23 research outputs found
G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Three-dimensional datasets, or three-way data, started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations _ features _ contexts). With an increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount.These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real three-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. G-Tric can replicate real-world datasets and create new ones that match researchers’ needs across several properties, including data type (numeric or symbolic), dimension, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled by defining the number of missing values, noise, and errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches. Besides reviewing the current state-of-the-art regarding triclustering approaches, comparison studies and evaluation metrics, this work also analyzes how the lack of frameworks to generate synthetic data influences existent evaluation methodologies, limiting the scope of performance insights that can be extracted from each algorithm. As well as exemplifying how the set of decisions made on these evaluations can impact the quality and validity of those results. Alternatively, a different methodology that takes advantage of synthetic data with ground truth is presented. This approach, combined with the proposal of an extension to an existing clustering extrinsic measure, enables to assess solutions’ quality under new perspectives
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Application of Multi-Sensor Fusion Technology in Target Detection and Recognition
Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems
Acoustic modelling, data augmentation and feature extraction for in-pipe machine learning applications
Gathering measurements from infrastructure, private premises, and harsh environments can be difficult and expensive. From this perspective, the development of
new machine learning algorithms is strongly affected by the availability of training
and test data. We focus on audio archives for in-pipe events. Although several
examples of pipe-related applications can be found in the literature, datasets of
audio/vibration recordings are much scarcer, and the only references found relate
to leakage detection and characterisation. Therefore, this work proposes a methodology to relieve the burden of data collection for acoustic events in deployed pipes.
The aim is to maximise the yield of small sets of real recordings and demonstrate
how to extract effective features for machine learning. The methodology developed
requires the preliminary creation of a soundbank of audio samples gathered with
simple weak annotations. For practical reasons, the case study is given by a range
of appliances, fittings, and fixtures connected to pipes in domestic environments.
The source recordings are low-reverberated audio signals enhanced through a
bespoke spectral filter and containing the desired audio fingerprints. The soundbank is then processed to create an arbitrary number of synthetic augmented
observations. The data augmentation improves the quality and the quantity of
the metadata and automatically creates strong and accurate annotations that
are both machine and human-readable. Besides, the implemented processing
chain allows precise control of properties such as signal-to-noise ratio, duration
of the events, and the number of overlapping events. The inter-class variability
is expanded by recombining source audio blocks and adding simulated artificial
reverberation obtained through an acoustic model developed for the purpose.
Finally, the dataset is synthesised to guarantee separability and balance. A few
signal representations are optimised to maximise the classification performance,
and the results are reported as a benchmark for future developments. The contribution to the existing knowledge concerns several aspects of the processing chain
implemented. A novel quasi-analytic acoustic model is introduced to simulate
in-pipe reverberations, adopting a three-layer architecture particularly convenient
for batch processing. The first layer includes two algorithms: one for the numerical
calculation of the axial wavenumbers and one for the separation of the modes. The
latter, in particular, provides a workaround for a problem not explicitly treated in the
literature and related to the modal non-orthogonality given by the solid-liquid interface in the analysed domain. A set of results for different waveguides is reported
to compare the dispersive behaviour against different mechanical configurations.
Two more novel solutions are also included in the second layer of the model and
concern the integration of the acoustic sources. Specifically, the amplitudes of the
non-orthogonal modal potentials are obtained using either a distance minimisation
objective function or by solving an analytical decoupling problem. In both cases,
results show that sources sufficiently smooth can be approximated with a limited
number of modes keeping the error below 1%. The last layer proposes a bespoke
approach for the integration of the acoustic model into the synthesiser as a reverberation simulator. Additional elements of novelty relate to the other blocks of the
audio synthesiser. The statistical spectral filter, for instance, is a batch-processing
solution for the attenuation of the background noise of the source recordings. The
signal-to-noise ratio analysis for both moderate and high noise levels indicates
a clear improvement of several decibels against the closest filter example in the
literature. The recombination of the audio blocks and the system of fully tracked
annotations are also novel extensions of similar approaches recently adopted in
other contexts. Moreover, a bespoke synthesis strategy is proposed to guarantee
separable and balanced datasets. The last contribution concerns the extraction
of convenient sets of audio features. Elements of novelty are introduced for the
optimisation of the filter banks of the mel-frequency cepstral coefficients and the
scattering wavelet transform. In particular, compared to the respective standard
definitions, the average F-score performance of the optimised features is roughly
6% higher in the first case and 2.5% higher for the latter. Finally, the soundbank,
the synthetic dataset, and the fundamental blocks of the software library developed
are publicly available for further research
Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio
Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases
Bibliography of Lewis Research Center technical publications announced in 1985
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1985. All the publications were announced in the 1985 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
ICR ANNUAL REPORT 2019 (Volume 26)[All Pages]
This Annual Report covers from 1 January to 31 December 201
2006-2007, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 2006-2007.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1426/thumbnail.jp