Improving spectral‐based fault localization using static analysis

Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral‐based fault localization (SBFL). It helps to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. SBFL is an example of a dynamic analysis – an analysis of computer program that is performed by executing it with sufficient number of test cases. Static analysis, on the other hand, is performed in a non‐runtime environment. We introduce a weighting technique by combining these two kinds of program analysis. Static analysis is performed to categorize program statements into different classes and giving them weights based on the likelihood of being buggy statement. Statements are finally ranked on the basis of the weights computed by statements' categorization (static analysis) and scores computed by SBFL metrics (dynamic analysis). We evaluate the performance of our technique on Siemens test suite and Flex (having seeded bugs seeded by expert developers), Sed (having mixture of real and seeded bugs), and Space (having real bugs). In our evaluation, proposed weighting technique improves the performance of a wide variety of fault localization metrics up to 20% on single bug datasets and up to 42% on multi‐bug datasets. Copyright © 2017 John Wiley & Sons, Ltd.     

Protecting privacy for distance and rank based group nearest neighbor queries

World Wide Web - This paper proposes a novel approach to safeguarding location privacy for GNN (group nearest neighbor) queries. Given the locations of a group of dispersed users, the GNN query...     

A spatiotemporal compression based approach for efficient big data processing on Cloud

It is well known that processing big graph data can be costly on Cloud. Processing big graph data introduces complex and multiple iterations that raise challenges such as parallel memory bottlenecks, deadlocks, and inefficiency. To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud. By exploring spatial data correlation, we partition a graph data set into clusters. In a cluster, the workload can be shared by the inference based on time series similarity. By exploiting temporal correlation, in each time series or a single graph edge, temporal data compression is conducted. A novel data driven scheduling is also developed for data processing optimisation. The experiment results demonstrate that the spatiotemporal compression and scheduling achieve significant performance gains in terms of data size and data fidelity loss.     

The role of soil characteristics in the succession of two herbaceous lianas in a modified river floodplain

The Tamagawa floodplain of Japan experienced a series of changes in the structure of its plant community after 1960. Today, two dominant lianas – Pueraria lobata and Sicyos angulatus – have colonized the area and are competing for space. This study explores their competitive abilities in relation to soil factors and flooding disturbance downstream of the Tama River. Several colonies of these species were selected in three spots of the Tamagawa floodplain having different soil characteristics and flooding frequencies. Our results revealed that S. angulatus can only grow on fine sediment, whereas P. lobata can grow on soil with a wide range of particle sizes. Soil moisture and nitrogen requirements are higher for S. angulatus. S. angulatus grows only on fine sediment for two reasons: first, fine sediment stores a higher concentration of nitrogen and moisture; second, S. angulatus has a very shallow root system. P. lobata has an extensive root system with nodules, with which it can thrive on coarse and relatively dry soil with lower nitrogen. However, P. lobata requires higher soil phosphorus than S. angulatus does. The earlier patchy colonization of S. angulatus on the Tamagawa floodplain was provoked by flooding, when the perennial Miscanthus sacchariflorus and other species trapped fine sediments. Gradually, sediments became finer and nutrient‐rich due to vegetative decomposition, and S. angulatus invaded the area. The soil at that time was not deep enough for the root system of P. lobata. Subsequently, P. lobata outcompeted S. angulatus on the various types of soil that developed. Severe washing of fine substrates by floodwaters makes the area unsuitable for S. angulatus, whereas P. lobata continues to thrive. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimized algorithms for predictive range and KNN queries on moving objects

There have been many studies on management of moving objects recently. Most of them try to optimize the performance of predictive window queries. However, not much attention is paid to two other important query types: the predictive range query and the predictive k nearest neighbor query. In this article, we focus on these two types of queries. The novelty of our work mainly lies in the introduction of the Transformed Minkowski Sum, which can be used to determine whether a moving bounding rectangle intersects a moving circular query region. This enables us to use the traditional tree traversal algorithms to perform range and kNN searches. We theoretically show that our algorithms based on the Transformed Minkowski Sum are optimal in terms of the number of tree node accesses. We also experimentally verify the effectiveness of our technique and show that our algorithms outperform alternative approaches.     

A novel document ranking method using the discrete cosine transform

We propose a new spectral text retrieval method using the discrete cosine transform (DCT). By taking advantage of the properties of the DCT and by employing the fast query and compression techniques found in vector space methods (VSM), we show that we can process queries as fast as VSM and achieve a much higher precision.     

Discovering outlying aspects in large datasets

We address the problem of outlying aspects mining: given a query object and a reference multidimensional data set, how can we discover what aspects (i.e., subsets of features or subspaces) make the query object most outlying? Outlying aspects mining can be used to explain any data point of interest, which itself might be an inlier or outlier. In this paper, we investigate several open challenges faced by existing outlying aspects mining techniques and propose novel solutions, including (a) how to design effective scoring functions that are unbiased with respect to dimensionality and yet being computationally efficient, and (b) how to efficiently search through the exponentially large search space of all possible subspaces. We formalize the concept of dimensionality unbiasedness, a desirable property of outlyingness measures. We then characterize existing scoring measures as well as our novel proposed ones in terms of efficiency, dimensionality unbiasedness and interpretability. Finally, we evaluate the effectiveness of different methods for outlying aspects discovery and demonstrate the utility of our proposed approach on both large real and synthetic data sets.     

Investigation of Raman Modes and Born‐Effective Charges in AgNb1/2Ta1/2O3: A Density‐Functional and Raman Scattering Study

Using ab initio density‐functional theory, the Born‐effective charge tensors and zone‐center phonon mode frequencies are computed for AgNb_1/2Ta_1/2O₃ in monoclinic P2/m and orthorombic Pbcm symmetries. The experimental mode frequencies are obtained from deconvolution of Raman spectrum of prepared AgNb_1/2Ta_1/2O₃ samples and are compared with computed mode frequencies. The Raman modes with high (>350 cm^?1) and low frequencies (<90 cm^?1) correspond to Ag and O vibrations, respectively. The modes in intermediate frequency band (120–350 cm^?1) are dominated by Nb(Ta)–O vibrations. The computed effective charge tensors of cations at A (Ag) and B (Nb, Ta) sites are found to be diagonal. The off‐diagonal components of charge tensor are found sizeable only for O ions in orthorhombic AgNb_1/2Ta_1/2O₃ with Pbcm symmetry. Further, charge tensor structure of O ions is found to depend on site symmetry in the unit cell. Charge tensor components for Nb, Ta, and O ions differ significantly from their nominal ionic values suggesting (1) large local dipole moments induced by off‐centering of Nb(Ta) ions and tilting(rotations) of Nb(Ta)O₆ octahedra, (2) hybridization between d‐orbitals of Nb(Ta) and p‐orbitals of O atoms. Furthermore, the electronic structure, directional dependence of effective charges and performance of LDA (GGA) exchange‐correlation functionals with regard to computed values are also discussed.     

Scalable and fast SVM regression using modern hardware

Support Vector Machine (SVM) regression is an important technique in data mining. The SVM training is expensive and its cost is dominated by: (i) the kernel value computation, and (ii) a search operation which finds extreme training data points for adjusting the regression function in every training iteration. Existing training algorithms for SVM regression are not scalable to large datasets because: (i) each training iteration repeatedly performs expensive kernel value computations, which is inefficient and requires holding the whole training dataset in memory; (ii) the search operation used in each training iteration considers the whole search space which is very expensive. In this article, we significantly improve the scalability and efficiency of SVM regression by exploiting the high performance of Graphics Processing Units (GPUs) and solid state drives (SSDs). Our key ideas are as follows. (i) To reduce the cost of repeated kernel value computations and avoid holding the whole training dataset in the GPU memory, we precompute all the kernel values and store them in the CPU memory extended by the SSD; together with an efficient strategy to read the precomputed kernel values, reusing precomputed kernel values with an efficient retrieval is much faster than computing them on-the-fly. This also alleviates the restriction that the training dataset has to fit into the GPU memory, and hence makes our algorithm scalable to large datasets, especially for large datasets with very high dimensionality. (ii) To enhance the performance of the frequently used search operation, we design an algorithm that minimizes the search space and the number of accesses to the GPU global memory; this optimized search algorithm also avoids branch divergence (one of the causes for poor performance) among GPU threads to achieve high utilization of the GPU resources. Our proposed techniques together form a scalable solution to the SVM regression which we call SIGMA. Our extensive experimental results show that SIGMA is highly efficient and can handle very large datasets which the state-of-the-art GPU-based algorithm cannot handle. On the datasets of size that the state-of-the-art GPU-based algorithm can handle, SIGMA consistently outperforms the state-of-the-art GPU-based algorithm by an order of magnitude and achieves up to 86 times speedup.