2023 |
| 147. | Cristian A. Secco; Lennart B. Sina; Midhad Blazevic; Kawa Nazemi Visual Analytics for Forecasting Technological Trends from Text Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), pp. 251-258, IEEE CPS, 2023. @inproceedings{Secco2023,
title = {Visual Analytics for Forecasting Technological Trends from Text},
author = {Cristian A. Secco and Lennart B. Sina and Midhad Blazevic and Kawa Nazemi },
doi = {10.1109/IV60283.2023.00051},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {251-258},
publisher = {IEEE CPS},
abstract = {Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on
macro level, micro level, and monitoring topics of interest.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on
macro level, micro level, and monitoring topics of interest. |
| 146. | Kawa Nazemi Artificial Intelligence in Visual Analytics Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, pp. 230 - 237, IEEE CPS, 2023. @inproceedings{Nazemi2023,
title = {Artificial Intelligence in Visual Analytics},
author = {Kawa Nazemi},
doi = {10.1109/IV60283.2023.00048},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award},
journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-},
pages = {230 - 237},
publisher = {IEEE CPS},
abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.},
keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual Tasks},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics. |
| 145. | Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Visual Analytics for Corporate Foresight - A Conceptual Approach Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), pp. 244-250, IEEE CPS, 2023. @inproceedings{SinaIV2023,
title = {Visual Analytics for Corporate Foresight - A Conceptual Approach},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00050},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {244-250},
publisher = {IEEE CPS},
abstract = {Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.},
keywords = {Artificial Intelligence, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data. |
| 144. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV 2023), pp. 259- 267, IEEE CPS, 2023. @inproceedings{blaz2023,
title = {Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00052},
year = {2023},
date = {2023-11-24},
urldate = {2023-11-24},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV 2023)},
pages = {259- 267},
publisher = {IEEE CPS},
abstract = {The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.},
keywords = {Artifical Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model. |
| 143. | Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Hybrid Forecasting Methods - A Systematic Review Journal Article In: Electronics, vol. 12, no. 7, 2023. @article{SSB*23,
title = {Hybrid Forecasting Methods - A Systematic Review},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/9/2019},
doi = {10.3390/electronics12092019},
year = {2023},
date = {2023-04-27},
urldate = {2023-04-27},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.},
keywords = {hybrid forecsting, PRISMA study},
pubstate = {published},
tppubtype = {article}
}
Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization. |
| 142. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System Journal Article In: Electronics, vol. 12, no. 7, 2023, ISSN: 2079-9292. @article{electronics12071699,
title = {Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/7/1699},
doi = {10.3390/electronics12071699},
issn = {2079-9292},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.},
keywords = {latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling},
pubstate = {published},
tppubtype = {article}
}
Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities. |
| 141. | Mina Schütz Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations Inproceedings In: Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Udo Kruschwitz; Annalina Caputo (Ed.): Advances in Information Retrieval, pp. 468–475, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-28241-6. @inproceedings{Sc23,
title = {Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations},
author = {Mina Schütz},
editor = {Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Udo Kruschwitz and Annalina Caputo},
doi = {10.1007/978-3-031-28241-6_54},
isbn = {978-3-031-28241-6},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Advances in Information Retrieval},
pages = {468--475},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way. |
| 140. | Christoph Demus; Mina Schütz; Nadine Probol; Jonas Pitz; Melanie Siegel; Dirk Labudde Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse Book Chapter In: Thomas-Gabriel Rüdiger; P. Saskia Bayerl (Ed.): Handbuch Cyberkriminologie, pp. 1–32, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-35450-3. @inbook{DSPP23,
title = {Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse},
author = {Christoph Demus and Mina Schütz and Nadine Probol and Jonas Pitz and Melanie Siegel and Dirk Labudde},
editor = {Thomas-Gabriel Rüdiger and P. Saskia Bayerl},
url = {https://doi.org/10.1007/978-3-658-35450-3_13-1},
doi = {10.1007/978-3-658-35450-3_13-1},
isbn = {978-3-658-35450-3},
year = {2023},
date = {2023-01-01},
booktitle = {Handbuch Cyberkriminologie},
pages = {1--32},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
abstract = {Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert. |
2022 |
| 139. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm 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. @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},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
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}
}
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. |
| 138. | Lukas Kaupp; Bernhard Humm; Kawa Nazemi; Stephan Simons Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis Journal Article In: Sensors, vol. 22, no. 21, 2022, ISSN: 1424-8220. @article{s22218259,
title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis},
author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons},
doi = {10.3390/s22218259},
issn = {1424-8220},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
journal = {Sensors},
volume = {22},
number = {21},
abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.},
keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, smart factory, Smart manufacturing},
pubstate = {published},
tppubtype = {article}
}
Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert. |
| 137. | Ebad Banissi, Anna Ursyn, Mark W. McK. Bannatyne, João Moura Pires, Nuno Datia, Kawa Nazemi, Boris Kovalerchuk, Razvan Andonie, Minoru Nakayama, Filippo Sciarrone, Weidong Huang, Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Marco Temperini, Urska Cvek, Marjan Trutschl, Heimo Mueller, Harri Siirtola, Wai Lok Woo, Rita Francese, Veronica Rossano, Tania Di Mascio, Fatma Bouali, Gilles Venturini, Sebastian Kernbach, Delfina Malandrino, Rocco Zaccagnin, Jian J Zhang, Xiaosong Yang,; Vladimir Geroimenko (Ed.) Proceedings of 2022 26th International Conference Information Visualisation (IV) Bachelor Thesis 2022, ISBN: 978-1-6654-9007-8. @bachelorthesis{nokey,
title = {Proceedings of 2022 26th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi, Anna Ursyn, Mark W. McK. Bannatyne, João Moura Pires, Nuno Datia, Kawa Nazemi, Boris Kovalerchuk, Razvan Andonie, Minoru Nakayama, Filippo Sciarrone, Weidong Huang, Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Marco Temperini, Urska Cvek, Marjan Trutschl, Heimo Mueller, Harri Siirtola, Wai Lok Woo, Rita Francese, Veronica Rossano, Tania Di Mascio, Fatma Bouali, Gilles Venturini, Sebastian Kernbach, Delfina Malandrino, Rocco Zaccagnin, Jian J Zhang, Xiaosong Yang, and Vladimir Geroimenko},
doi = {10.1109/IV56949.2022.00001},
isbn = {978-1-6654-9007-8},
year = {2022},
date = {2022-08-08},
urldate = {2022-08-08},
abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it is intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are just a few state-of-the-art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the
scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation. },
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it is intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are just a few state-of-the-art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the
scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation. |
| 136. | Christoph Demus; Jonas Pitz; Mina Schütz; Nadine Probol; Melanie Siegel; Dirk Labudde DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis Inproceedings In: Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pp. 143–153, Association for Computational Linguistics, Seattle, Washington (Hybrid), 2022. @inproceedings{DPSP22,
title = {DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis},
author = {Christoph Demus and Jonas Pitz and Mina Schütz and Nadine Probol and Melanie Siegel and Dirk Labudde},
url = {https://aclanthology.org/2022.woah-1.14},
doi = {10.18653/v1/2022.woah-1.14},
year = {2022},
date = {2022-07-01},
booktitle = {Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)},
pages = {143--153},
publisher = {Association for Computational Linguistics},
address = {Seattle, Washington (Hybrid)},
abstract = {In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments' contexts and do conversation analyses in order to research the contagion of offensive language in conversations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments' contexts and do conversation analyses in order to research the contagion of offensive language in conversations. |
| 135. | Lennart B. Sina; Kawa Nazemi Visual Analytics for Systematic Reviews According to PRISMA Inproceedings In: 2022 26th International Conference Information Visualisation (IV), pp. 307 - 313, IEEE, 2022. @inproceedings{SiNa22,
title = {Visual Analytics for Systematic Reviews According to PRISMA},
author = {Lennart B. Sina and Kawa Nazemi},
doi = {10.1109/IV56949.2022.00059},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 26th International Conference Information Visualisation (IV)},
pages = {307 - 313},
publisher = {IEEE},
abstract = {Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement. |
| 134. | Midhad Blazevic; Lennart B. Sina; Kawa Nazemi Visual Collaboration - An Approach for Visual Analytical Collaborative Research Inproceedings In: 2022 26th International Conference Information Visualisation (IV), pp. 293 - 299, IEEE, 2022. @inproceedings{BSN22,
title = { Visual Collaboration - An Approach for Visual Analytical Collaborative Research},
author = {Midhad Blazevic and Lennart B. Sina and Kawa Nazemi},
doi = {10.1109/IV56949.2022.00057},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 26th International Conference Information Visualisation (IV)},
pages = {293 - 299},
publisher = {IEEE},
abstract = {Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines. |
| 133. | Medina Andresel; Sergiu Gordea; Srdjan Stevanetic; Mina Schütz An Approach for Curating Collections of Historical Documents with
the Use of Topic Detection Technologies Journal Article In: Int. J. Digit. Curation, vol. 17, no. 1, pp. 12, 2022. @article{AGSS22,
title = {An Approach for Curating Collections of Historical Documents with
the Use of Topic Detection Technologies},
author = {Medina Andresel and Sergiu Gordea and Srdjan Stevanetic and Mina Schütz},
url = {http://www.ijdc.net/article/view/819},
year = {2022},
date = {2022-01-01},
journal = {Int. J. Digit. Curation},
volume = {17},
number = {1},
pages = {12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 132. | Juliane Köhler; Gautam Kishore Shahi; Julia Maria Struss; Michael Wiegand; Melanie Siegel; Mina Schütz Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection Inproceedings In: Guglielmo Faggioli; Nicola Ferro; Allan Hanbury; Martin Potthast (Ed.): Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, pp. 404-421, Bologna, Italy, 2022. @inproceedings{KSSW22,
title = {Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection},
author = {Juliane Köhler and Gautam Kishore Shahi and Julia Maria Struss and Michael Wiegand and Melanie Siegel and Mina Schütz},
editor = {Guglielmo Faggioli and Nicola Ferro and Allan Hanbury and Martin Potthast},
url = {https://ceur-ws.org/Vol-3180/paper-30.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum},
pages = {404-421},
address = {Bologna, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 131. | Mina Schütz; Jaqueline Böck; Medina Andresel; Armin Kirchknopf; Daria Liakhovets; Djordje Slijepčević; Alexander Schindler AIT_FHSTP at CheckThat! 2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer Inproceedings In: Guglielmo Faggioli; Nicola Ferro; Allan Hanbury; Martin Potthast (Ed.): Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, pp. 660-670, Bologna, Italy, 2022. @inproceedings{SBAK22,
title = {AIT_FHSTP at CheckThat! 2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer},
author = {Mina Schütz and Jaqueline Böck and Medina Andresel and Armin Kirchknopf and Daria Liakhovets and Djordje Slijepčević and Alexander Schindler},
editor = {Guglielmo Faggioli and Nicola Ferro and Allan Hanbury and Martin Potthast},
url = {https://ceur-ws.org/Vol-3180/paper-53.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum},
pages = {660-670},
address = {Bologna, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 130. | Daria Liakhovets; Mina Schütz; Jaqueline Böck; Medina Andresel; Armin Kirchknopf; Andreas Babic; Djordje Slijepčević; Jasmin Lampert; Alexander Schindler; Matthias Zeppelzauer Transfer Learning for Automatic Sexism Detection with Multilingual Transformer Models Inproceedings In: Manuel Montes-y-Gómez; Julio Gonzalo; Francisco Rangel; Marco Casavantes; Miguel Ángel Álvarez-Carmona; Gemma Bel-Enguix; Hugo Jair Escalante; Larissa Freitas; Antonio Miranda-Escalada; Francisco Rodríguez-Sánchez; Aiala Rosá; Marco Antonio Sobrevilla-Cabezudo; Mariona Taulé; Rafael Valencia-García (Ed.): Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022), CEUR-WS.org, A Coruna, Spain, 2022. @inproceedings{LSBA22,
title = {Transfer Learning for Automatic Sexism Detection with Multilingual Transformer Models},
author = {Daria Liakhovets and Mina Schütz and Jaqueline Böck and Medina Andresel and Armin Kirchknopf and Andreas Babic and Djordje Slijepčević and Jasmin Lampert and Alexander Schindler and Matthias Zeppelzauer},
editor = {Manuel Montes-y-Gómez and Julio Gonzalo and Francisco Rangel and Marco Casavantes and Miguel Ángel Álvarez-Carmona and Gemma Bel-Enguix and Hugo Jair Escalante and Larissa Freitas and Antonio Miranda-Escalada and Francisco Rodríguez-Sánchez and Aiala Rosá and Marco Antonio Sobrevilla-Cabezudo and Mariona Taulé and Rafael Valencia-García},
url = {https://ceur-ws.org/Vol-3202/exist-paper1.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)},
publisher = {CEUR-WS.org},
address = {A Coruna, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 129. | Christoph Demus; Mina Schütz; Jonas Pitz; Nadine Probol; Melanie Siegel; Dirk Labudde Automatische Klassifikation offensiver deutscher Sprache in sozialen Netzwerken Book Chapter In: Sylvia Jaki; Stefan Steiger (Ed.): Digitale Hate Speech, 2022. @inbook{DSPP22,
title = {Automatische Klassifikation offensiver deutscher Sprache in sozialen Netzwerken},
author = {Christoph Demus and Mina Schütz and Jonas Pitz and Nadine Probol and Melanie Siegel and Dirk Labudde},
editor = {Sylvia Jaki and Stefan Steiger},
year = {2022},
date = {2022-01-01},
booktitle = {Digitale Hate Speech},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
| 128. | Lam Pham; Alexander Schindler; Mina Schütz; Jasmin Lampert; Sven Schlarb; Ross King Deep Learning Frameworks Applied For Audio-Visual Scene Classification Inproceedings In: Peter Haber; Thomas J. Lampoltshammer; Helmut Leopold; Manfred Mayr (Ed.): Data Science -- Analytics and Applications, pp. 39–44, Springer Fachmedien Wiesbaden, Wiesbaden, 2022, ISBN: 978-3-658-36295-9. @inproceedings{PSSL22,
title = {Deep Learning Frameworks Applied For Audio-Visual Scene Classification},
author = {Lam Pham and Alexander Schindler and Mina Schütz and Jasmin Lampert and Sven Schlarb and Ross King},
editor = {Peter Haber and Thomas J. Lampoltshammer and Helmut Leopold and Manfred Mayr},
isbn = {978-3-658-36295-9},
year = {2022},
date = {2022-01-01},
booktitle = {Data Science -- Analytics and Applications},
pages = {39--44},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
abstract = {In this paper, we present deep learning frameworks for audio-visual scene classification (SC) and indicate how individual visual, audio features as well as their combination affect SC performance. Our extensive experiments are conducted on DCASE 2021 (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B Development and Evaluation datasets. Our results on Development dataset achieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio input only, visual input only, and both audio-visual input, respectively. The highest classification accuracy of 93.9%, obtained from an ensemble of audio-based and visual-based frameworks, shows an improvement of 16.5% compared with DCASE 2021 baseline. Our best results on Evaluation dataset is 91.5%, outperforming DCASE baseline of 77.1%},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present deep learning frameworks for audio-visual scene classification (SC) and indicate how individual visual, audio features as well as their combination affect SC performance. Our extensive experiments are conducted on DCASE 2021 (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B Development and Evaluation datasets. Our results on Development dataset achieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio input only, visual input only, and both audio-visual input, respectively. The highest classification accuracy of 93.9%, obtained from an ensemble of audio-based and visual-based frameworks, shows an improvement of 16.5% compared with DCASE 2021 baseline. Our best results on Evaluation dataset is 91.5%, outperforming DCASE baseline of 77.1% |
| 127. | Combining social media open source data with relevance analysis and expert knowledge to improve situational awareness in crisis and disaster management - concept Inproceedings In: IDIMT-2022 : digitalization of society, business and management in a pandemic : 30th Interdisciplinary Information Management Talks, Linz Trauner Verlag, 2022. @inproceedings{ILSN22,
title = {Combining social media open source data with relevance analysis and expert knowledge to improve situational awareness in crisis and disaster management - concept},
doi = {10.35011/IDIMT-2022-153},
year = {2022},
date = {2022-01-01},
booktitle = {IDIMT-2022 : digitalization of society, business and management in a pandemic : 30th Interdisciplinary Information Management Talks},
publisher = {Linz Trauner Verlag},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 126. | Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery Book Springer Nature, Cham, 2022, ISBN: 978-3-030-93118-6. @book{Kovalerchuk2022,
title = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3},
isbn = {978-3-030-93118-6},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {Springer Nature},
address = {Cham},
series = {Studies in Computational Intelligence},
abstract = {This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.},
key = {SP2022},
keywords = {Artificial Intelligence, Computational Intelligence, Machine Learning, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {book}
}
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes. |
| 125. | Kawa Nazemi; Tim Feiter; Lennart B. Sina; Dirk Burkhardt; Alexander Kock Visual Analytics for Strategic Decision Making in Technology Management Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 31–61, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. @inbook{Nazemi2022,
title = {Visual Analytics for Strategic Decision Making in Technology Management},
author = {Kawa Nazemi and Tim Feiter and Lennart B. Sina and Dirk Burkhardt and Alexander Kock},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_2},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {31--61},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work.},
keywords = {Artificial Intelligence, Machine Leanring, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work. |
| 124. | Boris Kovalerchuk; Răzvan Andonie; Nuno Datia; Kawa Nazemi; Ebad Banissi Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 1–27, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. @inbook{Kovalerchuk2022b,
title = {Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions},
author = {Boris Kovalerchuk and Răzvan Andonie and Nuno Datia and Kawa Nazemi and Ebad Banissi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_1},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {1--27},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations. |
| 123. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. @inbook{Kaupp2022,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403--436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research. |
