Last Year

Master
[1]
M. Abouhamra, “AligNarr: Aligning Narratives of Different Length for Movie Summarization,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.
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BibTeX
@mastersthesis{AbouhamraMSc2019, TITLE = {{AligNarr}: Aligning Narratives of Different Length for Movie Summarization}, AUTHOR = {Abouhamra, Mostafa}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.}, }
Endnote
%0 Thesis %A Abouhamra, Mostafa %Y Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T AligNarr: Aligning Narratives of Different Length for Movie Summarization : %G eng %U http://hdl.handle.net/21.11116/0000-0004-5836-D %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 54 p. %V master %9 master %X Automatic text alignment is an important problem in natural language processing. It can be used to create the data needed to train different language models. Most research about automatic summarization revolves around summarizing news articles or scientific papers, which are somewhat small texts with simple and clear structure. The bigger the difference in size between the summary and the original text, the harder the problem will be since important information will be sparser and identifying them can be more difficult. Therefore, creating datasets from larger texts can help improve automatic summarization. In this project, we try to develop an algorithm which can automatically create a dataset for abstractive automatic summarization for bigger narrative text bodies such as movie scripts. To this end, we chose sentences as summary text units and scenes as script text units and developed an algorithm which uses some of the latest natural language processing techniques to align scenes and sentences based on the similarity in their meanings. Solving this alignment problem can provide us with important information about how to evaluate the meaning of a text, which can help us create better abstractive summariza- tion models. We developed a method which uses different similarity scoring techniques (embedding similarity, word inclusion and entity inclusion) to align script scenes and sum- mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that the bigger the differences in the number of text units being aligned, the more difficult the alignment problem is. We also critiqued of our own similarity scoring techniques and dif- ferent alignment algorithms based on integer linear programming and local optimization and showed their limitations and discussed ideas to improve them.
[2]
M. Anis, “Proactive Learning Algorithms: A Survey of the State of the Art and Implementation of Novel and Concrete Algorithm for (Unstructured) Data Classification,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Artificial Intelligence (AI) has become one of the most researched fields nowadays. Ma- chine Learning (ML) is one of the most popular AI domains, where systems are created with the capability of automatic learning and improving from the learning experience. The current revolution in the size and cost of electronic storage allows for the existence of enormous amount of data that can be used for ML training. Unfortunately, not all of this data is labelled. The process of manually labelling documents can be expen- sive, time consuming and subject to human errors. Active Learning (AL) addresses this challenge by finding a sample of the enormous data corpus that, if labelled, can substitute the use of the whole dataset. AL routes this sample to a human labeller to formulate the training dataset needed for the ML model. AL assumes that there exists a single, infallible and indefatigable labeller. These assumptions cannot cope to real world problems. The main focus of this work is to introduce Proactive Learning (PL) to an existing AL system. PL aims at generalizing the problem, solved by AL, by relaxing all of its assumptions about the user. The main addition of this project is enhancing automatic text classification by combining knowledge from the domain of PL and from Instance Relabelling paradigms to update the currently implemented AL system. The implemented PL system is tested on the 20 Newsgroups, Reuters and AG News datasets. The system is capable of reaching impressive results in detecting and predicting users actions, which allows the system to efficiently route labelling tasks to the best users, leading to minimize the risk of receiving wrong labels.
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BibTeX
@mastersthesis{, TITLE = {Proactive Learning Algorithms: A Survey of the State of the Art and Implementation of Novel and Concrete Algorithm for (Unstructured) Data Classi{fi}cation}, AUTHOR = {Anis, Myriam}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019-08-06}, ABSTRACT = {Arti{fi}cial Intelligence (AI) has become one of the most researched {fi}elds nowadays. Ma- chine Learning (ML) is one of the most popular AI domains, where systems are created with the capability of automatic learning and improving from the learning experience. The current revolution in the size and cost of electronic storage allows for the existence of enormous amount of data that can be used for ML training. Unfortunately, not all of this data is labelled. The process of manually labelling documents can be expen- sive, time consuming and subject to human errors. Active Learning (AL) addresses this challenge by {fi}nding a sample of the enormous data corpus that, if labelled, can substitute the use of the whole dataset. AL routes this sample to a human labeller to formulate the training dataset needed for the ML model. AL assumes that there exists a single, infallible and indefatigable labeller. These assumptions cannot cope to real world problems. The main focus of this work is to introduce Proactive Learning (PL) to an existing AL system. PL aims at generalizing the problem, solved by AL, by relaxing all of its assumptions about the user. The main addition of this project is enhancing automatic text classi{fi}cation by combining knowledge from the domain of PL and from Instance Relabelling paradigms to update the currently implemented AL system. The implemented PL system is tested on the 20 Newsgroups, Reuters and AG News datasets. The system is capable of reaching impressive results in detecting and predicting users actions, which allows the system to efficiently route labelling tasks to the best users, leading to minimize the risk of receiving wrong labels.}, }
Endnote
%0 Thesis %A Anis, Myriam %Y Klakow, Dietrich %A referee: Petrenko, Pavlo %A referee: Hampp, Thomas %A referee: Klakow, Dietrich %A referee: Mirza, Paramita %+ International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Proactive Learning Algorithms: A Survey of the State of the Art and Implementation of Novel and Concrete Algorithm for (Unstructured) Data Classification : %G eng %U http://hdl.handle.net/21.11116/0000-0005-9C5B-6 %I Universität des Saarlandes %C Saarbrücken %D 2019 %8 06.08.2019 %P 86 p. %V master %9 master %X Artificial Intelligence (AI) has become one of the most researched fields nowadays. Ma- chine Learning (ML) is one of the most popular AI domains, where systems are created with the capability of automatic learning and improving from the learning experience. The current revolution in the size and cost of electronic storage allows for the existence of enormous amount of data that can be used for ML training. Unfortunately, not all of this data is labelled. The process of manually labelling documents can be expen- sive, time consuming and subject to human errors. Active Learning (AL) addresses this challenge by finding a sample of the enormous data corpus that, if labelled, can substitute the use of the whole dataset. AL routes this sample to a human labeller to formulate the training dataset needed for the ML model. AL assumes that there exists a single, infallible and indefatigable labeller. These assumptions cannot cope to real world problems. The main focus of this work is to introduce Proactive Learning (PL) to an existing AL system. PL aims at generalizing the problem, solved by AL, by relaxing all of its assumptions about the user. The main addition of this project is enhancing automatic text classification by combining knowledge from the domain of PL and from Instance Relabelling paradigms to update the currently implemented AL system. The implemented PL system is tested on the 20 Newsgroups, Reuters and AG News datasets. The system is capable of reaching impressive results in detecting and predicting users actions, which allows the system to efficiently route labelling tasks to the best users, leading to minimize the risk of receiving wrong labels.
[3]
N. Cheema, “In Silico User Testing for Mid-Air Interactions with Deep Reinforcement Learning,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
User interface design for Virtual Reality and other embodied interaction contexts has to carefully consider ergonomics. A common problem is that mid-air inter- action may cause excessive arm fatigue, known as the “Gorilla arm” effect. To predict and prevent such problems at a low cost, this thesis investigates user test- ing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). This is implemented in a pointing task and four experimental conditions, demonstrating that the sim- ulated fatigue data matches ground truth human data. Additionally, two effort models are compared against each other: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements that are both more efficient and natural, whereas with instantaneous joint torques, the RL agent can easily generate movements that are unnatural or only reach the targets slowly and inaccurately. This thesis demonstrates that deep RL combined with the 3CC-r provides a viable tool for predicting both interaction movements and user experience in silico, without users.
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BibTeX
@mastersthesis{, TITLE = {In Silico User Testing for Mid-Air Interactions with Deep Reinforcement Learning}, AUTHOR = {Cheema, Noshaba}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019-09-30}, ABSTRACT = {User interface design for Virtual Reality and other embodied interaction contexts has to carefully consider ergonomics. A common problem is that mid-air inter- action may cause excessive arm fatigue, known as the {\textquotedblleft}Gorilla arm{\textquotedblright} effect. To predict and prevent such problems at a low cost, this thesis investigates user test- ing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). This is implemented in a pointing task and four experimental conditions, demonstrating that the sim- ulated fatigue data matches ground truth human data. Additionally, two effort models are compared against each other: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements that are both more efficient and natural, whereas with instantaneous joint torques, the RL agent can easily generate movements that are unnatural or only reach the targets slowly and inaccurately. This thesis demonstrates that deep RL combined with the 3CC-r provides a viable tool for predicting both interaction movements and user experience in silico, without users.}, }
Endnote
%0 Thesis %A Cheema, Noshaba %Y Slusallek, Philipp %A referee: Hämäläinen, Perttu %A referee: Lehtinen, Jaakko %A referee: Slusallek, Philipp %A referee: Hämäläinen, Perttu %+ International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T In Silico User Testing for Mid-Air Interactions with Deep Reinforcement Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0005-9C66-9 %I Universität des Saarlandes %C Saarbrücken %D 2019 %8 30.09.2019 %P 65 p. %V master %9 master %X User interface design for Virtual Reality and other embodied interaction contexts has to carefully consider ergonomics. A common problem is that mid-air inter- action may cause excessive arm fatigue, known as the “Gorilla arm” effect. To predict and prevent such problems at a low cost, this thesis investigates user test- ing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). This is implemented in a pointing task and four experimental conditions, demonstrating that the sim- ulated fatigue data matches ground truth human data. Additionally, two effort models are compared against each other: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements that are both more efficient and natural, whereas with instantaneous joint torques, the RL agent can easily generate movements that are unnatural or only reach the targets slowly and inaccurately. This thesis demonstrates that deep RL combined with the 3CC-r provides a viable tool for predicting both interaction movements and user experience in silico, without users.
[4]
N. Cheema, “Fine-Grained Semantic Segmentation of Motion Capture Data using Convolutional Neural Networks,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Human motion capture data has been widely used in data-driven character animation. In order to generate realistic, natural-looking motions, most data-driven approaches require considerable efforts of pre-processing, including motion segmentation, annotation, and so on. Existing (semi-) automatic solutions either require hand-crafted features for motion segmentation or do not produce the semantic annotations required for motion synthesis and building large-scale motion databases. In this thesis, an approach for a semi-automatic framework for semantic segmentation of motion capture data based on (semi-) supervised machine learning techniques is developed. The motion capture data is first transformed into a “motion image” to apply common convolutional neural networks for image segmentation. Convolutions over the time domain enable the extraction of temporal information and dilated convolutions are used to enlarge the receptive field exponentially using comparably few layers and parameters. The finally developed dilated temporal fully-convolutional model is compared against state-of-the-art models in action segmentation, as well as a popular network for sequence modeling. The models are further tested on noisy and inaccurate training labels and the developed model is found to be surprisingly robust and self-correcting.
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BibTeX
@mastersthesis{, TITLE = {Fine-Grained Semantic Segmentation of Motion Capture Data using Convolutional Neural Networks}, AUTHOR = {Cheema, Noshaba}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019-03-29}, ABSTRACT = {Human motion capture data has been widely used in data-driven character animation. In order to generate realistic, natural-looking motions, most data-driven approaches require considerable efforts of pre-processing, including motion segmentation, annotation, and so on. Existing (semi-) automatic solutions either require hand-crafted features for motion segmentation or do not produce the semantic annotations required for motion synthesis and building large-scale motion databases. In this thesis, an approach for a semi-automatic framework for semantic segmentation of motion capture data based on (semi-) supervised machine learning techniques is developed. The motion capture data is {fi}rst transformed into a {\textquotedblleft}motion image{\textquotedblright} to apply common convolutional neural networks for image segmentation. Convolutions over the time domain enable the extraction of temporal information and dilated convolutions are used to enlarge the receptive {fi}eld exponentially using comparably few layers and parameters. The {fi}nally developed dilated temporal fully-convolutional model is compared against state-of-the-art models in action segmentation, as well as a popular network for sequence modeling. The models are further tested on noisy and inaccurate training labels and the developed model is found to be surprisingly robust and self-correcting.}, }
Endnote
%0 Thesis %A Cheema, Noshaba %Y Slusallek, Philipp %A referee: Hosseini, Somayeh %A referee: Slusallek, Philipp %A referee: Theobalt, C. %+ International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Graphics, MPI for Informatics, Max Planck Society %T Fine-Grained Semantic Segmentation of Motion Capture Data using Convolutional Neural Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0005-9C5E-3 %I Universität des Saarlandes %C Saarbrücken %D 2019 %8 29.03.2019 %P 127 p. %V master %9 master %X Human motion capture data has been widely used in data-driven character animation. In order to generate realistic, natural-looking motions, most data-driven approaches require considerable efforts of pre-processing, including motion segmentation, annotation, and so on. Existing (semi-) automatic solutions either require hand-crafted features for motion segmentation or do not produce the semantic annotations required for motion synthesis and building large-scale motion databases. In this thesis, an approach for a semi-automatic framework for semantic segmentation of motion capture data based on (semi-) supervised machine learning techniques is developed. The motion capture data is first transformed into a “motion image” to apply common convolutional neural networks for image segmentation. Convolutions over the time domain enable the extraction of temporal information and dilated convolutions are used to enlarge the receptive field exponentially using comparably few layers and parameters. The finally developed dilated temporal fully-convolutional model is compared against state-of-the-art models in action segmentation, as well as a popular network for sequence modeling. The models are further tested on noisy and inaccurate training labels and the developed model is found to be surprisingly robust and self-correcting.
[5]
S. Ganguly, “Empirical Evaluation of Common Assumptions in Building Political Bias Datasets,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
In today’s world, bias and polarization are some of the biggest problems plaguing our society. In such volatile environments, news media play a crucial role as the gatekeepers of the information. Given the huge impact they can have on societal evolution, they have long been studied by researchers. Researchers and practitioners often build political bias datasets for a variety of tasks ranging from examining bias of news outlets and articles to studying and designing algorithmic news retrieval systems for online platforms. Often, researchers make certain simplifying assumptions in building such datasets. In this thesis, we empirically validate three such common assumptions given the im- portance of such datasets. The three assumptions are, (i) raters’ political leaning does not affect their ratings of political articles, (ii) news articles follow the leaning of their source outlet, and (iii) political leaning of a news outlet is stable across reporting on different topics. We constructed a manually annotated ground-truth dataset of news articles, published by several popular news media outlets in the U.S., on “Gun policy” and “Immigration” along with their political bias leanings using Amazon Mechanical Turk and used it to validate these assumptions. Our findings suggest that, (i) in certain cases, liberal and conservative raters’ label leanings of news articles differently, (ii) in many cases, the news articles do not follow the political leaning of their source outlet, and (iii) for certain outlets, the political leaning of the outlet does not remain unchanged while reporting on different topics/issues. We believe, our work offers important guidelines for future attempts at building political bias datasets which in turn will help them in building better algorithmic news retrieval systems for online platforms.
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BibTeX
@mastersthesis{, TITLE = {Empirical Evaluation of Common Assumptions in Building Political Bias Datasets}, AUTHOR = {Ganguly, Soumen}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019-09-01}, ABSTRACT = {In today{\textquoteright}s world, bias and polarization are some of the biggest problems plaguing our society. In such volatile environments, news media play a crucial role as the gatekeepers of the information. Given the huge impact they can have on societal evolution, they have long been studied by researchers. Researchers and practitioners often build political bias datasets for a variety of tasks ranging from examining bias of news outlets and articles to studying and designing algorithmic news retrieval systems for online platforms. Often, researchers make certain simplifying assumptions in building such datasets. In this thesis, we empirically validate three such common assumptions given the im- portance of such datasets. The three assumptions are, (i) raters{\textquoteright} political leaning does not affect their ratings of political articles, (ii) news articles follow the leaning of their source outlet, and (iii) political leaning of a news outlet is stable across reporting on different topics. We constructed a manually annotated ground-truth dataset of news articles, published by several popular news media outlets in the U.S., on {\textquotedblleft}Gun policy{\textquotedblright} and {\textquotedblleft}Immigration{\textquotedblright} along with their political bias leanings using Amazon Mechanical Turk and used it to validate these assumptions. Our {fi}ndings suggest that, (i) in certain cases, liberal and conservative raters{\textquoteright} label leanings of news articles differently, (ii) in many cases, the news articles do not follow the political leaning of their source outlet, and (iii) for certain outlets, the political leaning of the outlet does not remain unchanged while reporting on different topics/issues. We believe, our work offers important guidelines for future attempts at building political bias datasets which in turn will help them in building better algorithmic news retrieval systems for online platforms.}, }
Endnote
%0 Thesis %A Ganguly, Soumen %Y An, Jinsun %A referee: Gummadi, Krishna %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society %T Empirical Evaluation of Common Assumptions in Building Political Bias Datasets : %G eng %U http://hdl.handle.net/21.11116/0000-0002-B37F-6 %I Universität des Saarlandes %C Saarbrücken %D 2019 %8 01.09.2019 %P 53 p. %V master %9 master %X In today’s world, bias and polarization are some of the biggest problems plaguing our society. In such volatile environments, news media play a crucial role as the gatekeepers of the information. Given the huge impact they can have on societal evolution, they have long been studied by researchers. Researchers and practitioners often build political bias datasets for a variety of tasks ranging from examining bias of news outlets and articles to studying and designing algorithmic news retrieval systems for online platforms. Often, researchers make certain simplifying assumptions in building such datasets. In this thesis, we empirically validate three such common assumptions given the im- portance of such datasets. The three assumptions are, (i) raters’ political leaning does not affect their ratings of political articles, (ii) news articles follow the leaning of their source outlet, and (iii) political leaning of a news outlet is stable across reporting on different topics. We constructed a manually annotated ground-truth dataset of news articles, published by several popular news media outlets in the U.S., on “Gun policy” and “Immigration” along with their political bias leanings using Amazon Mechanical Turk and used it to validate these assumptions. Our findings suggest that, (i) in certain cases, liberal and conservative raters’ label leanings of news articles differently, (ii) in many cases, the news articles do not follow the political leaning of their source outlet, and (iii) for certain outlets, the political leaning of the outlet does not remain unchanged while reporting on different topics/issues. We believe, our work offers important guidelines for future attempts at building political bias datasets which in turn will help them in building better algorithmic news retrieval systems for online platforms.
[6]
V. Lazova, “Texture Completion of People in Diverse Clothing,” Universität des Saarlandes, Saarbrücken, 2019.
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BibTeX
@mastersthesis{Lazova_Master2019, TITLE = {Texture Completion of People in Diverse Clothing}, AUTHOR = {Lazova, Verica}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, }
Endnote
%0 Thesis %A Lazova, Verica %Y Insafutdinov, Eldar %A referee: Pons-Moll, Gerard %A referee: Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Texture Completion of People in Diverse Clothing : %G eng %U http://hdl.handle.net/21.11116/0000-0003-91DB-2 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 46 p. %V master %9 master
[7]
M. M. Mounir Sourial, “Automatic Neural Network Architecture Optimization,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Deep learning has recently become a very hot topic in Computer Science. It has invaded many applications in Computer Science achieving exceptional performances compared to other existing methods. However, neural networks have a strong memory limitation which is considered to be one of its main challenges. This is why remarkable research focus is recently directed towards model compression. This thesis studies a divide-and-conquer approach that transforms an existing trained neural network into another network with less number of parameters with the target of decrasing its memory footprint. It takes into account the resulting loss in performance. It is based on existing layer transformation techniques like Canonical Polyadic (CP) and SVD affine transformations. Given an artificial neural network, trained on a certain dataset, an agent optimizes the architecture of the neural network in a bottom-up man- ner. It cuts the network in sub-networks of length 1. It optimizes each sub-network using layer transformations. Then it chooses the most- promising sub-networks to construct sub-networks of length 2. This process is repeated until it constructs an artificial neural network that covers the functionalities of the original neural network. This thesis offers an extensive analysis of the proposed approach. We tested this tech- nique with different known neural network architectures with popular datasets. We could outperform recent techniques in both the compression rate and network perfor- mance on LeNet5 with MNIST. We could compress ResNet-20 to 25% of their original size achieving performance comparable with networks in the literature with double this size.
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BibTeX
@mastersthesis{, TITLE = {Automatic Neural Network Architecture Optimization}, AUTHOR = {Mounir Sourial, Maggie Moheb}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019-05-15}, ABSTRACT = {Deep learning has recently become a very hot topic in Computer Science. It has invaded many applications in Computer Science achieving exceptional performances compared to other existing methods. However, neural networks have a strong memory limitation which is considered to be one of its main challenges. This is why remarkable research focus is recently directed towards model compression. This thesis studies a divide-and-conquer approach that transforms an existing trained neural network into another network with less number of parameters with the target of decrasing its memory footprint. It takes into account the resulting loss in performance. It is based on existing layer transformation techniques like Canonical Polyadic (CP) and SVD affine transformations. Given an arti{fi}cial neural network, trained on a certain dataset, an agent optimizes the architecture of the neural network in a bottom-up man- ner. It cuts the network in sub-networks of length 1. It optimizes each sub-network using layer transformations. Then it chooses the most- promising sub-networks to construct sub-networks of length 2. This process is repeated until it constructs an arti{fi}cial neural network that covers the functionalities of the original neural network. This thesis offers an extensive analysis of the proposed approach. We tested this tech- nique with different known neural network architectures with popular datasets. We could outperform recent techniques in both the compression rate and network perfor- mance on LeNet5 with MNIST. We could compress ResNet-20 to 25% of their original size achieving performance comparable with networks in the literature with double this size.}, }
Endnote
%0 Thesis %A Mounir Sourial, Maggie Moheb %Y Weikum, Gerhard %A referee: Cardinaux, Fabian %A referee: Weikum, Gerhard %A referee: Yates, Andrew %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automatic Neural Network Architecture Optimization : %G eng %U http://hdl.handle.net/21.11116/0000-0005-9C58-9 %I Universität des Saarlandes %C Saarbrücken %D 2019 %8 15.05.2019 %P 76 p. %V master %9 master %X Deep learning has recently become a very hot topic in Computer Science. It has invaded many applications in Computer Science achieving exceptional performances compared to other existing methods. However, neural networks have a strong memory limitation which is considered to be one of its main challenges. This is why remarkable research focus is recently directed towards model compression. This thesis studies a divide-and-conquer approach that transforms an existing trained neural network into another network with less number of parameters with the target of decrasing its memory footprint. It takes into account the resulting loss in performance. It is based on existing layer transformation techniques like Canonical Polyadic (CP) and SVD affine transformations. Given an artificial neural network, trained on a certain dataset, an agent optimizes the architecture of the neural network in a bottom-up man- ner. It cuts the network in sub-networks of length 1. It optimizes each sub-network using layer transformations. Then it chooses the most- promising sub-networks to construct sub-networks of length 2. This process is repeated until it constructs an artificial neural network that covers the functionalities of the original neural network. This thesis offers an extensive analysis of the proposed approach. We tested this tech- nique with different known neural network architectures with popular datasets. We could outperform recent techniques in both the compression rate and network perfor- mance on LeNet5 with MNIST. We could compress ResNet-20 to 25% of their original size achieving performance comparable with networks in the literature with double this size.
PhD
[8]
A. Abujabal, “Question Answering over Knowledge Bases with Continuous Learning,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words.
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BibTeX
@phdthesis{Abujabalphd2013, TITLE = {Question Answering over Knowledge Bases with Continuous Learning}, AUTHOR = {Abujabal, Abdalghani}, LANGUAGE = {eng}, DOI = {10.22028/D291-27968}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words.}, }
Endnote
%0 Thesis %A Abujabal, Abdalghani %Y Weikum, Gerhard %A referee: Linn, Jimmy %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Question Answering over Knowledge Bases with Continuous Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0003-AEC0-0 %R 10.22028/D291-27968 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 141 p. %V phd %9 phd %X Answering complex natural language questions with crisp answers is crucial towards satisfying the information needs of advanced users. With the rapid growth of knowledge bases (KBs) such as Yago and Freebase, this goal has become attainable by translating questions into formal queries like SPARQL queries. Such queries can then be evaluated over knowledge bases to retrieve crisp answers. To this end, three research issues arise: (i) how to develop methods that are robust to lexical and syntactic variations in questions and can handle complex questions, (ii) how to design and curate datasets to advance research in question answering, and (iii) how to efficiently identify named entities in questions. In this dissertation, we make the following five contributions in the areas of question answering (QA) and named entity recognition (NER). For issue (i), we make the following contributions: We present QUINT, an approach for answering natural language questions over knowledge bases using automatically learned templates. Templates are an important asset for QA over KBs, simplifying the semantic parsing of input questions and generating formal queries for interpretable answers. QUINT is capable of answering both simple and compositional questions. We introduce NEQA, a framework for continuous learning for QA over KBs. NEQA starts with a small seed of training examples in the form of question-answer pairs, and improves its performance over time. NEQA combines both syntax, through template-based answering, and semantics, via a semantic similarity function. %when templates fail to do so. Moreover, it adapts to the language used after deployment by periodically retraining its underlying models. For issues (i) and (ii), we present TEQUILA, a framework for answering complex questions with explicit and implicit temporal conditions over KBs. TEQUILA is built on a rule-based framework that detects and decomposes temporal questions into simpler sub-questions that can be answered by standard KB-QA systems. TEQUILA reconciles the results of sub-questions into final answers. TEQUILA is accompanied with a dataset called TempQuestions, which consists of 1,271 temporal questions with gold-standard answers over Freebase. This collection is derived by judiciously selecting time-related questions from existing QA datasets. For issue (ii), we publish ComQA, a large-scale manually-curated dataset for QA. ComQA contains questions that represent real information needs and exhibit a wide range of difficulties such as the need for temporal reasoning, comparison, and compositionality. ComQA contains paraphrase clusters of semantically-equivalent questions that can be exploited by QA systems. We harness a combination of community question-answering platforms and crowdsourcing to construct the ComQA dataset. For issue (iii), we introduce a neural network model based on subword units for named entity recognition. The model learns word representations using a combination of characters, bytes and phonemes. While achieving comparable performance with word-level based models, our model has an order-of-magnitude smaller vocabulary size and lower memory requirements, and it handles out-of-vocabulary words. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27438
[9]
J. A. Biega, “Enhancing Privacy and Fairness in Search Systems,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.
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BibTeX
@phdthesis{biegaphd2019, TITLE = {Enhancing Privacy and Fairness in Search Systems}, AUTHOR = {Biega, Joanna Asia}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-278861}, DOI = {10.22028/D291-27886}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.}, }
Endnote
%0 Thesis %A Biega, Joanna Asia %Y Weikum, Gerhard %A referee: Gummadi, Krishna %A referee: Nejdl, Wolfgang %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society External Organizations %T Enhancing Privacy and Fairness in Search Systems : %G eng %U http://hdl.handle.net/21.11116/0000-0003-9AED-5 %R 10.22028/D291-27886 %U urn:nbn:de:bsz:291--ds-278861 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 111 p. %V phd %9 phd %X Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27389
[10]
A. Dheghani Amirabad, “From genes to transcripts : integrative modeling and analysis of regulatory networks,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Although all the cells in an organism posses the same genome, the regulatory mechanisms lead to highly specific cell types. Elucidating these regulatory mechanisms is a great challenge in systems biology research. Nonetheless, it is known that a large fraction of our genome is comprised of regulatory elements, the precise mechanisms by which different combinations of regulatory elements are involved in controlling gene expression and cell identity are poorly understood. This thesis describes algorithms and approaches for modeling and analysis of different modes of gene regulation. We present POSTIT a novel algorithm for modeling and inferring transcript isoform regulation from transcriptomics and epigenomics data. POSTIT uses multi-task learning with structured-sparsity inducing regularizer to share the regulatory information between isoforms of a gene, which is shown to lead to accurate isoform expression prediction and inference of regulators. Furthermore, it can use isoform expression level and annotation as informative priors for gene expression prediction. Hence, it constitute a novel accurate approach applicable to gene or transcript isoform centric analysis using expression data. In an application to microRNA (miRNA) target prioritization, we demonstrate that it out-competes classical gene centric methods. Moreover, pinpoints important transcription factors and miRNAs that regulate differentially expressed isoforms in any biological system. Competing endogenous RNA (ceRNA) interactions mediated by miRNAs were postulated as an important cellular regulatory network, in which cross-talk between different transcripts involves competition for joint regulators. We developed a novel statistical method, called SPONGE, for large-scale inference of ceRNA networks. In this framework, we designed an efficient empirical p-value computation approach, by sampling from derived null models, which addresses important confounding factors such as sample size, number of involved regulators and strength of correlation. In an application to a large pan-cancer dataset with 31 cancers we discovered protein-coding and non-coding RNAs that are generic ceRNAs in cancer. Finally, we present an integrative analysis of miRNA and protein-based posttranscriptional regulation. We postulate a competitive regulation of the RNAbinding protein IMP2 with miRNAs binding the same RNAs using expression and RNA binding data. This function of IMP2 is relevant in the contribution to disease in the context of adult cellular metabolism. As a summary, in this thesis we have presented a number of different novel approaches for inference and the integrative analysis of regulatory networks that we believe will find wide applicability in the biological sciences.
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BibTeX
@phdthesis{Dehghaniphd2019, TITLE = {From genes to transcripts : integrative modeling and analysis of regulatory networks}, AUTHOR = {Dheghani Amirabad, Azim}, LANGUAGE = {eng}, DOI = {10.22028/D291-28659}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Although all the cells in an organism posses the same genome, the regulatory mechanisms lead to highly specific cell types. Elucidating these regulatory mechanisms is a great challenge in systems biology research. Nonetheless, it is known that a large fraction of our genome is comprised of regulatory elements, the precise mechanisms by which different combinations of regulatory elements are involved in controlling gene expression and cell identity are poorly understood. This thesis describes algorithms and approaches for modeling and analysis of different modes of gene regulation. We present POSTIT a novel algorithm for modeling and inferring transcript isoform regulation from transcriptomics and epigenomics data. POSTIT uses multi-task learning with structured-sparsity inducing regularizer to share the regulatory information between isoforms of a gene, which is shown to lead to accurate isoform expression prediction and inference of regulators. Furthermore, it can use isoform expression level and annotation as informative priors for gene expression prediction. Hence, it constitute a novel accurate approach applicable to gene or transcript isoform centric analysis using expression data. In an application to microRNA (miRNA) target prioritization, we demonstrate that it out-competes classical gene centric methods. Moreover, pinpoints important transcription factors and miRNAs that regulate differentially expressed isoforms in any biological system. Competing endogenous RNA (ceRNA) interactions mediated by miRNAs were postulated as an important cellular regulatory network, in which cross-talk between different transcripts involves competition for joint regulators. We developed a novel statistical method, called SPONGE, for large-scale inference of ceRNA networks. In this framework, we designed an efficient empirical p-value computation approach, by sampling from derived null models, which addresses important confounding factors such as sample size, number of involved regulators and strength of correlation. In an application to a large pan-cancer dataset with 31 cancers we discovered protein-coding and non-coding RNAs that are generic ceRNAs in cancer. Finally, we present an integrative analysis of miRNA and protein-based posttranscriptional regulation. We postulate a competitive regulation of the RNAbinding protein IMP2 with miRNAs binding the same RNAs using expression and RNA binding data. This function of IMP2 is relevant in the contribution to disease in the context of adult cellular metabolism. As a summary, in this thesis we have presented a number of different novel approaches for inference and the integrative analysis of regulatory networks that we believe will find wide applicability in the biological sciences.}, }
Endnote
%0 Thesis %A Dheghani Amirabad, Azim %Y Schulz, Marcel %A referee: Keller, Andreas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations %T From genes to transcripts : integrative modeling and analysis of regulatory networks : %G eng %U http://hdl.handle.net/21.11116/0000-0005-438D-1 %R 10.22028/D291-28659 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 139 p. %V phd %9 phd %X Although all the cells in an organism posses the same genome, the regulatory mechanisms lead to highly specific cell types. Elucidating these regulatory mechanisms is a great challenge in systems biology research. Nonetheless, it is known that a large fraction of our genome is comprised of regulatory elements, the precise mechanisms by which different combinations of regulatory elements are involved in controlling gene expression and cell identity are poorly understood. This thesis describes algorithms and approaches for modeling and analysis of different modes of gene regulation. We present POSTIT a novel algorithm for modeling and inferring transcript isoform regulation from transcriptomics and epigenomics data. POSTIT uses multi-task learning with structured-sparsity inducing regularizer to share the regulatory information between isoforms of a gene, which is shown to lead to accurate isoform expression prediction and inference of regulators. Furthermore, it can use isoform expression level and annotation as informative priors for gene expression prediction. Hence, it constitute a novel accurate approach applicable to gene or transcript isoform centric analysis using expression data. In an application to microRNA (miRNA) target prioritization, we demonstrate that it out-competes classical gene centric methods. Moreover, pinpoints important transcription factors and miRNAs that regulate differentially expressed isoforms in any biological system. Competing endogenous RNA (ceRNA) interactions mediated by miRNAs were postulated as an important cellular regulatory network, in which cross-talk between different transcripts involves competition for joint regulators. We developed a novel statistical method, called SPONGE, for large-scale inference of ceRNA networks. In this framework, we designed an efficient empirical p-value computation approach, by sampling from derived null models, which addresses important confounding factors such as sample size, number of involved regulators and strength of correlation. In an application to a large pan-cancer dataset with 31 cancers we discovered protein-coding and non-coding RNAs that are generic ceRNAs in cancer. Finally, we present an integrative analysis of miRNA and protein-based posttranscriptional regulation. We postulate a competitive regulation of the RNAbinding protein IMP2 with miRNAs binding the same RNAs using expression and RNA binding data. This function of IMP2 is relevant in the contribution to disease in the context of adult cellular metabolism. As a summary, in this thesis we have presented a number of different novel approaches for inference and the integrative analysis of regulatory networks that we believe will find wide applicability in the biological sciences. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27669
[11]
M. Döring, “Computational Approaches for Improving Treatment and Prevention of Viral Infections,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.
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BibTeX
@phdthesis{Doringphd2013, TITLE = {Computational Approaches for Improving Treatment and Prevention of Viral Infections}, AUTHOR = {D{\"o}ring, Matthias}, LANGUAGE = {eng}, DOI = {10.22028/D291-27946}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.}, }
Endnote
%0 Thesis %A Döring, Matthias %Y Pfeifer, Nico %A referee: Lengauer, Thomas %A referee: Kalinina, Olga V. %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Computational Approaches for Improving Treatment and Prevention of Viral Infections : %G eng %U http://hdl.handle.net/21.11116/0000-0003-AEBA-8 %R 10.22028/D291-27946 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 337 p. %V phd %9 phd %X The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27443
[12]
P. Ebert, “What we leave behind : reproducibility in chromatin analysis within and across species,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries.
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@phdthesis{Ebertphd2019, TITLE = {What we leave behind : reproducibility in chromatin analysis within and across species}, AUTHOR = {Ebert, Peter}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-278311}, DOI = {doi.org/10.22028/D291-27831}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries.}, }
Endnote
%0 Thesis %A Ebert, Peter %Y Lengauer, Thomas %A referee: Lenhof, Hans-Peter %A referee: Weikum, Gerhard %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T What we leave behind : reproducibility in chromatin analysis within and across species : %G eng %U http://hdl.handle.net/21.11116/0000-0003-9ADF-5 %R doi.org/10.22028/D291-27831 %U urn:nbn:de:bsz:291--ds-278311 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 152 p. %V phd %9 phd %X Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27387
[13]
D. Gupta, “Search and Analytics Using Semantic Annotations,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.
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@phdthesis{GUPTAphd2019, TITLE = {Search and Analytics Using Semantic Annotations}, AUTHOR = {Gupta, Dhruv}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-300780}, DOI = {10.22028/D291-30078}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections.}, }
Endnote
%0 Thesis %A Gupta, Dhruv %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Bedathur, Srikanta %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Search and Analytics Using Semantic Annotations : %G eng %U http://hdl.handle.net/21.11116/0000-0005-7695-E %R 10.22028/D291-30078 %U urn:nbn:de:bsz:291--ds-300780 %F OTHER: hdl:20.500.11880/28516 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P xxviii, 211 p. %V phd %9 phd %X Search systems help users locate relevant information in the form of text documents for keyword queries. Using text alone, it is often difficult to satisfy the user's information need. To discern the user's intent behind queries, we turn to semantic annotations (e.g., named entities and temporal expressions) that natural language processing tools can now deliver with great accuracy. This thesis develops methods and an infrastructure that leverage semantic annotations to efficiently and effectively search large document collections. This thesis makes contributions in three areas: indexing, querying, and mining of semantically annotated document collections. First, we describe an indexing infrastructure for semantically annotated document collections. The indexing infrastructure can support knowledge-centric tasks such as information extraction, relationship extraction, question answering, fact spotting and semantic search at scale across millions of documents. Second, we propose methods for exploring large document collections by suggesting semantic aspects for queries. These semantic aspects are generated by considering annotations in the form of temporal expressions, geographic locations, and other named entities. The generated aspects help guide the user to relevant documents without the need to read their contents. Third and finally, we present methods that can generate events, structured tables, and insightful visualizations from semantically annotated document collections. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28516
[14]
Y. Ibrahim, “Understanding Quantities in Web Tables and Text,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: - We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. - We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. - We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. - We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems.
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@phdthesis{yusraphd2019, TITLE = {Understanding Quantities in Web Tables and Text}, AUTHOR = {Ibrahim, Yusra}, LANGUAGE = {eng}, DOI = {10.22028/D291-29657}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: -- We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. -- We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. -- We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. -- We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems.}, }
Endnote
%0 Thesis %A Ibrahim, Yusra %Y Weikum, Gerhard %A referee: Riedewald, Mirek %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Understanding Quantities in Web Tables and Text : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4384-A %R 10.22028/D291-29657 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 116 p. %V phd %9 phd %X There is a wealth of schema-free tables on the web. The text accompanying these tables explains and qualifies the numerical quantities given in the tables. Despite this ubiquity of tabular data, there is little research that harnesses this wealth of data by semantically understanding the information that is conveyed rather ambiguously in these tables. This information can be disambiguated only by the help of the accompanying text. In the process of understanding quantity mentions in tables and text, we are faced with the following challenges; First, there is no comprehensive knowledge base for anchoring quantity mentions. Second, tables are created ad-hoc without a standard schema and with ambiguous header names; also table cells usually contain abbreviations. Third, quantities can be written in multiple forms and units of measures. Fourth, the text usually refers to the quantities in tables using aggregation, approximation, and different scales. In this thesis, we target these challenges through the following contributions: - We present the Quantity Knowledge Base (QKB), a knowledge base for representing Quantity mentions. We construct the QKB by importing information from Freebase, Wikipedia, and other online sources. - We propose Equity: a system for automatically canonicalizing header names and cell values onto concepts, classes, entities, and uniquely represented quantities registered in a knowledge base. We devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities, and quantities. Then, we cast the inference problem into an efficient algorithm based on random walks over weighted graphs. baselines. - We introduce the quantity alignment problem: computing bidirectional links between textual mentions of quantities and the corresponding table cells. We propose BriQ: a system for computing such alignments. BriQ copes with the specific challenges of approximate quantities, aggregated quantities, and calculated quantities. - We design ExQuisiTe: a web application that identifies mentions of quantities in text and tables, aligns quantity mentions in the text with related quantity mentions in tables, and generates salient suggestions for extractive text summarization systems. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28300
[15]
D. Issac, “On some covering, partition and connectivity problems in graphs,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
We look at some graph problems related to covering, partition, and connectivity. First, we study the problems of covering and partitioning edges with bicliques, especially from the viewpoint of parameterized complexity. For the partition problem, we develop much more efficient algorithms than the ones previously known. In contrast, for the cover problem, our lower bounds show that the known algorithms are probably optimal. Next, we move on to graph coloring, which is probably the most extensively studied partition problem in graphs. Hadwiger’s conjecture is a long-standing open problem related to vertex coloring. We prove the conjecture for a special class of graphs, namely squares of 2-trees, and show that square graphs are important in connection with Hadwiger’s conjecture. Then, we study a coloring problem that has been emerging recently, called rainbow coloring. This problem lies in the intersection of coloring and connectivity. We study different variants of rainbow coloring and present bounds and complexity results on them. Finally, we move on to another parameter related to connectivity called spanning tree congestion (STC). We give tight bounds for STC in general graphs and random graphs. While proving the results on
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@phdthesis{Issacphd2019, TITLE = {On some covering, partition and connectivity problems in graphs}, AUTHOR = {Issac, Davis}, LANGUAGE = {eng}, DOI = {10.22028/D291-29620}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {We look at some graph problems related to covering, partition, and connectivity. First, we study the problems of covering and partitioning edges with bicliques, especially from the viewpoint of parameterized complexity. For the partition problem, we develop much more efficient algorithms than the ones previously known. In contrast, for the cover problem, our lower bounds show that the known algorithms are probably optimal. Next, we move on to graph coloring, which is probably the most extensively studied partition problem in graphs. Hadwiger{\textquoteright}s conjecture is a long-standing open problem related to vertex coloring. We prove the conjecture for a special class of graphs, namely squares of 2-trees, and show that square graphs are important in connection with Hadwiger{\textquoteright}s conjecture. Then, we study a coloring problem that has been emerging recently, called rainbow coloring. This problem lies in the intersection of coloring and connectivity. We study different variants of rainbow coloring and present bounds and complexity results on them. Finally, we move on to another parameter related to connectivity called spanning tree congestion (STC). We give tight bounds for STC in general graphs and random graphs. While proving the results on}, }
Endnote
%0 Thesis %A Issac, Davis %Y Karrenbauer, Andreas %A referee: Mehlhorn, Kurt %A referee: Chandran, L. Sunil %+ Algorithms and Complexity, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society %T On some covering, partition and connectivity problems in graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0004-D665-9 %R 10.22028/D291-29620 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 191 p. %V phd %9 phd %X We look at some graph problems related to covering, partition, and connectivity. First, we study the problems of covering and partitioning edges with bicliques, especially from the viewpoint of parameterized complexity. For the partition problem, we develop much more efficient algorithms than the ones previously known. In contrast, for the cover problem, our lower bounds show that the known algorithms are probably optimal. Next, we move on to graph coloring, which is probably the most extensively studied partition problem in graphs. Hadwiger’s conjecture is a long-standing open problem related to vertex coloring. We prove the conjecture for a special class of graphs, namely squares of 2-trees, and show that square graphs are important in connection with Hadwiger’s conjecture. Then, we study a coloring problem that has been emerging recently, called rainbow coloring. This problem lies in the intersection of coloring and connectivity. We study different variants of rainbow coloring and present bounds and complexity results on them. Finally, we move on to another parameter related to connectivity called spanning tree congestion (STC). We give tight bounds for STC in general graphs and random graphs. While proving the results on %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28007
[16]
S. Karaev, “Matrix factorization over diods and its applications in data mining,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse (“negative numbers”) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used – the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data.
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@phdthesis{Karaevphd2019, TITLE = {Matrix factorization over diods and its applications in data mining}, AUTHOR = {Karaev, Sanjar}, LANGUAGE = {eng}, DOI = {10.22028/D291-28661}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse ({\textquotedblleft}negative numbers{\textquotedblright}) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used -- the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data.}, }
Endnote
%0 Thesis %A Karaev, Sanjar %Y Miettinen, Pauli %A referee: Weikum, Gerhard %A referee: van Leeuwen, Matthijs %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Matrix factorization over diods and its applications in data mining : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4369-A %R 10.22028/D291-28661 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 113 p. %V phd %9 phd %X Matrix factorizations are an important tool in data mining, and they have been used extensively for finding latent patterns in the data. They often allow to separate structure from noise, as well as to considerably reduce the dimensionality of the input matrix. While classical matrix decomposition methods, such as nonnegative matrix factorization (NMF) and singular value decomposition (SVD), proved to be very useful in data analysis, they are limited by the underlying algebraic structure. NMF, in particular, tends to break patterns into smaller bits, often mixing them with each other. This happens because overlapping patterns interfere with each other, making it harder to tell them apart. In this thesis we study matrix factorization over algebraic structures known as dioids, which are characterized by the lack of additive inverse (“negative numbers”) and the idempotency of addition (a + a = a). Using dioids makes it easier to separate overlapping features, and, in particular, it allows to better deal with the above mentioned pattern breaking problem. We consider different types of dioids, that range from continuous (subtropical and tropical algebras) to discrete (Boolean algebra). Among these, the Boolean algebra is perhaps the most well known, and there exist methods that allow one to obtain high quality Boolean matrix factorizations in terms of the reconstruction error. In this work, however, a different objective function is used – the description length of the data, which enables us to obtain compact and highly interpretable results. The tropical and subtropical algebras, on the other hand, are much less known in the data mining field. While they find applications in areas such as job scheduling and discrete event systems, they are virtually unknown in the context of data analysis. We will use them to obtain idempotent nonnegative factorizations that are similar to NMF, but are better at separating the most prominent features of the data. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27903
[17]
T. Leimkühler, “Artificial Intelligence for Efficient Image-based View Synthesis,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Synthesizing novel views from image data is a widely investigated topic in both computer graphics and computer vision, and has many applications like stereo or multi-view rendering for virtual reality, light field reconstruction, and image post-processing. While image-based approaches have the advantage of reduced computational load compared to classical model-based rendering, efficiency is still a major concern. This thesis demonstrates how concepts and tools from artificial intelligence can be used to increase the efficiency of image-based view synthesis algorithms. In particular it is shown how machine learning can help to generate point patterns useful for a variety of computer graphics tasks, how path planning can guide image warping, how sparsity-enforcing optimization can lead to significant speedups in interactive distribution effect rendering, and how probabilistic inference can be used to perform real-time 2D-to-3D conversion.
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BibTeX
@phdthesis{Leimphd2019, TITLE = {Artificial Intelligence for Efficient Image-based View Synthesis}, AUTHOR = {Leimk{\"u}hler, Thomas}, LANGUAGE = {eng}, DOI = {10.22028/D291-28379}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Synthesizing novel views from image data is a widely investigated topic in both computer graphics and computer vision, and has many applications like stereo or multi-view rendering for virtual reality, light field reconstruction, and image post-processing. While image-based approaches have the advantage of reduced computational load compared to classical model-based rendering, efficiency is still a major concern. This thesis demonstrates how concepts and tools from artificial intelligence can be used to increase the efficiency of image-based view synthesis algorithms. In particular it is shown how machine learning can help to generate point patterns useful for a variety of computer graphics tasks, how path planning can guide image warping, how sparsity-enforcing optimization can lead to significant speedups in interactive distribution effect rendering, and how probabilistic inference can be used to perform real-time 2D-to-3D conversion.}, }
Endnote
%0 Thesis %A Leimkühler, Thomas %Y Seidel, Hans-Peter %A referee: Ritschel, Tobias %A referee: Lensch, Hendrik %A referee: Drettakis, George %+ Computer Graphics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society External Organizations %T Artificial Intelligence for Efficient Image-based View Synthesis : %G eng %U http://hdl.handle.net/21.11116/0000-0004-A589-7 %R 10.22028/D291-28379 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 136 p. %V phd %9 phd %X Synthesizing novel views from image data is a widely investigated topic in both computer graphics and computer vision, and has many applications like stereo or multi-view rendering for virtual reality, light field reconstruction, and image post-processing. While image-based approaches have the advantage of reduced computational load compared to classical model-based rendering, efficiency is still a major concern. This thesis demonstrates how concepts and tools from artificial intelligence can be used to increase the efficiency of image-based view synthesis algorithms. In particular it is shown how machine learning can help to generate point patterns useful for a variety of computer graphics tasks, how path planning can guide image warping, how sparsity-enforcing optimization can lead to significant speedups in interactive distribution effect rendering, and how probabilistic inference can be used to perform real-time 2D-to-3D conversion. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27664
[18]
E. Levinkov, “Generalizations of the Multicut Problem for Computer Vision,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.
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BibTeX
@phdthesis{Levinkovphd2013, TITLE = {Generalizations of the Multicut Problem for Computer Vision}, AUTHOR = {Levinkov, Evgeny}, LANGUAGE = {eng}, DOI = {10.22028/D291-27909}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.}, }
Endnote
%0 Thesis %A Levinkov, Evgeny %Y Andres, Bjoern %A referee: Lempitsky, Victor %A referee: Rother, Carsten %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generalizations of the Multicut Problem for Computer Vision : %G eng %U http://hdl.handle.net/21.11116/0000-0003-9B27-3 %R 10.22028/D291-27909 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 151 p. %V phd %9 phd %X Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27415
[19]
S. Nikumbh, “Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.
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@phdthesis{Nikumbhphd2019, TITLE = {Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in {3D}}, AUTHOR = {Nikumbh, Sarvesh}, LANGUAGE = {eng}, DOI = {10.22028/D291-28153}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.}, }
Endnote
%0 Thesis %A Nikumbh, Sarvesh %Y Pfeifer, Nico %A referee: Marschall, Tobias %A referee: Ebert, Peter %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D : %G eng %U http://hdl.handle.net/21.11116/0000-0004-A5CE-A %R 10.22028/D291-28153 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 150 p. %V phd %9 phd %X With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27471
[20]
K. Popat, “Credibility Analysis of Textual Claimswith Explainable Evidence,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.
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@phdthesis{Popatphd2019, TITLE = {Credibility Analysis of Textual Claimswith Explainable Evidence}, AUTHOR = {Popat, Kashyap}, LANGUAGE = {eng}, DOI = {10.22028/D291-30005}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.}, }
Endnote
%0 Thesis %A Popat, Kashyap %Y Weikum, Gerhard %A referee: Naumann, Felix %A referee: Yates, Andrew %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Credibility Analysis of Textual Claimswith Explainable Evidence : %G eng %U http://hdl.handle.net/21.11116/0000-0005-654D-4 %R 10.22028/D291-30005 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 134 p. %V phd %9 phd %X Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28481
[21]
N. Robertini, “Model-based Human Performance Capture in Outdoor Scenes,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Technologies for motion and performance capture of real actors have enabled the creation of realisticlooking virtual humans through detail and deformation transfer at the cost of extensive manual work and sophisticated in-studio marker-based systems. This thesis pushes the boundaries of performance capture by proposing automatic algorithms for robust 3D skeleton and detailed surface tracking in less constrained multi-view outdoor scenarios. Contributions include new multi-layered human body representations designed for effective model-based time-consistent reconstruction in complex dynamic environments with varying illumination, from a set of vision cameras. We design dense surface refinement approaches to enable smooth silhouette-free model-to-image alignment, as well as coarse-to-fine tracking techniques to enable joint estimation of skeleton motion and finescale surface deformations in complicated scenarios. High-quality results attained on challenging application scenarios confirm the contributions and show great potential for the automatic creation of personalized 3D virtual humans.
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BibTeX
@phdthesis{Robertini_PhD2019, TITLE = {Model-based Human Performance Capture in Outdoor Scenes}, AUTHOR = {Robertini, Nadia}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-285887}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Technologies for motion and performance capture of real actors have enabled the creation of realisticlooking virtual humans through detail and deformation transfer at the cost of extensive manual work and sophisticated in-studio marker-based systems. This thesis pushes the boundaries of performance capture by proposing automatic algorithms for robust 3D skeleton and detailed surface tracking in less constrained multi-view outdoor scenarios. Contributions include new multi-layered human body representations designed for effective model-based time-consistent reconstruction in complex dynamic environments with varying illumination, from a set of vision cameras. We design dense surface refinement approaches to enable smooth silhouette-free model-to-image alignment, as well as coarse-to-fine tracking techniques to enable joint estimation of skeleton motion and finescale surface deformations in complicated scenarios. High-quality results attained on challenging application scenarios confirm the contributions and show great potential for the automatic creation of personalized 3D virtual humans.}, }
Endnote
%0 Thesis %A Robertini, Nadia %Y Theobalt, Christian %A referee: Seidel, Hans-Peter %+ Computer Graphics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society Computer Graphics, MPI for Informatics, Max Planck Society %T Model-based Human Performance Capture in Outdoor Scenes : %G eng %U http://hdl.handle.net/21.11116/0000-0004-9B2E-B %U urn:nbn:de:bsz:291--ds-285887 %F OTHER: hdl:20.500.11880/27667 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P XIX, 136, XI p. %V phd %9 phd %X Technologies for motion and performance capture of real actors have enabled the creation of realisticlooking virtual humans through detail and deformation transfer at the cost of extensive manual work and sophisticated in-studio marker-based systems. This thesis pushes the boundaries of performance capture by proposing automatic algorithms for robust 3D skeleton and detailed surface tracking in less constrained multi-view outdoor scenarios. Contributions include new multi-layered human body representations designed for effective model-based time-consistent reconstruction in complex dynamic environments with varying illumination, from a set of vision cameras. We design dense surface refinement approaches to enable smooth silhouette-free model-to-image alignment, as well as coarse-to-fine tracking techniques to enable joint estimation of skeleton motion and finescale surface deformations in complicated scenarios. High-quality results attained on challenging application scenarios confirm the contributions and show great potential for the automatic creation of personalized 3D virtual humans. %U https://scidok.sulb.uni-saarland.de/handle/20.500.11880/27667
[22]
H. Sattar, “Intents and Preferences Prediction Based on Implicit Human Cues,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Visual search is an important task, and it is part of daily human life. Thus, it has been a long-standing goal in Computer Vision to develop methods aiming at analysing human search intent and preferences. As the target of the search only exists in mind of the person, search intent prediction remains challenging for machine perception. In this thesis, we focus on advancing techniques for search target and preference prediction from implicit human cues. First, we propose a search target inference algorithm from human fixation data recorded during visual search. In contrast to previous work that has focused on individual instances as a search target in a closed world, we propose the first approach to predict the search target in open-world settings by learning the compatibility between observed fixations and potential search targets. Second, we further broaden the scope of search target prediction to categorical classes, such as object categories and attributes. However, state of the art models for categorical recognition, in general, require large amounts of training data, which is prohibitive for gaze data. To address this challenge, we propose a novel Gaze Pooling Layer that integrates gaze information into CNN-based architectures as an attention mechanism – incorporating both spatial and temporal aspects of human gaze behaviour. Third, we go one step further and investigate the feasibility of combining our gaze embedding approach, with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Forth, for the first time, we studied the effect of body shape on people preferences of outfits. We propose a novel and robust multi-photo approach to estimate the body shapes of each user and build a conditional model of clothing categories given body-shape. We demonstrate that in real-world data, clothing categories and body-shapes are correlated. We show that our approach estimates a realistic looking body shape that captures a user’s weight group and body shape type, even from a single image of a clothed person. However, an accurate depiction of the naked body is considered highly private and therefore, might not be consented by most people. First, we studied the perception of such technology via a user study. Then, in the last part of this thesis, we ask if the automatic extraction of such information can be effectively evaded. In summary, this thesis addresses several different tasks that aims to enable the vision system to analyse human search intent and preferences in real-world scenarios. In particular, the thesis proposes several novel ideas and models in visual search target prediction from human fixation data, for the first time studied the correlation between shape and clothing categories opening a new direction in clothing recommendation systems, and introduces a new topic in privacy and computer vision, aimed at preventing automatic 3D shape extraction from images.
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@phdthesis{Sattar_PhD2019, TITLE = {Intents and Preferences Prediction Based on Implicit Human Cues}, AUTHOR = {Sattar, Hosnieh}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-281920}, DOI = {10.22028/D291-28192}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Visual search is an important task, and it is part of daily human life. Thus, it has been a long-standing goal in Computer Vision to develop methods aiming at analysing human search intent and preferences. As the target of the search only exists in mind of the person, search intent prediction remains challenging for machine perception. In this thesis, we focus on advancing techniques for search target and preference prediction from implicit human cues. First, we propose a search target inference algorithm from human fixation data recorded during visual search. In contrast to previous work that has focused on individual instances as a search target in a closed world, we propose the first approach to predict the search target in open-world settings by learning the compatibility between observed fixations and potential search targets. Second, we further broaden the scope of search target prediction to categorical classes, such as object categories and attributes. However, state of the art models for categorical recognition, in general, require large amounts of training data, which is prohibitive for gaze data. To address this challenge, we propose a novel Gaze Pooling Layer that integrates gaze information into CNN-based architectures as an attention mechanism -- incorporating both spatial and temporal aspects of human gaze behaviour. Third, we go one step further and investigate the feasibility of combining our gaze embedding approach, with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Forth, for the first time, we studied the effect of body shape on people preferences of outfits. We propose a novel and robust multi-photo approach to estimate the body shapes of each user and build a conditional model of clothing categories given body-shape. We demonstrate that in real-world data, clothing categories and body-shapes are correlated. We show that our approach estimates a realistic looking body shape that captures a user{\textquoteright}s weight group and body shape type, even from a single image of a clothed person. However, an accurate depiction of the naked body is considered highly private and therefore, might not be consented by most people. First, we studied the perception of such technology via a user study. Then, in the last part of this thesis, we ask if the automatic extraction of such information can be effectively evaded. In summary, this thesis addresses several different tasks that aims to enable the vision system to analyse human search intent and preferences in real-world scenarios. In particular, the thesis proposes several novel ideas and models in visual search target prediction from human fixation data, for the first time studied the correlation between shape and clothing categories opening a new direction in clothing recommendation systems, and introduces a new topic in privacy and computer vision, aimed at preventing automatic 3D shape extraction from images.}, }
Endnote
%0 Thesis %A Sattar, Hosnieh %Y Fritz, Mario %A referee: Schiele, Bernt %A referee: Sugano, Yusuke %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Intents and Preferences Prediction Based on Implicit Human Cues : %G eng %U http://hdl.handle.net/21.11116/0000-0004-8E7F-F %R 10.22028/D291-28192 %U urn:nbn:de:bsz:291--ds-281920 %F OTHER: hdl:20.500.11880/27625 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P X, 136 p. %V phd %9 phd %X Visual search is an important task, and it is part of daily human life. Thus, it has been a long-standing goal in Computer Vision to develop methods aiming at analysing human search intent and preferences. As the target of the search only exists in mind of the person, search intent prediction remains challenging for machine perception. In this thesis, we focus on advancing techniques for search target and preference prediction from implicit human cues. First, we propose a search target inference algorithm from human fixation data recorded during visual search. In contrast to previous work that has focused on individual instances as a search target in a closed world, we propose the first approach to predict the search target in open-world settings by learning the compatibility between observed fixations and potential search targets. Second, we further broaden the scope of search target prediction to categorical classes, such as object categories and attributes. However, state of the art models for categorical recognition, in general, require large amounts of training data, which is prohibitive for gaze data. To address this challenge, we propose a novel Gaze Pooling Layer that integrates gaze information into CNN-based architectures as an attention mechanism – incorporating both spatial and temporal aspects of human gaze behaviour. Third, we go one step further and investigate the feasibility of combining our gaze embedding approach, with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Forth, for the first time, we studied the effect of body shape on people preferences of outfits. We propose a novel and robust multi-photo approach to estimate the body shapes of each user and build a conditional model of clothing categories given body-shape. We demonstrate that in real-world data, clothing categories and body-shapes are correlated. We show that our approach estimates a realistic looking body shape that captures a user’s weight group and body shape type, even from a single image of a clothed person. However, an accurate depiction of the naked body is considered highly private and therefore, might not be consented by most people. First, we studied the perception of such technology via a user study. Then, in the last part of this thesis, we ask if the automatic extraction of such information can be effectively evaded. In summary, this thesis addresses several different tasks that aims to enable the vision system to analyse human search intent and preferences in real-world scenarios. In particular, the thesis proposes several novel ideas and models in visual search target prediction from human fixation data, for the first time studied the correlation between shape and clothing categories opening a new direction in clothing recommendation systems, and introduces a new topic in privacy and computer vision, aimed at preventing automatic 3D shape extraction from images. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27625
[23]
M. Simeonovski, “Accountable infrastructure and its impact on internet security and privacy,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
The Internet infrastructure relies on the correct functioning of the basic underlying protocols, which were designed for functionality. Security and privacy have been added post hoc, mostly by applying cryptographic means to different layers of communication. In the absence of accountability, as a fundamental property, the Internet infrastructure does not have a built-in ability to associate an action with the responsible entity, neither to detect or prevent misbehavior. In this thesis, we study accountability from a few different perspectives. First, we study the need of having accountability in anonymous communication networks as a mechanism that provides repudiation for the proxy nodes by tracing back selected outbound traffic in a provable manner. Second, we design a framework that provides a foundation to support the enforcement of the right to be forgotten law in a scalable and automated manner. The framework provides a technical mean for the users to prove their eligibility for content removal from the search results. Third, we analyze the Internet infrastructure determining potential security risks and threats imposed by dependencies among the entities on the Internet. Finally, we evaluate the feasibility of using hop count filtering as a mechanism for mitigating Distributed Reflective Denial-of-Service attacks, and conceptually show that it cannot work to prevent these attacks.
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BibTeX
@phdthesis{Simeonphd2019, TITLE = {Accountable infrastructure and its impact on internet security and privacy}, AUTHOR = {Simeonovski, Milivoj}, LANGUAGE = {eng}, DOI = {10.22028/D291-29890}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {The Internet infrastructure relies on the correct functioning of the basic underlying protocols, which were designed for functionality. Security and privacy have been added post hoc, mostly by applying cryptographic means to different layers of communication. In the absence of accountability, as a fundamental property, the Internet infrastructure does not have a built-in ability to associate an action with the responsible entity, neither to detect or prevent misbehavior. In this thesis, we study accountability from a few different perspectives. First, we study the need of having accountability in anonymous communication networks as a mechanism that provides repudiation for the proxy nodes by tracing back selected outbound traffic in a provable manner. Second, we design a framework that provides a foundation to support the enforcement of the right to be forgotten law in a scalable and automated manner. The framework provides a technical mean for the users to prove their eligibility for content removal from the search results. Third, we analyze the Internet infrastructure determining potential security risks and threats imposed by dependencies among the entities on the Internet. Finally, we evaluate the feasibility of using hop count filtering as a mechanism for mitigating Distributed Reflective Denial-of-Service attacks, and conceptually show that it cannot work to prevent these attacks.}, }
Endnote
%0 Thesis %A Simeonovski, Milivoj %Y Backes, Michael %A referee: Rossow, Christian %+ International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Accountable infrastructure and its impact on internet security and privacy : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4392-A %R 10.22028/D291-29890 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 143 p. %V phd %9 phd %X The Internet infrastructure relies on the correct functioning of the basic underlying protocols, which were designed for functionality. Security and privacy have been added post hoc, mostly by applying cryptographic means to different layers of communication. In the absence of accountability, as a fundamental property, the Internet infrastructure does not have a built-in ability to associate an action with the responsible entity, neither to detect or prevent misbehavior. In this thesis, we study accountability from a few different perspectives. First, we study the need of having accountability in anonymous communication networks as a mechanism that provides repudiation for the proxy nodes by tracing back selected outbound traffic in a provable manner. Second, we design a framework that provides a foundation to support the enforcement of the right to be forgotten law in a scalable and automated manner. The framework provides a technical mean for the users to prove their eligibility for content removal from the search results. Third, we analyze the Internet infrastructure determining potential security risks and threats imposed by dependencies among the entities on the Internet. Finally, we evaluate the feasibility of using hop count filtering as a mechanism for mitigating Distributed Reflective Denial-of-Service attacks, and conceptually show that it cannot work to prevent these attacks. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28321
[24]
J. Steil, “Mobile Eye Tracking for Everyone,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
Eye tracking and gaze-based human-computer interfaces have become a practical modality in desktop settings, since remote eye tracking is efficient and affordable. However, remote eye tracking remains constrained to indoor, laboratory-like conditions, in which lighting and user position need to be controlled. Mobile eye tracking has the potential to overcome these limitations and to allow people to move around freely and to use eye tracking on a daily basis during their everyday routine. However, mobile eye tracking currently faces two fundamental challenges that prevent it from being practically usable and that, consequently, have to be addressed before mobile eye tracking can truly be used by everyone: Mobile eye tracking needs to be advanced and made fully functional in unconstrained environments, and it needs to be made socially acceptable. Numerous sensing and analysis methods were initially developed for remote eye tracking and have been successfully applied for decades. Unfortunately, these methods are limited in terms of functionality and correctness, or even unsuitable for application in mobile eye tracking. Therefore, the majority of fundamental definitions, eye tracking methods, and gaze estimation approaches cannot be borrowed from remote eye tracking without adaptation. For example, the definitions of specific eye movements, like classical fixations, need to be extended to mobile settings where natural user and head motion are omnipresent. Corresponding analytical methods need to be adjusted or completely reimplemented based on novel approaches encoding the human gaze behaviour. Apart from these technical challenges, an entirely new, and yet under-explored, topic required for the breakthrough of mobile eye tracking as everyday technology is the overcoming of social obstacles. A first crucial key issue to defuse social objections is the building of acceptance towards mobile eye tracking. Hence, it is essential to replace the bulky appearance of current head-mounted eye trackers with an unobtrusive, appealing, and trendy design. The second high-priority theme of increasing importance for everyone is privacy and its protection, given that research and industry have not focused on or taken care of this problem at all. To establish true confidence, future devices have to find a fine balance between protecting users’ and bystanders’ privacy and attracting and convincing users of their necessity, utility, and potential with useful and beneficial features. The solution of technical challenges and social obstacles is the prerequisite for the development of a variety of novel and exciting applications in order to establish mobile eye tracking as a new paradigm, which ease our everyday life. This thesis addresses core technical challenges of mobile eye tracking that currently prevent it from being widely adopted. Specifically, this thesis proves that 3D data used for the calibration of mobile eye trackers improves gaze estimation and significantly reduces the parallax error. Further, it presents the first effective fixation detection method for head-mounted devices that is robust against the prevalence of user and gaze target motion. In order to achieve social acceptability, this thesis proposes an innovative and unobtrusive design for future mobile eye tracking devices and builds the first prototype with fully frame-embedded eye cameras combined with a calibration-free deep-trained appearance-based gaze estimation approach. To protect users’ and bystanders’ privacy in the presence of head-mounted eye trackers, this thesis presents another first-of-its-kind prototype. It is able to identify privacy-sensitive situations to automatically enable and disable the eye tracker’s first-person camera by means of a mechanical shutter, leveraging the combination of deep scene and eye movement features. Nevertheless, solving technical challenges and social obstacles alone is not sufficient to make mobile eye tracking attractive for the masses. The key to success is the development of convincingly useful, innovative, and essential applications. To extend the protection of users’ privacy on the software side as well, this thesis presents the first privacy-aware VR gaze interface using differential privacy. This method adds noise to recorded eye tracking data so that privacy-sensitive information like a user’s gender or identity is protected without impeding the utility of the data itself. In addition, the first large-scale online survey is conducted to understand users’ concerns with eye tracking. To develop and evaluate novel applications, this thesis presents the first publicly available long-term eye tracking datasets. They are used to show the unsupervised detection of users’ activities from eye movements alone using novel and efficient video-based encoding approaches as well as to propose the first proof-of-concept method to forecast users’ attentive behaviour during everyday mobile interactions from phone-integrated and body-worn sensors. This opens up possibilities for the development of a variety of novel and exciting applications. With more advanced features, accompanied by technological progress and sensor miniaturisation, eye tracking is increasingly integrated into conventional glasses as well as virtual and augmented reality (VR/AR) head-mounted displays, becoming an integral component of mobile interfaces. This thesis paves the way for the development of socially acceptable, privacy-aware, but highly functional mobile eye tracking devices and novel applications, so that mobile eye tracking can develop its full potential to become an everyday technology for everyone.
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BibTeX
@phdthesis{Steilphd2019, TITLE = {Mobile Eye Tracking for Everyone}, AUTHOR = {Steil, Julian}, LANGUAGE = {eng}, DOI = {10.22028/D291-30004}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Eye tracking and gaze-based human-computer interfaces have become a practical modality in desktop settings, since remote eye tracking is efficient and affordable. However, remote eye tracking remains constrained to indoor, laboratory-like conditions, in which lighting and user position need to be controlled. Mobile eye tracking has the potential to overcome these limitations and to allow people to move around freely and to use eye tracking on a daily basis during their everyday routine. However, mobile eye tracking currently faces two fundamental challenges that prevent it from being practically usable and that, consequently, have to be addressed before mobile eye tracking can truly be used by everyone: Mobile eye tracking needs to be advanced and made fully functional in unconstrained environments, and it needs to be made socially acceptable. Numerous sensing and analysis methods were initially developed for remote eye tracking and have been successfully applied for decades. Unfortunately, these methods are limited in terms of functionality and correctness, or even unsuitable for application in mobile eye tracking. Therefore, the majority of fundamental definitions, eye tracking methods, and gaze estimation approaches cannot be borrowed from remote eye tracking without adaptation. For example, the definitions of specific eye movements, like classical fixations, need to be extended to mobile settings where natural user and head motion are omnipresent. Corresponding analytical methods need to be adjusted or completely reimplemented based on novel approaches encoding the human gaze behaviour. Apart from these technical challenges, an entirely new, and yet under-explored, topic required for the breakthrough of mobile eye tracking as everyday technology is the overcoming of social obstacles. A first crucial key issue to defuse social objections is the building of acceptance towards mobile eye tracking. Hence, it is essential to replace the bulky appearance of current head-mounted eye trackers with an unobtrusive, appealing, and trendy design. The second high-priority theme of increasing importance for everyone is privacy and its protection, given that research and industry have not focused on or taken care of this problem at all. To establish true confidence, future devices have to find a fine balance between protecting users{\textquoteright} and bystanders{\textquoteright} privacy and attracting and convincing users of their necessity, utility, and potential with useful and beneficial features. The solution of technical challenges and social obstacles is the prerequisite for the development of a variety of novel and exciting applications in order to establish mobile eye tracking as a new paradigm, which ease our everyday life. This thesis addresses core technical challenges of mobile eye tracking that currently prevent it from being widely adopted. Specifically, this thesis proves that 3D data used for the calibration of mobile eye trackers improves gaze estimation and significantly reduces the parallax error. Further, it presents the first effective fixation detection method for head-mounted devices that is robust against the prevalence of user and gaze target motion. In order to achieve social acceptability, this thesis proposes an innovative and unobtrusive design for future mobile eye tracking devices and builds the first prototype with fully frame-embedded eye cameras combined with a calibration-free deep-trained appearance-based gaze estimation approach. To protect users{\textquoteright} and bystanders{\textquoteright} privacy in the presence of head-mounted eye trackers, this thesis presents another first-of-its-kind prototype. It is able to identify privacy-sensitive situations to automatically enable and disable the eye tracker{\textquoteright}s first-person camera by means of a mechanical shutter, leveraging the combination of deep scene and eye movement features. Nevertheless, solving technical challenges and social obstacles alone is not sufficient to make mobile eye tracking attractive for the masses. The key to success is the development of convincingly useful, innovative, and essential applications. To extend the protection of users{\textquoteright} privacy on the software side as well, this thesis presents the first privacy-aware VR gaze interface using differential privacy. This method adds noise to recorded eye tracking data so that privacy-sensitive information like a user{\textquoteright}s gender or identity is protected without impeding the utility of the data itself. In addition, the first large-scale online survey is conducted to understand users{\textquoteright} concerns with eye tracking. To develop and evaluate novel applications, this thesis presents the first publicly available long-term eye tracking datasets. They are used to show the unsupervised detection of users{\textquoteright} activities from eye movements alone using novel and efficient video-based encoding approaches as well as to propose the first proof-of-concept method to forecast users{\textquoteright} attentive behaviour during everyday mobile interactions from phone-integrated and body-worn sensors. This opens up possibilities for the development of a variety of novel and exciting applications. With more advanced features, accompanied by technological progress and sensor miniaturisation, eye tracking is increasingly integrated into conventional glasses as well as virtual and augmented reality (VR/AR) head-mounted displays, becoming an integral component of mobile interfaces. This thesis paves the way for the development of socially acceptable, privacy-aware, but highly functional mobile eye tracking devices and novel applications, so that mobile eye tracking can develop its full potential to become an everyday technology for everyone.}, }
Endnote
%0 Thesis %A Steil, Julian %Y Bulling, Andreas %A referee: Krüger, Antonio %A referee: Kasneci, Enkelejda %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Mobile Eye Tracking for Everyone : %G eng %U http://hdl.handle.net/21.11116/0000-0005-652F-6 %R 10.22028/D291-30004 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 272 p. %V phd %9 phd %X Eye tracking and gaze-based human-computer interfaces have become a practical modality in desktop settings, since remote eye tracking is efficient and affordable. However, remote eye tracking remains constrained to indoor, laboratory-like conditions, in which lighting and user position need to be controlled. Mobile eye tracking has the potential to overcome these limitations and to allow people to move around freely and to use eye tracking on a daily basis during their everyday routine. However, mobile eye tracking currently faces two fundamental challenges that prevent it from being practically usable and that, consequently, have to be addressed before mobile eye tracking can truly be used by everyone: Mobile eye tracking needs to be advanced and made fully functional in unconstrained environments, and it needs to be made socially acceptable. Numerous sensing and analysis methods were initially developed for remote eye tracking and have been successfully applied for decades. Unfortunately, these methods are limited in terms of functionality and correctness, or even unsuitable for application in mobile eye tracking. Therefore, the majority of fundamental definitions, eye tracking methods, and gaze estimation approaches cannot be borrowed from remote eye tracking without adaptation. For example, the definitions of specific eye movements, like classical fixations, need to be extended to mobile settings where natural user and head motion are omnipresent. Corresponding analytical methods need to be adjusted or completely reimplemented based on novel approaches encoding the human gaze behaviour. Apart from these technical challenges, an entirely new, and yet under-explored, topic required for the breakthrough of mobile eye tracking as everyday technology is the overcoming of social obstacles. A first crucial key issue to defuse social objections is the building of acceptance towards mobile eye tracking. Hence, it is essential to replace the bulky appearance of current head-mounted eye trackers with an unobtrusive, appealing, and trendy design. The second high-priority theme of increasing importance for everyone is privacy and its protection, given that research and industry have not focused on or taken care of this problem at all. To establish true confidence, future devices have to find a fine balance between protecting users’ and bystanders’ privacy and attracting and convincing users of their necessity, utility, and potential with useful and beneficial features. The solution of technical challenges and social obstacles is the prerequisite for the development of a variety of novel and exciting applications in order to establish mobile eye tracking as a new paradigm, which ease our everyday life. This thesis addresses core technical challenges of mobile eye tracking that currently prevent it from being widely adopted. Specifically, this thesis proves that 3D data used for the calibration of mobile eye trackers improves gaze estimation and significantly reduces the parallax error. Further, it presents the first effective fixation detection method for head-mounted devices that is robust against the prevalence of user and gaze target motion. In order to achieve social acceptability, this thesis proposes an innovative and unobtrusive design for future mobile eye tracking devices and builds the first prototype with fully frame-embedded eye cameras combined with a calibration-free deep-trained appearance-based gaze estimation approach. To protect users’ and bystanders’ privacy in the presence of head-mounted eye trackers, this thesis presents another first-of-its-kind prototype. It is able to identify privacy-sensitive situations to automatically enable and disable the eye tracker’s first-person camera by means of a mechanical shutter, leveraging the combination of deep scene and eye movement features. Nevertheless, solving technical challenges and social obstacles alone is not sufficient to make mobile eye tracking attractive for the masses. The key to success is the development of convincingly useful, innovative, and essential applications. To extend the protection of users’ privacy on the software side as well, this thesis presents the first privacy-aware VR gaze interface using differential privacy. This method adds noise to recorded eye tracking data so that privacy-sensitive information like a user’s gender or identity is protected without impeding the utility of the data itself. In addition, the first large-scale online survey is conducted to understand users’ concerns with eye tracking. To develop and evaluate novel applications, this thesis presents the first publicly available long-term eye tracking datasets. They are used to show the unsupervised detection of users’ activities from eye movements alone using novel and efficient video-based encoding approaches as well as to propose the first proof-of-concept method to forecast users’ attentive behaviour during everyday mobile interactions from phone-integrated and body-worn sensors. This opens up possibilities for the development of a variety of novel and exciting applications. With more advanced features, accompanied by technological progress and sensor miniaturisation, eye tracking is increasingly integrated into conventional glasses as well as virtual and augmented reality (VR/AR) head-mounted displays, becoming an integral component of mobile interfaces. This thesis paves the way for the development of socially acceptable, privacy-aware, but highly functional mobile eye tracking devices and novel applications, so that mobile eye tracking can develop its full potential to become an everyday technology for everyone. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/28498
[25]
M. Voigt, “Decidable fragments of first-order logic and of first-order linear arithmetic with uninterpreted predicates,” Universität des Saarlandes, Saarbrücken, 2019.
Abstract
First-order logic is one of the most prominent formalisms in computer science and mathematics. Since there is no algorithm capable of solving its satisfiability problem, first-order logic is said to be undecidable. The classical decision problem is the quest for a delineation between the decidable and the undecidable parts. The results presented in this thesis shed more light on the boundary and open new perspectives on the landscape of known decidable fragments. In the first part we focus on the new concept of separateness of variables and explore its applicability to the classical decision problem and beyond. Two disjoint sets of first-order variables are separated in a given formula if none of its atoms contains variables from both sets. This notion facilitates the definition of decidable extensions of many well-known decidable first-order fragments. We demonstrate this for several prefix fragments, several guarded fragments, the two-variable fragment, and for the fluted fragment. Although the extensions exhibit the same expressive power as the respective originals, certain logical properties can be expressed much more succinctly. In two cases the succinctness gap cannot be bounded using elementary functions. This fact already hints at computationally hard satisfiability problems. Indeed, we derive non-elementary lower bounds for the separated fragment, an extension of the Bernays-Schönfinkel-Ramsey fragment (E*A*-prefix sentences). On the semantic level, separateness of quantified variables may lead to weaker dependences than we encounter in general. We investigate this property in the context of model-checking games. The focus of the second part of the thesis is on linear arithmetic with uninterpreted predicates. Two novel decidable fragments are presented, both based on the Bernays-Schönfinkel-Ramsey fragment. On the negative side, we identify several small fragments of the language for which satisfiability is undecidable.
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BibTeX
@phdthesis{voigtphd2019, TITLE = {Decidable fragments of first-order logic and of first-order linear arithmetic with uninterpreted predicates}, AUTHOR = {Voigt, Marco}, LANGUAGE = {eng}, DOI = {10.22028/D291-28428}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {First-order logic is one of the most prominent formalisms in computer science and mathematics. Since there is no algorithm capable of solving its satisfiability problem, first-order logic is said to be undecidable. The classical decision problem is the quest for a delineation between the decidable and the undecidable parts. The results presented in this thesis shed more light on the boundary and open new perspectives on the landscape of known decidable fragments. In the first part we focus on the new concept of separateness of variables and explore its applicability to the classical decision problem and beyond. Two disjoint sets of first-order variables are separated in a given formula if none of its atoms contains variables from both sets. This notion facilitates the definition of decidable extensions of many well-known decidable first-order fragments. We demonstrate this for several prefix fragments, several guarded fragments, the two-variable fragment, and for the fluted fragment. Although the extensions exhibit the same expressive power as the respective originals, certain logical properties can be expressed much more succinctly. In two cases the succinctness gap cannot be bounded using elementary functions. This fact already hints at computationally hard satisfiability problems. Indeed, we derive non-elementary lower bounds for the separated fragment, an extension of the Bernays-Sch{\"o}nfinkel-Ramsey fragment (E*A*-prefix sentences). On the semantic level, separateness of quantified variables may lead to weaker dependences than we encounter in general. We investigate this property in the context of model-checking games. The focus of the second part of the thesis is on linear arithmetic with uninterpreted predicates. Two novel decidable fragments are presented, both based on the Bernays-Sch{\"o}nfinkel-Ramsey fragment. On the negative side, we identify several small fragments of the language for which satisfiability is undecidable.}, }
Endnote
%0 Thesis %A Voigt, Marco %Y Weidenbach, Christoph %A referee: Grädel, Erich %A referee: Leitsch, Alexander %A referee: Sturm, Thomas %+ Automation of Logic, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Automation of Logic, MPI for Informatics, Max Planck Society External Organizations External Organizations Automation of Logic, MPI for Informatics, Max Planck Society %T Decidable fragments of first-order logic and of first-order linear arithmetic with uninterpreted predicates : %G eng %U http://hdl.handle.net/21.11116/0000-0005-4373-E %R 10.22028/D291-28428 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 333 p. %V phd %9 phd %X First-order logic is one of the most prominent formalisms in computer science and mathematics. Since there is no algorithm capable of solving its satisfiability problem, first-order logic is said to be undecidable. The classical decision problem is the quest for a delineation between the decidable and the undecidable parts. The results presented in this thesis shed more light on the boundary and open new perspectives on the landscape of known decidable fragments. In the first part we focus on the new concept of separateness of variables and explore its applicability to the classical decision problem and beyond. Two disjoint sets of first-order variables are separated in a given formula if none of its atoms contains variables from both sets. This notion facilitates the definition of decidable extensions of many well-known decidable first-order fragments. We demonstrate this for several prefix fragments, several guarded fragments, the two-variable fragment, and for the fluted fragment. Although the extensions exhibit the same expressive power as the respective originals, certain logical properties can be expressed much more succinctly. In two cases the succinctness gap cannot be bounded using elementary functions. This fact already hints at computationally hard satisfiability problems. Indeed, we derive non-elementary lower bounds for the separated fragment, an extension of the Bernays-Schönfinkel-Ramsey fragment (E*A*-prefix sentences). On the semantic level, separateness of quantified variables may lead to weaker dependences than we encounter in general. We investigate this property in the context of model-checking games. The focus of the second part of the thesis is on linear arithmetic with uninterpreted predicates. Two novel decidable fragments are presented, both based on the Bernays-Schönfinkel-Ramsey fragment. On the negative side, we identify several small fragments of the language for which satisfiability is undecidable. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27767