@inproceedings{48304,
  author       = {{Daxenberger, Johannes and Eger, Steffen and Habernal, Ivan and Stab, Christian and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 2017 Conference on Empirical Methods in Natural          Language Processing}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{What is the Essence of a Claim? Cross-Domain Claim Identification}}},
  doi          = {{10.18653/v1/d17-1218}},
  year         = {{2018}},
}

@inproceedings{6859,
  abstract     = {{Signal processing in WASNs is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. 
In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented.}},
  author       = {{Afifi, Haitham and Schmalenstroeer, Joerg and Ullmann, Joerg and Haeb-Umbach, Reinhold and Karl, Holger}},
  booktitle    = {{Speech Communication; 13th ITG-Symposium}},
  pages        = {{1--5}},
  title        = {{{MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks}}},
  year         = {{2018}},
}

@inproceedings{46350,
  abstract     = {{The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates.}},
  author       = {{van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike}},
  booktitle    = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}},
  pages        = {{524–540}},
  publisher    = {{Springer}},
  title        = {{{Accurate WiFi based indoor positioning with continuous location sampling}}},
  year         = {{2018}},
}

@article{46351,
  abstract     = {{Clustering is an important field in data mining that aims to reveal hidden patterns in data sets. It is widely popular in marketing or medical applications and used to identify groups of similar objects. Clustering possibly unbounded and evolving data streams is of particular interest due to the widespread deployment of large and fast data sources such as sensors. The vast majority of stream clustering algorithms employ a two-phase approach where the stream is first summarized in an online phase. Upon request, an offline phase reclusters the aggregations into the final clusters. In this setup, the online component will idle and wait for the next observation in times where the stream is slow. This paper proposes a new stream clustering algorithm called evoStream which performs evolutionary optimization in the idle times of the online phase to incrementally build and refine the final clusters. Since the online phase would idle otherwise, our approach does not reduce the processing speed while effectively removing the computational overhead of the offline phase. In extensive experiments on real data streams we show that the proposed algorithm allows to output clusters of high quality at any time within the stream without the need for additional computational resources.}},
  author       = {{Carnein, Matthias and Trautmann, Heike}},
  journal      = {{Big Data Research}},
  pages        = {{101–111}},
  title        = {{{evoStream — Evolutionary Stream Clustering Utilizing Idle Times}}},
  doi          = {{10.1016/j.bdr.2018.05.005}},
  volume       = {{14}},
  year         = {{2018}},
}

@article{46353,
  abstract     = {{Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.}},
  author       = {{Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}},
  journal      = {{Swarm and Evolutionary Computation}},
  pages        = {{196–215}},
  title        = {{{Multiobjective evolutionary algorithms based on target region preferences}}},
  doi          = {{10.1016/j.swevo.2018.02.006}},
  volume       = {{40}},
  year         = {{2018}},
}

@inproceedings{48839,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  keywords     = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}},
  pages        = {{585–592}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@inproceedings{48867,
  abstract     = {{Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.}},
  author       = {{Bossek, Jakob}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{evolutionary optimization, performance assessment, software-tools}},
  pages        = {{1350–1356}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}}},
  doi          = {{10.1145/3205651.3208312}},
  year         = {{2018}},
}

@inproceedings{48885,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-5764-7}},
  keywords     = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}},
  pages        = {{1737–1744}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}

@book{48880,
  author       = {{Grimme, Christian and Bossek, Jakob}},
  isbn         = {{978-3-658-21150-9}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Einführung in die Optimierung - Konzepte, Methoden und Anwendungen}}},
  doi          = {{10.1007/978-3-658-21151-6}},
  year         = {{2018}},
}

@article{48884,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  issn         = {{1063-6560}},
  journal      = {{Evolutionary Computation}},
  keywords     = {{automated algorithm selection, machine learning., performance modeling, Travelling Salesperson Problem}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@article{48866,
  abstract     = {{Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528}},
  author       = {{Bossek, Jakob}},
  issn         = {{2475-9066}},
  journal      = {{Journal of Open Source Software}},
  number       = {{22}},
  pages        = {{528}},
  title        = {{{Grapherator: A Modular Multi-Step Graph Generator}}},
  doi          = {{10.21105/joss.00528}},
  volume       = {{3}},
  year         = {{2018}},
}

@inproceedings{47253,
  author       = {{Wu, Yuxi and Gupta, Panya and Wei, Miranda and Acar, Yasemin and Fahl, Sascha and Ur, Blase}},
  booktitle    = {{Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18}},
  publisher    = {{ACM Press}},
  title        = {{{Your Secrets Are Safe}}},
  doi          = {{10.1145/3178876.3186088}},
  year         = {{2018}},
}

@inproceedings{47255,
  author       = {{Haney, Julie M. and Theofanos, Mary and Acar, Yasemin and Prettyman, Sandra Spickard}},
  booktitle    = {{Fourteenth Symposium on Usable Privacy and Security, SOUPS 2018, Baltimore, MD, USA, August 12-14, 2018}},
  editor       = {{Zurko, Mary Ellen and Lipford, Heather Richter}},
  pages        = {{357–373}},
  publisher    = {{USENIX Association}},
  title        = {{{"We make it a big deal in the company": Security Mindsets in Organizations that Develop Cryptographic Products}}},
  year         = {{2018}},
}

@techreport{47876,
  author       = {{Haney, Julie and Theofanos, Mary and Acar, Yasemin and Prettyman, Sandra Spickard}},
  publisher    = {{National Institute of Standards and Technology}},
  title        = {{{Organizational views of NIST cryptographic standards and testing and validation programs}}},
  doi          = {{10.6028/nist.ir.8241}},
  year         = {{2018}},
}

@inproceedings{47256,
  author       = {{Gorski, Peter Leo and Iacono, Luigi Lo and Wermke, Dominik and Stransky, Christian and Möller, Sebastian and Acar, Yasemin and Fahl, Sascha}},
  booktitle    = {{Fourteenth Symposium on Usable Privacy and Security, SOUPS 2018, Baltimore, MD, USA, August 12-14, 2018}},
  editor       = {{Zurko, Mary Ellen and Lipford, Heather Richter}},
  pages        = {{265–281}},
  publisher    = {{USENIX Association}},
  title        = {{{Developers Deserve Security Warnings, Too: On the Effect of Integrated Security Advice on Cryptographic API Misuse}}},
  year         = {{2018}},
}

@inproceedings{47254,
  author       = {{Oltrogge, Marten and Derr, Erik and Stransky, Christian and Acar, Yasemin and Fahl, Sascha and Rossow, Christian and Pellegrino, Giancarlo and Bugiel, Sven and Backes, Michael}},
  booktitle    = {{2018 IEEE Symposium on Security and Privacy (SP)}},
  publisher    = {{IEEE}},
  title        = {{{The Rise of the Citizen Developer: Assessing the Security Impact of Online App Generators}}},
  doi          = {{10.1109/sp.2018.00005}},
  year         = {{2018}},
}

@inproceedings{47252,
  author       = {{Wermke, Dominik and Huaman, Nicolas and Acar, Yasemin and Reaves, Bradley and Traynor, Patrick and Fahl, Sascha}},
  booktitle    = {{Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018}},
  pages        = {{222–235}},
  publisher    = {{ACM}},
  title        = {{{A Large Scale Investigation of Obfuscation Use in Google Play}}},
  doi          = {{10.1145/3274694.3274726}},
  year         = {{2018}},
}

@inproceedings{46348,
  abstract     = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Guenter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-5618-3}},
  pages        = {{585–592}},
  publisher    = {{ACM}},
  title        = {{{Local Search Effects in Bi-Objective Orienteering}}},
  doi          = {{10.1145/3205455.3205548}},
  year         = {{2018}},
}

@article{46352,
  abstract     = {{The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.}},
  author       = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}},
  journal      = {{Evolutionary Computation (ECJ)}},
  number       = {{4}},
  pages        = {{597–620}},
  title        = {{{Leveraging TSP Solver Complementarity through Machine Learning}}},
  doi          = {{10.1162/evco_a_00215}},
  volume       = {{26}},
  year         = {{2018}},
}

@inproceedings{46349,
  abstract     = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}},
  author       = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion}},
  isbn         = {{978-1-4503-5764-7/18/07}},
  pages        = {{1737–1744}},
  title        = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}},
  doi          = {{10.1145/3205651.3208233}},
  year         = {{2018}},
}