| Deep Learning to Encourage Citizen Involvement in Local Journalism Book Chapter Tessem, B., Nyre, L., Mesquita, M.d.S., Mulholland, P. In: Mari K. Niemi Ville J. E. Manninen, Anthony Ridge-Newman (Ed.): Chapter 3, pp. 211-226, Palgrave Macmillan Cham, 2022. @inbook{Tessem2022,
title = {Deep Learning to Encourage Citizen Involvement in Local Journalism},
author = {Tessem, B., Nyre, L., Mesquita, M.d.S., Mulholland, P.},
editor = {Ville J. E. Manninen, Mari K. Niemi, Anthony Ridge-Newman},
url = {https://link.springer.com/chapter/10.1007/978-3-030-95073-6_14},
doi = {https://doi.org/10.1007/978-3-030-95073-6_14},
year = {2022},
date = {2022-05-05},
urldate = {2022-05-05},
pages = {211-226},
publisher = {Palgrave Macmillan Cham},
chapter = {3},
abstract = {We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems. |
A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications Conference Ziming Wang, Ziyi Hu, Yemao Man, Morten Fjeld A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications, 2022. @conference{Wang2022,
title = {A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications},
author = {Ziming Wang, Ziyi Hu, Yemao Man, Morten Fjeld},
year = {2022},
date = {2022-04-29},
urldate = {2022-04-29},
booktitle = {A Collaborative System of Flying and Ground Robots with Universal Physical Coupling Interface (PCI), and the Potential Interactive Applications},
abstract = {Flying and ground robots complement each other in terms of their advantages and disadvantages. We propose a collaborative system combining flying and ground robots, using a universal physical coupling interface (PCI) that allows for momentary connections and disconnections between multiple robots/devices. The proposed system may better utilize the complementary advantages of both flying and ground robots. We also describe various potential scenarios where such a system could be of benefit to interact with humans - namely, remote field works and rescue missions, transportation, healthcare, and education. Finally, we discuss the opportunities and challenges of such systems and consider deeper questions which should be studied in future work.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Flying and ground robots complement each other in terms of their advantages and disadvantages. We propose a collaborative system combining flying and ground robots, using a universal physical coupling interface (PCI) that allows for momentary connections and disconnections between multiple robots/devices. The proposed system may better utilize the complementary advantages of both flying and ground robots. We also describe various potential scenarios where such a system could be of benefit to interact with humans - namely, remote field works and rescue missions, transportation, healthcare, and education. Finally, we discuss the opportunities and challenges of such systems and consider deeper questions which should be studied in future work. |
RedirectedDoors: Redirection While Opening Doors in Virtual Reality Conference Morten Fjeld, Yukai Hoshikawa, Kazuyuki Fujita, Kazuki Takashima, Yoshifumi Kitamura RedirectedDoors: Redirection While Opening Doors in Virtual Reality., 2022. @conference{Fjeld2022,
title = {RedirectedDoors: Redirection While Opening Doors in Virtual Reality},
author = {Morten Fjeld, Yukai Hoshikawa, Kazuyuki Fujita, Kazuki Takashima, Yoshifumi Kitamura },
year = {2022},
date = {2022-03-12},
urldate = {2022-03-12},
booktitle = {RedirectedDoors: Redirection While Opening Doors in Virtual Reality.},
abstract = {We propose RedirectedDoors, a novel technique for redirection in VR focused on door-opening behavior. This technique manipulates the user's walking direction by rotating the entire virtual environment at a certain angular ratio of the door being opened, while the virtual door's position is kept unmanipulated to ensure door-opening realism. Results of a user study using two types of door-opening interfaces (with and without a passive haptic prop) revealed that the estimated detection thresholds generally showed a higher space efficiency of redirection. Following the results, we derived usage guidelines for our technique that provide lower noticeability and higher acceptability.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
We propose RedirectedDoors, a novel technique for redirection in VR focused on door-opening behavior. This technique manipulates the user's walking direction by rotating the entire virtual environment at a certain angular ratio of the door being opened, while the virtual door's position is kept unmanipulated to ensure door-opening realism. Results of a user study using two types of door-opening interfaces (with and without a passive haptic prop) revealed that the estimated detection thresholds generally showed a higher space efficiency of redirection. Following the results, we derived usage guidelines for our technique that provide lower noticeability and higher acceptability. |
| Developing and Evaluating a University Recommender System Journal Article Mehdi Elahi; Alain D. Starke; Nabil El Ioini; Anna Alexander Lambrix; Christoph Trattner In: Frontiers in Artificial Intelligence , 2022. @article{Elahi2022,
title = {Developing and Evaluating a University Recommender System},
author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},
doi = {https://doi.org/10.3389/frai.2021.796268},
year = {2022},
date = {2022-02-02},
journal = {Frontiers in Artificial Intelligence },
abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features. |
| WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package Technical Report Are Tverberg, Ingrid Agasøster, Mads Grønbæck, Marius Monsen, Robert Strand, Kristian Eikeland, Eivind Throndsen, Lars Westvang, Tove B. Knudsen, Eivind Fiskerud, Rune Skår, Sergej Stoppel, Arne Berven, Glenn Skare Pedersen, Paul Macklin, Kenneth Cuomo, Loek Vredenberg, Kristian Tolonen,
Andreas L Opdahl, Bjørnar Tessem, Csaba Veres, Duc Tien Dang Nguyen, Enrico Motta, Vinay Jayarama Setty University of Bergen, MediaFutures 2021. @techreport{Tverberg2021,
title = {WP3 2021 M3.1 Report The industrial expectations to, needs from and wishes for the work package},
author = {Are Tverberg, Ingrid Agasøster, Mads Grønbæck, Marius Monsen, Robert Strand, Kristian Eikeland, Eivind Throndsen, Lars Westvang, Tove B. Knudsen, Eivind Fiskerud, Rune Skår, Sergej Stoppel, Arne Berven, Glenn Skare Pedersen, Paul Macklin, Kenneth Cuomo, Loek Vredenberg, Kristian Tolonen,
Andreas L Opdahl, Bjørnar Tessem, Csaba Veres, Duc Tien Dang Nguyen, Enrico Motta, Vinay Jayarama Setty},
url = {https://mediafutures.no/wp3-q2-2021-m3-1-report-by-the-industrial-partners-final-2/},
year = {2021},
date = {2021-07-25},
institution = {University of Bergen, MediaFutures},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
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