The INTERACT Project, funded by the DG-DEFIS of the European Commission and managed by the European Defence Agency (EDA), aims at enhancing the capabilities of European armed forces to safely, effectively and flexibly operate unmanned and manned systems in joint or combined operations. The challenge to achieve this lies in creating overarching interoperability concepts for defence systems in general and unmanned systems in particular. INTERACT proposes to use selected NATO STANAGs to engender compatibility for military systems. But the lack of a promulgated STANAG for UxS (Unmanned Systems) control in an all-domain context is identified as a major gap regarding this endeavour. As a response the INTERACT project is elaborating a set of interoperability concepts and standardisation proposals, which will enable the coordinated deployment of multiple and potential heterogeneous platforms by a single, standardised control station as well as the controlled hand-over of platforms between INTERACT compliant control nodes. The INTERACT solution creates a holistic approach and includes the proposal for concepts and design of a set of interoperable standardized interfaces between subsystems and payloads within an unmanned system (intra-system interoperability) to ease the upgrade and adoption of novel payloads and maintaining and upgrading equipment and components to the state-of-the-art, as well as the proposal for inter-system interface standardization in order to pave the way for future operational concepts where autonomous assets will flexibly operate together in organized heterogeneous UxS teams. Beneath the system interoperability INTERACT will also address the human-machine interaction by proposing a common design solution for standardisable user interfaces. The INTERACT consortium consists of 4 major European RTOs as a core team supported by a strong alliance of 15 representative European defence industries, SMEs and RTOs from 11 different nations.
Building upon the possibilities of technologies like big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions and reasoning tools, even with only small data samples. Due to the fact that the MAGNETO tools have to operate on highly sensitive data from criminal investigations, the data samples provided to the tool developers have been small, scarce, and often not correlated. The project team had to overcome these drawbacks. The developed reasoning tools are based on the MAGNETO ontology and knowledge base and enables LEA officers to uncover derived facts that are not expressed in the knowledge base explicitly, as well as discover new knowledge of relations between different objects and items of data. Two reasoning tools have been implemented, a probabilistic reasoning tool based on Markov Logic Networks and a logical reasoning tool. The design of the tools and their interfaces will be presented, as well as the results provided by the tools, when applied to operational use cases.
Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.
The main challenge of computer linguistics is to represent the meaning of text in a computer model. Statistics based methods with manually created features have been used for more than 30 years with a divide and conquer approach to mark interesting features in free text. Around 2010, deep learning concepts found their way into the text-understanding research community. Deep learning is very attractive and easy to apply but needs massive pools of annotated and high quality data from every target domain, which is generally not available especially for the military domain. When changing the application domain one needs additional or new data to adopt the language models to the new domain. To overcome the everlasting “data problem” we chose a novel two-step approach by first using formal representations of the meaning and then applying a rule-based mapping to the target domain. As an intermediate language representation, we used abstract meaning representation (AMR) and trained a general base model. This base model was then trained with additional data from the intended domains (transfer learning) evaluating the quality of the parser with a stepwise approach in which we measured the parser performance against the amount of training data. This approach answered the question of how much data we need to get the required quality when changing an application domain. The mapping of the meaning representation to the target domain model gave us more control over specifics of the domain, which are not generally representable by a machine learning approach with self-learned feature vectors.
KEYWORDS: Unmanned aerial vehicles, Sensors, Data fusion, Databases, Data modeling, Model-based design, Classification systems, Decision support systems, Systems modeling
Today, drone technology has been made available around the world. Anyone can purchase a drone from an online retailer. Government agencies and military are seeing a rise in drones used for terrorism, destruction and espionage. The emergence of threats caused by unfriendly or hostile drones requires proactive drone detection in order to decide on appropriate defensive actions. In this contribution, a high-level data fusion component for drone classification is presented. The high-level data fusion component is part of our counter UAV system MODEAS including decision support. The component provides well-defined interfaces which allow it to be integrated also into other counter UAV systems. The aim of the high-level data fusion component is to support an operator in his decision making by providing detailed information about detected drones together with assigned threat levels. To identify a detected and tracked drone with sufficient detail, a knowledge-based classification is performed, based on background knowledge like drone model specifications. By fusing the knowledge-based classification results with prior results of a sensor-based classification, the overall classification is improved. The fusion results, in addition to kinematic data, also contain specific capabilities of the respective drone like its maximum payload, endurance, and speed as well as recorded incidents with similar drones or their typical (commercial) usage, if known. Based on these fusion results, a threat analysis is performed. The component’s output then is a ranked list of dossiers for the most probable types of drones with regard to the observation data and their assigned threat levels.
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