165,925 research outputs found
Distributed Collaborative Monitoring in Software Defined Networks
We propose a Distributed and Collaborative Monitoring system, DCM, with the
following properties. First, DCM allow switches to collaboratively achieve flow
monitoring tasks and balance measurement load. Second, DCM is able to perform
per-flow monitoring, by which different groups of flows are monitored using
different actions. Third, DCM is a memory-efficient solution for switch data
plane and guarantees system scalability. DCM uses a novel two-stage Bloom
filters to represent monitoring rules using small memory space. It utilizes the
centralized SDN control to install, update, and reconstruct the two-stage Bloom
filters in the switch data plane. We study how DCM performs two representative
monitoring tasks, namely flow size counting and packet sampling, and evaluate
its performance. Experiments using real data center and ISP traffic data on
real network topologies show that DCM achieves highest measurement accuracy
among existing solutions given the same memory budget of switches
Finding Associations and Computing Similarity via Biased Pair Sampling
This version is ***superseded*** by a full version that can be found at
http://www.itu.dk/people/pagh/papers/mining-jour.pdf, which contains stronger
theoretical results and fixes a mistake in the reporting of experiments.
Abstract: Sampling-based methods have previously been proposed for the
problem of finding interesting associations in data, even for low-support
items. While these methods do not guarantee precise results, they can be vastly
more efficient than approaches that rely on exact counting. However, for many
similarity measures no such methods have been known. In this paper we show how
a wide variety of measures can be supported by a simple biased sampling method.
The method also extends to find high-confidence association rules. We
demonstrate theoretically that our method is superior to exact methods when the
threshold for "interesting similarity/confidence" is above the average pairwise
similarity/confidence, and the average support is not too low. Our method is
particularly good when transactions contain many items. We confirm in
experiments on standard association mining benchmarks that this gives a
significant speedup on real data sets (sometimes much larger than the
theoretical guarantees). Reductions in computation time of over an order of
magnitude, and significant savings in space, are observed.Comment: This is an extended version of a paper that appeared at the IEEE
International Conference on Data Mining, 2009. The conference version is (c)
2009 IEE
IR-UWB Detection and Fusion Strategies using Multiple Detector Types
Optimal detection of ultra wideband (UWB) pulses in a UWB transceiver
employing multiple detector types is proposed and analyzed in this paper. We
propose several fusion techniques for fusing decisions made by individual
IR-UWB detectors. We assess the performance of these fusion techniques for
commonly used detector types like matched filter, energy detector and amplitude
detector. In order to perform this, we derive the detection performance
equation for each of the detectors in terms of false alarm rate, shape of the
pulse and number of UWB pulses used in the detection and apply these in the
fusion algorithms. We show that the performance can be improved approximately
by 4 dB in terms of signal to noise ratio (SNR) for perfect detectability of a
UWB signal in a practical scenario by fusing the decisions from individual
detectors.Comment: Accepted for publishing in IEEE WCNC 201
Bayesian estimation of Differential Transcript Usage from RNA-seq data
Next generation sequencing allows the identification of genes consisting of
differentially expressed transcripts, a term which usually refers to changes in
the overall expression level. A specific type of differential expression is
differential transcript usage (DTU) and targets changes in the relative within
gene expression of a transcript. The contribution of this paper is to: (a)
extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian
model which is originally designed for identifying changes in overall
expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist
model for inferring DTU. cjBitSeq is a read based model and performs fully
Bayesian inference by MCMC sampling on the space of latent state of each
transcript per gene. BayesDRIMSeq is a count based model and estimates the
Bayes Factor of a DTU model against a null model using Laplace's approximation.
The proposed models are benchmarked against the existing ones using a recent
independent simulation study as well as a real RNA-seq dataset. Our results
suggest that the Bayesian methods exhibit similar performance with DRIMSeq in
terms of precision/recall but offer better calibration of False Discovery Rate.Comment: Revised version, accepted to Statistical Applications in Genetics and
Molecular Biolog
HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach
In this paper we present a novel hybrid (arraybased layout and vertical
bitmap layout) database representation approach for mining complete Maximal
Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms
of scalability, item search order and two horizontal and vertical projection
techniques. We also present a maximal algorithm using this hybrid database
representation approach. Different experimental results on real and sparse
benchmark datasets show that our approach is better than previous state of art
maximal algorithms.Comment: 8 Pages In the proceedings of 9th IEEE-INMIC 2005, Karachi, Pakistan,
200
First-grade Latino English language learners' performance on story problems in spanish versus english
To explore whether teaching English Language Learners (ELLs) with an emphasis on English story problem is appropriate, we compared the performance of a group of Latino first graders when working in Spanish and in English on two equivalent sets of story problems. The students’ performance was slightly higher in English than in Spanish, but lower than monolingual students from other studies. ELLs’ success in English indicated that the children’s knowledge of conversational English was sufficient to comprehend story problems, leading us to conclude that teaching through story problems is a viable approach with ELLs
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