Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

A Geometric Algorithm for Learning Oblique Decision Trees

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

A new approach for fault location identification in transmission system using stability analysis and SVMs

This paper presents a new approach to the location of fault in the high voltage power transmission system using Support Vector Machines (SVMs). A knowledge base is developed using transient stability studies for apparent impedance swing trajectory in the R-X plane. SVM technique is applied to identify the fault location in the system. Results are presented on sample 3-power station, a 9-bus system illustrate the implementation of the proposed method.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.

Reducing the babel in plant volatile communication: using the forest to see the trees

While plants of a single species emit a diversity of volatile organic compounds (VOCs) to attract or repel interacting organisms, these specific messages may be lost in the midst of the hundreds of VOCs produced by sympatric plants of different species, many of which may have no signal content. Receivers must be able to reduce the babel or noise in these VOCs in order to correctly identify the message. For chemical ecologists faced with vast amounts of data on volatile signatures of plants in different ecological contexts, it is imperative to employ accurate methods of classifying messages, so that suitable bioassays may then be designed to understand message content. We demonstrate the utility of `Random Forests' (RF), a machine-learning algorithm, for the task of classifying volatile signatures and choosing the minimum set of volatiles for accurate discrimination, using datam from sympatric Ficus species as a case study. We demonstrate the advantages of RF over conventional classification methods such as principal component analysis (PCA), as well as data-mining algorithms such as support vector machines (SVM), diagonal linear discriminant analysis (DLDA) and k-nearest neighbour (KNN) analysis. We show why a tree-building method such as RF, which is increasingly being used by the bioinformatics, food technology and medical community, is particularly advantageous for the study of plant communication using volatiles, dealing, as it must, with abundant noise.

On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations

It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M-1 and M-2 as given by C-d*w = M1(d*t) x Q(t*w).Where d is the number of documents present in the corpus anti w is the size of the vocabulary. The quality of the split depends on ``t'', the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics - this is shown by a `dip' at the right value for `t'.

A Low-Complexity Algorithm for Intrusion Detection in a PIR-Based Wireless Sensor Network

We present a low-complexity algorithm for intrusion detection in the presence of clutter arising from wind-blown vegetation, using Passive Infra-Red (PIR) sensors in a Wireless Sensor Network (WSN). The algorithm is based on a combination of Haar Transform (HT) and Support-Vector-Machine (SVM) based training and was field tested in a network setting comprising of 15-20 sensing nodes. Also contained in this paper is a closed-form expression for the signal generated by an intruder moving at a constant velocity. It is shown how this expression can be exploited to determine the direction of motion information and the velocity of the intruder from the signals of three well-positioned sensors.

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (sigma'(v0)), soil type, shear wave velocity (V-s) and earthquake parameters such as peak horizontal acceleration (a(max)) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are sigma'(v0), soil type. V-s, a(max) and M. In the other model based on shear wave velocity alone uses V-s, a(max) and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. (C) 2010 Elsevier B.V. All rights reserved.

Synthesis, Reactivity, Structural Aspects, and Solution Dynamics of Cyclopalladated Compounds of N,N `,N `'-Tris(2-anisyl)guanidine

N,N',N `'-Tris(2-anisyl)guanidine, (ArNH)(2)C=NAr (Ar = 2-(MeO)C6H4), was cyclopallaclated with Pd(OC(O)R)(2) (R = Me, CF3) in toluene at 70 degrees C to afford palladacycles Pd{kappa(2)(C,N)-C6H3-(OMe)-3(NHC(NHAr)(=NAr))-2}(mu-OC(O)R)](2)(R = Me (1a) and CF3 (1b)) in 87% and 95% yield, respectively. Palladacycle 1a was subjected to a metathetical reaction with LiBr in aqueous ethanol at 78 degrees C to afford palladacycle Pd{kappa(2)(C,N)-C6H3(OMe)-3(NHC(NHAr)(=NAr))-2}(mu-Br)](2) (2) in 90% yield. Palladacycle 2 was subjected to a bridge-splitting reaction with Lewis bases in CH2Cl2 to afford the monomeric palladacycles Pd{kappa(2)(C,N)-C6H3(OMe)-3(NHC(NHAr)(=NAr))-2}Br(L)] (L = 2,6-Me2C5H3N (3a), 2,4-Me2C5H3N (3b), 3,5-Me2C5H3N (3c), XyNC (Xy = 2,6-Me2C6H3; 4a), (BuNC)-Bu-t (4b), and PPh3 (5)) in 87-95% yield. Palladacycle 2 upon reaction with 2 equiv of XyNC in CH2Cl2 afforded an unanticipated palladacycle, Pd{kappa(2)(C,N)-C(=NXy)(C6H3(OMe)-4)-2(N=C-(NH Ar)(2))-3} Br(CNXy)] (6) in 93% yield, and the driving force for the formation of 6 was ascribed to a ring contraction followed by amine-imine tautomerization. Palladacycles 1 a,b revealed a dimeric transoid in-in conformation with ``open book'' framework in the solid state. In solution, 1 a exhibited a fluxional behavior ascribed to the six-membered ``(C,N)Pd'' ring inversion and partly dissociates to the pincer type and kappa(2)-O,O'-OAc monomeric palladacycles by an anchimerically assisted acetate cleavage process as studied by variable-temperature H-1 NMR data. Palladacycles 3a,b revealed a unique trans configuration around the palladium with lutidine being placed trans to the Pd-C bond, whereas cis stereochemistry was observed between the Pd-C bond and the Lewis base in 4a (as determined by X-ray diffraction data) and 5 (as determined by P-31 and C-13 NMR data). The aforementioned stereochemical difference was explained by invoking relative hardness/softness of the donor atoms around the palladium center. In solution, palladacycles 3a-c exist as a mixture of two interconverting boat conformers via a planar intermediate without any bond breaking due to the six-membered ``(C,N)Pd'' ring inversion, whereas palladacycles 4a,b and 5 exist as a single isomer, as deduced from detailed H-1 NMR studies.

Organometallics of diphosphazanes. Part 10. Dinuclear group 6 metal carbonyl complexes bridged by a cyclodiphosphazane in its cis or trans form

Mononuclear Group 6 metal tetracarbonyl complexes containing a cyclodiphosphazane ligand, [PhNP(OC(6)H(4)Me-p)](2) (L), have been used as synthons to prepare homo- and hetero-bimetallic complexes in which the cyclodiphosphazane bridges the two metal centres in its cis or trans isomeric forms. The dimolybdenum complex [Mo-2(eta(5)-C5H5)(2)(CO)(4)(mu-L)] has also been synthesized. The trends in P-31 NMR chemical shifts and the structural features as revealed by X-ray crystallography are discussed.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Organometallic chemistry of diphosphazanes .13. Palladium complexes of unsymmetrical diphosphazanes Ph(2)PN(Pr-i)PYY'

Unsymmetrical diphosphazanes Ph(2)PN(Pr-i)PYY' [YY' = O2C12H8 (L(1)), O2C20H12 (L(2)); Y = Ph and Y' = OC6H4Br-4 (L(3)), OC(6)H(4)Me-4 (L(4)), OC(6)H(3)Me(2)-3,5 (L(5)), N(2)C(3)HMe(2)-3,5 (L(6))] react with cis-[PdCl2(COD)] (COD = cycloocta-1,5-diene) giving the chelate complexes of the type cis-[PdCl2{eta(2)-Ph(2)PN(Pr-i)PYY'}] [YY' = O2C12H8 (1), O2C20H12 (2), Y = Ph and Y' = OC6H4Br-4 (3), OC(6)H(4)Me-4 (4), OC(6)H(3)Me(2)-3,5 (5), N(2)C(3)HMe(2)-3,5 (6)]. The P-N bond in 3 and 5 undergoes a facile cleavage in methanol solution to give cis-[PdCl2{eta(1)Ph(2)P(OMe)}{eta(1)-PhP(NHPri)(Y')}] [Y' = OC6H4Br-4 (7), OC(6)H(3)Me(2)-3,5 (8)]. Reactions of Pd-2(dba)(3) . CHCl3 (dba = dibenzylideneacetone) with the diphosphazanes Ph(2)PN(Pr-i)PPhY' [Y' = OC(6)H(4)Me-4 (L(4)), N(2)C(3)HMe(2)-3,5 (L(6)), N2C3H3 (L(7))] in the presence of MeI yields cis-[PdI2{eta(2)-Ph(2)PN(Pr-i)PPhMe}] (9); the P-O or P-N(pyrazolyl) bond of the starting ligands is cleaved and a p-C(Me) bond is formed. An analogous oxidative addition reaction in the presence of Ph(2)PN(Pr-i)PPh(2) (L(8)) yields cis-[PdI(Me)(eta(2)-L(8))] (10) and cis-[PdI2(eta 2-L(8))] (11). The structures of 8 and 9 have been determined by X-ray diffraction. Copyright (C) 1996 Elsevier Science Ltd