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Author ![]() |
Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl | ||||
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 ![]() |
Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang | ||||
Title | Historical Cryptology | Type | Book Chapter | ||
Year | 2024 | Publication | Learning and Experiencing Cryptography with CrypTool and SageMath | Abbreviated Journal | |
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Abstract | Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. | ||||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ MFK2024 | Serial | 4020 | ||
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Author ![]() |
Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski | ||||
Title | Metric Learning for Session-Based Recommendations | Type | Conference Article | ||
Year | 2021 | Publication | 43rd edition of the annual BCS-IRSG European Conference on Information Retrieval | Abbreviated Journal | |
Volume | 12656 | Issue | Pages | 650-665 | |
Keywords | Session-based recommendations; Deep metric learning; Learning to rank | ||||
Abstract | Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study. | ||||
Address | Virtual; March 2021 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECIR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ TZZ2021 | Serial | 3586 | ||
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Author ![]() |
Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas | ||||
Title | Orientation detection of trabecular bone | Type | Miscellaneous | ||
Year | 1996 | Publication | Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 | Abbreviated Journal | |
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ HNW1996 | Serial | 489 | ||
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Author ![]() |
Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu | ||||
Title | Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits | Type | Conference Article | ||
Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels | ||||
Abstract | We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. | ||||
Address | Amsterdam; The Netherlands; October 2016 | ||||
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Area | Expedition | Conference | ECCVW | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEG2016 | Serial | 2829 | ||
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B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva | ||||
Title | Learning Deep Features for Scene Recognition using Places Database | Type | Conference Article | ||
Year | 2014 | Publication | 28th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
Volume | Issue | Pages | 487-495 | ||
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Address | Montreal; Canada; December 2014 | ||||
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Area | Expedition | Conference | NIPS | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ ZLX2014 | Serial | 2621 | ||
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Author ![]() |
B. Moghaddam; David Guillamet; Jordi Vitria | ||||
Title | , Local Appearance-Based Models using High-Order Statistics of Image Features | Type | Miscellaneous | ||
Year | 2003 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) | Abbreviated Journal | |
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Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ MGV2003 | Serial | 395 | ||
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Author ![]() |
B. Moghaddam; David Guillamet; Jordi Vitria | ||||
Title | Local Appearance-Based Models using High-Order Statistics of Image Features | Type | Miscellaneous | ||
Year | 2003 | Publication | Mitsubishi Electrical Reasearch Lab Technical Report | Abbreviated Journal | |
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Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ TR2003-85 | Serial | 396 | ||
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Author ![]() |
B. Gotschy; Matthias S. Keil; H. Klos; I. Rystau | ||||
Title | Transition from static to dynamic Jahn-Teller distortion in (P(C6 H5)4)2 C60| | Type | Journal | ||
Year | 1994 | Publication | Solid State Communications, 92(12): 935–938 | Abbreviated Journal | |
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Notes | Approved | no | |||
Call Number | Admin @ si @ GKK1994 | Serial | 631 | ||
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Author ![]() |
B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols | ||||
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 | |
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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 ![]() |
Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez | ||||
Title | Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest | Type | Miscellaneous | ||
Year | 2016 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian detection; Random Forest | ||||
Abstract | Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ MVJ2016 | Serial | 2868 | ||
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Author ![]() |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana | ||||
Title | Context Proposals for Saliency Detection | Type | Journal Article | ||
Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 174 | Issue | Pages | 1-11 | |
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Abstract | One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated. In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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Notes | LAMP; 600.109; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ AWD2018 | Serial | 3241 | ||
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Author ![]() |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
Year | 2020 | Publication | SN Computer Science | Abbreviated Journal | SN |
Volume | 1 | Issue | 4 | Pages | 1-12 |
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Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
Call Number | Admin @ si @ AWD2020 | Serial | 3503 | ||
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Author ![]() |
Aymen Azaza | ||||
Title | Context, Motion and Semantic Information for Computational Saliency | Type | Book Whole | ||
Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of explicit context modelling for saliency estimation. Several important works in saliency are based on the usage of object proposals. However, these methods focus on the saliency of the object proposal itself and ignore the context. To introduce context in such saliency approaches, we couple every object proposal with its direct context. This allows us to evaluate the importance of the immediate surround (context) for its saliency. We propose several saliency features which are computed from the context proposals including features based on omni-directional and horizontal context continuity. Secondly, we investigate the usage of top-downmethods (high-level semantic information) for the task of saliency prediction since most computational methods are bottom-up or only include few semantic classes. We propose to consider a wider group of object classes. These objects represent important semantic information which we will exploit in our saliency prediction approach. Thirdly, we develop a method to detect video saliency by computing saliency from supervoxels and optical flow. In addition, we apply the context features developed in this thesis for video saliency detection. The method combines shape and motion features with our proposed context features. To summarize, we prove that extending object proposals with their direct context improves the task of saliency detection in both image and video data. Also the importance of the semantic information in saliency estimation is evaluated. Finally, we propose a newmotion feature to detect saliency in video data. The three proposed novelties are evaluated on standard saliency benchmark datasets and are shown to improve with respect to state-of-the-art. |
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Address | October 2018 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ali Douik | |
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ISSN | ISBN | 978-84-945373-9-4 | Medium | ||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ Aza2018 | Serial | 3218 | ||
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Author ![]() |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
Title | SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation | Type | Conference Article | ||
Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 14187 | Issue | Pages | 307–325 | |
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Abstract | Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of 93.72, 54.39, 84.65 and 98.04 respectively under one billion parameters. The code is made publicly available at: github.com/ayanban011/SwinDocSegmenter . | ||||
Address | San Jose; CA; USA; August 2023 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ BBL2023 | Serial | 3893 | ||
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