@inproceedings{16400, abstract = {{Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration.}}, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft) Demo Track}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Cloud-Native Threat Detection and Containment for Smart Manufacturing}}}, year = {{2020}}, } @inproceedings{13868, author = {{Pukrop, Simon and Mäcker, Alexander and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)}}, title = {{{Approximating Weighted Completion Time for Order Scheduling with Setup Times}}}, year = {{2020}}, } @article{10330, author = {{Kiesel, Dora and Riehmann, Patrick and Wachsmuth, Henning and Stein, Benno and Fröhlich, Bernd}}, journal = {{IEEE Transactions of Visualization & Computer Graphics}}, number = {{2}}, pages = {{1139--1148}}, title = {{{Visual Analysis of Argumentation in Essays}}}, volume = {{27}}, year = {{2020}}, } @inproceedings{25336, abstract = {{OpenPGP and S/MIME are two major standards for securing email communication introduced in the early 1990s. Three recent classes of attacks exploit weak cipher modes (EFAIL Malleability Gadgets, or EFAIL-MG), the flexibility of the MIME email structure (EFAIL Direct Exfiltration, or EFAIL-DE), and the Reply action of the email client (REPLY attacks). Although all three break message confidentiality by using standardized email features, only EFAIL-MG has been mitigated in IETF standards with the introduction of AEAD algorithms. So far, no uniform and reliable countermeasures have been adopted by email clients to prevent EFAIL-DE and REPLY attacks. Instead, email clients implement a variety of different ad-hoc countermeasures which are only partially effective, cause interoperability problems, and fragment the secure email ecosystem.We present the first generic countermeasure against both REPLY and EFAIL-DE attacks by checking the decryption context including SMTP headers and MIME structure during decryption. The decryption context is encoded into a string DC and used as Associated Data (AD) in the AEAD encryption. Thus the proposed solution seamlessly extends the EFAIL-MG countermeasures. The decryption context changes whenever an attacker alters the email source code in a critical way, for example, if the attacker changes the MIME structure or adds a new Reply-To header. The proposed solution does not cause any interoperability problems and legacy emails can still be decrypted. We evaluate our approach by implementing the decryption contexts in Thunderbird/Enigmail and by verifying their correct functionality after the email has been transported over all major email providers, including Gmail and iCloud Mail.}}, author = {{Schwenk, Jörg and Brinkmann, Marcus and Poddebniak, Damian and Müller, Jens and Somorovsky, Juraj and Schinzel, Sebastian}}, booktitle = {{Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security}}, isbn = {{9781450370899}}, keywords = {{decryption contexts, EFAIL, OpenPGP, S/MIME, AEAD}}, pages = {{1647–1664}}, publisher = {{Association for Computing Machinery}}, title = {{{Mitigation of Attacks on Email End-to-End Encryption}}}, doi = {{10.1145/3372297.3417878}}, year = {{2020}}, } @phdthesis{16935, author = {{Moussalem, Diego Campos}}, title = {{{Knowledge Graphs for Multilingual Language Translation and Generation}}}, doi = {{10.17619/UNIPB/1-980}}, year = {{2020}}, } @article{13770, author = {{Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}}, journal = {{Business & Information Systems Engineering}}, number = {{6}}, pages = {{467--481}}, publisher = {{Springer}}, title = {{{A Case for a New IT Ecosystem: On-The-Fly Computing}}}, doi = {{10.1007/s12599-019-00627-x}}, volume = {{62}}, year = {{2020}}, } @inproceedings{29298, abstract = {{Die Themen „Big Data“, „Künstliche Intelligenz und „Data Science“ werden seit einiger Zeit nicht nur in der breiten Öffentlichkeit kontrovers diskutiert, sondern stellen für die Ausbildung in den IT- und IT-nahen Berufen schon heute neue Herausforderungen dar, die in Zukunft durch die gesellschaftliche und technologische Weiterentwicklung hin zu einer Datengesellschaft noch größer werden. An dieser Stelle stellt sich die Frage, welche Aspekte dieses großen Themenkomplexes für Schule und Ausbildung von Wichtigkeit sind und wie diese Themen sinnstiftend und gewinnbringend in die informatische Ausbildung in verschiedenen Bildungsgängen integriert werden können. Im Rahmen des von uns im Jahr 2017 organisierten Symposiums zum Thema „Data Science“ wurden für die Bildung relevante Aspekte erörtert, wodurch als Kernelemente für den Unterricht Algorithmen der Künstlichen Intelligenz und ihre Anwendung in Industrie und Gesellschaft, Explorationen von Big Data sowie der Umgang mit eigenen Daten in sozialen Netzwerken herausgearbeitet wurden. Ziel ist, aus diesen Themenbereichen sowohl ein umfassendes Curriculum als auch Module für verschiedene Unterrichtsszenarien zu entwickeln und zu erproben. Durch diese Materialien soll es Lehrkräften aus der Informatik, Mathematik oder Technik ermöglicht werden, diese Themen auf Basis des Curriculums und der erprobten Unterrichtskonzepte selbst zu unterrichten. Hierfür wurde im Rahmen des Projekts ProDaBi (Projekt Data Science und Big Data in der Schule, https://www.prodabi.de), initiiert von der Telekom Stiftung, ein experimenteller Projektkurs entwickelt, den wir mit Schüler:innen der Sekundarstufe II an der Universität Paderborn im Schuljahr 2018/19 durchführten. Dieser Kurs enthält neben einem Modul zur Exploration von Big Data und einem weiteren Modul zum Maschinellen Lernen als Teil der Künstlichen Intelligenz auch eine Projektphase, die es in Zusammenarbeit mit lokalen Unternehmen den Schüler:innen ermöglicht, das Erlernte in ein reales Data Science-Projekt einzubringen. Aus den Erfahrungen dieses Projektkurses sowie den parallel durchgeführten Erprobungen einzelner Bausteine auch mit beruflichen Schulen werden ab dem Schuljahr 2019/20 die hierfür verwendeten Materialien weiterentwickelt und weiteren Kooperationspartnern zur Erprobung zur Verfügung gestellt. Damit wurden zum Ende des Projekts nicht nur vollständige Unterrichtsmaterialien, sondern auch ein umfassendes Curriculum entwickelt.}}, author = {{Opel, Simone Anna and Schlichtig, Michael}}, booktitle = {{Sammelband der 27. Fachtagung der BAG Berufliche Bildung}}, editor = {{Vollmer, Thomas and Karges, Torben and Richter, Tim and Schlömer, Britta and Schütt-Sayed, Sören}}, keywords = {{Berufsbildung, vocational education, Ausbildung, training, berufliche Weiterbildung, advanced vocational education, Digitalisierung, digitalization, Unterricht, teaching, Lehrmethode, teaching method, Interdisziplinarität, interdisciplinarity, Fachdidaktik, subject didactics, Curriculum, curriculum, gewerblich-technischer Beruf, vocational/technical occupation, Fachkraft, specialist, Qualifikationsanforderungen, qualification requirements, Kompetenz, competence, Lehrerbildung, teacher training, Bundesrepublik Deutschland, Federal Republic of Germany}}, location = {{Siegen}}, pages = {{176--194}}, publisher = {{wbv Media GmbH & Co. KG}}, title = {{{Data Science und Big Data in der beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II}}}, doi = {{https://doi.org/10.3278/6004722w}}, volume = {{55}}, year = {{2020}}, } @inproceedings{3776, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}}, pages = {{149--154}}, title = {{{Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity}}}, year = {{2020}}, } @inproceedings{20137, author = {{Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020)}}, pages = {{237--241}}, title = {{{Task Proposal: Abstractive Snippet Generation for Web Pages}}}, year = {{2020}}, } @inproceedings{3818, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2020}}, pages = {{4290--4300}}, title = {{{Detecting Media Bias in News Articles using Gaussian Bias Distributions}}}, year = {{2020}}, } @inproceedings{15826, author = {{Chen, Wei-Fan and Syed, Shahbaz and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Proceedings of the Web Conference 2020}}, pages = {{1309--1319}}, title = {{{Abstractive Snippet Generation}}}, year = {{2020}}, } @inproceedings{16868, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}}, location = {{Seattle, USA}}, pages = {{4334--4345}}, publisher = {{Association for Computational Linguistics}}, title = {{{Target Inference in Argument Conclusion Generation}}}, year = {{2020}}, } @inproceedings{20141, author = {{Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin}}, booktitle = {{Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)}}, pages = {{3023--3030}}, title = {{{CauseNet: Towards a Causality Graph Extracted from the Web}}}, doi = {{10.1145/3340531.3412763}}, year = {{2020}}, } @misc{18638, author = {{Kramer, Paul}}, publisher = {{Universität Paderborn}}, title = {{{Comparison of Zero-Knowledge Range Proofs}}}, year = {{2020}}, } @inproceedings{20838, author = {{Lösch, Achim and Platzner, Marco}}, booktitle = {{2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, isbn = {{9781728174457}}, title = {{{MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterogeneous Compute Nodes}}}, doi = {{10.1109/ipdpsw50202.2020.00012}}, year = {{2020}}, } @inproceedings{13226, abstract = {{The canonical problem for the class Quantum Merlin-Arthur (QMA) is that of estimating ground state energies of local Hamiltonians. Perhaps surprisingly, [Ambainis, CCC 2014] showed that the related, but arguably more natural, problem of simulating local measurements on ground states of local Hamiltonians (APX-SIM) is likely harder than QMA. Indeed, [Ambainis, CCC 2014] showed that APX-SIM is P^QMA[log]-complete, for P^QMA[log] the class of languages decidable by a P machine making a logarithmic number of adaptive queries to a QMA oracle. In this work, we show that APX-SIM is P^QMA[log]-complete even when restricted to more physical Hamiltonians, obtaining as intermediate steps a variety of related complexity-theoretic results. We first give a sequence of results which together yield P^QMA[log]-hardness for APX-SIM on well-motivated Hamiltonians: (1) We show that for NP, StoqMA, and QMA oracles, a logarithmic number of adaptive queries is equivalent to polynomially many parallel queries. These equalities simplify the proofs of our subsequent results. (2) Next, we show that the hardness of APX-SIM is preserved under Hamiltonian simulations (a la [Cubitt, Montanaro, Piddock, 2017]). As a byproduct, we obtain a full complexity classification of APX-SIM, showing it is complete for P, P^||NP, P^||StoqMA, or P^||QMA depending on the Hamiltonians employed. (3) Leveraging the above, we show that APX-SIM is P^QMA[log]-complete for any family of Hamiltonians which can efficiently simulate spatially sparse Hamiltonians, including physically motivated models such as the 2D Heisenberg model. Our second focus considers 1D systems: We show that APX-SIM remains P^QMA[log]-complete even for local Hamiltonians on a 1D line of 8-dimensional qudits. This uses a number of ideas from above, along with replacing the "query Hamiltonian" of [Ambainis, CCC 2014] with a new "sifter" construction.}}, author = {{Gharibian, Sevag and Piddock, Stephen and Yirka, Justin}}, booktitle = {{Proceedings of the 37th Symposium on Theoretical Aspects of Computer Science (STACS 2020)}}, pages = {{38}}, title = {{{Oracle complexity classes and local measurements on physical Hamiltonians}}}, year = {{2020}}, } @inproceedings{46319, abstract = {{The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns.}}, author = {{Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}}, booktitle = {{Proceedings of the Florida Artificial Intelligence Research Society Conference}}, title = {{{Towards Real-Time and Unsupervised Campaign Detection in Social Media}}}, year = {{2020}}, } @inproceedings{46328, abstract = {{In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{996–1003}}, title = {{{Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}}}, doi = {{10.1109/SSCI47803.2020.9308510}}, year = {{2020}}, } @inproceedings{46320, abstract = {{The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020.}}, author = {{Assenmacher, D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}}, booktitle = {{Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis}}, editor = {{Meiselwitz, G}}, isbn = {{978-3-030-49570-1}}, pages = {{201–214}}, publisher = {{Springer International Publishing}}, title = {{{A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}}}, doi = {{10.1007/978-3-030-49570-1_14}}, year = {{2020}}, } @inproceedings{46325, abstract = {{Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}}, isbn = {{978-3-030-43823-4}}, pages = {{137–143}}, title = {{{Towards Automated Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-43823-4_12}}, year = {{2020}}, }