@inproceedings{21238, author = {{Pauck, Felix and Wehrheim, Heike}}, booktitle = {{Software Engineering 2021}}, editor = {{Koziolek, Anne and Schaefer, Ina and Seidl, Christoph}}, pages = {{ 83--84 }}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Cooperative Android App Analysis with CoDiDroid}}}, doi = {{10.18420/SE2021_30 }}, year = {{2021}}, } @inproceedings{29138, author = {{Ahmed, Qazi Arbab}}, booktitle = {{2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)}}, title = {{{Hardware Trojans in Reconfigurable Computing}}}, doi = {{10.1109/vlsi-soc53125.2021.9606974}}, year = {{2021}}, } @inproceedings{20681, abstract = {{The battle of developing hardware Trojans and corresponding countermeasures has taken adversaries towards ingenious ways of compromising hardware designs by circumventing even advanced testing and verification methods. Besides conventional methods of inserting Trojans into a design by a malicious entity, the design flow for field-programmable gate arrays (FPGAs) can also be surreptitiously compromised to assist the attacker to perform a successful malfunctioning or information leakage attack. The advanced stealthy malicious look-up-table (LUT) attack activates a Trojan only when generating the FPGA bitstream and can thus not be detected by register transfer and gate level testing and verification. However, also this attack was recently revealed by a bitstream-level proof-carrying hardware (PCH) approach. In this paper, we present a novel attack that leverages malicious routing of the inserted Trojan circuit to acquire a dormant state even in the generated and transmitted bitstream. The Trojan's payload is connected to primary inputs/outputs of the FPGA via a programmable interconnect point (PIP). The Trojan is detached from inputs/outputs during place-and-route and re-connected only when the FPGA is being programmed, thus activating the Trojan circuit without any need for a trigger logic. Since the Trojan is injected in a post-synthesis step and remains unconnected in the bitstream, the presented attack can currently neither be prevented by conventional testing and verification methods nor by recent bitstream-level verification techniques.}}, author = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}}, booktitle = {{2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)}}, location = {{Alpexpo | Grenoble, France}}, publisher = {{2021 Design, Automation and Test in Europe Conference (DATE)}}, title = {{{Malicious Routing: Circumventing Bitstream-level Verification for FPGAs}}}, doi = {{10.23919/DATE51398.2021.9474026}}, year = {{2021}}, } @inproceedings{26406, author = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric and Leer, Richard}}, booktitle = {{SCAM '21: IEEE International Working Conference on Source Code Analysis and Manipulation (Engineering Track)}}, title = {{{Into the Woods: Experiences from Building a Dataflow Analysis Framework for C/C++}}}, year = {{2021}}, } @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{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}}, } @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{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}}, } @misc{19999, author = {{Mayer, Stefan}}, publisher = {{Universität Paderborn}}, title = {{{Optimierung von JMCTest beim Testen von Inter Method Contracts}}}, year = {{2020}}, } @inbook{17347, abstract = {{Peer-to-Peer news portals allow Internet users to write news articles and make them available online to interested readers. Despite the fact that authors are free in their choice of topics, there are a number of quality characteristics that an article must meet before it is published. In addition to meaningful titles, comprehensibly written texts and meaning- ful images, relevant tags are an important criteria for the quality of such news. In this case study, we discuss the challenges and common mistakes that Peer-to-Peer reporters face when tagging news and how incorrect information can be corrected through the orchestration of existing Natu- ral Language Processing services. Lastly, we use this illustrative example to give insight into the challenges of dealing with bottom-up taxonomies.}}, author = {{Bäumer, Frederik Simon and Kersting, Joschka and Buff, Bianca and Geierhos, Michaela}}, booktitle = {{Information and Software Technologies}}, editor = {{Audrius, Lopata and Rita, Butkienė and Daina, Gudonienė and Vilma, Sukackė}}, location = {{Kaunas, Litauen}}, pages = {{368----382}}, publisher = {{Springer}}, title = {{{Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P2P News Tagging}}}, doi = {{https://doi.org/10.1007/978-3-030-59506-7_30}}, volume = {{1283}}, year = {{2020}}, } @article{17358, abstract = {{Approximate circuits trade-off computational accuracy against improvements in hardware area, delay, or energy consumption. IP core vendors who wish to create such circuits need to convince consumers of the resulting approximation quality. As a solution we propose proof-carrying approximate circuits: The vendor creates an approximate IP core together with a certificate that proves the approximation quality. The proof certificate is bundled with the approximate IP core and sent off to the consumer. The consumer can formally verify the approximation quality of the IP core at a fraction of the typical computational cost for formal verification. In this paper, we first make the case for proof-carrying approximate circuits and then demonstrate the feasibility of the approach by a set of synthesis experiments using an exemplary approximation framework.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}}, issn = {{1557-9999}}, journal = {{IEEE Transactions On Very Large Scale Integration Systems}}, keywords = {{Approximate circuit synthesis, approximate computing, error metrics, formal verification, proof-carrying hardware}}, number = {{9}}, pages = {{2084 -- 2088}}, publisher = {{IEEE}}, title = {{{Proof-carrying Approximate Circuits}}}, doi = {{10.1109/TVLSI.2020.3008061}}, volume = {{28}}, year = {{2020}}, } @inproceedings{17407, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, title = {{{Extreme Algorithm Selection with Dyadic Feature Representation}}}, year = {{2020}}, } @inproceedings{17408, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{KI 2020: Advances in Artificial Intelligence}}, title = {{{Hybrid Ranking and Regression for Algorithm Selection}}}, year = {{2020}}, } @inproceedings{17424, author = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the ECMLPKDD 2020}}, title = {{{AutoML for Predictive Maintenance: One Tool to RUL Them All}}}, doi = {{10.1007/978-3-030-66770-2_8}}, year = {{2020}}, } @inproceedings{18686, author = {{Kersting, Joschka and Bäumer, Frederik Simon}}, booktitle = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020}}, keywords = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}}, location = {{Lisbon, Portugal}}, pages = {{119----123}}, publisher = {{IADIS}}, title = {{{SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH}}}, year = {{2020}}, } @inproceedings{20306, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Workshop MetaLearn 2020 @ NeurIPS 2020}}, location = {{Online}}, title = {{{Towards Meta-Algorithm Selection}}}, year = {{2020}}, } @techreport{20712, author = {{Schubert, Philipp and Bodden, Eric and Hermann, Ben}}, title = {{{Accelerating Static Call-Graph, Points-to and Data-Flow Analysis Through Persisted Summaries}}}, year = {{2020}}, } @unpublished{20748, abstract = {{On the circuit level, the design paradigm Approximate Computing seeks to trade off computational accuracy against a target metric, e.g., energy consumption. This trade-off is possible for many applications due to their inherent resiliency against inaccuracies. In the past, several automated approximation frameworks have been presented, which either utilize designated approximation techniques or libraries to replace approximable circuit parts with inaccurate versions. The frameworks invoke a search algorithm to iteratively explore the search space of performance degraded circuits, and validate their quality individually. In this paper, we propose to reverse this procedure. Rather than exploring the search space, we delineate the approximate parts of the search space which are guaranteed to lead to valid approximate circuits. Our methodology is supported by formal verification and independent of approximation techniques. Eventually, the user is provided with quality bounds of the individual approximable circuit parts. Consequently, our approach guarantees that any approximate circuit which implements these parts within the determined quality constraints satisfies the global quality constraints, superseding a subsequent quality verification. In our experimental results, we present the runtimes of our approach.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}}, booktitle = {{Fifth Workshop on Approximate Computing (AxC 2020)}}, pages = {{2}}, title = {{{Search Space Characterization for AxC Synthesis}}}, year = {{2020}}, } @inproceedings{18276, abstract = {{Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.}}, author = {{Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ACML 2020}}, location = {{Bangkok, Thailand}}, title = {{{Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}}}, year = {{2020}}, }