@inproceedings{13557, abstract = {{We present a searchable encryption scheme for dynamic document collections in a multi-user scenario. Our scheme features fine-grained access control to search results, as well as access control to operations such as adding documents to the document collection, or changing individual documents. The scheme features verifiability of search results. Our scheme also satisfies the forward privacy notion crucial for the security of dynamic searchable encryption schemes.}}, author = {{Blömer, Johannes and Löken, Nils}}, booktitle = {{12th International Symposium on Foundations and Practice of Security, FPS 2019}}, publisher = {{Springer}}, title = {{{Dynamic Searchable Encryption with Access Control}}}, volume = {{12056}}, year = {{2019}}, } @misc{13592, author = {{Pilot, Matthias}}, publisher = {{Universität Paderborn}}, title = {{{Efficient Finite-Field Arithmetic for Elliptic Curve Cryptography in Java}}}, year = {{2019}}, } @misc{13648, author = {{Scholz, Swante}}, publisher = {{Universität Paderborn}}, title = {{{Implementation and Comparison of Elliptic Curve Algorithms in Java}}}, year = {{2019}}, } @inproceedings{13652, author = {{Hinnenthal, Kristian and Scheideler, Christian and Struijs, Martijn}}, booktitle = {{33rd International Symposium on Distributed Computing (DISC 2019)}}, title = {{{Fast Distributed Algorithms for LP-Type Problems of Low Dimension}}}, doi = {{10.4230/LIPICS.DISC.2019.23}}, year = {{2019}}, } @phdthesis{13679, author = {{Brauer, Sascha}}, title = {{{Classification and Approximation of Geometric Location Problems}}}, doi = {{10.17619/UNIPB/1-816}}, year = {{2019}}, } @inproceedings{7626, author = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric}}, booktitle = {{Proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2019), Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2019)}}, location = {{Prague, Czech Republic}}, pages = {{393--410}}, title = {{{PhASAR: An Inter-Procedural Static Analysis Framework for C/C++}}}, doi = {{10.1007/978-3-030-17465-1_22}}, volume = {{II}}, year = {{2019}}, } @inproceedings{31067, author = {{Guettatfi, Zakarya and Platzner, Marco and Kermia, Omar and Khouas, Abdelhakim}}, booktitle = {{2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, publisher = {{IEEE}}, title = {{{An Approach for Mapping Periodic Real-Time Tasks to Reconfigurable Hardware}}}, doi = {{10.1109/ipdpsw.2019.00027}}, year = {{2019}}, } @inproceedings{15332, abstract = {{Artificial intelligence (AI) has the potential for far-reaching – in our opinion – irreversible changes. They range from effects on the individual and society to new societal and social issues. The question arises as to how students can learn the basic functioning of AI systems, what areas of life and society are affected by these and – most important – how their own lives are affected by these changes. Therefore, we are developing and evaluating school materials for the German ”Science Year AI”. It can be used for students of all school types from the seventh grade upwards and will be distributed to about 2000 schools in autumn with the support of the Federal Ministry of Education and Research. The material deals with the following aspects of AI: Discussing everyday experiences with AI, how does machine learning work, historical development of AI concepts, difference between man and machine, future distribution of roles between man and machine, in which AI world do we want to live and how much AI would we like to have in our lives. Through an accompanying evaluation, high quality of the technical content and didactic preparation is achieved in order to guarantee the long-term applicability in the teaching context in the different age groups and school types. In this paper, we describe the current state of the material development, the challenges arising, and the results of tests with different classes to date. We also present first ideas for evaluating the results.}}, author = {{Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte, Carsten}}, booktitle = {{ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings}}, editor = {{Jasutė, Eglė and Pozdniakov, Sergei}}, isbn = {{978-9925-553-27-3}}, keywords = {{Artificial Intelligence, Machine Learning, Teaching Material, Societal Aspects, Ethics. Social Aspects, Science Year, Simulation Game}}, location = {{Lanarca}}, pages = {{65 -- 73}}, title = {{{Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material}}}, volume = {{12}}, year = {{2019}}, } @inproceedings{15643, author = {{Opel, Simone Anna and Schlichtig, Michael and Schulte, Carsten}}, booktitle = {{WiPSCE}}, pages = {{11:1--11:2}}, publisher = {{ACM}}, title = {{{Developing Teaching Materials on Artificial Intelligence by Using a Simulation Game (Work in Progress)}}}, year = {{2019}}, } @inproceedings{13259, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Hagen, Matthias and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom}}, pages = {{76--82}}, title = {{{Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition}}}, year = {{2019}}, } @inproceedings{13904, abstract = {{In this paper, we introduce updatable anonymous credential systems (UACS) and use them to construct a new privacy-preserving incentive system. In a UACS, a user holding a credential certifying some attributes can interact with the corresponding issuer to update his attributes. During this, the issuer knows which update function is run, but does not learn the user's previous attributes. Hence the update process preserves anonymity of the user. One example for a class of update functions are additive updates of integer attributes, where the issuer increments an unknown integer attribute value v by some known value k. This kind of update is motivated by an application of UACS to incentive systems. Users in an incentive system can anonymously accumulate points, e.g. in a shop at checkout, and spend them later, e.g. for a discount.}}, author = {{Blömer, Johannes and Bobolz, Jan and Diemert, Denis Pascal and Eidens, Fabian}}, booktitle = {{Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security - CCS '19}}, location = {{London}}, title = {{{Updatable Anonymous Credentials and Applications to Incentive Systems}}}, doi = {{10.1145/3319535.3354223}}, year = {{2019}}, } @inproceedings{46343, abstract = {{This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets.}}, author = {{Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}}, editor = {{Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}}, pages = {{126–138}}, publisher = {{Springer}}, title = {{{Multimodality in Multi-Objective Optimization — More Boon than Bane?}}}, doi = {{10.1007/978-3-030-12598-1_11}}, volume = {{11411}}, year = {{2019}}, } @article{46345, abstract = {{It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.}}, author = {{Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}}, journal = {{Evolutionary Computation (ECJ)}}, number = {{1}}, pages = {{3–45}}, title = {{{Automated Algorithm Selection: Survey and Perspectives}}}, doi = {{10.1162/evco_a_00242}}, volume = {{27}}, year = {{2019}}, } @article{46344, abstract = {{Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, journal = {{Business and Information Systems Engineering (BISE)}}, number = {{3}}, pages = {{277–297}}, title = {{{Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}}}, volume = {{61}}, year = {{2019}}, } @inproceedings{46340, abstract = {{Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers.}}, author = {{Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}}, booktitle = {{Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}}, pages = {{65–74}}, title = {{{A Recommender System Based on Omni-Channel Customer Data}}}, year = {{2019}}, } @inproceedings{46341, abstract = {{Customer Segmentation aims to identify groups of customers that share similar interest or behaviour. It is an essential tool in marketing and can be used to target customer segments with tailored marketing strategies. Customer segmentation is often based on clustering techniques. This analysis is typically performed as a snapshot analysis where segments are identified at a specific point in time. However, this ignores the fact that customer segments are highly volatile and segments change over time. Once segments change, the entire analysis needs to be repeated and strategies adapted. In this paper we explore stream clustering as a tool to alleviate this problem. We propose a new stream clustering algorithm which allows to identify and track customer segments over time. The biggest challenge is that customer segmentation often relies on the transaction history of a customer. Since this data changes over time, it is necessary to update customers which have already been incorporated into the clustering. We show how to perform this step incrementally, without the need for periodic re-computations. As a result, customer segmentation can be performed continuously, faster and is more scalable. We demonstrate the performance of our algorithm using a large real-life case study.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, booktitle = {{Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)}}, pages = {{280–292}}, title = {{{Customer Segmentation Based on Transactional Data Using Stream Clustering}}}, year = {{2019}}, } @inproceedings{46342, abstract = {{There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures.}}, author = {{Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}}, booktitle = {{AIP Conference Proceedings}}, pages = {{020052--1--020052--4}}, publisher = {{AIP Publishing}}, title = {{{Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}}}, doi = {{10.1063/1.5090019}}, year = {{2019}}, } @inbook{46336, abstract = {{Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called black-box problems, and function evaluations are considered to be expensive. In case of continuous single-objective optimization problems, exploratory landscape analysis (ELA), a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself, is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been—if at all—spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related algorithm selection problem (ASP), it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package’s built-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI). It facilitates the usage of the package—especially for people who are not familiar with R—and thus makes flacco a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.}}, author = {{Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Applications in Statistical Computing}}, editor = {{Bauer, Nadja and Ickstadt, Katja and Lübke, Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}}, pages = {{93–123}}, publisher = {{Springer}}, title = {{{Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco}}}, doi = {{10.1007/978-3-030-25147-5_7}}, year = {{2019}}, } @book{46335, author = {{Trautmann, Heike}}, isbn = {{978-3-030-25147-5}}, publisher = {{Springer International Publishing}}, title = {{{Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement}}}, year = {{2019}}, } @article{46346, abstract = {{In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.}}, author = {{Kerschke, Pascal and Trautmann, Heike}}, journal = {{Evolutionary Computation (ECJ)}}, number = {{1}}, pages = {{99–127}}, title = {{{Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning}}}, doi = {{10.1162/evco_a_00236}}, volume = {{27}}, year = {{2019}}, }