Search results for "Training."

Combining conjunctive rule extraction with diffusion maps for network intrusion detection

2013

Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detectio…

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

2014

Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…

Automated Scenario Generation for Training of Humanitarian Responders in High-Risk Settings

2019

Deep Learning-Based Real-Time Object Detection in Inland Navigation

2019

International audience; Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data s…

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

2021

Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise-Basics and Options for Wearable Devices.

2018

The use of wearable devices or "wearables" in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the p…

Architecture of High Fidelity Simulation Tool for Crisis Management Training

2018

This paper addresses an ongoing project KriseSIM aiming at creating a virtual training tool for crisis management that should be realistic, flexible, scalable with highfidelity system architecture and user interface. Our goal is to accomplish high-level requirements developed through a codesign process with the crisis management stakeholders. In this paper, we describe a highfidelity training tool architecture, which follows the serious game design principles. The tool is designed as a resource management serious game genre, where the players will deal with limited resources and should respond based on various critical information flows. A preliminary result, i.e. the architecture and the t…

Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

2021

The study of human behaviors in cognitive sciences provides clues to understand and describe people’s personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes th…

Does training in syllable recognition improve reading speed? A computer-based trial with poor readers from second and third grade.

2013

Repeated reading of infrequent syllables has been shown to increase reading speed at the word level in a transparent orthography. This study confirms these results with a computer-based training method and extends them by comparing the training effects of short syllables and long frequent and infrequent syllables, controlling for rapid automatized naming. Our results, based on a sample of 150 poor readers of Finnish, showed clear gains in reading speed regarding all trained syllables, but a transfer effect to the word level was evident only in the case of long infrequent syllables. Rapid automatized naming was associated with initial reading speed, but not with the training effect. peerRevi…

Acoustic Detection of Moving Vehicles

2018

This chapter outlines a robust algorithm to detect the arrival of a vehicle of arbitrary type when other noises are present. It is done via analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. To achieve it with minimum number of false alarms, a construction of a training database of acoustic signatures of signals emitted by vehicles using the distribution of the energies among blocks of wavelet packet coefficients (waveband spectra, see Sect. 4.6) is combined with a procedure of random search for a near-optimal footprint (RSNOFP). The number of false alarms in the detection is minimized even under severe conditions such as: signals emi…