Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.Comment: IEEE Transactions on Intelligent Transportation System

Contextual and Human Factors in Information Fusion

Proceedings of: NATO Advanced Research Workshop on Human Systems Integration to Enhance Maritime Domain Awareness for Port/Harbour Security Systems, Opatija (Croatia), December 8-12, 2008Context and human factors may be essential to improving measurement processes for each sensor, and the particular context of each sensor could be used to obtain a global definition of context in multisensor environments. Reality may be captured by human sensorial domain based only on machine stimulus and then generate a feedback which can be used by the machine at its different processing levels, adapting its algorithms and methods accordingly. Reciprocally, human perception of the environment could also be modelled by context in the machine. In the proposed model, both machine and man take sensorial information from the environment and process it cooperatively until a decision or semantic synthesis is produced. In this work, we present a model for context representation and reasoning to be exploited by fusion systems. In the first place, the structure and representation of contextual information must be determined before being exploited by a specific application. Under complex circumstances, the use of context information and human interaction can help to improve a tracking system's performance (for instance, video-based tracking systems may fail when dealing with object interaction, occlusions, crosses, etc.).Publicad

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

Challenge and relief : a Foucauldian disciplinary analysis of retirement from professional association football in the United Kingdom

The aim of this study was to consider the retirement experiences of British male professional Association footballers by utilising Foucault’s (1991) analysis of discipline discussed in Discipline and Punish: the Birth of the Prison. Specifically, we drew upon Foucault to consider how, through the various techniques and instruments of discipline, the professional football context produces ‘docile footballing bodies’ and how this might influence a player’s experiences in retirement. We gathered our empirical material using a Foucauldian-informed interview framework (Avner et al., 2013) with 25 former professional male football players between the ages of 21-34. Our analysis suggested that retirement from football was both a challenge and a relief for our participants, and that their extended period of time within football’s strong disciplinary apparatus significantly influenced how they experienced their retirement

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Study and analysis of behaviour decision methods of non-player characters in first-person shooters

Non-player characters (NPCs) have a big importance in video games because if they did not exist, games would feel monotone and without life. With the increase of complexity and realism in video games graphics, the behaviour of NPCs needs to keep up to not break the experience of the player. For that reason, new decision methods for NPCs are studied to handle complex behaviours. First person shooters (FPSs) have a big role on implementing novel ways to define behaviour decision methods of NPCs such as behaviour trees and goal-oriented action planning while other game genres end up using their standards. Some decision methods are better than others depending on what kind of behaviour we want the NPCs to possess, thus, we propose to analyse, discuss, and compare different behaviour decision methods of NPCs and implement some examples to showcase these algorithms.Personagens não jogáveis (NPCs) são um dos tópicos mais importantes dos videojogos, pois é graças a eles que os jogos se tornam mais divertidos e menos repetitivos. Com o aumento do realismo e complexidade dos videojogos, é necessário que o comportamento dos NPCs se torne também mais realista. Para resolver esse problema, vários métodos de decisão para NPC foram criados. Jogos de tiro em primeira pessoa (FPSs) são responsáveis por serem os pioneiros em técnicas tais como behaviour trees e goal oriented action planning que são agora utilizados em vários géneros de videojogos como métodos de decisão de NPC. Alguns métodos de decisão são mais apropriados do que outros, dependendo do tipo de comportamento que pretendamos que o NPC exiba. É proposto neste projeto, analisar, comparar e implementar diferentes métodos de decisão de NPCs