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Author (up) Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit  url
openurl 
  Title From Optical Music Recognition to Handwritten Music Recognition: a Baseline Type Journal Article
  Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 123 Issue Pages 1-8  
  Keywords  
  Abstract Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.  
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  Notes DAG; 600.097; 601.302; 601.330; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ BRC2019 Serial 3275  
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Author (up) B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols edit   pdf
url  openurl
  Title Knowledge graph based methods for record linkage Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 127-133  
  Keywords  
  Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
 
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  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ GRP2020 Serial 3453  
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Author (up) Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl edit  url
openurl 
  Title Decryption of historical manuscripts: the DECRYPT project Type Journal Article
  Year 2020 Publication Cryptologia Abbreviated Journal CRYPT  
  Volume 44 Issue 6 Pages 545-559  
  Keywords automatic decryption; cipher collection; historical cryptology; image transcription  
  Abstract Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.  
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  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ MEF2020 Serial 3347  
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Author (up) C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger edit   pdf
url  doi
openurl 
  Title Limitations of visual gamma corrections in LCD displays Type Journal Article
  Year 2014 Publication Displays Abbreviated Journal Dis  
  Volume 35 Issue 5 Pages 227–239  
  Keywords Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration  
  Abstract A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.  
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  Notes CIC; DAG; 600.052; 600.077; 600.074 Approved no  
  Call Number Admin @ si @ PRK2014 Serial 2511  
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Author (up) Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil edit  doi
openurl 
  Title Automatic evaluation of practices in Moodle for Self Learning in Engineering Type Journal
  Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE  
  Volume 5 Issue 2 Pages 97-106  
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  Notes IAM; DAG; 600.075; 600.077 Approved no  
  Call Number Admin @ si @ SRM2015 Serial 2610  
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