1,721,004 research outputs found
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
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
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
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
Deep Representation Learning with Genetic Programming
Get PDFIn this thesis, we propose the development of a new Deep Learning (DL) model based on the Genetic Programming (GP) framework. Although DL is typically considered a machine learning (ML) field solely concerned with certain class of artificial neural networks (ANNs), in this thesis we approach DL as a paradigm shift, where the two classical stages of a ML workflow, i.e. feature extraction and prediction, are fusioned in a single ML algorithm, represented by an unified pipeline composed of non-linear transformations. Our literature review, that revolves around recent efforts from the research community at developing new DL architectures that depart from the classical ANN-based models, supports our point of view. As result from our research, we propose two GP-based DL models, one for unsupervised learning and another for supervised learning. The unsupervised learning model consists in a GP framework aimed at evolving autoencoder algorithms. To the best of the author’s knowledge, this is the first time autoencoder algorithms are generated though a ML model other than ANNs. The performance obtained by the evolved autoencoders is comparable to the performance of deep networks proposed ten years ago; nevertheless, we consider the GP framework more general than that of ANNs, due to the fact that GP searchs for the entire solution’s structure, whereas in the ANNs framework only the weights are optimized, hence the relevance of this result.En esta tesis se propone el desarrollo de un nuevo modelo de aprendizaje profundo basado en el entorno de trabajo de Programación Genética (PG). Aunque el aprendizaje profundo es normalmente considerado una área concerniente exclusivamente al estudio de cierto de tipo de redes neuronales artificiales, en esta tesis se aborda el aprendizaje profundo como un cambio de paradigma, en donde las dos etapas clásicas de una solución basada en aprendizaje maquina, i.e. la extracción de características y la predicción, se fusionan en un solo algoritmo de aprendizaje, representado por una estructura única compuesta por una secuencia de transformaciones no-lineales. La revisión de literatura que se presenta en esta tesis defiende esta nueva perspectiva, al presentar nuevos modelos de aprendizaje profundo propuestos recientemente en la literatura científica, que no basan su implementación en el uso de redes neuronales artificiales. Como resultado de esta investigación, se proponen dos modelos de aprendizaje profundo basados en PG, uno para aprendizaje no supervisado y otro para aprendizaje supervisado. El modelo propuesto para aprendizaje no supervisado consiste en un método basado en PG para la evolución de algoritmos autocodificantes. De acuerdo a la experiencia del autor de esta tesis, esta es la primera vez que algoritmos autocodificantes se obtienen mediante un modelo de aprendizaje maquina distinto al de las redes neuronales artificiales. El rendimiento obtenido por los algoritmos autocodificantes generados mediante PG es comparable a los que obtenían las redes neuronales profundas de hace diez anos; sin embargo, nosotros consideramos que el marco de trabajo de la PG es mas general que el de las redes neuronales artificiales, dado que no solamente se busca un vector de pesos, sino toda la estructura de la solución, y que ahí radica la relevancia de este resultado
Real time extraction of high level structures using a semi-calibrated stereo system
Get PDFThis research presents a novel methodology that combines stereo vision and parallel processing,
based on GPU and the use of binary descriptors, for fast High-Level Structures extraction. Typical
stereo algorithms require an image rectification stage that has to run on a frame-to-frame basis,
increasing the computational burden and with the possibility of compromising high frame rate
operation. Hence, it is proposed to use a semi-calibrated stereo approach, meaning that only
calibration of extrinsic parameters of the stereo rig is carried out, thus avoiding a rectification
process of the frames captured by the stereo camera. For the latter, the proposed approach relies on
feature matching of salient points detected on the stereo images, from which image correspondences
are obtained. These correspondences are triangulated to generate a point cloud that is passed to a
plane fitting module. As feature matching is a cumbersome task, this study presents a novel GPU
architecture to accelerate such process, thus achieving a real-time performance of up to 50 fps for
the whole process. To demonstrate our approach, we also present an augmented reality application
that exploits the planes extracted with our approach
An adaptive pixel value ordering based reversiblewatermarking scheme for image authentication
Get PDFProtecting information within communication systems is quite important since a digital content may be manipulated by unauthorized people during transmission. Image watermarking is a hiding information technique that has been thoroughly investigated by the cybersecurity community in recent years. A traditional watermarking scheme permanently distorts an image in order to hide information, which is why reversible watermarking schemes (RWS) emerged for application domains where original image is compulsory when involving decision making at the receiver end of the communication. Reversible watermarking schemes have the capability to extract the hidden information and remove the introduced distortion, such that original image is recovered. In this work, a reversible watermarking scheme for images authentication called PVO-GA (Pixel Value Ordering Genetic Algorithm), which is based on pixel value ordering technique to embed the watermark is developed. The proposed scheme improves the two important characteristics in reversible watermarking, such as imperceptibility and embedding capacity reported in state of the art works. A fine grained genetic algorithm is incorporated in order to optimize the insertion process since solutions space is expanded. The proposed genetic algorithm finds the optimal/near optimal PVO-GA parameters, which provide an effective trade-off between imperceptibility and embedding capacity properties.La protección de información dentro de un sistema de comunicación es de suma importancia ya que un contenido digital puede caer en manos no autorizadas durante su transmisión. El uso de marcas de agua para ocultar información en imágenes es un tema por el que los investigadores han apostado durante los últimos años. Un esquema de marca de agua convencional distorsiona permanentemente la imagen que se utiliza para transportar la información oculta. Proponer esquemas reversibles se vuelve importante en aplicaciones donde el uso de la imagen original es indispensable para la toma de decisiones en el extremo receptor de la comunicación, ya que proporcionan la ventaja de extraer la información oculta y eliminar la distorsión introducida, recuperando la imagen original usada para transportarla. En este trabajo se desarrolla PVO-GA (Pixel Value Ordering Genetic Algorithm), un esquema de marca de agua reversible para la autenticación de imágenes usando la técnica de ordenamiento de pixeles para la inserción de la marca de agua. El esquema propuesto mejora las dos características que deben considerarse en la evaluación de un esquema reversible: imperceptibilidad y capacidad de inserción, respecto a trabajos en el estado del arte. Dado que el espacio de soluciones es expandido, se integra un algoritmo genético evolutivo para la búsqueda de los parámetros adecuados del esquema propuesto
Finite state machine watermarking scheme using genetic algorithms for IP cores protection
Get PDFThis thesis proposes an improved procedure to watermark Intellectual Property
Cores at Register Transfer Level using Genetic Algorithms. First, watermarking
signature and Intellectual Property Core's behavioral description are translated into
Finite State Machines in preparation for merging. The resulting Finite State Machine
contains the watermarked Intellectual Property Core maintaining its original
functionality without disruption. Next, a reduction procedure is applied to the watermarked
design. At this stage, dealing with hanging states is challenging, if any
of these is deleted, the watermark could be removed and possibly the original Intellectual
Property Core functionality would not be disrupted. Both Finite State
Machine merging and reduction are NP-Complete problems. In this study an improved
objective function is proposed to accurately model the Finite State Machine
reduction problem while applying Genetic Algorithms as optimization techniques at
both stages. Empirical results show a significant improvement in terms of the number
of final hanging states and watermark embedding strength as regards previous
reported approaches.
Results of applying the proposed technique to watermark a number of Finite
State Machines are presented and discussed
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