Abstract

In most of the Optical Character Recognition softwares, a substantial percentage of errors are caused by the incorrect segmentation of degraded words. This is especially true for recognizing old books, newspapers and historical manuscripts. In this paper, we propose multiple segmentation methods which address the problem of cuts and merges in degraded words. We have created an annotated dataset of 1034 word images with pixel level ground truth for quantitative evaluation of the methods. We compare the methods with a baseline implementation based on connected component analysis. We report substantial improvement in accuracy both at character and at word level.

Abstract

Plasmonic hotspots in single gold nanostars are located at the tips and can be excited selectively by laser light as evidenced by photoelectron emission microscopy. Selectivity is achieved through wavelength and polarization of the excitation light. Comparing photoelectron emission intensity and dark-field scattering spectra of the same individual nanostars reveals differences in terms of observable plasmon resonance wavelengths and field enhancements. Differences are explained with the underlying near- and far-field processes of the two techniques.

Abstract

Surface enhanced Raman scattering (SERS) spectra of 4-mercaptobenzoic acid (4-MBA) self-assembled monolayers (SAMs) on gold substrates is presented for SAMs onto which gold nanoparticles of various shapes have been electrostatically immobilized. SERS spectra of 4-MBA SAMs are enhanced in the presence of immobilized gold nanocrystals by a factor of 10 7-10 9 relative to 4-MBA in solution. Large enhancement factors are a likely result of plasmon coupling between the nanoparticles (localized surface plasmon) and the smooth gold substrate (surface plasmon polariton), creating large localized electromagnetic fields at their interface, where 4-MBA molecules reside in this sandwich architecture. Moreover, enhancement factors depend on nanoparticle shape, and vary by a factor of 10 2.

Abstract

The present invention provides a method for manufacture of supported noble metal based alloy catalysts with a high degree of alloying and a small crystallite size. The method is based on the use of polyol solvents as reaction medium and comprises of a two-step reduction process in the presence of a support material. In the first step, the first metal (M1= transition metal; eg Co, Cr, Ru) is activated by increasing the reaction temperature to 80 to 160 C. In the second step, the second metal (M2= noble metal; eg Pt, Pd, Au and mixtures thereof) is added and the slurry is heated to the boiling point of the polyol solvent in a range of 160 to 300 C. Due to this two-step method, an uniform reduction occurs, resulting in noble metal based catalysts with a high degree of alloying and a small crystallite size of less than 3 nm. Due to the high degree of alloying, the lattice constants are lowered. The catalysts manufactured …

Abstract

The Text Analysis Conference MultiLing Pilot of 2011 posed a multi-lingual summarization task to the summarization community, aiming to quantify and measure the performance of multi-lingual, multi-document summarization systems. The task was to create a 240–250 word summary from 10 news texts, describing a given topic. The texts of each topic were provided in seven languages (Arabic, Czech, English, French, Greek, Hebrew, Hindi) and each participant generated summaries for at least 2 languages. The evaluation of the summaries was performed using automatic (AutoSummENG, Rouge) and manual processes (Overall Responsiveness score). The participating systems were 8, some of which providing summaries across all languages. This paper provides a brief description for the collection of the data, the evaluation methodology, the problems and challenges faced, and an overview of participation and corresponding results.

Abstract

With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS) information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity. Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology.

Abstract

Invalidity search poses different challenges when compared to conventional Information Retrieval problems. Presently, the success of invalidity search relies on the queries created from a patent application by the patent examiner. Since a lot of time is spent in constructing relevant queries, automatically creating them from a patent would save the examiner a lot of effort. In this paper, we address the problem of automatically creating queries from an input patent. An optimal query can be formed by extracting important keywords or phrases from a patent by using Key Phrase Extraction (KPE) techniques. Several KPE algorithms have been proposed in the literature but their performance on query construction for patents has not yet been explored. We systematically evaluate and analyze the performance of queries created by using state-of-the-art KPE techniques for invalidity search task. Our experiments show that t queries formed by KPE approaches perform better than those formed by selecting phrases based on tf or tf-idf scores.

Abstract

The world of E-commerce is expanding, posing a large arena of products, their descriptions, customer and professional reviews that are pertinent to them. Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific attributes. In this paper, we propose a novel supervised domain independent model for product attribute extraction from user reviews. The user generated content contains unstructured and semi-structured text where conventional language grammar dependent tools like parts-of-speech taggers, named entity recognizers, parsers do not perform at expected levels. We used Wikipedia and Web to identify product attributes from customer reviews and achieved F1score of 0.73.

Abstract

A synergistic approach is described combining computer simulation with experiment in the interpretation of small angle neutron scattering (SANS) and inelastic scattering experiments on the structure and dynamics of proteins and other biopolymers. Simulation models can be tested by calculating neutron scattering structure factors and comparing the results directly with experimental scattering profiles. If the scattering profiles agree the simulations can be used to provide a detailed decomposition and interpretation of the experiments, and if not, the models can be rationally adjusted. Comparison with neutron experiment can be made at the level of the scattering functions or, less directly, of structural and dynamical quantities derived from them. This methodology is discussed in the context of the protein glass transition, protein-solvent dynamical coupling, the density of the hydration shell of proteins and the structural analysis of lignocellulosic biomass.

Abstract

MapReduce framework has received a wide acclaim over the past few years for large scale computing. It has become a standard paradigm for batch oriented workloads. As the adoption of this paradigm has increased rapidly, scheduling of these MapReduce jobs has become a problem of great interest in research community. We propose an approach which tries to maintain harmony among the jobs running on the cluster, and in turn decrease their runtime. In our model, the scheduler is made aware of different types of jobs running on the cluster. The scheduler tries to allocate a task on a node if the incoming task does not affect the tasks already running on that node. From the list of available pending tasks, our algorithm selects the one that is most compatible with the tasks already running on that node. We bring up heuristic and machine learning based solutions to our approach and try to maintain a resource balance on the cluster by not overloading any of the nodes, thereby reducing the overall runtime of the jobs. The results show a saving of runtime of around 21% in the case of heuristic based approach and around 27% in the case of machine learning based approach when compared to Yahoo's Capacity scheduler.