497 research outputs found
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Cyber insurance of information systems: Security and privacy cyber insurance contracts for ICT and helathcare organizations
Nowadays, more-and-more aspects of our daily activities are digitalized. Data and assets in the cyber-space, both for individuals and organizations, must be safeguarded. Thus, the insurance sector must face the challenge of digital transformation in the 5G era with the right set of tools. In this paper, we present CyberSure-an insurance framework for information systems. CyberSure investigates the interplay between certification, risk management, and insurance of cyber processes. It promotes continuous monitoring as the new building block for cyber insurance in order to overcome the current obstacles of identifying in real-time contractual violations by the insured party and receiving early warning notifications prior the violation. Lightweight monitoring modules capture the status of the operating components and send data to the CyberSure backend system which performs the core decision making. Therefore, an insured system is certified dynamically, with the risk and insurance perspectives being evaluated at runtime as the system operation evolves. As new data become available, the risk management and the insurance policies are adjusted and fine-tuned. When an incident occurs, the insurance company possesses adequate information to assess the situation fast, estimate accurately the level of a potential loss, and decrease the required period for compensating the insured customer. The framework is applied in the ICT and healthcare domains, assessing the system of medium-size organizations. GDPR implications are also considered with the overall setting being effective and scalable
Data-efficient Online Classification with Siamese Networks and Active Learning
An ever increasing volume of data is nowadays becoming available in a
streaming manner in many application areas, such as, in critical infrastructure
systems, finance and banking, security and crime and web analytics. To meet
this new demand, predictive models need to be built online where learning
occurs on-the-fly. Online learning poses important challenges that affect the
deployment of online classification systems to real-life problems. In this
paper we investigate learning from limited labelled, nonstationary and
imbalanced data in online classification. We propose a learning method that
synergistically combines siamese neural networks and active learning. The
proposed method uses a multi-sliding window approach to store data, and
maintains separate and balanced queues for each class. Our study shows that the
proposed method is robust to data nonstationarity and imbalance, and
significantly outperforms baselines and state-of-the-art algorithms in terms of
both learning speed and performance. Importantly, it is effective even when
only 1% of the labels of the arriving instances are available.Comment: 2020 International Joint Conference on Neural Networks (IJCNN),
Glasgow, UK, 202
Natriuretic peptide receptor-3 underpins the disparate regulation of endothelial and vascular smooth muscle cell proliferation by C-type natriuretic peptide
CM Panayiotou was the recipient of a Wellcome Trust Prize
PhD studentship. RS Khambata was the recipient of a Medical
Research Council PhD studentshi
Dynamically Personalizing Search Results for Mobile Users
International audienceWe introduce a novel situation-aware approach to personalize search results for mobile users. By providing a mobile user with appropriate information that dynamically satisfies his interests according to his situation, we tackle the problem of information overload. To build situation-aware user profile we rely on evidence issued from retrieval situations. A retrieval situation refers to the spatio-temporal context of the user when submitting a query to the search engine. A situation is represented as a combination of geographical and temporal concepts inferred from concrete time and location information by some ontological knowledge. User's interests are inferred from past search activities related to the identified situations. They are represented using concepts issued from a thematic ontology. We also involve a method to maintain the user's interests over his ongoing search activity and to personalize the search results
Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone
This work investigates the problem of simultaneous tracking and jamming of a
rogue drone in 3D space with a team of cooperative unmanned aerial vehicles
(UAVs). We propose a decentralized estimation, decision and control framework
in which a team of UAVs cooperate in order to a) optimally choose their
mobility control actions that result in accurate target tracking and b) select
the desired transmit power levels which cause uninterrupted radio jamming and
thus ultimately disrupt the operation of the rogue drone. The proposed decision
and control framework allows the UAVs to reconfigure themselves in 3D space
such that the cooperative simultaneous tracking and jamming (CSTJ) objective is
achieved; while at the same time ensures that the unwanted inter-UAV jamming
interference caused during CSTJ is kept below a specified critical threshold.
Finally, we formulate this problem under challenging conditions i.e., uncertain
dynamics, noisy measurements and false alarms. Extensive simulation experiments
illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS
Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems
This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and air-conditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology
Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning
In this work we propose a coverage planning control approach which allows a
mobile agent, equipped with a controllable sensor (i.e., a camera) with limited
sensing domain (i.e., finite sensing range and angle of view), to cover the
surface area of an object of interest. The proposed approach integrates
ray-tracing into the coverage planning process, thus allowing the agent to
identify which parts of the scene are visible at any point in time. The problem
of integrated ray-tracing and coverage planning control is first formulated as
a constrained optimal control problem (OCP), which aims at determining the
agent's optimal control inputs over a finite planning horizon, that minimize
the coverage time. Efficiently solving the resulting OCP is however very
challenging due to non-convex and non-linear visibility constraints. To
overcome this limitation, the problem is converted into a Markov decision
process (MDP) which is then solved using reinforcement learning. In particular,
we show that a controller which follows an optimal control law can be learned
using off-policy temporal-difference control (i.e., Q-learning). Extensive
numerical experiments demonstrate the effectiveness of the proposed approach
for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09
December 2022, Cancun, Mexic
Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
In this work, a novel distributed search-planning framework is proposed,
where a dynamically varying team of autonomous agents cooperate in order to
search multiple objects of interest in three-dimension (3-D). It is assumed
that the agents can enter and exit the mission space at any point in time, and
as a result the number of agents that actively participate in the mission
varies over time. The proposed distributed search-planning framework takes into
account the agent dynamical and sensing model, and the dynamically varying
number of agents, and utilizes model predictive control (MPC) to generate
cooperative search trajectories over a finite rolling planning horizon. This
enables the agents to adapt their decisions on-line while considering the plans
of their peers, maximizing their search planning performance, and reducing the
duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202
Long-term quality of life postacute kidney injury in cardiac surgery patients.
Acute renal failure after cardiac surgery is known to be associated with significant short-term morbidity and mortality. There have as yet been no major reports on long-term quality of life (QOL). This study assessed the impact of acute kidney injury (AKI) and renal replacement therapy (RRT) on long-term survival and QOL after cardiac surgery. The need for long-term RRT is also assessed
Bleeding complications in patients on warfarin undergoing joint injection/aspiration: systematic review and meta-analysis
Steroid injections in joints are commonly administered for the management of inflammatory or degenerative conditions. There is substantial controversy as to whether to continue warfarin when undertaking joint injection or aspiration. To assess the rate of bleeding complications in patients on warfarin undergoing joint injection/aspiration. Systematic review and meta-analysis. A literature search of 3 online databases was conducted by 2 reviewers using the Cochrane methodology for systematic reviews. Eligibility criteria were any study that reported bleeding complication rates in adult patients on warfarin undergoing a joint injection/aspiration whilst taking warfarin anticoagulation. Studies reporting on less than 5 patients were excluded. Meta-analysis was conducted using a random effects model. The search of databases resulted in a total of 1547 articles. After screening, 8 articles were deemed suitable for inclusion in the analysis, involving 871 injection/aspiration procedures. There were only 5 reported cases of bleeding. On meta-analysis the estimated bleeding complication rate was 1.5% (95% CI 0.5–4.5%). This meta-analysis shows that it is safe to perform joint injection and aspiration in patients on warfarin without routine prior testing of INR
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