---
_id: '28350'
abstract:
- lang: eng
text: "In recent years, we observe an increasing amount of software with machine
learning components being deployed. This poses the question of quality assurance
for such components: how can we validate whether specified requirements are fulfilled
by a machine learned software? Current testing and verification approaches either
focus on a single requirement (e.g., fairness) or specialize on a single type
of machine learning model (e.g., neural networks).\r\nIn this paper, we propose
property-driven testing of machine learning models. Our approach MLCheck encompasses
(1) a language for property specification, and (2) a technique for systematic
test case generation. The specification language is comparable to property-based
testing languages. Test case generation employs advanced verification technology
for a systematic, property dependent construction of test suites, without additional
user supplied generator functions. We evaluate MLCheck using requirements and
data sets from three different application areas (software\r\ndiscrimination,
learning on knowledge graphs and security). Our evaluation shows that despite
its generality MLCheck can even outperform specialised testing approaches while
having a comparable runtime"
author:
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Sharma A, Demir C, Ngonga Ngomo A-C, Wehrheim H. MLCHECK–Property-Driven Testing
of Machine Learning Classifiers. In: Proceedings of the 20th IEEE International
Conference on Machine Learning and Applications (ICMLA). IEEE.'
apa: Sharma, A., Demir, C., Ngonga Ngomo, A.-C., & Wehrheim, H. (n.d.). MLCHECK–Property-Driven
Testing of Machine Learning Classifiers. Proceedings of the 20th IEEE International
Conference on Machine Learning and Applications (ICMLA).
bibtex: '@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven
Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE
International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE},
author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim,
Heike} }'
chicago: Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim.
“MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In Proceedings
of the 20th IEEE International Conference on Machine Learning and Applications
(ICMLA). IEEE, n.d.
ieee: A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven
Testing of Machine Learning Classifiers.”
mla: Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning
Classifiers.” Proceedings of the 20th IEEE International Conference on Machine
Learning and Applications (ICMLA), IEEE.
short: 'A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of
the 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
IEEE, n.d.'
date_created: 2021-12-07T11:11:36Z
date_updated: 2022-01-06T06:58:02Z
department:
- _id: '7'
- _id: '77'
- _id: '574'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '10'
name: SFB 901 - Subproject B2
publication: Proceedings of the 20th IEEE International Conference on Machine Learning
and Applications (ICMLA)
publication_status: accepted
publisher: IEEE
status: public
title: MLCHECK–Property-Driven Testing of Machine Learning Classifiers
type: conference
user_id: '477'
year: '2021'
...
---
_id: '26049'
abstract:
- lang: eng
text: 'Content is the new oil. Users consume billions of terabytes a day while surfing
on news sites or blogs, posting on social media sites, and sending chat messages
around the globe. While content is heterogeneous, the dominant form of web content
is text. There are situations where more diversity needs to be introduced into
text content, for example, to reuse it on websites or to allow a chatbot to base
its models on the information conveyed rather than of the language used. In order
to achieve this, paraphrasing techniques have been developed: One example is Text
spinning, a technique that automatically paraphrases text while leaving the intent
intact. This makes it easier to reuse content, or to change the language generated
by the bot more human. One method for modifying texts is a combination of translation
and back-translation. This paper presents NATTS, a naive approach that uses transformer-based
translation models to create diversified text, combining translation steps in
one model. An advantage of this approach is that it can be fine-tuned and handle
technical language.'
author:
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
last_name: Bäumer
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Sergej
full_name: Denisov, Sergej
last_name: Denisov
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Bäumer FS, Kersting J, Denisov S, Geierhos M. IN OTHER WORDS: A NAIVE APPROACH
TO TEXT SPINNING. In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET
2021 AND APPLIED COMPUTING 2021. IADIS; 2021:221--225.'
apa: 'Bäumer, F. S., Kersting, J., Denisov, S., & Geierhos, M. (2021). IN OTHER
WORDS: A NAIVE APPROACH TO TEXT SPINNING. PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, 221--225.'
bibtex: '@inproceedings{Bäumer_Kersting_Denisov_Geierhos_2021, title={IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING}, booktitle={PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}, publisher={IADIS},
author={Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos,
Michaela}, year={2021}, pages={221--225} }'
chicago: 'Bäumer, Frederik Simon, Joschka Kersting, Sergej Denisov, and Michaela
Geierhos. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” In PROCEEDINGS
OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021,
221--225. IADIS, 2021.'
ieee: 'F. S. Bäumer, J. Kersting, S. Denisov, and M. Geierhos, “IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING,” in PROCEEDINGS OF THE INTERNATIONAL CONFERENCES
ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, Lisbon, Portugal, 2021, pp.
221--225.'
mla: 'Bäumer, Frederik Simon, et al. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.”
PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED
COMPUTING 2021, IADIS, 2021, pp. 221--225.'
short: 'F.S. Bäumer, J. Kersting, S. Denisov, M. Geierhos, in: PROCEEDINGS OF THE
INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, IADIS,
2021, pp. 221--225.'
conference:
end_date: 15.10.2021
location: Lisbon, Portugal
name: 18th International Conference on Applied Computing
start_date: 13.10.2021
date_created: 2021-10-11T15:26:58Z
date_updated: 2022-01-06T06:57:16Z
ddc:
- '000'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2021-10-15T15:54:41Z
date_updated: 2021-10-15T15:54:41Z
file_id: '26282'
file_name: Bäumer et al. (2021), Baeumer2021.pdf
file_size: 411667
relation: main_file
success: 1
file_date_updated: 2021-10-15T15:54:41Z
has_accepted_license: '1'
keyword:
- Software Requirements
- Natural Language Processing
- Transfer Learning
- On-The-Fly Computing
language:
- iso: eng
page: 221--225
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 INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND
APPLIED COMPUTING 2021
publisher: IADIS
status: public
title: 'IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING'
type: conference
user_id: '58701'
year: '2021'
...
---
_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: '28998'
author:
- first_name: Dennis
full_name: Suermann, Dennis
last_name: Suermann
citation:
ama: Suermann D. Schutz Und Stabilisierung von Overlay-Netzwerken Mithilfe Des
Relay-Layers.; 2021.
apa: Suermann, D. (2021). Schutz und Stabilisierung von Overlay-Netzwerken mithilfe
des Relay-Layers.
bibtex: '@book{Suermann_2021, title={Schutz und Stabilisierung von Overlay-Netzwerken
mithilfe des Relay-Layers}, author={Suermann, Dennis}, year={2021} }'
chicago: Suermann, Dennis. Schutz Und Stabilisierung von Overlay-Netzwerken Mithilfe
Des Relay-Layers, 2021.
ieee: D. Suermann, Schutz und Stabilisierung von Overlay-Netzwerken mithilfe
des Relay-Layers. 2021.
mla: Suermann, Dennis. Schutz Und Stabilisierung von Overlay-Netzwerken Mithilfe
Des Relay-Layers. 2021.
short: D. Suermann, Schutz Und Stabilisierung von Overlay-Netzwerken Mithilfe Des
Relay-Layers, 2021.
date_created: 2021-12-16T06:41:25Z
date_updated: 2022-01-06T06:58:43Z
department:
- _id: '79'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '13'
name: SFB 901 - Subproject C1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Schutz und Stabilisierung von Overlay-Netzwerken mithilfe des Relay-Layers
type: bachelorsthesis
user_id: '15504'
year: '2021'
...
---
_id: '29151'
abstract:
- lang: eng
text: Automation becomes a vital part in the High-Performance computing system in
situational dynamics to take the decisions on the fly. Heterogeneous compute nodes
consist of computing resources such as CPU, GPU and FPGA and are the important
components of the high-performance computing system that can adapt the automation
to achieve the given goal. While implanting automation in the computing resources,
management of the resources is one of the essential aspects that need to be taken
care of. Tasks are continuously executed on the resources using its unique characteristics.
Effective scheduling is essential to make the best use of the characteristics
provided by each resource. Scheduling enables the execution of each task by allocating
resources so that they take advantage of all the characteristics of the compute
resources. Various scheduling heuristics can be used to create effective scheduling,
which might require the execution time to schedule the task efficiently. Providing
actual execution time is not possible in many cases; hence we can provide the
estimations for the actual execution time . The purpose of this master's thesis
is to design a predictive model or system that estimates the execution time required
to execute tasks using historical execution time data on the heterogeneous compute
nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive
Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction
accuracy in order to determine which technique produces reliable predictions for
the execution time. These estimations must be generated in an online learning
environment in which data points arrive in any sequence, one by one, and the regression
model must learn from them. After evaluating the regression algorithms, it is
seen that the XCSF regressor provides the highest overall prediction accuracy
for the supplied data sets. The regression technique's parameters also play a
significant role in achieving an acceptable prediction accuracy. As a remark,
when using online learning in regression analysis, the accuracy depends upon both
the order of sequential data points that are coming to train the model and the
parameter configuration for each regression technique.
author:
- first_name: Chinmay
full_name: Kashikar, Chinmay
last_name: Kashikar
citation:
ama: Kashikar C. A Comparison of Machine Learning Techniques for the On-Line
Characterization of Tasks Executed on Heterogeneous Compute Nodes. Paderborn
University; 2021.
apa: Kashikar, C. (2021). A Comparison of Machine Learning Techniques for the
On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes.
Paderborn University.
bibtex: '@book{Kashikar_2021, place={Paderborn}, title={A Comparison of Machine
Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous
Compute Nodes}, publisher={Paderborn University}, author={Kashikar, Chinmay},
year={2021} }'
chicago: 'Kashikar, Chinmay. A Comparison of Machine Learning Techniques for
the On-Line Characterization of Tasks Executed on Heterogeneous Compute Nodes.
Paderborn: Paderborn University, 2021.'
ieee: 'C. Kashikar, A Comparison of Machine Learning Techniques for the On-line
Characterization of Tasks Executed on Heterogeneous Compute Nodes. Paderborn:
Paderborn University, 2021.'
mla: Kashikar, Chinmay. A Comparison of Machine Learning Techniques for the On-Line
Characterization of Tasks Executed on Heterogeneous Compute Nodes. Paderborn
University, 2021.
short: C. Kashikar, A Comparison of Machine Learning Techniques for the On-Line
Characterization of Tasks Executed on Heterogeneous Compute Nodes, Paderborn University,
Paderborn, 2021.
date_created: 2022-01-04T09:24:52Z
date_updated: 2022-01-06T06:58:46Z
department:
- _id: '78'
language:
- iso: eng
place: Paderborn
project:
- _id: '14'
name: SFB 901 - Subproject C2
- _id: '4'
name: SFB 901 - Project Area C
- _id: '1'
name: SFB 901
publisher: Paderborn University
status: public
supervisor:
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
- first_name: Tim
full_name: Hansmeier, Tim
id: '49992'
last_name: Hansmeier
orcid: 0000-0003-1377-3339
title: A Comparison of Machine Learning Techniques for the On-line Characterization
of Tasks Executed on Heterogeneous Compute Nodes
type: mastersthesis
user_id: '49992'
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: '27053'
author:
- first_name: Leon
full_name: Everling, Leon
last_name: Everling
citation:
ama: Everling L. Selbststabilisierender Bakery Algorithmus Für Verteilte Systeme.;
2021.
apa: Everling, L. (2021). Selbststabilisierender Bakery Algorithmus für verteilte
Systeme.
bibtex: '@book{Everling_2021, title={Selbststabilisierender Bakery Algorithmus für
verteilte Systeme}, author={Everling, Leon}, year={2021} }'
chicago: Everling, Leon. Selbststabilisierender Bakery Algorithmus Für Verteilte
Systeme, 2021.
ieee: L. Everling, Selbststabilisierender Bakery Algorithmus für verteilte Systeme.
2021.
mla: Everling, Leon. Selbststabilisierender Bakery Algorithmus Für Verteilte
Systeme. 2021.
short: L. Everling, Selbststabilisierender Bakery Algorithmus Für Verteilte Systeme,
2021.
date_created: 2021-11-02T10:13:51Z
date_updated: 2022-01-06T06:57:33Z
department:
- _id: '79'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '5'
name: SFB 901 - Subproject A1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Selbststabilisierender Bakery Algorithmus für verteilte Systeme
type: bachelorsthesis
user_id: '15504'
year: '2021'
...
---
_id: '27072'
author:
- first_name: Vaibhav
full_name: Adsul, Vaibhav
last_name: Adsul
citation:
ama: Adsul V. Peer-to-Peer Matching for Distributed Systems.; 2021.
apa: Adsul, V. (2021). Peer-to-Peer Matching for Distributed Systems.
bibtex: '@book{Adsul_2021, title={Peer-to-Peer Matching for Distributed Systems},
author={Adsul, Vaibhav}, year={2021} }'
chicago: Adsul, Vaibhav. Peer-to-Peer Matching for Distributed Systems, 2021.
ieee: V. Adsul, Peer-to-Peer Matching for Distributed Systems. 2021.
mla: Adsul, Vaibhav. Peer-to-Peer Matching for Distributed Systems. 2021.
short: V. Adsul, Peer-to-Peer Matching for Distributed Systems, 2021.
date_created: 2021-11-03T06:12:47Z
date_updated: 2022-01-06T06:57:33Z
department:
- _id: '79'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '5'
name: SFB 901 - Subproject A1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Peer-to-Peer Matching for Distributed Systems
type: mastersthesis
user_id: '15504'
year: '2021'
...
---
_id: '27503'
author:
- first_name: Asif
full_name: Hasnain, Asif
last_name: Hasnain
citation:
ama: Hasnain A. Automating Network Resource Allocation for Coflows with Deadlines.;
2021. doi:10.17619/UNIPB/1-1241
apa: Hasnain, A. (2021). Automating Network Resource Allocation for Coflows with
Deadlines. https://doi.org/10.17619/UNIPB/1-1241
bibtex: '@book{Hasnain_2021, title={Automating Network Resource Allocation for Coflows
with Deadlines}, DOI={10.17619/UNIPB/1-1241
}, author={Hasnain, Asif}, year={2021} }'
chicago: Hasnain, Asif. Automating Network Resource Allocation for Coflows with
Deadlines, 2021. https://doi.org/10.17619/UNIPB/1-1241
.
ieee: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines.
2021.
mla: Hasnain, Asif. Automating Network Resource Allocation for Coflows with Deadlines.
2021, doi:10.17619/UNIPB/1-1241
.
short: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines,
2021.
date_created: 2021-11-16T13:05:12Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '75'
doi: '10.17619/UNIPB/1-1241 '
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
status: public
supervisor:
- first_name: Holger
full_name: Karl, Holger
last_name: Karl
title: Automating Network Resource Allocation for Coflows with Deadlines
type: dissertation
user_id: '15504'
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: '21005'
abstract:
- lang: eng
text: Data-parallel applications are developed using different data programming
models, e.g., MapReduce, partition/aggregate. These models represent diverse resource
requirements of application in a datacenter network, which can be represented
by the coflow abstraction. The conventional method of creating hand-crafted coflow
heuristics for admission or scheduling for different workloads is practically
infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based
coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level
performance objective, i.e., maximize successful coflow admissions, without manual
feature engineering. LCS is trained on a production trace, which has online coflow
arrivals. The evaluation results show that LCS is able to learn a reasonable admission
policy that admits more coflows than state-of-the-art Varys heuristic while meeting
their deadlines.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Learning Coflow Admissions. In: IEEE INFOCOM 2021 -
IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE
Communications Society. doi:10.1109/INFOCOMWKSHPS51825.2021.9484599'
apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Coflow Admissions. In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
Vancouver BC Canada: IEEE Communications Society. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599'
bibtex: '@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={10.1109/INFOCOMWKSHPS51825.2021.9484599},
booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif
and Karl, Holger} }'
chicago: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
IEEE Communications Society, n.d. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599.
ieee: A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver
BC Canada.
mla: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” IEEE INFOCOM
2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS),
IEEE Communications Society, doi:10.1109/INFOCOMWKSHPS51825.2021.9484599.
short: 'A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer
Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.'
conference:
end_date: 2021-05-13
location: Vancouver BC Canada
name: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
start_date: 2021-05-10
date_created: 2021-01-16T18:24:19Z
date_updated: 2022-01-06T06:54:42Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/INFOCOMWKSHPS51825.2021.9484599
keyword:
- Coflow scheduling
- Reinforcement learning
- Deadlines
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9484599
project:
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '4'
name: SFB 901 - Project Area C
- _id: '1'
name: SFB 901
publication: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)
publication_status: accepted
publisher: IEEE Communications Society
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9484599
status: public
title: Learning Coflow Admissions
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '21084'
author:
- first_name: Julian
full_name: Werthmann, Julian
id: '50024'
last_name: Werthmann
citation:
ama: Werthmann J. Derandomization and Local Graph Problems in the Node-Capacitated
Clique.; 2021.
apa: Werthmann, J. (2021). Derandomization and Local Graph Problems in the Node-Capacitated
Clique.
bibtex: '@book{Werthmann_2021, title={Derandomization and Local Graph Problems in
the Node-Capacitated Clique}, author={Werthmann, Julian}, year={2021} }'
chicago: Werthmann, Julian. Derandomization and Local Graph Problems in the Node-Capacitated
Clique, 2021.
ieee: J. Werthmann, Derandomization and Local Graph Problems in the Node-Capacitated
Clique. 2021.
mla: Werthmann, Julian. Derandomization and Local Graph Problems in the Node-Capacitated
Clique. 2021.
short: J. Werthmann, Derandomization and Local Graph Problems in the Node-Capacitated
Clique, 2021.
date_created: 2021-01-26T13:58:14Z
date_updated: 2022-01-06T06:54:44Z
department:
- _id: '79'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '5'
name: SFB 901 - Subproject A1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Derandomization and Local Graph Problems in the Node-Capacitated Clique
type: mastersthesis
user_id: '15504'
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: '21197'
author:
- first_name: Ma
full_name: Mengshi, Ma
last_name: Mengshi
citation:
ama: Mengshi M. Self-Stabilizing Arrow Protocol on Spanning Trees with a Low
Diameter.; 2021.
apa: Mengshi, M. (2021). Self-stabilizing Arrow Protocol on Spanning Trees with
a Low Diameter.
bibtex: '@book{Mengshi_2021, title={Self-stabilizing Arrow Protocol on Spanning
Trees with a Low Diameter}, author={Mengshi, Ma}, year={2021} }'
chicago: Mengshi, Ma. Self-Stabilizing Arrow Protocol on Spanning Trees with
a Low Diameter, 2021.
ieee: M. Mengshi, Self-stabilizing Arrow Protocol on Spanning Trees with a Low
Diameter. 2021.
mla: Mengshi, Ma. Self-Stabilizing Arrow Protocol on Spanning Trees with a Low
Diameter. 2021.
short: M. Mengshi, Self-Stabilizing Arrow Protocol on Spanning Trees with a Low
Diameter, 2021.
date_created: 2021-02-09T07:09:22Z
date_updated: 2022-01-06T06:54:49Z
department:
- _id: '79'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '4'
name: SFB 901 - Project Area C
- _id: '5'
name: SFB 901 - Subproject A1
- _id: '13'
name: SFB 901 - Subproject C1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Self-stabilizing Arrow Protocol on Spanning Trees with a Low Diameter
type: bachelorsthesis
user_id: '15504'
year: '2021'
...
---
_id: '21242'
author:
- first_name: Hedda
full_name: Lüttenberg, Hedda
id: '60612'
last_name: Lüttenberg
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Martin
full_name: Poniatowski, Martin
id: '32441'
last_name: Poniatowski
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Nancy
full_name: Wünderlich, Nancy
id: '36392'
last_name: Wünderlich
citation:
ama: Lüttenberg H, Beverungen D, Poniatowski M, Kundisch D, Wünderlich N. Drei Strategien
zur Etablierung digitaler Plattformen in der Industrie. Wirtschaftsinformatik
& Management. 2021;13(2):120-131.
apa: Lüttenberg, H., Beverungen, D., Poniatowski, M., Kundisch, D., & Wünderlich,
N. (2021). Drei Strategien zur Etablierung digitaler Plattformen in der Industrie.
Wirtschaftsinformatik & Management, 13(2), 120–131.
bibtex: '@article{Lüttenberg_Beverungen_Poniatowski_Kundisch_Wünderlich_2021, title={Drei
Strategien zur Etablierung digitaler Plattformen in der Industrie}, volume={13},
number={2}, journal={Wirtschaftsinformatik & Management}, author={Lüttenberg,
Hedda and Beverungen, Daniel and Poniatowski, Martin and Kundisch, Dennis and
Wünderlich, Nancy}, year={2021}, pages={120–131} }'
chicago: 'Lüttenberg, Hedda, Daniel Beverungen, Martin Poniatowski, Dennis Kundisch,
and Nancy Wünderlich. “Drei Strategien zur Etablierung digitaler Plattformen in
der Industrie.” Wirtschaftsinformatik & Management 13, no. 2 (2021):
120–31.'
ieee: H. Lüttenberg, D. Beverungen, M. Poniatowski, D. Kundisch, and N. Wünderlich,
“Drei Strategien zur Etablierung digitaler Plattformen in der Industrie,” Wirtschaftsinformatik
& Management, vol. 13, no. 2, pp. 120–131, 2021.
mla: Lüttenberg, Hedda, et al. “Drei Strategien zur Etablierung digitaler Plattformen
in der Industrie.” Wirtschaftsinformatik & Management, vol. 13, no.
2, 2021, pp. 120–31.
short: H. Lüttenberg, D. Beverungen, M. Poniatowski, D. Kundisch, N. Wünderlich,
Wirtschaftsinformatik & Management 13 (2021) 120–131.
date_created: 2021-02-16T13:33:57Z
date_updated: 2022-01-06T06:54:51Z
department:
- _id: '276'
intvolume: ' 13'
issue: '2'
language:
- iso: ger
page: 120-131
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '17'
name: SFB 901 - Subproject C5
- _id: '97'
name: its OWL - Digital Business
publication: Wirtschaftsinformatik & Management
publication_status: published
status: public
title: Drei Strategien zur Etablierung digitaler Plattformen in der Industrie
type: journal_article
user_id: '477'
volume: 13
year: '2021'
...
---
_id: '21525'
author:
- first_name: Dominik
full_name: Gutt, Dominik
last_name: Gutt
- first_name: Jürgen
full_name: Neumann, Jürgen
id: '32456'
last_name: Neumann
- first_name: Wael
full_name: Jabr, Wael
last_name: Jabr
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
citation:
ama: 'Gutt D, Neumann J, Jabr W, Kundisch D. The Fate of the App: Economic Implications
of Updating under Reputation Resetting. In: ; 2021.'
apa: 'Gutt, D., Neumann, J., Jabr, W., & Kundisch, D. (2021). The Fate of the
App: Economic Implications of Updating under Reputation Resetting. Presented at
the Sixth Workshop on Information System Design and Economic Behavior (ISDEB 2021),
Virtual Conference/Workshop.'
bibtex: '@inproceedings{Gutt_Neumann_Jabr_Kundisch_2021, title={The Fate of the
App: Economic Implications of Updating under Reputation Resetting}, author={Gutt,
Dominik and Neumann, Jürgen and Jabr, Wael and Kundisch, Dennis}, year={2021}
}'
chicago: 'Gutt, Dominik, Jürgen Neumann, Wael Jabr, and Dennis Kundisch. “The Fate
of the App: Economic Implications of Updating under Reputation Resetting,” 2021.'
ieee: 'D. Gutt, J. Neumann, W. Jabr, and D. Kundisch, “The Fate of the App: Economic
Implications of Updating under Reputation Resetting,” presented at the Sixth Workshop
on Information System Design and Economic Behavior (ISDEB 2021), Virtual Conference/Workshop,
2021.'
mla: 'Gutt, Dominik, et al. The Fate of the App: Economic Implications of Updating
under Reputation Resetting. 2021.'
short: 'D. Gutt, J. Neumann, W. Jabr, D. Kundisch, in: 2021.'
conference:
location: Virtual Conference/Workshop
name: Sixth Workshop on Information System Design and Economic Behavior (ISDEB 2021)
date_created: 2021-03-17T13:11:24Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '276'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '8'
name: SFB 901 - Subproject A4
status: public
title: 'The Fate of the App: Economic Implications of Updating under Reputation Resetting'
type: conference
user_id: '477'
year: '2021'
...
---
_id: '21543'
abstract:
- lang: eng
text: "Services often consist of multiple chained components such as microservices
in a service mesh, or machine learning functions in a pipeline. Providing these
services requires online coordination including scaling the service, placing instance
of all components in the network, scheduling traffic to these instances, and routing
traffic through the network. Optimized service coordination is still a hard problem
due to many influencing factors such as rapidly arriving user demands and limited
node and link capacity. Existing approaches to solve the problem are often built
on rigid models and assumptions, tailored to specific scenarios. If the scenario
changes and the assumptions no longer hold, they easily break and require manual
adjustments by experts. Novel self-learning approaches using deep reinforcement
learning (DRL) are promising but still have limitations as they only address simplified
versions of the problem and are typically centralized and thus do not scale to
practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed
self-learning service coordination approach using DRL. After centralized training,
we deploy a distributed DRL agent at each node in the network, making fast coordination
decisions locally in parallel with the other nodes. Each agent only observes its
direct neighbors and does not need global knowledge. Hence, our approach scales
independently from the size of the network. In our extensive evaluation using
real-world network topologies and traffic traces, we show that our proposed approach
outperforms a state-of-the-art conventional heuristic as well as a centralized
DRL approach (60% higher throughput on average) while requiring less time per
online decision (1 ms)."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Haydar
full_name: Qarawlus, Haydar
last_name: Qarawlus
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination
Using Deep Reinforcement Learning. In: IEEE International Conference on Distributed
Computing Systems (ICDCS). IEEE; 2021.'
apa: 'Schneider, S. B., Qarawlus, H., & Karl, H. (2021). Distributed Online
Service Coordination Using Deep Reinforcement Learning. In IEEE International
Conference on Distributed Computing Systems (ICDCS). Washington, DC, USA:
IEEE.'
bibtex: '@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online
Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International
Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider,
Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }'
chicago: Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed
Online Service Coordination Using Deep Reinforcement Learning.” In IEEE International
Conference on Distributed Computing Systems (ICDCS). IEEE, 2021.
ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed
Computing Systems (ICDCS), Washington, DC, USA, 2021.
mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Distributed
Computing Systems (ICDCS), IEEE, 2021.
short: 'S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference
on Distributed Computing Systems (ICDCS), IEEE, 2021.'
conference:
location: Washington, DC, USA
name: IEEE International Conference on Distributed Computing Systems (ICDCS)
date_created: 2021-03-18T17:15:47Z
date_updated: 2022-01-06T06:55:04Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2021-03-18T17:12:56Z
date_updated: 2021-03-18T17:12:56Z
file_id: '21544'
file_name: public_author_version.pdf
file_size: 606321
relation: main_file
title: Distributed Online Service Coordination Using Deep Reinforcement Learning
file_date_updated: 2021-03-18T17:12:56Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- reinforcement learning
- distributed
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE International Conference on Distributed Computing Systems (ICDCS)
publisher: IEEE
related_material:
link:
- relation: software
url: https://github.com/ RealVNF/distributed-drl-coordination
status: public
title: Distributed Online Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2021'
...
---
_id: '21569'
abstract:
- lang: eng
text: Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für
eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit
nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle
häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden,
wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung
(GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen
aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen.
Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur
und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen
wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen
unterstützt.
author:
- first_name: Sebastian
full_name: Gottschalk, Sebastian
id: '47208'
last_name: Gottschalk
- first_name: Enes
full_name: Yigitbas, Enes
id: '8447'
last_name: Yigitbas
orcid: 0000-0002-5967-833X
citation:
ama: 'Gottschalk S, Yigitbas E. Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung:
Ein Überblick über Software-Tools und deren Datennutzung. Vol 1. Gesellschaft
für Informatik; 2021.'
apa: 'Gottschalk, S., & Yigitbas, E. (2021). Von datenbasierter zu datengetriebener
Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung
(Vol. 1). Gesellschaft für Informatik.'
bibtex: '@book{Gottschalk_Yigitbas_2021, series={WI-MAW-Rundbrief}, title={Von datenbasierter
zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools
und deren Datennutzung}, volume={1}, publisher={Gesellschaft für Informatik},
author={Gottschalk, Sebastian and Yigitbas, Enes}, year={2021}, collection={WI-MAW-Rundbrief}
}'
chicago: 'Gottschalk, Sebastian, and Enes Yigitbas. Von datenbasierter zu datengetriebener
Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung.
Vol. 1. WI-MAW-Rundbrief. Gesellschaft für Informatik, 2021.'
ieee: 'S. Gottschalk and E. Yigitbas, Von datenbasierter zu datengetriebener
Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung,
vol. 1. Gesellschaft für Informatik, 2021.'
mla: 'Gottschalk, Sebastian, and Enes Yigitbas. Von datenbasierter zu datengetriebener
Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung.
Vol. 1, Gesellschaft für Informatik, 2021.'
short: 'S. Gottschalk, E. Yigitbas, Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung:
Ein Überblick über Software-Tools und deren Datennutzung, Gesellschaft für Informatik,
2021.'
date_created: 2021-03-25T10:02:18Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '66'
- _id: '534'
intvolume: ' 1'
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://fa-wi-maw.gi.de/fileadmin/FA/WI-MAW/Rundbriefe/3037624_GI_45_Rundbrief_JG27.pdf
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '17'
name: SFB 901 - Subproject C5
publication_identifier:
unknown:
- 1610-5753
publisher: Gesellschaft für Informatik
series_title: WI-MAW-Rundbrief
status: public
title: 'Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick
über Software-Tools und deren Datennutzung'
type: report
user_id: '47208'
volume: 1
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: '21628'
abstract:
- lang: eng
text: "This thesis considers the realization of distributed data structures and
the construction of distributed protocols for self-stabilizing overlay networks.\r\n\r\nIn
the first part of this thesis, we provide distributed protocols for queues, stacks
and priority queues that serve the insertion and deletion of elements within a
logarithmic amount of rounds.\r\nOur protocols respect semantic constraints such
as sequential consistency or serializability and the individual semantic constraints
given by the type (queue, stack, priority queue) of the data structure.\r\nWe
furthermore provide a protocol that handles joining and leaving nodes.\r\nAs an
important side product, we present a novel protocol solving the distributed $k$-selection
problem in a logarithmic amount of rounds, that is, to find the $k$-smallest elements
among a polynomial number of elements spread among $n$ nodes.\r\n\t\r\nThe second
part of this thesis is devoted to the construction of protocols for self-stabilizing
overlay networks, i.e., distributed protocols that transform an overlay network
from any initial (potentially illegitimate) state into a legitimate state in finite
time.\r\nWe present protocols for self-stabilizing generalized De Bruijn graphs,
self-stabilizing quadtrees and self-stabilizing supervised skip rings.\r\nEach
of those protocols comes with unique properties that makes it interesting for
certain distributed applications.\r\nGeneralized De Bruijn networks provide routing
within a constant amount of hops, thus serving the interest in networks that require
a low latency for requests.\r\nThe protocol for the quadtree guarantees monotonic
searchability as well as a geometric variant of monotonic searchability, making
it interesting for wireless networks or applications needed in the area of computational
geometry.\r\nThe supervised skip ring can be used to construct a self-stabilizing
publish-subscribe system.\r\n"
author:
- first_name: Michael
full_name: Feldmann, Michael
id: '23538'
last_name: Feldmann
citation:
ama: Feldmann M. Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks.; 2021. doi:10.17619/UNIPB/1-1113
apa: Feldmann, M. (2021). Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks. https://doi.org/10.17619/UNIPB/1-1113
bibtex: '@book{Feldmann_2021, title={Algorithms for Distributed Data Structures
and Self-Stabilizing Overlay Networks}, DOI={10.17619/UNIPB/1-1113},
author={Feldmann, Michael}, year={2021} }'
chicago: Feldmann, Michael. Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks, 2021. https://doi.org/10.17619/UNIPB/1-1113.
ieee: M. Feldmann, Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks. 2021.
mla: Feldmann, Michael. Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks. 2021, doi:10.17619/UNIPB/1-1113.
short: M. Feldmann, Algorithms for Distributed Data Structures and Self-Stabilizing
Overlay Networks, 2021.
date_created: 2021-04-15T08:23:52Z
date_updated: 2022-01-06T06:55:08Z
ddc:
- '006'
department:
- _id: '79'
doi: 10.17619/UNIPB/1-1113
file:
- access_level: closed
content_type: application/pdf
creator: mfeldma2
date_created: 2021-04-15T08:21:15Z
date_updated: 2021-04-15T08:21:15Z
file_id: '21629'
file_name: Dissertation_Michael_Feldmann.pdf
file_size: 2617069
relation: main_file
success: 1
file_date_updated: 2021-04-15T08:21:15Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '5'
name: SFB 901 - Subproject A1
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Algorithms for Distributed Data Structures and Self-Stabilizing Overlay Networks
type: dissertation
user_id: '23538'
year: '2021'
...