@article {Vassilakis_fgcs17,
	title = {exhiSTORY: Smart Exhibits That Tell Their Own Stories},
	journal = {Future Generation of Computer Systems},
	volume = {81},
	year = {2018},
	pages = {542-556},
	abstract = {Museum exhibitions are designed to tell a story; this story is woven by curators and in its context a particular aspect of each exhibit, fitting to the message that the story is intended to convey, is highlighted. Adding new exhibits to the story requires curators to identify for each exhibit its aspects that fit to the message of the story and position the exhibit at the right place in the story thread. The availability of rich semantic information for exhibits, allows for exploiting the wealth of meanings that museum exhibits express, enabling the automated or semi-automated generation of practically countless stories that can be told. Personalization algorithms can then be employed to choose from these stories the ones most suitable for each individual user, based on the semantics of the stories and information within the user profile. In this work we examine how opportunities arising from technological advances in the fields of IoT and semantics can be used to develop smart, self-organizing exhibits that cooperate with each other and provide visitors with comprehensible, rich, diverse, personalized and highly stimulating experiences. These notions are included in the design of a system named exhiSTORY, which also exploits previously ignored information and identifies previously unseen semantic links. We present the architecture of the system and discuss its application potential.},
	keywords = {Cultural heritage, IoT, personalization, Quality of Experience (QoE), Self-organizing exhibitions, semantics},
	doi = {https://doi.org/10.1016/j.future.2017.10.038},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X17324093},
	author = {Costas Vassilakis and Vassilis Poulopoulos and Angela Antoniou and Manolis Wallace and Giorgos Lepouras and Martin Lopez Nores}
}
@conference {591,
	title = {A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence},
	booktitle = {In Proceedings of the IEEE Workshop on Cyber Security \& Resilience in the Internet of Things (CSRIoT @ IEEE Services)},
	year = {2016},
	month = {July 2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Milan, Italy},
	abstract = {The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that {\textendash}given the appropriate tools and methods{\textendash} may be identified, crawled and subsequently leveraged to actionable cyber- threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker fo- rums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state- of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.},
	keywords = {crawling architecture, cyber-security, cyber-threat intelligence, IoT, language models, Machine Learning},
	url = {http://users.uop.gr/ trifon/papers/pdf/csriot19-KCTS.pdf},
	author = {Paris Koloveas and Thanasis Chantzios and Christos Tryfonopoulos and Spiros Skiadopoulos}
}
