---
_id: '22481'
abstract:
- lang: eng
text: During the industrial processing of materials for the manufacture of new products,
surface defects can quickly occur. In order to achieve high quality without a
long time delay, it makes sense to inspect the work pieces so that defective work
pieces can be sorted out right at the beginning of the process. At the same time,
the evaluation unit should come close the perception of the human eye regarding
detection of defects in surfaces. Such defects often manifest themselves by a
deviation of the existing structure. The only restriction should be that only
matt surfaces should be considered here. Therefore in this work, different classification
and image processing algorithms are applied to surface data to identify possible
surface damages. For this purpose, the Gabor filter and the FST (Fused Structure
and Texture) features generated with it, as well as the salience metric are used
on the image processing side. On the classification side, however, deep neural
networks, Convolutional Neural Networks (CNN), and autoencoders are used to make
a decision. A distinction is also made between training using class labels and
without. It turns out later that the salience metric are best performed by CNN.
On the other hand, if there is no labeled training data available, a novelty classification
can easily be achieved by using autoencoders as well as the salience metric and
some filters.
author:
- first_name: Tom
full_name: Sander, Tom
last_name: Sander
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
- first_name: Volker
full_name: Geneis, Volker
last_name: Geneis
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Franz-Barthold
full_name: Gockel, Franz-Barthold
last_name: Gockel
citation:
ama: 'Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction. In: 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE; 2021. doi:10.1109/icit46573.2021.9453646'
apa: 'Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H.,
& Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction. In 22nd IEEE International
Conference on Industrial Technology (ICIT). Valencia, Spain : IEEE. https://doi.org/10.1109/icit46573.2021.9453646'
bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021,
place={Valencia, Spain }, title={Detection of Defects on Irregular Structured
Surfaces by Image Processing Methods for Feature Extraction}, DOI={10.1109/icit46573.2021.9453646},
booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)},
publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich
and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold},
year={2021} }'
chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian
Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction.” In 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE, 2021. https://doi.org/10.1109/icit46573.2021.9453646.'
ieee: T. Sander et al., “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction,” in 22nd IEEE International
Conference on Industrial Technology (ICIT), Valencia, Spain , 2021.
mla: Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction.” 22nd IEEE International
Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453646.
short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B.
Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT),
IEEE, Valencia, Spain , 2021.'
conference:
end_date: 2021-03-12
location: 'Valencia, Spain '
name: 22nd IEEE International Conference on Industrial Technology (ICIT)
start_date: 2021-03-10
date_created: 2021-06-20T23:32:11Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/icit46573.2021.9453646
keyword:
- Image Processing
- Defect Detection
- wooden surfaces
- Machine Learning
- Neural Networks
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9453646
place: 'Valencia, Spain '
publication: 22nd IEEE International Conference on Industrial Technology (ICIT)
publication_identifier:
isbn:
- '9781728157306'
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregular Structured Surfaces by Image Processing Methods
for Feature Extraction
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '35889'
abstract:
- lang: eng
text: Network and service coordination is important to provide modern services consisting
of multiple interconnected components, e.g., in 5G, network function virtualization
(NFV), or cloud and edge computing. In this paper, I outline my dissertation research,
which proposes six approaches to automate such network and service coordination.
All approaches dynamically react to the current demand and optimize coordination
for high service quality and low costs. The approaches range from centralized
to distributed methods and from conventional heuristic algorithms and mixed-integer
linear programs to machine learning approaches using supervised and reinforcement
learning. I briefly discuss their main ideas and advantages over other state-of-the-art
approaches and compare strengths and weaknesses.
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
citation:
ama: Schneider SB. Conventional and Machine Learning Approaches for Network and
Service Coordination.; 2021.
apa: Schneider, S. B. (2021). Conventional and Machine Learning Approaches for
Network and Service Coordination.
bibtex: '@book{Schneider_2021, title={Conventional and Machine Learning Approaches
for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021}
}'
chicago: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination, 2021.
ieee: S. B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination. 2021.
mla: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination. 2021.
short: S.B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination, 2021.
date_created: 2023-01-10T15:08:50Z
date_updated: 2023-01-10T15:09:05Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2023-01-10T15:07:03Z
date_updated: 2023-01-10T15:07:03Z
file_id: '35890'
file_name: main.pdf
file_size: 133340
relation: main_file
file_date_updated: 2023-01-10T15:07:03Z
has_accepted_license: '1'
keyword:
- nfv
- coordination
- machine learning
- reinforcement learning
- phd
- digest
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'
status: public
title: Conventional and Machine Learning Approaches for Network and Service Coordination
type: working_paper
user_id: '35343'
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: '15332'
abstract:
- lang: eng
text: "Artificial intelligence (AI) has the potential for far-reaching – in our
opinion – irreversible changes.\r\nThey range from effects on the individual and
society to new societal and social issues. The question arises\r\nas to how students
can learn the basic functioning of AI systems, what areas of life and society
are affected\r\nby these and – most important – how their own lives are affected
by these changes. Therefore, we are developing and evaluating school materials
for the German ”Science Year AI”. It can be used for students of all\r\nschool
types from the seventh grade upwards and will be distributed to about 2000 schools
in autumn with\r\nthe support of the Federal Ministry of Education and Research.
The material deals with the following aspects\r\nof AI: Discussing everyday experiences
with AI, how does machine learning work, historical development\r\nof AI concepts,
difference between man and machine, future distribution of roles between man and
machine,\r\nin which AI world do we want to live and how much AI would we like
to have in our lives. Through an\r\naccompanying evaluation, high quality of the
technical content and didactic preparation is achieved in order\r\nto guarantee
the long-term applicability in the teaching context in the different age groups
and school types.\r\nIn this paper, we describe the current state of the material
development, the challenges arising, and the results\r\nof tests with different
classes to date. We also present first ideas for evaluating the results."
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Lea
full_name: Budde, Lea
id: '32443'
last_name: Budde
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
citation:
ama: 'Schlichtig M, Opel SA, Budde L, Schulte C. Understanding Artificial Intelligence
– A Project for the Development of Comprehensive Teaching Material. In: Jasutė
E, Pozdniakov S, eds. ISSEP 2019 - 12th International Conference on Informatics
in Schools: Situation, Evaluation and Perspectives, Local Proceedings. Vol
12. ; 2019:65-73.'
apa: 'Schlichtig, M., Opel, S. A., Budde, L., & Schulte, C. (2019). Understanding
Artificial Intelligence – A Project for the Development of Comprehensive Teaching
Material. In E. Jasutė & S. Pozdniakov (Eds.), ISSEP 2019 - 12th International
conference on informatics in schools: Situation, evaluation and perspectives,
Local Proceedings (Vol. 12, pp. 65–73).'
bibtex: '@inproceedings{Schlichtig_Opel_Budde_Schulte_2019, title={Understanding
Artificial Intelligence – A Project for the Development of Comprehensive Teaching
Material}, volume={12}, booktitle={ISSEP 2019 - 12th International conference
on informatics in schools: Situation, evaluation and perspectives, Local Proceedings},
author={Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte,
Carsten}, editor={Jasutė, Eglė and Pozdniakov, Sergei}, year={2019}, pages={65–73}
}'
chicago: 'Schlichtig, Michael, Simone Anna Opel, Lea Budde, and Carsten Schulte.
“Understanding Artificial Intelligence – A Project for the Development of Comprehensive
Teaching Material.” In ISSEP 2019 - 12th International Conference on Informatics
in Schools: Situation, Evaluation and Perspectives, Local Proceedings, edited
by Eglė Jasutė and Sergei Pozdniakov, 12:65–73, 2019.'
ieee: 'M. Schlichtig, S. A. Opel, L. Budde, and C. Schulte, “Understanding Artificial
Intelligence – A Project for the Development of Comprehensive Teaching Material,”
in ISSEP 2019 - 12th International conference on informatics in schools: Situation,
evaluation and perspectives, Local Proceedings, Lanarca, 2019, vol. 12, pp.
65–73.'
mla: 'Schlichtig, Michael, et al. “Understanding Artificial Intelligence – A Project
for the Development of Comprehensive Teaching Material.” ISSEP 2019 - 12th
International Conference on Informatics in Schools: Situation, Evaluation and
Perspectives, Local Proceedings, edited by Eglė Jasutė and Sergei Pozdniakov,
vol. 12, 2019, pp. 65–73.'
short: 'M. Schlichtig, S.A. Opel, L. Budde, C. Schulte, in: E. Jasutė, S. Pozdniakov
(Eds.), ISSEP 2019 - 12th International Conference on Informatics in Schools:
Situation, Evaluation and Perspectives, Local Proceedings, 2019, pp. 65–73.'
conference:
end_date: 2019-11-20
location: Lanarca
name: 'ISSEP 2019 - 12th International conference on informatics in schools: Situation,
evaluation and perspectives'
start_date: 2019-11-18
date_created: 2019-12-16T17:50:08Z
date_updated: 2022-07-26T11:41:41Z
department:
- _id: '67'
editor:
- first_name: Eglė
full_name: Jasutė, Eglė
last_name: Jasutė
- first_name: Sergei
full_name: Pozdniakov, Sergei
last_name: Pozdniakov
intvolume: ' 12'
keyword:
- Artificial Intelligence
- Machine Learning
- Teaching Material
- Societal Aspects
- Ethics. Social Aspects
- Science Year
- Simulation Game
language:
- iso: eng
main_file_link:
- url: http://cyprusconferences.org/issep2019/wp-content/uploads/2019/10/LocalISSEP-v5.pdf
page: 65 - 73
publication: 'ISSEP 2019 - 12th International conference on informatics in schools:
Situation, evaluation and perspectives, Local Proceedings'
publication_identifier:
isbn:
- 978-9925-553-27-3
publication_status: published
quality_controlled: '1'
status: public
title: Understanding Artificial Intelligence – A Project for the Development of Comprehensive
Teaching Material
type: conference
user_id: '32312'
volume: 12
year: '2019'
...
---
_id: '48877'
abstract:
- lang: eng
text: OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them online
to work and collaborate more efficiently. In this paper, we present an R package
to interface with the OpenML platform and illustrate its usage in combination
with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5,
2016). We show how the OpenML package allows R users to easily search, download
and upload data sets and machine learning tasks. Furthermore, we also show how
to upload results of experiments, share them with others and download results
from other users. Beyond ensuring reproducibility of results, the OpenML platform
automates much of the drudge work, speeds up research, facilitates collaboration
and increases the users’ visibility online.
author:
- first_name: Giuseppe
full_name: Casalicchio, Giuseppe
last_name: Casalicchio
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Michel
full_name: Lang, Michel
last_name: Lang
- first_name: Dominik
full_name: Kirchhoff, Dominik
last_name: Kirchhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Benjamin
full_name: Hofner, Benjamin
last_name: Hofner
- first_name: Heidi
full_name: Seibold, Heidi
last_name: Seibold
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
citation:
ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to
the Machine Learning Platform OpenML. Computational Statistics. 2019;34(3):977–991.
doi:10.1007/s00180-017-0742-2'
apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner,
B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package
to Connect to the Machine Learning Platform OpenML. Computational Statistics,
34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2'
bibtex: '@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019,
title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML},
volume={34}, DOI={10.1007/s00180-017-0742-2},
number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe
and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal
and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
year={2019}, pages={977–991} }'
chicago: 'Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal
Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl.
“OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” Computational
Statistics 34, no. 3 (2019): 977–991. https://doi.org/10.1007/s00180-017-0742-2.'
ieee: 'G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp.
977–991, 2019, doi: 10.1007/s00180-017-0742-2.'
mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML.” Computational Statistics, vol. 34, no. 3, 2019,
pp. 977–991, doi:10.1007/s00180-017-0742-2.'
short: G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner,
H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:51:17Z
department:
- _id: '819'
doi: 10.1007/s00180-017-0742-2
intvolume: ' 34'
issue: '3'
keyword:
- Databases
- Machine learning
- R
- Reproducible research
language:
- iso: eng
page: 977–991
publication: Computational Statistics
publication_identifier:
issn:
- 0943-4062
status: public
title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML'
type: journal_article
user_id: '102979'
volume: 34
year: '2019'
...
---
_id: '3852'
abstract:
- lang: eng
text: "In automated machine learning (AutoML), the process of engineering machine
learning applications with respect to a specific problem is (partially) automated.\r\nVarious
AutoML tools have already been introduced to provide out-of-the-box machine learning
functionality.\r\nMore specifically, by selecting machine learning algorithms
and optimizing their hyperparameters, these tools produce a machine learning pipeline
tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
follows an evolutionary approach, it suffers from performance issues when dealing
with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
a hierarchical planning to configure machine learning pipelines that are unlimited
in length.\r\nWe evaluate our approach and find its performance to be competitive
with other AutoML tools, including TPOT."
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. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...
---
_id: '2331'
abstract:
- lang: eng
text: A user generally writes software requirements in ambiguous and incomplete
form by using natural language; therefore, a software developer may have difficulty
in clearly understanding what the meanings are. To solve this problem with automation,
we propose a classifier for semantic annotation with manually pre-defined semantic
categories. To improve our classifier, we carefully designed syntactic features
extracted by constituency and dependency parsers. Even with a small dataset and
a large number of classes, our proposed classifier records an accuracy of 0.75,
which outperforms the previous model, REaCT.
article_type: original
author:
- first_name: 'Yeongsu '
full_name: 'Kim, Yeongsu '
last_name: Kim
- first_name: Seungwoo
full_name: Lee, Seungwoo
last_name: Lee
- first_name: Markus
full_name: Dollmann, Markus
id: '27578'
last_name: Dollmann
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: Kim Y, Lee S, Dollmann M, Geierhos M. Improving Classifiers for Semantic Annotation
of Software Requirements with Elaborate Syntactic Structure. International
Journal of Advanced Science and Technology. 2018;112:123-136. doi:10.14257/ijast.2018.112.12
apa: Kim, Y., Lee, S., Dollmann, M., & Geierhos, M. (2018). Improving Classifiers
for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.
International Journal of Advanced Science and Technology, 112, 123–136.
https://doi.org/10.14257/ijast.2018.112.12
bibtex: '@article{Kim_Lee_Dollmann_Geierhos_2018, title={Improving Classifiers for
Semantic Annotation of Software Requirements with Elaborate Syntactic Structure},
volume={112}, DOI={10.14257/ijast.2018.112.12},
journal={International Journal of Advanced Science and Technology}, publisher={SERSC
Australia}, author={Kim, Yeongsu and Lee, Seungwoo and Dollmann, Markus and Geierhos,
Michaela}, year={2018}, pages={123–136} }'
chicago: 'Kim, Yeongsu , Seungwoo Lee, Markus Dollmann, and Michaela Geierhos. “Improving
Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic
Structure.” International Journal of Advanced Science and Technology 112
(2018): 123–36. https://doi.org/10.14257/ijast.2018.112.12.'
ieee: Y. Kim, S. Lee, M. Dollmann, and M. Geierhos, “Improving Classifiers for Semantic
Annotation of Software Requirements with Elaborate Syntactic Structure,” International
Journal of Advanced Science and Technology, vol. 112, pp. 123–136, 2018.
mla: Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software
Requirements with Elaborate Syntactic Structure.” International Journal of
Advanced Science and Technology, vol. 112, SERSC Australia, 2018, pp. 123–36,
doi:10.14257/ijast.2018.112.12.
short: Y. Kim, S. Lee, M. Dollmann, M. Geierhos, International Journal of Advanced
Science and Technology 112 (2018) 123–136.
date_created: 2018-04-13T09:19:22Z
date_updated: 2022-01-06T06:55:49Z
ddc:
- '000'
department:
- _id: '36'
- _id: '1'
- _id: '579'
doi: 10.14257/ijast.2018.112.12
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:16:29Z
date_updated: 2018-11-02T15:16:29Z
file_id: '5297'
file_name: 12.pdf
file_size: 586968
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:16:29Z
has_accepted_license: '1'
intvolume: ' 112'
keyword:
- Software Engineering
- Natural Language Processing
- Semantic Annotation
- Machine Learning
- Feature Engineering
- Syntactic Structure
language:
- iso: eng
page: 123-136
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: International Journal of Advanced Science and Technology
publication_identifier:
eissn:
- 2207-6360
issn:
- 2005-4238
publication_status: published
publisher: SERSC Australia
quality_controlled: '1'
status: public
title: Improving Classifiers for Semantic Annotation of Software Requirements with
Elaborate Syntactic Structure
type: journal_article
user_id: '477'
volume: 112
year: '2018'
...
---
_id: '48884'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
on a large set of well-known benchmark instances and demonstrate complementary
performance, in that different instances may be solved most effectively by different
algorithms. We leverage this complementarity to build an algorithm selector, which
selects the best TSP solver on a per-instance basis and thus achieves significantly
improved performance compared to the single best solver, representing an advance
in the state of the art in solving the Euclidean TSP. Our in-depth analysis of
the selectors provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620.
doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff,
Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018},
pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation,
vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...
---
_id: '1098'
abstract:
- lang: eng
text: An end user generally writes down software requirements in ambiguous expressions
using natural language; hence, a software developer attuned to programming language
finds it difficult to understand th meaning of the requirements. To solve this
problem we define semantic categories for disambiguation and classify/annotate
the requirement into the categories by using machine-learning models. We extensively
use a language frame closely related to such categories for designing features
to overcome the problem of insufficient training data compare to the large number
of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming
the previous model, REaCT.
article_type: original
author:
- first_name: Yeong-Su
full_name: Kim, Yeong-Su
last_name: Kim
- first_name: 'Seung-Woo '
full_name: 'Lee, Seung-Woo '
last_name: Lee
- first_name: Markus
full_name: Dollmann, Markus
id: '27578'
last_name: Dollmann
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: Kim Y-S, Lee S-W, Dollmann M, Geierhos M. Semantic Annotation of Software Requirements
with Language Frame. International Journal of Software Engineering for Smart
Device. 2017;4(2):1-6.
apa: Kim, Y.-S., Lee, S.-W., Dollmann, M., & Geierhos, M. (2017). Semantic Annotation
of Software Requirements with Language Frame. International Journal of Software
Engineering for Smart Device, 4(2), 1–6.
bibtex: '@article{Kim_Lee_Dollmann_Geierhos_2017, title={Semantic Annotation of
Software Requirements with Language Frame}, volume={4}, number={2}, journal={International
Journal of Software Engineering for Smart Device}, publisher={Global Vision School
Publication}, author={Kim, Yeong-Su and Lee, Seung-Woo and Dollmann, Markus and
Geierhos, Michaela}, year={2017}, pages={1–6} }'
chicago: 'Kim, Yeong-Su, Seung-Woo Lee, Markus Dollmann, and Michaela Geierhos.
“Semantic Annotation of Software Requirements with Language Frame.” International
Journal of Software Engineering for Smart Device 4, no. 2 (2017): 1–6.'
ieee: Y.-S. Kim, S.-W. Lee, M. Dollmann, and M. Geierhos, “Semantic Annotation of
Software Requirements with Language Frame,” International Journal of Software
Engineering for Smart Device, vol. 4, no. 2, pp. 1–6, 2017.
mla: Kim, Yeong-Su, et al. “Semantic Annotation of Software Requirements with Language
Frame.” International Journal of Software Engineering for Smart Device,
vol. 4, no. 2, Global Vision School Publication, 2017, pp. 1–6.
short: Y.-S. Kim, S.-W. Lee, M. Dollmann, M. Geierhos, International Journal of
Software Engineering for Smart Device 4 (2017) 1–6.
date_created: 2018-01-25T15:23:15Z
date_updated: 2022-01-06T06:50:55Z
ddc:
- '000'
department:
- _id: '36'
- _id: '1'
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-12-12T15:30:59Z
date_updated: 2018-12-12T15:30:59Z
file_id: '6196'
file_name: Semantic_Annotation_of_Software_Requirements.pdf
file_size: 244655
relation: main_file
success: 1
file_date_updated: 2018-12-12T15:30:59Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '2'
keyword:
- Natural Language Processing
- Semantic Annotation
- Machine Learning
language:
- iso: eng
page: 1-6
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: International Journal of Software Engineering for Smart Device
publication_identifier:
issn:
- 2205-8494
publication_status: published
publisher: Global Vision School Publication
quality_controlled: '1'
status: public
title: Semantic Annotation of Software Requirements with Language Frame
type: journal_article
user_id: '477'
volume: 4
year: '2017'
...
---
_id: '46396'
abstract:
- lang: eng
text: The steady supply of new optimization methods makes the algorithm selection
problem (ASP) an increasingly pressing and challenging task, specially for real-world
black-box optimization problems. The introduced approach considers the ASP as
a cost-sensitive classification task which is based on Exploratory Landscape Analysis.
Low-level features gathered by systematic sampling of the function on the feasible
set are used to predict a well-performing algorithm out of a given portfolio.
Example-specific label costs are defined by the expected runtime of each candidate
algorithm. We use one-sided support vector regression to solve this learning problem.
The approach is illustrated by means of the optimization problems and algorithms
of the BBOB’09/10 workshop.
author:
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
citation:
ama: 'Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory
Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association
for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209'
apa: Bischl, B., Mersmann, O., Trautmann, H., & Preuß, M. (2012). Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.
Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. https://doi.org/10.1145/2330163.2330209
bibtex: '@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY,
USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape
Analysis and Cost-Sensitive Learning}, DOI={10.1145/2330163.2330209},
booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation}, publisher={Association for Computing Machinery}, author={Bischl,
Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320},
collection={GECCO ’12} }'
chicago: 'Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”
In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012.
https://doi.org/10.1145/2330163.2330209.'
ieee: 'B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection
Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings
of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012,
pp. 313–320, doi: 10.1145/2330163.2330209.'
mla: Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis
and Cost-Sensitive Learning.” Proceedings of the 14th Annual Conference on
Genetic and Evolutionary Computation, Association for Computing Machinery,
2012, pp. 313–320, doi:10.1145/2330163.2330209.
short: 'B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation, Association for Computing
Machinery, New York, NY, USA, 2012, pp. 313–320.'
date_created: 2023-08-04T15:51:56Z
date_updated: 2023-10-16T13:48:48Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2330163.2330209
keyword:
- machine learning
- exploratory landscape analysis
- fitness landscape
- benchmarking
- evolutionary optimization
- bbob test set
- algorithm selection
language:
- iso: eng
page: 313–320
place: New York, NY, USA
publication: Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- '9781450311779'
publisher: Association for Computing Machinery
series_title: GECCO ’12
status: public
title: Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive
Learning
type: conference
user_id: '15504'
year: '2012'
...