Discriminative pattern mining in software fault detection

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

An item response analysis of the motor and behavioral subscales of the unified Huntington's disease rating scale in huntington disease gene expansion carriers

Although the Unified Huntington's Disease Rating Scale (UHDRS) is widely used in the assessment of Huntington disease (HD), the ability of individual items to discriminate individual differences in motor or behavioral manifestations has not been extensively studied in HD gene expansion carriers without a motor-defined clinical diagnosis (ie, prodromal-HD or prHD). To elucidate the relationship between scores on individual motor and behavioral UHDRS items and total score for each subscale, a nonparametric item response analysis was performed on retrospective data from 2 multicenter longitudinal studies. Motor and behavioral assessments were supplied for 737 prHD individuals with data from 2114 visits (PREDICT-HD) and 686 HD individuals with data from 1482 visits (REGISTRY). Option characteristic curves were generated for UHDRS subscale items in relation to their subscale score. In prHD, overall severity of motor signs was low, and participants had scores of 2 or above on very few items. In HD, motor items that assessed ocular pursuit, saccade initiation, finger tapping, tandem walking, and to a lesser extent, saccade velocity, dysarthria, tongue protrusion, pronation/supination, Luria, bradykinesia, choreas, gait, and balance on the retropulsion test were found to discriminate individual differences across a broad range of motor severity. In prHD, depressed mood, anxiety, and irritable behavior demonstrated good discriminative properties. In HD, depressed mood demonstrated a good relationship with the overall behavioral score. These data suggest that at least some UHDRS items appear to have utility across a broad range of severity, although many items demonstrate problematic features.

Diversity of strains of Salmonella enterica serotype Enteritidis from English poultry farms assessed by multiple genetic fingerprinting

Reliable and sufficiently discriminative methods are needed for differentiating individual strains of Salmonella enterica serotype Enteritidis beyond the phenotypic level; however, a consensus has not been reached as to which molecular method is best suited for this purpose. In addition, data are lacking on the molecular fingerprinting of serotype Enteritidis from poultry environments in the United Kingdom. This study evaluated the combined use of classical methods (phage typing) with three well-established molecular methods (ribotyping, macrorestriction analysis of genomic DNA, and plasmid profiling) in the assessment of diversity within 104 isolates of serotype Enteritidis from eight unaffiliated poultry farms in England. The most sensitive technique for identifying polymorphism was PstI-SphII ribotyping, distinguishing a total of 22 patterns, 10 of which were found among phage type 4 isolates. Pulsed-field gel electrophoresis of XhaI-digested genomic DNA segregated the isolates into only six types with minor differences between them. In addition, 14 plasmid profiles were found among this population. When all of the typing methods were combined, 54 types of strains were differentiated, and most of the poultry farms presented a variety of strains, which suggests that serotype Enteritidis organisms representing different genomic groups are circulating in England. In conclusion, geographical and animal origins of Salmonella serotype Enteritidis isolates may have a considerable influence on selecting the best typing strategy for individual programs, and a single method cannot be relied on for discriminating between strains.

Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules

Infrared polarization and intensity imagery provide complementary and discriminative information in image understanding and interpretation. In this paper, a novel fusion method is proposed by effectively merging the information with various combination rules. It makes use of both low-frequency and highfrequency images components from support value transform (SVT), and applies fuzzy logic in the combination process. Images (both infrared polarization and intensity images) to be fused are firstly decomposed into low-frequency component images and support value image sequences by the SVT. Then the low-frequency component images are combined using a fuzzy combination rule blending three sub-combination methods of (1) region feature maximum, (2) region feature weighting average, and (3) pixel value maximum; and the support value image sequences are merged using a fuzzy combination rule fusing two sub-combination methods of (1) pixel energy maximum and (2) region feature weighting. With the variables of two newly defined features, i.e. the low-frequency difference feature for low-frequency component images and the support-value difference feature for support value image sequences, trapezoidal membership functions are proposed and developed in tuning the fuzzy fusion process. Finally the fused image is obtained by inverse SVT operations. Experimental results of visual inspection and quantitative evaluation both indicate the superiority of the proposed method to its counterparts in image fusion of infrared polarization and intensity images.

The Cognitive Behaviour Therapy Scale for Children and Young People (CBTS-CYP): development and psychometric properties

Background: There is increased interest in developing training in cognitive behaviour therapy (CBT) with children and young people. However, the assessment of clinical competence has relied upon the use of measures such as the Cognitive Therapy Scale-Revised (CTSR: Blackburn et al., 2001) which has been validated to assess competence with adults. The appropriateness of this measure to assess competence when working with children and young people has been questioned. Aim: This paper describes the development and initial evaluation of the Cognitive Behaviour Therapy Scale for Children and Young People (CBTSCYP) developed specifically to assess competence in CBT with children and young people. Method: A cross section of child CBT practitioners (n = 61) were consulted to establish face validity. Internal reliability, convergent validity and discriminative ability were assessed in two studies. In the first, 12 assessors independently rated a single video using both the Cognitive Behaviour Therapy Scale for Children and Young People (CBTS-CYP) and Cognitive Therapy Scale-Revised (CTS-Revised: Blackburn et al., 2001). In the second, 48 different recordings of CBT undertaken with children and young people were rated on both the CBTS-CYP and CTS-R. Results: Face validity and internal reliability of the CBTS-CYP were high, and convergent validity with the CTS-R was good. The CBTS-CYP compared well with the CTSR in discriminative ability. Conclusion: The CBTS-CYP provides an appropriate way of assessing competence in using CBT with children and young people. Further work is required to assess robustness with younger children and the impact of group training in reducing interrater variations.