@misc{45788, author = {{Bülling, Jonas}}, title = {{{Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump}}}, year = {{2021}}, } @misc{45787, author = {{Mishra, Avishek}}, title = {{{Computational Text Professionalization using Neural Sequence-to-Sequence Models}}}, year = {{2021}}, } @inproceedings{30909, author = {{Clausing, Lennart}}, booktitle = {{Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies}}, publisher = {{ACM}}, title = {{{ReconOS64: High-Performance Embedded Computing for Industrial Analytics on a Reconfigurable System-on-Chip}}}, doi = {{10.1145/3468044.3468056}}, year = {{2021}}, } @inproceedings{30908, author = {{Ghasemzadeh Mohammadi, Hassan and Jentzsch, Felix and Kuschel, Maurice and Arshad, Rahil and Rautmare, Sneha and Manjunatha, Suraj and Platzner, Marco and Boschmann, Alexander and Schollbach, Dirk }}, booktitle = {{ Machine Learning and Principles and Practice of Knowledge Discovery in Databases}}, publisher = {{Springer}}, title = {{{FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics}}}, doi = {{https://doi.org/10.1007/978-3-030-93736-2_27}}, year = {{2021}}, } @inproceedings{21178, abstract = {{When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.}}, author = {{Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}}, location = {{Online}}, pages = {{224--233}}, publisher = {{Association for Computational Linguistics}}, title = {{{Belief-based Generation of Argumentative Claims}}}, doi = {{10.18653/v1/2021.eacl-main.17}}, year = {{2021}}, } @article{31132, author = {{Dann, Andreas Peter and Plate, Henrik and Hermann, Ben and Ponta, Serena Elisa and Bodden, Eric}}, issn = {{0098-5589}}, journal = {{IEEE Transactions on Software Engineering}}, keywords = {{Software}}, pages = {{1--1}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Identifying Challenges for OSS Vulnerability Scanners - A Study & Test Suite}}}, doi = {{10.1109/tse.2021.3101739}}, year = {{2021}}, } @inproceedings{26405, author = {{Schubert, Philipp and Sattler, Florian and Schiebel, Fabian Benedikt and Hermann, Ben and Bodden, Eric}}, booktitle = {{2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)}}, title = {{{Modeling the Effects of Global Variables in Data-Flow Analysis for C/C++}}}, year = {{2021}}, } @inproceedings{22014, author = {{Seutter, Janina and Müller, Michelle and Neumann, Jürgen and Kundisch, Dennis}}, location = {{Virtual Conference/Workshop}}, publisher = {{Proceedings of the International Conference on Challenges in Managing Smart Products and Services (CHIMSPAS 2021)}}, title = {{{Do Smart Product Service Systems Crowd Out Interactions in Online Communities? – Empirical Evidence from a Cooking Community}}}, year = {{2021}}, } @inproceedings{23411, author = {{Müller, Michelle and Neumann, Jürgen and Kundisch, Dennis}}, location = {{Newport Beach, California, USA}}, title = {{{Toss a Coin to Your Host? – Why Guests Do Not Always End Up Paying for the Cost of Regulatory Policies}}}, year = {{2021}}, } @inproceedings{19551, author = {{Kurek, Rafael}}, booktitle = {{Information Security and Privacy - 25th Australasian Conference, {ACISP} 2020, Perth, WA, Australia, November 30 - December 2, 2020, Proceedings}}, editor = {{K. Liu, Joseph and Cui, Hui}}, pages = {{330--349}}, publisher = {{Springer}}, title = {{{Efficient Forward-Secure Threshold Public Key Encryption}}}, doi = {{10.1007/978-3-030-55304-3\_17}}, volume = {{12248}}, year = {{2020}}, } @inproceedings{19553, author = {{Kurek, Rafael}}, booktitle = {{Advances in Information and Computer Security - 15th International Workshop on Security, {IWSEC} 2020, Fukui, Japan, September 2-4, 2020, Proceedings}}, editor = {{Aoki, Kazumaro and Kanaoka, Akira}}, pages = {{239--260}}, publisher = {{Springer}}, title = {{{Efficient Forward-Secure Threshold Signatures}}}, doi = {{10.1007/978-3-030-58208-1\_14}}, volume = {{12231}}, year = {{2020}}, } @inproceedings{19606, abstract = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Model-based, Tool Support}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}}, doi = {{10.1007/978-3-030-64266-2_14}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{19607, abstract = {{Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{19656, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS)}}, publisher = {{Springer}}, title = {{{Automatic Fairness Testing of Machine Learning Models}}}, year = {{2020}}, } @inproceedings{19739, author = {{Szopinski, Daniel}}, booktitle = {{Proceedings of the 15th International Conference on Design Science Research in Information Systems and Technology (DESRIST)}}, location = {{Virtual Conference/Workshop}}, title = {{{Active Business Model Development Tools: Design Requirements}}}, year = {{2020}}, } @inproceedings{19741, author = {{Szopinski, Daniel}}, booktitle = {{Proceedings of the 41st International Conference on Information Systems (ICIS)}}, location = {{Virtual Conference/Workshop}}, title = {{{Exploring design principles for stimuli in business model development tools}}}, year = {{2020}}, } @inproceedings{19782, author = {{Müller, Michelle and Neumann, Jürgen and Gutt, Dominik and Kundisch, Dennis}}, booktitle = {{Proceedings of the 41th International Conference on Information Systems (ICIS)}}, location = {{Virtual Conference/Workshop}}, title = {{{Toss a Coin to your Host - How Guests End up Paying for the Cost of Regulatory Policies}}}, year = {{2020}}, } @misc{19999, author = {{Mayer, Stefan}}, publisher = {{Universität Paderborn}}, title = {{{Optimierung von JMCTest beim Testen von Inter Method Contracts}}}, year = {{2020}}, } @misc{20221, author = {{Yeole, Paresh Kishor}}, title = {{{Plurality Consensus in Hybrid Networks}}}, year = {{2020}}, }