Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders

Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan

A Correlation Framework for Continuous User Authentication Using Data Mining

Merged with duplicate records: 10026.1/572, 10026.1/334 and 10026.1/724 on 01.02.2017 by CS (TIS)The increasing security breaches revealed in recent surveys and security threats reported in the media reaffirms the lack of current security measures in IT systems. While most reported work in this area has focussed on enhancing the initial login stage in order to counteract against unauthorised access, there is still a problem detecting when an intruder has compromised the front line controls. This could pose a senous threat since any subsequent indicator of an intrusion in progress could be quite subtle and may remain hidden to the casual observer. Having passed the frontline controls and having the appropriate access privileges, the intruder may be in the position to do virtually anything without further challenge. This has caused interest'in the concept of continuous authentication, which inevitably involves the analysis of vast amounts of data. The primary objective of the research is to develop and evaluate a suitable correlation engine in order to automate the processes involved in authenticating and monitoring users in a networked system environment. The aim is to further develop the Anoinaly Detection module previously illustrated in a PhD thesis [I] as part of the conceptual architecture of an Intrusion Monitoring System (IMS) framework

Active aging in place supported by caregiver-centered modular low-cost platform

Aging in place happens when people age in the residence of their choice, usually their homes because is their preference for living as long as possible. This research work is focused on the conceptualization and implementation of a platform to support active aging in place with a particular focus on the caregivers and their requirements to accomplish their tasks with comfort and supervision. An engagement dimension is also a plus provided by the platform since it supports modules to make people react to challenges, stimulating them to be naturally more active. The platform is supported by IoT, using low-cost technology to increment the platform modularly. Is a modular platform capable of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding the person under supervision, as well as providing conditions for constant and more effective monitoring, through modules and tools that support decision making and tasks realization for active living. The constant monitoring allows knowing the routine of daily activities of the senior. The use of machine learning techniques allows the platform to identify, in real-time, situations of potential risk, allowing to trigger triage processes with the older adult, and consequently trigger the necessary actions so that the caregiver can intervene in useful time.O envelhecimento no local acontece quando as pessoas envelhecem na residência da sua escolha, geralmente nas suas próprias casas porque é a sua preferência para viver o máximo de tempo possível. Este trabalho de investigação foca-se na conceptualização e implementação de uma plataforma de apoio ao envelhecimento ativo no local, com particular enfoque nos cuidadores e nas suas necessidades para cumprir as suas tarefas com conforto e supervisão. Uma dimensão de engajamento também é um diferencial da plataforma, pois esta integra módulos de desafios para fazer as pessoas reagirem aos mesmos, estimulando-as a serem naturalmente mais ativas. A plataforma é suportada por IoT, utilizando tecnologia de baixo custo para incrementar a plataforma de forma modular. É uma plataforma modular capaz de responder às necessidades específicas do envelhecimento dos idosos no local e dos seus cuidadores, obtendo dados relativos à pessoa sob supervisão, bem como fornecendo condições para um acompanhamento constante e mais eficaz, através de módulos e ferramentas que apoiam a tomada de decisões e realização de tarefas para a vida ativa. A monitorização constante permite conhecer a rotina das atividades diárias do idoso, permitindo que, com a utilização de técnicas de machine learning, a plataforma seja capaz de detetar em tempo real situações de risco potencial, permitindo desencadear um processo de triagem junto do idoso, e consequentemente despoletar as ações necessárias para que o prestador de cuidados possa intervir em tempo útil

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Cyber security incident handling, warning and response system for the european critical information infrastructures (cyberSANE)

This paper aims to enhance the security and resilience of Critical Information Infrastructures (CIIs) by providing a dynamic collaborative, warning and response system (CyberSANE system) supporting and guiding security officers and operators (e.g. Incident Response professionals) to recognize, identify, dynamically analyse, forecast, treat and respond to their threats and risks and handle their daily cyber incidents. The proposed solution provides a first of a kind approach for handling cyber security incidents in the digital environments with highly interconnected, complex and diverse nature

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Artificial Intelligence Enabled Methods for Human Action Recognition using Surveillance Videos

Computer vision applications have been attracting researchers and academia. It is more so with cloud computing resources enabling such applications. Analysing video surveillance applications became an important research area due to its widespread applications. For instance, CCTV camera are used in public places in order to monitor situations, identify any theft or crime instances. In presence of thousands of such surveillance videos streaming simultaneously, manual analysis is very tedious and time consuming task. There is need for automated approach for analysis and giving notifications or findings to officers concerned. It is very useful to police and investigation agencies to ascertain facts, recover evidences and even exploit digital forensics. In this context, this paper throws light on different methods of human action recognition (HAR) using machine learning (ML) and deep learning (DL) that come under Artificial Intelligence (AI). It also reviews methods on privacy preserving action recognition and Generative Adversarial Networks (GANs). This paper also provides different datasets being used for human action recognition research besides giving an account of research gaps that help in pursuing further research in the area of human action recognition