@article {2026,
	title = {A Functional Taxonomy of Intelligent Systems in Education},
	year = {2026},
	abstract = {This structured integrative review surveys Intelligent Systems in Education through a function-first taxonomy that groups systems by pedagogical role rather than algorithm. It synthesizes eight families{\textemdash}adaptive tutoring, assessment and feedback, student modeling, analytics, collaboration support, content generation, game-based learning, and cross-function integration{\textemdash}and traces how data moves among them. Across families, the field is shifting from stand-alone tools toward interoperable ecosystems. The review highlights recurring challenges in long-term adaptivity, generative reliability, fairness and privacy, teacher{\textendash}artificial intelligence (AI) collaboration, and system-level evaluation.},
	doi = {https://dx.doi.org/10.2139/ssrn.7183660},
	author = {N. Kadir and D. Herremans}
}
@article {2022,
	title = {Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models},
	journal = {IEEE Access},
	volume = {13},
	year = {2025},
	pages = {117788-117807},
	abstract = {The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin{\textquoteright}s next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2025.3584243},
	url = {https://ieeexplore.ieee.org/document/11058926},
	author = {D. Herremans and K.W. Low}
}
@article {132,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	year = {2018},
	keywords = {music, word2vec},
	doi = {10.1007/s00521-018-3923-1},
	url = {http://link.springer.com/article/10.1007/s00521-018-3923-1},
	author = {C.-H. Chuan and K. Agres and D. Herremans}
}
@article {93,
	title = {A Functional Taxonomy of Music Generation Systems},
	journal = {ACM Computing Surveys},
	volume = {50},
	year = {2017},
	pages = {30},
	chapter = {69:1},
	abstract = {Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.},
	doi = {10.1145/3108242},
	url = {https://dl.acm.org/citation.cfm?id=3108242},
	author = {D. Herremans and C.-H. Chuan and E. Chew}
}
@proceedings {245,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2014},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@proceedings {247,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2013},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@conference {antor13,
	title = {FuX, an Android app that generates counterpoint},
	booktitle = {IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC)},
	year = {2013},
	month = {04/2013},
	pages = {48-55},
	doi = {10.1109/CICAC.2013.6595220},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=6595220\&queryText\%3Dfux+an+android+app},
	author = {D. Herremans and K. S{\"o}rensen}
}
