Mining sentiments from songs using latent dirichlet allocation

Song-selection and mood are interdependent. If we capture a song’s sentiment, we can determine the mood of the listener, which can serve as a basis for recommendation systems. Songs are generally classified according to genres, which don’t entirely reflect sentiments. Thus, we require an unsupervised scheme to mine them. Sentiments are classified into either two (positive/negative) or multiple (happy/angry/sad/...) classes, depending on the application. We are interested in analyzing the feelings invoked by a song, involving multi-class sentiments. To mine the hidden sentimental structure behind a song, in terms of “topics”, we consider its lyrics and use Latent Dirichlet Allocation (LDA). Each song is a mixture of moods. Topics mined by LDA can represent moods. Thus we get a scheme of collecting similar-mood songs. For validation, we use a dataset of songs containing 6 moods annotated by users of a particular website.

IceCube: efficient targeted mining in data cubes

We address the problem of mining targeted association rules over multidimensional market-basket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distributed over cells of an n-dimensional cube. In this framework, a targeted association rule is of the form {X -> Y} R, where R is a convex region in the cube and X. Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying targeted association rules. Then, we discuss the concepts of bottom-up aggregation and cubing, leading to the CellUnion technique. This approach is further extended, using notions of cube-count interleaving and credit-based pruning, to derive the IceCube algorithm. Our experiments demonstrate that IceCube consistently provides the best execution time performance, especially for large and complex data cubes.

Algorithm for mining outliers in categorical data through ranking

The rapid growth in the field of data mining has lead to the development of various methods for outlier detection. Though detection of outliers has been well explored in the context of numerical data, dealing with categorical data is still evolving. In this paper, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase, this algorithm explores a clustering of the given data, followed by the ranking phase for determining the set of most likely outliers. The proposed algorithm is expected to perform better as it can identify different types of outliers, employing two independent ranking schemes based on the attribute value frequencies and the inherent clustering structure in the given data. Unlike some existing methods, the computational complexity of this algorithm is not affected by the number of outliers to be detected. The efficacy of this algorithm is demonstrated through experiments on various public domain categorical data sets.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.

Mining large-scale response networks reveals `topmost activities' in Mycobacterium tuberculosis infection

Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.

DEPRESSION OF MAGNETlCALLY FIXED EMISSION ZONE IN LOW PRESSURE MERCURY ARCS

A transverse magnetic field was used to fix the cathode spot of a low pressure mercury arc with liquid cathode It was noticed that such fixation causes consider-abledepression of the emission zone below the mercury level.This depression varies with the arc current and the magnetic field and is associated with an increase in the arc voltage drop. It indicates appreciable pressure in the emission zone.

Remarkable role of positional isomers in the design of sensors for the ratiometric detection of copper and mercury ions in water

Cation sensing properties of the three positional isomers of rhodamine based sensors (1-3) are studied in water. The sensors differ only in the position of pyridine's nitrogen. The chemosensor 1, with pyridine nitrogen at ortho-position, showed a selective colorimetric detection of Cu(II) ions in water, at physiological pH 7.4 and also in medium containing BSA (bovine serum albumin) and blood serum. Notably the compound 2 and 3, with pyridine end located at meta-and para-positions did not show any color change with Cu(II) ions, although both the compounds showed turn-on change both in color and fluorescence with Hg(II) ions specifically. All the probes showed ratiometric changes with the specific metal ions. The changing position of nitrogen also changed the complexation pattern of the sensors with the metal ions. Probe 1 showed 2 : 1 complexation with Cu(II), whereas 2 and 3 showed 1 : 1 complexation with Hg(II) ions. The mechanism investigation showed that the change in color upon addition of metal ions is due to the ring-opening of the spirolactam ring of the probes. Cu(II) interacted with ligand 1 through a three-point interaction mode comprising carbonyl oxygen, amido nitrogen and pyridine nitrogen end. But in case of 2 and 3, Hg2+ only interacted through pyridine nitrogen ends. Quantitative estimation of Cu2+ and Hg2+ in complex biological media such as bovine albumin protein (BSA) and human blood serum were performed using these sensors. Rapid on-site detection as well as discrimination of these toxic ions was demonstrated using easily prepared portable test-strips.

A Probe for the Selective and Parts-per-Billion-Level Detection of Copper(II) and Mercury(II) using a Micellar Medium and Its Utility in Cell Imaging

A novel colorimetric probe 1 based on the picolyl moiety has been designed and synthesized. Probe 1 is composed of a pyrene and a bispicolyl amine (BPA) unit, in which the BPA moiety acts as a binding unit and the binding phenomenon is sensed from the changes in the signaling subunit. The probe detects Cu2+ specifically in water and both Cu2+ and Hg2+ efficiently in neutral Brij-58 micellar media. The probe shows a color change visible to the naked eye upon addition of metal ions. Notably, in a micellar medium, probe 1 can detect both the Cu2+ and Hg2+ ions even at parts-per-billion levels. Furthermore, the probe shows ratiometric detection of both the metal ions making the sensing quantitative. The two metal ions could be discriminated both visibly under a UV lamp and with the use of fluorescence spectroscopy. The probe could be also used in biological cell lines for the detection of both Hg2+ and Cu2+ ions.

Discovering Math APIs by Mining Unit Tests

In today's API-rich world, programmer productivity depends heavily on the programmer's ability to discover the required APIs. In this paper, we present a technique and tool, called MATHFINDER, to discover APIs for mathematical computations by mining unit tests of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code to compute the expression by mapping its subexpressions to API method calls. For each subexpression, MATHFINDER searches for a method such that there is a mapping between method inputs and variables of the subexpression. The subexpression, when evaluated on the test inputs of the method under this mapping, should produce results that match the method output on a large number of tests. We implemented MATHFINDER as an Eclipse plugin for discovery of third-party Java APIs and performed a user study to evaluate its effectiveness. In the study, the use of MATHFINDER resulted in a 2x improvement in programmer productivity. In 96% of the subexpressions queried for in the study, MATHFINDER retrieved the desired API methods as the top-most result. The top-most pseudo-code snippet to implement the entire expression was correct in 93% of the cases. Since the number of methods and unit tests to mine could be large in practice, we also implement MATHFINDER in a MapReduce framework and evaluate its scalability and response time.

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.

Mercury exposure Aasessment in fish and humans from Sundarban Mangrove Wetland of India

Present study had documented total mercury levels in six commonly consumed fish species, and performed across-sectional study on local residents to gauge their intake of fish (via dietary survey) and mercury exposure (via hair biomarker analyses). Mean total mercury content in edible composites of locally-caught fishes (topse, hilsa, mackerel, topse, sardinella, khoira) was low and ranged from 0.01 to 0.11 mu g g(-1) mercury, dry weight. In a cross-sectional study of 58 area residents, the mercury content in hair ranged from 0.25 to 1.23 mu g g(-1), with a mean of 0.65 +/- 0.23 mu g g(-1), Flair mercury level was not influenced by gender, age, or occupation. Mean number of meals consumed per week was 3.1 +/- 1.1, and all participants consumed at least one fish meal per week. When related to fish consumption, a significant positive association was found between number of fish meals consumed per week and hair mercury levels.

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).

Size and temperature dependence of the photoluminescence properties of NIR emitting ternary alloyed mercury cadmium telluride quantum dots

Exciton-phonon coupling and nonradiative relaxation processes have been investigated in near-infrared (NIR) emitting ternary alloyed mercury cadmium telluride (CdHgTe) quantum dots. Organically capped CdHgTe nanocrystals of sizes varying from 2.5-4.2 nm have been synthesized where emission is in the NIR region of 650-855 nm. Temperature-dependent (15-300 K) photoluminescence (PL) and the decay dynamics of PL at 300 K have been studied to understand the photophysical properties. The PL decay kinetics shows the transition from triexponential to biexponential on increasing the size of the quantom dots (QDs), informing the change in the distribution of the emitting states. The energy gap is found to be following the Varshni relation with a temperature coefficient of 2.1-2.8 x 10(-4) eV K-1. The strength of the electron-phonon coupling, which is reflected in the Huang and Rhys factor S, is found in the range of 1.17-1.68 for QDs with a size of 2.5-4.2 nm. The integrated PL intensity is nearly constant until 50 K, and slowly decreases up to 140 K, beyond which it decreases at a faster rate. The mechanism for PL quenching with temperature is attributed to the presence of nonradiative relaxation channels, where the excited carriers are thermally stimulated to the surface defect/trap states. At temperatures of different region (<140 K and 140-300 K), traps of low (13-25 meV) and high (65-140 meV) activation energies seem to be controlling the quenching of the PL emission. The broadening of emission linewidth is found to due to exciton-acoustic phonon scattering and exciton-longitudinal optical (LO) phonon coupling. The exciton-acoustic phonon scattering coefficient is found to be enhanced up to 55 MU eV K-1 due to a stronger confinement effect. These findings give insight into understanding the photophysical properties of CdHgTe QDs and pave the way for their possible applications in the fields of NIR photodetectors and other optoelectronic devices.

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/).