@inproceedings {28317,
	title = {Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3622{\textendash}3626},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450386517},
	doi = {10.1145/3474085.3477933},
	url = {https://dl.acm.org/doi/10.1145/3474085.3477933},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Korhonen, Jari and Su, Yicheng and You, Junyong and Hicks, Steven and Midoglu, Cise}
}
@inproceedings {28321,
	title = {Reproducibility Companion Paper: Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450386517},
	doi = {10.1145/3474085.3477934},
	url = {https://dl.acm.org/doi/10.1145/3474085.3477934},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Liao, Yuqing and Li, Xinke and Tong, Zekun and Zhao, Yabang and Lim, Andrew and Kuang, Zhenzhong and Midoglu, Cise}
}
@inproceedings {28211,
	title = {Reproducibility Companion Paper: Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3630{\textendash}3632},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this companion paper, we provide details of the artifacts to support the replication of "Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework", which was presented at MM{\textquoteright}20. The Inter-intra Contrastive (IIC) framework aims to extract more discriminative temporal information by extending intra-negative samples in contrastive self-supervised learning. In this paper, we first summarize our contribution. Then we explain the file structure of the source code and detailed settings. Since our proposal is a framework which contain a lot of different settings, we provide some custom settings to help other researchers to use our methods easily. The source code is available at https://github.com/BestJuly/IIC.},
	isbn = {9781450386517},
	doi = {10.1145/347408510.1145/3474085.3477939},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3474085https://dl.acm.org/doi/10.1145/3474085.3477939https://dl.acm.org/doi/pdf/10.1145/3474085.3477939},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Tao, Li and Wang, Xueting and Yamasaki, Toshihiko and Chen, Jingjing and Hicks, Steven}
}
@inproceedings {26171,
	title = {Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	month = {06/2018},
	pages = {472-477},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients{\textquoteright} depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.},
	isbn = {9781450351928},
	doi = {10.1145/3204949.3208125},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and T{\o}rresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Cesar, Pablo and Zink, Michael and Murray, Niall}
}
