32 research outputs found
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home
An increasing number of systems use indoor positioning for many scenarios
such as asset tracking, health care, games, manufacturing, logistics, shopping,
and security. Many technologies are available and the use of depth cameras is
becoming more and more attractive as this kind of device becomes affordable and
easy to handle. This paper contributes to the effort of creating an indoor
positioning system based on low cost depth cameras (Kinect). A method is
proposed to optimize the calibration of the depth cameras, to describe the
multi-camera data fusion and to specify a global positioning projection to
maintain the compatibility with outdoor positioning systems.
The monitoring of the people trajectories at home is intended for the early
detection of a shift in daily activities which highlights disabilities and loss
of autonomy. This system is meant to improve homecare health management at home
for a better end of life at a sustainable cost for the community
People management framework using a 2D camera for human-robot social interactions
International audienc
A Data Fusion System to Study Synchronization in Social Activities
As the world population gets older, the healthcare system must be adapted,
among others by providing continuous health monitoring at home and in the city.
The social activities have a significant role in everyone health status. Hence,
this paper proposes a system to perform a data fusion of signals sampled on
several subjects during social activities. This study implies the time
synchronization of data coming from several sensors whether these are embedded
on people or integrated in the environment. The data fusion is applied to
several experiments including physical, cognitive and rest activities, with
social aspects. The simultaneous and continuous analysis of four subjects
cardiac activity and GPS coordinates provides a new way to distinguish
different collaborative activities comparing the measurements between the
subjects and along time.Comment: Healthcom 201
Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms
DOI is not yet properly functionnal, go to IEEEXplore directly : https://ieeexplore.ieee.org/abstract/document/9340892International audienceCurrent Navigation Among Movable Obstacles (NAMO) algorithms focus on finding a path for the robot that only optimizes the displacement cost of navigating and moving obstacles out of its way. However, in a human environment, this focus may lead the robot to leave the space in a socially inappropriate state that may hamper human activity (i.e. by blocking access to doors, corridors, rooms or objects of interest). In this paper, we tackle this problem of "Social Placement Choice" by building a social occupation costmap, built using only geometrical information. We present how existing NAMO algorithms can be extended by exploiting this new cost map. Then, we show the effectiveness of this approach with simulations, and provide additional evaluation criteria to assess the social acceptability of plans
Navigation in Human Flows : Planning with Adaptive Motion Grid
International audienceAn important challenge for mobile robots is to navigate efficiently in human populated environments. In this context, we examine how human presence grids can be extended to model human motions, considering only embedded sensors. The proposed flow grid computes in each cell a discrete distribution of the human motion. The model is defined to take into account the most recent observations, so as to adapt to changes. More, it is expanded with a predictive motion pattern. Then we revisit the cost function of the A* pathplanning algorithm to take into account the risk of encountering humans. We compare the standard A* with variants exploiting the human presence likelihood [1] and the proposed flow grid. Experiments in simulation show that the Flow grid A* is able to compute paths minimizing the risk of navigating against human flows, and to adapt to their variations. Experiments with a mobile robot confirms the ability of the model to map human flows and to optimize paths
People management framework using a 2D camera for human-robot social interactions
International audienc
Socially Compliant Navigation in Dense Crowds
International audienceNavigating in complex and highly dynamic environments such as crowds is still a major challenge for autonomous vehicle such as autonomous wheelchairs or even autonomous cars. This article presents a new way of navigating in crowds by using behavioral clustering for the surrounding agents and representing the crowd as a set of moving polygons. Once the environment has been modelled in this way and the robot has all the information it needs, we then propose a navigation algorithm that is able to guide the vehicle through the scene. The key-points of this algorithm are that (1) it can avoid densely-populated areas in order to minimize the risk of being on a collision course with any of the surrounding dynamic obstacles, (2) it generates socially compliant trajectories
Characterization of a multi-user indoor positioning system based on low cost depth vision (Kinect) for monitoring human activity in a smart home
International audienceAn increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community