Search results for " Extraction"
showing 10 items of 1344 documents
Information Abstraction from Crises Related Tweets Using Recurrent Neural Network
2016
Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.
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…
The impact of sample reduction on PCA-based feature extraction for supervised learning
2006
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…
Area-Based Depth Estimation for Monochromatic Feature-Sparse Orthographic Capture
2018
With the rapid development of light field technology, depth estimation has been highlighted as one of the critical problems in the field, and a number of approaches have been proposed to extract the depth of the scene. However, depth estimation by stereo matching becomes difficult and unreliable when the captured images lack both color and feature information. In this paper, we propose a scheme that extracts robust depth from monochromatic, feature-sparse scenes recorded in orthographic sub-aperture images. Unlike approaches which rely on the rich color and texture information across the sub-aperture views, our approach is based on depth from focus techniques. First, we superimpose shifted …
POLARIZATION-BASED CAR DETECTION
2018
International audience; Road scene understanding is a vital task for driving assistance systems. Robust vehicle detection is a precondition for diverse applications particularly for obstacle avoidance and secure navigation. Color images provide limited information about the physical properties of the object. This results in unstable vehicle detection caused mainly from road scene complexity (strong reflexions, noises and radiometric distortions). Instead, polarimetric images, characteristic of the light wave, can robustly describe important physical properties of the object (e.g., the surface geometric structure, material and roughness etc). This modality gives rich physical informations wh…
Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals.
2019
In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were e…
Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering
2016
Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It i…
Dynamics-based action recognition for motor intention prediction
2020
Abstract Powered lower-limb prostheses presented in the previous chapter require a natural and easy-to-use interface for communicating amputee’s motor intention in order to select the appropriate motor program in a given context or simply to commute from an active (powered) to a passive mode of functioning. To be accepted by amputees, such an interface should (1) not put additional cognitive load on the end-user, (2) be reliable and (3) be minimally invasive. In this chapter we present one possible solution for achieving that goal: a robust method for autonomously detecting and recognizing motor intents from a wearable sensor network mounted on a sound leg. The sensor network provides a rea…
Cueing animations: Dynamic signaling aids information extraction and comprehension
2013
The effectiveness of animations containing two novel forms of animation cueing that target relations between event units rather than individual entities was compared with that of animations containing conventional entity-based cueing or no cues. These relational event unit cues (progressive path and local coordinated cues) were specifically designed to support key learning processes posited by the Animation Processing Model (Lowe & Boucheix, 2008). Four groups of undergraduates (N ¼ 84) studied a usercontrollable animation of a piano mechanism and then were assessed for mental model quality (via a written comprehension test) and knowledge of the mechanism’s dynamics (via a novel non-verbal …
An introduction to knowledge computing
2014
This paper deals with the challenges related to self-management and evolution of massive knowledge collections. We can assume that a self-managed knowledge graph needs a kind of a hybrid of: an explicit declarative self-knowledge (as knowledge about own properties and capabilities) and an explicit procedural self-knowledge (as knowledge on how to utilize own properties and the capabilities for the self-management).We offer an extension to a traditional RDF model of describing knowledge graphs according to the Semantic Web standards so that it will also allow to a knowledge entity to autonomously perform or query from remote services different computational executions needed. We also introdu…