738 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Approximate Methods for Marginal Likelihood Estimation
We consider the estimation of the marginal likelihood in Bayesian statistics, a essential and
important task known to be computationally expensive when the dimension of the parameter space
is large. We propose a general algorithm with numerous extensions that can be widely applied to a
variety of problem settings and excels particularly when dealing with near log-concave posteriors.
Our method hinges on a novel idea that uses MCMC samples to partition the parameter space
and forms local approximations over these partition sets as a means of estimating the marginal
likelihood. In this dissertation, we provide both the motivation and the groundwork for developing
what we call the Hybrid estimator. Our numerical experiments show the versatility and accuracy of
the proposed estimator, even as the parameter space becomes increasingly high-dimensional and
complicated
Investigating Trade-offs For Fair Machine Learning Systems
Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race,
or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have
been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this
thesis addresses trade-offs that practitioners face when debiasing Machine Learning
systems.
At first, we perform a literature review to investigate the current state of the
art for debiasing Machine Learning systems. This includes an overview of existing
debiasing techniques and how they are evaluated (e.g., how is bias measured).
As a second contribution, we propose a benchmarking approach that allows for
an evaluation and comparison of bias mitigation methods and their trade-offs (i.e.,
how much performance is sacrificed for improving fairness).
Afterwards, we propose a debiasing method ourselves, which modifies already
trained Machine Learning models, with the goal to improve both, their fairness and
accuracy.
Moreover, this thesis addresses the challenge of how to deal with fairness with
regards to age. This question is answered with an empirical evaluation on real-world
datasets
Estimating Higher-Order Mixed Memberships via the Tensor Perturbation Bound
Higher-order multiway data is ubiquitous in machine learning and statistics
and often exhibits community-like structures, where each component (node) along
each different mode has a community membership associated with it. In this
paper we propose the tensor mixed-membership blockmodel, a generalization of
the tensor blockmodel positing that memberships need not be discrete, but
instead are convex combinations of latent communities. We establish the
identifiability of our model and propose a computationally efficient estimation
procedure based on the higher-order orthogonal iteration algorithm (HOOI) for
tensor SVD composed with a simplex corner-finding algorithm. We then
demonstrate the consistency of our estimation procedure by providing a per-node
error bound, which showcases the effect of higher-order structures on
estimation accuracy. To prove our consistency result, we develop the
tensor perturbation bound for HOOI under independent,
possibly heteroskedastic, subgaussian noise that may be of independent
interest. Our analysis uses a novel leave-one-out construction for the
iterates, and our bounds depend only on spectral properties of the underlying
low-rank tensor under nearly optimal signal-to-noise ratio conditions such that
tensor SVD is computationally feasible. Whereas other leave-one-out analyses
typically focus on sequences constructed by analyzing the output of a given
algorithm with a small part of the noise removed, our leave-one-out analysis
constructions use both the previous iterates and the additional tensor
structure to eliminate a potential additional source of error. Finally, we
apply our methodology to real and simulated data, including applications to two
flight datasets and a trade network dataset, demonstrating some effects not
identifiable from the model with discrete community memberships
Object Detection and Classification in the Visible and Infrared Spectrums
The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition
Entropic Gromov-Wasserstein Distances: Stability, Algorithms, and Distributional Limits
The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric
measure spaces, but suffers from computational hardness. The entropic
Gromov-Wasserstein (EGW) distance serves as a computationally efficient proxy
for the GW distance. Recently, it was shown that the quadratic GW and EGW
distances admit variational forms that tie them to the well-understood optimal
transport (OT) and entropic OT (EOT) problems. By leveraging this connection,
we derive two notions of stability for the EGW problem with the quadratic or
inner product cost. The first stability notion enables us to establish
convexity and smoothness of the objective in this variational problem. This
results in the first efficient algorithms for solving the EGW problem that are
subject to formal guarantees in both the convex and non-convex regimes. The
second stability notion is used to derive a comprehensive limit distribution
theory for the empirical EGW distance and, under additional conditions,
asymptotic normality, bootstrap consistency, and semiparametric efficiency
thereof.Comment: 66 pages, 3 figure
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