4,851 research outputs found
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Data analysis and navigation in high-dimensional chemical and biological spaces
The goal of this master thesis is to develop and validate a visual data-mining
approach suitable for the screening of chemicals in the context of REACH [Registration, Evaluation, Authorization and
Restriction of Chemicals]. The
proposed approach will facilitate the development and validation of non-testing
methods via the exploration of environmental endpoints and their relationship with
the chemical structure and physicochemical properties of chemicals.
The use of an interactive chemical space data exploration tool using 3D visualization
and navigation will enrich the information available with additional variables like
size, texture and color of the objects of the scene (compounds). The features that
distinguish this approach and make it unique are (i) the integration of multiple data
sources allowing the recovery in real time of complementary information of the
studied compounds, (ii) the integration of several algorithms for the data analysis
(dimensional reduction, generation of composite variables and clustering) and (iii)
direct user interaction with the data through the virtual navigation mechanism. All
this is achieved without the need for specialized hardware or the use of specific
devices and high-cost virtual reality and mixed reality
Hyperspectral-Augmented Target Tracking
With the global war on terrorism, the nature of military warfare has changed significantly. The United States Air Force is at the forefront of research and development in the field of intelligence, surveillance, and reconnaissance that provides American forces on the ground and in the air with the capability to seek, monitor, and destroy mobile terrorist targets in hostile territory. One such capability recognizes and persistently tracks multiple moving vehicles in complex, highly ambiguous urban environments. The thesis investigates the feasibility of augmenting a multiple-target tracking system with hyperspectral imagery. The research effort evaluates hyperspectral data classification using fuzzy c-means and the self-organizing map clustering algorithms for remote identification of moving vehicles. Results demonstrate a resounding 29.33% gain in performance from the baseline kinematic-only tracking to the hyperspectral-augmented tracking. Through a novel methodology, the hyperspectral observations are integrated in the MTT paradigm. Furthermore, several novel ideas are developed and implemented—spectral gating of hyperspectral observations, a cost function for hyperspectral observation-to-track association, and a self-organizing map filtering method. It appears that relatively little work in the target tracking and hyperspectral image classification literature exists that addresses these areas. Finally, two hyperspectral sensor modes are evaluated—Pushbroom and Region-of-Interest. Both modes are based on realistic technologies, and investigating their performance is the goal of performance-driven sensing. Performance comparison of the two modes can drive future design of hyperspectral sensors
Dynamic Content-based Indexing in Mobile edge Networks
Recently, we have seen a huge growth in the usage of mobile devices, and with this growth,
the data generated has also increased, being in a huge scale, user generated, e.g, photos,
books, texts or messages/e-mails. Usually this data requires a permanent storage and its
respective indexing for users to efficiently access it however, due to the unpredictability
of this data, a concern regarding its indexing starts to raise as it can be hard to predict
labels and indexes capable of representing every possible set of data.
For instance, during a birthday party, users may want to share photos and videos of
this event which can be seen as uploading streams of data to a content sharing system.
This content stream will most likely have no index, unless it is explicitly generated, making
its retrieval difficult. However, when clustering this stream, as data keeps increasing,
we might, somewhere in the future, be capable of detecting similarities between each
photo (e.g. a guest’s face) and might want to index them. Indices can directly impact a
system’s performance however, there is a drawback from having either too many or too
few indices, posing a challenge when it comes to evolving content.
We propose Chives, a Content-Based Indexing framework, built on top of a content
sharing publish/subscribe system at the edge named Thyme, where we evaluate unsupervised
learning in data stream techniques to generate indices. It also offers a content-based
query to automatically subscribe to indices containing similar content, e.g images.
After evaluating our proposal in a simulated environment, we can see that our framework
offers a great abstraction, allowing an easy extension, furthermore our implementation
can generate indices from data streams and the indexing follows a clustering criteria,
generating the indices as conditions are met. Furthermore, results show that our clustering
quality and consequently its generated indices rely strongly on the quality of the
image discrimination and its ability to extract features representing its face. In Conclusion,
more studies should be done regarding this framework as such, our solution is built
in a way where we can exclusively study each component and upgrade it in future work.Recentemente, tem-se observado um enorme crescimento na adesão a dispositivos móveis
e com este crescimento, tem também aumentado a quantidade de dados partilhados,
sendo em grande escala, gerado pelos utilizadores, por exemplo, fotos, livros, textos ou até
mensagens/e-mails. Normalmente estes dados necessitam de um local de armazenamento
permanente e a sua respectiva indexação de modo a poderem ser acedidos de forma
eficiente por parte dos utilizadores no entanto, dada a imprevisibilidade destes dados,
pode surgir um problema relativamente à indexação dado que poderá ser difícil prever
etiquetas e índices capazes de representar qualquer conjunto de dados.
Por exemplo, durante uma festa de anos, utilizadores poderão partilhar fotografias e
vídeos deste evento que poderá ser também interpretado como um upload de dados em
stream para um sistema de partilha de conteúdo. Esta stream de dados, muito provavelmente
não terá nenhum índice capaz de o descrever, tornando difícil a obtenção deste
visto que não existe representação semântica desta. No entanto, ao agrupar esta stream, à
medida que os dados vão crescendo, poderemos, algures no tempo ser capaz de detectar
semelhanças entre cada fotografia (por exemplo. a cara de um convidado) e podemos
querer indexar. Índices podem causar um impacto directo sobre o sistema, no entanto o
inverso pode acontecer quando existe índices em défice ou em excesso, apresentando um
desafio acerca de dados evolutivos.
Nós propomos uma framework de indexação baseada em conteúdo, construído por
cima de um sistema de partilha de conteúdo que usa um sistema de Publish/Subscribe na
edge denominado Thyme, onde avaliamos técnicas de aprendizagem não supervisionada
em data streams para gerar dinamicamente índices.
Depois de avaliar a nossa framework, conseguimos concluir que esta oferece uma boa
abstração, facilitando a sua extensão, para além disso a nossa proposta permite gerar
índices quando as condições definidas para o clustering são respeitadas. Para além disso,
os resultados demonstram que o clustering realizado pelo nosso algoritmo dependem
fortemente da qualidade de discriminação de imagens e das características obtidas por
este discriminador em relação às faces. Concluindo, mais estudos devem feitos em relação
à framework, como tal esta foi construída de modo a permitir uma rápida e fácil extensão para futuros melhoramentos
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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