---
_id: '10759'
author:
- first_name: Giovanni
full_name: Squillero, Giovanni
last_name: Squillero
- first_name: Paolo
full_name: Burelli, Paolo
last_name: Burelli
- first_name: Antonio
full_name: M. Mora, Antonio
last_name: M. Mora
- first_name: Alexandros
full_name: Agapitos, Alexandros
last_name: Agapitos
- first_name: William
full_name: S. Bush, William
last_name: S. Bush
- first_name: Stefano
full_name: Cagnoni, Stefano
last_name: Cagnoni
- first_name: Carlos
full_name: Cotta, Carlos
last_name: Cotta
- first_name: Ivanoe
full_name: De Falco, Ivanoe
last_name: De Falco
- first_name: Antonio
full_name: Della Cioppa, Antonio
last_name: Della Cioppa
- first_name: Federico
full_name: Divina, Federico
last_name: Divina
- first_name: A.E.
full_name: Eiben, A.E.
last_name: Eiben
- first_name: Anna
full_name: I. Esparcia-Alc{\'a}zar, Anna
last_name: I. Esparcia-Alc{\'a}zar
- first_name: Francisco
full_name: Fern{\'a}ndez de Vega, Francisco
last_name: Fern{\'a}ndez de Vega
- first_name: Kyrre
full_name: Glette, Kyrre
last_name: Glette
- first_name: Evert
full_name: Haasdijk, Evert
last_name: Haasdijk
- first_name: J.
full_name: Ignacio Hidalgo, J.
last_name: Ignacio Hidalgo
- first_name: Michael
full_name: Kampouridis, Michael
last_name: Kampouridis
- first_name: Paul
full_name: Kaufmann, Paul
last_name: Kaufmann
- first_name: Michalis
full_name: Mavrovouniotis, Michalis
last_name: Mavrovouniotis
- first_name: Trung
full_name: Thanh Nguyen, Trung
last_name: Thanh Nguyen
- first_name: Robert
full_name: Schaefer, Robert
last_name: Schaefer
- first_name: Kevin
full_name: Sim, Kevin
last_name: Sim
- first_name: Ernesto
full_name: Tarantino, Ernesto
last_name: Tarantino
- first_name: Neil
full_name: Urquhart, Neil
last_name: Urquhart
- first_name: Mengjie
full_name: Zhang (editors), Mengjie
last_name: Zhang (editors)
citation:
ama: Squillero G, Burelli P, M. Mora A, et al. Applications of Evolutionary Computation
- 20th European Conference, EvoApplications. Springer; 2017.
apa: Squillero, G., Burelli, P., M. Mora, A., Agapitos, A., S. Bush, W., Cagnoni,
S., … Zhang (editors), M. (2017). Applications of Evolutionary Computation
- 20th European Conference, EvoApplications. Springer.
bibtex: '@book{Squillero_Burelli_M. Mora_Agapitos_S. Bush_Cagnoni_Cotta_De Falco_Della
Cioppa_Divina_et al._2017, series={Lecture Notes in Computer Science}, title={Applications
of Evolutionary Computation - 20th European Conference, EvoApplications}, publisher={Springer},
author={Squillero, Giovanni and Burelli, Paolo and M. Mora, Antonio and Agapitos,
Alexandros and S. Bush, William and Cagnoni, Stefano and Cotta, Carlos and De
Falco, Ivanoe and Della Cioppa, Antonio and Divina, Federico and et al.}, year={2017},
collection={Lecture Notes in Computer Science} }'
chicago: Squillero, Giovanni, Paolo Burelli, Antonio M. Mora, Alexandros Agapitos,
William S. Bush, Stefano Cagnoni, Carlos Cotta, et al. Applications of Evolutionary
Computation - 20th European Conference, EvoApplications. Lecture Notes in
Computer Science. Springer, 2017.
ieee: G. Squillero et al., Applications of Evolutionary Computation -
20th European Conference, EvoApplications. Springer, 2017.
mla: Squillero, Giovanni, et al. Applications of Evolutionary Computation - 20th
European Conference, EvoApplications. Springer, 2017.
short: G. Squillero, P. Burelli, A. M. Mora, A. Agapitos, W. S. Bush, S. Cagnoni,
C. Cotta, I. De Falco, A. Della Cioppa, F. Divina, A.E. Eiben, A. I. Esparcia-Alc{\’a}zar,
F. Fern{\’a}ndez de Vega, K. Glette, E. Haasdijk, J. Ignacio Hidalgo, M. Kampouridis,
P. Kaufmann, M. Mavrovouniotis, T. Thanh Nguyen, R. Schaefer, K. Sim, E. Tarantino,
N. Urquhart, M. Zhang (editors), Applications of Evolutionary Computation - 20th
European Conference, EvoApplications, Springer, 2017.
date_created: 2019-07-10T12:06:37Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Applications of Evolutionary Computation - 20th European Conference, EvoApplications
type: book
user_id: '3118'
year: '2017'
...
---
_id: '10760'
author:
- first_name: Paul
full_name: Kaufmann, Paul
last_name: Kaufmann
- first_name: Roman
full_name: Kalkreuth, Roman
last_name: Kalkreuth
citation:
ama: 'Kaufmann P, Kalkreuth R. Parametrizing Cartesian Genetic Programming: An Empirical
Study. In: KI 2017: Advances in Artificial Intelligence: 40th Annual German
Conference on AI. Springer International Publishing; 2017. doi:10.1007/978-3-319-67190-1_26'
apa: 'Kaufmann, P., & Kalkreuth, R. (2017). Parametrizing Cartesian Genetic
Programming: An Empirical Study. In KI 2017: Advances in Artificial Intelligence:
40th Annual German Conference on AI. Springer International Publishing. https://doi.org/10.1007/978-3-319-67190-1_26'
bibtex: '@inproceedings{Kaufmann_Kalkreuth_2017, title={Parametrizing Cartesian
Genetic Programming: An Empirical Study}, DOI={10.1007/978-3-319-67190-1_26},
booktitle={KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference
on AI}, publisher={Springer International Publishing}, author={Kaufmann, Paul
and Kalkreuth, Roman}, year={2017} }'
chicago: 'Kaufmann, Paul, and Roman Kalkreuth. “Parametrizing Cartesian Genetic
Programming: An Empirical Study.” In KI 2017: Advances in Artificial Intelligence:
40th Annual German Conference on AI. Springer International Publishing, 2017.
https://doi.org/10.1007/978-3-319-67190-1_26.'
ieee: 'P. Kaufmann and R. Kalkreuth, “Parametrizing Cartesian Genetic Programming:
An Empirical Study,” in KI 2017: Advances in Artificial Intelligence: 40th
Annual German Conference on AI, 2017.'
mla: 'Kaufmann, Paul, and Roman Kalkreuth. “Parametrizing Cartesian Genetic Programming:
An Empirical Study.” KI 2017: Advances in Artificial Intelligence: 40th Annual
German Conference on AI, Springer International Publishing, 2017, doi:10.1007/978-3-319-67190-1_26.'
short: 'P. Kaufmann, R. Kalkreuth, in: KI 2017: Advances in Artificial Intelligence:
40th Annual German Conference on AI, Springer International Publishing, 2017.'
date_created: 2019-07-10T12:06:38Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
doi: 10.1007/978-3-319-67190-1_26
language:
- iso: eng
publication: 'KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference
on AI'
publisher: Springer International Publishing
status: public
title: 'Parametrizing Cartesian Genetic Programming: An Empirical Study'
type: conference
user_id: '3118'
year: '2017'
...
---
_id: '10761'
author:
- first_name: Paul
full_name: Kaufmann, Paul
last_name: Kaufmann
- first_name: Nam
full_name: Ho, Nam
last_name: Ho
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Kaufmann P, Ho N, Platzner M. Evaluation Methodology for Complex Non-deterministic
Functions: A Case Study in Metaheuristic Optimization of Caches. In: Adaptive
Hardware and Systems (AHS). IEEE; 2017. doi:10.1109/AHS.2017.8046380'
apa: 'Kaufmann, P., Ho, N., & Platzner, M. (2017). Evaluation Methodology for
Complex Non-deterministic Functions: A Case Study in Metaheuristic Optimization
of Caches. In Adaptive Hardware and Systems (AHS). IEEE. https://doi.org/10.1109/AHS.2017.8046380'
bibtex: '@inproceedings{Kaufmann_Ho_Platzner_2017, title={Evaluation Methodology
for Complex Non-deterministic Functions: A Case Study in Metaheuristic Optimization
of Caches}, DOI={10.1109/AHS.2017.8046380},
booktitle={Adaptive Hardware and Systems (AHS)}, publisher={IEEE}, author={Kaufmann,
Paul and Ho, Nam and Platzner, Marco}, year={2017} }'
chicago: 'Kaufmann, Paul, Nam Ho, and Marco Platzner. “Evaluation Methodology for
Complex Non-Deterministic Functions: A Case Study in Metaheuristic Optimization
of Caches.” In Adaptive Hardware and Systems (AHS). IEEE, 2017. https://doi.org/10.1109/AHS.2017.8046380.'
ieee: 'P. Kaufmann, N. Ho, and M. Platzner, “Evaluation Methodology for Complex
Non-deterministic Functions: A Case Study in Metaheuristic Optimization of Caches,”
in Adaptive Hardware and Systems (AHS), 2017.'
mla: 'Kaufmann, Paul, et al. “Evaluation Methodology for Complex Non-Deterministic
Functions: A Case Study in Metaheuristic Optimization of Caches.” Adaptive
Hardware and Systems (AHS), IEEE, 2017, doi:10.1109/AHS.2017.8046380.'
short: 'P. Kaufmann, N. Ho, M. Platzner, in: Adaptive Hardware and Systems (AHS),
IEEE, 2017.'
date_created: 2019-07-10T12:07:01Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
doi: 10.1109/AHS.2017.8046380
language:
- iso: eng
publication: Adaptive Hardware and Systems (AHS)
publisher: IEEE
status: public
title: 'Evaluation Methodology for Complex Non-deterministic Functions: A Case Study
in Metaheuristic Optimization of Caches'
type: conference
user_id: '3118'
year: '2017'
...
---
_id: '10762'
author:
- first_name: Paul
full_name: Kaufmann, Paul
last_name: Kaufmann
- first_name: Roman
full_name: Kalkreuth, Roman
last_name: Kalkreuth
citation:
ama: 'Kaufmann P, Kalkreuth R. An Empirical Study on the Parametrization of Cartesian
Genetic Programming. In: Genetic and Evolutionary Computation (GECCO), Compendium.
ACM; 2017. doi:10.1145/3067695.3075980'
apa: Kaufmann, P., & Kalkreuth, R. (2017). An Empirical Study on the Parametrization
of Cartesian Genetic Programming. In Genetic and Evolutionary Computation (GECCO),
Compendium. ACM. https://doi.org/10.1145/3067695.3075980
bibtex: '@inproceedings{Kaufmann_Kalkreuth_2017, title={An Empirical Study on the
Parametrization of Cartesian Genetic Programming}, DOI={10.1145/3067695.3075980},
booktitle={Genetic and Evolutionary Computation (GECCO), Compendium}, publisher={ACM},
author={Kaufmann, Paul and Kalkreuth, Roman}, year={2017} }'
chicago: Kaufmann, Paul, and Roman Kalkreuth. “An Empirical Study on the Parametrization
of Cartesian Genetic Programming.” In Genetic and Evolutionary Computation
(GECCO), Compendium. ACM, 2017. https://doi.org/10.1145/3067695.3075980.
ieee: P. Kaufmann and R. Kalkreuth, “An Empirical Study on the Parametrization of
Cartesian Genetic Programming,” in Genetic and Evolutionary Computation (GECCO),
Compendium, 2017.
mla: Kaufmann, Paul, and Roman Kalkreuth. “An Empirical Study on the Parametrization
of Cartesian Genetic Programming.” Genetic and Evolutionary Computation (GECCO),
Compendium, ACM, 2017, doi:10.1145/3067695.3075980.
short: 'P. Kaufmann, R. Kalkreuth, in: Genetic and Evolutionary Computation (GECCO),
Compendium, ACM, 2017.'
date_created: 2019-07-10T12:07:03Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
doi: 10.1145/3067695.3075980
publication: Genetic and Evolutionary Computation (GECCO), Compendium
publisher: ACM
status: public
title: An Empirical Study on the Parametrization of Cartesian Genetic Programming
type: conference
user_id: '3118'
year: '2017'
...
---
_id: '10780'
author:
- first_name: Zakarya
full_name: Guettatfi, Zakarya
last_name: Guettatfi
- first_name: Philipp
full_name: Hübner, Philipp
last_name: Hübner
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
- first_name: Bernhard
full_name: Rinner, Bernhard
last_name: Rinner
citation:
ama: 'Guettatfi Z, Hübner P, Platzner M, Rinner B. Computational self-awareness
as design approach for visual sensor nodes. In: 12th International Symposium
on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC). ; 2017:1-8.
doi:10.1109/ReCoSoC.2017.8016147'
apa: Guettatfi, Z., Hübner, P., Platzner, M., & Rinner, B. (2017). Computational
self-awareness as design approach for visual sensor nodes. In 12th International
Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)
(pp. 1–8). https://doi.org/10.1109/ReCoSoC.2017.8016147
bibtex: '@inproceedings{Guettatfi_Hübner_Platzner_Rinner_2017, title={Computational
self-awareness as design approach for visual sensor nodes}, DOI={10.1109/ReCoSoC.2017.8016147},
booktitle={12th International Symposium on Reconfigurable Communication-centric
Systems-on-Chip (ReCoSoC)}, author={Guettatfi, Zakarya and Hübner, Philipp and
Platzner, Marco and Rinner, Bernhard}, year={2017}, pages={1–8} }'
chicago: Guettatfi, Zakarya, Philipp Hübner, Marco Platzner, and Bernhard Rinner.
“Computational Self-Awareness as Design Approach for Visual Sensor Nodes.” In
12th International Symposium on Reconfigurable Communication-Centric Systems-on-Chip
(ReCoSoC), 1–8, 2017. https://doi.org/10.1109/ReCoSoC.2017.8016147.
ieee: Z. Guettatfi, P. Hübner, M. Platzner, and B. Rinner, “Computational self-awareness
as design approach for visual sensor nodes,” in 12th International Symposium
on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), 2017, pp.
1–8.
mla: Guettatfi, Zakarya, et al. “Computational Self-Awareness as Design Approach
for Visual Sensor Nodes.” 12th International Symposium on Reconfigurable Communication-Centric
Systems-on-Chip (ReCoSoC), 2017, pp. 1–8, doi:10.1109/ReCoSoC.2017.8016147.
short: 'Z. Guettatfi, P. Hübner, M. Platzner, B. Rinner, in: 12th International
Symposium on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC), 2017,
pp. 1–8.'
date_created: 2019-07-10T12:13:15Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '78'
doi: 10.1109/ReCoSoC.2017.8016147
keyword:
- embedded systems
- image sensors
- power aware computing
- wireless sensor networks
- Zynq-based VSN node prototype
- computational self-awareness
- design approach
- platform levels
- power consumption
- visual sensor networks
- visual sensor nodes
- Cameras
- Hardware
- Middleware
- Multicore processing
- Operating systems
- Runtime
- Reconfigurable platforms
- distributed embedded systems
- performance-resource trade-off
- self-awareness
- visual sensor nodes
language:
- iso: eng
page: 1-8
publication: 12th International Symposium on Reconfigurable Communication-centric
Systems-on-Chip (ReCoSoC)
status: public
title: Computational self-awareness as design approach for visual sensor nodes
type: conference
user_id: '3118'
year: '2017'
...
---
_id: '10784'
author:
- first_name: J.
full_name: Fürnkranz, J.
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds.
Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.'
apa: Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut
& G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining
(Vol. 107, pp. 1000–1005). Springer.
bibtex: '@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, volume={107},
booktitle={Encyclopedia of Machine Learning and Data Mining}, publisher={Springer},
author={Fürnkranz, J. and Hüllermeier, Eyke}, editor={Sammut, C. and Webb, G.I.Editors},
year={2017}, pages={1000–1005} }'
chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia
of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, 107:1000–1005.
Springer, 2017.
ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia
of Machine Learning and Data Mining, vol. 107, C. Sammut and G. I. Webb, Eds.
Springer, 2017, pp. 1000–1005.
mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia
of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, vol.
107, Springer, 2017, pp. 1000–05.
short: 'J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia
of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.'
date_created: 2019-07-10T15:44:32Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
editor:
- first_name: C.
full_name: Sammut, C.
last_name: Sammut
- first_name: G.I.
full_name: Webb, G.I.
last_name: Webb
intvolume: ' 107'
language:
- iso: eng
page: 1000-1005
publication: Encyclopedia of Machine Learning and Data Mining
publisher: Springer
status: public
title: Preference Learning
type: book_chapter
user_id: '49109'
volume: 107
year: '2017'
...
---
_id: '1080'
author:
- first_name: Jan
full_name: Bürmann, Jan
last_name: Bürmann
citation:
ama: Bürmann J. Complexity of Signalling in Routing Games under Uncertainty.
Universität Paderborn; 2017.
apa: Bürmann, J. (2017). Complexity of Signalling in Routing Games under Uncertainty.
Universität Paderborn.
bibtex: '@book{Bürmann_2017, title={Complexity of Signalling in Routing Games under
Uncertainty}, publisher={Universität Paderborn}, author={Bürmann, Jan}, year={2017}
}'
chicago: Bürmann, Jan. Complexity of Signalling in Routing Games under Uncertainty.
Universität Paderborn, 2017.
ieee: J. Bürmann, Complexity of Signalling in Routing Games under Uncertainty.
Universität Paderborn, 2017.
mla: Bürmann, Jan. Complexity of Signalling in Routing Games under Uncertainty.
Universität Paderborn, 2017.
short: J. Bürmann, Complexity of Signalling in Routing Games under Uncertainty,
Universität Paderborn, 2017.
date_created: 2017-12-19T10:07:35Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '63'
- _id: '541'
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '7'
name: SFB 901 - Subproject A3
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Alexander
full_name: Skopalik, Alexander
id: '40384'
last_name: Skopalik
title: Complexity of Signalling in Routing Games under Uncertainty
type: mastersthesis
user_id: '14052'
year: '2017'
...
---
_id: '1081'
author:
- first_name: Vipin Ravindran
full_name: Vijayalakshmi, Vipin Ravindran
last_name: Vijayalakshmi
citation:
ama: Vijayalakshmi VR. Bounding the Inefficiency of Equilibria in Congestion
Games under Taxation. Universität Paderborn; 2017.
apa: Vijayalakshmi, V. R. (2017). Bounding the Inefficiency of Equilibria in
Congestion Games under Taxation. Universität Paderborn.
bibtex: '@book{Vijayalakshmi_2017, title={Bounding the Inefficiency of Equilibria
in Congestion Games under Taxation}, publisher={Universität Paderborn}, author={Vijayalakshmi,
Vipin Ravindran}, year={2017} }'
chicago: Vijayalakshmi, Vipin Ravindran. Bounding the Inefficiency of Equilibria
in Congestion Games under Taxation. Universität Paderborn, 2017.
ieee: V. R. Vijayalakshmi, Bounding the Inefficiency of Equilibria in Congestion
Games under Taxation. Universität Paderborn, 2017.
mla: Vijayalakshmi, Vipin Ravindran. Bounding the Inefficiency of Equilibria
in Congestion Games under Taxation. Universität Paderborn, 2017.
short: V.R. Vijayalakshmi, Bounding the Inefficiency of Equilibria in Congestion
Games under Taxation, Universität Paderborn, 2017.
date_created: 2017-12-19T10:08:44Z
date_updated: 2022-01-06T06:50:51Z
department:
- _id: '63'
- _id: '541'
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '7'
name: SFB 901 - Subproject A3
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Alexander
full_name: Skopalik, Alexander
id: '40384'
last_name: Skopalik
title: Bounding the Inefficiency of Equilibria in Congestion Games under Taxation
type: mastersthesis
user_id: '14052'
year: '2017'
...
---
_id: '109'
author:
- first_name: Felix
full_name: Pauck, Felix
id: '22398'
last_name: Pauck
citation:
ama: Pauck F. Cooperative Static Analysis of Android Applications. Universität
Paderborn; 2017.
apa: Pauck, F. (2017). Cooperative static analysis of Android applications.
Universität Paderborn.
bibtex: '@book{Pauck_2017, title={Cooperative static analysis of Android applications},
publisher={Universität Paderborn}, author={Pauck, Felix}, year={2017} }'
chicago: Pauck, Felix. Cooperative Static Analysis of Android Applications.
Universität Paderborn, 2017.
ieee: F. Pauck, Cooperative static analysis of Android applications. Universität
Paderborn, 2017.
mla: Pauck, Felix. Cooperative Static Analysis of Android Applications. Universität
Paderborn, 2017.
short: F. Pauck, Cooperative Static Analysis of Android Applications, Universität
Paderborn, 2017.
date_created: 2017-10-17T12:41:12Z
date_updated: 2022-01-06T06:50:52Z
ddc:
- '000'
department:
- _id: '77'
file:
- access_level: open_access
content_type: application/pdf
creator: fpauck
date_created: 2019-08-07T08:55:58Z
date_updated: 2019-08-07T09:03:48Z
file_id: '12905'
file_name: fpauck_2017.pdf
file_size: 5093611
relation: main_file
title: Master's Thesis
file_date_updated: 2019-08-07T09:03:48Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '12'
name: SFB 901 - Subprojekt B4
- _id: '3'
name: SFB 901 - Project Area B
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
title: Cooperative static analysis of Android applications
type: mastersthesis
user_id: '22398'
year: '2017'
...
---
_id: '1094'
abstract:
- lang: eng
text: Many university students struggle with motivational problems, and gamification
has the potential to address these problems. However, gamification is hardly used
in education, because current approaches to gamification require instructors to
engage in the time-consuming preparation of their course contents for use in quizzes,
mini-games and the like. Drawing on research on limited attention and present
bias, we propose a "lean" approach to gamification, which relies on gamifying
learning activities (rather than learning contents) and increasing their salience.
In this paper, we present the app StudyNow that implements such a lean gamification
approach. With this app, we aim to enable more students and instructors to benefit
from the advantages of gamification.
author:
- first_name: Matthias
full_name: Feldotto, Matthias
id: '14052'
last_name: Feldotto
orcid: 0000-0003-1348-6516
- first_name: Thomas
full_name: John, Thomas
id: '3952'
last_name: John
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Paul
full_name: Hemsen, Paul
id: '22546'
last_name: Hemsen
- first_name: Katrin
full_name: Klingsieck, Katrin
last_name: Klingsieck
- first_name: Alexander
full_name: Skopalik, Alexander
id: '40384'
last_name: Skopalik
citation:
ama: 'Feldotto M, John T, Kundisch D, Hemsen P, Klingsieck K, Skopalik A. Making
Gamification Easy for the Professor: Decoupling Game and Content with the StudyNow
Mobile App. In: Proceedings of the 12th International Conference on Design
Science Research in Information Systems and Technology (DESRIST). LNCS. ;
2017:462-467. doi:10.1007/978-3-319-59144-5_32'
apa: 'Feldotto, M., John, T., Kundisch, D., Hemsen, P., Klingsieck, K., & Skopalik,
A. (2017). Making Gamification Easy for the Professor: Decoupling Game and Content
with the StudyNow Mobile App. In Proceedings of the 12th International Conference
on Design Science Research in Information Systems and Technology (DESRIST)
(pp. 462–467). https://doi.org/10.1007/978-3-319-59144-5_32'
bibtex: '@inproceedings{Feldotto_John_Kundisch_Hemsen_Klingsieck_Skopalik_2017,
series={LNCS}, title={Making Gamification Easy for the Professor: Decoupling Game
and Content with the StudyNow Mobile App}, DOI={10.1007/978-3-319-59144-5_32},
booktitle={Proceedings of the 12th International Conference on Design Science
Research in Information Systems and Technology (DESRIST)}, author={Feldotto, Matthias
and John, Thomas and Kundisch, Dennis and Hemsen, Paul and Klingsieck, Katrin
and Skopalik, Alexander}, year={2017}, pages={462–467}, collection={LNCS} }'
chicago: 'Feldotto, Matthias, Thomas John, Dennis Kundisch, Paul Hemsen, Katrin
Klingsieck, and Alexander Skopalik. “Making Gamification Easy for the Professor:
Decoupling Game and Content with the StudyNow Mobile App.” In Proceedings of
the 12th International Conference on Design Science Research in Information Systems
and Technology (DESRIST), 462–67. LNCS, 2017. https://doi.org/10.1007/978-3-319-59144-5_32.'
ieee: 'M. Feldotto, T. John, D. Kundisch, P. Hemsen, K. Klingsieck, and A. Skopalik,
“Making Gamification Easy for the Professor: Decoupling Game and Content with
the StudyNow Mobile App,” in Proceedings of the 12th International Conference
on Design Science Research in Information Systems and Technology (DESRIST),
2017, pp. 462–467.'
mla: 'Feldotto, Matthias, et al. “Making Gamification Easy for the Professor: Decoupling
Game and Content with the StudyNow Mobile App.” Proceedings of the 12th International
Conference on Design Science Research in Information Systems and Technology (DESRIST),
2017, pp. 462–67, doi:10.1007/978-3-319-59144-5_32.'
short: 'M. Feldotto, T. John, D. Kundisch, P. Hemsen, K. Klingsieck, A. Skopalik,
in: Proceedings of the 12th International Conference on Design Science Research
in Information Systems and Technology (DESRIST), 2017, pp. 462–467.'
date_created: 2018-01-05T08:37:52Z
date_updated: 2022-01-06T06:50:53Z
ddc:
- '000'
department:
- _id: '63'
- _id: '541'
- _id: '276'
doi: 10.1007/978-3-319-59144-5_32
file:
- access_level: closed
content_type: application/pdf
creator: feldi
date_created: 2018-10-31T17:01:09Z
date_updated: 2018-10-31T17:01:09Z
file_id: '5230'
file_name: Feldotto2017_Chapter_MakingGamificationEasyForThePr.pdf
file_size: 1576363
relation: main_file
success: 1
file_date_updated: 2018-10-31T17:01:09Z
has_accepted_license: '1'
language:
- iso: eng
page: 462-467
publication: Proceedings of the 12th International Conference on Design Science Research
in Information Systems and Technology (DESRIST)
series_title: LNCS
status: public
title: 'Making Gamification Easy for the Professor: Decoupling Game and Content with
the StudyNow Mobile App'
type: conference
user_id: '14052'
year: '2017'
...
---
_id: '1095'
abstract:
- lang: eng
text: 'Many university students struggle with motivational problems, and gamification
has the potential to address these problems. However, using gamification currently
is rather tedious and time-consuming for instructors because current approaches
to gamification require instructors to engage in the time-consuming preparation
of course contents (e.g., for quizzes or mini-games). In reply to this issue,
we propose a “lean” approach to gamification, which relies on gamifying learning
activities rather than learning contents. The learning activities that are gamified
in the lean approach can typically be drawn from existing course syllabi (e.g.,
attend certain lectures, hand in assignments, read book chapters and articles).
Hence, compared to existing approaches, lean gamification substantially lowers
the time requirements posed on instructors for gamifying a given course. Drawing
on research on limited attention and the present bias, we provide the theoretical
foundation for the lean gamification approach. In addition, we present a mobile
application that implements lean gamification and outline a mixed-methods study
that is currently under way for evaluating whether lean gamification does indeed
have the potential to increase students’ motivation. We thereby hope to allow
more students and instructors to benefit from the advantages of gamification. '
author:
- first_name: Thomas
full_name: John, Thomas
id: '3952'
last_name: John
- first_name: Matthias
full_name: Feldotto, Matthias
id: '14052'
last_name: Feldotto
orcid: 0000-0003-1348-6516
- first_name: Paul
full_name: Hemsen, Paul
id: '22546'
last_name: Hemsen
- first_name: Katrin
full_name: Klingsieck, Katrin
last_name: Klingsieck
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Mike
full_name: Langendorf, Mike
last_name: Langendorf
citation:
ama: 'John T, Feldotto M, Hemsen P, Klingsieck K, Kundisch D, Langendorf M. Towards
a Lean Approach for Gamifying Education. In: Proceedings of the 25th European
Conference on Information Systems (ECIS). ; 2017:2970-2979.'
apa: John, T., Feldotto, M., Hemsen, P., Klingsieck, K., Kundisch, D., & Langendorf,
M. (2017). Towards a Lean Approach for Gamifying Education. In Proceedings
of the 25th European Conference on Information Systems (ECIS) (pp. 2970–2979).
bibtex: '@inproceedings{John_Feldotto_Hemsen_Klingsieck_Kundisch_Langendorf_2017,
title={Towards a Lean Approach for Gamifying Education}, booktitle={Proceedings
of the 25th European Conference on Information Systems (ECIS)}, author={John,
Thomas and Feldotto, Matthias and Hemsen, Paul and Klingsieck, Katrin and Kundisch,
Dennis and Langendorf, Mike}, year={2017}, pages={2970–2979} }'
chicago: John, Thomas, Matthias Feldotto, Paul Hemsen, Katrin Klingsieck, Dennis
Kundisch, and Mike Langendorf. “Towards a Lean Approach for Gamifying Education.”
In Proceedings of the 25th European Conference on Information Systems (ECIS),
2970–79, 2017.
ieee: T. John, M. Feldotto, P. Hemsen, K. Klingsieck, D. Kundisch, and M. Langendorf,
“Towards a Lean Approach for Gamifying Education,” in Proceedings of the 25th
European Conference on Information Systems (ECIS), 2017, pp. 2970–2979.
mla: John, Thomas, et al. “Towards a Lean Approach for Gamifying Education.” Proceedings
of the 25th European Conference on Information Systems (ECIS), 2017, pp. 2970–79.
short: 'T. John, M. Feldotto, P. Hemsen, K. Klingsieck, D. Kundisch, M. Langendorf,
in: Proceedings of the 25th European Conference on Information Systems (ECIS),
2017, pp. 2970–2979.'
date_created: 2018-01-05T08:39:41Z
date_updated: 2022-01-06T06:50:53Z
ddc:
- '000'
department:
- _id: '63'
- _id: '541'
- _id: '178'
- _id: '185'
file:
- access_level: closed
content_type: application/pdf
creator: feldi
date_created: 2018-10-31T17:02:07Z
date_updated: 2018-10-31T17:02:07Z
file_id: '5232'
file_name: TOWARDS A LEAN APPROACH TO GAMIFYING EDUCATION.pdf
file_size: 485333
relation: main_file
success: 1
file_date_updated: 2018-10-31T17:02:07Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aisel.aisnet.org/ecis2017_rip/46
oa: '1'
page: 2970-2979
publication: Proceedings of the 25th European Conference on Information Systems (ECIS)
status: public
title: Towards a Lean Approach for Gamifying Education
type: conference
user_id: '14052'
year: '2017'
...
---
_id: '110'
abstract:
- lang: eng
text: 'We consider an extension of the dynamic speed scaling scheduling model introduced
by Yao et al.: A set of jobs, each with a release time, deadline, and workload,
has to be scheduled on a single, speed-scalable processor. Both the maximum allowed
speed of the processor and the energy costs may vary continuously over time. The
objective is to find a feasible schedule that minimizes the total energy costs.
Theoretical algorithm design for speed scaling problems often tends to discretize
problems, as our tools in the discrete realm are often better developed or understood.
Using the above speed scaling variant with variable, continuous maximal processor
speeds and energy prices as an example, we demonstrate that a more direct approach
via tools from variational calculus can not only lead to a very concise and elegant
formulation and analysis, but also avoids the “explosion of variables/constraints”
that often comes with discretizing. Using well-known tools from calculus of variations,
we derive combinatorial optimality characteristics for our continuous problem
and provide a quite concise and simple correctness proof.'
author:
- first_name: Antonios
full_name: Antoniadis, Antonios
last_name: Antoniadis
- first_name: Peter
full_name: Kling, Peter
last_name: Kling
- first_name: Sebastian
full_name: Ott, Sebastian
last_name: Ott
- first_name: Sören
full_name: Riechers, Sören
last_name: Riechers
citation:
ama: 'Antoniadis A, Kling P, Ott S, Riechers S. Continuous Speed Scaling with Variability:
A Simple and Direct Approach. Theoretical Computer Science. 2017:1-13.
doi:10.1016/j.tcs.2017.03.021'
apa: 'Antoniadis, A., Kling, P., Ott, S., & Riechers, S. (2017). Continuous
Speed Scaling with Variability: A Simple and Direct Approach. Theoretical Computer
Science, 1–13. https://doi.org/10.1016/j.tcs.2017.03.021'
bibtex: '@article{Antoniadis_Kling_Ott_Riechers_2017, title={Continuous Speed Scaling
with Variability: A Simple and Direct Approach}, DOI={10.1016/j.tcs.2017.03.021},
journal={Theoretical Computer Science}, publisher={Elsevier}, author={Antoniadis,
Antonios and Kling, Peter and Ott, Sebastian and Riechers, Sören}, year={2017},
pages={1–13} }'
chicago: 'Antoniadis, Antonios, Peter Kling, Sebastian Ott, and Sören Riechers.
“Continuous Speed Scaling with Variability: A Simple and Direct Approach.” Theoretical
Computer Science, 2017, 1–13. https://doi.org/10.1016/j.tcs.2017.03.021.'
ieee: 'A. Antoniadis, P. Kling, S. Ott, and S. Riechers, “Continuous Speed Scaling
with Variability: A Simple and Direct Approach,” Theoretical Computer Science,
pp. 1–13, 2017.'
mla: 'Antoniadis, Antonios, et al. “Continuous Speed Scaling with Variability: A
Simple and Direct Approach.” Theoretical Computer Science, Elsevier, 2017,
pp. 1–13, doi:10.1016/j.tcs.2017.03.021.'
short: A. Antoniadis, P. Kling, S. Ott, S. Riechers, Theoretical Computer Science
(2017) 1–13.
date_created: 2017-10-17T12:41:13Z
date_updated: 2022-01-06T06:50:55Z
ddc:
- '040'
department:
- _id: '63'
doi: 10.1016/j.tcs.2017.03.021
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T13:07:43Z
date_updated: 2018-03-21T13:07:43Z
file_id: '1567'
file_name: 110-TCSSubmission.pdf
file_size: 494600
relation: main_file
success: 1
file_date_updated: 2018-03-21T13:07:43Z
has_accepted_license: '1'
language:
- iso: eng
page: 1-13
project:
- _id: '1'
name: SFB 901
- _id: '16'
name: SFB 901 - Subprojekt C4
- _id: '4'
name: SFB 901 - Project Area C
publication: Theoretical Computer Science
publisher: Elsevier
status: public
title: 'Continuous Speed Scaling with Variability: A Simple and Direct Approach'
type: journal_article
user_id: '477'
year: '2017'
...
---
_id: '117'
author:
- first_name: Pascal
full_name: Bemmann, Pascal
id: '32571'
last_name: Bemmann
citation:
ama: Bemmann P. Attribute-Based Signatures Using Structure Preserving Signatures.
Universität Paderborn; 2017.
apa: Bemmann, P. (2017). Attribute-based Signatures using Structure Preserving
Signatures. Universität Paderborn.
bibtex: '@book{Bemmann_2017, title={Attribute-based Signatures using Structure Preserving
Signatures}, publisher={Universität Paderborn}, author={Bemmann, Pascal}, year={2017}
}'
chicago: Bemmann, Pascal. Attribute-Based Signatures Using Structure Preserving
Signatures. Universität Paderborn, 2017.
ieee: P. Bemmann, Attribute-based Signatures using Structure Preserving Signatures.
Universität Paderborn, 2017.
mla: Bemmann, Pascal. Attribute-Based Signatures Using Structure Preserving Signatures.
Universität Paderborn, 2017.
short: P. Bemmann, Attribute-Based Signatures Using Structure Preserving Signatures,
Universität Paderborn, 2017.
date_created: 2017-10-17T12:41:14Z
date_updated: 2022-01-06T06:51:06Z
department:
- _id: '64'
project:
- _id: '1'
name: SFB 901
- _id: '13'
name: SFB 901 - Subprojekt C1
- _id: '4'
name: SFB 901 - Project Area C
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Johannes
full_name: Blömer, Johannes
id: '23'
last_name: Blömer
title: Attribute-based Signatures using Structure Preserving Signatures
type: mastersthesis
user_id: '25078'
year: '2017'
...
---
_id: '11717'
abstract:
- lang: eng
text: In this work, we address the limited availability of large annotated databases
for real-life audio event detection by utilizing the concept of transfer learning.
This technique aims to transfer knowledge from a source domain to a target domain,
even if source and target have different feature distributions and label sets.
We hypothesize that all acoustic events share the same inventory of basic acoustic
building blocks and differ only in the temporal order of these acoustic units.
We then construct a deep neural network with convolutional layers for extracting
the acoustic units and a recurrent layer for capturing the temporal order. Under
the above hypothesis, transfer learning from a source to a target domain with
a different acoustic event inventory is realized by transferring the convolutional
layers from the source to the target domain. The recurrent layer is, however,
learnt directly from the target domain. Experiments on the transfer from a synthetic
source database to the reallife target database of DCASE 2016 demonstrate that
transfer learning leads to improved detection performance on average. However,
the successful transfer to detect events which are very different from what was
seen in the source domain, could not be verified.
author:
- first_name: Prerna
full_name: Arora, Prerna
last_name: Arora
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Arora P, Haeb-Umbach R. A Study on Transfer Learning for Acoustic Event Detection
in a Real Life Scenario. In: IEEE 19th International Workshop on Multimedia
Signal Processing (MMSP). ; 2017.'
apa: Arora, P., & Haeb-Umbach, R. (2017). A Study on Transfer Learning for Acoustic
Event Detection in a Real Life Scenario. In IEEE 19th International Workshop
on Multimedia Signal Processing (MMSP).
bibtex: '@inproceedings{Arora_Haeb-Umbach_2017, title={A Study on Transfer Learning
for Acoustic Event Detection in a Real Life Scenario}, booktitle={IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP)}, author={Arora, Prerna and Haeb-Umbach,
Reinhold}, year={2017} }'
chicago: Arora, Prerna, and Reinhold Haeb-Umbach. “A Study on Transfer Learning
for Acoustic Event Detection in a Real Life Scenario.” In IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP), 2017.
ieee: P. Arora and R. Haeb-Umbach, “A Study on Transfer Learning for Acoustic Event
Detection in a Real Life Scenario,” in IEEE 19th International Workshop on
Multimedia Signal Processing (MMSP), 2017.
mla: Arora, Prerna, and Reinhold Haeb-Umbach. “A Study on Transfer Learning for
Acoustic Event Detection in a Real Life Scenario.” IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP), 2017.
short: 'P. Arora, R. Haeb-Umbach, in: IEEE 19th International Workshop on Multimedia
Signal Processing (MMSP), 2017.'
date_created: 2019-07-12T05:26:54Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_AroraHaeb.pdf
oa: '1'
publication: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_AroraHaeb_poster.pdf
status: public
title: A Study on Transfer Learning for Acoustic Event Detection in a Real Life Scenario
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11735'
abstract:
- lang: eng
text: This report describes the computation of gradients by algorithmic differentiation
for statistically optimum beamforming operations. Especially the derivation of
complex-valued functions is a key component of this approach. Therefore the real-valued
algorithmic differentiation is extended via the complex-valued chain rule. In
addition to the basic mathematic operations the derivative of the eigenvalue problem
with complex-valued eigenvectors is one of the key results of this report. The
potential of this approach is shown with experimental results on the CHiME-3 challenge
database. There, the beamforming task is used as a front-end for an ASR system.
With the developed derivatives a joint optimization of a speech enhancement and
speech recognition system w.r.t. the recognition optimization criterion is possible.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. On the Computation
of Complex-Valued Gradients with Application to Statistically Optimum Beamforming.;
2017.
apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R.
(2017). On the Computation of Complex-valued Gradients with Application to
Statistically Optimum Beamforming.
bibtex: '@book{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={On the
Computation of Complex-valued Gradients with Application to Statistically Optimum
Beamforming}, author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas
and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and
Reinhold Haeb-Umbach. On the Computation of Complex-Valued Gradients with Application
to Statistically Optimum Beamforming, 2017.
ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, On
the Computation of Complex-valued Gradients with Application to Statistically
Optimum Beamforming. 2017.
mla: Boeddeker, Christoph, et al. On the Computation of Complex-Valued Gradients
with Application to Statistically Optimum Beamforming. 2017.
short: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, On the
Computation of Complex-Valued Gradients with Application to Statistically Optimum
Beamforming, 2017.
date_created: 2019-07-12T05:27:15Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/ArXiv_2017_BoeddekerHanebrinkHaeb_Article.pdf
oa: '1'
status: public
title: On the Computation of Complex-valued Gradients with Application to Statistically
Optimum Beamforming
type: report
user_id: '40767'
year: '2017'
...
---
_id: '11736'
abstract:
- lang: eng
text: In this paper we show how a neural network for spectral mask estimation for
an acoustic beamformer can be optimized by algorithmic differentiation. Using
the beamformer output SNR as the objective function to maximize, the gradient
is propagated through the beamformer all the way to the neural network which provides
the clean speech and noise masks from which the beamformer coefficients are estimated
by eigenvalue decomposition. A key theoretical result is the derivative of an
eigenvalue problem involving complex-valued eigenvectors. Experimental results
on the CHiME-3 challenge database demonstrate the effectiveness of the approach.
The tools developed in this paper are a key component for an end-to-end optimization
of speech enhancement and speech recognition.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. Optimizing Neural-Network
Supported Acoustic Beamforming by Algorithmic Differentiation. In: Proc. IEEE
Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2017.'
apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R.
(2017). Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic
Differentiation. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal
Processing (ICASSP).
bibtex: '@inproceedings{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={Optimizing
Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation},
booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)},
author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann,
Jahn and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and
Reinhold Haeb-Umbach. “Optimizing Neural-Network Supported Acoustic Beamforming
by Algorithmic Differentiation.” In Proc. IEEE Intl. Conf. on Acoustics, Speech
and Signal Processing (ICASSP), 2017.
ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, “Optimizing
Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation,”
in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
2017.
mla: Boeddeker, Christoph, et al. “Optimizing Neural-Network Supported Acoustic
Beamforming by Algorithmic Differentiation.” Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2017.
short: 'C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc.
IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.'
date_created: 2019-07-12T05:27:16Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_boeddeker_paper.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
status: public
title: Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11737'
abstract:
- lang: eng
text: The benefits of both a logarithmic spectral amplitude (LSA) estimation and
a modeling in a generalized spectral domain (where short-time amplitudes are raised
to a generalized power exponent, not restricted to magnitude or power spectrum)
are combined in this contribution to achieve a better tradeoff between speech
quality and noise suppression in single-channel speech enhancement. A novel gain
function is derived to enhance the logarithmic generalized spectral amplitudes
of noisy speech. Experiments on the CHiME-3 dataset show that it outperforms the
famous minimum mean squared error (MMSE) LSA gain function of Ephraim and Malah
in terms of noise suppression by 1.4 dB, while the good speech quality of the
MMSE-LSA estimator is maintained.
author:
- first_name: Alleksej
full_name: Chinaev, Alleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. A Generalized Log-Spectral Amplitude Estimator for
Single-Channel Speech Enhancement. In: Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP). ; 2017.'
apa: Chinaev, A., & Haeb-Umbach, R. (2017). A Generalized Log-Spectral Amplitude
Estimator for Single-Channel Speech Enhancement. In Proc. IEEE Intl. Conf.
on Acoustics, Speech and Signal Processing (ICASSP).
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2017, title={A Generalized Log-Spectral
Amplitude Estimator for Single-Channel Speech Enhancement}, booktitle={Proc. IEEE
Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Chinaev,
Alleksej and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Chinaev, Alleksej, and Reinhold Haeb-Umbach. “A Generalized Log-Spectral
Amplitude Estimator for Single-Channel Speech Enhancement.” In Proc. IEEE Intl.
Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.
ieee: A. Chinaev and R. Haeb-Umbach, “A Generalized Log-Spectral Amplitude Estimator
for Single-Channel Speech Enhancement,” in Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2017.
mla: Chinaev, Alleksej, and Reinhold Haeb-Umbach. “A Generalized Log-Spectral Amplitude
Estimator for Single-Channel Speech Enhancement.” Proc. IEEE Intl. Conf. on
Acoustics, Speech and Signal Processing (ICASSP), 2017.
short: 'A. Chinaev, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech
and Signal Processing (ICASSP), 2017.'
date_created: 2019-07-12T05:27:17Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/ChinHaeb17.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/ChinHaeb17_Slides.pdf
status: public
title: A Generalized Log-Spectral Amplitude Estimator for Single-Channel Speech Enhancement
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11754'
abstract:
- lang: eng
text: Recent advances in discriminatively trained mask estimation networks to extract
a single source utilizing beamforming techniques demonstrate, that the integration
of statistical models and deep neural networks (DNNs) are a promising approach
for robust automatic speech recognition (ASR) applications. In this contribution
we demonstrate how discriminatively trained embeddings on spectral features can
be tightly integrated into statistical model-based source separation to separate
and transcribe overlapping speech. Good generalization to unseen spatial configurations
is achieved by estimating a statistical model at test time, while still leveraging
discriminative training of deep clustering embeddings on a separate training set.
We formulate an expectation maximization (EM) algorithm which jointly estimates
a model for deep clustering embeddings and complex-valued spatial observations
in the short time Fourier transform (STFT) domain at test time. Extensive simulations
confirm, that the integrated model outperforms (a) a deep clustering model with
a subsequent beamforming step and (b) an EM-based model with a beamforming step
alone in terms of signal to distortion ratio (SDR) and perceptually motivated
metric (PESQ) gains. ASR results on a reverberated dataset further show, that
the aforementioned gains translate to reduced word error rates (WERs) even in
reverberant environments.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Haeb-Umbach R. Tight integration of spatial and spectral features
for BSS with Deep Clustering embeddings. In: INTERSPEECH 2017, Stockholm, Schweden.
; 2017.'
apa: Drude, L., & Haeb-Umbach, R. (2017). Tight integration of spatial and spectral
features for BSS with Deep Clustering embeddings. In INTERSPEECH 2017, Stockholm,
Schweden.
bibtex: '@inproceedings{Drude_Haeb-Umbach_2017, title={Tight integration of spatial
and spectral features for BSS with Deep Clustering embeddings}, booktitle={INTERSPEECH
2017, Stockholm, Schweden}, author={Drude, Lukas and Haeb-Umbach, Reinhold}, year={2017}
}'
chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and
Spectral Features for BSS with Deep Clustering Embeddings.” In INTERSPEECH
2017, Stockholm, Schweden, 2017.
ieee: L. Drude and R. Haeb-Umbach, “Tight integration of spatial and spectral features
for BSS with Deep Clustering embeddings,” in INTERSPEECH 2017, Stockholm, Schweden,
2017.
mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and Spectral
Features for BSS with Deep Clustering Embeddings.” INTERSPEECH 2017, Stockholm,
Schweden, 2017.
short: 'L. Drude, R. Haeb-Umbach, in: INTERSPEECH 2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:37Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_slides.pdf
status: public
title: Tight integration of spatial and spectral features for BSS with Deep Clustering
embeddings
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11770'
abstract:
- lang: eng
text: 'In this contribution we show how to exploit text data to support word discovery
from audio input in an underresourced target language. Given audio, of which a
certain amount is transcribed at the word level, and additional unrelated text
data, the approach is able to learn a probabilistic mapping from acoustic units
to characters and utilize it to segment the audio data into words without the
need of a pronunciation dictionary. This is achieved by three components: an unsupervised
acoustic unit discovery system, a supervisedly trained acoustic unit-to-grapheme
converter, and a word discovery system, which is initialized with a language model
trained on the text data. Experiments for multiple setups show that the initialization
of the language model with text data improves the word segementation performance
by a large margin.'
author:
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Benedikt
full_name: Boenninghoff, Benedikt
last_name: Boenninghoff
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Glarner T, Boenninghoff B, Walter O, Haeb-Umbach R. Leveraging Text Data for
Word Segmentation for Underresourced Languages. In: INTERSPEECH 2017, Stockholm,
Schweden. ; 2017.'
apa: Glarner, T., Boenninghoff, B., Walter, O., & Haeb-Umbach, R. (2017). Leveraging
Text Data for Word Segmentation for Underresourced Languages. In INTERSPEECH
2017, Stockholm, Schweden.
bibtex: '@inproceedings{Glarner_Boenninghoff_Walter_Haeb-Umbach_2017, title={Leveraging
Text Data for Word Segmentation for Underresourced Languages}, booktitle={INTERSPEECH
2017, Stockholm, Schweden}, author={Glarner, Thomas and Boenninghoff, Benedikt
and Walter, Oliver and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Glarner, Thomas, Benedikt Boenninghoff, Oliver Walter, and Reinhold Haeb-Umbach.
“Leveraging Text Data for Word Segmentation for Underresourced Languages.” In
INTERSPEECH 2017, Stockholm, Schweden, 2017.
ieee: T. Glarner, B. Boenninghoff, O. Walter, and R. Haeb-Umbach, “Leveraging Text
Data for Word Segmentation for Underresourced Languages,” in INTERSPEECH 2017,
Stockholm, Schweden, 2017.
mla: Glarner, Thomas, et al. “Leveraging Text Data for Word Segmentation for Underresourced
Languages.” INTERSPEECH 2017, Stockholm, Schweden, 2017.
short: 'T. Glarner, B. Boenninghoff, O. Walter, R. Haeb-Umbach, in: INTERSPEECH
2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:55Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Glarner_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Glarner_poster.pdf
status: public
title: Leveraging Text Data for Word Segmentation for Underresourced Languages
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '1180'
abstract:
- lang: eng
text: These days, there is a strong rise in the needs for machine learning applications,
requiring an automation of machine learning engineering which is referred to as
AutoML. In AutoML the selection, composition and parametrization of machine learning
algorithms is automated and tailored to a specific problem, resulting in a machine
learning pipeline. Current approaches reduce the AutoML problem to optimization
of hyperparameters. Based on recursive task networks, in this paper we present
one approach from the field of automated planning and one evolutionary optimization
approach. Instead of simply parametrizing a given pipeline, this allows for structure
optimization of machine learning pipelines, as well. We evaluate the two approaches
in an extensive evaluation, finding both approaches to have their strengths in
different areas. Moreover, the two approaches outperform the state-of-the-art
tool Auto-WEKA in many settings.
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: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence.
Dortmund; 2017.'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning:
Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational
Intelligence. Dortmund.'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic
Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th
Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr,
Felix and Hüllermeier, Eyke}, year={2017} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine
Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop
Computational Intelligence. Dortmund, 2017.'
ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical
Planning Versus Evolutionary Optimization,” in 27th Workshop Computational
Intelligence, Dortmund, 2017.'
mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence,
2017.'
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence,
Dortmund, 2017.'
conference:
end_date: 2017-11-24
location: Dortmund
name: 27th Workshop Computational Intelligence
start_date: 2017-11-23
date_created: 2018-02-22T07:19:18Z
date_updated: 2022-01-06T06:51:09Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:28:09Z
date_updated: 2018-11-06T15:28:09Z
file_id: '5387'
file_name: CI Workshop AutoML.pdf
file_size: 323589
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:28:09Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000074341/4643874
oa: '1'
place: Dortmund
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 27th Workshop Computational Intelligence
publication_status: published
status: public
title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization'
type: conference
user_id: '49109'
year: '2017'
...