@article{15738, author = {{Ohto, Tatsuhiko and Dodia, Mayank and Xu, Jianhang and Imoto, Sho and Tang, Fujie and Zysk, Frederik and Kühne, Thomas D. and Shigeta, Yasuteru and Bonn, Mischa and Wu, Xifan and Nagata, Yuki}}, issn = {{1948-7185}}, journal = {{The Journal of Physical Chemistry Letters}}, pages = {{4914--4919}}, title = {{{Accessing the Accuracy of Density Functional Theory through Structure and Dynamics of the Water–Air Interface}}}, doi = {{10.1021/acs.jpclett.9b01983}}, volume = {{10}}, year = {{2019}}, } @article{15740, author = {{Guc, Maxim and Kodalle, Tim and Kormath Madam Raghupathy, Ramya and Mirhosseini, Hossein and Kühne, Thomas D. and Becerril-Romero, Ignacio and Pérez-Rodríguez, Alejandro and Kaufmann, Christian A. and Izquierdo-Roca, Victor}}, issn = {{1932-7447}}, journal = {{The Journal of Physical Chemistry C}}, pages = {{1285--1291}}, title = {{{Vibrational Properties of RbInSe2: Raman Scattering Spectroscopy and First-Principle Calculations}}}, doi = {{10.1021/acs.jpcc.9b08781}}, volume = {{124}}, year = {{2019}}, } @article{15741, abstract = {{ In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario}}, author = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, title = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}}, doi = {{10.1016/j.automatica.2019.108759}}, year = {{2019}}, } @misc{15746, author = {{Otte, Oliver}}, title = {{{Outsourced Decryption of Attribute-based Ciphertexts}}}, year = {{2019}}, } @misc{15747, author = {{Wördenweber, Nico Christof}}, title = {{{On the Security of the Rouselakis-Waters Ciphertext-Policy Attribute-Based Encryption Scheme in the Random Oracle Model}}}, year = {{2019}}, } @inproceedings{15812, abstract = {{Connectionist temporal classification (CTC) is a sequence-level loss that has been successfully applied to train recurrent neural network (RNN) models for automatic speech recognition. However, one major weakness of CTC is the conditional independence assumption that makes it difficult for the model to learn label dependencies. In this paper, we propose stimulated CTC, which uses stimulated learning to help CTC models learn label dependencies implicitly by using an auxiliary RNN to generate the appropriate stimuli. This stimuli comes in the form of an additional stimulation loss term which encourages the model to learn said label dependencies. The auxiliary network is only used during training and the inference model has the same structure as a standard CTC model. The proposed stimulated CTC model achieves about 35% relative character error rate improvements on a synthetic gesture keyboard recognition task and over 30% relative word error rate improvements on the Librispeech automatic speech recognition tasks over a baseline model trained with CTC only.}}, author = {{Heymann, Jahn and Khe Chai Sim, Bo Li}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Improving CTC Using Stimulated Learning for Sequence Modeling}}}, year = {{2019}}, } @inproceedings{15816, abstract = {{Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is available. However, there has been a longstanding debate whether enhancement should also be carried out on the ASR training data. In an extensive experimental evaluation on the acoustically very challenging CHiME-5 dinner party data we show that: (i) cleaning up the training data can lead to substantial error rate reductions, and (ii) enhancement in training is advisable as long as enhancement in test is at least as strong as in training. This approach stands in contrast and delivers larger gains than the common strategy reported in the literature to augment the training database with additional artificially degraded speech. Together with an acoustic model topology consisting of initial CNN layers followed by factorized TDNN layers we achieve with 41.6% and 43.2% WER on the DEV and EVAL test sets, respectively, a new single-system state-of-the-art result on the CHiME-5 data. This is a 8% relative improvement compared to the best word error rate published so far for a speech recognizer without system combination.}}, author = {{Zorila, Catalin and Boeddeker, Christoph and Doddipatla, Rama and Haeb-Umbach, Reinhold}}, booktitle = {{ASRU 2019, Sentosa, Singapore}}, title = {{{An Investigation Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription}}}, year = {{2019}}, } @misc{15819, author = {{Leutnant, Matthias}}, title = {{{Experimentelle Untersuchung des SEM-Algorithmus}}}, year = {{2019}}, } @inproceedings{15838, abstract = {{In the field of software analysis a trade-off between scalability and accuracy always exists. In this respect, Android app analysis is no exception, in particular, analyzing large or many apps can be challenging. Dealing with many small apps is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH. These particular benchmarks are not only used for the evaluation of novel tools but also in continuous integration pipelines of existing mature tools to maintain and guarantee a certain quality-level. Considering this latter usage it becomes very important to be able to achieve benchmark results as fast as possible. Hence, benchmarks have to be optimized for this purpose. One approach to do so is app merging. We implemented the Android Merge Tool (AMT) following this approach and show that its novel aspects can be used to produce scaled up and accurate benchmarks. For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy trade-off anymore. We show this throughout detailed experiments on DROIDBENCH employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark execution times are largely reduced without losing benchmark accuracy. Moreover, we argue why AMT is an advantageous successor of the state-of-the-art app merging tool (APKCOMBINER) in analysis lift-up scenarios.}}, author = {{Pauck, Felix and Zhang, Shikun}}, booktitle = {{2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)}}, isbn = {{9781728141367}}, keywords = {{Program Analysis, Android App Analysis, Taint Analysis, App Merging, Benchmark}}, title = {{{Android App Merging for Benchmark Speed-Up and Analysis Lift-Up}}}, doi = {{10.1109/asew.2019.00019}}, year = {{2019}}, } @article{15875, author = {{Camberg, Alan Adam and Tröster, Thomas and Bohner, F. and Tölle, J.}}, issn = {{1757-899X}}, journal = {{IOP Conference Series: Materials Science and Engineering}}, pages = {{012057}}, title = {{{Predicting plasticity and fracture of severe pre-strained EN AW-5182 by Yld2000 yield locus and Hosford-Coulomb fracture model in sheet forming applications}}}, doi = {{10.1088/1757-899X/651/1/012057}}, volume = {{651}}, year = {{2019}}, } @misc{15883, author = {{Kumar Jeyakumar, Shankar}}, title = {{{Incremental learning with Support Vector Machine on embedded platforms}}}, year = {{2019}}, } @inproceedings{15908, author = {{Müller, Jens and Brinkmann, Marcus and Poddebniak, Damian and Böck, Hanno and Schinzel, Sebastian and Somorovsky, Juraj and Schwenk, Jörg}}, booktitle = {{28th {USENIX} Security Symposium ({USENIX} Security 19)}}, isbn = {{978-1-939133-06-9}}, pages = {{1011--1028}}, publisher = {{{USENIX} Association}}, title = {{{"Johnny, you are fired!" -- Spoofing OpenPGP and S/MIME Signatures in Emails}}}, year = {{2019}}, } @inproceedings{15909, author = {{Merget, Robert and Somorovsky, Juraj and Aviram, Nimrod and Young, Craig and Fliegenschmidt, Janis and Schwenk, Jörg and Shavitt, Yuval}}, booktitle = {{28th {USENIX} Security Symposium ({USENIX} Security 19)}}, isbn = {{978-1-939133-06-9}}, pages = {{1029--1046}}, publisher = {{{USENIX} Association}}, title = {{{Scalable Scanning and Automatic Classification of TLS Padding Oracle Vulnerabilities}}}, year = {{2019}}, } @inproceedings{15910, author = {{Engelbertz, Nils and Mladenov, Vladislav and Somorovsky, Juraj and Herring, David and Erinola, Nurullah and Schwenk, Jörg}}, booktitle = {{Open Identity Summit 2019}}, editor = {{Roßnagel, Heiko and Wagner, Sven and Hühnlein, Detlef}}, pages = {{ 95--106 }}, publisher = {{Gesellschaft für Informatik, Bonn}}, title = {{{Security Analysis of XAdES Validation in the CEF Digital Signature Services (DSS)}}}, year = {{2019}}, } @misc{15920, abstract = {{Secure hardware design is the most important aspect to be considered in addition to functional correctness. Achieving hardware security in today’s globalized Integrated Cir- cuit(IC) supply chain is a challenging task. One solution that is widely considered to help achieve secure hardware designs is Information Flow Tracking(IFT). It provides an ap- proach to verify that the systems adhere to security properties either by static verification during design phase or dynamic checking during runtime. Proof-Carrying Hardware(PCH) is an approach to verify a functional design prior to using it in hardware. It is a two-party verification approach, where the target party, the consumer requests new functionalities with pre-defined properties to the producer. In response, the producer designs the IP (Intellectual Property) cores with the requested functionalities that adhere to the consumer-defined properties. The producer provides the IP cores and a proof certificate combined into a proof-carrying bitstream to the consumer to verify it. If the verification is successful, the consumer can use the IP cores in his hardware. In essence, the consumer can only run verified IP cores. Correctly applied, PCH techniques can help consumers to defend against many unintentional modifications and malicious alterations of the modules they receive. There are numerous published examples of how to use PCH to detect any change in the functionality of a circuit, i.e., pairing a PCH approach with functional equivalence checking for combinational or sequential circuits. For non-functional properties, since opening new covert channels to leak secret information from secure circuits is a viable attack vector for hardware trojans, i.e., intentionally added malicious circuitry, IFT technique is employed to make sure that secret/untrusted information never reaches any unclassified/trusted outputs. This master thesis aims to explore the possibility of adapting Information Flow Tracking into a Proof-Carrying Hardware scenario. It aims to create a method that combines Infor- mation Flow Tracking(IFT) with a PCH approach at bitstream level enabling consumers to validate the trustworthiness of a module’s information flow without the computational costs of a complete flow analysis.}}, author = {{Keerthipati, Monica}}, publisher = {{Universität Paderborn}}, title = {{{A Bitstream-Level Proof-Carrying Hardware Technique for Information Flow Tracking}}}, year = {{2019}}, } @inproceedings{15921, abstract = {{Ranking plays a central role in a large number of applications driven by RDF knowledge graphs. Over the last years, many popular RDF knowledge graphs have grown so large that rankings for the facts they contain cannot be computed directly using the currently common 64-bit platforms. In this paper, we tackle two problems: Computing ranks on such large knowledge bases efficiently and incrementally. First, we present D-HARE, a distributed approach for computing ranks on very large knowledge graphs. D-HARE assumes the random surfer model and relies on data partitioning to compute matrix multiplications and transpositions on disk for matrices of arbitrary size. Moreover, the data partitioning underlying D-HARE allows the execution of most of its steps in parallel. As very large knowledge graphs are often updated periodically, we tackle the incremental computation of ranks on large knowledge bases as a second problem. We address this problem by presenting I-HARE, an approximation technique for calculating the overall ranking scores of a knowledge without the need to recalculate the ranking from scratch at each new revision. We evaluate our approaches by calculating ranks on the 3 × 10^9 and 2.4 × 10^9 triples from Wikidata resp. LinkedGeoData. Our evaluation demonstrates that D-HARE is the first holistic approach for computing ranks on very large RDF knowledge graphs. In addition, our incremental approach achieves a root mean squared error of less than 10E−7 in the best case. Both D-HARE and I-HARE are open-source and are available at: https://github.com/dice-group/incrementalHARE. }}, author = {{Desouki, Abdelmoneim Amer and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 30th ACM Conference on Hypertext and Social Media - HT '19}}, isbn = {{9781450368858}}, keywords = {{Knowledge Graphs, Ranking, RDF}}, pages = {{163--171}}, publisher = {{ACM}}, title = {{{Ranking on Very Large Knowledge Graphs}}}, doi = {{10.1145/3342220.3343660}}, year = {{2019}}, } @inproceedings{14568, author = {{Heindorf, Stefan and Scholten, Yan and Engels, Gregor and Potthast, Martin}}, booktitle = {{INFORMATIK}}, pages = {{289--290}}, title = {{{Debiasing Vandalism Detection Models at Wikidata (Extended Abstract)}}}, doi = {{10.18420/inf2019_48}}, year = {{2019}}, } @article{14817, author = {{Sommer, Christoph and Basagni, Stefano}}, issn = {{1570-8705}}, journal = {{Ad Hoc Networks}}, title = {{{Advances and novel applications of mobile wireless networking}}}, doi = {{10.1016/j.adhoc.2019.101975}}, year = {{2019}}, } @inproceedings{14819, author = {{Heinovski, Julian and Stratmann, Lukas and Buse, Dominik S. and Klingler, Florian and Franke, Mario and Oczko, Marie-Christin H. and Sommer, Christoph and Scharlau, Ingrid and Dressler, Falko}}, booktitle = {{2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)}}, isbn = {{9781728102702}}, title = {{{Modeling Cycling Behavior to Improve Bicyclists' Safety at Intersections - A Networking Perspective}}}, doi = {{10.1109/wowmom.2019.8793008}}, year = {{2019}}, } @article{14821, author = {{Seipelt, Agnes Regina}}, isbn = {{978-3-96233-182-5}}, journal = {{Weberiana}}, keywords = {{Weber, Wien, Zensur}}, pages = {{107–161}}, publisher = {{Allitera}}, title = {{{Aufführungs- und zensurbedingte Veränderungen im Wiener Manuskript der Freischütz-Erstaufführung 1821 in Wien}}}, volume = {{29}}, year = {{2019}}, }