4,348 research outputs found
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning
Motivated by what is required for real-time path planning, the paper starts
out by presenting sRMPD, a new recursive "local" planner founded on the key
notion that, unless made necessary by an obstacle, there must be no deviation
from the shortest path between any two points, which would normally be a
straight line path in the configuration space. Subsequently, we increase the
power of sRMPD by using it as a "connect" subroutine call in a higher-level
sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence,
mRMPD spawns a larger number of space exploring trees in regions of the
configuration space that are characterized by a higher density of obstacles.
The overall effect is a hybrid tree growing strategy with a trade-off between
random exploration as made possible by multi-RRT based logic and immediate
exploitation of opportunities to connect two states as made possible by sRMPD.
The mRMPD planner can be biased with regard to this trade-off for solving
different kinds of planning problems efficiently. Based on the test cases we
have run, our experiments show that mRMPD can reduce planning time by up to 80%
compared to basic RRT
The Noetic Prism
Definitions of ‘knowledge’ and its relationships with ‘data’ and ‘information’ are varied, inconsistent and often contradictory. In particular the traditional hierarchy of data-information-knowledge and its various revisions do not stand up to close scrutiny. We suggest that the problem lies in a flawed analysis that sees data, information and knowledge as separable concepts that are transformed into one another through processing. We propose instead that we can describe collectively all of the materials of computation as ‘noetica’, and that the terms data, information and knowledge can be reconceptualised as late-binding, purpose-determined aspects of the same body of material. Changes in complexity of noetica occur due to value-adding through the imposition of three different principles: increase in aggregation (granularity), increase in set relatedness (shape), and increase in contextualisation through the formation of networks (scope). We present a new model in which granularity, shape and scope are seen as the three vertices of a triangular prism, and show that all value-adding through computation can be seen as movement within the prism space. We show how the conceptual framework of the noetic prism provides a new and comprehensive analysis of the foundations of computing and information systems, and how it can provide a fresh analysis of many of the common problems in the management of intellectual resources
Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields
The aim of this paper is to propose diffusion strategies for distributed
estimation over adaptive networks, assuming the presence of spatially
correlated measurements distributed according to a Gaussian Markov random field
(GMRF) model. The proposed methods incorporate prior information about the
statistical dependency among observations, while at the same time processing
data in real-time and in a fully decentralized manner. A detailed mean-square
analysis is carried out in order to prove stability and evaluate the
steady-state performance of the proposed strategies. Finally, we also
illustrate how the proposed techniques can be easily extended in order to
incorporate thresholding operators for sparsity recovery applications.
Numerical results show the potential advantages of using such techniques for
distributed learning in adaptive networks deployed over GMRF.Comment: Submitted to IEEE Transactions on Signal Processing. arXiv admin
note: text overlap with arXiv:1206.309
Fixed-point MAP decoding of channel codes
This paper describes the fixed-point model of the maximum a posteriori (MAP) decoding algorithm of turbo and low-density parity-check (LDPC) codes, the most advanced channel codes adopted by modern communication systems for forward error correction (FEC). Fixed-point models of the decoding algorithms are developed in a unified framework based on the use of the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. This approach aims at bridging the gap toward the design of a universal, multistandard decoder of channel codes, capable of supporting the two classes of codes and having reduced requirements in terms of silicon area and power consumption and so suitable to mobile applications. The developed models allow the identification of key parameters such as dynamic range and number of bits, whose impact on the error correction performance of the algorithm is of pivotal importance for the definition of the architectural tradeoffs between complexity and performance. This is done by taking the turbo and LDPC codes of two recent communication standards such asWiMAX and 3GPP-LTE as a reference benchmark for a mobile scenario and by analyzing their performance over additive white Gaussian noise (AWGN) channel for different values of the fixed-point parameters
Network-Oblivious Algorithms
A framework is proposed for the design and analysis of network-oblivious algorithms, namely algorithms that can run unchanged, yet efficiently, on a variety of machines characterized by different degrees of parallelism and communication capabilities. The framework prescribes that a network-oblivious algorithm be specified on a parallel model of computation where the only parameter is the problem\u2019s input size, and then evaluated on a model with two parameters, capturing parallelism granularity and communication latency. It is shown that for a wide class of network-oblivious algorithms, optimality in the latter model implies optimality in the decomposable bulk synchronous parallel model, which is known to effectively describe a wide and significant class of parallel platforms. The proposed framework can be regarded as an attempt to port the notion of obliviousness, well established in the context of cache hierarchies, to the realm of parallel computation. Its effectiveness is illustrated by providing optimal network-oblivious algorithms for a number of key problems. Some limitations of the oblivious approach are also discussed
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
Label-Dependencies Aware Recurrent Neural Networks
In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as CRFs. In this work we propose a solution far simpler but very
effective: an evolution of the simple Jordan RNN, where labels are re-injected
as input into the network, and converted into embeddings, in the same way as
words. We compare this RNN variant to all the other RNN models, Elman and
Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language
Understanding (SLU). Thanks to label embeddings and their combination at the
hidden layer, the proposed variant, which uses more parameters than Elman and
Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other
RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best
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