@inproceedings {43291,
	title = {ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset},
	journal = {International Conference on Multimedia Modeling (MMM)},
	volume = {13833},
	year = {2023},
	pages = {502 - 514},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people{\textquoteright}s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called\&nbsp;ScopeSense\&nbsp;from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.},
	isbn = {978-3-031-27076-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2.pdfhttps://link.springer.com/10.1007/978-3-031-27077-2_39https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2_39},
	author = {Riegler, Michael and Thambawita, Vajira and Chatterjee, Ayan and Nguyen, Thu and Hicks, Steven and Telle-Hansen, Vibeke and Svein Arne {Pettersen} and Johansen, Dag and Jain, Ramesh and Halvorsen, P{\r a}l},
	editor = {Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Larson, Martha and Alan F. {Smeaton} and Rudinac, Stevan and Dao, Minh-Son and Trattner, Christoph and Chen, Phoebe}
}
@article {43053,
	title = {Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs},
	journal = {Algorithms},
	year = {2023},
	month = {Jan-01-2023},
	pages = {30},
	publisher = {MDPI},
	doi = {10.3390/a16010030},
	url = {https://www.mdpi.com/1999-4893/16/1/30},
	author = {Nguyen, Phuc-Thinh and Dao, Minh-Son and Riegler, Michael and Rage Uday {Kiran} and Dang, Thai-Thinh and Le, Duy-Dong and Nguyen-Ly, Kieu-Chinh and Pham, Thanh-Qui and Nguyen, Van-Luong}
}
@inproceedings {42708,
	title = {Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children},
	journal = {ICDAR {\textquoteright}22: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2022},
	pages = {41-49},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450392419},
	doi = {10.1145/351273110.1145/3512731.3534209},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3512731},
	author = {Salehi, Pegah and Syed Zohaib {Hassan} and Saeed Shafiee {Sabet} and Gunn Astrid {Baugerud} and Miriam Sinkerud {Johnson} and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@inproceedings {28008,
	title = {Multimodal Virtual Avatars for Investigative Interviews with Children},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR {\textquoteright}21)},
	year = {2021},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469269},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944},
	author = {Gunn Astrid {Baugerud} and Miriam S. {Johnson} and Ragnhild Klingenber {R{\o}ed} and Michael E. {Lamb} and Powell, Martine and Thambawita, Vajira and Hicks, Steven and Salehi, Pegah and Syed Zohaib {Hassan} and Halvorsen, P{\r a}l and Riegler, Michael},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@inproceedings {28225,
	title = {Temperature Forecasting using Tower Networks},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval},
	year = {2021},
	month = {08/2021},
	pages = {18-23},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469099},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944https://dl.acm.org/doi/10.1145/3463944.3469099https://dl.acm.org/doi/pdf/10.1145/3463944.3469099},
	author = {Siri S. {Eide} and Riegler, Michael and Hugo Lewi {Hammer} and John Bj{\o}rnar {Bremnes}},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
