1,720,968 research outputs found
Emergent self-awareness in multi-sensor physical agents
No full textThe cognitive approach to the development of autonomous vehicles takes inspiration from human reasoning, and, conversely to the computationalist approach, rejects formulating fixed mathematical models to describe each possible behavior of vehicles and objects around them. The computationalist approach indeed has a weakness: developers cannot examine and mathematically formulate all possible real-world situations that drivers may encounter. Cognitive approaches provide a solution to this problem, as they suggest that vehicles should continually learn through experience as humans do, which would allow them to progressively grasp rare and unexpected behaviors. This thesis mainly addresses the pivotal question of detecting when some unexpected behavior is occurring - referred to as anomaly detection - within a cognitive self-awareness framework. The adopted framework is characterized by several desirable characteristics, such as being Bayesian, hierarchical, multi-sensorial, data-driven, and interpretable.
A set of modules for anomaly detection and localization of an agent are proposed. Dynamic Bayesian Networks are used, as they are interpretable probabilistic models allowing hierarchical representation of variables potentially coming from multiple sensors; moreover, the links between variables inside Dynamic Bayesian Networks can be learned from data. All methods in the thesis adopt a filter called the Markov Jump Particle Filter that can be described through a Dynamic Bayesian Network on a minimum of three levels. When elaborating image data, Variational Autoencoders are adopted to perform dimensionality reduction while maintaining a probabilistic representation; novel methods for joining Variational Autoencoders and Bayesian filters are proposed.
This thesis focuses on the elaboration of vehicular video and odometry data. First, self-awareness anomaly detection approaches to separately handle video and odometry data are proposed; then, an approach fusing the two modalities is introduced; the capability to localize the vehicle is also added.
The proposed methods are evaluated on real-world and simulated data from terrestrial and aerial vehicles.El enfoque cognitivo para el desarrollo de vehículos autónomos se inspira en el razonamiento humano y, a la inversa del enfoque computacional, rechaza la formulación de modelos matemáticos fijos para describir cada posible comportamiento de los vehículos y objetos alrededor de ellos. En efecto, el enfoque computacional tiene una debilidad: los desarrolladores no pueden examinar y formular matemáticamente todas las posibles situaciones del mundo real que los conductores podrían encontrar. Los enfoques cognitivos proporcionan una solución a este problema, ya que sugieren que los vehículos deberían aprender continuamente a través de la experiencia como hacen los humanos, lo que les permitiría reconocer progresivamente comportamientos raros e inesperados. Esta tesis trata principalmente la question fundamental de detectar cuándo está ocurriendo algún comportamiento inesperado - definida como detección de anomalías - dentro de un marco de autoconciencia cognitiva. El marco adoptado tiene características deseables, como ser bayesiano, jerárquico, multisensorial, basado en datos e interpretable.
Se propone un conjunto de módulos para la detección de anomalías y localización de un agente. Se utilizan Redes Bayesianas Dinámicas, ya que son modelos probabilísticos interpretables que permiten una representación jerárquica de variables potencialmente provenientes de sensores múltiples; además, los vínculos entre variables dentro de las Redes Bayesianas Dinámicas se pueden aprender de los datos.
Todos los métodos en la tesis adoptan un filtro llamado Filtro de Partículas de Salto de Markov, que puede describirse a través de una Red Bayesiana Dinámica con un mínimo de tres niveles. Los datos de imágenes se procesan usando un Autocodificadores Variacionales para realizar una reducción de dimensionalidad manteniendo una representación probabilística; se proponen métodos novedosos para unir Autocodificadores Variacionales y filtros Bayesianos.
Esta tesis se enfoca en la elaboración de datos de vehículos de video y odometría. En primer lugar, se proponen enfoques de detección de anomalías para elaborar por separado datos de vídeo y odometría; luego se introduce un enfoque que fusiona las dos modalidades; también se añade la capacidad de localizar el vehículo.
Los métodos propuestos se evalúan con datos del mundo real y simulados, tanto de vehículos terrestres como aéreos
Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems
No full textAnomaly detection techniques constitute a fundamental resource in many applications such as medical image analysis, fraud detection or video surveillance. These techniques represent an essential step also for artificial self-aware systems that can continually learn from new situations. In this chapter, we present a semisupervised method for the detection of anomalies for this type of self-aware agents. The described method leverages the message-passing capability of Generalized Dynamic Bayesian Networks (GDBNs) to provide anomalies at different abstraction levels for diverse types of time-series data (i.e., both low-dimensional and high-dimensional). The detected anomalies could consequently be employed to enable the system to evolve by integrating the new acquired knowledge. To present a case study for the description of the anomaly detection method, we propose to use multisensory data from a semiautonomous vehicle performing different tasks in a closed environment
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A data-driven approach for the localization of interacting agents via a multi-modal Dynamic Bayesian Network framework
Get PDFProceedings of 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 29 Nov. - 2 Dec. 2022, Madrid, SpainThis paper proposes a multi-modal situational inter-action model for collaborative agents by fusing multi-sensorial information in a Multi-Agent Hierarchical Dynamic Bayesian Network (MAH-DBN) framework. The proposed model is learned in a data-driven methodology to estimate the states of interacting agents only from video sequences. This can be regarded as a two-fold methodology for improving visual-based localization and interaction between autonomous agents. In the learning stage, the odometry model is used to drive the video learning model for a robust localization and interaction modeling. During the testing phase, the learned Multi-Agent Hierarchical DBN (MAH-DBN) model is used for the localization of collaborative agents only from video sequences by proposing an inference method called Multi-Agent Coupled Markov Jump Particle Filter (MAC-MJPF)
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Simultaneous localization and anomaly detection from first-person video data through a Coupled Dynamic Bayesian Network model
No full textProceedings of 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 29 Nov. - 2 Dec. 2022, Madrid, SpainThis paper proposes a method to localize a moving agent - such as an autonomous surveillance vehicle - inside a known environment using First Person Viewpoint video data. Anomalies w.r.t. expected vehicle motion and image content are extracted to guide the localization, signal when the localization results are not trustworthy and explain the reason for the failure. During the training phase, a Dynamic Bayesian Network model is learned, which couples positional and video data. To learn it, clustering is performed on the odometry data, and a modified Kalman Variational Autoencoder is built over the video data. During the testing phase, a Coupled Markov Jump Particle Filter leverages the learned Dynamic Bayesian Network to extract anomalies and to estimate the vehicle’s position, given only camera data. The proposed method is evaluated on two real-world datasets of a vehicle performing perimeter monitoring of a closed environment and of a shopping cart moving in a supermarket
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
No full textThis paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used
for reducing the dimensionality of video frames, generating
latent space information that is comparable to low-dimensional
sensory data (e.g., positioning, steering angle), making feasible
the development of a consistent multi-modal architecture for
autonomous vehicles. An Adapted Markov Jump Particle Filter
defined by discrete and continuous inference levels is employed to
predict the following frames and detecting anomalies in new video
sequences. Our method is evaluated on different video scenarios
where a semi-autonomous vehicle performs a set of tasks in a
closed environment
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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