Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.

High Value of Proton Relaxivity Achieved by Graphene Oxide-Cobalt Ferrite Nanoparticle Composite: A Potential Contrast Agent in Magnetic Resonance Imaging

This work investigates the potential of graphene oxide-cobalt ferrite nanoparticle (GO-CoFe2O4) composite as image contrast enhancing material in Magnetic Resonance Imaging (MRI). In the preset work, GO-CoFe2O4 composites were produced by a two-step synthesis process. In the first step, graphene oxide (GO) was synthesized, and in the second step CoFe2O4 nanoparticles were synthesized in a reaction mixture containing GO to yield graphene GO-CoFe2O4 composite. Proton relaxivity value obtained from the composite was 361 mM(-1)s(-1). This value of proton relaxivity is higher than a majority of reported relaxivity values obtained using several ferrite based contrast agents.

Association Rule Sharing Model for Privacy Preservation and Collaborative Data Mining Efficiency

The disclosure of information and its misuse in Privacy Preserving Data Mining (PPDM) systems is a concern to the parties involved. In PPDM systems data is available amongst multiple parties collaborating to achieve cumulative mining accuracy. The vertically partitioned data available with the parties involved cannot provide accurate mining results when compared to the collaborative mining results. To overcome the privacy issue in data disclosure this paper describes a Key Distribution-Less Privacy Preserving Data Mining (KDLPPDM) system in which the publication of local association rules generated by the parties is published. The association rules are securely combined to form the combined rule set using the Commutative RSA algorithm. The combined rule sets established are used to classify or mine the data. The results discussed in this paper compare the accuracy of the rules generated using the C4. 5 based KDLPPDM system and the CS. 0 based KDLPPDM system using receiver operating characteristics curves (ROC).

Effect of viscosity contrast on plume formation in density stratified fluids

We perform two and three dimensional numerical simulations of plume formation in density and viscosity stratified fluid systems. We show that the ambient to plume fluid viscosity ratio strongly affects the near wall plume structures (line or sheet plumes) such as plume spacing and shape of plumes. We observe that where mushroom-like plumes are observed for lower viscosity ratios, taller plumes with bulbous heads form for high viscosity ratios. Plume structure and spacing are in good agreement with experimental results. By studying the geometry of the line plumes and the flow in the circulation cells, we discuss the mechanisms of their formation and the dynamics of merging. We show that an increase in the viscosity ratio decreases the total length of line plumes in the planform which indicates a decreased mixing at higher viscosity ratios. (C) 2015 Elsevier Ltd. All rights reserved.

MnFe2O4-Fe3O4 core-shell nanoparticles as a potential contrast agent for magnetic resonance imaging

In recent years, magnetic core-shell nanoparticles have received widespread attention due to their unique properties that can be used for various applications. We introduce here a magnetic core-shell nanoparticle system for potential application as a contrast agent in magnetic resonance imaging (MRI). MnFe2O4-Fe3O4 core-shell nanoparticles were synthesized by the wet-chemical synthesis method. Detailed structural and compositional charaterization confirmed the formation of a core-shell microstructure for the nanoparticles. Magnetic charaterization revealed the superparamagnetic nature of the as-synthesized core-shell nanoparticles. Average size and saturation magnetization values obtained for the as-synthesized core-shell nanoparticle were 12.5 nm and 69.34 emu g(-1) respectively. The transverse relaxivity value of the water protons obtained in the presence of the core-shell nanoparticles was 184.1 mM(-1) s(-1). To investigate the effect of the core-shell geometry towards enhancing the relaxivity value, transverse relaxivity values were also obtained in the presence of separately synthesized single phase Fe3O4 and MnFe2O4 nanoparticles. Average size and saturation magnetization values for the as-synthesized Fe3O4 nanoparticles were 12 nm and 65.8 emu g(-1) respectively. Average size and saturation magnetization values for the MnFe2O4 nanoparticles were 9 nm and 61.5 emu g(-1) respectively. The transverse relaxivity value obtained in the presence of single phase Fe3O4 and MnFe2O4 nanoparticles was 96.6 and 83.2 mM(-1) s(-1) respectively. All the nanoparticles (core-shell and single phase) were coated with chitosan by a surfactant exchange reaction before determining the relaxivity values. For similar nanoparticle sizes and saturation magnetization values, the highest value of the transverse relaxivity in the case of core-shell nanoparticles clearly illustrated that the difference in the magnetic nature of the core and shell phases in the core-shell nanoparticles creates greater magnetic inhomogeneity in the surrounding medium yielding a high value for proton relaxivity. The MnFe2O4-Fe3O4 core-shell nanoparticles exhibited extremely low toxicity towards the MCF-7 cell line. Taken together, this opens up new avenues for the use of core-shell nanoparticles in MRI.

Ultrafine graphene oxide-CoFe2O4 nanoparticle composite as T-1 and T-2 contrast agent for magnetic resonance imaging

Graphene oxide-CoFe2O4 nanoparticle composites were synthesized using a two step synthesis method in which graphene oxide was initially synthesized followed by precipitation of CoFe2O4 nanoparticles in a reaction mixture containing graphene oxide. Samples were extracted from the reaction mixture at different times at 80 degrees C. All the extracted samples contained CoFe2O4 nanoparticles formed over the graphene oxide. It was observed that the increase in the reflux time significantly increased the saturation magnetization value for the superparamagnetic nanoparticles in the composite. It was also noticed that the size of the nanoparticles increased with increase in the reflux time. Transverse relaxivity of the water protons increased monotonically with increase in the reflux time. Whereas, the longitudinal relaxivity value initially increased and then decreased with the reflux time. Graphene oxide-CoFe2O4 nanoparticle composites also exhibit biocompatibility towards the MCF-7 cell line.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).