Microblogs Information Retrieval for Disaster Management

2019 ◽  
pp. 1085-1112
Author(s):  
Imen Bizid ◽  
Nibal Nayef ◽  
Sami Faiz ◽  
Patrice Boursier
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Disaster Management ◽  
Prediction Models ◽  
User Behavior ◽  
Early Stage ◽  
First Responders ◽  
Experimental Results ◽  
Direct Access ◽  
New Approach

This chapter proposes a new approach for microblog information retrieval during unexpected disasters. This approach consists of identifying prominent microblog users who are susceptible to share relevant and exclusive information during a specific disaster. By tracking these users, emergency first responders would benefit from a direct access to the valuable information shared in real time in microblogs. In order to identify such users, we represent each microblog user according to his behavior at each particular disaster phase. Through the proposed users' representation, different prediction models are learned in order to identify prominent users at an early stage of each disaster phase. We experimented with different user representations, taking into account both the microblog user behavior and disaster context specificities. We also analyzed the importance of the different microblog users' features categories according to the disaster phase context. The achieved experimental results show the efficiency of our phase-aware-user characterization approach.

Microblogs Information Retrieval for Disaster Management

2017 ◽  
pp. 268-295
Author(s):  
Imen Bizid ◽  
Nibal Nayef ◽  
Sami Faiz ◽  
Patrice Boursier
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Disaster Management ◽  
Prediction Models ◽  
User Behavior ◽  
Early Stage ◽  
First Responders ◽  
Experimental Results ◽  
Direct Access ◽  
New Approach

This chapter proposes a new approach for microblog information retrieval during unexpected disasters. This approach consists of identifying prominent microblog users who are susceptible to share relevant and exclusive information during a specific disaster. By tracking these users, emergency first responders would benefit from a direct access to the valuable information shared in real time in microblogs. In order to identify such users, we represent each microblog user according to his behavior at each particular disaster phase. Through the proposed users' representation, different prediction models are learned in order to identify prominent users at an early stage of each disaster phase. We experimented with different user representations, taking into account both the microblog user behavior and disaster context specificities. We also analyzed the importance of the different microblog users' features categories according to the disaster phase context. The achieved experimental results show the efficiency of our phase-aware-user characterization approach.

Real-time digital simulator for distributed systems

SIMULATION ◽  
2021 ◽  
pp. 003754972098686
Author(s):  
Jinchao Chen ◽  
Chenglie Du ◽  
Pengcheng Han ◽  
Xiaoyan Du
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Experimental Results ◽  
Data Driven ◽  
New Approach ◽  
Support Tool ◽  
Time Performance ◽  
Simulator Design ◽  
Data Buses ◽  
Real Time Digital Simulator

Simulation has been widely adopted as a support tool for the validation and experimentation of distributed systems. It allows different devices and applications to be evaluated and analyzed without requiring the actual presence of those machines. Although the simulation plays an important role in investigating and evaluating the behaviors of devices, it results in a serious simulator building problem as the distributed systems become more and more complicated and dynamically data driven. Most of the existing simulators are designed and developed to target a specific type of application, lacking the capabilities to be a configurable and standardized tool for researchers. To solve the adaptability and reusability problems of simulators, this paper proposes a new approach to design and implement a configurable real-time digital simulator for hardware devices that are connected via data buses in distributed systems. First, the proposed simulator uses a logic automaton to simulate the activities of a real device, and generates the incentive data for tested equipment according to the predefined XML-based files. Then with a virtual bus, the simulator can receive, handle, and send data in various network environments, improving the flexibility and adaptability of a simulator design. Experimental results show that the proposed simulator has a high real-time performance, and can meet the increasing requirements of modern simulations of distributed systems.

Obstacles Perception for Serpentine Robot Based on Optical Flow Modules and Multi-Sensor Fusion

2011 ◽  
Vol 55-57 ◽  
pp. 1699-1704
Author(s):  
Jie Zhao ◽  
Zhen Feng Han ◽  
Gang Feng Liu ◽  
Yong Min Yang
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Real Time ◽  
Sensor Fusion ◽  
Image Sequence ◽  
Higher Order ◽  
Experimental Results ◽  
New Approach ◽  
Environment Perception ◽  
Indoor And Outdoor

To move efficiently in an unknown environment, a mobile robot must use observations taken by various sensors to detect obstacles. This paper describes a new approach to detect obstacles for serpentine robot. It captures the image sequence and analyzed the optical flow modules to estimate the deepness of the scene. This avoids one or higher order differential in the traditional optical flow calculation. The data of ultrasonic sensor and attitude transducer sensor are fused into the algorithm to improve the real-time capability and the robustness. The detecting results are presented by fuzzy diagrams which is concise and convenient. Indoor and outdoor experimental results demonstrate that this method can provide useful and comprehensive environment perception for the robot.

scholarly journals Real Time Multimodal Depression Analysis

2019 ◽  
Vol 8 (9) ◽  
pp. 2298-2304
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Decision Fusion ◽  
Prediction Performance ◽  
Experimental Results ◽  
Training Data ◽  
Abnormal Behavior ◽  
Daily Lives ◽  
Eeg Data ◽  
The Individual

Severe mental disorder is recognized as Depression. State of low mood and aversion to activity causes abnormal behavior of a person in both professional and daily lives. As per WHO, around 350 million people worldwide are victimized by depression. Importance of automated real time mental health analysis is increasing day by day. In this paper, we proposed a system of automated depression diagnosis. This is a new approach to predict the depression severity corresponding to HAM-D score values obtained from prediction models. The proposed framework is designed keeping in mind a multi-modal approach, aiming at capturing facial characteristics, speech properties and brain waves. Further, a decision fusion technique has been implemented to integrate the obtained information in real-time. Using statistical features extracted from the speech recording, facial video and EEG data, the individual prediction models classify the subject according to severity of depression and the outputs are then fused to increase the performance parameters. The training data was obtained from 50 subjects, who provided all three recordings necessary for analysis. In unimodal systems the EEG data provides 80%, Speech 78% and Facial recording 72% accuracy, which is much inferior to a multimodal framework which provides 92% accuracy. The experimental results show that the proposed multimodal framework significantly improves the depression prediction performance, compared to other techniques. Inferior to a multimodal framework which provides 92% accuracy. The experimental results show that the proposed multimodal framework significantly improves the depression prediction performance, compared to other techniques

New approach of fast matrix access for real-time SAR system

2009 ◽  
Author(s):  
Tengfei Liu ◽  
Shanqing Hu ◽  
Teng Long
Keyword(s):  
Real Time ◽  
New Approach
Sign in / Sign up

Export Citation Format

Share Document