Dagstuhl News January - December 2005

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Strategic Voting and Social Networks

With the ever increasing ubiquity of social networks in our everyday lives, comes an increasing urgency for us to understand their impact on human behavior. Social networks quantify the ways in which we communicate with each other, and therefore shape the flow of information through the community. It is this same flow of information that we utilize to make sound, strategic decisions. This thesis focuses on one particular type of decisions: voting. When a community engages in voting, it is soliciting the opinions of its members, who present it in the form of a ballot. The community may then choose a course of action based on the submitted ballots. Individual voters, however, are under no obligation to submit sincere ballots that accurately reflects their opinions; they may instead submit a strategic ballot in hopes of affecting the election's outcome to their advantage. This thesis examines the interplay between social network structure and strategic voting behavior. In particular, we will explore how social network structure affects the flow of information through a population, and thereby affect the strategic behavior of voters, and ultimately, the outcomes of elections. We will begin by considering how network structure affects information propagation. This work builds upon the rich body of literature called opinion dynamics by proposing a model for skeptical agents --- agents that distrust other agents for holding opinions that differ too wildly from their own. We show that network structure is one of several factors that affects the degree of penetration that radical opinions can achieve through the community. Next, we propose a model for strategic voting in social networks, where voters are self-interested and rational, but may only use the limited information available through their social network contacts to formulate strategic ballots. In particular, we study the ``Echo Chamber Effect'', the tendency for humans to favor connections with similar people, and show that it leads to the election of less suitable candidates. We also extend this voter model by using boundedly-rational heuristics to scale up our simulations to larger populations. We propose a general framework for voting agents embedded in social networks, and show that our heuristic models can demonstrate a variation of the ``Micromega Law'' which relates the popularity of smaller parties to the size of the population. Finally, we examine another avenue for strategic behavior: choosing when to cast your vote. We propose a type of voting mechanism called ``Sticker Voting'', where voters cast ballots by placing stickers on their favored alternatives, thereby publicly and irrevocably declaring their support. We present a complete analysis of several simple instances of the Sticker Voting game and discuss how our results reflect human voting behavior

Mining, Modeling and Predicting Mobility

Mobility is a central aspect of our life, and our movements reveal much more about us than simply our whereabouts. In this thesis, we are interested in mobility and study it from three different perspectives: the modeling perspective, the information-theoretic perspective, and the data mining perspective. For the modeling perspective, we represent mobility as a probabilistic process described by both observable and latent variables, and we introduce formally the notion of individual and collective dimensions in mobility models. Ideally, we should take advantage of both dimensions to learn accurate mobility models, but the nature of data might limit us. We take a data-driven approach to study three scenarios, which differ on the nature of mobility data, and present, for each scenario, a mobility model that is tailored for it. The first scenario is individual-specific as we have mobility data about individuals but are unable to cross reference data from them. In the second scenario, we introduce the collective model that we use to overcome the sparsity of individual traces, and for which we assume that individuals in the same group exhibit similar mobility patterns. Finally, we present the ideal scenario, for which we can take advantage of both the individual and collective dimensions, and analyze collective mobility patterns in order to create individual models. In the second part of the thesis, we take an information-theoretic approach in order to quantify mobility uncertainty and its evolution with location updates. We discretize the userâs world to obtain a map that we represent as a mobility graph. We model mobility as a random walk on this graph âequivalent to a Markov chain âand quantify trajectory uncertainty as the entropy of the distribution over possible trajectories. In this setting, a location update amounts to conditioning on a particular state of the Markov chain, which requires the computation of the entropy of conditional Markov trajectories. Our main result enables us to compute this entropy through a transformation of the original Markov chain. We apply our framework to real-world mobility datasets and show that the influence of intermediate locations on trajectory entropy depends on the nature of these locations. We build on this finding and design a segmentation algorithm that uncovers intermediate destinations along a trajectory. The final perspective from which we analyze mobility is the data mining perspective: we go beyond simple mobility and analyze geo-tagged data that is generated by online social medias and that describes the whole user experience. We postulate that mining geo-tagged data enables us to obtain a rich representation of the user experience and all that surrounds its mobility. We propose a hierarchical probabilistic model that enables us to uncover specific descriptions of geographical regions, by analyzing the geo-tagged content generated by online social medias. By applying our method to a dataset of 8 million geo-tagged photos, we are able to associate with each neighborhood the tags that describe it specifically, and to find the most unique neighborhoods in a city

A topological solution to object segmentation and tracking

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: segmenting visual input into discrete units, and tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question: can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input, and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary material containing mathematical proof

StoryNet: A 5W1H-based knowledge graph to connect stories

Title from PDF of title page viewed January 19, 2022Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (page 149-164)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021Stories are a powerful medium through which the human community has exchanged information since the dawn of the information age. They have taken multiple forms like articles, movies, books, plays, short films, magazines, mythologies, etc. With the ever-growing complexity of information representation, exchange, and interaction, it became highly important to find ways that convey the stories more effectively. With a world that is diverging more and more, it is harder to draw parallels and connect the information from all around the globe. Even though there have been efforts to consolidate the information on a large scale like Wikipedia, Wiki Data, etc, they are devoid of any real-time happenings. With the recent advances in Natural Language Processing (NLP), we propose a framework to connect these stories together making it easier to find the links between them thereby helping us understand and explore the links between the stories and possibilities that revolve around them. Our framework is based on the 5W + 1H (What, Who, Where, When, Why, and How) format that represents stories in a format that is both easily understandable by humans and accurately generated by the deep learning models. We have used 311 calls and cyber security datasets as case studies for which a few NLP techniques like classification, Topic Modelling, Question Answering, and Question Generation were used along with the 5W1H framework to segregate the stories into clusters. This is a generic framework and can be used to apply to any field. We have evaluated two approaches for generating results - training-based and rule-based. For the rule-based approach, we used Stanford NLP parsers to identify patterns for the 5W + 1H terms, and for the training based approach, BERT embeddings were used and both were compared using an ensemble score (average of CoLA, SST-2, MRPC, QQP, STS-B, MNLI, QNLI, and RTE) along with BLEU and ROUGE scores. A few approaches are studied for training-based analysis - using BERT, Roberta, XLNet, ALBERT, ELECTRA, and AllenNLP Transformer QA with the datasets - CVE, NVD, SQuAD v1.1, and SQuAD v2.0, and compared them with custom annotations for identifying 5W + 1H. We've presented the performance and accuracy of both approaches in the results section. Our method gave a boost in the score from 30% (baseline) to 91% when trained on the 5W+1H annotations.Introduction -- Related work -- The 5W1H Framework and the models included -- StoryNet Application: Evaluation and Results -- Conclusion and Future Wor

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Towards 3D facial morphometry:facial image analysis applications in anesthesiology and 3D spectral nonrigid registration

In anesthesiology, the detection and anticipation of difficult tracheal intubation is crucial for patient safety. When undergoing general anesthesia, a patient who is unexpectedly difficult to intubate risks potential life-threatening complications with poor clinical outcomes, ranging from severe harm to brain damage or death. Conversely, in cases of suspected difficulty, specific equipment and personnel will be called upon to increase safety and the chances of successful intubation. Research in anesthesiology has associated a certain number of morphological features of the face and neck with higher risk of difficult intubation. Detecting and analyzing these and other potential features, thus allowing the prediction of difficulty of tracheal intubation in a robust, objective, and automatic way, may therefore improve the patients' safety. In this thesis, we first present a method to automatically classify images of the mouth cavity according to the visibility of certain oropharyngeal structures. This method is then integrated into a novel and completely automatic method, based on frontal and profile images of the patient's face, to predict the difficulty of intubation. We also provide a new database of three dimensional (3D) facial scans and present the initial steps towards a complete 3D model of the face suitable for facial morphometry applications, which include difficult tracheal intubation prediction. In order to develop and test our proposed method, we collected a large database of multimodal recordings of over 2700 patients undergoing general anesthesia. In the first part of this thesis, using two dimensional (2D) facial image analysis methods, we automatically extract morphological and appearance-based features from these images. These are used to train a classifier, which learns to discriminate between patients as being easy or difficult to intubate. We validate our approach on two different scenarios, one of them being close to a real-world clinical scenario, using 966 patients, and demonstrate that the proposed method achieves performance comparable to medical diagnosis-based predictions by experienced anesthesiologists. In the second part of this thesis, we focus on the development of a new 3D statistical model of the face to overcome some of the limitations of 2D methods. We first present EPFL3DFace, a new database of 3D facial expression scans, containing 120 subjects, performing 35 different facial expressions. Then, we develop a nonrigid alignment method to register the scans and allow for statistical analysis. Our proposed method is based on spectral geometry processing and makes use of an implicit representation of the scans in order to be robust to noise or holes in the surfaces. It presents the significant advantage of reducing the number of free parameters to optimize for in the alignment process by two orders of magnitude. We apply our proposed method on the data collected and discuss qualitative results. At its current level of performance, our fully automatic method to predict difficult intubation already has the potential to reduce the cost, and increase the availability of such predictions, by not relying on qualified anesthesiologists with years of medical training. Further data collection, in order to increase the number of patients who are difficult to intubate, as well as extracting morphological features from a 3D representation of the face are key elements to further improve the performance

Automated retinal layer segmentation and pre-apoptotic monitoring for three-dimensional optical coherence tomography

The aim of this PhD thesis was to develop segmentation algorithm adapted and optimized to retinal OCT data that will provide objective 3D layer thickness which might be used to improve diagnosis and monitoring of retinal pathologies. Additionally, a 3D stack registration method was produced by modifying an existing algorithm. A related project was to develop a pre-apoptotic retinal monitoring based on the changes in texture parameters of the OCT scans in order to enable treatment before the changes become irreversible; apoptosis refers to the programmed cell death that can occur in retinal tissue and lead to blindness. These issues can be critical for the examination of tissues within the central nervous system. A novel statistical model for segmentation has been created and successfully applied to a large data set. A broad range of future research possibilities into advanced pathologies has been created by the results obtained. A separate model has been created for choroid segmentation located deep in retina, as the appearance of choroid is very different from the top retinal layers. Choroid thickness and structure is an important index of various pathologies (diabetes etc.). As part of the pre-apoptotic monitoring project it was shown that an increase in proportion of apoptotic cells in vitro can be accurately quantified. Moreover, the data obtained indicates a similar increase in neuronal scatter in retinal explants following axotomy (removal of retinas from the eye), suggesting that UHR-OCT can be a novel non-invasive technique for the in vivo assessment of neuronal health. Additionally, an independent project within the computer science department in collaboration with the school of psychology has been successfully carried out, improving analysis of facial dynamics and behaviour transfer between individuals. Also, important improvements to a general signal processing algorithm, dynamic time warping (DTW), have been made, allowing potential application in a broad signal processing field.EThOS - Electronic Theses Online ServiceGBUnited Kingdo