Institut für Informatik und Computational Science
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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.
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.
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.
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 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.
PLATON
(2019)
Lesson planning is both an important and demanding task—especially as part of teacher training. This paper presents the requirements for a lesson planning system and evaluates existing systems regarding these requirements. One major drawback of existing software tools is that most are limited to a text- or form-based representation of the lesson designs. In this article, a new approach with a graphical, time-based representation with (automatic) analyses methods is proposed and the system architecture and domain model are described in detail. The approach is implemented in an interactive, web-based prototype called PLATON, which additionally supports the management of lessons in units as well as the modelling of teacher and student-generated resources. The prototype was evaluated in a study with 61 prospective teachers (bachelor’s and master’s preservice teachers as well as teacher trainees in post-university teacher training) in Berlin, Germany, with a focus on usability. The results show that this approach proofed usable for lesson planning and offers positive effects for the perception of time and self-reflection.
PLATON
(2019)
Lesson planning is both an important and demanding task—especially as part of teacher training. This paper presents the requirements for a lesson planning system and evaluates existing systems regarding these requirements. One major drawback of existing software tools is that most are limited to a text- or form-based representation of the lesson designs. In this article, a new approach with a graphical, time-based representation with (automatic) analyses methods is proposed and the system architecture and domain model are described in detail. The approach is implemented in an interactive, web-based prototype called PLATON, which additionally supports the management of lessons in units as well as the modelling of teacher and student-generated resources. The prototype was evaluated in a study with 61 prospective teachers (bachelor’s and master’s preservice teachers as well as teacher trainees in post-university teacher training) in Berlin, Germany, with a focus on usability. The results show that this approach proofed usable for lesson planning and offers positive effects for the perception of time and self-reflection.
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.
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.