Pres reportatge mates_17juny2010

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Pili Royo

Educação Tecnologia

el treball per competències i la gestiódels recursos digitals a l’aula ,[object Object]

Matemàgia (4t d’ESO)Pili Royo IES Montilivi (Girona) Programa de formaciód’acompanyants del projecte 1x1 22 d’abril de 2010

Reportatge “matemàtiques a l’institut” (2n d’ESO)

PLANIFICACIÓ DE L’ACTIVITAT. MATERIAL ALUMNES

MATEMÀTIQUES A L’IES-SEP MONTILIVI (GIRONA)

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Future Of Print

Ulbe Jelluma

Manually selecting subsets of photos from large collections in order to present them to friends or colleagues or to print them as photo books can be a tedious task. Today, fully automatic approaches are at hand for supporting users. They make use of pixel information extracted from the images, analyze contextual information such as capture time and focal aperture, or use both to determine a proper subset of photos. However, these approaches miss the most important factor in the photo selection process: the user. The goal of our approach is to consider individual interests. By recording and analyzing gaze information from the user's viewing photo collections, we obtain information on user's interests and use this information in the creation of personal photo selections. In a controlled experiment with 33 participants, we show that the selections can be significantly improved over a baseline approach by up to 22% when taking individual viewing behavior into account. We also obtained significantly better results for photos taken at an event participants were involved in compared with photos from another event.

Smart photo selection: interpret gaze as personal interest

Ansgar Scherp

Cemetery in Romania

Giena Juice

We introduce a framework for automated semantic document annotation that is composed of four processes, namely concept extraction, concept activation, annotation selection, and evaluation. The framework is used to implement and compare different annotation strategies motivated by the literature. For concept extraction, we apply entity detection with semantic hierarchical knowledge bases, Tri-gram, RAKE, and LDA. For concept activation, we compare a set of statistical, hierarchy-based, and graph-based methods. For selecting annotations, we compare top-k as well as kNN. In total, we define 43 different strategies including novel combinations like using graph-based activation with kNN. We have evaluated the strategies using three different datasets of varying size from three scientific disciplines (economics, politics, and computer science) that contain 100, 000 manually labeled documents in total. We obtain the best results on all three datasets by our novel combination of entity detection with graph-based activation (e.g., HITS and Degree) and kNN. For the economic and political science datasets, the best F-measure is .39 and .28, respectively. For the computer science dataset, the maximum F-measure of .33 can be reached. The experiments are the by far largest on scholarly content annotation, which typically are up to a few hundred documents per dataset only. Gregor Große-Bölting, Chifumi Nishioka, and Ansgar Scherp. 2015. A Comparison of Different Strategies for Automated Semantic Document Annotation. In Proceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015). ACM, New York, NY, USA, , Article 8 , 8 pages. DOI=http://dx.doi.org/10.1145/2815833.2815838

A Comparison of Different Strategies for Automated Semantic Document Annotation

Ansgar Scherp

We propose a pipeline for text extraction from infographics that makes use of a novel combination of data mining and computer vision techniques. The pipeline defines a sequence of steps to identify characters, cluster them into text lines, determine their rotation angle, and apply state-of-the-art OCR to recognize the text. In this paper, we formally define the pipeline and present its current implementation. In addition, we have conducted preliminary evaluations over a data corpus of 121 manually annotated infographics from a broad range of illustration types such as bar charts, pie charts, and line charts, maps, and others. We assess the results of our text extraction pipeline by comparing it with two baselines. Finally, we sketch an outline for future work and possibilities for improving the pipeline. - http://ceur-ws.org/Vol-1458/

Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...

Ansgar Scherp

Terratrèmol al Japó en nombres

Pili Royo

Mathmagics Abeam 08

Pili Royo

Dea Presentacio

Pili Royo

The talk presents selected results of our research in the area of text and data mining in social media and scientific literature. (1) First, we consider the area of classifying microblogging postings like tweets on Twitter. Typically, the classification results are evaluated against a gold standard, which is either the hashtags of the tweets’ authors or manual annotations. We claim that there are fundamental differences between these two kinds of gold standard classifications and conducted an experiment with 163 participants to manually classify tweets from ten topics. Our results show that the human annotators are more likely to classify tweets like other human annotators than like the tweets’ authors (i. e., the hashtags). This may influence the evaluation of classification methods like LDA and we argue that researchers should reflect the kind of gold standard used when interpreting their results. (2) Second, we present a framework for semantic document annotation that aims to compare different existing as well as new annotation strategies. For entity detection, we compare semantic taxonomies, trigrams, RAKE, and LDA. For concept activation, we cover a set of statistical, hierarchy-based, and graph-based methods. The strategies are evaluated over 100,000 manually labeled scientific documents from economics, politics, and computer science. (3) Finally, we present a processing pipeline for extracting text of varying size, rotation, color, and emphases from scholarly figures. The pipeline does not need training nor does it make any assumptions about the characteristics of the scholarly figures. We conducted a preliminary evaluation with 121 figures from a broad range of illustration types. URL: https://www.ukp.tu-darmstadt.de/ukp-home/news-singleview/artikel/guest-speaker-ansgar-scherp/

Knowledge Discovery in Social Media and Scientific Digital Libraries

Ansgar Scherp

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Ricoh|IBM Transpromo

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Smart photo selection: interpret gaze as personal interest

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A Comparison of Different Strategies for Automated Semantic Document Annotation

Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...

Terratrèmol al Japó en nombres

Mathmagics Abeam 08

Dea Presentacio

Knowledge Discovery in Social Media and Scientific Digital Libraries

Pres reportatge mates_17juny2010

2. Matemàgia (4t d’ESO)Pili Royo IES Montilivi (Girona) Programa de formaciód’acompanyants del projecte 1x1 22 d’abril de 2010

3. Reportatge “matemàtiques a l’institut” (2n d’ESO)

4. CONTINGUTS

5. CRITERIS D’AVALUACIÓ

6. CRITERIS D’AVALUACIÓ

7. PLANIFICACIÓ DE L’ACTIVITAT. MATERIAL ALUMNES

8. Fitxa de seguimentdiari

9. MATEMÀTIQUES A L’IES-SEP MONTILIVI (GIRONA)

10. Valoraciódelsalumnes El tercer trimestre del curs 2008-2009, alguns nois i noies de 2n d’ESO vàrem triar el credit variable de mates. Havíem de fer repàs de mates, i la Pili ens va proposar fer un taller optatiu, un petit reportatge. La primera part del reportatge va ser sortir per tot l’institut a fer fotos de coses que tinguessin alguna forma geomètrica, com per exemple el cartell de l’entrada, que indica cap a on és Recepció. Va ser molt divertit el fet de buscar petites coses que mai a simple vista ens fixàvem. L’altra part era elaborar el powerpoint per fer una exposició oral. En conclusió, vàrem aprendre i ens ho vam passar molt bé. David Zamorano (3r B) i Borja Peruga (3r D)

Pres reportatge mates_17juny2010

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Pres reportatge mates_17juny2010