@inbook{22057,
abstract = {We construct more efficient cryptosystems with provable
security against adaptive attacks, based on simple and natural hardness
assumptions in the standard model. Concretely, we describe:
– An adaptively-secure variant of the efficient, selectively-secure LWE-
based identity-based encryption (IBE) scheme of Agrawal, Boneh,
and Boyen (EUROCRYPT 2010). In comparison to the previously
most efficient such scheme by Yamada (CRYPTO 2017) we achieve
smaller lattice parameters and shorter public keys of size O(log λ),
where λ is the security parameter.
– Adaptively-secure variants of two efficient selectively-secure pairing-
based IBEs of Boneh and Boyen (EUROCRYPT 2004). One is based
on the DBDH assumption, has the same ciphertext size as the cor-
responding BB04 scheme, and achieves full adaptive security with
public parameters of size only O(log λ). The other is based on a q-
type assumption and has public key size O(λ), but a ciphertext is
only a single group element and the security reduction is quadrat-
ically tighter than the corresponding scheme by Jager and Kurek
(ASIACRYPT 2018).
– A very efficient adaptively-secure verifiable random function where
proofs, public keys, and secret keys have size O(log λ).
As a technical contribution we introduce blockwise partitioning, which
leverages the assumption that a cryptographic hash function is weak
near-collision resistant to prove full adaptive security of cryptosystems.},
author = {Jager, Tibor and Kurek, Rafael and Niehues, David},
booktitle = {Public-Key Cryptography – PKC 2021},
isbn = {9783030752446},
issn = {0302-9743},
title = {{Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance}},
doi = {10.1007/978-3-030-75245-3_22},
year = {2021},
}
@inproceedings{22158,
author = {Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin},
booktitle = {Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings},
title = {{Generating Informative Conclusions for Argumentative Texts}},
year = {2021},
}
@inproceedings{22160,
author = {Al-Khatib, Khalid and Trautner, Lukas and Wachsmuth, Henning and Hou, Yufang and Stein, Benno},
booktitle = {Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)},
title = {{Employing Argumentation Knowledge Graphs for Neural Argument Generation}},
year = {2021},
}
@inproceedings{22014,
author = {Seutter, Janina and Müller, Michelle and Neumann, Jürgen and Kundisch, Dennis},
location = {Virtual Conference/Workshop},
title = {{Do Smart Product Service Systems Crowd Out Interactions in Online Communities? – Empirical Evidence from a Cooking Community}},
year = {2021},
}
@techreport{22211,
author = {Lorenz, Johannes and Sureth-Sloane, Caren and Diller, Markus},
title = {{Inconsistent tax transfer prices: tax filings, audits, and double taxation}},
doi = {10.52569/acpj5634},
year = {2021},
}
@inbook{22052,
abstract = {In this study, we describe a text processing pipeline that transforms user-generated text into structured data. To do this, we train neural and transformer-based models for aspect-based sentiment analysis. As most research deals with explicit aspects from product or service data, we extract and classify implicit and explicit aspect phrases from German-language physician review texts. Patients often rate on the basis of perceived friendliness or competence. The vocabulary is difficult, the topic sensitive, and the data user-generated. The aspect phrases come with various wordings using insertions and are not noun-based, which makes the presented case equally relevant and reality-based. To find complex, indirect aspect phrases, up-to-date deep learning approaches must be combined with supervised training data. We describe three aspect phrase datasets, one of them new, as well as a newly annotated aspect polarity dataset. Alongside this, we build an algorithm to rate the aspect phrase importance. All in all, we train eight transformers on the new raw data domain, compare 54 neural aspect extraction models and, based on this, create eight aspect polarity models for our pipeline. These models are evaluated by using Precision, Recall, and F-Score measures. Finally, we evaluate our aspect phrase importance measure algorithm.},
author = {Kersting, Joschka and Geierhos, Michaela},
booktitle = {Natural Language Processing and Information Systems},
editor = {Kapetanios, Epaminondas and Horacek, Helmut and Métais, Elisabeth and Meziane, Farid},
location = {Saarbrücken, Germany},
pages = {N.N.},
publisher = {Springer},
title = {{Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting}},
volume = {12801},
year = {2021},
}
@inproceedings{21953,
author = {Witschen, Linus Matthias and Wiersema, Tobias and Raeisi Nafchi, Masood and Bockhorn, Arne and Platzner, Marco},
booktitle = {Proceedings of International Symposium on Applied Reconfigurable Computing (ARC'21)},
editor = {Hannig, Frank and Derrien, Steven and Diniz, Pedro and Chillet, Daniel},
location = {Virtual conference},
publisher = {Springer Lecture Notes in Computer Science},
title = {{Timing Optimization for Virtual FPGA Configurations}},
year = {2021},
}
@misc{22216,
author = {Rehnen, Jakob Werner},
title = {{Decomposition of Arithmetic Components for the Approximate Circuit Synthesis with EvoApproxLib}},
year = {2021},
}
@article{16295,
abstract = {It is a challenging task to identify the objectives on which a certain decision was based, in particular if several, potentially conflicting criteria are equally important and a continuous set of optimal compromise decisions exists. This task can be understood as the inverse problem of multiobjective optimization, where the goal is to find the objective function vector of a given Pareto set. To this end, we present a method to construct the objective function vector of an unconstrained multiobjective optimization problem (MOP) such that the Pareto critical set contains a given set of data points with prescribed KKT multipliers. If such an MOP can not be found, then the method instead produces an MOP whose Pareto critical set is at least close to the data points. The key idea is to consider the objective function vector in the multiobjective KKT conditions as variable and then search for the objectives that minimize the Euclidean norm of the resulting system of equations. By expressing the objectives in a finite-dimensional basis, we transform this problem into a homogeneous, linear system of equations that can be solved efficiently. Potential applications of this approach include the identification of objectives (both from clean and noisy data) and the construction of surrogate models for expensive MOPs.},
author = {Gebken, Bennet and Peitz, Sebastian},
journal = {Journal of Global Optimization},
pages = {3--29},
publisher = {Springer},
title = {{Inverse multiobjective optimization: Inferring decision criteria from data}},
doi = {10.1007/s10898-020-00983-z},
volume = {80},
year = {2021},
}
@proceedings{22230,
editor = {Sousa Santos, Beatriz and Domik, Gitta},
isbn = {ISBN 978-3-03868-132-8 },
location = {Vienna},
publisher = {Eurographics Association },
title = {{EUROGRAPHICS 2021: Education Papers Frontmatter}},
doi = {10.2312/EGED.20212000},
year = {2021},
}