---
_id: '13770'
author:
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Karl H, Kundisch D, Meyer auf der Heide F, Wehrheim H. A Case for a New IT
Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering.
2020;62(6):467-481. doi:10.1007/s12599-019-00627-x'
apa: 'Karl, H., Kundisch, D., Meyer auf der Heide, F., & Wehrheim, H. (2020).
A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information
Systems Engineering, 62(6), 467–481. https://doi.org/10.1007/s12599-019-00627-x'
bibtex: '@article{Karl_Kundisch_Meyer auf der Heide_Wehrheim_2020, title={A Case
for a New IT Ecosystem: On-The-Fly Computing}, volume={62}, DOI={10.1007/s12599-019-00627-x},
number={6}, journal={Business & Information Systems Engineering}, publisher={Springer},
author={Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and
Wehrheim, Heike}, year={2020}, pages={467–481} }'
chicago: 'Karl, Holger, Dennis Kundisch, Friedhelm Meyer auf der Heide, and Heike
Wehrheim. “A Case for a New IT Ecosystem: On-The-Fly Computing.” Business &
Information Systems Engineering 62, no. 6 (2020): 467–81. https://doi.org/10.1007/s12599-019-00627-x.'
ieee: 'H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for
a New IT Ecosystem: On-The-Fly Computing,” Business & Information Systems
Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi: 10.1007/s12599-019-00627-x.'
mla: 'Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.”
Business & Information Systems Engineering, vol. 62, no. 6, Springer,
2020, pp. 467–81, doi:10.1007/s12599-019-00627-x.'
short: H. Karl, D. Kundisch, F. Meyer auf der Heide, H. Wehrheim, Business &
Information Systems Engineering 62 (2020) 467–481.
date_created: 2019-10-10T13:41:06Z
date_updated: 2022-12-02T09:27:17Z
ddc:
- '004'
department:
- _id: '276'
- _id: '75'
- _id: '63'
- _id: '77'
doi: 10.1007/s12599-019-00627-x
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2019-12-12T10:24:47Z
date_updated: 2019-12-12T10:24:47Z
file_id: '15311'
file_name: Karl2019_Article_ACaseForANewITEcosystemOn-The-.pdf
file_size: 454532
relation: main_file
success: 1
file_date_updated: 2019-12-12T10:24:47Z
has_accepted_license: '1'
intvolume: ' 62'
issue: '6'
language:
- iso: eng
page: 467-481
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '3'
name: SFB 901 - Project Area B
- _id: '4'
name: SFB 901 - Project Area C
- _id: '82'
name: SFB 901 - Project Area T
- _id: '5'
name: SFB 901 - Subproject A1
- _id: '6'
name: SFB 901 - Subproject A2
- _id: '7'
name: SFB 901 - Subproject A3
- _id: '8'
name: SFB 901 - Subproject A4
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '13'
name: SFB 901 - Subproject C1
- _id: '14'
name: SFB 901 - Subproject C2
- _id: '15'
name: SFB 901 - Subproject C3
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '17'
name: SFB 901 - Subproject C5
- _id: '83'
name: SFB 901 -Subproject T1
- _id: '84'
name: SFB 901 -Subproject T2
- _id: '107'
name: SFB 901 -Subproject T3
- _id: '158'
name: 'SFB 901 - T4: SFB 901 -Subproject T4'
publication: Business & Information Systems Engineering
publication_status: published
publisher: Springer
status: public
title: 'A Case for a New IT Ecosystem: On-The-Fly Computing'
type: journal_article
user_id: '477'
volume: 62
year: '2020'
...
---
_id: '29298'
abstract:
- lang: ger
text: "Die Themen „Big Data“, „Künstliche Intelligenz und „Data Science“ werden
seit einiger Zeit nicht nur in der breiten Öffentlichkeit kontrovers diskutiert,
sondern stellen für die Ausbildung in den IT- und IT-nahen Berufen schon heute
neue Herausforderungen dar, die in Zukunft durch die gesellschaftliche und technologische
Weiterentwicklung hin zu einer Datengesellschaft noch größer werden.\r\nAn dieser
Stelle stellt sich die Frage, welche Aspekte dieses großen Themenkomplexes für
Schule und Ausbildung von Wichtigkeit sind und wie diese Themen sinnstiftend und
gewinnbringend in die informatische Ausbildung in verschiedenen Bildungsgängen
integriert werden können. Im Rahmen des von uns im Jahr 2017 organisierten Symposiums
zum Thema „Data Science“ wurden für die Bildung relevante Aspekte erörtert, wodurch
als Kernelemente für den Unterricht Algorithmen der Künstlichen Intelligenz und
ihre Anwendung in Industrie und Gesellschaft, Explorationen von Big Data sowie
der Umgang mit eigenen Daten in sozialen Netzwerken herausgearbeitet wurden. Ziel
ist, aus diesen Themenbereichen sowohl ein umfassendes Curriculum als auch Module
für verschiedene Unterrichtsszenarien zu entwickeln und zu erproben. Durch diese
Materialien soll es Lehrkräften aus der Informatik, Mathematik oder Technik ermöglicht
werden, diese Themen auf Basis des Curriculums und der erprobten Unterrichtskonzepte
selbst zu unterrichten.\r\nHierfür wurde im Rahmen des Projekts ProDaBi (Projekt
Data Science und Big Data in der Schule, https://www.prodabi.de), initiiert von
der Telekom Stiftung, ein experimenteller Projektkurs entwickelt, den wir mit
Schüler:innen der Sekundarstufe II an der Universität Paderborn im Schuljahr 2018/19
durchführten. Dieser Kurs enthält neben einem Modul zur Exploration von Big Data
und einem weiteren Modul zum Maschinellen Lernen als Teil der Künstlichen Intelligenz
auch eine Projektphase, die es in Zusammenarbeit mit lokalen Unternehmen den Schüler:innen\r\nermöglicht,
das Erlernte in ein reales Data Science-Projekt einzubringen. Aus den Erfahrungen
dieses Projektkurses sowie den parallel durchgeführten Erprobungen einzelner Bausteine
auch mit beruflichen Schulen werden ab dem Schuljahr 2019/20 die hierfür verwendeten
Materialien weiterentwickelt und weiteren Kooperationspartnern zur Erprobung zur
Verfügung gestellt. Damit wurden zum Ende des Projekts nicht nur vollständige
Unterrichtsmaterialien, sondern auch ein umfassendes Curriculum entwickelt."
- lang: eng
text: "The topics ”Big Data”, “Artificial Intelligence” and “Data Science” are controversially
discussed among the general public, but they present new challenges for training
in IT and IT-related professions. These challenges will become more important
in the future as a result of further social and technological development towards
a data society.\r\nAt this point, the question arises as to which aspects of this
large complex of topics are important for school and education, and how these
topics can be integrated in a meaningful and profitable way into informatics education
in vocational education. In 2017, we organized a symposium towards the topic “Data
Science” and discussed relevant aspects for general and vocational education.
Algorithms of artificial intelligence and their application in industry and society,
explorations of Big Data as well as the handling of one's own data in social networks
were worked out as core elements for teaching. For this reason, our aim is to
develop a comprehensive curriculum on this topic from these subject areas and
to develop and test modules for various teaching scenarios in order to enable
teachers from computer science, mathematics or technology to teach these topics
themselves.\r\nFor this purpose, an experimental project course was developed
within the framework of the ProDaBi project (Project Data Science and Big Data
at School, https://www.prodabi.de), which we conducted with students from upper
secondary classes at the University of Paderborn in the school year 2018/19. In
this course we try to address all these aspects. This course consists of several
modules: One module has been designed to teach the exploration of Big Data. Another
module encompasses aspects of machine learning as part of artificial intelligence.
The course concludes in a project phase which, in cooperation with local companies,
will enable the students to apply what they have learned into a real Data Science
project. Based on the experiences of this project course and the parallel testing
of individual modules with vocational schools, we will further develop the material
and make it available to other cooperation partners for testing, so that not only
complete teaching materials but also a comprehensive curriculum will have been
developed until the end of the project."
author:
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
citation:
ama: 'Opel SA, Schlichtig M. Data Science und Big Data in der beruflichen Bildung
– Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II. In: Vollmer
T, Karges T, Richter T, Schlömer B, Schütt-Sayed S, eds. Sammelband der 27.
Fachtagung der BAG Berufliche Bildung. Vol 55. Berufsbildung, Arbeit und Innovation.
wbv Media GmbH & Co. KG; 2020:176-194. doi:https://doi.org/10.3278/6004722w'
apa: Opel, S. A., & Schlichtig, M. (2020). Data Science und Big Data in der
beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe
II. In T. Vollmer, T. Karges, T. Richter, B. Schlömer, & S. Schütt-Sayed (Eds.),
Sammelband der 27. Fachtagung der BAG Berufliche Bildung (Vol. 55, pp.
176–194). wbv Media GmbH & Co. KG. https://doi.org/10.3278/6004722w
bibtex: '@inproceedings{Opel_Schlichtig_2020, place={Bielefeld}, series={Berufsbildung,
Arbeit und Innovation}, title={Data Science und Big Data in der beruflichen Bildung
– Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II}, volume={55},
DOI={https://doi.org/10.3278/6004722w},
booktitle={Sammelband der 27. Fachtagung der BAG Berufliche Bildung}, publisher={wbv
Media GmbH & Co. KG}, author={Opel, Simone Anna and Schlichtig, Michael},
editor={Vollmer, Thomas and Karges, Torben and Richter, Tim and Schlömer, Britta
and Schütt-Sayed, Sören}, year={2020}, pages={176–194}, collection={Berufsbildung,
Arbeit und Innovation} }'
chicago: 'Opel, Simone Anna, and Michael Schlichtig. “Data Science und Big Data
in der beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für
die Sekundarstufe II.” In Sammelband der 27. Fachtagung der BAG Berufliche
Bildung, edited by Thomas Vollmer, Torben Karges, Tim Richter, Britta Schlömer,
and Sören Schütt-Sayed, 55:176–94. Berufsbildung, Arbeit und Innovation. Bielefeld:
wbv Media GmbH & Co. KG, 2020. https://doi.org/10.3278/6004722w.'
ieee: 'S. A. Opel and M. Schlichtig, “Data Science und Big Data in der beruflichen
Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II,”
in Sammelband der 27. Fachtagung der BAG Berufliche Bildung, Siegen, 2020,
vol. 55, pp. 176–194, doi: https://doi.org/10.3278/6004722w.'
mla: Opel, Simone Anna, and Michael Schlichtig. “Data Science und Big Data in der
beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe
II.” Sammelband der 27. Fachtagung der BAG Berufliche Bildung, edited by
Thomas Vollmer et al., vol. 55, wbv Media GmbH & Co. KG, 2020, pp. 176–94,
doi:https://doi.org/10.3278/6004722w.
short: 'S.A. Opel, M. Schlichtig, in: T. Vollmer, T. Karges, T. Richter, B. Schlömer,
S. Schütt-Sayed (Eds.), Sammelband der 27. Fachtagung der BAG Berufliche Bildung,
wbv Media GmbH & Co. KG, Bielefeld, 2020, pp. 176–194.'
conference:
end_date: 2019-03-13
location: Siegen
name: 20. Hochschultage Berufliche Bildung (HTBB) "Digitale Welt - Bildung und Arbeit
in Transformationsgesellschaften".
start_date: 2019-03-11
date_created: 2022-01-12T16:43:38Z
date_updated: 2022-01-12T17:04:10Z
department:
- _id: '67'
doi: https://doi.org/10.3278/6004722w
editor:
- first_name: Thomas
full_name: Vollmer, Thomas
last_name: Vollmer
- first_name: Torben
full_name: Karges, Torben
last_name: Karges
- first_name: Tim
full_name: Richter, Tim
last_name: Richter
- first_name: Britta
full_name: Schlömer, Britta
last_name: Schlömer
- first_name: Sören
full_name: Schütt-Sayed, Sören
last_name: Schütt-Sayed
intvolume: ' 55'
keyword:
- Berufsbildung
- vocational education
- Ausbildung
- training
- berufliche Weiterbildung
- advanced vocational education
- Digitalisierung
- digitalization
- Unterricht
- teaching
- Lehrmethode
- teaching method
- Interdisziplinarität
- interdisciplinarity
- Fachdidaktik
- subject didactics
- Curriculum
- curriculum
- gewerblich-technischer Beruf
- vocational/technical occupation
- Fachkraft
- specialist
- Qualifikationsanforderungen
- qualification requirements
- Kompetenz
- competence
- Lehrerbildung
- teacher training
- Bundesrepublik Deutschland
- Federal Republic of Germany
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://library.oapen.org/handle/20.500.12657/43933
oa: '1'
page: 176-194
place: Bielefeld
publication: Sammelband der 27. Fachtagung der BAG Berufliche Bildung
publication_status: published
publisher: wbv Media GmbH & Co. KG
series_title: Berufsbildung, Arbeit und Innovation
status: public
title: Data Science und Big Data in der beruflichen Bildung – Konzeption und Erprobung
eines Projektkurses für die Sekundarstufe II
type: conference
user_id: '32312'
volume: 55
year: '2020'
...
---
_id: '3776'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Khalid
full_name: Al-Khatib, Khalid
last_name: Al-Khatib
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Chen W-F, Al-Khatib K, Wachsmuth H, Stein B. Analyzing Political Bias and
Unfairness in News Articles at Different Levels of Granularity. In: Proceedings
of the Fourth Workshop on Natural Language Processing and Computational Social
Science. ; 2020:149-154.'
apa: Chen, W.-F., Al-Khatib, K., Wachsmuth, H., & Stein, B. (2020). Analyzing
Political Bias and Unfairness in News Articles at Different Levels of Granularity.
Proceedings of the Fourth Workshop on Natural Language Processing and Computational
Social Science, 149–154.
bibtex: '@inproceedings{Chen_Al-Khatib_Wachsmuth_Stein_2020, title={Analyzing Political
Bias and Unfairness in News Articles at Different Levels of Granularity}, booktitle={Proceedings
of the Fourth Workshop on Natural Language Processing and Computational Social
Science}, author={Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and
Stein, Benno}, year={2020}, pages={149–154} }'
chicago: Chen, Wei-Fan, Khalid Al-Khatib, Henning Wachsmuth, and Benno Stein. “Analyzing
Political Bias and Unfairness in News Articles at Different Levels of Granularity.”
In Proceedings of the Fourth Workshop on Natural Language Processing and Computational
Social Science, 149–54, 2020.
ieee: W.-F. Chen, K. Al-Khatib, H. Wachsmuth, and B. Stein, “Analyzing Political
Bias and Unfairness in News Articles at Different Levels of Granularity,” in Proceedings
of the Fourth Workshop on Natural Language Processing and Computational Social
Science, 2020, pp. 149–154.
mla: Chen, Wei-Fan, et al. “Analyzing Political Bias and Unfairness in News Articles
at Different Levels of Granularity.” Proceedings of the Fourth Workshop on
Natural Language Processing and Computational Social Science, 2020, pp. 149–54.
short: 'W.-F. Chen, K. Al-Khatib, H. Wachsmuth, B. Stein, in: Proceedings of the
Fourth Workshop on Natural Language Processing and Computational Social Science,
2020, pp. 149–154.'
date_created: 2018-08-02T11:40:56Z
date_updated: 2022-05-09T15:03:31Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/2020.nlpcss-1.16.pdf
page: 149-154
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: Proceedings of the Fourth Workshop on Natural Language Processing and
Computational Social Science
status: public
title: Analyzing Political Bias and Unfairness in News Articles at Different Levels
of Granularity
type: conference
user_id: '82920'
year: '2020'
...
---
_id: '20137'
author:
- first_name: Shahbaz
full_name: Syed, Shahbaz
last_name: Syed
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Matthias
full_name: Hagen, Matthias
last_name: Hagen
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
citation:
ama: 'Syed S, Chen W-F, Hagen M, Stein B, Wachsmuth H, Potthast M. Task Proposal:
Abstractive Snippet Generation for Web Pages. In: Proceedings of the 13th International
Conference on Natural Language Generation (INLG 2020). ; 2020:237-241.'
apa: 'Syed, S., Chen, W.-F., Hagen, M., Stein, B., Wachsmuth, H., & Potthast,
M. (2020). Task Proposal: Abstractive Snippet Generation for Web Pages. Proceedings
of the 13th International Conference on Natural Language Generation (INLG 2020),
237–241.'
bibtex: '@inproceedings{Syed_Chen_Hagen_Stein_Wachsmuth_Potthast_2020, title={Task
Proposal: Abstractive Snippet Generation for Web Pages}, booktitle={Proceedings
of the 13th International Conference on Natural Language Generation (INLG 2020)},
author={Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and
Wachsmuth, Henning and Potthast, Martin}, year={2020}, pages={237–241} }'
chicago: 'Syed, Shahbaz, Wei-Fan Chen, Matthias Hagen, Benno Stein, Henning Wachsmuth,
and Martin Potthast. “Task Proposal: Abstractive Snippet Generation for Web Pages.”
In Proceedings of the 13th International Conference on Natural Language Generation
(INLG 2020), 237–41, 2020.'
ieee: 'S. Syed, W.-F. Chen, M. Hagen, B. Stein, H. Wachsmuth, and M. Potthast, “Task
Proposal: Abstractive Snippet Generation for Web Pages,” in Proceedings of
the 13th International Conference on Natural Language Generation (INLG 2020),
2020, pp. 237–241.'
mla: 'Syed, Shahbaz, et al. “Task Proposal: Abstractive Snippet Generation for Web
Pages.” Proceedings of the 13th International Conference on Natural Language
Generation (INLG 2020), 2020, pp. 237–41.'
short: 'S. Syed, W.-F. Chen, M. Hagen, B. Stein, H. Wachsmuth, M. Potthast, in:
Proceedings of the 13th International Conference on Natural Language Generation
(INLG 2020), 2020, pp. 237–241.'
date_created: 2020-10-20T13:00:06Z
date_updated: 2022-05-09T15:03:11Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/2020.inlg-1.30.pdf
page: 237-241
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: Proceedings of the 13th International Conference on Natural Language
Generation (INLG 2020)
status: public
title: 'Task Proposal: Abstractive Snippet Generation for Web Pages'
type: conference
user_id: '82920'
year: '2020'
...
---
_id: '3818'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Khalid
full_name: Al-Khatib, Khalid
last_name: Al-Khatib
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Chen W-F, Al-Khatib K, Stein B, Wachsmuth H. Detecting Media Bias in News
Articles using Gaussian Bias Distributions. In: Findings of the Association
for Computational Linguistics: EMNLP 2020. ; 2020:4290-4300.'
apa: 'Chen, W.-F., Al-Khatib, K., Stein, B., & Wachsmuth, H. (2020). Detecting
Media Bias in News Articles using Gaussian Bias Distributions. Findings of
the Association for Computational Linguistics: EMNLP 2020, 4290–4300.'
bibtex: '@inproceedings{Chen_Al-Khatib_Stein_Wachsmuth_2020, title={Detecting Media
Bias in News Articles using Gaussian Bias Distributions}, booktitle={Findings
of the Association for Computational Linguistics: EMNLP 2020}, author={Chen, Wei-Fan
and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}, year={2020}, pages={4290–4300}
}'
chicago: 'Chen, Wei-Fan, Khalid Al-Khatib, Benno Stein, and Henning Wachsmuth. “Detecting
Media Bias in News Articles Using Gaussian Bias Distributions.” In Findings
of the Association for Computational Linguistics: EMNLP 2020, 4290–4300, 2020.'
ieee: 'W.-F. Chen, K. Al-Khatib, B. Stein, and H. Wachsmuth, “Detecting Media Bias
in News Articles using Gaussian Bias Distributions,” in Findings of the Association
for Computational Linguistics: EMNLP 2020, 2020, pp. 4290–4300.'
mla: 'Chen, Wei-Fan, et al. “Detecting Media Bias in News Articles Using Gaussian
Bias Distributions.” Findings of the Association for Computational Linguistics:
EMNLP 2020, 2020, pp. 4290–300.'
short: 'W.-F. Chen, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association
for Computational Linguistics: EMNLP 2020, 2020, pp. 4290–4300.'
date_created: 2018-08-02T13:38:46Z
date_updated: 2022-05-09T15:00:46Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/2020.findings-emnlp.383.pdf
page: 4290-4300
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2020'
status: public
title: Detecting Media Bias in News Articles using Gaussian Bias Distributions
type: conference
user_id: '82920'
year: '2020'
...
---
_id: '15826'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Shahbaz
full_name: Syed, Shahbaz
last_name: Syed
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
- first_name: Matthias
full_name: Hagen, Matthias
last_name: Hagen
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
citation:
ama: 'Chen W-F, Syed S, Stein B, Hagen M, Potthast M. Abstractive Snippet Generation.
In: Proceedings of the Web Conference 2020. ; 2020:1309-1319.'
apa: Chen, W.-F., Syed, S., Stein, B., Hagen, M., & Potthast, M. (2020). Abstractive
Snippet Generation. Proceedings of the Web Conference 2020, 1309–1319.
bibtex: '@inproceedings{Chen_Syed_Stein_Hagen_Potthast_2020, title={Abstractive
Snippet Generation}, booktitle={Proceedings of the Web Conference 2020}, author={Chen,
Wei-Fan and Syed, Shahbaz and Stein, Benno and Hagen, Matthias and Potthast, Martin},
year={2020}, pages={1309–1319} }'
chicago: Chen, Wei-Fan, Shahbaz Syed, Benno Stein, Matthias Hagen, and Martin Potthast.
“Abstractive Snippet Generation.” In Proceedings of the Web Conference 2020,
1309–19, 2020.
ieee: W.-F. Chen, S. Syed, B. Stein, M. Hagen, and M. Potthast, “Abstractive Snippet
Generation,” in Proceedings of the Web Conference 2020, 2020, pp. 1309–1319.
mla: Chen, Wei-Fan, et al. “Abstractive Snippet Generation.” Proceedings of the
Web Conference 2020, 2020, pp. 1309–19.
short: 'W.-F. Chen, S. Syed, B. Stein, M. Hagen, M. Potthast, in: Proceedings of
the Web Conference 2020, 2020, pp. 1309–1319.'
date_created: 2020-02-06T10:51:33Z
date_updated: 2022-05-09T15:03:53Z
department:
- _id: '600'
- _id: '568'
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/pdf/10.1145/3366423.3380206
page: 1309-1319
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: Proceedings of the Web Conference 2020
status: public
title: Abstractive Snippet Generation
type: conference
user_id: '82920'
year: '2020'
...
---
_id: '16868'
author:
- first_name: Milad
full_name: Alshomary, Milad
id: '73059'
last_name: Alshomary
- first_name: Shahbaz
full_name: Syed, Shahbaz
last_name: Syed
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Alshomary M, Syed S, Potthast M, Wachsmuth H. Target Inference in Argument
Conclusion Generation. In: Proceedings of 58th Annual Meeting of the Association
for Computational Linguistics (ACL 2020). Proceedings of 58th Annual Meeting
of the Association for Computational Linguistics. Association for Computational
Linguistics; 2020:4334-4345.'
apa: Alshomary, M., Syed, S., Potthast, M., & Wachsmuth, H. (2020). Target Inference
in Argument Conclusion Generation. Proceedings of 58th Annual Meeting of the
Association for Computational Linguistics (ACL 2020), 4334–4345.
bibtex: '@inproceedings{Alshomary_Syed_Potthast_Wachsmuth_2020, series={Proceedings
of 58th Annual Meeting of the Association for Computational Linguistics}, title={Target
Inference in Argument Conclusion Generation}, booktitle={Proceedings of 58th Annual
Meeting of the Association for Computational Linguistics (ACL 2020)}, publisher={Association
for Computational Linguistics}, author={Alshomary, Milad and Syed, Shahbaz and
Potthast, Martin and Wachsmuth, Henning}, year={2020}, pages={4334–4345}, collection={Proceedings
of 58th Annual Meeting of the Association for Computational Linguistics} }'
chicago: Alshomary, Milad, Shahbaz Syed, Martin Potthast, and Henning Wachsmuth.
“Target Inference in Argument Conclusion Generation.” In Proceedings of 58th
Annual Meeting of the Association for Computational Linguistics (ACL 2020),
4334–45. Proceedings of 58th Annual Meeting of the Association for Computational
Linguistics. Association for Computational Linguistics, 2020.
ieee: M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Target Inference in
Argument Conclusion Generation,” in Proceedings of 58th Annual Meeting of the
Association for Computational Linguistics (ACL 2020), Seattle, USA, 2020,
pp. 4334–4345.
mla: Alshomary, Milad, et al. “Target Inference in Argument Conclusion Generation.”
Proceedings of 58th Annual Meeting of the Association for Computational Linguistics
(ACL 2020), Association for Computational Linguistics, 2020, pp. 4334–45.
short: 'M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of 58th
Annual Meeting of the Association for Computational Linguistics (ACL 2020), Association
for Computational Linguistics, 2020, pp. 4334–4345.'
conference:
end_date: 2020.07.10
location: Seattle, USA
name: 58th Annual Meeting of the Association for Computational Linguistics (ACL
2020)
start_date: 2020.07.05
date_created: 2020-04-27T10:11:00Z
date_updated: 2022-05-09T15:06:52Z
ddc:
- '000'
department:
- _id: '600'
- _id: '568'
file:
- access_level: closed
content_type: application/pdf
creator: sile2804
date_created: 2020-05-22T06:41:25Z
date_updated: 2020-05-22T06:41:25Z
file_id: '17054'
file_name: acl20-conclusion-generation-frame.pdf
file_size: 1572275
relation: main_file
success: 1
file_date_updated: 2020-05-22T06:41:25Z
has_accepted_license: '1'
language:
- iso: eng
page: 4334-4345
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: Proceedings of 58th Annual Meeting of the Association for Computational
Linguistics (ACL 2020)
publisher: Association for Computational Linguistics
series_title: Proceedings of 58th Annual Meeting of the Association for Computational
Linguistics
status: public
title: Target Inference in Argument Conclusion Generation
type: conference
user_id: '82920'
year: '2020'
...
---
_id: '20141'
author:
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Yan
full_name: Scholten, Yan
last_name: Scholten
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Martin
full_name: Potthast, Martin
last_name: Potthast
citation:
ama: 'Heindorf S, Scholten Y, Wachsmuth H, Ngonga Ngomo A-C, Potthast M. CauseNet:
Towards a Causality Graph Extracted from the Web. In: Proceedings of the 28th
ACM International Conference on Information and Knowledge Management (CIKM 2020).
; 2020:3023-3030. doi:10.1145/3340531.3412763'
apa: 'Heindorf, S., Scholten, Y., Wachsmuth, H., Ngonga Ngomo, A.-C., & Potthast,
M. (2020). CauseNet: Towards a Causality Graph Extracted from the Web. Proceedings
of the 28th ACM International Conference on Information and Knowledge Management
(CIKM 2020), 3023–3030. https://doi.org/10.1145/3340531.3412763'
bibtex: '@inproceedings{Heindorf_Scholten_Wachsmuth_Ngonga Ngomo_Potthast_2020,
title={CauseNet: Towards a Causality Graph Extracted from the Web}, DOI={10.1145/3340531.3412763},
booktitle={Proceedings of the 28th ACM International Conference on Information
and Knowledge Management (CIKM 2020)}, author={Heindorf, Stefan and Scholten,
Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin},
year={2020}, pages={3023–3030} }'
chicago: 'Heindorf, Stefan, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga
Ngomo, and Martin Potthast. “CauseNet: Towards a Causality Graph Extracted from
the Web.” In Proceedings of the 28th ACM International Conference on Information
and Knowledge Management (CIKM 2020), 3023–30, 2020. https://doi.org/10.1145/3340531.3412763.'
ieee: 'S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, and M. Potthast,
“CauseNet: Towards a Causality Graph Extracted from the Web,” in Proceedings
of the 28th ACM International Conference on Information and Knowledge Management
(CIKM 2020), 2020, pp. 3023–3030, doi: 10.1145/3340531.3412763.'
mla: 'Heindorf, Stefan, et al. “CauseNet: Towards a Causality Graph Extracted from
the Web.” Proceedings of the 28th ACM International Conference on Information
and Knowledge Management (CIKM 2020), 2020, pp. 3023–30, doi:10.1145/3340531.3412763.'
short: 'S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, M. Potthast,
in: Proceedings of the 28th ACM International Conference on Information and Knowledge
Management (CIKM 2020), 2020, pp. 3023–3030.'
date_created: 2020-10-20T13:11:14Z
date_updated: 2022-10-15T19:57:01Z
department:
- _id: '574'
doi: 10.1145/3340531.3412763
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2020/CIKM-20/heindorf_2020a_public.pdf
oa: '1'
page: 3023-3030
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the 28th ACM International Conference on Information and
Knowledge Management (CIKM 2020)
status: public
title: 'CauseNet: Towards a Causality Graph Extracted from the Web'
type: conference
user_id: '11871'
year: '2020'
...
---
_id: '18638'
author:
- first_name: Paul
full_name: Kramer, Paul
id: '64594'
last_name: Kramer
citation:
ama: Kramer P. Comparison of Zero-Knowledge Range Proofs. Universität Paderborn;
2020.
apa: Kramer, P. (2020). Comparison of Zero-Knowledge Range Proofs. Universität
Paderborn.
bibtex: '@book{Kramer_2020, title={Comparison of Zero-Knowledge Range Proofs}, publisher={Universität
Paderborn}, author={Kramer, Paul}, year={2020} }'
chicago: Kramer, Paul. Comparison of Zero-Knowledge Range Proofs. Universität
Paderborn, 2020.
ieee: P. Kramer, Comparison of Zero-Knowledge Range Proofs. Universität Paderborn,
2020.
mla: Kramer, Paul. Comparison of Zero-Knowledge Range Proofs. Universität
Paderborn, 2020.
short: P. Kramer, Comparison of Zero-Knowledge Range Proofs, Universität Paderborn,
2020.
date_created: 2020-08-29T13:27:11Z
date_updated: 2022-10-17T10:56:59Z
department:
- _id: '7'
- _id: '64'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '13'
name: SFB 901 - Subproject C1
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Johannes
full_name: Blömer, Johannes
id: '23'
last_name: Blömer
title: Comparison of Zero-Knowledge Range Proofs
type: bachelorsthesis
user_id: '25078'
year: '2020'
...
---
_id: '20838'
author:
- first_name: Achim
full_name: Lösch, Achim
last_name: Lösch
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Lösch A, Platzner M. MigHEFT: DAG-based Scheduling of Migratable Tasks on
Heterogeneous Compute Nodes. In: 2020 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW). ; 2020. doi:10.1109/ipdpsw50202.2020.00012'
apa: 'Lösch, A., & Platzner, M. (2020). MigHEFT: DAG-based Scheduling of Migratable
Tasks on Heterogeneous Compute Nodes. 2020 IEEE International Parallel and
Distributed Processing Symposium Workshops (IPDPSW). https://doi.org/10.1109/ipdpsw50202.2020.00012'
bibtex: '@inproceedings{Lösch_Platzner_2020, title={MigHEFT: DAG-based Scheduling
of Migratable Tasks on Heterogeneous Compute Nodes}, DOI={10.1109/ipdpsw50202.2020.00012},
booktitle={2020 IEEE International Parallel and Distributed Processing Symposium
Workshops (IPDPSW)}, author={Lösch, Achim and Platzner, Marco}, year={2020} }'
chicago: 'Lösch, Achim, and Marco Platzner. “MigHEFT: DAG-Based Scheduling of Migratable
Tasks on Heterogeneous Compute Nodes.” In 2020 IEEE International Parallel
and Distributed Processing Symposium Workshops (IPDPSW), 2020. https://doi.org/10.1109/ipdpsw50202.2020.00012.'
ieee: 'A. Lösch and M. Platzner, “MigHEFT: DAG-based Scheduling of Migratable Tasks
on Heterogeneous Compute Nodes,” 2020, doi: 10.1109/ipdpsw50202.2020.00012.'
mla: 'Lösch, Achim, and Marco Platzner. “MigHEFT: DAG-Based Scheduling of Migratable
Tasks on Heterogeneous Compute Nodes.” 2020 IEEE International Parallel and
Distributed Processing Symposium Workshops (IPDPSW), 2020, doi:10.1109/ipdpsw50202.2020.00012.'
short: 'A. Lösch, M. Platzner, in: 2020 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW), 2020.'
date_created: 2020-12-23T09:07:11Z
date_updated: 2023-01-03T22:07:12Z
department:
- _id: '78'
doi: 10.1109/ipdpsw50202.2020.00012
language:
- iso: eng
publication: 2020 IEEE International Parallel and Distributed Processing Symposium
Workshops (IPDPSW)
publication_identifier:
isbn:
- '9781728174457'
publication_status: published
status: public
title: 'MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterogeneous Compute
Nodes'
type: conference
user_id: '398'
year: '2020'
...
---
_id: '13226'
abstract:
- lang: eng
text: "The canonical problem for the class Quantum Merlin-Arthur (QMA) is that of\r\nestimating
ground state energies of local Hamiltonians. Perhaps surprisingly,\r\n[Ambainis,
CCC 2014] showed that the related, but arguably more natural,\r\nproblem of simulating
local measurements on ground states of local Hamiltonians\r\n(APX-SIM) is likely
harder than QMA. Indeed, [Ambainis, CCC 2014] showed that\r\nAPX-SIM is P^QMA[log]-complete,
for P^QMA[log] the class of languages decidable\r\nby a P machine making a logarithmic
number of adaptive queries to a QMA oracle.\r\nIn this work, we show that APX-SIM
is P^QMA[log]-complete even when restricted\r\nto more physical Hamiltonians,
obtaining as intermediate steps a variety of\r\nrelated complexity-theoretic results.\r\n
\ We first give a sequence of results which together yield P^QMA[log]-hardness\r\nfor
APX-SIM on well-motivated Hamiltonians: (1) We show that for NP, StoqMA,\r\nand
QMA oracles, a logarithmic number of adaptive queries is equivalent to\r\npolynomially
many parallel queries. These equalities simplify the proofs of our\r\nsubsequent
results. (2) Next, we show that the hardness of APX-SIM is preserved\r\nunder
Hamiltonian simulations (a la [Cubitt, Montanaro, Piddock, 2017]). As a\r\nbyproduct,
we obtain a full complexity classification of APX-SIM, showing it is\r\ncomplete
for P, P^||NP, P^||StoqMA, or P^||QMA depending on the Hamiltonians\r\nemployed.
(3) Leveraging the above, we show that APX-SIM is P^QMA[log]-complete\r\nfor any
family of Hamiltonians which can efficiently simulate spatially sparse\r\nHamiltonians,
including physically motivated models such as the 2D Heisenberg\r\nmodel.\r\n
\ Our second focus considers 1D systems: We show that APX-SIM remains\r\nP^QMA[log]-complete
even for local Hamiltonians on a 1D line of 8-dimensional\r\nqudits. This uses
a number of ideas from above, along with replacing the \"query\r\nHamiltonian\"
of [Ambainis, CCC 2014] with a new \"sifter\" construction."
author:
- first_name: Sevag
full_name: Gharibian, Sevag
id: '71541'
last_name: Gharibian
orcid: 0000-0002-9992-3379
- first_name: Stephen
full_name: Piddock, Stephen
last_name: Piddock
- first_name: Justin
full_name: Yirka, Justin
last_name: Yirka
citation:
ama: 'Gharibian S, Piddock S, Yirka J. Oracle complexity classes and local measurements
on physical Hamiltonians. In: Proceedings of the 37th Symposium on Theoretical
Aspects of Computer Science (STACS 2020). ; 2020:38.'
apa: Gharibian, S., Piddock, S., & Yirka, J. (2020). Oracle complexity classes
and local measurements on physical Hamiltonians. Proceedings of the 37th Symposium
on Theoretical Aspects of Computer Science (STACS 2020), 38.
bibtex: '@inproceedings{Gharibian_Piddock_Yirka_2020, title={Oracle complexity classes
and local measurements on physical Hamiltonians}, booktitle={Proceedings of the
37th Symposium on Theoretical Aspects of Computer Science (STACS 2020)}, author={Gharibian,
Sevag and Piddock, Stephen and Yirka, Justin}, year={2020}, pages={38} }'
chicago: Gharibian, Sevag, Stephen Piddock, and Justin Yirka. “Oracle Complexity
Classes and Local Measurements on Physical Hamiltonians.” In Proceedings of
the 37th Symposium on Theoretical Aspects of Computer Science (STACS 2020),
38, 2020.
ieee: S. Gharibian, S. Piddock, and J. Yirka, “Oracle complexity classes and local
measurements on physical Hamiltonians,” in Proceedings of the 37th Symposium
on Theoretical Aspects of Computer Science (STACS 2020), 2020, p. 38.
mla: Gharibian, Sevag, et al. “Oracle Complexity Classes and Local Measurements
on Physical Hamiltonians.” Proceedings of the 37th Symposium on Theoretical
Aspects of Computer Science (STACS 2020), 2020, p. 38.
short: 'S. Gharibian, S. Piddock, J. Yirka, in: Proceedings of the 37th Symposium
on Theoretical Aspects of Computer Science (STACS 2020), 2020, p. 38.'
date_created: 2019-09-16T07:41:31Z
date_updated: 2023-10-09T04:17:41Z
department:
- _id: '623'
- _id: '7'
external_id:
arxiv:
- '1909.05981'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1909.05981
oa: '1'
page: '38'
publication: Proceedings of the 37th Symposium on Theoretical Aspects of Computer
Science (STACS 2020)
publication_status: published
status: public
title: Oracle complexity classes and local measurements on physical Hamiltonians
type: conference
user_id: '71541'
year: '2020'
...
---
_id: '46319'
abstract:
- lang: eng
text: The detection of orchestrated and potentially manipulative campaigns in social
media is far more meaningful than an- alyzing single account behaviour but also
more challenging in terms of pattern recognition, data processing, and com- putational
complexity. While supervised learning methods need an enormous amount of reliable
ground truth data to find rather inflexible patterns, classical unsupervised learn-
ing techniques need a lot of computational power to handle large amount of data.
This makes them infeasible for real- time analysis. In this work, we demonstrate
the applicability of text stream clustering for the real-time detection of coordi-
nated campaigns.
author:
- first_name: D
full_name: Assenmacher, D
last_name: Assenmacher
- first_name: L
full_name: Adam, L
last_name: Adam
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: C
full_name: Grimme, C
last_name: Grimme
citation:
ama: 'Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised
Campaign Detection in Social Media. In: Proceedings of the Florida Artificial
Intelligence Research Society Conference. ; 2020.'
apa: Assenmacher, D., Adam, L., Trautmann, H., & Grimme, C. (2020). Towards
Real-Time and Unsupervised Campaign Detection in Social Media. Proceedings
of the Florida Artificial Intelligence Research Society Conference.
bibtex: '@inproceedings{Assenmacher_Adam_Trautmann_Grimme_2020, place={Florida,
USA}, title={Towards Real-Time and Unsupervised Campaign Detection in Social Media},
booktitle={Proceedings of the Florida Artificial Intelligence Research Society
Conference}, author={Assenmacher, D and Adam, L and Trautmann, Heike and Grimme,
C}, year={2020} }'
chicago: Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time
and Unsupervised Campaign Detection in Social Media.” In Proceedings of the
Florida Artificial Intelligence Research Society Conference. Florida, USA,
2020.
ieee: D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and
Unsupervised Campaign Detection in Social Media,” 2020.
mla: Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection
in Social Media.” Proceedings of the Florida Artificial Intelligence Research
Society Conference, 2020.
short: 'D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the
Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020.'
date_created: 2023-08-04T07:29:36Z
date_updated: 2023-10-16T12:59:10Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
place: Florida, USA
publication: Proceedings of the Florida Artificial Intelligence Research Society Conference
status: public
title: Towards Real-Time and Unsupervised Campaign Detection in Social Media
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46328'
abstract:
- lang: eng
text: In this paper, we rely on previous work proposing a modularized version of
CMA-ES, which captures several alterations to the conventional CMA-ES developed
in recent years. Each alteration provides significant advantages under certain
problem properties, e.g., multi-modality, high conditioning. These distinct advancements
are implemented as modules which result in 4608 unique versions of CMA-ES. Previous
findings illustrate the competitive advantage of enabling and disabling the aforementioned
modules for different optimization problems. Yet, this modular CMA-ES is lacking
a method to automatically determine when the activation of specific modules is
auspicious and when it is not. We propose a well-performing instance-specific
algorithm configuration model which selects an (almost) optimal configuration
of modules for a given problem instance. In addition, the structure of this configuration
model is able to capture inter-dependencies between modules, e.g., two (or more)
modules might only be advantageous in unison for some problem types, making the
orchestration of modules a crucial task. This is accomplished by chaining multiple
random forest classifiers together into a so-called Classifier Chain based on
a set of numerical features extracted by means of Exploratory Landscape Analysis
(ELA) to describe the given problem instances.
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Hao
full_name: Wang, Hao
last_name: Wang
- first_name: Thomas H. W.
full_name: Bäck, Thomas H. W.
last_name: Bäck
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Prager RP, Trautmann H, Wang H, Bäck THW, Kerschke P. Per-Instance Configuration
of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape
Analysis. In: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI). ; 2020:996–1003. doi:10.1109/SSCI47803.2020.9308510'
apa: Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P.
(2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier
Chains and Exploratory Landscape Analysis. Proceedings of the IEEE Symposium
Series on Computational Intelligence (SSCI), 996–1003. https://doi.org/10.1109/SSCI47803.2020.9308510
bibtex: '@inproceedings{Prager_Trautmann_Wang_Bäck_Kerschke_2020, place={Canberra,
Australia}, title={Per-Instance Configuration of the Modularized CMA-ES by Means
of Classifier Chains and Exploratory Landscape Analysis}, DOI={10.1109/SSCI47803.2020.9308510},
booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)}, author={Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and
Bäck, Thomas H. W. and Kerschke, Pascal}, year={2020}, pages={996–1003} }'
chicago: Prager, Raphael Patrick, Heike Trautmann, Hao Wang, Thomas H. W. Bäck,
and Pascal Kerschke. “Per-Instance Configuration of the Modularized CMA-ES by
Means of Classifier Chains and Exploratory Landscape Analysis.” In Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), 996–1003.
Canberra, Australia, 2020. https://doi.org/10.1109/SSCI47803.2020.9308510.
ieee: 'R. P. Prager, H. Trautmann, H. Wang, T. H. W. Bäck, and P. Kerschke, “Per-Instance
Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory
Landscape Analysis,” in Proceedings of the IEEE Symposium Series on Computational
Intelligence (SSCI), 2020, pp. 996–1003, doi: 10.1109/SSCI47803.2020.9308510.'
mla: Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized
CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp.
996–1003, doi:10.1109/SSCI47803.2020.9308510.
short: 'R.P. Prager, H. Trautmann, H. Wang, T.H.W. Bäck, P. Kerschke, in: Proceedings
of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia,
2020, pp. 996–1003.'
date_created: 2023-08-04T07:37:30Z
date_updated: 2023-10-16T13:04:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1109/SSCI47803.2020.9308510
language:
- iso: eng
page: 996–1003
place: Canberra, Australia
publication: Proceedings of the IEEE Symposium Series on Computational Intelligence
(SSCI)
status: public
title: Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier
Chains and Exploratory Landscape Analysis
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46320'
abstract:
- lang: eng
text: The identification of coordinated campaigns within Social Media is a complex
task that is often hindered by missing labels and large amounts of data that have
to be processed. We propose a new two-phase framework that uses unsupervised stream
clustering for detecting suspicious trends over time in a first step. Afterwards,
traditional offline analyses are applied to distinguish between normal trend evolution
and malicious manipulation attempts. We demonstrate the applicability of our framework
in the context of the final days of the Brexit in 2019/2020.
author:
- first_name: D
full_name: Assenmacher, D
last_name: Assenmacher
- first_name: L
full_name: Clever, L
last_name: Clever
- first_name: JS
full_name: Pohl, JS
last_name: Pohl
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: C
full_name: Grimme, C
last_name: Grimme
citation:
ama: 'Assenmacher D, Clever L, Pohl J, Trautmann H, Grimme C. A Two-Phase Framework
for Detecting Manipulation Campaigns in Social Media. In: Meiselwitz G, ed. Proceedings
of the International Conference on Human-Computer Interaction (HCII 2020): Social
Computing and Social Media. Design, Ethics, User Behavior, and Social Network
Analysis. Springer International Publishing; 2020:201–214. doi:10.1007/978-3-030-49570-1_14'
apa: 'Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., & Grimme, C. (2020).
A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In
G. Meiselwitz (Ed.), Proceedings of the International Conference on Human-Computer
Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User
Behavior, and Social Network Analysis (pp. 201–214). Springer International
Publishing. https://doi.org/10.1007/978-3-030-49570-1_14'
bibtex: '@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham},
title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media},
DOI={10.1007/978-3-030-49570-1_14},
booktitle={Proceedings of the International Conference on Human-Computer Interaction
(HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior,
and Social Network Analysis}, publisher={Springer International Publishing}, author={Assenmacher,
D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}, editor={Meiselwitz,
G}, year={2020}, pages={201–214} }'
chicago: 'Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase
Framework for Detecting Manipulation Campaigns in Social Media.” In Proceedings
of the International Conference on Human-Computer Interaction (HCII 2020): Social
Computing and Social Media. Design, Ethics, User Behavior, and Social Network
Analysis, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing,
2020. https://doi.org/10.1007/978-3-030-49570-1_14.'
ieee: 'D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase
Framework for Detecting Manipulation Campaigns in Social Media,” in Proceedings
of the International Conference on Human-Computer Interaction (HCII 2020): Social
Computing and Social Media. Design, Ethics, User Behavior, and Social Network
Analysis, 2020, pp. 201–214, doi: 10.1007/978-3-030-49570-1_14.'
mla: 'Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation
Campaigns in Social Media.” Proceedings of the International Conference on
Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design,
Ethics, User Behavior, and Social Network Analysis, edited by G Meiselwitz,
Springer International Publishing, 2020, pp. 201–214, doi:10.1007/978-3-030-49570-1_14.'
short: 'D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, C. Grimme, in: G. Meiselwitz
(Ed.), Proceedings of the International Conference on Human-Computer Interaction
(HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior,
and Social Network Analysis, Springer International Publishing, Cham, 2020, pp.
201–214.'
date_created: 2023-08-04T07:30:29Z
date_updated: 2023-10-16T12:59:28Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-49570-1_14
editor:
- first_name: G
full_name: Meiselwitz, G
last_name: Meiselwitz
language:
- iso: eng
page: 201–214
place: Cham
publication: 'Proceedings of the International Conference on Human-Computer Interaction
(HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and
Social Network Analysis'
publication_identifier:
isbn:
- 978-3-030-49570-1
publisher: Springer International Publishing
status: public
title: A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46325'
abstract:
- lang: eng
text: Clustering is an important technique in data analysis which can reveal hidden
patterns and unknown relationships in the data. A common problem in clustering
is the proper choice of parameter settings. To tackle this, automated algorithm
configuration is available which can automatically find the best parameter settings.
In practice, however, many of our today’s data sources are data streams due to
the widespread deployment of sensors, the internet-of-things or (social) media.
Stream clustering aims to tackle this challenge by identifying, tracking and updating
clusters over time. Unfortunately, none of the existing approaches for automated
algorithm configuration are directly applicable to the streaming scenario. In
this paper, we explore the possibility of automated algorithm configuration for
stream clustering algorithms using an ensemble of different configurations. In
first experiments, we demonstrate that our approach is able to automatically find
superior configurations and refine them over time.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Albert
full_name: Bifet, Albert
last_name: Bifet
- first_name: Bernhard
full_name: Pfahringer, Bernhard
last_name: Pfahringer
citation:
ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration
of Stream Clustering Algorithms. In: Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECMLPKDD ’19). ; 2020:137–143. doi:10.1007/978-3-030-43823-4_12'
apa: Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). Towards
Automated Configuration of Stream Clustering Algorithms. Proceedings of the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECMLPKDD ’19), 137–143. https://doi.org/10.1007/978-3-030-43823-4_12
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg,
Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms},
DOI={10.1007/978-3-030-43823-4_12},
booktitle={Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein,
Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020},
pages={137–143} }'
chicago: Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“Towards Automated Configuration of Stream Clustering Algorithms.” In Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Databases (ECMLPKDD ’19), 137–143. Würzburg, Germany,
2020. https://doi.org/10.1007/978-3-030-43823-4_12.
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated
Configuration of Stream Clustering Algorithms,” in Proceedings of the European
Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Databases (ECMLPKDD ’19), 2020, pp. 137–143, doi: 10.1007/978-3-030-43823-4_12.'
mla: Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering
Algorithms.” Proceedings of the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19),
2020, pp. 137–143, doi:10.1007/978-3-030-43823-4_12.
short: 'M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.'
date_created: 2023-08-04T07:35:24Z
date_updated: 2023-10-16T13:03:15Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-43823-4_12
language:
- iso: eng
page: 137–143
place: Würzburg, Germany
publication: Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)
publication_identifier:
isbn:
- 978-3-030-43823-4
status: public
title: Towards Automated Configuration of Stream Clustering Algorithms
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46321'
abstract:
- lang: eng
text: Social bots have recently gained attention in the context of public opinion
manipulation on social media platforms. While a lot of research effort has been
put into the classification and detection of such automated programs, it is still
unclear how technically sophisticated those bots are, which platforms they target,
and where they originate from. To answer these questions, we gathered repository
data from open source collaboration platforms to identify the status-quo of social
bot development as well as first insights into the overall skills of publicly
available bot code.
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Lena
full_name: Frischlich , Lena
last_name: 'Frischlich '
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Lena
full_name: Adam, Lena
last_name: Adam
citation:
ama: 'Assenmacher D, Frischlich L, Trautmann H, Grimme C, Adam L. Inside the tool
set of automation: Free social bot code revisited. In: Grimme C, Preuß M, Takes
F, Waldherr A, eds. Disinformation in Open Online Media. Lecture Notes
in Computer Science. Springer; 2020:101–114.'
apa: 'Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., & Adam, L.
(2020). Inside the tool set of automation: Free social bot code revisited. In
C. Grimme, M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in
open online media (pp. 101–114). Springer.'
bibtex: '@inproceedings{Assenmacher_Frischlich _Trautmann_Grimme_Adam_2020, place={Wiesbaden},
series={Lecture Notes in Computer Science}, title={Inside the tool set of automation:
Free social bot code revisited}, booktitle={Disinformation in open online media},
publisher={Springer}, author={Assenmacher, Dennis and Frischlich , Lena and Trautmann,
Heike and Grimme, Christian and Adam, Lena}, editor={Grimme, Christian and Preuß,
Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={101–114}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme,
and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.”
In Disinformation in Open Online Media, edited by Christian Grimme, Mike
Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science.
Wiesbaden: Springer, 2020.'
ieee: 'D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside
the tool set of automation: Free social bot code revisited,” in Disinformation
in open online media, 2020, pp. 101–114.'
mla: 'Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social
Bot Code Revisited.” Disinformation in Open Online Media, edited by Christian
Grimme et al., Springer, 2020, pp. 101–114.'
short: 'D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, L. Adam, in: C.
Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online
Media, Springer, Wiesbaden, 2020, pp. 101–114.'
date_created: 2023-08-04T07:31:13Z
date_updated: 2023-10-16T13:00:15Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
- first_name: Frank
full_name: Takes, Frank
last_name: Takes
- first_name: Annie
full_name: Waldherr, Annie
last_name: Waldherr
language:
- iso: eng
page: 101–114
place: Wiesbaden
publication: Disinformation in open online media
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Inside the tool set of automation: Free social bot code revisited'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46326'
abstract:
- lang: eng
text: Machine learning has become one of the most important tools in data analysis.
However, selecting the most appropriate machine learning algorithm and tuning
its hyperparameters to their optimal values remains a difficult task. This is
even more difficult for streaming applications where automated approaches are
often not available to help during algorithm selection and configuration. This
paper proposes the first approach for automated algorithm selection and configuration
of stream clustering algorithms. We train an ensemble of different stream clustering
algorithms and configurations in parallel and use the best performing configuration
to obtain a clustering solution. By drawing new configurations from better performing
ones, we are able to improve the ensemble performance over time. In large experiments
on real and artificial data we show how our ensemble approach can improve upon
default configurations and can also compete with a-posteriori algorithm configuration.
Our approach is considerably faster than a-posteriori approaches and applicable
in real-time. In addition, it is not limited to stream clustering and can be generalised
to all streaming applications, including stream classification and regression.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Albert
full_name: Bifet, Albert
last_name: Bifet
- first_name: Bernhard
full_name: Pfahringer, Bernhard
last_name: Pfahringer
citation:
ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm
Selection and Configuration of Stream Clustering Algorithms. In: Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020).
; 2020:80–95. doi:10.1007/978-3-030-53552-0_10'
apa: 'Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). confStream:
Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.
Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020), 80–95. https://doi.org/10.1007/978-3-030-53552-0_10'
bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens,
Greece}, title={confStream: Automated Algorithm Selection and Configuration of
Stream Clustering Algorithms}, DOI={10.1007/978-3-030-53552-0_10},
booktitle={Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert
and Pfahringer, Bernhard}, year={2020}, pages={80–95} }'
chicago: 'Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer.
“ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms.” In Proceedings of the 14$^th$ Learning and Intelligent Optimization
Conference (LION 2020), 80–95. Athens, Greece, 2020. https://doi.org/10.1007/978-3-030-53552-0_10.'
ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated
Algorithm Selection and Configuration of Stream Clustering Algorithms,” in Proceedings
of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020),
2020, pp. 80–95, doi: 10.1007/978-3-030-53552-0_10.'
mla: 'Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration
of Stream Clustering Algorithms.” Proceedings of the 14$^th$ Learning and Intelligent
Optimization Conference (LION 2020), 2020, pp. 80–95, doi:10.1007/978-3-030-53552-0_10.'
short: 'M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the
14$^th$ Learning and Intelligent Optimization Conference (LION 2020), Athens,
Greece, 2020, pp. 80–95.'
date_created: 2023-08-04T07:36:03Z
date_updated: 2023-10-16T13:03:36Z
department:
- _id: '34'
- _id: '819'
doi: 10.1007/978-3-030-53552-0_10
language:
- iso: eng
page: 80–95
place: Athens, Greece
publication: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference
(LION 2020)
status: public
title: 'confStream: Automated Algorithm Selection and Configuration of Stream Clustering
Algorithms'
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46327'
abstract:
- lang: eng
text: In online media environments, nostalgia can be used as important ingredient
of propaganda strategies, specifically, by creating societal pessimism. This work
addresses the automated detection of nostalgic text as a first step towards automatically
identifying nostalgia-based manipulation strategies. We compare the performance
of standard machine learning approaches on this challenge and demonstrate the
successful transfer of the best performing approach to real-world nostalgia detection
in a case study.
author:
- first_name: Clever
full_name: Lena, Clever
last_name: Lena
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Lena C, Frischlich L, Trautmann H, Grimme C. Automated detection of nostalgic
text in the context of societal pessimism. In: Grimme C, Preuß M, Takes F, Waldherr
A, eds. Disinformation in Open Online Media. ; 2020:48–58.'
apa: Lena, C., Frischlich, L., Trautmann, H., & Grimme, C. (2020). Automated
detection of nostalgic text in the context of societal pessimism. In C. Grimme,
M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in open online
media (pp. 48–58).
bibtex: '@inproceedings{Lena_Frischlich_Trautmann_Grimme_2020, place={Hamburg, Deutschland},
title={Automated detection of nostalgic text in the context of societal pessimism},
booktitle={Disinformation in open online media}, author={Lena, Clever and Frischlich,
Lena and Trautmann, Heike and Grimme, Christian}, editor={Grimme, Christian and
Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={48–58}
}'
chicago: Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated
Detection of Nostalgic Text in the Context of Societal Pessimism.” In Disinformation
in Open Online Media, edited by Christian Grimme, Mike Preuß, Frank Takes,
and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020.
ieee: C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection
of nostalgic text in the context of societal pessimism,” in Disinformation
in open online media, 2020, pp. 48–58.
mla: Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context
of Societal Pessimism.” Disinformation in Open Online Media, edited by
Christian Grimme et al., 2020, pp. 48–58.
short: 'C. Lena, L. Frischlich, H. Trautmann, C. Grimme, in: C. Grimme, M. Preuß,
F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Hamburg, Deutschland,
2020, pp. 48–58.'
date_created: 2023-08-04T07:36:43Z
date_updated: 2023-10-16T13:03:56Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
- first_name: Frank
full_name: Takes, Frank
last_name: Takes
- first_name: Annie
full_name: Waldherr, Annie
last_name: Waldherr
language:
- iso: eng
page: 48–58
place: Hamburg, Deutschland
publication: Disinformation in open online media
status: public
title: Automated detection of nostalgic text in the context of societal pessimism
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46329'
abstract:
- lang: eng
text: The past decade has been characterized by a strong increase in the use of
social media and a continuous growth of public online discussion. With the failure
of purely manual moderation, platform operators started searching for semi-automated
solutions, where the application of Natural Language Processing (NLP) and Machine
Learning (ML) techniques is promising. However, this requires huge financial investments
for algorithmic implementations, data collection, and model training, which only
big players can afford. To support smaller or medium-sized media enterprises (SME),
we developed an integrated comment moderation system as an IT platform. This platform
acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating
such a platform, however, requires a robust technology stack, integrated workflows
and well-defined interfaces between all parties. In this paper, we develop and
discuss a suitable IT architecture and present a prototypical implementation.
author:
- first_name: Dennis M.
full_name: Riehle, Dennis M.
last_name: Riehle
- first_name: Marco
full_name: Niemann, Marco
last_name: Niemann
- first_name: Jens
full_name: Brunk, Jens
last_name: Brunk
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
citation:
ama: 'Riehle DM, Niemann M, Brunk J, Assenmacher D, Trautmann H, Becker J. Building
an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool. In: Meiselwitz G, ed. Social Computing and Social Media. Participation,
User Experience, Consumer Experience, and Applications of Social Computing.
Springer International Publishing; 2020:71–86.'
apa: Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., &
Becker, J. (2020). Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), Social Computing
and Social Media. Participation, User Experience, Consumer Experience, and Applications
of Social Computing (pp. 71–86). Springer International Publishing.
bibtex: '@inproceedings{Riehle_Niemann_Brunk_Assenmacher_Trautmann_Becker_2020,
place={Cham}, title={Building an Integrated Comment Moderation System – Towards
a Semi-automatic Moderation Tool}, booktitle={Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing}, publisher={Springer International Publishing}, author={Riehle, Dennis
M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike
and Becker, Jörg}, editor={Meiselwitz, Gabriele}, year={2020}, pages={71–86} }'
chicago: 'Riehle, Dennis M., Marco Niemann, Jens Brunk, Dennis Assenmacher, Heike
Trautmann, and Jörg Becker. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” In Social Computing and Social
Media. Participation, User Experience, Consumer Experience, and Applications of
Social Computing, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International
Publishing, 2020.'
ieee: D. M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, and J. Becker,
“Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation
Tool,” in Social Computing and Social Media. Participation, User Experience,
Consumer Experience, and Applications of Social Computing, 2020, pp. 71–86.
mla: Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System
– Towards a Semi-Automatic Moderation Tool.” Social Computing and Social Media.
Participation, User Experience, Consumer Experience, and Applications of Social
Computing, edited by Gabriele Meiselwitz, Springer International Publishing,
2020, pp. 71–86.
short: 'D.M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, J. Becker,
in: G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User
Experience, Consumer Experience, and Applications of Social Computing, Springer
International Publishing, Cham, 2020, pp. 71–86.'
date_created: 2023-08-04T07:38:42Z
date_updated: 2023-10-16T13:04:36Z
department:
- _id: '34'
- _id: '819'
editor:
- first_name: Gabriele
full_name: Meiselwitz, Gabriele
last_name: Meiselwitz
language:
- iso: eng
page: 71–86
place: Cham
publication: Social Computing and Social Media. Participation, User Experience, Consumer
Experience, and Applications of Social Computing
publication_identifier:
isbn:
- 978-3-030-49576-3
publisher: Springer International Publishing
status: public
title: Building an Integrated Comment Moderation System – Towards a Semi-automatic
Moderation Tool
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '46333'
abstract:
- lang: eng
text: ' Recently, social bots, (semi-) automatized accounts in social media, gained
global attention in the context of public opinion manipulation. Dystopian scenarios
like the malicious amplification of topics, the spreading of disinformation, and
the manipulation of elections through “opinion machines” created headlines around
the globe. As a consequence, much research effort has been put into the classification
and detection of social bots. Yet, it is still unclear how easy an average online
media user can purchase social bots, which platforms they target, where they originate
from, and how sophisticated these bots are. This work provides a much needed new
perspective on these questions. By providing insights into the markets of social
bots in the clearnet and darknet as well as an exhaustive analysis of freely available
software tools for automation during the last decade, we shed light on the availability
and capabilities of automated profiles in social media platforms. Our results
confirm the increasing importance of social bot technology but also uncover an
as yet unknown discrepancy of theoretical and practically achieved artificial
intelligence in social bots: while literature reports on a high degree of intelligence
for chat bots and assumes the same for social bots, the observed degree of intelligence
in social bot implementations is limited. In fact, the overwhelming majority of
available services and software are of supportive nature and merely provide modules
of automation instead of fully fledged “intelligent” social bots. '
author:
- first_name: Dennis
full_name: Assenmacher, Dennis
last_name: Assenmacher
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Lena
full_name: Frischlich, Lena
last_name: Frischlich
- first_name: Thorsten
full_name: Quandt, Thorsten
last_name: Quandt
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors. Social Media
+ Society. 2020;6(3):2056305120939264. doi:10.1177/2056305120939264'
apa: 'Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., &
Grimme, C. (2020). Demystifying Social Bots: On the Intelligence of Automated
Social Media Actors. Social Media + Society, 6(3), 2056305120939264.
https://doi.org/10.1177/2056305120939264'
bibtex: '@article{Assenmacher_Clever_Frischlich_Quandt_Trautmann_Grimme_2020, title={Demystifying
Social Bots: On the Intelligence of Automated Social Media Actors}, volume={6},
DOI={10.1177/2056305120939264},
number={3}, journal={Social Media + Society}, author={Assenmacher, Dennis and
Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and
Grimme, Christian}, year={2020}, pages={2056305120939264} }'
chicago: 'Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike
Trautmann, and Christian Grimme. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society 6, no. 3 (2020):
2056305120939264. https://doi.org/10.1177/2056305120939264.'
ieee: 'D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C.
Grimme, “Demystifying Social Bots: On the Intelligence of Automated Social Media
Actors,” Social Media + Society, vol. 6, no. 3, p. 2056305120939264, 2020,
doi: 10.1177/2056305120939264.'
mla: 'Assenmacher, Dennis, et al. “Demystifying Social Bots: On the Intelligence
of Automated Social Media Actors.” Social Media + Society, vol. 6, no.
3, 2020, p. 2056305120939264, doi:10.1177/2056305120939264.'
short: D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, C. Grimme,
Social Media + Society 6 (2020) 2056305120939264.
date_created: 2023-08-04T07:41:37Z
date_updated: 2023-10-16T13:06:34Z
department:
- _id: '34'
- _id: '819'
doi: 10.1177/2056305120939264
intvolume: ' 6'
issue: '3'
language:
- iso: eng
page: '2056305120939264'
publication: Social Media + Society
status: public
title: 'Demystifying Social Bots: On the Intelligence of Automated Social Media Actors'
type: journal_article
user_id: '15504'
volume: 6
year: '2020'
...