114,530 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
Models of Cognition: Neurological possibility does not indicate neurological plausibility
Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
Entropy/IP: Uncovering Structure in IPv6 Addresses
In this paper, we introduce Entropy/IP: a system that discovers Internet
address structure based on analyses of a subset of IPv6 addresses known to be
active, i.e., training data, gleaned by readily available passive and active
means. The system is completely automated and employs a combination of
information-theoretic and machine learning techniques to probabilistically
model IPv6 addresses. We present results showing that our system is effective
in exposing structural characteristics of portions of the IPv6 Internet address
space populated by active client, service, and router addresses.
In addition to visualizing the address structure for exploration, the system
uses its models to generate candidate target addresses for scanning. For each
of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates
for scanning. We achieve some success in 14 datasets, finding up to 40% of the
generated addresses to be active. In 11 of these datasets, we find active
network identifiers (e.g., /64 prefixes or `subnets') not seen in training.
Thus, we provide the first evidence that it is practical to discover subnets
and hosts by scanning probabilistically selected areas of the IPv6 address
space not known to contain active hosts a priori.Comment: Paper presented at the ACM IMC 2016 in Santa Monica, USA
(https://dl.acm.org/citation.cfm?id=2987445). Live Demo site available at
http://www.entropy-ip.com
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