---
_id: '29220'
abstract:
- lang: eng
text: "Modern services often comprise several components, such as chained virtual
network functions, microservices, or\r\nmachine learning functions. Providing
such services requires to decide how often to instantiate each component, where
to place these instances in the network, how to chain them and route traffic through
them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep
reinforcement learning (DRL) approaches for self-learning network and service
management have emerged recently. These model-free DRL approaches are more flexible
but typically learn tabula rasa, i.e., disregard existing understanding of networks,
services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel
model-based AI approach that leverages existing understanding of networks and
services for more efficient and effective coordination without time-intensive
training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic
model. This allows FutureCoord to estimate the impact of future incoming traffic
and effectively optimize long-term effects, taking fluctuating demand and Quality
of Service (QoS) requirements into account. Our extensive evaluation based on
real-world network topologies, services, and traffic traces indicates that FutureCoord
clearly outperforms state-of-the-art model-free and model-based approaches with
up to 51% higher flow success ratios."
author:
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination
Beyond Certainty. In: IEEE/IFIP Network Operations and Management Symposium
(NOMS). IEEE; 2022.'
apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network
and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and
Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium
(NOMS), Budapest.'
bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network
and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations
and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and
Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }'
chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What
You Know: Network and Service Coordination Beyond Certainty.” In IEEE/IFIP
Network Operations and Management Symposium (NOMS). IEEE, 2022.'
ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and
Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations
and Management Symposium (NOMS), Budapest, 2022.'
mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination
Beyond Certainty.” IEEE/IFIP Network Operations and Management Symposium (NOMS),
IEEE, 2022.'
short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and
Management Symposium (NOMS), IEEE, 2022.'
conference:
end_date: 2022-04-29
location: Budapest
name: IEEE/IFIP Network Operations and Management Symposium (NOMS)
start_date: 2022-04-25
date_created: 2022-01-11T08:43:26Z
date_updated: 2022-01-11T08:44:04Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2022-01-11T08:39:57Z
date_updated: 2022-01-11T08:39:57Z
file_id: '29222'
file_name: author_version.pdf
file_size: 528653
relation: main_file
file_date_updated: 2022-01-11T08:39:57Z
has_accepted_license: '1'
keyword:
- network management
- service management
- AI
- Monte Carlo Tree Search
- model-based
- QoS
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
publication: IEEE/IFIP Network Operations and Management Symposium (NOMS)
publisher: IEEE
quality_controlled: '1'
status: public
title: 'Use What You Know: Network and Service Coordination Beyond Certainty'
type: conference
user_id: '35343'
year: '2022'
...
---
_id: '26539'
abstract:
- lang: eng
text: In control design most control strategies are model-based and require accurate
models to be applied successfully. Due to simplifications and the model-reality-gap
physics-derived models frequently exhibit deviations from real-world-systems.
Likewise, purely data-driven methods often do not generalise well enough and may
violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired
loss functions separately have shown promising results to conquer these drawbacks.
In this contribution we extend existing methods towards the identification of
non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN
and a physics-inspired loss term (-L) to successfully identify the system's dynamics,
while maintaining the consistency with physical laws. The proposed method is demonstrated
on two real-world nonlinear systems and outperforms existing techniques regarding
complexity and reliability.
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Composed Physics- and Data-driven System Identification
for Non-autonomous Systems in Control Engineering. In: 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC). ; 2022:67-76.
doi:10.1109/AIRC56195.2022.9836982'
apa: Götte, R.-S., & Timmermann, J. (2022). Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering. 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76. https://doi.org/10.1109/AIRC56195.2022.9836982
bibtex: '@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering}, DOI={10.1109/AIRC56195.2022.9836982},
booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022},
pages={67–76} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” In 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76, 2022. https://doi.org/10.1109/AIRC56195.2022.9836982.
ieee: 'R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System
Identification for Non-autonomous Systems in Control Engineering,” in 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), Cairo, Egypt, 2022, pp. 67–76, doi: 10.1109/AIRC56195.2022.9836982.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 2022, pp. 67–76, doi:10.1109/AIRC56195.2022.9836982.
short: 'R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.'
conference:
end_date: 2021-12-10
location: Cairo, Egypt
name: 3rd International Conference on Artificial Intelligence, Robotics and Control
start_date: 2021-12-08
date_created: 2021-10-19T14:47:17Z
date_updated: 2024-11-13T08:43:28Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836982
keyword:
- data-driven
- physics-based
- physics-informed
- neural networks
- system identification
- hybrid modelling
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.08148
oa: '1'
page: 67-76
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)
quality_controlled: '1'
status: public
title: Composed Physics- and Data-driven System Identification for Non-autonomous
Systems in Control Engineering
type: conference
user_id: '43992'
year: '2022'
...
---
_id: '31066'
abstract:
- lang: eng
text: 'While trade-offs between modeling effort and model accuracy remain a major
concern with system identification, resorting to data-driven methods often leads
to a complete disregard for physical plausibility. To address this issue, we propose
a physics-guided hybrid approach for modeling non-autonomous systems under control.
Starting from a traditional physics-based model, this is extended by a recurrent
neural network and trained using a sophisticated multi-objective strategy yielding
physically plausible models. While purely data-driven methods fail to produce
satisfying results, experiments conducted on real data reveal substantial accuracy
improvements by our approach compared to a physics-based model. '
author:
- first_name: Oliver
full_name: Schön, Oliver
last_name: Schön
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent
Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55.
; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282'
apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12),
19–24. https://doi.org/10.1016/j.ifacol.2022.07.282
bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55},
DOI={https://doi.org/10.1016/j.ifacol.2022.07.282},
number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems
(ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2022}, pages={19–24} }'
chicago: Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective
Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical
Systems.” In 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS
2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282.
ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in
14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022),
Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.'
mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks
for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24,
doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.'
conference:
end_date: 2022-07-01
location: Casablanca, Morocco
name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
start_date: 2022-06-29
date_created: 2022-05-05T06:22:55Z
date_updated: 2024-11-13T08:43:16Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2022.07.282
intvolume: ' 55'
issue: '12'
keyword:
- neural networks
- physics-guided
- data-driven
- multi-objective optimization
- system identification
- machine learning
- dynamical systems
language:
- iso: eng
page: 19-24
publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
quality_controlled: '1'
status: public
title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous
Dynamical Systems
type: conference
user_id: '43992'
volume: 55
year: '2022'
...
---
_id: '37155'
abstract:
- lang: eng
text: 'Artificial intelligence (AI) has moved beyond the planning phase in many
organisations and it is often accompanied by uncertainties and fears of job loss
among employees. It is crucial to manage employees{\textquoteright} attitudes
towards the deployment of an AI-based technology effectively and counteract possible
resistance behaviour. We present lessons learned from an industry case where we
conducted interviews with affected employees. We evaluated our results with managers
across industries and found that that the deployment of AI-based technologies
does not differ from other IT, but that the change is perceived differently due
to misguided expectations. '
author:
- first_name: Stefan
full_name: Stieglitz, Stefan
last_name: Stieglitz
- first_name: Nicholas R. J.
full_name: Möllmann (Frick), Nicholas R. J.
last_name: Möllmann (Frick)
- first_name: Milad
full_name: Mirbabaie, Milad
id: '88691'
last_name: Mirbabaie
- first_name: Lennart
full_name: Hofeditz, Lennart
last_name: Hofeditz
- first_name: Björn
full_name: Ross, Björn
last_name: Ross
citation:
ama: 'Stieglitz S, Möllmann (Frick) NRJ, Mirbabaie M, Hofeditz L, Ross B. Recommendations
for Managing AI-Driven Change Processes: When Expectations Meet Reality. International
Journal of Management Practice. Published online 2021.'
apa: 'Stieglitz, S., Möllmann (Frick), N. R. J., Mirbabaie, M., Hofeditz, L., &
Ross, B. (2021). Recommendations for Managing AI-Driven Change Processes: When
Expectations Meet Reality. International Journal of Management Practice.'
bibtex: '@article{Stieglitz_Möllmann (Frick)_Mirbabaie_Hofeditz_Ross_2021, title={Recommendations
for Managing AI-Driven Change Processes: When Expectations Meet Reality}, journal={International
Journal of Management Practice}, publisher={Inderscience}, author={Stieglitz,
Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz,
Lennart and Ross, Björn}, year={2021} }'
chicago: 'Stieglitz, Stefan, Nicholas R. J. Möllmann (Frick), Milad Mirbabaie, Lennart
Hofeditz, and Björn Ross. “Recommendations for Managing AI-Driven Change Processes:
When Expectations Meet Reality.” International Journal of Management Practice,
2021.'
ieee: 'S. Stieglitz, N. R. J. Möllmann (Frick), M. Mirbabaie, L. Hofeditz, and B.
Ross, “Recommendations for Managing AI-Driven Change Processes: When Expectations
Meet Reality,” International Journal of Management Practice, 2021.'
mla: 'Stieglitz, Stefan, et al. “Recommendations for Managing AI-Driven Change Processes:
When Expectations Meet Reality.” International Journal of Management Practice,
Inderscience, 2021.'
short: S. Stieglitz, N.R.J. Möllmann (Frick), M. Mirbabaie, L. Hofeditz, B. Ross,
International Journal of Management Practice (2021).
date_created: 2023-01-17T15:37:55Z
date_updated: 2023-01-18T07:59:08Z
keyword:
- Artificial Intelligence
- Change Management
- Resistance
- AI-Driven Change
- AI Deployment
- AI Perception
language:
- iso: eng
publication: International Journal of Management Practice
publication_identifier:
issn:
- 1477-9064
publisher: Inderscience
status: public
title: 'Recommendations for Managing AI-Driven Change Processes: When Expectations
Meet Reality'
type: journal_article
user_id: '80546'
year: '2021'
...
---
_id: '27491'
abstract:
- lang: eng
text: ' Students often have a lack of understanding and awareness of where, how,
and why personal data about them is collected and processed. Especially, when
interacting with data-driven digital artifacts, an appropriate perception of the
data collection and processing is necessary for self-determination. This dissertation
deals with the development and evaluation of a concept called data awareness which
aims to foster students’ self-determination interacting with data-driven digital
artifacts.'
author:
- first_name: Lukas
full_name: Höper, Lukas
id: '58041'
last_name: Höper
citation:
ama: 'Höper L. Developing and Evaluating the Concept Data Awareness for K12 Computing
Education. In: 21st Koli Calling International Conference on Computing Education
Research. Koli Calling ’21. Association for Computing Machinery; 2021. doi:10.1145/3488042.3490509'
apa: Höper, L. (2021). Developing and Evaluating the Concept Data Awareness for
K12 Computing Education. 21st Koli Calling International Conference on Computing
Education Research. https://doi.org/10.1145/3488042.3490509
bibtex: '@inproceedings{Höper_2021, place={New York, NY, USA}, series={Koli Calling
’21}, title={Developing and Evaluating the Concept Data Awareness for K12 Computing
Education}, DOI={10.1145/3488042.3490509},
booktitle={21st Koli Calling International Conference on Computing Education Research},
publisher={Association for Computing Machinery}, author={Höper, Lukas}, year={2021},
collection={Koli Calling ’21} }'
chicago: 'Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for
K12 Computing Education.” In 21st Koli Calling International Conference on
Computing Education Research. Koli Calling ’21. New York, NY, USA: Association
for Computing Machinery, 2021. https://doi.org/10.1145/3488042.3490509.'
ieee: 'L. Höper, “Developing and Evaluating the Concept Data Awareness for K12 Computing
Education,” 2021, doi: 10.1145/3488042.3490509.'
mla: Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for K12
Computing Education.” 21st Koli Calling International Conference on Computing
Education Research, Association for Computing Machinery, 2021, doi:10.1145/3488042.3490509.
short: 'L. Höper, in: 21st Koli Calling International Conference on Computing Education
Research, Association for Computing Machinery, New York, NY, USA, 2021.'
date_created: 2021-11-16T07:59:49Z
date_updated: 2024-09-16T08:32:39Z
department:
- _id: '67'
doi: 10.1145/3488042.3490509
keyword:
- data awareness
- machine learning
- data science education
- data-driven digital artifacts
- artificial intelligence
language:
- iso: eng
place: New York, NY, USA
publication: 21st Koli Calling International Conference on Computing Education Research
publication_identifier:
isbn:
- '9781450384889'
publisher: Association for Computing Machinery
quality_controlled: '1'
series_title: Koli Calling '21
status: public
title: Developing and Evaluating the Concept Data Awareness for K12 Computing Education
type: conference
user_id: '58041'
year: '2021'
...
---
_id: '16933'
abstract:
- lang: eng
text: The continuous innovation of its business models is an important task for
a company to stay competitive. During this process, the company has to validate
various hypotheses about its business models by adapting to uncertain and changing
customer needs effectively and efficiently. This adaptation, in turn, can be supported
by the concept of Software Product Lines (SPLs). SPLs reduce the time to market
by deriving products for customers with changing requirements using a common set
of features, structured as a feature model. Analogously, we support the process
of business model adaptation by applying the engineering process of SPLs to the
structure of the Business Model Canvas (BMC). We call this concept a Business
Model Decision Line (BMDL). The BMDL matches business domain knowledge in the
form of a feature model with customer needs to derive hypotheses about the business
model together with experiments for validation. Our approach is effective by providing
a comprehensive overview of possible business model adaptations and efficient
by reusing experiments for different hypotheses. We implement our approach in
a tool and illustrate the usefulness with an example of developing business models
for a mobile application.
author:
- first_name: Sebastian
full_name: Gottschalk, Sebastian
id: '47208'
last_name: Gottschalk
- first_name: Florian
full_name: Rittmeier, Florian
id: '5281'
last_name: Rittmeier
- first_name: Gregor
full_name: Engels, Gregor
id: '107'
last_name: Engels
citation:
ama: 'Gottschalk S, Rittmeier F, Engels G. Hypothesis-driven Adaptation of Business
Models based on Product Line Engineering. In: Proceedings of the 22nd IEEE
International Conference on Business Informatics. IEEE; 2020. doi:10.1109/CBI49978.2020.00022'
apa: 'Gottschalk, S., Rittmeier, F., & Engels, G. (2020). Hypothesis-driven
Adaptation of Business Models based on Product Line Engineering. In Proceedings
of the 22nd IEEE International Conference on Business Informatics. Antwerp:
IEEE. https://doi.org/10.1109/CBI49978.2020.00022'
bibtex: '@inproceedings{Gottschalk_Rittmeier_Engels_2020, title={Hypothesis-driven
Adaptation of Business Models based on Product Line Engineering}, DOI={10.1109/CBI49978.2020.00022},
booktitle={Proceedings of the 22nd IEEE International Conference on Business Informatics},
publisher={IEEE}, author={Gottschalk, Sebastian and Rittmeier, Florian and Engels,
Gregor}, year={2020} }'
chicago: Gottschalk, Sebastian, Florian Rittmeier, and Gregor Engels. “Hypothesis-Driven
Adaptation of Business Models Based on Product Line Engineering.” In Proceedings
of the 22nd IEEE International Conference on Business Informatics. IEEE, 2020.
https://doi.org/10.1109/CBI49978.2020.00022.
ieee: S. Gottschalk, F. Rittmeier, and G. Engels, “Hypothesis-driven Adaptation
of Business Models based on Product Line Engineering,” in Proceedings of the
22nd IEEE International Conference on Business Informatics, Antwerp, 2020.
mla: Gottschalk, Sebastian, et al. “Hypothesis-Driven Adaptation of Business Models
Based on Product Line Engineering.” Proceedings of the 22nd IEEE International
Conference on Business Informatics, IEEE, 2020, doi:10.1109/CBI49978.2020.00022.
short: 'S. Gottschalk, F. Rittmeier, G. Engels, in: Proceedings of the 22nd IEEE
International Conference on Business Informatics, IEEE, 2020.'
conference:
end_date: 2020-06-24
location: Antwerp
name: 22nd IEEE International Conference on Business Informatics
start_date: 2020-06-22
date_created: 2020-05-04T12:16:54Z
date_updated: 2022-01-06T06:52:59Z
ddc:
- '006'
department:
- _id: '66'
doi: 10.1109/CBI49978.2020.00022
file:
- access_level: open_access
content_type: application/pdf
creator: sego
date_created: 2020-07-14T09:33:00Z
date_updated: 2020-07-14T09:33:00Z
file_id: '17383'
file_name: CBI.pdf
file_size: 569290
relation: main_file
file_date_updated: 2020-07-14T09:33:00Z
has_accepted_license: '1'
keyword:
- Business Model Decision Line
- Business Model Adaptation
- Hypothesis-driven Adaptation
- Software Product Line
- Feature Model
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '17'
name: SFB 901 - Subproject C5
publication: Proceedings of the 22nd IEEE International Conference on Business Informatics
publisher: IEEE
status: public
title: Hypothesis-driven Adaptation of Business Models based on Product Line Engineering
type: conference
user_id: '47208'
year: '2020'
...
---
_id: '22737'
author:
- first_name: Matthias
full_name: Becker, Matthias
last_name: Becker
- first_name: Markus
full_name: Luckey, Markus
last_name: Luckey
- first_name: Steffen
full_name: Becker, Steffen
last_name: Becker
citation:
ama: 'Becker M, Luckey M, Becker S. Model-driven Performance Engineering of Self-adaptive
Systems: A Survey. In: {Proceedings of the 8th International ACM SIGSOFT Conference
on Quality of Software Architectures (QoSA)}. New York, NY, USA: ACM; 2012:117-122.
doi:10.1145/2304696.2304716'
apa: 'Becker, M., Luckey, M., & Becker, S. (2012). Model-driven Performance
Engineering of Self-adaptive Systems: A Survey. In {Proceedings of the 8th
International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)}
(pp. 117–122). New York, NY, USA: ACM. https://doi.org/10.1145/2304696.2304716'
bibtex: '@inproceedings{Becker_Luckey_Becker_2012, place={New York, NY, USA}, title={Model-driven
Performance Engineering of Self-adaptive Systems: A Survey}, DOI={10.1145/2304696.2304716},
booktitle={{Proceedings of the 8th International ACM SIGSOFT Conference on Quality
of Software Architectures (QoSA)}}, publisher={ACM}, author={Becker, Matthias
and Luckey, Markus and Becker, Steffen}, year={2012}, pages={117–122} }'
chicago: 'Becker, Matthias, Markus Luckey, and Steffen Becker. “Model-Driven Performance
Engineering of Self-Adaptive Systems: A Survey.” In {Proceedings of the 8th
International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)},
117–22. New York, NY, USA: ACM, 2012. https://doi.org/10.1145/2304696.2304716.'
ieee: 'M. Becker, M. Luckey, and S. Becker, “Model-driven Performance Engineering
of Self-adaptive Systems: A Survey,” in {Proceedings of the 8th International
ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)}, 2012,
pp. 117–122.'
mla: 'Becker, Matthias, et al. “Model-Driven Performance Engineering of Self-Adaptive
Systems: A Survey.” {Proceedings of the 8th International ACM SIGSOFT Conference
on Quality of Software Architectures (QoSA)}, ACM, 2012, pp. 117–22, doi:10.1145/2304696.2304716.'
short: 'M. Becker, M. Luckey, S. Becker, in: {Proceedings of the 8th International
ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)}, ACM, New
York, NY, USA, 2012, pp. 117–122.'
date_created: 2021-07-15T08:38:08Z
date_updated: 2022-01-06T06:55:39Z
doi: 10.1145/2304696.2304716
keyword:
- model-driven performance engineering
- self-*
- Self-adaptation
- software performance
page: 117-122
place: New York, NY, USA
publication: '{Proceedings of the 8th International ACM SIGSOFT Conference on Quality
of Software Architectures (QoSA)}'
publication_identifier:
isbn:
- 978-1-4503-1346-9
publisher: ACM
status: public
title: 'Model-driven Performance Engineering of Self-adaptive Systems: A Survey'
type: conference
user_id: '4870'
year: '2012'
...
---
_id: '6092'
abstract:
- lang: eng
text: The topic of the present edition is visual masking paradigms-as powerful tool
for demonstrating the processing of nonconscious visual information. In the present
issue one article presents an improved methodology for disentangling perceptual
and temporal influences in markers. Another paper demonstrates that preemptive
control, or DPS, mediates the allocation of attention towards possible targets.
One of the contributions specify conditions under which DPS-like effects are found
as opposed to conditions under which stimulus-driven effects are found. A study
of two illusions which the prime may cause in a trailing stimulus, a temporal
pre-dating of the mask and a perception of motion in later stimuli adjacent to
the prime is presented in the issue. Another contribution addresses how the percept
of a stimulus is altered by a temporal and spatial interplay of two backward masks
or of one forward mask and two backwards masks. (PsycINFO Database Record (c)
2016 APA, all rights reserved)
author:
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Ulrich
full_name: Ansorge, Ulrich
last_name: Ansorge
- first_name: Bruno G.
full_name: Breitmeyer, Bruno G.
last_name: Breitmeyer
citation:
ama: Scharlau I, Ansorge U, Breitmeyer BG. Trends and styles in visual masking.
Advances in Cognitive Psychology. 2006;2(1):1-5.
apa: Scharlau, I., Ansorge, U., & Breitmeyer, B. G. (2006). Trends and styles
in visual masking. Advances in Cognitive Psychology, 2(1), 1–5.
bibtex: '@article{Scharlau_Ansorge_Breitmeyer_2006, title={Trends and styles in
visual masking.}, volume={2}, number={1}, journal={Advances in Cognitive Psychology},
author={Scharlau, Ingrid and Ansorge, Ulrich and Breitmeyer, Bruno G.}, year={2006},
pages={1–5} }'
chicago: 'Scharlau, Ingrid, Ulrich Ansorge, and Bruno G. Breitmeyer. “Trends and
Styles in Visual Masking.” Advances in Cognitive Psychology 2, no. 1 (2006):
1–5.'
ieee: I. Scharlau, U. Ansorge, and B. G. Breitmeyer, “Trends and styles in visual
masking.,” Advances in Cognitive Psychology, vol. 2, no. 1, pp. 1–5, 2006.
mla: Scharlau, Ingrid, et al. “Trends and Styles in Visual Masking.” Advances
in Cognitive Psychology, vol. 2, no. 1, 2006, pp. 1–5.
short: I. Scharlau, U. Ansorge, B.G. Breitmeyer, Advances in Cognitive Psychology
2 (2006) 1–5.
date_created: 2018-12-10T07:08:33Z
date_updated: 2022-06-06T20:08:22Z
department:
- _id: '424'
extern: '1'
intvolume: ' 2'
issue: '1'
keyword:
- visual masking
- visual information
- attention
- stimulus-driven effects
- motion perception
- Attention
- Illusions (Perception)
- Motion Perception
- Visual Masking
language:
- iso: eng
page: 1 - 5
publication: Advances in Cognitive Psychology
publication_identifier:
issn:
- 1895-1171
publication_status: published
status: public
title: Trends and styles in visual masking.
type: journal_article
user_id: '42165'
volume: 2
year: '2006'
...