@article {2022,
	title = {Single Image Video Prediction with Auto-Regressive GANs},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {3533},
	abstract = {In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use \&ldquo;one shot\&rdquo; generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a \&ldquo;complementary mask\&rdquo; module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.},
	issn = {1424-8220},
	doi = {10.3390/s22093533},
	url = {https://www.mdpi.com/1424-8220/22/9/3533},
	author = {Huang, Jiahui and Chia, Yew Ken and Yu, Samson and Yee, Kevin and K{\"u}ster, Dennis and Krumhuber, Eva G. and Herremans, D and Roig, G.}
}
@article {2021,
	title = {aiSTROM - A roadmap for developing a successful AI strategy},
	journal = {IEEE Access},
	year = {2021},
	abstract = { A total of 34\% of AI research and development projects fail or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. In this perspective paper, a new STrategic ROadMap, aiSTROM, is presented that empowers managers to create an AI strategy. A comprehensive approach is provided that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, the top $n$ potential projects (typically 3-5) are first identified. For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas) and outsourcing development. Looking at new technologies, one has to consider challenges such as bias, the legality of black-box models, and keeping humans in the loop. Next, like any project, value-based key performance indicators (KPIs) need to be defined to track and validate the progress. Depending on the company{\textquoteright}s risk strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, one should make sure that the strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a practical tool for managers and lead developers.},
	doi = {10.1109/ACCESS.2021.3127548},
	url = {https://arxiv.org/abs/2107.06071},
	author = {Herremans, D}
}
@article {97,
	title = {Generating guitar solos by integer programming},
	journal = {Journal of the Operational Research Society},
	year = {2017},
	pages = {971-985},
	doi = {https://doi.org/10.1080/01605682.2017.1390528},
	url = {http://www.tandfonline.com/eprint/Prks6BcYNEivw87vSu86/full},
	author = {Cunha, N. and Subramanian A. and Herremans, D}
}
@conference {82,
	title = {The effect of repetitive structure on enjoyment and altered states in uplifting trance music},
	booktitle = {2nd International Conference on Music and Consciousness (MUSCON 2), Brighton},
	year = {2015},
	author = {Agres, K and Bigo, L and Herremans, D and Conklin, D}
}
