Structural SVM with Partial Ranking for Activity Segmentation and Classification

© 1994-2012 IEEE. Structural SVM is an extension of the support vector machine for the joint prediction of structured labels from multiple measurements. Following a large margin principle, the training of structural SVM ensures that the ground-Truth labeling of each sample receives a score higher than that of any other labeling. However, no specific score ranking is imposed among the other labelings. In this letter, we extend the standard constraint set of structural SVM with constraints between 'almost-correct' labelings and less desirable ones to obtain a partial-ranking structural SVM (PR-SSVM) approach. Experimental results on action segmentation and classification with two challenging datasets (the TUM Kitchen mocap dataset and the CMU-MMAC video dataset) show that the proposed method achieves better detection and false alarm rates and higher F1 scores than both the conventional structural SVM and a comparable unstructured predictor. The proposed method also achieves higher accuracy than the state of the art on these datasets in excess of 14 and 31 percentage points, respectively

Evaluating the Quality of the Indonesian Scientific Journal References using ParsCit, CERMINE and GROBID

There are several open-source tools available to extract the bibliographic references of the Pdf. Those tools based on the various approaches including rule-based approach, knowledge-based approach, machine learning-based approach, and the combination. To improve the services of the Indonesian Scientific Journal Database (ISJD), Center for Scientific Data and Documentation – Indonesian Institute of Sciences (PDDI-LIPI) intends to have an automatic bibliographic references extraction tool. The paper aims to analyze the quality of the reference metadata of the local journals with the three open-source tools, namely ParsCit, CERMINE and GROBID. The accuracy test of the three tools are poor. Those are 0.555, 0.633, and 0.605 for ParsCit, CERMINE, and GROBID respectively. It caused by many authors do not use a reference manager when they write the bibliography section. On the such condition this paper proposed to build an application to identify and correct errors in the bibliographic references of paper in ISJD. This application become a liaison between ISJD and open source tool for the bibliographic reference extraction. This paper proposed the combination of building software and using an open source

Harvesting Entities from the Web Using Unique Identifiers -- IBEX

In this paper we study the prevalence of unique entity identifiers on the Web. These are, e.g., ISBNs (for books), GTINs (for commercial products), DOIs (for documents), email addresses, and others. We show how these identifiers can be harvested systematically from Web pages, and how they can be associated with human-readable names for the entities at large scale. Starting with a simple extraction of identifiers and names from Web pages, we show how we can use the properties of unique identifiers to filter out noise and clean up the extraction result on the entire corpus. The end result is a database of millions of uniquely identified entities of different types, with an accuracy of 73--96% and a very high coverage compared to existing knowledge bases. We use this database to compute novel statistics on the presence of products, people, and other entities on the Web.Comment: 30 pages, 5 figures, 9 tables. Complete technical report for A. Talaika, J. A. Biega, A. Amarilli, and F. M. Suchanek. IBEX: Harvesting Entities from the Web Using Unique Identifiers. WebDB workshop, 201