TERRORIST THREAT has increased in Europe and worldwide since Paris attacks in 2016. High density crowded places, such as critical infrastructures and public open areas, are being particularly targeted by those who instill terror. Both public and private sectors need next generation face recognition systems able to cover such large, crowded and unconstrained environments, and to massively process faces with high performance; altogether with the detection abnormal crowd behaviors such as mass panic, stampedes or bottlenecks. However, computational requirements and highly-priced hardware are hampering these deployments. Also the analysis of faces and crowds arises increasing concerns about privacy, and it is necessary to ensure compliance with the EU General Data Protection Regulation (GDPR) and other local regulations. We will bring an easy-to-install and user-friendly solution, covering cybersecurity needs and market replication. The AWARE PROJECT provides a disruptive solution to solve major pains: (1) First solution able to analyze crowd behavior. (2) Use of an edge computing architecture and Deep Neural Network accelerators to achieve outstanding processing speed and cost-efficiency. (3) Ensuring data privacy and GDPR accomplishment. AWARE is at TRL6, while HERTA is well-positioned worldwide as one of the few face recognition providers in crowded and unconstrained environments. The global face recognition MARKET is becoming very competitive, and expected to increase to USD 7,76 Billion by 2022 at a CAGR of 13,8%, The team involved in the project has over 25 years of experience in the innovation and commercialization of biometrics. COMPETITORS: FaceFirst (USA) and NEC (Japan) solutions are slower and do not provide: crowd behavior analysis, secure databases, hardware scalability and integrated with any type of camera. ACCUMULATED ADDITIONAL REVENUES OF 143 M€ and accumulated additional Profit of 67,9 M€ by the 3rd year; creating 30 new high-quality employments.

We are entering the Cyber-Physical age, in which both objects and people will become nodes of the same digital network for exchanging information. Therefore, in our imaginary, the general expectation is that “things” or systems will become somewhat smart as people, allowing rapid and close interactions not only system-system, but also human-system, system-human. More scientifically, we expect that such Cyber-Physical Systems (CPSs) will at least react in real-time, have enough computational power for the assigned tasks, consume the least possible energy for such task (energy efficiency), scale up through modularity, allow for an easy programmability across performance scaling and exploit at best existing standards at minimal costs. The whole set of these expectations impose scientific and technological challenges that need to be properly addressed. The AXIOM project (Agile, eXtensible, fast I/O Module) aims at researching new software/hardware architectures for CPSs to meet the above expectations. The technical approach aims at solving fundamental problems to enable easy programmability of multi-core multi-board systems through the open-source OmpSs programming model, leveraging Distributed Shared Memory (DSM) inspired concepts across the modules. The OmpSs will allow accelerating functions through an FPGA (Agility). In particular, to the best of our knowledge, this is the first time that DSM will be effectively demonstrated on an embedded modular system (eXtensibility). Modular scalability will be possible thanks to a fast interconnect that will enrich the module. To this aim, an innovative modular ARM-based board with enhanced capabilities for interfacing with the physical world will be designed and demonstrated in key scenarios such as Smart Video-Surveillance and Smart Living/Home (Domotic).

Many low-power devices such as smartphones, tablets, notebooks as well as several other embedded systems can't always cope with the increased demand for processing power, memory and storage required by modern applications in gaming, vision, security, robotics, aerospace, etc. As a result, most such applications are only executed on high-end servers. RAPID tackles this challenge by taking advantage of high-performance accelerators and high-bandwidth networks. Following our approach, compute or storage intensive tasks are seamlessly offloaded from the low-power devices to more powerful heterogeneous accelerators, supporting multiple virtual CPUs and GPUs. We propose, for the first time, a secure unified model where almost any device or infrastructure, ranging from smartphone, notebook, laptop and desktop to private and public cloud can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way. RAPID offers a registration mechanism, which permits the accelerated entities to automatically find and connect to nearby accelerators with the required resources. Next, a runtime system, running on each such accelerated entity, takes into account several parameters such as the local status, the environmental conditions, the task requirements, and the status of the accelerators it is connected to in order to decide whether local tasks (or incoming tasks if the entity also acts as an accelerator) should be executed locally or remotely. Novel scheduling algorithms, admission control policies, Service Level Agreements and license policies are employed to serve multiple accelerated applications efficiently on heterogeneous cloud infrastructures. An easy-to-use task-based programming model will be defined, while a novel runtime will automatically offload and execute the tasks transparently to the programmer. Within the RAPID project the first public acceleration cloud service will become available and commercially exploitable.

EITHOS will develop a novel Identity Theft Observatory System, empowering European citizens, Law Enforcements Agencies (LEAs), and policy makers to further contribute to the prevention, detection, and investigation of identity theft related crime. It will provide a common gateway for identity theft information and intelligence in Europe, built on top of a modern technological back end, based on two pillars: (1) Inform & Educate European citizens through the observatory itself and via innovative awareness campaigns regarding the safety of their personal data and identity; (2) Identify & address the challenges that Police Authorities face against identity theft and develop a robust software toolset to support them and enhance their investigations. The proposed system will provide easy access to information and intelligence about previous and current identity theft related trends (such as the methods that fraudsters follow to steal information, personal data protection, or the ways that victims can be supported) through its front end, while offering a cutting-edge AI-based technological toolkit via its back end. Additionally, the project will analyse the societal impact of identity theft, as well as the legal framework under which the utilisation of AI remote technologies and e-evidence exchange can be achieved. Focusing on educating civil society and addressing the obstacles that hinder LEAs to efficiently fight identity theft, EITHOS differentiates from previous projects and other market solutions which often concentrate on digital identity management and secure transactions.

Energy efficiency is becoming increasingly important in today's world of battery powered mobile devices and power limited servers. While performance optimisation is a familiar topic for developers, few are even aware of the effects that source code changes will have on the energy profiles of their programs. Without knowledge of these effects, compiler and operating system writers cannot create automatic energy optimisers. To realise the needed energy savings, we require the capability to track energy consumption and associate it to code and data at a fine granularity. Furthermore, compilers and operating systems must exploit this capability to optimise applications automatically. This proposal presents a novel approach to software-centric modelling, measurement, accounting and optimisation of energy-efficiency on many-core systems. Energy consumption will be matched against programming language abstractions, from basic-blocks to functions, loops, and parallel constructs, and from variables to data structures, providing developers with the information that they need. The project will use this fine grained accounting to build novel compiler optimisations that target energy consumption. It will create low energy runtime systems that adapt to environmental changes. It will develop energy efficient operating system scheduling that manages multi-tasking for heterogeneous many-cores. The project aims to improve performance per Watt by at least 40%.