of Artificial Intelligence, Volume 74, Issue C•Sep 2018, pp 294-302 • https://doi.org/10.1016/j.engappai.2018.07.004AbstractNeural approximations of the optimal stationary closed-loop control strategies for discounted infinite-horizon stochastic optimal control problems are investigated. It is shown that for a family of such problems, the minimal number of ...
- article
Lqg online learning
Neural Computation, Volume 29, Issue 8•August 2017, pp 2203-2291 • https://doi.org/10.1162/neco_a_00976Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical ...
- article
Supervised and semi-supervised classifiers for the detection of flood-prone areas
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 21, Issue 13•July 2017, pp 3673-3685 • https://doi.org/10.1007/s00500-015-1983-zSupervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers ...
- short-paper
Graph-restricted game approach for investigating human movement qualities
MOCO '17: Proceedings of the 4th International Conference on Movement Computing•June 2017, Article No.: 30, pp 1-4• https://doi.org/10.1145/3077981.3078030A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints ...
- research-article
Optimal distributed task scheduling in volunteer clouds
Computers and Operations Research, Volume 81, Issue C•May 2017, pp 231-246 • https://doi.org/10.1016/j.cor.2016.11.004A framework for task scheduling policies in a large-scale distributed cloud.A mathematical formulation driven by real system requirements.Model with: FIFO queue, tasks with deadlines, the actual load on the machines.Application of the distributed ...
- article
A SOM-based Chan---Vese model for unsupervised image segmentation
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 21, Issue 8•April 2017, pp 2047-2067 • https://doi.org/10.1007/s00500-015-1906-zActive Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its ...
- research-article
A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory
Engineering Applications of Artificial Intelligence, Volume 56, Issue C•November 2016, pp 157-174 • https://doi.org/10.1016/j.engappai.2016.08.018A hierarchical method for the approximate computation of the consensus state of a network of agents is investigated. The method is motivated theoretically by spectral graph theory arguments. In a first phase, the graph is divided into a number of ...
- research-article
Automatic Classification of Leading Interactions in a String Quartet
ACM Transactions on Interactive Intelligent Systems, Volume 6, Issue 1•May 2016, Article No.: 5, pp 1-27 • https://doi.org/10.1145/2818739The aim of the present work is to analyze automatically the leading interactions between the musicians of a string quartet, using machine-learning techniques applied to nonverbal features of the musicians’ behavior, which are detected through the help ...