@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{9582711,

title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},

author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},

url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},

doi = {10.1109/IV53921.2021.00041},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

booktitle = {2021 25th International Conference Information Visualisation (IV)},

pages = {211-217},

publisher = {IEEE },

abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},

keywords = {Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}