---
_id: '33510'
abstract:
- lang: eng
text: In the manufacture of real wood products, defects can quickly occur during
the production process. To quickly sort out these defects, a system is needed
that finds damage in the irregularly structured surfaces of the product. The difficulty
in this task is that each surface is visually different and no standard defects
can be defined. Thus, damage detection using correlation does not work, so this
paper will test different machine learning methods. To evaluate different machine
learning methods, a data set is needed. For this reason, the available samples
were recorded manually using a static fixed camera. Subsequently, the images were
divided into sub-images, which resulted in a relatively small data set. Next,
a convolutional neural network (CNN) was constructed to classify the images. However,
this approach did not lead to a generalized solution, so the dataset was hashed
using the a- and pHash. These hash values were then trained with a fully supervised
system that will later serve as a reference model, in the semi-supervised learning
procedures. To improve the supervised model and not have to label every data point,
semi-supervised learning methods are used in the following. For this purpose,
the CEAL method (wrapper method) is considered in the first and then the Π-Model
(intrinsically semi-supervised).
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: Geneiß, Volker
last_name: Geneiß
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
citation:
ama: 'Sander T, Lange S, Hilleringmann U, Geneiß V, Hedayat C, Kuhn H. Detection
of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised
Learning Methods. In: 2022 Smart Systems Integration (SSI). IEEE; 2022.
doi:10.1109/ssi56489.2022.9901433'
apa: Sander, T., Lange, S., Hilleringmann, U., Geneiß, V., Hedayat, C., & Kuhn,
H. (2022). Detection of Defects on Irregularly Structured Surfaces using Supervised
and Semi-Supervised Learning Methods. 2022 Smart Systems Integration (SSI).
2022 Smart Systems Integration (SSI), Grenoble, France. https://doi.org/10.1109/ssi56489.2022.9901433
bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneiß_Hedayat_Kuhn_2022, place={Grenoble,
France}, title={Detection of Defects on Irregularly Structured Surfaces using
Supervised and Semi-Supervised Learning Methods}, DOI={10.1109/ssi56489.2022.9901433},
booktitle={2022 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Sander,
Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat,
Christian and Kuhn, Harald}, year={2022} }'
chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneiß, Christian
Hedayat, and Harald Kuhn. “Detection of Defects on Irregularly Structured Surfaces
Using Supervised and Semi-Supervised Learning Methods.” In 2022 Smart Systems
Integration (SSI). Grenoble, France: IEEE, 2022. https://doi.org/10.1109/ssi56489.2022.9901433.'
ieee: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, and H. Kuhn,
“Detection of Defects on Irregularly Structured Surfaces using Supervised and
Semi-Supervised Learning Methods,” presented at the 2022 Smart Systems Integration
(SSI), Grenoble, France, 2022, doi: 10.1109/ssi56489.2022.9901433.'
mla: Sander, Tom, et al. “Detection of Defects on Irregularly Structured Surfaces
Using Supervised and Semi-Supervised Learning Methods.” 2022 Smart Systems
Integration (SSI), IEEE, 2022, doi:10.1109/ssi56489.2022.9901433.
short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, H. Kuhn, in:
2022 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2022.'
conference:
end_date: 2022-04-28
location: Grenoble, France
name: 2022 Smart Systems Integration (SSI)
start_date: 2022-04-27
date_created: 2022-10-04T11:35:55Z
date_updated: 2022-10-04T11:37:39Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/ssi56489.2022.9901433
keyword:
- Machine Learning
- CNN
- Hashing
- semi-supervised learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9901433
place: Grenoble, France
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2022 Smart Systems Integration (SSI)
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregularly Structured Surfaces using Supervised and
Semi-Supervised Learning Methods
type: conference
user_id: '38240'
year: '2022'
...
---
_id: '48878'
abstract:
- lang: eng
text: Due to the rise of continuous data-generating applications, analyzing data
streams has gained increasing attention over the past decades. A core research
area in stream data is stream classification, which categorizes or detects data
points within an evolving stream of observations. Areas of stream classification
are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing
a wide range of (social) media applications. Research in stream classification
is related to developing methods that adapt to the changing and potentially volatile
data stream. It focuses on individual aspects of the stream classification pipeline,
e.g., designing suitable algorithm architectures, an efficient train and test
procedure, or detecting so-called concept drifts. As a result of the many different
research questions and strands, the field is challenging to grasp, especially
for beginners. This survey explores, summarizes, and categorizes work within the
domain of stream classification and identifies core research threads over the
past few years. It is structured based on the stream classification process to
facilitate coordination within this complex topic, including common application
scenarios and benchmarking data sets. Thus, both newcomers to the field and experts
who want to widen their scope can gain (additional) insight into this research
area and find starting points and pointers to more in-depth literature on specific
issues and research directions in the field.
author:
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(18):9094.
doi:10.3390/app12189094'
apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review. Applied
Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094'
bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094},
number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital
Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek,
Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }'
chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.'
ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
Stream Classification Pipeline: A Literature Review,” Applied Sciences,
vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.'
mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital
Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.'
short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
12 (2022) 9094.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:50:56Z
department:
- _id: '819'
doi: 10.3390/app12189094
intvolume: ' 12'
issue: '18'
keyword:
- big data
- data mining
- data stream analysis
- machine learning
- stream classification
- supervised learning
language:
- iso: eng
page: '9094'
publication: Applied Sciences
publication_identifier:
issn:
- 2076-3417
publisher: '{Multidisciplinary Digital Publishing Institute}'
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '102979'
volume: 12
year: '2022'
...
---
_id: '34674'
abstract:
- lang: eng
text: 'Smart home systems contain plenty of features that enhance wellbeing in everyday
life through artificial intelligence (AI). However, many users feel insecure because
they do not understand the AI’s functionality and do not feel they are in control
of it. Combining technical, psychological and philosophical views on AI, we rethink
smart homes as interactive systems where users can partake in an intelligent agent’s
learning. Parallel to the goals of explainable AI (XAI), we explored the possibility
of user involvement in supervised learning of the smart home to have a first approach
to improve acceptance, support subjective understanding and increase perceived
control. In this work, we conducted two studies: In an online pre-study, we asked
participants about their attitude towards teaching AI via a questionnaire. In
the main study, we performed a Wizard of Oz laboratory experiment with human participants,
where participants spent time in a prototypical smart home and taught activity
recognition to the intelligent agent through supervised learning based on the
user’s behaviour. We found that involvement in the AI’s learning phase enhanced
the users’ feeling of control, perceived understanding and perceived usefulness
of AI in general. The participants reported positive attitudes towards training
a smart home AI and found the process understandable and controllable. We suggest
that involving the user in the learning phase could lead to better personalisation
and increased understanding and control by users of intelligent agents for smart
home automation.'
alternative_title:
- Increasing Perceived Control and Understanding
author:
- first_name: Leonie Nora
full_name: Sieger, Leonie Nora
id: '93402'
last_name: Sieger
- first_name: Julia
full_name: Hermann, Julia
last_name: Hermann
- first_name: Astrid
full_name: Schomäcker, Astrid
last_name: Schomäcker
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Christian
full_name: Meske, Christian
last_name: Meske
- first_name: Celine-Chiara
full_name: Hey, Celine-Chiara
last_name: Hey
- first_name: Ayşegül
full_name: Doğangün, Ayşegül
last_name: Doğangün
citation:
ama: 'Sieger LN, Hermann J, Schomäcker A, et al. User Involvement in Training Smart
Home Agents. In: International Conference on Human-Agent Interaction. ACM;
2022. doi:10.1145/3527188.3561914'
apa: 'Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey,
C.-C., & Doğangün, A. (2022). User Involvement in Training Smart Home Agents.
International Conference on Human-Agent Interaction. HAI ’22: International
Conference on Human-Agent Interaction, Christchurch, New Zealand. https://doi.org/10.1145/3527188.3561914'
bibtex: '@inproceedings{Sieger_Hermann_Schomäcker_Heindorf_Meske_Hey_Doğangün_2022,
title={User Involvement in Training Smart Home Agents}, DOI={10.1145/3527188.3561914},
booktitle={International Conference on Human-Agent Interaction}, publisher={ACM},
author={Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf,
Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}, year={2022}
}'
chicago: Sieger, Leonie Nora, Julia Hermann, Astrid Schomäcker, Stefan Heindorf,
Christian Meske, Celine-Chiara Hey, and Ayşegül Doğangün. “User Involvement in
Training Smart Home Agents.” In International Conference on Human-Agent Interaction.
ACM, 2022. https://doi.org/10.1145/3527188.3561914.
ieee: 'L. N. Sieger et al., “User Involvement in Training Smart Home Agents,”
presented at the HAI ’22: International Conference on Human-Agent Interaction,
Christchurch, New Zealand, 2022, doi: 10.1145/3527188.3561914.'
mla: Sieger, Leonie Nora, et al. “User Involvement in Training Smart Home Agents.”
International Conference on Human-Agent Interaction, ACM, 2022, doi:10.1145/3527188.3561914.
short: 'L.N. Sieger, J. Hermann, A. Schomäcker, S. Heindorf, C. Meske, C.-C. Hey,
A. Doğangün, in: International Conference on Human-Agent Interaction, ACM, 2022.'
conference:
end_date: 2022-12-08
location: Christchurch, New Zealand
name: 'HAI ''22: International Conference on Human-Agent Interaction'
start_date: 2022-12-05
date_created: 2022-12-21T09:48:43Z
date_updated: 2024-05-30T18:04:45Z
ddc:
- '000'
department:
- _id: '574'
- _id: '760'
doi: 10.1145/3527188.3561914
file:
- access_level: closed
content_type: application/pdf
creator: heindorf
date_created: 2024-05-30T18:04:31Z
date_updated: 2024-05-30T18:04:31Z
file_id: '54524'
file_name: User_Involvement_in_Training_Smart_Home_Agents_public.pdf
file_size: 1151728
relation: main_file
success: 1
file_date_updated: 2024-05-30T18:04:31Z
has_accepted_license: '1'
keyword:
- human-agent interaction
- smart homes
- supervised learning
- participation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2022/HAI_SmartHome/User_Involvement_in_Training_Smart_Home_Agents_public.pdf
oa: '1'
project:
- _id: '121'
grant_number: '438445824'
name: 'TRR 318 - B1: TRR 318 - Subproject B1'
publication: International Conference on Human-Agent Interaction
publication_status: published
publisher: ACM
quality_controlled: '1'
status: public
title: User Involvement in Training Smart Home Agents
type: conference
user_id: '11871'
year: '2022'
...