Unknown perpetrators of crime cannot be identified with the current forensic use of DNA. The VISAGE Project aims to overcome this major limitation by developing, validating, and implementing in the relevant forensic DNA service environment a set of prototype tools for predicting appearance, age, and ancestry in as much detail and as accurately, and effectively as possible from DNA traces. This VISAGE Toolkit will allow the construction of composite sketches of unknown trace donors directly from their crime scene traces, which will guide and focus criminal investigations towards finding them. The VISAGE Toolkit will include analysis prototype tools based on massively parallel sequencing for genotyping the large number of DNA predictors for appearance, age, and ancestry established within the Project, as well as an integrated statistical framework with prototype software for translating these genotype data into statistical probabilities on appearance, age and ancestry, which represents the intelligence information finally used for guiding criminal investigations towards the most probable group of suspects. The VISAGE Toolkit will consider ethical, societal, and legal dimensions of Forensic DNA Phenotyping as identified within the Project, by applying a privacy-by-design strategy. The interdisciplinary VISAGE Consortium includes European (and global) scientific leaders in Forensic DNA Phenotyping as well as in forensic massively parallel sequencing, leading European forensic DNA service providers, and one of the leading social scientists in the field of forensic DNA analysis, ensuring that the Project goals will be achieved on time. The outcome of the VISAGE Project will have a major impact on solving more crimes more rapidly by providing previously unused intelligence information from trace DNA to find unknown perpetrators, which will lead to reduced impact on victims, reduced societal distress, preventing miscarriages of justice, thereby avoiding unnecessary costs.

The RED-Alert project will bring data mining and predictive analytics tools to the next level, developing novel natural language processing (NLP), semantic media analysis (SMA), social network analysis (SNA), Complex Event Processing (CEP) and artificial intelligence (AI) technologies. These technologies will be combined for the first time and validated by 6 law enforcement agencies (LEAs) to collect, process, visualize and store online data related to terrorist groups, allowing them to take coordinated action in real-time while preserving the privacy of citizens. The RED-Alert solution will outperform state-of-the-art solutions in terms of number of languages supported, privacy-preserving capabilities, usability, detection performance, real-time capabilities and integration capabilities. The RED-Alert approach combines for the first time the CEP methodology with NLP/SMA and SNA applications in the context of social media data analytics, transforming (unstructured) social media data into (structured) events enhanced by semantic attributes. For example, a tweet will be an event consisting of content (expressed as NLP features e.g. concepts, sentiment, entities, etc.) and context (time and the author including SNA features e.g. number of followers, number of links, etc.). Turning unstructured social media data into structured events is key, as it allows the system to use (event) rules (event temporal logic, event logic patterns, even counting, absence of events) to infer insights or create alerts in real-time. The project impact is supported by the participation of Europol and specific dissemination activities around the World Counter-Terrorism Summit, organized by one of the partners. The total requested EC funding is 5M Euros and the project duration 36 months.