Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Interoperability of armed forces unmanned systems: the INTERACT project

2023 , Müller, Wilmuth , Segor, Florian , Mühlenberg, Dirk , Driessen, Bart , Geus, Vincent De , Papp, Zita , Svenmarck, Peter , Kalis, Antonis , Amditis, Angelos

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.

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Knowledge Engineering for Crime Investigation

2022 , Müller, Wilmuth , Mühlenberg, Dirk , Pallmer, Dirk , Zeltmann, Uwe , Ellmauer, Christian , Pérez Carrasco, Francisco José , Garcia, Alberto Garcia , Demestichas, Konstantinos , Peppes, Nikolaos , Touska, Despoina , Gkountakos, Konstantinos , Muńoz Navarro, Eva , Martinez, Santiago

Building upon the possibilities of technologies like ontology engineering, knowledge representational models, text mining, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), 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. A series of tools have been developed within PREVISION which provide LEAS with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and OGCs. The paper at hand focuses on the developed ontology and the tools for text mining, Extract Transform Load, Semantic Reasoning and the knowledge base and knowledge visualization.

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Multimedia analysis platform for crime prevention and investigation. Results of MAGNETO project

2021 , Perez, Francisco J. , Garrido, Victor J. , Garcia, Alberto , Zambrano, Marcelo , Kozik, Rafal , Choras, Michal , Mühlenberg, Dirk , Pallmer, Dirk , Müller, Wilmuth

Nowadays, the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.

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Towards information extraction from ISR reports for decision support using a two-stage learning-based approach

2019 , Mühlenberg, Dirk , Kuwertz, Achim , Schenkel, P. , Müller, Wilmuth

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.

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Interoperability Open Architecture of Unmanned Systems

2023 , Müller, Wilmuth , Segor, Florian , Mühlenberg, Dirk , Driessen, Bart , Geus, Vincent de

Within the INTERACT Project, funded by the DG-DEFIS of the European Commission and managed by the European Defence Agency (EDA), interoperability concepts aiming at enhancing the capabilities of European armed forces to safely, effectively and flexibly operate unmanned and manned systems in joint or combined operations have been developed. The challenge lied in creating overarching interoperability concepts for defence systems in general and unmanned systems in particular. The different interoperability concepts have been integrated into an open interoperability architecture for unmanned systems. In this paper the developed open interoperability architecture is presented.

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Knowledge Engineering and Ontology for Crime Investigation

2022 , Müller, Wilmuth , Mühlenberg, Dirk , Pallmer, Dirk , Zeltmann, Uwe , Ellmauer, Christian , Demestichas, Konstantinos

Building upon the possibilities of technologies like ontology engineering, knowledge representational models, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), 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. A series of tools have been developed within PREVISION which provide LEAs with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and Organized Crime Groups (OCG). The paper at hand focuses on the developed ontology, the tool for Semantic Reasoning and the knowledge base and knowledge visualization.

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Machine learning for discovery analytics to support criminal investigations

2020 , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Loumiotis, Ioannis , Remoundou, Konstantina , Kosmides, Pavlos , Demestichas, Konstantinos

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.

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A Multi-Agent System for ISR Asset Planning

2023 , Müller, Wilmuth , Reinert, Frank , Pfirrmann, Uwe , Mühlenberg, Dirk , Ellmauer, Christian

In this paper an approach for an optimal planning of intelligence, surveillance, and reconnaissance (ISR) asset deployment in order to satisfy the information needs of a commander is presented. Based on the processes of information requirements management (IRM) and collection management (CM), a two-step approach has been developed. In the first step, an operator assigns to each target on which reconnaissance or surveillance has to be performed a set of suitable assets. The operator may assign the suitable assets to a target either directly, based on his experience and knowledge, or supported by an interactive asset selection assistant component of the application, or supported by an intelli-gent multi-agent system, which generates automatically an asset assignment proposal. In the second step an optimal asset assign-ment and execution order is computed. The multi-agent system consists of three types of intelligent agents, a target agent representing the targets on which reconnaissance or surveillance has to be performed, the asset agents representing the assets available to the operator, and an interface agent responsible for the communication with the other components of the application.

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Evolving from Data to Knowledge Mining to Uncover Hidden Relationships

2021 , Demestichas, Konstantinos , Remoundou, Konstantina , Loumiotis, Ioannis , Adamopoulou, Evgenia , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Kozik, Rafal , Choras, Michael , Faure, David , Horincar, Roxana , Brodie of Brodie, Edward Benedict , Jacobe de Naurois, Charlotte , Chandramouli, Krishna , Rosca, Alexandra

Nowadays, law enforcement agencies - LEAs - are forced to deal with extreme volumes of data, being in need to analyse from heterogeneous data sources, uncover hidden relationships, trends and patterns of incidents and ultimately reach solid evidence to be used in court. In this chapter, a system is presented that can assist LEA officers in fighting crime, that, following the collection of the primary data, it applies semantic reasoning tools that allow the system to relate pieces of data, based on their inner relationships, and extract new information based on the asserted facts and rules defined by the LEAs. Then, the results derived by the reasoners and the initial data are fused using appropriate tools including a trajectory, a person and an event fusion tool, to be finally visualized by the proposed bipartite graphs. The proposed system is expected to decrease the time required to solve a crime by LEA's officers.

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Reasoning with small data samples for organised crime

2020 , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Loumiotis, Ioannis , Remoundou, Konstantina , Kosmides, Pavlos , Demestichas, Konstantinos

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.

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