---
_id: '26746'
abstract:
- lang: eng
text: "Previous research in proof-carrying hardware has established the feasibility
and utility of the approach, and provided a concrete solution for employing it
for the certification of functional equivalence checking against a specification,
but fell short in connecting it to state-of-the-art formal verification insights,
methods and tools. Due to the immense complexity of modern circuits, and verification
challenges such as the state explosion problem for sequential circuits, this restriction
of readily-available verification solutions severely limited the applicability
of the approach in wider contexts.\r\n\r\nThis thesis closes the gap between the
PCH approach and current advances in formal hardware verification, provides methods
and tools to express and certify a wide range of circuit properties, both functional
and non-functional, and presents for the first time prototypes in which circuits
that are implemented on actual reconfigurable hardware are verified with PCH methods.
Using these results, designers can now apply PCH to establish trust in more complex
circuits, by using more diverse properties which they can express using modern,
efficient property specification techniques."
- lang: ger
text: "Die bisherige Forschung zu Proof-Carrying Hardware (PCH) hat dessen Machbarkeit
und Nützlichkeit gezeigt und einen Ansatz zur Zertifizierung der funktionalen
Äquivalenz zu einer Spezifikation geliefert, jedoch ohne PCH mit aktuellen Erkenntnissen,
Methoden oder Werkzeugen formaler Hardwareverifikation zu verknüpfen. Aufgrund
der Komplexität moderner Schaltungen und Verifikationsherausforderungen wie der
Zustandsexplosion bei sequentiellen Schaltungen, limitiert diese Einschränkung
sofort verfügbarer Verifikationslösungen die Anwendbarkeit des Ansatzes in einem
größeren Kontext signifikant.\r\n\r\nDiese Dissertation schließt die Lücke zwischen
PCH und modernen Entwicklungen in der Schaltungsverifikation und stellt Methoden
und Werkzeuge zur Verfügung, welche die Zertifizierung einer großen Bandbreite
von Schaltungseigenschaften ermöglicht; sowohl funktionale, als auch nicht-funktionale.
Überdies werden erstmals Prototypen vorgestellt in welchen Schaltungen mittels
PCH verifiziert werden, die auf tatsächlicher rekonfigurierbarer Hardware realisiert
sind. Dank dieser Ergebnisse können Entwickler PCH zur Herstellung von Vertrauen
in weit komplexere Schaltungen verwenden, unter Zuhilfenahme einer größeren Vielfalt
von Eigenschaften, welche durch moderne, effiziente Spezifikationstechniken ausgedrückt
werden können."
author:
- first_name: Tobias
full_name: Wiersema, Tobias
id: '3118'
last_name: Wiersema
citation:
ama: Wiersema T. Guaranteeing Properties of Reconfigurable Hardware Circuits
with Proof-Carrying Hardware. Paderborn University; 2021.
apa: Wiersema, T. (2021). Guaranteeing Properties of Reconfigurable Hardware
Circuits with Proof-Carrying Hardware. Paderborn University.
bibtex: '@book{Wiersema_2021, place={Paderborn}, title={Guaranteeing Properties
of Reconfigurable Hardware Circuits with Proof-Carrying Hardware}, publisher={Paderborn
University}, author={Wiersema, Tobias}, year={2021} }'
chicago: 'Wiersema, Tobias. Guaranteeing Properties of Reconfigurable Hardware
Circuits with Proof-Carrying Hardware. Paderborn: Paderborn University, 2021.'
ieee: 'T. Wiersema, Guaranteeing Properties of Reconfigurable Hardware Circuits
with Proof-Carrying Hardware. Paderborn: Paderborn University, 2021.'
mla: Wiersema, Tobias. Guaranteeing Properties of Reconfigurable Hardware Circuits
with Proof-Carrying Hardware. Paderborn University, 2021.
short: T. Wiersema, Guaranteeing Properties of Reconfigurable Hardware Circuits
with Proof-Carrying Hardware, Paderborn University, Paderborn, 2021.
date_created: 2021-10-25T06:35:41Z
date_updated: 2022-01-06T06:57:26Z
ddc:
- '006'
department:
- _id: '78'
keyword:
- Proof-Carrying Hardware
- Formal Verification
- Sequential Circuits
- Non-Functional Properties
- Functional Properties
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://nbn-resolving.de/urn:nbn:de:hbz:466:2-39800
oa: '1'
page: '293'
place: Paderborn
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
publication_status: published
publisher: Paderborn University
status: public
supervisor:
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
title: Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying
Hardware
type: dissertation
user_id: '3118'
year: '2021'
...
---
_id: '27045'
abstract:
- lang: eng
text: 'Due to the lack of established real-world benchmark suites for static taint
analyses of Android applications, evaluations of these analyses are often restricted
and hard to compare. Even in evaluations that do use real-world apps, details
about the ground truth in those apps are rarely documented, which makes it difficult
to compare and reproduce the results. To push Android taint analysis research
forward, this paper thus recommends criteria for constructing real-world benchmark
suites for this specific domain, and presents TaintBench, the first real-world
malware benchmark suite with documented taint flows. TaintBench benchmark apps
include taint flows with complex structures, and addresses static challenges that
are commonly agreed on by the community. Together with the TaintBench suite, we
introduce the TaintBench framework, whose goal is to simplify real-world benchmarking
of Android taint analyses. First, a usability test shows that the framework improves
experts’ performance and perceived usability when documenting and inspecting taint
flows. Second, experiments using TaintBench reveal new insights for the taint
analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world
malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources
and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions
of both tools are less accurate than their predecessors.'
author:
- first_name: Linghui
full_name: Luo, Linghui
last_name: Luo
- first_name: Felix
full_name: Pauck, Felix
id: '22398'
last_name: Pauck
- first_name: Goran
full_name: Piskachev, Goran
id: '41936'
last_name: Piskachev
orcid: 0000-0003-4424-5838
- first_name: Manuel
full_name: Benz, Manuel
last_name: Benz
- first_name: Ivan
full_name: Pashchenko, Ivan
last_name: Pashchenko
- first_name: Martin
full_name: Mory, Martin
id: '65667'
last_name: Mory
orcid: 0000-0001-5609-0031
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
- first_name: Ben
full_name: Hermann, Ben
id: '66173'
last_name: Hermann
orcid: 0000-0001-9848-2017
- first_name: Fabio
full_name: Massacci, Fabio
last_name: Massacci
citation:
ama: 'Luo L, Pauck F, Piskachev G, et al. TaintBench: Automatic real-world malware
benchmarking of Android taint analyses. Empirical Software Engineering.
Published online 2021. doi:10.1007/s10664-021-10013-5'
apa: 'Luo, L., Pauck, F., Piskachev, G., Benz, M., Pashchenko, I., Mory, M., Bodden,
E., Hermann, B., & Massacci, F. (2021). TaintBench: Automatic real-world malware
benchmarking of Android taint analyses. Empirical Software Engineering.
https://doi.org/10.1007/s10664-021-10013-5'
bibtex: '@article{Luo_Pauck_Piskachev_Benz_Pashchenko_Mory_Bodden_Hermann_Massacci_2021,
title={TaintBench: Automatic real-world malware benchmarking of Android taint
analyses}, DOI={10.1007/s10664-021-10013-5},
journal={Empirical Software Engineering}, author={Luo, Linghui and Pauck, Felix
and Piskachev, Goran and Benz, Manuel and Pashchenko, Ivan and Mory, Martin and
Bodden, Eric and Hermann, Ben and Massacci, Fabio}, year={2021} }'
chicago: 'Luo, Linghui, Felix Pauck, Goran Piskachev, Manuel Benz, Ivan Pashchenko,
Martin Mory, Eric Bodden, Ben Hermann, and Fabio Massacci. “TaintBench: Automatic
Real-World Malware Benchmarking of Android Taint Analyses.” Empirical Software
Engineering, 2021. https://doi.org/10.1007/s10664-021-10013-5.'
ieee: 'L. Luo et al., “TaintBench: Automatic real-world malware benchmarking
of Android taint analyses,” Empirical Software Engineering, 2021, doi:
10.1007/s10664-021-10013-5.'
mla: 'Luo, Linghui, et al. “TaintBench: Automatic Real-World Malware Benchmarking
of Android Taint Analyses.” Empirical Software Engineering, 2021, doi:10.1007/s10664-021-10013-5.'
short: L. Luo, F. Pauck, G. Piskachev, M. Benz, I. Pashchenko, M. Mory, E. Bodden,
B. Hermann, F. Massacci, Empirical Software Engineering (2021).
date_created: 2021-11-02T05:13:49Z
date_updated: 2022-01-06T06:57:32Z
ddc:
- '000'
department:
- _id: '77'
- _id: '76'
doi: 10.1007/s10664-021-10013-5
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10664-021-10013-5.pdf
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
publication: Empirical Software Engineering
publication_identifier:
issn:
- 1382-3256
- 1573-7616
publication_status: published
status: public
title: 'TaintBench: Automatic real-world malware benchmarking of Android taint analyses'
type: journal_article
user_id: '15249'
year: '2021'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21092'
abstract:
- lang: eng
text: "Automated Machine Learning (AutoML) seeks to automatically find so-called
machine learning pipelines that maximize the prediction performance when being
used to train a model on a given dataset. One of the main and yet open challenges
in AutoML is an effective use of computational resources: An AutoML process involves
the evaluation of many candidate pipelines, which are costly but often ineffective
because they are canceled due to a timeout.\r\nIn this paper, we present an approach
to predict the runtime of two-step machine learning pipelines with up to one pre-processor,
which can be used to anticipate whether or not a pipeline will time out. Separate
runtime models are trained offline for each algorithm that may be used in a pipeline,
and an overall prediction is derived from these models. We empirically show that
the approach increases successful evaluations made by an AutoML tool while preserving
or even improving on the previously best solutions."
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline
Runtimes in the Context of Automated Machine Learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting
Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning
Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE
Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE},
author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier,
Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
n.d.
ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine
Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE
Transactions on Pattern Analysis and Machine Intelligence.
mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context
of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, IEEE.
short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (n.d.).
date_created: 2021-01-27T13:45:52Z
date_updated: 2022-01-06T06:54:45Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: accepted
publisher: IEEE
status: public
title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '17905'
abstract:
- lang: eng
text: 'This chapter concentrates on aspect-based sentiment analysis, a form of opinion
mining where algorithms detect sentiments expressed about features of products,
services, etc. We especially focus on novel approaches for aspect phrase extraction
and classification trained on feature-rich datasets. Here, we present two new
datasets, which we gathered from the linguistically rich domain of physician reviews,
as other investigations have mainly concentrated on commercial reviews and social
media reviews so far. To give readers a better understanding of the underlying
datasets, we describe the annotation process and inter-annotator agreement in
detail. In our research, we automatically assess implicit mentions or indications
of specific aspects. To do this, we propose and utilize neural network models
that perform the here-defined aspect phrase extraction and classification task,
achieving F1-score values of about 80% and accuracy values of more than 90%. As
we apply our models to a comparatively complex domain, we obtain promising results. '
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. Towards Aspect Extraction and Classification for Opinion
Mining with Deep Sequence Networks. In: Loukanova R, ed. Natural Language Processing
in Artificial Intelligence -- NLPinAI 2020. Vol 939. Studies in Computational
Intelligence (SCI). Cham: Springer; 2021:163--189. doi:10.1007/978-3-030-63787-3_6'
apa: 'Kersting, J., & Geierhos, M. (2021). Towards Aspect Extraction and Classification
for Opinion Mining with Deep Sequence Networks. In R. Loukanova (Ed.), Natural
Language Processing in Artificial Intelligence -- NLPinAI 2020 (Vol. 939,
pp. 163--189). Cham: Springer. https://doi.org/10.1007/978-3-030-63787-3_6'
bibtex: '@inbook{Kersting_Geierhos_2021, place={Cham}, series={Studies in Computational
Intelligence (SCI)}, title={Towards Aspect Extraction and Classification for Opinion
Mining with Deep Sequence Networks}, volume={939}, DOI={10.1007/978-3-030-63787-3_6},
booktitle={Natural Language Processing in Artificial Intelligence -- NLPinAI 2020},
publisher={Springer}, author={Kersting, Joschka and Geierhos, Michaela}, editor={Loukanova,
RoussankaEditor}, year={2021}, pages={163--189}, collection={Studies in Computational
Intelligence (SCI)} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “Towards Aspect Extraction and
Classification for Opinion Mining with Deep Sequence Networks.” In Natural
Language Processing in Artificial Intelligence -- NLPinAI 2020, edited by
Roussanka Loukanova, 939:163--189. Studies in Computational Intelligence (SCI).
Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63787-3_6.'
ieee: 'J. Kersting and M. Geierhos, “Towards Aspect Extraction and Classification
for Opinion Mining with Deep Sequence Networks,” in Natural Language Processing
in Artificial Intelligence -- NLPinAI 2020, vol. 939, R. Loukanova, Ed. Cham:
Springer, 2021, pp. 163--189.'
mla: Kersting, Joschka, and Michaela Geierhos. “Towards Aspect Extraction and Classification
for Opinion Mining with Deep Sequence Networks.” Natural Language Processing
in Artificial Intelligence -- NLPinAI 2020, edited by Roussanka Loukanova,
vol. 939, Springer, 2021, pp. 163--189, doi:10.1007/978-3-030-63787-3_6.
short: 'J. Kersting, M. Geierhos, in: R. Loukanova (Ed.), Natural Language Processing
in Artificial Intelligence -- NLPinAI 2020, Springer, Cham, 2021, pp. 163--189.'
date_created: 2020-08-13T09:29:52Z
date_updated: 2022-01-06T06:53:23Z
ddc:
- '000'
department:
- _id: '579'
doi: 10.1007/978-3-030-63787-3_6
editor:
- first_name: Roussanka
full_name: Loukanova, Roussanka
last_name: Loukanova
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2021-04-08T08:14:05Z
date_updated: 2021-04-08T08:14:05Z
file_id: '21594'
file_name: Kersting-Geierhos2021_Chapter_TowardsAspectExtractionAndClas.pdf
file_size: 512065
relation: main_file
success: 1
file_date_updated: 2021-04-08T08:14:05Z
has_accepted_license: '1'
intvolume: ' 939'
language:
- iso: eng
page: '163--189 '
place: Cham
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: Natural Language Processing in Artificial Intelligence -- NLPinAI 2020
publication_identifier:
unknown:
- 978-3-030-63786-6 ; 978-3-030-63787-3
publication_status: published
publisher: Springer
series_title: Studies in Computational Intelligence (SCI)
status: public
title: Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence
Networks
type: book_chapter
user_id: '58701'
volume: 939
year: '2021'
...
---
_id: '22304'
author:
- first_name: Stefan
full_name: Schott, Stefan
id: '54847'
last_name: Schott
citation:
ama: Schott S. Android App Analysis Benchmark Case Generation. Paderborn;
2021.
apa: Schott, S. (2021). Android App Analysis Benchmark Case Generation. Paderborn.
bibtex: '@book{Schott_2021, place={Paderborn}, title={Android App Analysis Benchmark
Case Generation}, author={Schott, Stefan}, year={2021} }'
chicago: Schott, Stefan. Android App Analysis Benchmark Case Generation.
Paderborn, 2021.
ieee: S. Schott, Android App Analysis Benchmark Case Generation. Paderborn,
2021.
mla: Schott, Stefan. Android App Analysis Benchmark Case Generation. 2021.
short: S. Schott, Android App Analysis Benchmark Case Generation, Paderborn, 2021.
date_created: 2021-06-09T06:04:12Z
date_updated: 2022-01-06T06:55:31Z
ddc:
- '000'
department:
- _id: '77'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: fpauck
date_created: 2021-06-09T06:01:45Z
date_updated: 2021-06-09T06:01:45Z
file_id: '22305'
file_name: Masters_Thesis_Stefan_Schott_unsigned.pdf
file_size: 1918444
relation: main_file
file_date_updated: 2021-06-09T06:01:45Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
place: Paderborn
project:
- _id: '1'
name: SFB 901
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '3'
name: SFB 901 - Project Area B
status: public
supervisor:
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
title: Android App Analysis Benchmark Case Generation
type: mastersthesis
user_id: '477'
year: '2021'
...
---
_id: '22913'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded
Rationality, and Rational Metareasoning. In: ; 2021.'
apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated
Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD
Workshop on Automating Data Science, Bilbao (Virtual).
bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine
Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier,
Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021}
}'
chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever.
“Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,”
2021.
ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning,
Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop
on Automating Data Science, Bilbao (Virtual), 2021.
mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality,
and Rational Metareasoning. 2021.
short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.'
conference:
end_date: 2021-09-17
location: Bilbao (Virtual)
name: ECML/PKDD Workshop on Automating Data Science
start_date: 2021-09-13
date_created: 2021-08-02T07:46:29Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
quality_controlled: '1'
status: public
title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '22927'
author:
- first_name: John
full_name: Derrick, John
last_name: Derrick
- first_name: Simon
full_name: Doherty, Simon
last_name: Doherty
- first_name: Brijesh
full_name: Dongol, Brijesh
last_name: Dongol
- first_name: Gerhard
full_name: Schellhorn, Gerhard
last_name: Schellhorn
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Derrick J, Doherty S, Dongol B, Schellhorn G, Wehrheim H. On Strong Observational
Refinement and Forward Simulation. In: Proceedings of the 35th International
Symposium on Distributed Computing (DISC). Leibniz International Proceedings
in Informatics. Schloß Dagstuhl.'
apa: Derrick, J., Doherty, S., Dongol, B., Schellhorn, G., & Wehrheim, H. (n.d.).
On Strong Observational Refinement and Forward Simulation. In Proceedings of
the 35th International Symposium on Distributed Computing (DISC). Schloß Dagstuhl.
bibtex: '@inproceedings{Derrick_Doherty_Dongol_Schellhorn_Wehrheim, series={Leibniz
International Proceedings in Informatics}, title={On Strong Observational Refinement
and Forward Simulation}, booktitle={Proceedings of the 35th International Symposium
on Distributed Computing (DISC)}, publisher={Schloß Dagstuhl}, author={Derrick,
John and Doherty, Simon and Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim,
Heike}, collection={Leibniz International Proceedings in Informatics} }'
chicago: Derrick, John, Simon Doherty, Brijesh Dongol, Gerhard Schellhorn, and Heike
Wehrheim. “On Strong Observational Refinement and Forward Simulation.” In Proceedings
of the 35th International Symposium on Distributed Computing (DISC). Leibniz
International Proceedings in Informatics. Schloß Dagstuhl, n.d.
ieee: J. Derrick, S. Doherty, B. Dongol, G. Schellhorn, and H. Wehrheim, “On Strong
Observational Refinement and Forward Simulation,” in Proceedings of the 35th
International Symposium on Distributed Computing (DISC).
mla: Derrick, John, et al. “On Strong Observational Refinement and Forward Simulation.”
Proceedings of the 35th International Symposium on Distributed Computing (DISC),
Schloß Dagstuhl.
short: 'J. Derrick, S. Doherty, B. Dongol, G. Schellhorn, H. Wehrheim, in: Proceedings
of the 35th International Symposium on Distributed Computing (DISC), Schloß Dagstuhl,
n.d.'
date_created: 2021-08-03T07:38:56Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '7'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
publication: Proceedings of the 35th International Symposium on Distributed Computing
(DISC)
publication_status: accepted
publisher: Schloß Dagstuhl
series_title: Leibniz International Proceedings in Informatics
status: public
title: On Strong Observational Refinement and Forward Simulation
type: conference
user_id: '477'
year: '2021'
...
---
_id: '22051'
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. Well-being in Plastic Surgery: Deep Learning Reveals
Patients’ Evaluations. In: Proceedings of the 10th International Conference
on Data Science, Technology and Applications (DATA 2021). SCITEPRESS; 2021:275--284.'
apa: 'Kersting, J., & Geierhos, M. (2021). Well-being in Plastic Surgery: Deep
Learning Reveals Patients’ Evaluations. Proceedings of the 10th International
Conference on Data Science, Technology and Applications (DATA 2021), 275--284.'
bibtex: '@inproceedings{Kersting_Geierhos_2021, place={Online}, title={Well-being
in Plastic Surgery: Deep Learning Reveals Patients’ Evaluations}, booktitle={Proceedings
of the 10th International Conference on Data Science, Technology and Applications
(DATA 2021)}, publisher={SCITEPRESS}, author={Kersting, Joschka and Geierhos,
Michaela}, year={2021}, pages={275--284} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “Well-Being in Plastic Surgery:
Deep Learning Reveals Patients’ Evaluations.” In Proceedings of the 10th International
Conference on Data Science, Technology and Applications (DATA 2021), 275--284.
Online: SCITEPRESS, 2021.'
ieee: 'J. Kersting and M. Geierhos, “Well-being in Plastic Surgery: Deep Learning
Reveals Patients’ Evaluations,” in Proceedings of the 10th International Conference
on Data Science, Technology and Applications (DATA 2021), Online, 2021, pp.
275--284.'
mla: 'Kersting, Joschka, and Michaela Geierhos. “Well-Being in Plastic Surgery:
Deep Learning Reveals Patients’ Evaluations.” Proceedings of the 10th International
Conference on Data Science, Technology and Applications (DATA 2021), SCITEPRESS,
2021, pp. 275--284.'
short: 'J. Kersting, M. Geierhos, in: Proceedings of the 10th International Conference
on Data Science, Technology and Applications (DATA 2021), SCITEPRESS, Online,
2021, pp. 275--284.'
conference:
end_date: 2021-07-08
location: Online
name: 10th International Conference on Data Science, Technology and Applications
(DATA 2021)
start_date: 2021-07-06
date_created: 2021-05-07T16:27:27Z
date_updated: 2022-01-06T06:55:23Z
department:
- _id: '579'
language:
- iso: eng
page: 275--284
place: Online
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: Proceedings of the 10th International Conference on Data Science, Technology
and Applications (DATA 2021)
publication_status: published
publisher: SCITEPRESS
status: public
title: 'Well-being in Plastic Surgery: Deep Learning Reveals Patients'' Evaluations'
type: conference
user_id: '58701'
year: '2021'
...
---
_id: '21953'
author:
- first_name: Linus Matthias
full_name: Witschen, Linus Matthias
id: '49051'
last_name: Witschen
- first_name: Tobias
full_name: Wiersema, Tobias
id: '3118'
last_name: Wiersema
- first_name: Masood
full_name: Raeisi Nafchi, Masood
last_name: Raeisi Nafchi
- first_name: Arne
full_name: Bockhorn, Arne
last_name: Bockhorn
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Witschen LM, Wiersema T, Raeisi Nafchi M, Bockhorn A, Platzner M. Timing Optimization
for Virtual FPGA Configurations. In: Hannig F, Derrien S, Diniz P, Chillet D,
eds. Proceedings of International Symposium on Applied Reconfigurable Computing
(ARC’21). Reconfigurable Computing: Architectures, Tools, and Applications.
Springer Lecture Notes in Computer Science. doi:10.1007/978-3-030-79025-7_4'
apa: Witschen, L. M., Wiersema, T., Raeisi Nafchi, M., Bockhorn, A., & Platzner,
M. (n.d.). Timing Optimization for Virtual FPGA Configurations. In F. Hannig,
S. Derrien, P. Diniz, & D. Chillet (Eds.), Proceedings of International
Symposium on Applied Reconfigurable Computing (ARC’21). Springer Lecture Notes
in Computer Science. https://doi.org/10.1007/978-3-030-79025-7_4
bibtex: '@inproceedings{Witschen_Wiersema_Raeisi Nafchi_Bockhorn_Platzner, series={Reconfigurable
Computing: Architectures, Tools, and Applications}, title={Timing Optimization
for Virtual FPGA Configurations}, DOI={10.1007/978-3-030-79025-7_4},
booktitle={Proceedings of International Symposium on Applied Reconfigurable Computing
(ARC’21)}, publisher={Springer Lecture Notes in Computer Science}, author={Witschen,
Linus Matthias and Wiersema, Tobias and Raeisi Nafchi, Masood and Bockhorn, Arne
and Platzner, Marco}, editor={Hannig, Frank and Derrien, Steven and Diniz, Pedro
and Chillet, Daniel}, collection={Reconfigurable Computing: Architectures, Tools,
and Applications} }'
chicago: 'Witschen, Linus Matthias, Tobias Wiersema, Masood Raeisi Nafchi, Arne
Bockhorn, and Marco Platzner. “Timing Optimization for Virtual FPGA Configurations.”
In Proceedings of International Symposium on Applied Reconfigurable Computing
(ARC’21), edited by Frank Hannig, Steven Derrien, Pedro Diniz, and Daniel
Chillet. Reconfigurable Computing: Architectures, Tools, and Applications. Springer
Lecture Notes in Computer Science, n.d. https://doi.org/10.1007/978-3-030-79025-7_4.'
ieee: 'L. M. Witschen, T. Wiersema, M. Raeisi Nafchi, A. Bockhorn, and M. Platzner,
“Timing Optimization for Virtual FPGA Configurations,” in Proceedings of International
Symposium on Applied Reconfigurable Computing (ARC’21), Virtual conference,
doi: 10.1007/978-3-030-79025-7_4.'
mla: Witschen, Linus Matthias, et al. “Timing Optimization for Virtual FPGA Configurations.”
Proceedings of International Symposium on Applied Reconfigurable Computing
(ARC’21), edited by Frank Hannig et al., Springer Lecture Notes in Computer
Science, doi:10.1007/978-3-030-79025-7_4.
short: 'L.M. Witschen, T. Wiersema, M. Raeisi Nafchi, A. Bockhorn, M. Platzner,
in: F. Hannig, S. Derrien, P. Diniz, D. Chillet (Eds.), Proceedings of International
Symposium on Applied Reconfigurable Computing (ARC’21), Springer Lecture Notes
in Computer Science, n.d.'
conference:
end_date: 2021-07-01
location: Virtual conference
name: International Symposium on Applied Reconfigurable Computing
start_date: 2021-06-29
date_created: 2021-05-04T14:18:46Z
date_updated: 2022-02-14T11:03:09Z
department:
- _id: '78'
doi: 10.1007/978-3-030-79025-7_4
editor:
- first_name: Frank
full_name: Hannig, Frank
last_name: Hannig
- first_name: Steven
full_name: Derrien, Steven
last_name: Derrien
- first_name: Pedro
full_name: Diniz, Pedro
last_name: Diniz
- first_name: Daniel
full_name: Chillet, Daniel
last_name: Chillet
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of International Symposium on Applied Reconfigurable Computing
(ARC'21)
publication_status: accepted
publisher: Springer Lecture Notes in Computer Science
series_title: 'Reconfigurable Computing: Architectures, Tools, and Applications'
status: public
title: Timing Optimization for Virtual FPGA Configurations
type: conference
user_id: '3118'
year: '2021'
...
---
_id: '21598'
abstract:
- lang: eng
text: Static analysis is used to automatically detect bugs and security breaches,
and aids compileroptimization. Whole-program analysis (WPA) can yield high precision,
however causes long analysistimes and thus does not match common software-development
workflows, making it often impracticalto use for large, real-world applications.This
paper thus presents the design and implementation ofModAlyzer, a novel static-analysisapproach
that aims at accelerating whole-program analysis by making the analysis modular
andcompositional. It shows how to computelossless, persisted summaries for callgraph,
points-to anddata-flow information, and it reports under which circumstances this
function-level compositionalanalysis outperforms WPA.We implementedModAlyzeras
an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications.
At analysis time,ModAlyzermodularly and losslessly summarizesthe analysis effect
of the library code those applications share, hence avoiding its repeated re-analysis.The
experimental results show that the reuse of these summaries can save, on average,
72% ofanalysis time over WPA. Moreover, because it is lossless, the module-wise
analysis fully retainsprecision and recall. Surprisingly, as our results show,
it sometimes even yields precision superior toWPA. The initial summary generation,
on average, takes about 3.67 times as long as WPA.
author:
- first_name: Philipp
full_name: Schubert, Philipp
id: '60543'
last_name: Schubert
orcid: 0000-0002-8674-1859
- first_name: Ben
full_name: Hermann, Ben
id: '66173'
last_name: Hermann
orcid: 0000-0001-9848-2017
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Schubert P, Hermann B, Bodden E. Lossless, Persisted Summarization of Static
Callgraph, Points-To and Data-Flow Analysis. In: European Conference on Object-Oriented
Programming (ECOOP). ; 2021.'
apa: Schubert, P., Hermann, B., & Bodden, E. (2021). Lossless, Persisted Summarization
of Static Callgraph, Points-To and Data-Flow Analysis. European Conference
on Object-Oriented Programming (ECOOP).
bibtex: '@inproceedings{Schubert_Hermann_Bodden_2021, title={Lossless, Persisted
Summarization of Static Callgraph, Points-To and Data-Flow Analysis}, booktitle={European
Conference on Object-Oriented Programming (ECOOP)}, author={Schubert, Philipp
and Hermann, Ben and Bodden, Eric}, year={2021} }'
chicago: Schubert, Philipp, Ben Hermann, and Eric Bodden. “Lossless, Persisted Summarization
of Static Callgraph, Points-To and Data-Flow Analysis.” In European Conference
on Object-Oriented Programming (ECOOP), 2021.
ieee: P. Schubert, B. Hermann, and E. Bodden, “Lossless, Persisted Summarization
of Static Callgraph, Points-To and Data-Flow Analysis,” 2021.
mla: Schubert, Philipp, et al. “Lossless, Persisted Summarization of Static Callgraph,
Points-To and Data-Flow Analysis.” European Conference on Object-Oriented Programming
(ECOOP), 2021.
short: 'P. Schubert, B. Hermann, E. Bodden, in: European Conference on Object-Oriented
Programming (ECOOP), 2021.'
date_created: 2021-04-08T11:24:59Z
date_updated: 2022-03-25T07:49:35Z
department:
- _id: '76'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://drops.dagstuhl.de/opus/volltexte/2021/14045/
oa: '1'
project:
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '1'
name: SFB 901
publication: European Conference on Object-Oriented Programming (ECOOP)
status: public
title: Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow
Analysis
type: conference
user_id: '60543'
year: '2021'
...
---
_id: '30866'
abstract:
- lang: eng
text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof
machine learning algorithms and their composition into an overall (software)\r\nsolution
- a machine learning pipeline - tailored to the learning task\r\n(dataset) at
hand. Over the last decade, AutoML has developed into an\r\nindependent research
field with hundreds of contributions. While AutoML offers\r\nmany prospects, it
is also known to be quite resource-intensive, which is one\r\nof its major points
of criticism. The primary cause for a high resource\r\nconsumption is that many
approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines
while searching for good candidates. This problem is\r\namplified in the context
of research on AutoML methods, due to large scale\r\nexperiments conducted with
many datasets and approaches, each of them being run\r\nwith several repetitions
to rule out random effects. In the spirit of recent\r\nwork on Green AI, this
paper is written in an attempt to raise the awareness of\r\nAutoML researchers
for the problem and to elaborate on possible remedies. To\r\nthis end, we identify
four categories of actions the community may take towards\r\nmore sustainable
research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking,
transparency and research incentives."
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards
Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850.
Published online 2021.'
apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier,
E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
In arXiv:2111.05850.'
bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards
Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850},
author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik
Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning:
Status Quo and Future Directions.” ArXiv:2111.05850, 2021.'
ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier,
“Towards Green Automated Machine Learning: Status Quo and Future Directions,”
arXiv:2111.05850. 2021.'
mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo
and Future Directions.” ArXiv:2111.05850, 2021.'
short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier,
ArXiv:2111.05850 (2021).
date_created: 2022-04-12T11:57:15Z
date_updated: 2022-04-12T12:01:23Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2111.05850'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2111.05850
status: public
title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions'
type: preprint
user_id: '38209'
year: '2021'
...
---
_id: '27284'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: Wever MD. Automated Machine Learning for Multi-Label Classification.;
2021. doi:10.17619/UNIPB/1-1302
apa: Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification.
https://doi.org/10.17619/UNIPB/1-1302
bibtex: '@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification},
DOI={10.17619/UNIPB/1-1302},
author={Wever, Marcel Dominik}, year={2021} }'
chicago: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification,
2021. https://doi.org/10.17619/UNIPB/1-1302.
ieee: M. D. Wever, Automated Machine Learning for Multi-Label Classification.
2021.
mla: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification.
2021, doi:10.17619/UNIPB/1-1302.
short: M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.
date_created: 2021-11-08T14:05:19Z
date_updated: 2022-04-13T09:39:56Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.17619/UNIPB/1-1302
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2022-04-13T09:35:25Z
date_updated: 2022-04-13T09:39:56Z
file_id: '30886'
file_name: dissertation_publish_upload.pdf
file_size: 8098177
relation: main_file
file_date_updated: 2022-04-13T09:39:56Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication_status: published
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Automated Machine Learning for Multi-Label Classification
type: dissertation
user_id: '33176'
year: '2021'
...
---
_id: '22052'
abstract:
- lang: eng
text: 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:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. Human Language Comprehension in Aspect Phrase Extraction
with Importance Weighting. In: Kapetanios E, Horacek H, Métais E, Meziane F, eds.
Natural Language Processing and Information Systems. Vol 12801. Lecture
Notes in Computer Science. Springer; 2021:231--242.'
apa: Kersting, J., & Geierhos, M. (2021). Human Language Comprehension in Aspect
Phrase Extraction with Importance Weighting. In E. Kapetanios, H. Horacek, E.
Métais, & F. Meziane (Eds.), Natural Language Processing and Information
Systems (Vol. 12801, pp. 231--242). Springer.
bibtex: '@inbook{Kersting_Geierhos_2021, place={Saarbrücken, Germany}, series={Lecture
Notes in Computer Science}, title={Human Language Comprehension in Aspect Phrase
Extraction with Importance Weighting}, volume={12801}, booktitle={Natural Language
Processing and Information Systems}, publisher={Springer}, author={Kersting, Joschka
and Geierhos, Michaela}, editor={Kapetanios, Epaminondas and Horacek, Helmut and
Métais, Elisabeth and Meziane, Farid}, year={2021}, pages={231--242}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “Human Language Comprehension
in Aspect Phrase Extraction with Importance Weighting.” In Natural Language
Processing and Information Systems, edited by Epaminondas Kapetanios, Helmut
Horacek, Elisabeth Métais, and Farid Meziane, 12801:231--242. Lecture Notes in
Computer Science. Saarbrücken, Germany: Springer, 2021.'
ieee: 'J. Kersting and M. Geierhos, “Human Language Comprehension in Aspect Phrase
Extraction with Importance Weighting,” in Natural Language Processing and Information
Systems, vol. 12801, E. Kapetanios, H. Horacek, E. Métais, and F. Meziane,
Eds. Saarbrücken, Germany: Springer, 2021, pp. 231--242.'
mla: Kersting, Joschka, and Michaela Geierhos. “Human Language Comprehension in
Aspect Phrase Extraction with Importance Weighting.” Natural Language Processing
and Information Systems, edited by Epaminondas Kapetanios et al., vol. 12801,
Springer, 2021, pp. 231--242.
short: 'J. Kersting, M. Geierhos, in: E. Kapetanios, H. Horacek, E. Métais, F. Meziane
(Eds.), Natural Language Processing and Information Systems, Springer, Saarbrücken,
Germany, 2021, pp. 231--242.'
conference:
end_date: 2021-06-25
location: Saarbrücken, Germany
name: 26th International Conference on Natural Language & Information Systems (NLDB
2021)
start_date: 2021-06-23
date_created: 2021-05-07T16:31:05Z
date_updated: 2022-07-14T08:00:56Z
ddc:
- '004'
department:
- _id: '579'
editor:
- first_name: Epaminondas
full_name: Kapetanios, Epaminondas
last_name: Kapetanios
- first_name: Helmut
full_name: Horacek, Helmut
last_name: Horacek
- first_name: Elisabeth
full_name: Métais, Elisabeth
last_name: Métais
- first_name: Farid
full_name: Meziane, Farid
last_name: Meziane
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2022-07-14T08:00:35Z
date_updated: 2022-07-14T08:00:35Z
file_id: '32362'
file_name: Kersting & Geierhos (2021b), Kersting2021b.pdf
file_size: 506329
relation: main_file
success: 1
file_date_updated: 2022-07-14T08:00:35Z
has_accepted_license: '1'
intvolume: ' 12801'
language:
- iso: eng
page: 231--242
place: Saarbrücken, Germany
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: Natural Language Processing and Information Systems
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting
type: book_chapter
user_id: '58701'
volume: 12801
year: '2021'
...
---
_id: '28199'
author:
- first_name: Felix
full_name: Pauck, Felix
id: '22398'
last_name: Pauck
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Pauck F, Wehrheim H. Jicer: Simplifying Cooperative Android App Analysis Tasks.
In: 2021 IEEE 21st International Working Conference on Source Code Analysis
and Manipulation (SCAM). ; 2021. doi:10.1109/scam52516.2021.00031'
apa: 'Pauck, F., & Wehrheim, H. (2021). Jicer: Simplifying Cooperative Android
App Analysis Tasks. 2021 IEEE 21st International Working Conference on Source
Code Analysis and Manipulation (SCAM). https://doi.org/10.1109/scam52516.2021.00031'
bibtex: '@inproceedings{Pauck_Wehrheim_2021, title={Jicer: Simplifying Cooperative
Android App Analysis Tasks}, DOI={10.1109/scam52516.2021.00031},
booktitle={2021 IEEE 21st International Working Conference on Source Code Analysis
and Manipulation (SCAM)}, author={Pauck, Felix and Wehrheim, Heike}, year={2021}
}'
chicago: 'Pauck, Felix, and Heike Wehrheim. “Jicer: Simplifying Cooperative Android
App Analysis Tasks.” In 2021 IEEE 21st International Working Conference on
Source Code Analysis and Manipulation (SCAM), 2021. https://doi.org/10.1109/scam52516.2021.00031.'
ieee: 'F. Pauck and H. Wehrheim, “Jicer: Simplifying Cooperative Android App Analysis
Tasks,” 2021, doi: 10.1109/scam52516.2021.00031.'
mla: 'Pauck, Felix, and Heike Wehrheim. “Jicer: Simplifying Cooperative Android
App Analysis Tasks.” 2021 IEEE 21st International Working Conference on Source
Code Analysis and Manipulation (SCAM), 2021, doi:10.1109/scam52516.2021.00031.'
short: 'F. Pauck, H. Wehrheim, in: 2021 IEEE 21st International Working Conference
on Source Code Analysis and Manipulation (SCAM), 2021.'
date_created: 2021-12-01T08:53:29Z
date_updated: 2022-11-17T14:26:19Z
department:
- _id: '77'
doi: 10.1109/scam52516.2021.00031
language:
- iso: eng
project:
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
publication: 2021 IEEE 21st International Working Conference on Source Code Analysis
and Manipulation (SCAM)
publication_status: published
status: public
title: 'Jicer: Simplifying Cooperative Android App Analysis Tasks'
type: conference
user_id: '477'
year: '2021'
...
---
_id: '23709'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Khalid
full_name: Al Khatib, Khalid
last_name: Al Khatib
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Chen W-F, Al Khatib K, Stein B, Wachsmuth H. Controlled Neural Sentence-Level
Reframing of News Articles. In: Findings of the Association for Computational
Linguistics: EMNLP 2021. ; 2021:2683-2693.'
apa: 'Chen, W.-F., Al Khatib, K., Stein, B., & Wachsmuth, H. (2021). Controlled
Neural Sentence-Level Reframing of News Articles. Findings of the Association
for Computational Linguistics: EMNLP 2021, 2683–2693.'
bibtex: '@inproceedings{Chen_Al Khatib_Stein_Wachsmuth_2021, title={Controlled Neural
Sentence-Level Reframing of News Articles}, booktitle={Findings of the Association
for Computational Linguistics: EMNLP 2021}, author={Chen, Wei-Fan and Al Khatib,
Khalid and Stein, Benno and Wachsmuth, Henning}, year={2021}, pages={2683–2693}
}'
chicago: 'Chen, Wei-Fan, Khalid Al Khatib, Benno Stein, and Henning Wachsmuth. “Controlled
Neural Sentence-Level Reframing of News Articles.” In Findings of the Association
for Computational Linguistics: EMNLP 2021, 2683–93, 2021.'
ieee: 'W.-F. Chen, K. Al Khatib, B. Stein, and H. Wachsmuth, “Controlled Neural
Sentence-Level Reframing of News Articles,” in Findings of the Association
for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–2693.'
mla: 'Chen, Wei-Fan, et al. “Controlled Neural Sentence-Level Reframing of News
Articles.” Findings of the Association for Computational Linguistics: EMNLP
2021, 2021, pp. 2683–93.'
short: 'W.-F. Chen, K. Al Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association
for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–2693.'
date_created: 2021-09-02T20:09:20Z
date_updated: 2022-05-09T15:00:09Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://aclanthology.org/2021.findings-emnlp.228.pdf
page: 2683 - 2693
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2021'
status: public
title: Controlled Neural Sentence-Level Reframing of News Articles
type: conference
user_id: '82920'
year: '2021'
...
---
_id: '22229'
author:
- first_name: Milad
full_name: Alshomary, Milad
id: '73059'
last_name: Alshomary
- first_name: Shahbaz
full_name: Syed, Shahbaz
last_name: Syed
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Alshomary M, Syed S, Potthast M, Wachsmuth H. Argument Undermining: Counter-Argument
Generation by Attacking Weak Premises. In: 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 of the Association for Computational Linguistics: ACL-IJCNLP
2021. Association for Computational Linguistics; 2021:1816–1827. doi:10.18653/v1/2021.findings-acl.159'
apa: 'Alshomary, M., Syed, S., Potthast, M., & Wachsmuth, H. (2021). Argument
Undermining: Counter-Argument Generation by Attacking Weak Premises. 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), 1816–1827. https://doi.org/10.18653/v1/2021.findings-acl.159'
bibtex: '@inproceedings{Alshomary_Syed_Potthast_Wachsmuth_2021, series={Findings
of the Association for Computational Linguistics: ACL-IJCNLP 2021}, title={Argument
Undermining: Counter-Argument Generation by Attacking Weak Premises}, DOI={10.18653/v1/2021.findings-acl.159},
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)}, publisher={Association for
Computational Linguistics}, author={Alshomary, Milad and Syed, Shahbaz and Potthast,
Martin and Wachsmuth, Henning}, year={2021}, pages={1816–1827}, collection={Findings
of the Association for Computational Linguistics: ACL-IJCNLP 2021} }'
chicago: 'Alshomary, Milad, Shahbaz Syed, Martin Potthast, and Henning Wachsmuth.
“Argument Undermining: Counter-Argument Generation by Attacking Weak Premises.”
In 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), 1816–1827. Findings of the Association
for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational
Linguistics, 2021. https://doi.org/10.18653/v1/2021.findings-acl.159.'
ieee: 'M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Argument Undermining:
Counter-Argument Generation by Attacking Weak Premises,” in 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), Online, 2021, pp. 1816–1827, doi: 10.18653/v1/2021.findings-acl.159.'
mla: 'Alshomary, Milad, et al. “Argument Undermining: Counter-Argument Generation
by Attacking Weak Premises.” 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), Association
for Computational Linguistics, 2021, pp. 1816–1827, doi:10.18653/v1/2021.findings-acl.159.'
short: 'M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: 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), Association for Computational Linguistics, 2021, pp. 1816–1827.'
conference:
location: Online
name: 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)
date_created: 2021-05-26T07:06:18Z
date_updated: 2022-05-09T15:06:36Z
department:
- _id: '600'
doi: 10.18653/v1/2021.findings-acl.159
language:
- iso: eng
main_file_link:
- url: https://aclanthology.org/2021.findings-acl.159.pdf
page: 1816–1827
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: 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)
publisher: Association for Computational Linguistics
series_title: 'Findings of the Association for Computational Linguistics: ACL-IJCNLP
2021'
status: public
title: 'Argument Undermining: Counter-Argument Generation by Attacking Weak Premises'
type: conference
user_id: '82920'
year: '2021'
...
---
_id: '21198'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset
Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published
online 2021.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021).
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD-2021), Delhi, India.'
bibtex: '@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm
Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise
Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm
Selectors from Imprecise Performance Data.” PAKDD, 2021.'
ieee: 'J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection
as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance
Data.” 2021.'
mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning:
Constructing Algorithm Selectors from Imprecise Performance Data. 2021.'
short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).
conference:
end_date: 2021-05-14
location: Delhi, India
name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
start_date: 2021-05-11
date_created: 2021-02-09T09:30:14Z
date_updated: 2022-08-24T12:49:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
series_title: PAKDD
status: public
title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data'
type: conference
user_id: '38209'
year: '2021'
...
---
_id: '27841'
abstract:
- lang: eng
text: Verification of software and processor hardware usually proceeds separately,
software analysis relying on the correctness of processors executing machine instructions.
This assumption is valid as long as the software runs on standard CPUs that have
been extensively validated and are in wide use. However, for processors exploiting
custom instruction set extensions to meet performance and energy constraints the
validation might be less extensive, challenging the correctness assumption. In
this paper we present a novel formal approach for hardware/software co-verification
targeting processors with custom instruction set extensions. We detail two different
approaches for checking whether the hardware fulfills the requirements expected
by the software analysis. The approaches are designed to explore a trade-off between
generality of the verification and computational effort. Then, we describe the
integration of software and hardware analyses for both techniques and describe
a fully automated tool chain implementing the approaches. Finally, we demonstrate
and compare the two approaches on example source code with custom instructions,
using state-of-the-art software analysis and hardware verification techniques.
author:
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Felix
full_name: Pauck, Felix
id: '22398'
last_name: Pauck
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
- first_name: Tobias
full_name: Wiersema, Tobias
id: '3118'
last_name: Wiersema
citation:
ama: Jakobs M-C, Pauck F, Platzner M, Wehrheim H, Wiersema T. Software/Hardware
Co-Verification for Custom Instruction Set Processors. IEEE Access. Published
online 2021. doi:10.1109/ACCESS.2021.3131213
apa: Jakobs, M.-C., Pauck, F., Platzner, M., Wehrheim, H., & Wiersema, T. (2021).
Software/Hardware Co-Verification for Custom Instruction Set Processors. IEEE
Access. https://doi.org/10.1109/ACCESS.2021.3131213
bibtex: '@article{Jakobs_Pauck_Platzner_Wehrheim_Wiersema_2021, title={Software/Hardware
Co-Verification for Custom Instruction Set Processors}, DOI={10.1109/ACCESS.2021.3131213},
journal={IEEE Access}, publisher={IEEE}, author={Jakobs, Marie-Christine and Pauck,
Felix and Platzner, Marco and Wehrheim, Heike and Wiersema, Tobias}, year={2021}
}'
chicago: Jakobs, Marie-Christine, Felix Pauck, Marco Platzner, Heike Wehrheim, and
Tobias Wiersema. “Software/Hardware Co-Verification for Custom Instruction Set
Processors.” IEEE Access, 2021. https://doi.org/10.1109/ACCESS.2021.3131213.
ieee: 'M.-C. Jakobs, F. Pauck, M. Platzner, H. Wehrheim, and T. Wiersema, “Software/Hardware
Co-Verification for Custom Instruction Set Processors,” IEEE Access, 2021,
doi: 10.1109/ACCESS.2021.3131213.'
mla: Jakobs, Marie-Christine, et al. “Software/Hardware Co-Verification for Custom
Instruction Set Processors.” IEEE Access, IEEE, 2021, doi:10.1109/ACCESS.2021.3131213.
short: M.-C. Jakobs, F. Pauck, M. Platzner, H. Wehrheim, T. Wiersema, IEEE Access
(2021).
date_created: 2021-11-25T14:12:22Z
date_updated: 2023-01-18T08:34:50Z
department:
- _id: '78'
doi: 10.1109/ACCESS.2021.3131213
funded_apc: '1'
keyword:
- Software Analysis
- Abstract Interpretation
- Custom Instruction
- Hardware Verification
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
publication: IEEE Access
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Software/Hardware Co-Verification for Custom Instruction Set Processors
type: journal_article
user_id: '22398'
year: '2021'
...