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In this paper a new method for annotating images in Persian scientific documents is suggested. Images in scientific documents contain valuable information. In many cases, by analyzing images one can understand the main idea and important results of the document. Due to explosive growth of image data, automatic image annotation has attracted extensive attention and become one of the growing subjects in the literature. Image annotation is the first step in image retrieval methods, in which descriptive tags are assigned to each image. Here, for image annotation the associated text is used. The caption and the part of the document that includes the reference to the image are considered. None phrases in the associated text are ranked based on five different methods: term frequency, inverse document frequency, term frequency–inverse document frequency, cosine similarity between word embedding of noun phrases in the text and the caption and using both term frequency–inverse document frequency and cosine similarity methods. Image tags in every method are the noun phrases with the highest rank. Suggested methods are evaluated on the test data from Iran scientific information database (Ganj), the main database of Persian scientific documents. Term frequency–inverse document frequency method gives the best results. [ABSTRACT FROM AUTHOR]

Metadata extraction facilitates the process of indexing and improves information retrieval. Also automation of this process increases efficiency more than manual extraction. The example of the thesis metadata are names of students, professors, title, field, degree, abstract, keywords, etc. In this paper the aim is automatic boundary detection of metadata from the main body in Persian theses. Therefore, 250 theses collected from IRANDOC system. Features were extracted from paragraphs of each thesis then paragraphs were classified using support vector machine into 2 classes: metadata and body. In this study, Bat algorithm is used to set the parameter of SVM. The result reveals that the proposed method predicts type of paragraphs with 96.6 percent accuracy. [ABSTRACT FROM AUTHOR]

Figures in scientific documents are rich sources of information. The first step in retrieving information from such figures is to build a valid figure database. To this end, we developed a system for generating figure database from scholarly Persian documents, in large scale. The first step is to parse files and extract figures and their corresponding descriptions. There are two general approaches for extracting figures from documents. One is based on image processing methods and another is based on processing the file primitives. The focus of this paper is on latter one. This approach is shown to be a better choice for the search engines because of its speed and scalability properties. We propose a structure based method that extracts the figures and their descriptions by analyzing the file layout. This information is saved in a database with a specific structure and is indexed for retrieval in the search engine. The proposed algorithm was implemented in Python programming language. As a benchmark we used the basic method in the literature which is based on the processing PDF file. We employed the proposed method in a case study on Iran scientific information database (Ganj). In this regard, 150 scientific documents were randomly chosen from Ganj database and analyzed using two mentioned methods. Based on our experimental results, the proposed method is more efficient than the basic method especially for Persian documents. There are many unanswered challenges for Persian documents when using the basic method. The number of noise images resulted from the basic method is high and Persian text extracted is not well organized. Our proposed method overcomes some of these drawbacks and is recommended for generating figure database from scientific Persian documents. The proposed method is able to correctly extract about 40% of the images with their corresponding descriptions which is 10% better than the basic method. [ABSTRACT FROM AUTHOR]

The emergent concept of ' Big Data' has shifted the paradigm from information retrieval to information extraction techniques. The information extraction techniques enables corpus analysis to draw useful interpretations and its possible applications. Selection of appropriate information extraction technique depends upon the type of data being dealt with and its possible applications. In an R&D environment, the published information is considered as an authenticated benchmark to study and analyse the growth pattern in that field of science, medicine, business. A rule based information extraction process, on the selected data extracted from a bibliographic database of published R&D papers is proposed in this paper. Aim of the study is to build up a database on relevant concepts, cleaning of retrieved data and automate the process of information retrieval in the local database. For this purpose, a concept based 'subject profiles' in the area of advanced semiconductors as well as the rules for text extraction from metadata retrieved from the bibliographic database was developed. This subset was used as an input to the knowledge domain to support R&D in the area of 'advanced semiconductor materials and devices' and provide information services on Intranet. Study found that concept based pattern matching on the datasets downloaded yielded better results as compared to the results by using the controlled vocabulary of the source database. [ABSTRACT FROM AUTHOR]

Parallel and high performance computing experts are obsessed with performance and scalability. Performance analysis and tuning are important and complex but there are a number of software tools to support this. One methodology is the detailed recording of parallel runtime behavior in event traces and their subsequent analysis. This regularly produces very large data sets with their own challenges for handling and data management. This paper evaluates the utilization of the MASi research data management service as a trace repository to store, manage, and find traces in an efficient and usable way. First, we give an introduction to trace technologies in general, metadata in OTF2 traces specifically, and the MASi research data management service. Then, the trace repository is described with its potential for both performance analysts and parallel tool developers, followed with how we implemented it using existing metadata and how it can utilized. Finally, we give an outlook on how we plan to put the repository into productive use for the benefit of researchers using traces. [ABSTRACT FROM AUTHOR]

In recent years, a large number of RDF datasets have been built and published on the Web in fields as diverse as linguistics or life sciences, as well as general datasets such as DBpedia or Wikidata. The joint exploitation of these datasets requires specific knowledge about their content, access points, and commonalities. However, not all datasets contain a self-description, and not all access points can handle the complex queries used to generate such a description. In this article, we provide a standard-based approach to generate the description of a dataset. The generated descriptions as well as the process of their computation are expressed using standard vocabularies and languages. We implemented our approach into a framework, called IndeGx, where each indexing feature and its computation is collaboratively and declaratively defined in a GitHub repository. We have experimented IndeGx on a set of 339 RDF datasets with endpoints listed in public catalogs, over 8 months. The results show that we can collect, as much as possible, important characteristics of the datasets depending on their availability and capacities. The resulting index captures the commonalities, variety and disparity in the offered content and services and it provides an important support to any application designed to query RDF datasets. [ABSTRACT FROM AUTHOR]