This proposal aims to develop an innovative and privacy-preserving decision-support system for European law enforcement authorities, leveraging advanced Big Data and AI technologies to effectively combat crimes and terrorism. The proposed system integrates Federated Learning, User and Entity Behavior Analytics (UEBA), and other Big Data and AI techniques to monitor social network data, deep and shallow web information, and police databases in a secure, collaborative, privacy-aware and ethical manner. The primary objective is to help Law Enforcement Authorities (LEAs) fighting cybercrime and terrorism by identifying key communities and users involved in activities such as hate speech, child sexual abuse, terrorism, or drug trafficking and to use this information to better allocate police resources. PRESERVE will leverage Federated Learning, a decentralised machine learning approach that allows model training on distributed data sources while preserving data privacy. By collaborating with multiple LEAs across Europe, PRESERVE will collectively combine social network data, deep and shallow web information, and police databases to analyse large amounts of spatial and temporal data related to criminal activities to identify patterns and correlations to provide better police-resource management on critical areas.

The aim of our project is to train police officers’ on the procedure, through gamification technologies in a safe and controlled virtual environment. Essential tasks during the creation of LAW-GAME serious game are to virtualise and accurately recreate the real world. We will introduce an attractive approach to the development of core competencies required for performing intelligence analysis, through a series of AI-assisted procedures for crime analysis and prediction of illegal acts, within the LAW-GAME game realm. Building upon an in-depth analysis of police officers’ learning needs, we will develop an advanced learning experience, embedded into 3 comprehensive “gaming modes” dedicated to train police officers and measure their proficiency in: 1. conducting forensic examination, through a one-player or multi-player cooperative gaming scenario, played through the role of a forensics expert. Developed AI tools for evidence recognition and CSI and car accident analysis, will provide guidance to the trainee. 2. effective questioning, threatening, cajoling, persuasion, or negotiation. The trainee will be exposed to the challenges of the police interview tactics and trained to increase her emotional intelligence by interviewing a highly-realistic 3D digital character, advanced with conversational AI. 3. recognizing and mitigating potential terrorist attacks. The trainees will impersonate an intelligence analyst tasked with preventing an impending terrorist attack under a didactic and exciting “bad and good” multiplayer and AI-assisted game experience. The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations. The learning methodology developed by the LAW-GAME consortium will be extensively validated by European end-users, in Greece, Lithuania, Romania, Moldavia and Estonia.

District heating (DH) systems are one of the most energy efficient heating systems in urban environments, with proven reliability within many decades already. DHs have traditionally been designed to be operated in a hierarchized way, with central energy production facilities delivering heat to a variety of distributed consumption locations. DHs are identified as key systems to achieve the de-carbonization of heating energy in European Cities. Renewable and waste heat sources are foreseen at the same time as de-carbonized heat sources and the way to guarantee competitive energy costs with limited influence of fossil fuel supply price volatility. To achieve this, conversion of DHs is needed regarding: - The reduction of their operation temperature to avoid current technical constraints in the integration of low-grade industrial heat sources, - The introduction of larger shares of renewable energy sources (RES) in the DH network. - The introduction of distributed heat sources (reject heat from cooling equipment...). - To guarantee economic viability with the trend of DH heat load reduction due to the evolution of the building stock toward NZEB (Near Zero Energy Buildings). RELaTED will provide an innovative concept of decentralized Ultra-Low Temperature (ULT) DH networks, which allow for the incorporation of low-grade heat sources with minimal constraints. Also, ULT DH reduce operational costs due to fewer heat losses, better energy performance of heat generation plants and extensive use of de-carbonized energy sources at low marginal costs. The RELaTED ULT DH concept will be demonstrated in four complementary operation environments (new and existing DH, locations, climatic conditions, dimension…) in Denmark, Estonia, Serbia and Spain. RELaTED approach will follow the strategy of the electrical smart grids, in which energy generation is decentralized and consumers evolve to prosumers (they consume and produce energy).

ARIES main goal is to deliver a comprehensive framework for reliable e-identity ecosystem comprising new technologies, processes and security features that ensure highest levels of quality in eID based on trustworthy security documents and biometrics for highly secure and privacy-respecting physical and virtual identity management, with the specific aim to tangibly achieve a reduction in levels of identity theft, fraud and associated crimes. The set of solutions will be designed to achieve required levels of multi-party trust with efficiency, ease of adoption and convenience for all end-users (citizens, law enforcement, businesses), consolidating Europe as world leader in enhanced identity-based services as a basis to boost the competitiveness of its economy. ARIES will leverage virtual and mobile IDs cryptographically derived from strong eID documents in order to prevent identity theft and related crimes in the physical (e.g. an airport) and virtual (e.g eCommerce) domains. Both, the derivation process, and the derived IDs will be univocally linked to citizens' biometric features, increasing the level of identity assurance during the credential issuance process and during authentication. Highest data protection standards will be followed to provide digital privacy-preserving features. Thus, the project will provide a global approach for ID Ecosystem in Europe to address European-specific concerns to improve identity, trust and security, and better support the law enforcement to address the new threats in cybersecurity while achieving far-reaching socio-economic positive impacts. ARIES will demonstrate its outcomes and the levels of identity prevention reduction achieved in two use case demonstrators (secure eCommerce and identity virtualization for secure travel), covering the complete vision of virtual id ecosystem and its practical application.

ALIGNER aims to bring together European actors concerned with Artificial Intelligence, Law Enforcement, and Policing to collectively identify and discuss needs for paving the way for a more secure Europe in which Artificial Intelligence supports LEAs while simultaneously empowering, benefit-ing, and protecting the public. To achieve this, ALIGNER will (1) Facilitate communication and cooperation between actors from law enforcement, policing, policy-making, research, industry, and civil society about the changing dynamics of crime patterns relevant to the use of AI by establishing a workshop series; (2) Identify the capability enhancement needs of European LEAs; (3) Identify, assess, and validate AI technologies with potential for LEA capability enhancement by implementing a technology watch process that includes impact and risk assessments; (4) Identify ethical, societal, and legal implications of the use of AI in law enforcement; (5) Identify means and methods for preventing the criminal use of AI via the development of a taxonomy of AI-supported crime; (6) Identify policy and research needs related to the use of AI in law enforcement by mapping practitioner needs and emerging crime patterns with identified AI technologies; and (7) Employ the gathered insights in order to incrementally develop and maintain an AI research roadmap. ALIGNER ensure that project results are applicable and relevant by not only including three LEA organisations as full partners of the project, but also establishing two external advisory boards, one for LEA practitioners and one for researchers, industry professionals, ethicists, and civil society. The members of these boards will build the core stakeholders participating at ALIGNER workshops.