@misc{109, author = {{Pauck, Felix}}, publisher = {{Universität Paderborn}}, title = {{{Cooperative static analysis of Android applications}}}, year = {{2017}}, } @article{1098, abstract = {{An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.}}, author = {{Kim, Yeong-Su and Lee, Seung-Woo and Dollmann, Markus and Geierhos, Michaela}}, issn = {{2205-8494}}, journal = {{International Journal of Software Engineering for Smart Device}}, keywords = {{Natural Language Processing, Semantic Annotation, Machine Learning}}, number = {{2}}, pages = {{1--6}}, publisher = {{Global Vision School Publication}}, title = {{{Semantic Annotation of Software Requirements with Language Frame}}}, volume = {{4}}, year = {{2017}}, } @article{110, abstract = {{We consider an extension of the dynamic speed scaling scheduling model introduced by Yao et al.: A set of jobs, each with a release time, deadline, and workload, has to be scheduled on a single, speed-scalable processor. Both the maximum allowed speed of the processor and the energy costs may vary continuously over time. The objective is to find a feasible schedule that minimizes the total energy costs. Theoretical algorithm design for speed scaling problems often tends to discretize problems, as our tools in the discrete realm are often better developed or understood. Using the above speed scaling variant with variable, continuous maximal processor speeds and energy prices as an example, we demonstrate that a more direct approach via tools from variational calculus can not only lead to a very concise and elegant formulation and analysis, but also avoids the “explosion of variables/constraints” that often comes with discretizing. Using well-known tools from calculus of variations, we derive combinatorial optimality characteristics for our continuous problem and provide a quite concise and simple correctness proof.}}, author = {{Antoniadis, Antonios and Kling, Peter and Ott, Sebastian and Riechers, Sören}}, journal = {{Theoretical Computer Science}}, pages = {{1--13}}, publisher = {{Elsevier}}, title = {{{Continuous Speed Scaling with Variability: A Simple and Direct Approach}}}, doi = {{10.1016/j.tcs.2017.03.021}}, year = {{2017}}, } @misc{117, author = {{Bemmann, Pascal}}, publisher = {{Universität Paderborn}}, title = {{{Attribute-based Signatures using Structure Preserving Signatures}}}, year = {{2017}}, } @misc{118, author = {{Chi Banh, Ngoc}}, publisher = {{Universität Paderborn}}, title = {{{An Asynchronous Adaptation of a Churn-resistant Overlay Network}}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @misc{119, author = {{Wever, Marcel Dominik}}, publisher = {{Universität Paderborn}}, title = {{{Active Learning of User Requirement Specifications in Dynamic Software Service Markets}}}, year = {{2017}}, } @inproceedings{120, abstract = {{Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}}, pages = {{1327----1334}}, title = {{{Active Coevolutionary Learning of Requirements Specifications from Examples}}}, doi = {{10.1145/3071178.3071258}}, year = {{2017}}, } @inproceedings{121, abstract = {{Research on ad copy design is well-studied in the context of offline marketing. However, researchers have only recently started to investigate ad copies in the context of paid search, and have not yet explored the potential of information cues to enhance customers’ search process. In this paper we analyze the impact of an information cue on user behavior in ad copies. Contrary to prevalent advice, results suggest that reducing the number of words in an ad is not always beneficial. Users act quite differently (and unexpectedly) in response to an information cue depending on their search phrases. In turn, practitioners could leverage the observed moderating effect of an information cue to enhance paid search success. Furthermore, having detected deviating user behavior in terms of clicks and conversions, we provide first indicative evidence of a self-selection mechanism at play when paid search users respond to differently phrased ad copies.}}, author = {{Schlangenotto, Darius and Kundisch, Dennis}}, booktitle = {{Proceedings of the 50th annual Hawaii International Conference on System Sciences (HICSS), Waikoloa Village, HI, USA}}, title = {{{Achieving more by saying less? On the Moderating Effect of Information Cues in Paid Search}}}, year = {{2017}}, } @inproceedings{122, abstract = {{Current research on paid search highlights its ability to enhance both online and offline conversions. Yet, research investigating the impact of placing paid search ads on less prominent positions on subsequent consumer behavior is limited to the online environment. This paper presents a field experiment using differences-in-differences analysis to investigate whether the targeting of a less prominent ad position can be beneficial for bricks-and-mortar retailers. Results indicate that paid search advertising budgets could be allocated more efficiently by targeting less prominent ad positions, thus allowing bricks-and-mortar retailers with a limited marketing budget to increase the reach of their marketing campaign, attract more consumers to their website and achieve an overall increase in conversions. Furthermore, the pay-per-click billing mechanism allows advertisers to increase their marketing reach at no additional cost. Consequently, bricks-and-mortar retailers should consider targeting less prominent ad positions to reduce advertising costs while simultaneously enhancing advertising benefits.}}, author = {{Schlangenotto, Darius and Kundisch, Dennis and Gutt, Dominik}}, booktitle = {{Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea}}, location = {{Seoul, South Korea}}, title = {{{Achieving More by Paying Less? How Bricks-and-Mortar Retailers Can Benefit by Bidding Less Aggressively in Paid Search}}}, year = {{2017}}, } @techreport{123, author = {{Jazayeri, Bahar and Zimmermann, Olaf and Engels, Gregor and Kundisch, Dennis}}, publisher = {{Universität Paderborn}}, title = {{{A Variability Model for Store-oriented Software Ecosystems: An Enterprise Perspective (Supplementary Material)}}}, year = {{2017}}, } @inproceedings{124, abstract = {{Pioneers of today’s software industry like Salesforce and Apple have established successful ecosystems around their software platforms. Architectural knowledge of the existing ecosystems is implicit and fragmented among online documentation. In protection of intellectual property, existing documentation hardly reveals influential business strategies that affect the ecosystem structure. Thus, other platform providers can hardly learn from the existing ecosystems in order to systematically make reasonable design decisions with respect to their business strategies to create their own ecosystems. In this paper, we identify a variability model for architectural design decisions of a store-oriented software ecosystem product line from an enterprise perspective, comprising business, application, and infrastructure views. We derive the variability model from fragmentary material of existing ecosystems and a rigorous literature review using a research method based on the design science paradigm. To show its validity, we describe real-world ecosystems from diverse domains using the variability model. This knowledge helps platform providers to develop customized ecosystems or to recreate existing designs in a systematic way. This, in turn, contributes to an increase in designer and developer productivity.}}, author = {{Jazayeri, Bahar and Zimmermann, Olaf and Engels, Gregor and Kundisch, Dennis}}, booktitle = {{Proceedings of the 15th International Conference on Service Oriented Computing (ICSOC)}}, publisher = {{Springer}}, title = {{{A Variability Model for Store-oriented Software Ecosystems: An Enterprise Perspective}}}, doi = {{10.1007/978-3-319-69035-3_42}}, volume = {{10601}}, year = {{2017}}, } @inproceedings{125, abstract = {{Searching for other participants is one of the most important operations in a distributed system.We are interested in topologies in which it is possible to route a packet in a fixed number of hops until it arrives at its destination.Given a constant $d$, this paper introduces a new self-stabilizing protocol for the $q$-ary $d$-dimensional de Bruijn graph ($q = \sqrt[d]{n}$) that is able to route any search request in at most $d$ hops w.h.p., while significantly lowering the node degree compared to the clique: We require nodes to have a degree of $\mathcal O(\sqrt[d]{n})$, which is asymptotically optimal for a fixed diameter $d$.The protocol keeps the expected amount of edge redirections per node in $\mathcal O(\sqrt[d]{n})$, when the number of nodes in the system increases by factor $2^d$.The number of messages that are periodically sent out by nodes is constant.}}, author = {{Feldmann, Michael and Scheideler, Christian}}, booktitle = {{Proceedings of the 19th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS)}}, isbn = {{978-3-319-69083-4}}, pages = {{250--264 }}, publisher = {{Springer, Cham}}, title = {{{A Self-Stabilizing General De Bruijn Graph}}}, doi = {{10.1007/978-3-319-69084-1_17}}, volume = {{10616}}, year = {{2017}}, } @inproceedings{126, abstract = {{Optimal price setting in peer-to-peer markets featuring online ratings requires incorporating interactions between prices and ratings. Additionally, recent literature reports that online ratings in peer-to-peer markets tend to be inflated overall, undermining the reliability of online ratings as a quality signal. This study proposes a two-period model for optimal price setting that takes (potentially inflated) ratings into account. Our theoretical findings suggest that sellers in the medium-quality segment have an incentive to lower first-period prices to monetize on increased second-period ratings and that the possibility on monetizing on second-period ratings depends on the reliability of the rating system. Additionally, we find that total profits and prices increase with online ratings and additional quality signals. Empirically, conducting Difference-in-Difference regressions on a comprehensive panel data set from Airbnb, we can validate that price increases lead to lower ratings, and we find empirical support for the prediction that additional quality signals increase prices. Our work comes with substantial implications for sellers in peer-to-peer markets looking for an optimal price setting strategy. Moreover, we argue that our theoretical finding on the weights between online ratings and additional quality signals translates to conventional online markets.}}, author = {{Neumann, Jürgen and Gutt, Dominik}}, booktitle = {{Proceedings of the 25th Conference on Information Systems (ECIS)}}, location = {{Guimaraes, Portugal}}, title = {{{A Homeowner’s Guide to Airbnb: Theory and Empirical Evidence for Optimal Pricing Conditional on Online Ratings}}}, year = {{2017}}, } @misc{100, author = {{Sergio Djoum Temdjim, Albin}}, publisher = {{Universität Paderborn}}, title = {{{Evaluation of Software Reputation Matching Based on App Reviews}}}, year = {{2017}}, } @misc{101, author = {{Rehmer, Lennart}}, publisher = {{Universität Paderborn}}, title = {{{Erweiterung eines kontextsensitiven Autovervollständigungstools zur natürlichsprachlichen Softwarespezifikation}}}, year = {{2017}}, } @phdthesis{102, author = {{Becker, Matthias}}, publisher = {{Universität Paderborn}}, title = {{{Engineering Self-Adaptive Systems with Simulation-Based Performence Prediction}}}, doi = {{10.17619/UNIPB/1-133}}, year = {{2017}}, } @misc{5954, author = {{N, N}}, publisher = {{Universität Paderborn}}, title = {{{Multi-Dimensional Bargaining Problem and Nash Solution - A procedural approach}}}, year = {{2017}}, } @misc{5957, author = {{N, N}}, publisher = {{Universität Paderborn}}, title = {{{Game Theory solutions for the Transshipment Problem in Logistic Networks}}}, year = {{2017}}, } @misc{5955, author = {{N, N}}, publisher = {{Universität Paderborn}}, title = {{{Bitcoin, Ethereum & Co. Was zeichnet eine gute Kryptowährung aus?}}}, year = {{2017}}, }