Ruairí de Fréin

Distributed formal concept analysis algorithms based on an iterative mapreduce framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganters classic algorithm by introducing a family of

The Synchronized Short-Time-Fourier-Transform: Properties and Definitions for Multichannel Source Separation

\mbox{MR}^\star

Integration of QoS Metrics, Rules and Semantic Uplift for Advanced IPTV Monitoring

algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithms lineage. To evaluate the factors that impact distributed algorithm performance, we compare our

Formal Concept Analysis via Atomic Priming

\mbox{MR}^{*}

Learning speech features in the presence of noise: Sparse convolutive robust non-negative matrix factorization

algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.

Effect of system load on video service metrics

This paper proposes the use of a synchronized linear transform, the synchronized short-time-Fourier-transform (sSTFT), for time-frequency analysis of anechoic mixtures. We address the short comings of the commonly used time-frequency linear transform in multichannel settings, namely the classical short-time-Fourier-transform (cSTFT). We propose a series of desirable properties for the linear transform used in a multichannel source separation scenario: stationary invertibility, relative delay, relative attenuation, and finally delay invariant relative windowed-disjoint orthogonality (DIRWDO). Multisensor source separation techniques which operate in the time-frequency domain, have an inherent error unless consideration is given to the multichannel properties proposed in this paper. The sSTFT preserves these relationships for multichannel data. The crucial innovation of the sSTFT is to locally synchronize the analysis to the observations as opposed to a global clock. Improvement in separation performance can be achieved because assumed properties of the time-frequency transform are satisfied when it is appropriately synchronized. Numerical experiments show the sSTFT improves instantaneous subsample relative parameter estimation in low noise conditions and achieves good synthesis.

Constructing Time-Frequency Dictionaries for Source Separation via Time-Frequency Masking and Source Localisation

Abstract Increasing and variable traffic demands due to triple play services pose significant Internet Protocol Television (IPTV) resource management challenges for service providers. Managing subscriber expectations via consolidated IPTV quality reporting will play a crucial role in guaranteeing return-on-investment for players in the increasingly competitive IPTV delivery ecosystem. We propose a fault diagnosis and problem isolation solution that addresses the IPTV monitoring challenge and recommends problem-specific remedial action. IPTV delivery-specific metrics are collected at various points in the delivery topology, the residential gateway and the Digital Subscriber Line Access Multiplexer through to the video Head-End. They are then pre-processed using new metric rules. A semantic uplift engine takes these raw metric logs; it then transforms them into World Wide Web Consortium’s standard Resource Description Framework for knowledge representation and annotates them with expert knowledge from the IPTV domain. This system is then integrated with a monitoring visualization framework that displays monitoring events, alarms, and recommends solutions. A suite of IPTV fault scenarios is presented and used to evaluate the feasibility of the solution. We demonstrate that professional service providers can provide timely reports on the quality of IPTV service delivery using this system.

Learning and Storing the Parts of Objects : IMF

Formal Concept Analysis (FCA) looks to decompose a matrix of objects-attributes into a set of sparse matrices capturing the underlying structure of a formal context. We propose a Rank Reduction (RR) method to prime approximate FCAs, namely RRFCA. While many existing FCA algorithms are complete, lectic ordering of the lattice may not minimize search/decomposition time. Initially, RRFCA decompositions are not unique or complete; however, a set of good closures with high support is learned quickly, and then, made complete. RRFCA has its novelty in that we propose a new multiplicative two-stage method. First, we describe the theoretical foundations underpinning our RR approach. Second, we provide a representative exemplar, showing how RRFCA can be implemented. Further experiments demonstrate that RRFCA methods are efficient, scalable and yield time-savings. We demonstrate the resulting methods lend themselves to parallelization.

Learning convolutive features for storage and transmission between networked sensors

We introduce a non-negative matrix factorization technique which learns speech features with temporal extent in the presence of non-stationary noise. Our proposed technique, namely Sparse convolutive robust non-negative matrix factorization, is robust in the presence of noise due to our explicit treatment of noise as an interfering source in the factorization. We derive multiplicative update rules using the alpha divergence objective. We show that our proposed method yields superior performance to sparse convolutive non-negative matrix factorization in a feature learning task on noisy data and comparable results to dedicated speech enhancement techniques.

Take Off a Load: Load-Adjusted Video Quality Prediction and Measurement

Model selection, in order to learn the mapping between the kernel metrics of a machine in a server cluster and a service quality metric on a clients machine, has been addressed by directly applying Linear Regression (LR) to the observations. The popularity of the LR approach is due to: 1) its implementation efficiency; 2) its low computational complexity; and finally, 3) it generally captures the data relatively accurately. LR, can however, produce misleading results if the LR model does not characterize the system: this deception is due in part to its accuracy. In the client-server service modeling literature LR is applied to the server and client metrics without treating the load on the system as the cause for the excitation of the system. By contrast, in this paper, we propose a generative model for the server and client metrics and a hierarchical model to explain the mapping between them, which is cognizant of the effects of the load on the system. Evaluations using real traces support the following conclusions: The system load accounts for ≥ 50% of the energy of a high proportion of the client and server metric traces — modeling the load is crucial; the load signal is localized in the frequency domain: we can remove the load by deconvolution; There is a significant phase shift between both the kernel and the service-level metrics, which, coupled with the load, heavily biases the results obtained from out-of-the-box LR without any system identification pre-processing.