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NAG: neural feature aggregation framework for credit card fraud detection
No full textThe state-of-the-art feature-engineering method for fraud classification of electronic pay-ments uses manually engineered feature aggregates, i.e., descriptive statistics of thetransaction history. However, this approach has limitations, primarily that of being dependenton expensive human expert knowledge. There have been attempts to replace manual aggre-gation through automatic feature extraction approaches. They, however, do not consider thespecific structure of the manual aggregates. In this paper, we define the novel Neural Aggre-gate Generator (NAG), a neural network-based feature extraction module that learns featureaggregates end-to-end on the fraud classification task. In contrast to other automatic featureextraction approaches, the network architecture of the NAG closely mimics the structureof feature aggregates. Furthermore, the NAG extends learnable aggregates over traditionalones through soft feature value matching and relative weighting of the importance of differ-ent feature constraints. We provide a proof to show the modeling capabilities of the NAG.We compare the performance of the NAG to the state-of-the-art approaches on a real-worlddataset with millions of transactions. More precisely, we show that features generated with theNAG lead to improved results over manual aggregates for fraud classification, thus demon-strating its viability to replace them. Moreover, we compare the NAG to other end-to-endapproaches such as the LSTM or a generic CNN. Here we also observe improved results. Weperform a robust evaluation of the NAG through a parameter budget study, an analysis of theimpact of different sequence lengths and also the predictions across days. Unlike the LSTMor the CNN, our approach also provides further interpretability through the inspection of itsparameters
Entwicklungsstand der CIO-Funktion und hochschulübergreifenden IT-Governance im Kontext der Digitalen Transformation an Hochschulen in Bayern
No full textDie Hochschulen befinden sich durch vielfältige Veränderungs-prozesse in Verbindung mit dem Einsatz von Informationstechnologien (IT) aufdem Weg der Digitalen Transformation. Diese Digitale Transformation der Hoch-schulen umfasst intensive Veränderungsprozesse in der gesamten Hochschulkulturin Lehre, Forschung und Verwaltung in übergreifender und strukturierter Weise.Seit vielen Jahren werden vielfältige Digitalisierungsvorhaben zur Modernisierungvon einzelnen Prozessen an den Hochschulen umgesetzt. Die Leitungen der Re-chenzentren leisten mit der Umsetzung von IT-Projekten einen zentralen Beitrag zudiesem Wandel. Mit der Einführung der CIO-Funktion in den Hochschulleitungenund der hochschulübergreifenden Kooperationen hat sich die IT-Governance wei-terentwickelt. Insbesondere für die Digitale Transformation werden Strukturen zurKoordination der übergreifenden Vorhaben benötigt, wobei zusätzlich zur IT-Lei-tung eine Vielzahl von Funktionsträgern mit fachlichen Aufgaben aus Forschung,Lehre und Verwaltung involviert ist. Es stellt sich die Frage, wie die Digitale Trans-formation an Hochschulen gesteuert werden kann und in welcher organisatorischenForm sich die Aufgaben und Verantwortlichkeiten im Hochschulkontext realisierenlassen. An der Weiterentwicklung der IT-Governance an bayerischen Hochschulenwird beispielhaft erläutert, welche übergreifenden Aufgaben der Koordination vonBedarf und Versorgung mit IT-Services zwischen und innerhalb der Hochschulenbestehen. Die CIO-Funktion wird durch die Verankerung in der Leitungsebene derFunktion des Chief Digital Officers (CDO) aus der Wirtschaft ähnlicher, auch wennin Hochschulen aufgrund der klassischen Ressort-Einteilung die Rolle oft als Vize-präsident:in für Digitalisierung bezeichnet wird
Evaluating the emotional bidding framework: new evidence from a decade of neurophysiology
No full textTen years ago the “emotional bidding framework” (Adam et al., Electronic Markets, 21(3), 197–207, 2011b) was published in this journal. It provided a conceptualization for the role of human emotion in electronic auctions along six propositions on how emotions emerge during the auction process and affect auction outcomes. While the framework emphasized the importance of immediate emotional responses and momentary changes in the bidders’ emotional state, the original articledid not include an evaluation of its propositions given the limited data on how bidders experience emotions in the moment that they occur. Ten years on, advances in the growing research field of NeuroIS allow to evaluate the propositions based on neurophysiological evidence. As a rejoinder of the original article, the present paper synthesizes these insights, refines the framework further, and identifies fruitful areas for future research based on remaining gaps in the body of knowledge
The large deviation behavior of lacunary sums
No full textWe study the large deviation behavior of lacunary sums (Sn /n)n∈N with Sn :=∑[k=1...n] f (a(k)U), n ∈ |N, where U is uniformly distributed on [0, 1], (a(k))k∈|N is an Hadamard gap sequence, and f : |R → |R is a 1-periodic, (Lipschitz-)continuous mapping. In the case of large gaps, we show that the normalized partial sums satisfy a large deviation principle at speed n and with a good rate function which is the same as in the case of independent and identically distributed random variables U(k), k ∈ |N, having uniform distribution on [0, 1]. When the lacunary sequence (a(k))k∈|N is a geometric progression, then we also obtain large deviation principles at speed n, but with a good rate function that is different from the independent case, its form depending in a subtle way on the interplay between the function f and the arithmetic properties of the gap sequence. Our work generalizes some results recently obtained by Aistleitner, Gantert, Kabluchko, Prochno, and Ramanan [Large deviation principles for lacunary sums, preprint, 2020] who initiated this line of research for the case of lacunary trigonometric sums
Müller-Brandeck-Bocquet, G. (Hrsg.) (2021). Deutsche Europapolitik von Adenauer bis Merkel.
No full textRezension zu: Müller-Brandeck-Bocquet, G. (Hrsg.) (2021). Deutsche
Europapolitik von Adenauer bis Merkel. Wiesbaden: Springer VS, 416 Seiten, ISBN: 978-3658353391
Four essays on statistical modelling of environmental data
No full textThis dissertation deals with geostatistical, time series, and regression analytical approaches for modelling spatio-temporal processes, using air quality data in the applications. The work is structured into four essays the abstracts of which are given in the following.
The first essay is titled 'Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany'. It is written in co-authorship by Prof. Dr. Harry Haupt and Dr. Angelika Schmid and published in 2018 in Spatial Statistics 28, pp. 331-351 (https://doi.org/10.1016/j.spasta.2018.04.004).
Abstract
Short-term predictions of air pollution require spatial modelling of trends, heterogeneities, and dependencies. Two-step methods allow real-time computations by separating spatial detrending and spatial extrapolation into two steps. Existing methods discuss trend models for specific environments and require specification search. Given more complex environments, specification search gets complicated by potential nonlinearities and heterogeneities. This research embeds a nonparametric trend modelling approach in real-time two-step methods. Form and complexity of trends are allowed to vary across heterogeneous environments. The proposed method avoids ad hoc specifications and potential generated predictor problems in previous contributions. Examining Belgian and German air quality and land use data, local trend patterns are investigated in a data driven way and are compared to results computed with existing methods and variations thereof. An important aspect of our empirical illustration is the heterogeneity and superior performance of local trend patterns for both research regions. The findings suggest that a nonparametric spatial trend modelling approach is a valuable tool for real-time predictions of pollution variables: it avoids specification search, provides useful exploratory insights and reduces computational costs.
The second essay is titled 'Predictability of hourly nitrogen dioxide concentration'. It is written in co-authorship with Prof. Dr. Harry Haupt and published in 2020 in Ecological Modelling 428, 109076 (https://doi.org/10.1016/j.ecolmodel.2020.109076).
Abstract
Temporal aggregation of air quality time series is typically used to investigate stylized facts of the underlying series such as multiple seasonal cycles. While aggregation reduces complexity, commonly used aggregates can suffer from non-representativeness or non-robustness. For example, definitions of specific events such as extremes are subjective and may be prone to data contaminations. The aim of this paper is to assess the predictability of hourly nitrogen dioxide concentrations and to explore how predictability depends on (i) level of temporal aggregation, (ii) hour of day, and (iii) concentration level. Exploratory tools are applied to identify structural patterns, problems related to commonly used aggregate statistics and suitable statistical modeling philosophies, capable of handling multiple seasonalities and non-stationarities. Hourly times series and subseries of daily measurements for each hour of day are used to investigate the predictability of pollutant levels for each hour of day, with prediction horizons ranging from one hour to one week ahead. Predictability is assessed by time series cross validation of a loss function based on out-of-sample prediction errors. Empirical evidence on hourly nitrogen dioxide measurements suggests that predictability strongly depends on conditions (i)-(iii) for all statistical models: for specific hours of day, models based on daily series outperform models based on hourly series, while in general predictability deteriorates with exposure level.
The third essay is titled 'Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations'. It is written in co-authorship with Dr. Markus Fritsch and published in 2021 in Atmospheric Environment 253, 118337 (https://doi.org/10.1016/j.atmosenv.2021.118337).
Abstract
Established land use regression (LUR) techniques such as linear regression utilize extensive selection of predictors and functional form to fit a model for every data set on a given pollutant. In this paper, an alternative to established LUR modeling is employed, which uses additive regression smoothers. Predictors and functional form are selected in a data-driven way and ambiguities resulting from specification search are mitigated. The approach is illustrated with nitrogen dioxide (NO2) data from German monitoring sites using the spatial predictors longitude, latitude, altitude and structural predictors; the latter include population density, land use classes, and road traffic intensity measures. The statistical performance of LUR modeling via additive regression smoothers is contrasted with LUR modeling based on parametric polynomials. Model evaluation is based on goodness of fit, predictive performance, and a diagnostic test for remaining spatial autocorrelation in the error terms.
Additionally, interpretation and counterfactual analysis for LUR modeling based on additive regression smoothers are discussed. Our results have three main implications for modeling air pollutant concentration levels: First, modeling via additive regression smoothers is supported by a specification test and exhibits superior in- and out-of-sample performance compared to modeling based on parametric polynomials. Second, different levels of prediction errors indicate that NO2 concentration levels observed at background and traffic/industrial monitoring sites stem from different processes. Third, accounting for agglomeration and infrastructure effects is important: NO2 concentration levels tend to increase around major cities, surrounding agglomeration areas, and their connecting road traffic network.
The fourth essay is titled 'Outlier detection and visualisation in multi-seasonal time series and its application to hourly nitrogen dioxide concentration'. It is written in single authorship and has not been published yet.
Abstract
Outlier detection in data on air pollutant recordings is conducted to uncover data points that refer to either invalid measurements or valid but unusually high concentration levels. As air pollutant data is typically characterised by multiple seasonalities, the task of outlier detection is associated with the question of how to deal with such non-stationarities. The present work proposes a method that combines time series segmentation, seasonal adjustment, and standardisation of random variables. While the former two are employed to obtain subseries of homoskedastic data, the latter ensures comparability across the subseries. Further, the standardised version of the seasonally adjusted subseries represents a scaled measure for the outlyingness of each data point in the original time series from its mean and therefore forms a suitable basis for outlier detection. In an empirical application to data on hourly NO2 concentration levels recorded at a traffic monitoring site in Cologne, Germany, over the years 2016 to 2019, the common boxplot criterion is used to examine each standardised seasonally adjusted subseries for positive outliers. The results of the analyses are put into their natural temporal order and displayed in a heatmap layout that provides information on when single and sequential outliers occur
From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction
No full textSentences that present a complex linguistic structure act as a major stumbling block for Natural Language Processing (NLP) applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easier to process, using a set of rewriting operations, such as reordering, deletion or splitting. These transformations are executed with the objective of converting the input into a simplified output, while preserving its main idea and keeping it grammatically sound. State-of-the-art syntactic TS approaches suffer from two major drawbacks: first, they follow a very conservative approach in that they tend to retain the input rather than transforming it, and second, they ignore the cohesive nature of texts, where context spread across clauses or sentences is needed to infer the true meaning of a statement. To address these problems, we present a discourse-aware TS framework that is able to split and rephrase complex English sentences within the semantic context in which they occur. By generating a fine-grained output with a simple canonical structure that is easy to analyze by downstream applications, we tackle the first issue. For this purpose, we decompose a source sentence into smaller units by using a linguistically grounded transformation stage. The result is a set of selfcontained propositions, with each of them presenting a minimal semantic unit. To address the second concern, we suggest not only to split the input into isolated sentences, but to also incorporate the semantic context in the form of hierarchical structures and semantic relationships between the split propositions. In that way, we generate a semantic hierarchy of minimal propositions that benefits downstream Open Information Extraction (IE) tasks. To function well, the TS approach that we propose requires syntactically well-formed input sentences. It targets generalpurpose texts in English, such as newswire or Wikipedia articles, which commonly contain a high proportion of complex assertions.
In a second step, we present a method that allows state-of-the-art Open IE systems to leverage the semantic hierarchy of simplified sentences created by our discourseaware TS approach in constructing a lightweight semantic representation of complex assertions in the form of semantically typed predicate-argument structures. In that way, important contextual information of the extracted relations is preserved that allows for a proper interpretation of the output. Thus, we address the problem of extracting incomplete, uninformative or incoherent relational tuples that is commonly to be observed in existing Open IE approaches. Moreover, assuming that shorter sentences with a more regular structure are easier to process, the extraction of relational tuples is facilitated, leading to a higher coverage and accuracy of the extracted relations when operating on the simplified sentences. Aside from taking advantage of the semantic hierarchy of minimal propositions in existing Open IE Abstract approaches, we also develop an Open IE reference system, Graphene. It implements a relation extraction pattern upon the simplified sentences.
The framework we propose is evaluated within our reference TS implementation DisSim. In a comparative analysis, we demonstrate that our approach outperforms the state of the art in structural TS both in an automatic and a manual analysis. It obtains the highest score on three simplification datasets from two different domains with regard to SAMSA (0.67, 0.57, 0.54), a recently proposed metric targeted at automatically measuring the syntactic complexity of sentences which highly correlates with human judgments on structural simplicity and grammaticality. These findings are supported by the ratings from the human evaluation, which indicate that our baseline implementation DisSim returns fine-grained simplified sentences that achieve a high level of syntactic correctness and largely preserve the meaning of the input. Furthermore, a comparative analysis with the annotations contained in the RST Discourse Treebank (RST-DT) reveals that we are able to capture the contextual hierarchy between the split sentences with a precision of approximately 90% and reach an average precision of almost 70% for the classification of the rhetorical relations that hold between them. Finally, an extrinsic evaluation shows that when applying our TS framework as a pre-processing step, the performance of state-ofthe-art Open IE systems can be improved by up to 32% in precision and 30% in recall of the extracted relational tuples.
Accordingly, we can conclude that our proposed discourse-aware TS approach succeeds in transforming sentences that present a complex linguistic structure into a sequence of simplified sentences that are to a large extent grammatically correct, represent atomic semantic units and preserve the meaning of the input. Moreover, the evaluation provides sufficient evidence that our framework is able to establish a semantic hierarchy between the split sentences, generating a fine-grained representation of complex assertions in the form of hierarchically ordered and semantically interconnected propositions. Finally, we demonstrate that state-of-the-art Open IE systems benefit from using our TS approach as a pre-processing step by increasing both the accuracy and coverage of the extracted relational tuples for the majority of the Open IE approaches under consideration. In addition, we outline that the semantic hierarchy of simplified sentences can be leveraged to enrich the output of existing Open IE systems with additional meta information, thus transforming the shallow semantic representation of state-of-the-art approaches into a canonical context-preserving representation of relational tuples
Multi-objective Network Virtualization and its Applicability to Industrial Networks
No full textNetwork virtualization provides high flexibility for deploying communication services in dense and heterogeneous environments. Two main approaches (dimensions) that are usually combined exist: Network Function Virtualization (NFV) technologies for functionality virtualization and Virtual Network Embedding (VNE) algorithms for resource virtualization. These approaches can be applied to different network levels, such as factory and enterprise levels of industrial networks. Several objectives and constraints, that might be conflicting, shall be considered when network virtualization is applied, mainly in complex topologies. This thesis proposes a network virtualization model that considers both virtualization dimensions, two network levels, and different objectives and constraints. The network levels considered are two primary levels in industrial networks. However, this consideration does not restrict the model to a particular environment or certain levels. The considered objectivities/constraints are topology, reliability, security, performance, and resource usage.
Based on this model, we first build an overall combined solution for autonomic and composite virtual networking. This solution considers both virtualization dimensions, two network levels, and target objectives. Furthermore, this solution combines three novel virtualization sub-approaches that consider performance, reliability, and performance. However, the sub-approaches apply to different combinations of levels and dimensions, and the reliability approach additionally considers the resource usage objective. After presenting all solutions, we map them to the defined model.
Regarding applicability to industrial networks, the combined approach is applied to an enterprise-level Industrial Internet of Things (IIoT) use case inspired by the smart factory concept in Industry 4.0. However, the sub-approaches are applied to more specific use cases. The performance and reliability solutions are integrated with relevant components of the Time Sensitive Networks (TSN) standard as a modern technology for industrial networks. The goal is to enrich the reliability and performance capabilities of TSN with the flexibility of network virtualization.
In the combined approach, we compose and embed an environment-aware Extended Virtual Network (EVN) that represents the physical devices, virtual application functions, and required Service Function Chains (SFCs). We use the graph transformation method to transform abstract application requirements (represented by an Application Request (AR)) into an EVN. Both EVN composition and embedding methods consider the Substrate Network (SN) topology and different security, reliability, performance, and resource usage policies. These policies are applied with a certain priority and depend on the properties of communicating entities such as location and type. The EVN is embedded using property-based node mapping, reliability-aware branching, and a greedy chain embedding heuristic. The chain embedding heuristic is evaluated using a random topology that represents the use case.
The performance sub-approach is NFV-based and is applied to a specific use case with Time-critical Traffic (TCT) flows. We develop and evaluate a complete framework for virtualizing Time-aware Shaper (TAS) using high-performance NFV. The reliability sub-approach is VNE-based and is applied to a specific factory level use case. We develop minimal and maximal branching heuristics based on a reliability-aware k-shortest path algorithm and compare them using a typical factory topology. We then integrate these algorithms with a Frame Replication and Elimination for Reliability (FRER) simulator to realize reliability policies by the autonomic and efficient configuration of a supporting technology.
The security sub-approaches are related to both virtualization dimensions and are applied to generic enterprise-level use cases. However, the applicability of the security aspect to industrial networks is only shown in the combined (EVN) approach and its use case. We research the autonomic security management in Network Function Virtualization Infrastructure (NFVI) with the main goal of early reaction to threats through SFC reconfiguration through Virtual Network Function (VNF) live migration. This goal is approached by supporting the security measurements with a decision making architecture that considers, on the one hand, the threats and events in the environment and, on the other hand, the Service Level Agreement (SLA) between the NFVI provider and user. For this purpose, we classify the VNF-specific attacks and define possible early detectable behavior patterns. Finally, we develop a security-aware VNE heuristic that considers the security requirements of the Virtual Network (VN) and the security capabilities of the SN. This approach is modified in the combined approach to consider deploying virtualized security VNFs
Lehrerpersönlichkeit durch Selbstwirksamkeit
No full textForschungsfrage:
Im Zentrum der Forschungsbemühungen steht die Lehrerpersönlichkeit, welche durch die Selbstwirksamkeit als Eigenschaft beschrieben werden soll.
Methodik:
Als Methode wird die qualitative Analyse der Hermeneutik verwendet. Dabei werden Begriffe und Modelle zur Lehrerpersönlichkeit und Selbstwirksamkeit erläutert und in ihrer Bedeutung für das pädagogische Handeln erschlossen.
Ergebnis:
Selbstwirksamkeit ist eine unbedingt notwendige persönliche Ressource, die regulierend auf kognitive, emotionale, motivationale und selektive Prozesse einwirkt und Entwicklungsprozesse unterstützt. Dennoch sind der Selbstwirksamkeit Grenzen gesetzt, so dass diese nicht als hinreichend für die Beschreibung der gesamten Lehrerpersönlichkeit angesehen werden kann. Wie die zunehmende Beanspruchung auf die Lehrerpersönlichkeit wirkt, wird durch die Belastungsforschung dargelegt. Abgesehen von begrifflichen Kontroversen ist die berufliche Selbstwirksamkeit entscheidend für die Bewältigung schwieriger Situationen. Auch in der Lehrerbildung wird die Bedeutung der Lehrerselbstwirksamkeit hervorgehoben. Neben Einflussmöglichkeiten im schulischen Rahmen stehen Ansatzpunkte im Mittelpunkt, die bei der Lehrerpersönlichkeit ansetzen und durch sie aktiv unterstützt werden können. Zudem wird versucht, aus dem Erkenntnisgewinn Konsequenzen für die Rekrutierung und Ausbildung der Lehramtsstudenten abzuleiten und bildungspolitische Ansprüche zu formulieren, da ein gewisses Maß an Selbstwirksamkeit zu den Mindestvoraussetzungen für den beruflichen Erfolgt gehört