A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behavior analysis

International audienceDetection of abnormal movements of mobile objects has recently received a lot of attention due to the increasing availability of movement data and their potential for ensuring security in many different contexts. As timely detection of these events is often important, most current approaches use automated data-driven approaches. While these approaches have proved to be effective in specific contexts, they are not easily accepted by operators in charge of surveillance due, among other reasons, to the lack of user involvement during the detection process. To improve the detection and analysis of maritime anomalies this paper explores the potential of spatial ontologies for modeling maritime operator knowledge. The goal of this research is to facilitate the integration of human knowledge by modeling it in the form of semantic rules to improve confidence and trust in the anomaly detection system

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

The Maritime Domain Awareness Center– A Human-Centered Design Approach

This paper contends that Maritime Domain Awareness Center (MDAC) design should be a holistic approach integrating established knowledge about human factors, decision making, cognitive tasks, complexity science, and human information interaction. The design effort should not be primarily a technology effort that focuses on computer screens, information feeds, display technologies, or user interfaces. The existence of a room with access to vast amounts of information and wall-to-wall video screens of ships, aircraft, weather data, and other regional information does not necessarily correlate to possessing situation awareness. Fundamental principles of human-centered information design should guide MDAC design and technology selection, and it is imperative that they be addressed early in system development. The design approach should address the reason and purpose for a given MDAC. Subsequent design efforts should address ergonomic interaction with information – the relationship of the brain to the information ecosystem provided by the MDAC, and the cognitive science of situation awareness and decision making. This understanding will guide technology functionality. The system user and decision maker should be the focus of the information design specifications, and this user population must participate and influence the information design. Accordingly, this paper provides a “design gestalt” by which to approach the design and development of an MDAC

Users’ Cognitive Load: A Key Aspect to Successfully Communicate Visual Climate Information

The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction, and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users’ experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort, and comments, among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users’ response time by half, significantly improved success ratios, and eased decision-making by filtering nonrelevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and nontechnical audiences.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreements 776787 (S2S4E), 776613 (EUCP), and (ClimatEurope). This work was also supported by the MEDSCOPE project. MEDSCOPE is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by AEMET (ES), ANR (FR), BSC (ES), CMCC (IT), CNR (IT), IMR (BE), and Météo-France (FR), with co-funding by the European Union (Grant 690462). The research team would like to thank the participants of the test who generously shared their time and opinions for the purposes of this research. This study is a part of the PhD of the corresponding author, Luz Calvo.Peer ReviewedPostprint (published version

Data Quality Assessment for Maritime Situation Awareness

International audienceThe Automatic Identification System (AIS) initially designed to ensure maritime security through continuous position reports has been progressively used for many extended objectives. In particular it supports a global monitoring of the maritime domain for various purposes like safety and security but also traffic management, logistics or protection of strategic areas, etc. In this monitoring, data errors, misuse, irregular behaviours at sea, malfeasance mechanisms and bad navigation practices have inevitably emerged either by inattentiveness or voluntary actions in order to circumvent, alter or exploit such a system in the interests of offenders. This paper introduces the AIS system and presents vulnerabilities and data quality assessment for decision making in maritime situational awareness cases. The principles of a novel methodological approach for modelling, analysing and detecting these data errors and falsification are introduced

Self-adaptive multi-agent systems for aided decision-making : an application to maritime surveillance

L'activité maritime s'est fortement développée ces dernières années et sert de support à de nombreuses activités illicites. Il est devenu nécessaire que les organismes impliqués dans la surveillance maritime disposent de systèmes efficaces pour les aider à identifier ces activités illicites. Les Systèmes de Surveillance Maritime doivent observer de manière efficace un espace maritime large, à identifier des anomalies de comportement des navires évoluant dans l'espace en question, et à déclencher des alertes documentées si ces anomalies amènent à penser que les navires ont un comportement suspect. Nous proposons un modèle générique de système multi-agents, que nous appelons MAS4AT, capable de remplir deux des différents rôles d'un système de surveillance : la représentation numérique des comportements des entités surveillées et des mécanismes d'apprentissage pour une meilleure efficacité. MAS4AT est intégré au système I2C.The maritime activity has widely grow in the last few years and is the witness of several illegal activities. It has become necessary that the organizations involved in the maritime surveillance possess efficient systems to help them in their identification. The maritime surveillance systems must observe a wide maritime area, identify the anomalies in the behaviours of the monitored ships et trigger alerts when these anomalies leads to a suspicious behavior. We propose a generic agent model, called MAS4AT, able to fulfil two main roles of a surveillance system: the numerical representation of the behaviours of the monitored entities and learning mechanisms for a better efficiency. MAS4AT is integrated in the system I2C

Bootstrap–CURE: A novel clustering approach for sensor data: an application to 3D printing industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government.Peer ReviewedPostprint (published version