Publications ⋆ Kawa Nazemi

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2022
3.
 Kaupp, Lukas;  Nazemi, Kawa;  Humm, Bernhard
Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Journal Article 
In: Electronics, vol. 11, no. 23, 2022, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics
@article{electronics11233942,

title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},

url = {https://www.mdpi.com/2079-9292/11/23/3942},

doi = {10.3390/electronics11233942},

issn = {2079-9292},

year  = {2022},

date = {2022-11-01},

urldate = {2022-01-01},

journal = {Electronics},

volume = {11},

number = {23},

abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},

keywords = {Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},

pubstate = {published},

tppubtype = {article}

}

Close
Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.
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https://www.mdpi.com/2079-9292/11/23/3942
doi:10.3390/electronics11233942
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2021
2.
 Schütz, Mina;  Schindler, Alexander;  Siegel, Melanie;  Nazemi, Kawa
Automatic Fake News Detection with Pre-trained Transformer Models Proceedings Article 
In:  Bimbo, Alberto Del;  Cucchiara, Rita;  Sclaroff, Stan;  Farinella, Giovanni Maria;  Mei, Tao;  Bertini, Marco;  Escalante, Hugo Jair;  Vezzani, Roberto (Ed.): Pattern Recognition. ICPR International Workshops and Challenges, pp. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer
@inproceedings{10.1007/978-3-030-68787-8_45,

title = {Automatic Fake News Detection with Pre-trained Transformer Models},

author = {Mina Schütz and Alexander Schindler and Melanie Siegel and Kawa Nazemi},

editor = {Alberto Del Bimbo and Rita Cucchiara and Stan Sclaroff and Giovanni Maria Farinella and Tao Mei and Marco Bertini and Hugo Jair Escalante and Roberto Vezzani},

url = {https://link.springer.com/chapter/10.1007/978-3-030-68787-8_45, Full PDF},

doi = {10.1007/978-3-030-68787-8_45},

isbn = {978-3-030-68787-8},

year  = {2021},

date = {2021-02-21},

booktitle = {Pattern Recognition. ICPR International Workshops and Challenges},

pages = {627--641},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output.},

keywords = {Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output.
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Full PDF
doi:10.1007/978-3-030-68787-8_45
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2015
1.
 Burkhardt, Dirk;  Nazemi, Kawa;  Tomic, Silvana;  Ginters, Egils
Best-practice Piloting of Integrated Social Media Analysis Solution for E-Participation in Cities Journal Article 
In: Procedia Computer Science, vol. 77, pp. 11 - 21, 2015, ISSN: 1877-0509, (ICTE in regional Development 2015 Valmiera, Latvia).
Abstract | Links | BibTeX | Tags: Decision Making, E-Governmant, Evaluation, Information Communication Technologies, Piloting, Policy modeling
@article{Burkhardt2015b,

title = {Best-practice Piloting of Integrated Social Media Analysis Solution for E-Participation in Cities},

author = {Dirk Burkhardt and Kawa Nazemi and Silvana Tomic and Egils Ginters},

editor = {Jimson Mathew and Ashutosh K. singh},

url = {https://www.sciencedirect.com/science/article/pii/S1877050915038648, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S1877050915038648/pdf?md5=169ec82a0af0b5b8e740685f17683d0a&pid=1-s2.0-S1877050915038648-main.pdf, full text},

doi = {https://doi.org/10.1016/j.procs.2015.12.354},

issn = {1877-0509},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Computer Science},

volume = {77},

pages = {11 - 21},

abstract = {Goal definitions and developments are challenging in large-scale projects, because of the different expertise and skills of the stakeholders. Development often fails its intended goal because of misunderstandings and unclear definitions and descriptions during the planning phase. The paper describes a novel approach to collecting requirements and defining development plans by provisioning a guideline which informs what has to be done, when and in what form. The User Case Requirement Analysis model was applied in the large-scale European project FUPOL during the development of a Social Media Analysis System. Based on this a successful task-based evaluation could be performed that shows the benefit of the model and the software.},

note = {ICTE in regional Development 2015 Valmiera, Latvia},

keywords = {Decision Making, E-Governmant, Evaluation, Information Communication Technologies, Piloting, Policy modeling},

pubstate = {published},

tppubtype = {article}

}

Close
Goal definitions and developments are challenging in large-scale projects, because of the different expertise and skills of the stakeholders. Development often fails its intended goal because of misunderstandings and unclear definitions and descriptions during the planning phase. The paper describes a novel approach to collecting requirements and defining development plans by provisioning a guideline which informs what has to be done, when and in what form. The User Case Requirement Analysis model was applied in the large-scale European project FUPOL during the development of a Social Media Analysis System. Based on this a successful task-based evaluation could be performed that shows the benefit of the model and the software.
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Elsevier Science Direct
full text
doi:https://doi.org/10.1016/j.procs.2015.12.354
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