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Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such adhoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Institutionelle Bildung ist für autistische Lernende mit vielgestaltigen und spezifischen Hindernissen verbunden. Dies gilt insbesondere im Zusammenhang mit Inklusion, deren Relevanz nicht zuletzt durch das Übereinkommen der Vereinten Nationen über die Rechte von Menschen mit Behinderung gegeben ist.
Diese Arbeit diskutiert zahlreiche lernrelevante Besonderheiten im Kontext von Autismus und zeigt Diskrepanzen zu den nicht immer ausreichend angemessenen institutionellen Lehrkonzepten. Eine zentrale These ist hierbei, dass die ungewöhnlich intensive Aufmerksamkeit von Autist*innen für ihre Spezialinteressen dafür genutzt werden kann, das Lernen mit fremdgestellten Inhalten zu erleichtern. Darauf aufbauend werden Lösungsansätze diskutiert, welche in einem neuartigen Konzept für ein digitales mehrgerätebasiertes Lernspiel resultieren.
Eine wesentliche Herausforderung bei der Konzeption spielbasierten Lernens besteht in der adäquaten Einbindung von Lerninhalten in einen fesselnden narrativen Kontext. Am Beispiel von Übungen zur emotionalen Deutung von Mimik, welche für das Lernen von sozioemotionalen Kompetenzen besonders im Rahmen von Therapiekonzepten bei Autismus Verwendung finden, wird eine angemessene Narration vorgestellt, welche die störungsarme Einbindung dieser sehr speziellen Lerninhalte ermöglicht.
Die Effekte der einzelnen Konzeptionselemente werden anhand eines prototypisch entwickelten Lernspiels untersucht. Darauf aufbauend zeigt eine quantitative Studie die gute Akzeptanz und Nutzerfreundlichkeit des Spiels und belegte vor allem die
Verständlichkeit der Narration und der Spielelemente. Ein weiterer Schwerpunkt liegt in der minimalinvasiven Untersuchung möglicher Störungen des Spielerlebnisses durch den Wechsel zwischen verschiedenen Endgeräten, für die ein innovatives Messverfahren entwickelt wurde.
Im Ergebnis beleuchtet diese Arbeit die Bedeutung und die Grenzen von spielbasierten Ansätzen für autistische Lernende. Ein großer Teil der vorgestellten Konzepte lässt sich auf andersartige Lernszenarien übertragen. Das dafür entwickelte technische Framework zur Realisierung narrativer Lernpfade ist ebenfalls darauf vorbereitet, für weitere Lernszenarien, gerade auch im institutionellen Kontext, Verwendung zu finden.
The soft error rate (SER) due to heavy-ion irradiation of a clock tree is investigated in this paper. A method for clock tree SER prediction is developed, which employs a dedicated soft error analysis tool to characterize the single-event transient (SET) sensitivities of clock inverters and other commercial tools to calculate the SER through fault-injection simulations. A test circuit including a flip-flop chain and clock tree in a 65 nm CMOS technology is developed through the automatic ASIC design flow. This circuit is analyzed with the developed method to calculate its clock tree SER. In addition, this circuit is implemented in a 65 nm test chip and irradiated by heavy ions to measure its SER resulting from the SETs in the clock tree. The experimental and calculation results of this case study present good correlation, which verifies the effectiveness of the developed method.
The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]
THIS INSTALLMENT OF Research for Practice provides curated reading guides to technology for underserved communities and to new developments in personal fabrication. First, Tawanna Dillahunt describes design considerations and technology for underserved and impoverished communities. Designing for the more than 1.6 billion impoverished individuals worldwide requires special consideration of community needs, constraints, and context. Her selections span protocols for poor-quality communication networks, community-driven content generation, and resource and public service discovery. Second, Stefanie Mueller and Patrick Baudisch provide an overview of recent advances in personal fabrication (for example, 3D printers).
In vielen Studiengängen kommt es durch die oft heterogenen Vorkenntnisse in der Studieneingangsphase zu mangelnder Motivation durch Über- oder Unterforderung. Dieses Problem tritt auch in der musiktheoretischen Grundausbildung an Hochschulen auf. Durch Einsatz von Elementen, die aus dem Unterhaltungskontext geläufig sind, kann eine Steigerung der Motivation erreicht werden. Die Nutzung solcher Elemente wird als Gamification bezeichnet.
Das Ziel der vorliegenden Arbeit ist es, am Fallbeispiel der musiktheoretischen Grundausbildung zu analysieren, ob Lerngelegenheiten durch einen gamifizierten interaktiven Prototyp einer Lernumgebung unterstützt werden können. Dazu wird die folgende Forschungsfrage gestellt: Inwieweit wirkt Gamification auf die Motivation bei den Lernenden zur Beschäftigung mit dem Thema (musikalische) Funktionsanalyse?
Um die Forschungsfragen zu beantworten, wurde zunächst ein systematisches, theoriegeleitetes Vorgehensmodell zur Gamification von Lernumgebungen entwickelt und angewandt. Der so entstandene Prototyp wurde anschließend um alle Game-Design-Elemente reduziert und im Rahmen einer experimentellen Studie mit zwei unabhängigen Versuchsgruppen mit der gamifizierten Variante verglichen.
Die Untersuchung zeigte, dass die Gamification einer Lernanwendung nach dem entwickelten Vorgehensmodell grundsätzlich das Potenzial besitzt, manche Aspekte des Nutzungserlebnisses (UX) positiv zu beeinflussen. Insbesondere hatte die Gamification positive Effekte auf die Joy of Use und die Immersivität. Allerdings blieb das Ausmaß der beobachteten Effekte deutlich hinter den Erwartungen zurück, die auf Basis verschiedener Motivationstheorien getroffen wurden.
Daher erscheint Gamification besonders in außeruniversitären Kontexten vielversprechend, in denen der Fokus auf einer Erhöhung der Joy of Use oder einer Steigerung der Immersivität liegt. Allerdings lassen sich durch die Untersuchung neue Erkenntnisse zur emotionalen Wirkung von Gamification und zu einem systematischen Vorgehen bei der Gamification von Lernanwendungen herausstellen.
Weiterführende Forschung könnte an diese Erkenntnisse anknüpfen, indem sie die emotionale Wirkung von Gamification und deren Einfluss auf die Motivation näher untersucht. Darüber hinaus sollte sie Gamification auch aus einer entscheidungstheoretischen Perspektive betrachten und Analysemethoden entwickeln, mit denen entschieden werden kann, ob der Einsatz von Gamification zur Motivationssteigerung in einem spezifischen Anwendungsfall zielführend ist. Unter Verwendung des entwickelten Vorgehensmodells kann es sinnvoll sein, näher zu untersuchen, welche Faktoren insgesamt für das Gelingen einer Gamification-Maßnahme in Bildungskontexten entscheidend sind. Die Erkenntnisse einer solchen Untersuchung könnten entscheidend zur Verbesserung und Validierung des Vorgehensmodells beitragen.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes
(2021)
Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control.
A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification.
Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks.
We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices.
In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.
In this paper, we consider the computational power of a new variant of networks of splicing processors in which each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined (negative, neutral, positive), the polarization of data is dynamically computed by means of a valuation mapping. Consequently, the protocol of communication is naturally defined by means of this polarization. We show that networks of polarized splicing processors (NPSP) of size 2 are computationally complete, which immediately settles the question of designing computationally complete NPSPs of minimal size. With two more nodes we can simulate every nondeterministic Turing machine without increasing the time complexity. Particularly, we prove that NPSP of size 4 can accept all languages in NP in polynomial time. Furthermore, another computational model that is universal, namely the 2-tag system, can be simulated by NPSP of size 3 preserving the time complexity. All these results can be obtained with NPSPs with valuations in the set as well. We finally show that Turing machines can simulate a variant of NPSPs and discuss the time complexity of this simulation.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.
In recent years, named entity linking (NEL) tools were primarily developed in terms of a general approach, whereas today numerous tools are focusing on specific domains such as e.g. the mapping of persons and organizations only, or the annotation of locations or events in microposts. However, the available benchmark datasets necessary for the evaluation of NEL tools do not reflect this focalizing trend. We have analyzed the evaluation process applied in the NEL benchmarking framework GERBIL [in: Proceedings of the 24th International Conference on World Wide Web (WWW’15), International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 2015, pp. 1133–1143, Semantic Web 9(5) (2018), 605–625] and all its benchmark datasets. Based on these insights we have extended the GERBIL framework to enable a more fine grained evaluation and in depth analysis of the available benchmark datasets with respect to different emphases. This paper presents the implementation of an adaptive filter for arbitrary entities and customized benchmark creation as well as the automated determination of typical NEL benchmark dataset properties, such as the extent of content-related ambiguity and diversity. These properties are integrated on different levels, which also enables to tailor customized new datasets out of the existing ones by remixing documents based on desired emphases. Besides a new system library to enrich provided NIF [in: International Semantic Web Conference (ISWC’13), Lecture Notes in Computer Science, Vol. 8219, Springer, Berlin, Heidelberg, 2013, pp. 98–113] datasets with statistical information, best practices for dataset remixing are presented, and an in depth analysis of the performance of entity linking systems on special focus datasets is presented.
Detect me if you can
(2019)
Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node’s neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection.
In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
A distinguishing feature of Answer Set Programming is that all atoms belonging to a stable model must be founded. That is, an atom must not only be true but provably true. This can be made precise by means of the constructive logic of Here-and-There, whose equilibrium models correspond to stable models. One way of looking at foundedness is to regard Boolean truth values as ordered by letting true be greater than false. Then, each Boolean variable takes the smallest truth value that can be proven for it. This idea was generalized by Aziz to ordered domains and applied to constraint satisfaction problems. As before, the idea is that a, say integer, variable gets only assigned to the smallest integer that can be justified. In this paper, we present a logical reconstruction of Aziz’ idea in the setting of the logic of Here-and-There. More precisely, we start by defining the logic of Here-and-There with lower bound founded variables along with its equilibrium models and elaborate upon its formal properties. Finally, we compare our approach with related ones and sketch future work.
Digitalisierung ermöglicht es uns, mit Partnern (z.B. Unternehmen, Institutionen) in einer IT-unterstützten Umgebung zu interagieren und Tätigkeiten auszuführen, die vormals manuell erledigt wurden. Ein Ziel der Digitalisierung ist dabei, Dienstleistungen unterschiedlicher fachlicher Domänen zu Prozessen zu kombinieren und vielen Nutzergruppen bedarfsgerecht zugänglich zu machen. Hierzu stellen Anbieter technische Dienste bereit, die in unterschiedliche Anwendungen integriert werden können.
Die Digitalisierung stellt die Anwendungsentwicklung vor neue Herausforderungen. Ein Aspekt ist die bedarfsgerechte Anbindung von Nutzern an Dienste. Zur Interaktion menschlicher Nutzer mit den Diensten werden Benutzungsschnittstellen benötigt, die auf deren Bedürfnisse zugeschnitten sind. Hierzu werden Varianten für spezifische Nutzergruppen (fachliche Varianten) und variierende Umgebungen (technische Varianten) benötigt. Zunehmend müssen diese mit Diensten anderer Anbieter kombiniert werden können, um domänenübergreifend Prozesse zu Anwendungen mit einem erhöhten Mehrwert für den Endnutzer zu verknüpfen (z.B. eine Flugbuchung mit einer optionalen Reiseversicherung).
Die Vielfältigkeit der Varianten lässt die Erstellung von Benutzungsschnittstellen komplex und die Ergebnisse sehr individuell erscheinen. Daher werden die Varianten in der Praxis vorwiegend manuell erstellt. Dies führt zur parallelen Entwicklung einer Vielzahl sehr ähnlicher Anwendungen, die nur geringes Potential zur Wiederverwendung besitzen. Die Folge sind hohe Aufwände bei Erstellung und Wartung. Dadurch wird häufig auf die Unterstützung kleiner Nutzerkreise mit speziellen Anforderungen verzichtet (z.B. Menschen mit physischen Einschränkungen), sodass diese weiterhin von der Digitalisierung ausgeschlossen bleiben.
Die Arbeit stellt eine konsistente Lösung für diese neuen Herausforderungen mit den Mitteln der modellgetriebenen Entwicklung vor. Sie präsentiert einen Ansatz zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen und deren automatischer Generierung für digitale Dienste in einem verteilten Umfeld. Die Arbeit schafft eine Lösung zur Wiederverwendung und gemeinschaftlichen Nutzung von Benutzungsschnittstellen über Anbietergrenzen hinweg. Sie führt zu einer Infrastruktur, in der eine Vielzahl von Anbietern ihre Expertise in gemeinschaftliche Anwendungen einbringen können.
Die Beiträge bestehen im Einzelnen in Konzepten und Metamodellen zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen sowie einem Verfahren zu deren vollständig automatisierten Transformation in funktionale Benutzungsschnittstellen. Zur Umsetzung der gemeinschaftlichen Nutzbarkeit werden diese ergänzt um eine universelle Repräsentation der Modelle, einer Methodik zur Anbindung unterschiedlicher Dienst-Anbieter sowie einer Architektur zur verteilten Nutzung der Artefakte und Verfahren in einer dienstorientierten Umgebung.
Der Ansatz bietet die Chance, unterschiedlichste Menschen bedarfsgerecht an der Digitalisierung teilhaben zu lassen. Damit setzt die Arbeit Impulse für zukünftige Methoden zur Anwendungserstellung in einem zunehmend vielfältigen Umfeld.
Zum Einfluss von Adaptivität auf die Wahrnehmung von Komplexität in der Mensch-Technik-Interaktion
(2021)
Wir leben in einer Gesellschaft, die von einem stetigen Wunsch nach Innovation und Fortschritt geprägt ist. Folgen dieses Wunsches sind die immer weiter fortschreitende Digitalisierung und informatische Vernetzung aller Lebensbereiche, die so zu immer komplexeren sozio-technischen Systemen führen. Ziele dieser Systeme sind u. a. die Unterstützung von Menschen, die Verbesserung ihrer Lebenssituation oder Lebensqualität oder die Erweiterung menschlicher Möglichkeiten. Doch haben neue komplexe technische Systeme nicht nur positive soziale und gesellschaftliche Effekte. Oft gibt es unerwünschte Nebeneffekte, die erst im Gebrauch sichtbar werden, und sowohl Konstrukteur*innen als auch Nutzer*innen komplexer vernetzter Technologien fühlen sich oft orientierungslos. Die Folgen können von sinkender Akzeptanz bis hin zum kompletten Verlust des Vertrauens in vernetze Softwaresysteme reichen. Da komplexe Anwendungen, und damit auch immer komplexere Mensch-Technik-Interaktionen, immer mehr an Relevanz gewinnen, ist es umso wichtiger, wieder Orientierung zu finden. Dazu müssen wir zuerst diejenigen Elemente identifizieren, die in der Interaktion mit vernetzten sozio-technischen Systemen zu Komplexität beitragen und somit Orientierungsbedarf hervorrufen.
Mit dieser Arbeit soll ein Beitrag geleistet werden, um ein strukturiertes Reflektieren über die Komplexität vernetzter sozio-technischer Systeme im gesamten Konstruktionsprozess zu ermöglichen. Dazu wird zuerst eine Definition von Komplexität und komplexen Systemen erarbeitet, die über das informatische Verständnis von Komplexität (also der Kompliziertheit von Problemen, Algorithmen oder Daten) hinausgeht. Im Vordergrund soll vielmehr die sozio-technische Interaktion mit und in komplexen vernetzten Systemen stehen. Basierend auf dieser Definition wird dann ein Analysewerkzeug entwickelt, welches es ermöglicht, die Komplexität in der Interaktion mit sozio-technischen Systemen sichtbar und beschreibbar zu machen.
Ein Bereich, in dem vernetzte sozio-technische Systeme zunehmenden Einzug finden, ist jener digitaler Bildungstechnologien. Besonders adaptiven Bildungstechnologien wurde in den letzten Jahrzehnten ein großes Potential zugeschrieben. Zwei adaptive Lehr- bzw. Trainingssysteme sollen deshalb exemplarisch mit dem in dieser Arbeit entwickelten Analysewerkzeug untersucht werden. Hierbei wird ein besonderes Augenmerkt auf den Einfluss von Adaptivität auf die Komplexität von Mensch-Technik-Interaktionssituationen gelegt. In empirischen Untersuchungen werden die Erfahrungen von Konstrukteur*innen und Nutzer*innen jener adaptiver Systeme untersucht, um so die entscheidenden Kriterien für Komplexität ermitteln zu können. Auf diese Weise können zum einen wiederkehrende Orientierungsfragen bei der Entwicklung adaptiver Bildungstechnologien aufgedeckt werden. Zum anderen werden als komplex wahrgenommene Interaktionssituationen identifiziert. An diesen Situationen kann gezeigt werden, wo aufgrund der Komplexität des Systems die etablierten Alltagsroutinen von Nutzenden nicht mehr ausreichen, um die Folgen der Interaktion mit dem System vollständig erfassen zu können. Dieses Wissen kann sowohl Konstrukteur*innen als auch Nutzer*innen helfen, in Zukunft besser mit der inhärenten Komplexität moderner Bildungstechnologien umzugehen.
We introduce a type and effect system, for an imperative object calculus, which infers sharing possibly introduced by the evaluation of an expression, represented as an equivalence relation among its free variables. This direct representation of sharing effects at the syntactic level allows us to express in a natural way, and to generalize, widely-used notions in literature, notably uniqueness and borrowing. Moreover, the calculus is pure in the sense that reduction is defined on language terms only, since they directly encode store. The advantage of this non-standard execution model with respect to a behaviorally equivalent standard model using a global auxiliary structure is that reachability relations among references are partly encoded by scoping. (C) 2018 Elsevier B.V. All rights reserved.
teaspoon
(2018)
Answer Set Programming (ASP) is an approach to declarative problem solving, combining a rich yet simple modeling language with high performance solving capacities. We here develop an ASP-based approach to curriculum-based course timetabling (CB-CTT), one of the most widely studied course timetabling problems. The resulting teaspoon system reads a CB-CTT instance of a standard input format and converts it into a set of ASP facts. In turn, these facts are combined with a first-order encoding for CB-CTT solving, which can subsequently be solved by any off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting it to the best known bounds obtained so far via dedicated implementations. Furthermore, we extend the teaspoon system to multi-objective course timetabling and consider minimal perturbation problems.
Forschendes Lernen und die digitale Transformation sind zwei der wichtigsten Einflüsse auf die Entwicklung der Hochschuldidaktik im deutschprachigen Raum. Während das forschende Lernen als normative Theorie das sollen beschreibt, geben die digitalen Werkzeuge, alte wie neue, das können in vielen Bereichen vor.
In der vorliegenden Arbeit wird ein Prozessmodell aufgestellt, was den Versuch unternimmt, das forschende Lernen hinsichtlich interaktiver, gruppenbasierter Prozesse zu systematisieren. Basierend auf dem entwickelten Modell wurde ein Softwareprototyp implementiert, der den gesamten Forschungsprozess begleiten kann. Dabei werden Gruppenformation, Feedback- und Reflexionsprozesse und das Peer Assessment mit Bildungstechnologien unterstützt. Die Entwicklungen wurden in einem qualitativen Experiment eingesetzt, um Systemwissen über die Möglichkeiten und Grenzen der digitalen Unterstützung von forschendem Lernen zu gewinnen.
plasp 3
(2019)
We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.
E-Assessment etablieren
(2020)
Elektronische Lernstandserhebungen, sogenannte E-Assessments, bieten für Lehrende und Studierende viele Vorteile z. B. hinsichtlich schneller Rückmeldungen oder kompetenzorientierter Fragenformate, und ermöglichen es, unabhängig von Ort und Zeit Prüfungen zu absolvieren. In diesem Beitrag werden die Einführung von summativen Lernstandserhebungen, sogenannter E-Klausuren, am Beispiel der Universität Potsdam, der Aufbau einer länderübergreifenden Initiative für E-Assessment sowie technische Möglichkeiten für dezentrale elektronische Klausuren vorgestellt. Dabei werden der aktuelle Stand, die Ziele und die gewählte stufenweise Umsetzungsstrategie der Universität Potsdam skizziert. Darauf aufbauend folgt eine Beschreibung des Vorgehens, der Kooperationsmöglichkeiten für den Wissens- und Erfahrungsaustausch sowie Herausforderungen der E-Assessment- Initiative. Abschließend werden verschiedene E-Klausurformen und technische Möglichkeiten zur Umsetzung komplexer Prüfungsumgebungen klassifiziert sowie mit ihren charakteristischen Vor- und Nachteilen diskutiert und eine integrierte Lösung vorgeschlagen.
Das größte der fächerübergreifenden Projekte im Potsdamer Projekt Qualitätspakt Lehre hatte die flächendeckende Etablierung von digitalen Medien als einen integralen Bestandteil von Lehre und Studium zum Gegenstand. Im Teilprojekt E-Learning in Studienbereichen (eLiS) wurden dafür Maßnahmen in den Feldern Organisations-, technische und Inhaltsentwicklung zusammengeführt. Der vorliegende Beitrag präsentiert auf Basis von Ausgangslage und Zielsetzungen die Ergebnisse rund um die Digitalisierung von Lehre und Studium an der Universität Potsdam. Exemplarisch werden fünf Dienste näher vorgestellt, die inzwischen größtenteils in den Regelbetrieb der Hochschule übergegangen sind: die Videoplattform Media.UP, die mobile App Reflect.UP, die persönliche Lernumgebung Campus. UP, das Self-Service-Portal Cook.UP und das Anzeigesystem Freiraum.UP. Dabei wird jeweils ein technischer Blick „unter die Haube“ verbunden mit einer Erläuterung der Nutzungsmöglichkeiten, denen eine aktuelle Einschätzung von Lehrenden und Studierenden der Hochschule gegenübergestellt wird. Der Beitrag schließt mit einer Einbettung der vorgestellten Entwicklungen in einen größeren Kontext und einem Ausblick auf die weiterhin anstehenden Aufgaben.
Die Setzung strategischer Ziele sowie die Zuordnung und Umsetzung dazugehörender Maßnahmen sind ein wesentliches Element, um die Innovationsfähigkeit von Organisationen zu erhalten. In den vergangenen Jahren ist auch an Hochschulen die Strategiebildung deutlich vorangetrieben worden. Dies betrifft verschiedene Handlungsfelder, und es werden verschiedene Ansätze verfolgt. Der vorliegende Beitrag greift am Beispiel der Universität Potsdam drei in den vergangenen Jahren adressierte Strategiebereiche heraus: IT, E-Learning und Forschungsdaten. Die damit verbundenen Prozesse waren in unterschiedlichem Maß von Partizipation geprägt. Die gesammelten Erfahrungen werden reflektiert, und es werden Empfehlungen für Strategieentwicklungsprozesse abgeleitet.
The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60 degrees simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors.
A common feature in Answer Set Programming is the use of a second negation, stronger than default negation and sometimes called explicit, strong or classical negation. This explicit negation is normally used in front of atoms, rather than allowing its use as a regular operator. In this paper we consider the arbitrary combination of explicit negation with nested expressions, as those defined by Lifschitz, Tang and Turner. We extend the concept of reduct for this new syntax and then prove that it can be captured by an extension of Equilibrium Logic with this second negation. We study some properties of this variant and compare to the already known combination of Equilibrium Logic with Nelson's strong negation.
The target article discusses the question of how educational makerspaces can become places supportive of knowledge construction. This question is too often neglected by people who run makerspaces, as they mostly explain how to use different tools and focus on the creation of a product. In makerspaces, often pupils also engage in physical computing activities and thus in the creation of interactive artifacts containing embedded systems, such as smart shoes or wristbands, plant monitoring systems or drink mixing machines. This offers the opportunity to reflect on teaching physical computing in computer science education, where similarly often the creation of the product is so strongly focused upon that the reflection of the learning process is pushed into the background.
MUP
(2020)
Message Queuing Telemetry Transport (MQTT) is one of the dominating protocols for edge- and cloud-based Internet of Things (IoT) solutions. When a security vulnerability of an IoT device is known, it has to be fixed as soon as possible. This requires a firmware update procedure. In this paper, we propose a secure update protocol for MQTT-connected devices which ensures the freshness of the firmware, authenticates the new firmware and considers constrained devices. We show that the update protocol is easy to integrate in an MQTT-based IoT network using a semantic approach. The feasibility of our approach is demonstrated by a detailed performance analysis of our prototype implementation on a IoT device with 32 kB RAM. Thereby, we identify design issues in MQTT 5 which can help to improve the support of constrained devices.
MUP
(2020)
Message Queuing Telemetry Transport (MQTT) is one of the dominating protocols for edge- and cloud-based Internet of Things (IoT) solutions. When a security vulnerability of an IoT device is known, it has to be fixed as soon as possible. This requires a firmware update procedure. In this paper, we propose a secure update protocol for MQTT-connected devices which ensures the freshness of the firmware, authenticates the new firmware and considers constrained devices. We show that the update protocol is easy to integrate in an MQTT-based IoT network using a semantic approach. The feasibility of our approach is demonstrated by a detailed performance analysis of our prototype implementation on a IoT device with 32 kB RAM. Thereby, we identify design issues in MQTT 5 which can help to improve the support of constrained devices.
In a recent line of research, two familiar concepts from logic programming semantics (unfounded sets and splitting) were extrapolated to the case of epistemic logic programs. The property of epistemic splitting provides a natural and modular way to understand programs without epistemic cycles but, surprisingly, was only fulfilled by Gelfond's original semantics (G91), among the many proposals in the literature. On the other hand, G91 may suffer from a kind of self-supported, unfounded derivations when epistemic cycles come into play. Recently, the absence of these derivations was also formalised as a property of epistemic semantics called foundedness. Moreover, a first semantics proved to satisfy foundedness was also proposed, the so-called Founded Autoepistemic Equilibrium Logic (FAEEL). In this paper, we prove that FAEEL also satisfies the epistemic splitting property something that, together with foundedness, was not fulfilled by any other approach up to date. To prove this result, we provide an alternative characterisation of FAEEL as a combination of G91 with a simpler logic we called Founded Epistemic Equilibrium Logic (FEEL), which is somehow an extrapolation of the stable model semantics to the modal logic S5.
Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic structures, most approaches choose to learn a local model and then perform global decoding over the local probability distributions, often imposing constraints that are specific to the task at hand. Specifically for argumentation parsing, two decoding approaches have been recently proposed: Minimum Spanning Trees (MST) and Integer Linear Programming (ILP), following similar trends in discourse parsing. In contrast to discourse parsing though, where trees are not always used as underlying annotation schemes, argumentation structures so far have always been represented with trees. Using the 'argumentative microtext corpus' [in: Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 / Vol. 2, College Publications, London, 2016, pp. 801-815] as underlying data and replicating three different decoding mechanisms, in this paper we propose a novel ILP decoder and an extension to our earlier MST work, and then thoroughly compare the approaches. The result is that our new decoder outperforms related work in important respects, and that in general, ILP and MST yield very similar performance.
Flux-P
(2012)
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.
Answer Set Programming (ASP) has become a popular and widespread paradigm for practical Knowledge Representation thanks to its expressiveness and the available enhancements of its input language. One of such enhancements is the use of aggregates, for which different semantic proposals have been made. In this paper, we show that any ASP aggregate interpreted under Gelfond and Zhang's (GZ) semantics can be replaced (under strong equivalence) by a propositional formula. Restricted to the original GZ syntax, the resulting formula is reducible to a disjunction of conjunctions of literals but the formulation is still applicable even when the syntax is extended to allow for arbitrary formulas (including nested aggregates) in the condition. Once GZ-aggregates are represented as formulas, we establish a formal comparison (in terms of the logic of Here-and-There) to Ferraris' (F) aggregates, which are defined by a different formula translation involving nested implications. In particular, we prove that if we replace an F-aggregate by a GZ-aggregate in a rule head, we do not lose answer sets (although more can be gained). This extends the previously known result that the opposite happens in rule bodies, i.e., replacing a GZ-aggregate by an F-aggregate in the body may yield more answer sets. Finally, we characterize a class of aggregates for which GZ- and F-semantics coincide.
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.
TrainTrap
(2020)
Die Fehlerkorrektur in der Codierungstheorie beschäftigt sich mit der Erkennung und Behebung von Fehlern bei der Übertragung und auch Sicherung von Nachrichten.
Hierbei wird die Nachricht durch zusätzliche Informationen in ein Codewort kodiert.
Diese Kodierungsverfahren besitzen verschiedene Ansprüche, wie zum Beispiel die maximale Anzahl der zu korrigierenden Fehler und die Geschwindigkeit der Korrektur.
Ein gängiges Codierungsverfahren ist der BCH-Code, welches industriell für bis zu vier Fehler korrigiere Codes Verwendung findet. Ein Nachteil dieser Codes ist die technische Durchlaufzeit für die Berechnung der Fehlerstellen mit zunehmender Codelänge.
Die Dissertation stellt ein neues Codierungsverfahren vor, bei dem durch spezielle Anordnung kleinere Codelängen eines BCH-Codes ein langer Code erzeugt wird. Diese Anordnung geschieht über einen weiteren speziellen Code, einem LDPC-Code, welcher für eine schneller Fehlererkennung konzipiert ist.
Hierfür wird ein neues Konstruktionsverfahren vorgestellt, welches einen Code für einen beliebige Länge mit vorgebbaren beliebigen Anzahl der zu korrigierenden Fehler vorgibt. Das vorgestellte Konstruktionsverfahren erzeugt zusätzlich zum schnellen Verfahren der Fehlererkennung auch eine leicht und schnelle Ableitung eines Verfahrens zu Kodierung der Nachricht zum Codewort. Dies ist in der Literatur für die LDPC-Codes bis zum jetzigen Zeitpunkt einmalig.
Durch die Konstruktion eines LDPC-Codes wird ein Verfahren vorgestellt wie dies mit einem BCH-Code kombiniert wird, wodurch eine Anordnung des BCH-Codes in Blöcken erzeugt wird. Neben der allgemeinen Beschreibung dieses Codes, wird ein konkreter Code für eine 2-Bitfehlerkorrektur beschrieben. Diese besteht aus zwei Teilen, welche in verschiedene Varianten beschrieben und verglichen werden. Für bestimmte Längen des BCH-Codes wird ein Problem bei der Korrektur aufgezeigt, welche einer algebraischen Regel folgt.
Der BCH-Code wird sehr allgemein beschrieben, doch existiert durch bestimmte Voraussetzungen ein BCH-Code im engerem Sinne, welcher den Standard vorgibt. Dieser BCH-Code im engerem Sinne wird in dieser Dissertation modifiziert, so dass das algebraische Problem bei der 2-Bitfehler Korrektur bei der Kombination mit dem LDPC-Code nicht mehr existiert. Es wird gezeigt, dass nach der Modifikation der neue Code weiterhin ein BCH-Code im allgemeinen Sinne ist, welcher 2-Bitfehler korrigieren und 3-Bitfehler erkennen kann. Bei der technischen Umsetzung der Fehlerkorrektur wird des Weiteren gezeigt, dass die Durchlaufzeiten des modifizierten Codes im Vergleich zum BCH-Code schneller ist und weiteres Potential für Verbesserungen besitzt.
Im letzten Kapitel wird gezeigt, dass sich dieser modifizierte Code mit beliebiger Länge eignet für die Kombination mit dem LDPC-Code, wodurch dieses Verfahren nicht nur umfänglicher in der Länge zu nutzen ist, sondern auch durch die schnellere Dekodierung auch weitere Vorteile gegenüber einem BCH-Code im engerem Sinne besitzt.
In this work we tackle the problem of checking strong equivalence of logic programs that may contain local auxiliary atoms, to be removed from their stable models and to be forbidden in any external context. We call this property projective strong equivalence (PSE). It has been recently proved that not any logic program containing auxiliary atoms can be reformulated, under PSE, as another logic program or formula without them – this is known as strongly persistent forgetting. In this paper, we introduce a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There, in which we deal with a new connective ‘|’ we call fork. We provide a semantic characterisation of PSE for forks and use it to show that, in this extension, it is always possible to forget auxiliary atoms under strong persistence. We further define when the obtained fork is representable as a regular formula.
Multi-sided platforms (MSP) strongly affect markets and play a crucial part within the digital and networked economy. Although empirical evidence indicates their occurrence in many industries, research has not investigated the game-changing impact of MSP on traditional markets to a sufficient extent. More specifically, we have little knowledge of how MSP affect value creation and customer interaction in entire markets, exploiting the potential of digital technologies to offer new value propositions. Our paper addresses this research gap and provides an initial systematic approach to analyze the impact of MSP on the insurance industry. For this purpose, we analyze the state of the art in research and practice in order to develop a reference model of the value network for the insurance industry. On this basis, we conduct a case-study analysis to discover and analyze roles which are occupied or even newly created by MSP. As a final step, we categorize MSP with regard to their relation to traditional insurance companies, resulting in a classification scheme with four MSP standard types: Competition, Coordination, Cooperation, Collaboration.
Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.
A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.