647 research outputs found
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
Leveraging semantic text analysis to improve the performance of transformer-based relation extraction
Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29
Text-based Sentiment Analysis and Music Emotion Recognition
Nowadays, with the expansion of social media, large amounts of user-generated
texts like tweets, blog posts or product reviews are shared online. Sentiment polarity
analysis of such texts has become highly attractive and is utilized in recommender
systems, market predictions, business intelligence and more. We also witness deep
learning techniques becoming top performers on those types of tasks. There are
however several problems that need to be solved for efficient use of deep neural
networks on text mining and text polarity analysis.
First of all, deep neural networks are data hungry. They need to be fed with
datasets that are big in size, cleaned and preprocessed as well as properly labeled.
Second, the modern natural language processing concept of word embeddings as a
dense and distributed text feature representation solves sparsity and dimensionality
problems of the traditional bag-of-words model. Still, there are various uncertainties
regarding the use of word vectors: should they be generated from the same dataset
that is used to train the model or it is better to source them from big and popular
collections that work as generic text feature representations? Third, it is not easy for
practitioners to find a simple and highly effective deep learning setup for various
document lengths and types. Recurrent neural networks are weak with longer texts
and optimal convolution-pooling combinations are not easily conceived. It is thus
convenient to have generic neural network architectures that are effective and can
adapt to various texts, encapsulating much of design complexity.
This thesis addresses the above problems to provide methodological and practical
insights for utilizing neural networks on sentiment analysis of texts and achieving
state of the art results. Regarding the first problem, the effectiveness of various
crowdsourcing alternatives is explored and two medium-sized and emotion-labeled
song datasets are created utilizing social tags. One of the research interests of Telecom
Italia was the exploration of relations between music emotional stimulation and
driving style. Consequently, a context-aware music recommender system that aims
to enhance driving comfort and safety was also designed. To address the second
problem, a series of experiments with large text collections of various contents and
domains were conducted. Word embeddings of different parameters were exercised
and results revealed that their quality is influenced (mostly but not only) by the
size of texts they were created from. When working with small text datasets, it is
thus important to source word features from popular and generic word embedding
collections. Regarding the third problem, a series of experiments involving convolutional
and max-pooling neural layers were conducted. Various patterns relating
text properties and network parameters with optimal classification accuracy were
observed. Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
Given that labeled data are becoming the bottleneck of the current deep learning
systems, a future research direction could be the exploration of various data programming
possibilities for constructing even bigger labeled datasets. Investigation
of feature-level or decision-level ensemble techniques in the context of deep neural
networks could also be fruitful. Different feature types do usually represent complementary
characteristics of data. Combining word embedding and traditional text
features or utilizing recurrent networks on document splits and then aggregating the
predictions could further increase prediction accuracy of such models
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
Remote sensing (RS) systems have been collecting
massive volumes of datasets for decades, managing and analyzing
of which are not practical using common software packages and
desktop computing resources. In this regard, Google has developed
a cloud computing platform, called Google Earth Engine (GEE), to
effectively address the challenges of big data analysis. In particular,
this platformfacilitates processing big geo data over large areas and
monitoring the environment for long periods of time. Although this
platformwas launched in 2010 and has proved its high potential for
different applications, it has not been fully investigated and utilized
for RS applications until recent years. Therefore, this study aims
to comprehensively explore different aspects of the GEE platform,
including its datasets, functions, advantages/limitations, and various
applications. For this purpose, 450 journal articles published in
150 journals between January 2010 andMay 2020 were studied. It
was observed that Landsat and Sentinel datasets were extensively
utilized by GEE users. Moreover, supervised machine learning
algorithms, such as Random Forest, were more widely applied to
image classification tasks. GEE has also been employed in a broad
range of applications, such as Land Cover/land Use classification,
hydrology, urban planning, natural disaster, climate analyses, and
image processing. It was generally observed that the number of
GEE publications have significantly increased during the past few
years, and it is expected that GEE will be utilized by more users
from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version
Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes
Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute
Redes sociais online : extração de conhecimento e análise espaço-temporal de eventos de difusão de informação
Orientador: Fernando José Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociaisAbstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connectionsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric
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