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Unter Ambient Intelligence (AmI) wird die Integration verschiedener Technologien zu einer den Menschen umgebenden, (nahezu) unsichtbaren Gesamtheit verstanden. Diese Intelligente Umgebung wird möglich durch die Miniaturisierung hochintegrierter Bauteile (Sensoren, Aktuatoren und Rechnern), deren zunehmende Intelligenz und vor allem deren lokale und globale zunehmend drahtlose Vernetzung. Unter dem Titel Man-u-Faktur 2012 (man and factoring in 2012) wurde an der Technischen Universität Kaiserslautern im Rahmen des Forschungsschwerpunkts Ambient Intelligence ein Szenario entwickelt, das ein beeindruckendes Gesamtbild einer Technik, die den Menschen in den Mittelpunkt rückt, beschreibt. Man-u-Faktur 2012 steht dabei für ein Weiterdrehen des Rads der Industrialisierung von der heute üblichen variantenreichen, technologiezentrierten Massenfertigung hin zu einer kundenindividuellen, mitarbeiterzentrierten Maßfertigung. Im Speziellen wird hierunter der Aufbau massiv verteiler kunden- aber auch mitarbeiterfreundlicher Produktionsanlagen verstanden, die sich im hochdynamischen Umfeld entsprechend der jeweiligen Gegebenheiten anzupassen wissen. Der Mensch ist überall dort präsent, wo flexibles Arbeiten oder flexible Entscheidungen im Vordergrund stehen. In diesem Bericht wird der Einfluss von Ambient Intelligence beispielhaft auf die Vision einer Fahrradproduktion in der Man-u-Faktur 2012 angewandt. Aus diesem Szenario werden anschließend sowohl die zu entwickelnden Schlüsseltechnologien als auch die Einflüsse auf Wirtschaft und Gesellschaft abgeleitet.
Dieses Szenario ist eine Erweiterung eines Teilszenarios von Human Centered Manufacturing. Dabei geht es um die Montage der Energieelektrik für industrielle Anlagen. Im Jahr 2015 enthält die Ausrüstung eines Elektromonteurs bei der Verdrahtung von Schaltschränken u.a. einen Schutzhelm mit integrierter Farbkamera, integriertem Mikrofon und einem Lautsprecher im Ohrbereich sowie einen automatisch gesteuerten Laserpointer. Auf der Baustelle sind keine Pläne mehr erforderlich. Der Monteur benötigt keinen Plan während der Montage.
Robuste Optimierung wird zur Entscheidungsunterstützung eines komplexen Beschaffungs- und Transportmodells genutzt, um die Risikoeinstellung der Entscheidenden abzubilden und gleichzeitig ein robustes Ergebnis zu erzielen. Die Modellierung des Problems ist umfassend dargestellt und Ergebnisse der nicht-deterministischen Planung bei verschiedenen Parametern und Risikoeinstellungen gegenübergestellt. Die Datenunsicherheit wird an einem Praxisfall erläutert und Methoden und -empfehlungen zum Umgang mit dieser dargestellt.
In this thesis we develop a shape optimization framework for isogeometric analysis in the optimize first–discretize then setting. For the discretization we use
isogeometric analysis (iga) to solve the state equation, and search optimal designs in a space of admissible b-spline or nurbs combinations. Thus a quite
general class of functions for representing optimal shapes is available. For the
gradient-descent method, the shape derivatives indicate both stopping criteria and search directions and are determined isogeometrically. The numerical treatment requires solvers for partial differential equations and optimization methods, which introduces numerical errors. The tight connection between iga and geometry representation offers new ways of refining the geometry and analysis discretization by the same means. Therefore, our main concern is to develop the optimize first framework for isogeometric shape optimization as ground work for both implementation and an error analysis. Numerical examples show that this ansatz is practical and case studies indicate that it allows local refinement.
The goal of this work is to develop statistical natural language models and processing techniques
based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short-
Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more
robust, and easier to train than traditional methods, i.e., words list and rule-based models. They
improve the output of recognition systems and make them more accessible to users for browsing
and reading. These techniques are required, especially for historical books which might take
years of effort and huge costs to manually transcribe them.
The contributions of this thesis are several new methods which have high-performance computing and accuracy. First, an error model for improving recognition results is designed. As
a second contribution, a hyphenation model for difficult transcription for alignment purposes
is suggested. Third, a dehyphenation model is used to classify the hyphens in noisy transcription. The fourth contribution is using LSTM networks for normalizing historical orthography.
A size normalization alignment is implemented to equal the size of strings, before the training
phase. Using the LSTM networks as a language model to improve the recognition results is
the fifth contribution. Finally, the sixth contribution is a combination of Weighted Finite-State
Transducers (WFSTs), and LSTM applied on multiple recognition systems. These contributions
will be elaborated in more detail.
Context-dependent confusion rules is a new technique to build an error model for Optical
Character Recognition (OCR) corrections. The rules are extracted from the OCR confusions
which appear in the recognition outputs and are translated into edit operations, e.g., insertions,
deletions, and substitutions using the Levenshtein edit distance algorithm. The edit operations
are extracted in a form of rules with respect to the context of the incorrect string to build an
error model using WFSTs. The context-dependent rules assist the language model to find the
best candidate corrections. They avoid the calculations that occur in searching the language
model and they also make the language model able to correct incorrect words by using context-
dependent confusion rules. The context-dependent error model is applied on the university of
Washington (UWIII) dataset and the Nastaleeq script in Urdu dataset. It improves the OCR
results from an error rate of 1.14% to an error rate of 0.68%. It performs better than the
state-of-the-art single rule-based which returns an error rate of 1.0%.
This thesis describes a new, simple, fast, and accurate system for generating correspondences
between real scanned historical books and their transcriptions. The alignment has many challenges, first, the transcriptions might have different modifications, and layout variations than the
original book. Second, the recognition of the historical books have misrecognition, and segmentation errors, which make the alignment more difficult especially the line breaks, and pages will
not have the same correspondences. Adapted WFSTs are designed to represent the transcription. The WFSTs process Fraktur ligatures and adapt the transcription with a hyphenations
model that allows the alignment with respect to the varieties of the hyphenated words in the line
breaks of the OCR documents. In this work, several approaches are implemented to be used for
the alignment such as: text-segments, page-wise, and book-wise approaches. The approaches
are evaluated on German calligraphic (Fraktur) script historical documents dataset from “Wan-
derungen durch die Mark Brandenburg” volumes (1862-1889). The text-segmentation approach
returns an error rate of 2.33% without using a hyphenation model and an error rate of 2.0%
using a hyphenation model. Dehyphenation methods are presented to remove the hyphen from
the transcription. They provide the transcription in a readable and reflowable format to be used
for alignment purposes. We consider the task as classification problem and classify the hyphens
from the given patterns as hyphens for line breaks, combined words, or noise. The methods are
applied on clean and noisy transcription for different languages. The Decision Trees classifier
returns better performance on UWIII dataset and returns an accuracy of 98%. It returns 97%
on Fraktur script.
A new method for normalizing historical OCRed text using LSTM is implemented for different texts, ranging from Early New High German 14th - 16th centuries to modern forms in New
High German applied on the Luther bible. It performed better than the rule-based word-list
approaches. It provides a transcription for various purposes such as part-of-speech tagging and
n-grams. Also two new techniques are presented for aligning the OCR results and normalize the
size by using adding Character-Epsilons or Appending-Epsilons. They allow deletion and insertion in the appropriate position in the string. In normalizing historical wordforms to modern
wordforms, the accuracy of LSTM on seen data is around 94%, while the state-of-the-art combined rule-based method returns 93%. On unseen data, LSTM returns 88% and the combined
rule-based method returns 76%. In normalizing modern wordforms to historical wordforms, the
LSTM delivers the best performance and returns 93.4% on seen data and 89.17% on unknown
data.
In this thesis, a deep investigation has been done on constructing high-performance language
modeling for improving the recognition systems. A new method to construct a language model
using LSTM is designed to correct OCR results. The method is applied on UWIII and Urdu
script. The LSTM approach outperforms the state-of-the-art, especially for unseen tokens
during training. On the UWIII dataset, the LSTM returns reduction in OCR error rates from
1.14% to 0.48%. On the Nastaleeq script in Urdu dataset, the LSTM reduces the error rate
from 6.9% to 1.58%.
Finally, the integration of multiple recognition outputs can give higher performance than a
single recognition system. Therefore, a new method for combining the results of OCR systems is
explored using WFSTs and LSTM. It uses multiple OCR outputs and votes for the best output
to improve the OCR results. It performs better than the ISRI tool, Pairwise of Multiple Sequence and it helps to improve the OCR results. The purpose is to provide correct transcription
so that it can be used for digitizing books, linguistics purposes, N-grams, and part-of-speech
tagging. The method consists of two alignment steps. First, two recognition systems are aligned
using WFSTs. The transducers are designed to be more flexible and compatible with the different symbols in line and page breaks to avoid the segmentation and misrecognition errors.
The LSTM model then is used to vote the best candidate correction of the two systems and
improve the incorrect tokens which are produced during the first alignment. The approaches
are evaluated on OCRs output from the English UWIII and historical German Fraktur dataset
which are obtained from state-of-the-art OCR systems. The Experiments show that the error
rate of ISRI-Voting is 1.45%, the error rate of the Pairwise of Multiple Sequence is 1.32%, the
error rate of the Line-to-Page alignment is 1.26% and the error rate of the LSTM approach has
the best performance with 0.40%.
The purpose of this thesis is to contribute methods providing correct transcriptions corresponding to the original book. This is considered to be the first step towards an accurate and
more effective use of the documents in digital libraries.
This dissertation focuses on the visualization of urban microclimate data sets,
which describe the atmospheric impact of individual urban features. The application
and adaptation of visualization and analysis concepts to enhance the
insight into observational data sets used this specialized area are explored, motivated
through application problems encountered during active involvement
in urban microclimate research at the Arizona State University in Tempe, Arizona.
Besides two smaller projects dealing with the analysis of thermographs
recorded with a hand-held device and visualization techniques used for building
performance simulation results, the main focus of the work described in
this document is the development of a prototypic tool for the visualization
and analysis of mobile transect measurements. This observation technique involves
a sensor platform mounted to a vehicle, which is then used to traverse
a heterogeneous neighborhood to investigate the relationships between urban
form and microclimate. The resulting data sets are among the most complex
modes of in-situ observations due to their spatio-temporal dependence, their
multivariate nature, but also due to the various error sources associated with
moving platform observations.
The prototype enables urban climate researchers to preprocess their data,
to explore a single transect in detail, and to aggregate observations from multiple
traverses conducted over diverse routes for a visual delineation of climatic
microenvironments. Extending traditional analysis methods, the suggested visualization
tool provides techniques to relate the measured attributes to each
other and to the surrounding land cover structure. In addition to that, an
improved method for sensor lag correction is described, which shows the potential
to increase the spatial resolution of measurements conducted with slow
air temperature sensors.
In summary, the interdisciplinary approach followed in this thesis triggers
contributions to geospatial visualization and visual analytics, as well as to urban
climatology. The solutions developed in the course of this dissertation are
meant to support domain experts in their research tasks, providing means to
gain a qualitative overview over their specific data sets and to detect patterns,
which can then be further analyzed using domain-specific tools and methods.
Temporal Data Management and Incremental Data Recomputation with Wide-column Stores and MapReduce
(2017)
In recent years, ”Big Data” has become an important topic in academia
and industry. To handle the challenges and problems caused by Big Data,
new types of data storage systems called ”NoSQL stores” (means ”Not-only-
SQL”) have emerged.
”Wide-column stores” are one kind of NoSQL stores. Compared to relational database systems, wide-column stores introduce a new data model,
new IRUD (Insert, Retrieve, Update and Delete) semantics with support for
schema-flexibility, single-row transactions and data expiration constraints.
Moreover, each column stores multiple data versions with associated time-
stamps. Well-known examples are Google’s ”Big-table” and its open sourced
counterpart ”HBase”. Recently, such systems are increasingly used in business intelligence and data warehouse environments to provide decision support, controlling and revision capabilities.
Besides managing the current values, data warehouses also require management and processing of historical, time-related data. Data warehouses
frequently employ techniques for processing changes in various data sources
and incrementally applying such changes to the warehouse to keep it up-to-
date. Although both incremental data warehousing maintenance and temporal data management have been the subject of intensive research in the
relational database and finally commercial database products have picked up
the ability for temporal data processing and management, such capabilities
have not been explored systematically for today’s wide-column stores.
This thesis helps to address the shortcomings mentioned above. It care-
fully analyzes the properties of wide-column stores and the applicability
of mechanisms for temporal data management and incremental data ware-
house maintenance known from relational databases, extends well-known approaches and develops new capabilities for providing equivalent support in
wide-column stores.
Botrytis cinerea, der Erreger der Graufäule, infiziert hunderte verschiedene Pflanzenspezies und verursacht weltweit enorme landwirtschaftliche Verluste. Dabei tötet er das Wirtsgewebe sehr schnell mithilfe lytischer Enzyme und Nekrose-induzierender Metaboliten und Proteine ab. Das Signal-Mucin Msb2 ist in B. cinerea, wie in anderen pathogenen Pilzen, wichtig für die Oberflächenerkennung, Differenzierung von Appressorien und die Penetration des Pflanzengewebes. Msb2 agiert oberhalb der BMP1 Pathogenitäts-MAPK-Kaskade. In dieser Studie konnte eine direkte Interaktion zwischen Msb2 und BMP1, sowie zwischen den beiden Sensorproteinen Msb2 und Sho1 nachgewiesen werden. Dennoch führte die Deletion von sho1 lediglich zu geringfügigen Defekten im Wachstum, der Hyphendifferenzierung und der Bildung von Infektionsstrukturen. Sho1 zeigte nur einen geringen Einfluss auf die Aktivierung von BMP1. Das Fehlen von Sho1 verursachte keine Virulenzdefekte, während der Doppel-KO von msb2 und sho1 zu einer stärkeren Reduzierung der Läsionsausbreitung im Vergleich zu msb2 Mutanten führte. Es wurden mehrere keimungsregulierte, BMP1 abhängige Gene deletiert und die Mutanten phänotypisch charakterisiert. Keines der Gene für lytische Enzyme oder putative Effektorproteine beeinflusste die Virulenz. Mutanten, denen das für ein Ankyrin-repeat Protein codierende arp1 Gen fehlt, zeigten eine gestörte Oberflächenerkennung, gravierende Wachstumsdefekte und reduzierte Virulenz.
B. cinerea VELVET-Mutanten sind in der lichtabhängigen Differenzierung und der Ausbreitung nekrotischer Läsionen beeinträchtigt. In dieser Arbeit ermöglichte die Charakterisierung mehrerer Mutanten ein besseres Verständnis der molekularen Vorgänge, aufgrund derer der VELVET-Komplex die Entwicklung und Pathogenese in B. cinerea reguliert. Quantitative Vergleiche der in planta Transkriptome und Sekretome führten zur Identifizierung eines für drei VELVET-Mutanten gemeinsamen Sets an herunterregulierten Genen, welche für CAZymes, Proteasen und Virulenz-assoziierte Proteine codieren. Die meisten dieser Gene wurden zusätzlich im Wildtyp während der Infektion verstärkt exprimiert, was zusätzliche Hinweise auf deren Relevanz im Infektionsprozess lieferte. Die drastisch verringerte Expression von Genen für Proteasen konnte mit niedrigerer Proteaseaktivität und der unvollständigen Mazeration des Gewebes an der Infektionsstelle in Verbindung gebracht werden. Der neu etablierte quantitative Sekretom-Vergleich des Wildtyps und der VELVET-Mutanten mithilfe 15N-markierter Proteine korrelierte eindeutig mit den Transkriptomdaten sekretierter Proteine. Damit wurde gezeigt, dass die Abundanz der Proteine maßgeblich von deren mRNA-Level abhängt. Die Unfähigkeit zur Ansäuerung des Wirtsgewebes ist einer der interessantesten phänotypischen Aspekte der VELVET-Mutanten. Während Citrat die dominierende von B. cinerea ausgeschiedene Säure ist, sekretierten VELVET-Mutanten deutlich weniger Citrat. Weder für Oxalat noch für Gluconat konnte eine wichtige Rolle während der Infektion bestätigt werden. Die Läsionsausbreitung der Mutanten wurde sowohl durch Zugabe von Vollmedium, als auch durch künstlich konstant niedrig eingestellte pH-Werte an den Infektionsstellen gefördert, während die Einstellung auf neutrale pH-Werte die Expansion beim B. cinerea Wildtyp stark beeinträchtigte. Damit ist die Ansäuerung in Tomatenblättern ein wichtiger Virulenzmechanismus, der möglicherweise essentiell für die Aktivität der sekretierten Proteine ist.
Überraschenderweise scheint eine Ansäuerung des Gewebes für die erfolgreiche Infektion der Ackerbohne Vicia faba nicht notwendig zu sein. Weder B. cinerea noch der am nächsten verwandte Botrytis fabae, welcher sich als Spezialist auf V. faba aggressiver verhält, zeigten während der erfolgreichen Infektion eine Ansäuerung des Ackerbohnenblattgewebes. B. fabae ist auf wenige Wirtspflanzen der Fabaceae begrenzt. Die Grundlagen der Wirtsspezifität sind bisher unklar. Vergleichende Transkriptom- und Sekretom-Analysen ergaben Hinweise für die molekularen Ursachen der unterschiedlichen Wirtsspektren von B. cinerea und B. fabae. In dieser Arbeit konnte die schlechte Infektion durch B. fabae auf Tomatenblättern mit einer deutlich niedrigeren Proteaseaktivität in Verbindung gebracht werden, während artifiziell konstant niedrige pH-Werte die Läsionsausbreitung kaum förderten. Im Gegensatz zur Infektion von Tomatenblättern wurden jedoch auf V. faba insgesamt deutlich niedrigere Proteaseaktivitäten in den Sekretomen beider Spezies gemessen. Diese Daten weisen darauf hin, dass die beiden Spezies nicht nur generell unterschiedliche Infektionsstrategien anwenden, sondern dass die Virulenzmechanismen zusätzlich vom infizierten Pflanzengewebe abhängig sind.
The neural networks have been extensively used for tasks based on image sensors. These models have, in the past decade, consistently performed better than other machine learning methods on tasks of computer vision. It is understood that methods for transfer learning from neural networks trained on large datasets can reduce the total data requirement while training new neural network models. These methods tend not to perform well when the data recording sensor or the recording environment is unique from the existing large datasets. The machine learning literature provides various methods for prior-information inclusion in a learning model. Such methods employ methods like designing biases into the data representation vectors, enforcing priors or physical constraints on the models. Including such information into neural networks for the image frames and image-sequence classification is hard because of the very high dimensional neural network mapping function and little information about the relation between the neural network parameters. In this thesis, we introduce methods for evaluating the statistically learned data representation and combining these information descriptors. We have introduced methods for including information into neural networks. In a series of experiments, we have demonstrated methods for adding the existing model or task information to neural networks. This is done by 1) Adding architectural constraints based on the physical shape information of the input data, 2) including weight priors on neural networks by training them to mimic statistical and physical properties of the data (hand shapes), and 3) by including the knowledge about the classes involved in the classification tasks to modify the neural network outputs. These methods are demonstrated, and their positive influence on the hand shape and hand gesture classification tasks are reported. This thesis also proposes methods for combination of statistical and physical models with parametrized learning models and show improved performances with constant data size. Eventually, these proposals are tied together to develop an in-car hand-shape and hand-gesture classifier based on a Time of Flight sensor.
In the past, information and knowledge dissemination was relegated to the
brick-and-mortar classrooms, newspapers, radio, and television. As these
processes were simple and centralized, the models behind them were well
understood and so were the empirical methods for optimizing them. In today’s
world, the internet and social media has become a powerful tool for information
and knowledge dissemination: Wikipedia gets more than 1 million edits per day,
Stack Overflow has more than 17 million questions, 25% of US population visits
Yahoo! News for articles and discussions, Twitter has more than 60 million
active monthly users, and Duolingo has 25 million users learning languages
online. These developments have introduced a paradigm shift in the process of
dissemination. Not only has the nature of the task moved from being centralized
to decentralized, but the developments have also blurred the boundary between
the creator and the consumer of the content, i.e., information and knowledge.
These changes have made it necessary to develop new models, which are better
suited to understanding and analysing the dissemination, and to develop new
methods to optimize them.
At a broad level, we can view the participation of users in the process of
dissemination as falling in one of two settings: collaborative or competitive.
In the collaborative setting, the participants work together in crafting
knowledge online, e.g., by asking questions and contributing answers, or by
discussing news or opinion pieces. In contrast, as competitors, they vie for
the attention of their followers on social media. This thesis investigates both
these settings.
The first part of the thesis focuses on the understanding and analysis of
content being created online collaboratively. To this end, I propose models for
understanding the complexity of the content of collaborative online discussions
by looking exclusively at the signals of agreement and disagreement expressed
by the crowd. This leads to a formal notion of complexity of opinions and
online discussions. Next, I turn my attention to the participants of the crowd,
i.e., the creators and consumers themselves, and propose an intuitive model for
both, the evolution of their expertise and the value of the content they
collaboratively contribute and learn from on online Q&A based forums. The
second part of the thesis explores the competitive setting. It provides methods
to help the creators gain more attention from their followers on social media.
In particular, I consider the problem of controlling the timing of the posts of
users with the aim of maximizing the attention that their posts receive under
the idealized setting of full-knowledge of timing of posts of others. To solve
it, I develop a general reinforcement learning based method which is shown to
have good performance on the when-to-post problem and which can be employed in
many other settings as well, e.g., determining the reviewing times for spaced
repetition which lead to optimal learning. The last part of the thesis looks at
methods for relaxing the idealized assumption of full knowledge. This basic
question of determining the visibility of one’s posts on the followers’ feeds
becomes difficult to answer on the internet when constantly observing the feeds
of all the followers becomes unscalable. I explore the links of this problem to
the well-studied problem of web-crawling to update a search engine’s index and
provide algorithms with performance guarantees for feed observation policies
which minimize the error in the estimate of visibility of one’s posts.