92,402 research outputs found
Poseidon: a 2-tier Anomaly-based Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon
is payload-based, and presents a two-tier architecture: the first stage
consists of a Self-Organizing Map, while the second one is a modified PAYL
system. Our benchmarks on the 1999 DARPA data set show a higher detection rate
and lower number of false positives than PAYL and PHAD
Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD
Biological Principles in Self-Organization of Young Brain - Viewed from Kohonen Model
Variants of the Kohonen model are proposed to study biological principles of
self-organization in a model of young brain. We suggest a function to measure
aquired knowledge and use it to auto-adapt the topology of neuronal
connectivity, yielding substantial organizational improvement relative to the
standard model. In the early phase of organization with most intense learning,
we observe that neural connectivity is of Small World type, which is very
efficient to organize neurons in response to stimuli. In analogy to human brain
where pruning of neural connectivity (and neuron cell death) occurs in early
life, this feature is present also in our model, which is found to stabilize
neuronal response to stimuli
eXamine: a Cytoscape app for exploring annotated modules in networks
Background. Biological networks have growing importance for the
interpretation of high-throughput "omics" data. Statistical and combinatorial
methods allow to obtain mechanistic insights through the extraction of smaller
subnetwork modules. Further enrichment analyses provide set-based annotations
of these modules.
Results. We present eXamine, a set-oriented visual analysis approach for
annotated modules that displays set membership as contours on top of a
node-link layout. Our approach extends upon Self Organizing Maps to
simultaneously lay out nodes, links, and set contours.
Conclusions. We implemented eXamine as a freely available Cytoscape app.
Using eXamine we study a module that is activated by the virally-encoded
G-protein coupled receptor US28 and formulate a novel hypothesis about its
functioning
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity
Using a large database (~ 215 000 records) of relevant articles, we
empirically study the "complex systems" field and its claims to find universal
principles applying to systems in general. The study of references shared by
the papers allows us to obtain a global point of view on the structure of this
highly interdisciplinary field. We show that its overall coherence does not
arise from a universal theory but instead from computational techniques and
fruitful adaptations of the idea of self-organization to specific systems. We
also find that communication between different disciplines goes through
specific "trading zones", ie sub-communities that create an interface around
specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and
Technology (2012) 10.1002/asi.2264
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
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