24,991 research outputs found
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world
since it came in sight in December 2019. It has already caused thousands of
causalities and infected several millions worldwide. Any technological tool
that can be provided to healthcare practitioners to save time, effort, and
possibly lives has crucial importance. The main tools practitioners currently
use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction
(RT-PCR) and Computed Tomography (CT), which require significant time,
resources and acknowledged experts. X-ray imaging is a common and easily
accessible tool that has great potential for Covid-19 diagnosis. In this study,
we propose a novel approach for Covid-19 recognition from chest X-ray images.
Despite the importance of the problem, recent studies in this domain produced
not so satisfactory results due to the limited datasets available for training.
Recall that Deep Learning techniques can generally provide state-of-the-art
performance in many classification tasks when trained properly over large
datasets, such data scarcity can be a crucial obstacle when using them for
Covid-19 detection. Alternative approaches such as representation-based
classification (collaborative or sparse representation) might provide
satisfactory performance with limited size datasets, but they generally fall
short in performance or speed compared to Machine Learning methods. To address
this deficiency, Convolution Support Estimation Network (CSEN) has recently
been proposed as a bridge between model-based and Deep Learning approaches by
providing a non-iterative real-time mapping from query sample to ideally sparse
representation coefficient' support, which is critical information for class
decision in representation based techniques.Comment: 10 page
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Coronavirus disease 2019 (COVID-19) has rapidly become a global health
concern after its first known detection in December 2019. As a result, accurate
and reliable advance warning system for the early diagnosis of COVID-19 has now
become a priority. The detection of COVID-19 in early stages is not a
straightforward task from chest X-ray images according to expert medical
doctors because the traces of the infection are visible only when the disease
has progressed to a moderate or severe stage. In this study, our first aim is
to evaluate the ability of recent \textit{state-of-the-art} Machine Learning
techniques for the early detection of COVID-19 from chest X-ray images. Both
compact classifiers and deep learning approaches are considered in this study.
Furthermore, we propose a recent compact classifier, Convolutional Support
Estimator Network (CSEN) approach for this purpose since it is well-suited for
a scarce-data classification task. Finally, this study introduces a new
benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage
COVID-19 pneumonia samples (very limited or no infection signs) labelled by the
medical doctors and 12 544 samples for control (normal) class. A detailed set
of experiments shows that the CSEN achieves the top (over 97%) sensitivity with
over 95.5% specificity. Moreover, DenseNet-121 network produces the leading
performance among other deep networks with 95% sensitivity and 99.74%
specificity.Comment: 12 page
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Managing the KM Trade-Off: Knowledge Centralization versus Distribution
KM is more an archipelago of theories and practices rather than a monolithic approach. We propose a conceptual map that organizes some major approaches to KM according to their assumptions on the nature of knowledge. The paper introduces the two major views on knowledge objectivist, subjectivist - and explodes each of them into two major approaches to KM: knowledge as a market, and knowledge as intellectual capital (the objectivistic perspective); knowledge as mental models, and knowledge as practice (the subjectivist perspective). We argue that the dichotomy between objective and subjective approaches is intrinsic to KM within complex organizations, as each side of the dichotomy responds to different, and often conflicting, needs: on the one hand, the need to maximize the value of knowledge through its replication; on the other hand, the need to keep knowledge appropriate to an increasingly complex and changing environment. Moreover, as a proposal for a deeper discussion, such trade-off will be suggested as the origin of other relevant KM related trade-offs that will be listed. Managing these trade-offs will be proposed as a main challenge of KM
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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