Abstract

We present a method to identify the owner of a photo album taken off a social networking site. We consider this as a problem of prominent person mining. We introduce a new notion of prominent persons, and propose a greedy solution based on an eigenface representation. We mine prominent persons in a subset of dimensions in the eigenface space. We present excellent results on multiple datasets downloaded from the Internet. Index Terms—Eigenface, Clustering, Frequent Itemset Mining, Apriori, Prominent Person Mining

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

In web search, understanding the intent behind a user query can help in tasks such as placing of ads relevant to the query, routing the query to an appropriate vertical search and building a user profile for personalization. Such an intent could be represented by categories of the information a user is looking for, often expressed through a short query. In this paper, we address query categorization, which involves classifying a given query into one or more pre-defined categories. We propose an information retrieval based approach similar to document retrieval to solve query categorization. For a given query, we retrieve and rank the categories just as in document retrieval, effectively resulting in query categorization. Unlike previous works, the simplicity of the proposed approach makes it practical in a web search scenario, while achieving performance comparable with other systems, when evaluated on KDD Cup 20051 data set. Further, we also report an improvement of 4.2% in terms of precision at position 1, when compared with the best results of KDD Cup 2005.

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

This paper proposes an approach of extracting simple and effective features that enhances multilingual document ranking (MLDR). There is limited prior research on capturing the concept of multilingual document similarity in determining the ranking of documents. However, the literature available has worked heavily with language specific tools, making them hard to reimplement for other languages. Our approach extracts various multilingual and monolingual similarity features using a basic language resource (bilingual dictionary). No language-specific tools are used, hence making this approach extensible for other languages. We used the datasets provided by Forum for Information Retrieval Evaluation (FIRE) for their 2010 Adhoc Cross-Lingual document retrieval task on Indian languages. Experiments have been performed with different ranking algorithms and their results are compared. The results obtained showcase the effectiveness of the features considered in enhancing multilingual document ranking.

Abstract

Blogs has been one of the important resources of information on the internet. Now-a-days lot of Indian language content being generated in the form of blogs. People express their opinions on various situations and events. The content in the blogs may contain named entities–names of people, places, and organizations. Named entities also contain names of eminent personalities who are famous in or out of that language community. The goal of this paper is to find the influence of a personality among cross-language bloggers. The approach we follow is to collect information from blog pages and index the named entities along with their probabilities of occurrence by removing irrelevant information from the blog. When user searches to find the influence of a personality through a query in Indian language, we use a cross language lexicon in the form of multiple language parallel lists to transliterate the query into other Indian languages and mine blogs to return the influence of the personality across Indian language bloggers. An overview of the system and preliminary results are described.

Abstract

Cross lingual information access (CLIA) systems are required to access the large amounts of multilingual content generated on the world wide web in the form of blogs, news articles and documents. In this paper, we discuss our approach to query formation for CLIA systems where language resources are replaced by Wikipedia. We claim that Wikipedia, with its rich multilingual content and structure, forms an ideal platform to build a CLIA system. Our approach is particularly useful for under-resourced languages, as all the languages don’t have the resources (tools) with sufficient accuracies. We propose a context aware language-independent query formation method which, with the help of bilingual dictionaries, forms queries in the target language. Results are encouraging with a precision of 69.75% and thus endorse our claim on using Wikipedia for building CLIA systems.

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.