43,152 research outputs found
LUNES: Agent-based Simulation of P2P Systems (Extended Version)
We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which
allows to simulate complex networks composed of a high number of nodes. LUNES
is modular, since it splits the three phases of network topology creation,
protocol simulation and performance evaluation. This permits to easily
integrate external software tools into the main software architecture. The
simulation of the interaction protocols among network nodes is performed via a
simulation middleware that supports both the sequential and the
parallel/distributed simulation approaches. In the latter case, a specific
mechanism for the communication overhead-reduction is used; this guarantees
high levels of performance and scalability. To demonstrate the efficiency of
LUNES, we test the simulator with gossip protocols executed on top of networks
(representing peer-to-peer overlays), generated with different topologies.
Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation
of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011
International Conference on High Performance Computing and Simulation (HPCS
2011
Latent Fisher Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.Comment: 12 page
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
Data mining reactor fuel grab load trace data to support nuclear core condition monitoring
A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described
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