1,721,027 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Algorithms for learning from spatial and mobility data
Get PDFData from the numerous mobile devices, location-based applications, and collection
sensors used currently can provide important insights about human and natural processes. These insights can inform decision making in designing and optimising in frastructure such as transportation or energy. However, extracting patterns related to
spatial properties is challenging due to the large quantity of the data produced and the
complexity of the processes it describes. We propose scalable, multi-resolution approximation and heuristic algorithms that make use of spatial proximity properties to
solve fundamental data mining and optimisation problems with a better running time
and accuracy. We observe that abstracting from individual data points and working
with units of neighbouring points based on various measures on similarity, improves
computational efficiency and diminishes the effects of noise and overfitting. We consider applications in: mobility data compression, transit network planning, and solar
power output prediction.
Firstly, in order to understand transportation needs, it is essential to have efficient ways
to represent large amounts of travel data. In analysing spatial trajectories (for example
taxis travelling in a city), one of the main challenges is computing distances between
trajectories efficiently; due to their size and complexity this task is computationally
expensive. We build data structures and algorithms to sketch trajectory data that make
queries such as distance computation, nearest neighbour search and clustering, which
are key to finding mobility patterns, more computationally efficient. We use locality
sensitive hashing, a technique that associates similar objects to the same hash.
Secondly, to build efficient infrastructure it is necessary to satisfy travel demand by
placing resources optimally. This is difficult due to external constraints (such as limits
on budget) and the complexity of existing road networks that allow for a large number
of candidate locations. For this purpose, we present heuristic algorithms for efficient
transit network design with a case study on cycling lane placement. The heuristic is
based on a new type of clustering by projection, that is both computationally efficient
and gives good results in practice.
Lastly, we devise a novel method to forecast solar power output based on numerical
weather predictions, clear sky predictions and persistence data. The ensemble of a
multivariate linear regression model, support vector machines model, and an artificial neural network gives more accurate predictions than any of the individual models.
Analysing the performance of the models in a suite of frameworks reveals that building
separate models for each self-contained area based on weather patterns gives a better
accuracy than a single model that predicts the total. The ensemble can be further improved by giving performance-based weights to the individual models. This suggests
that the models identify different patterns in the data, which motivated the choice of an
ensemble architecture
On learning the structure of clusters in graphs
Get PDFGraph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs.
The first part of the thesis studies the classical spectral clustering algorithm, and presents a tighter analysis on its performance. This result explains why it works under a much weaker and more natural condition than the ones studied in the literature, and helps to close the gap between the theoretical guarantees of the spectral clustering algorithm and its excellent empirical performance.
The second part of the thesis builds on the theoretical guarantees of the previous part and shows that, when the clusters of the underlying graph have certain structures, spectral clustering with fewer than k eigenvectors is able to produce better output than classical spectral clustering in which k eigenvectors are employed, where k is the number of clusters. This presents the first work that discusses and analyses the performance of spectral clustering with fewer than k eigenvectors, and shows that general structures of clusters can be learned with spectral methods.
The third part of the thesis considers efficient learning of the structure of clusters with local algorithms, whose runtime depends only on the size of the target clusters and is independent of the underlying input graph. While the objective of classical local clustering algorithms is to find a cluster which is sparsely connected to the rest of the graph, this part of the thesis presents a local algorithm that finds a pair of clusters which are densely connected to each other. This result demonstrates that certain structures of clusters can be learned efficiently in the local setting, even in the massive graphs which are ubiquitous in real-world applications.
The final part of the thesis studies the problem of learning densely connected clusters in hypergraphs. The developed algorithm is based on a new heat diffusion process, whose analysis extends a sequence of recent work on the spectral theory of hypergraphs. It allows the structure of clusters to be learned in datasets modelling higher-order relations of objects and can be applied to efficiently analyse many complex datasets occurring in practice.
All of the presented theoretical results are further extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data
Machine learning and privacy preserving algorithms for spatial and temporal sensing
Get PDFSensing physical and social environments are ubiquitous in modern mobile phones,
IoT devices, and infrastructure-based settings. Information engraved in such
data, especially the time and location attributes have unprecedented potential
to characterize individual and crowd behaviour, natural and technological processes.
However, it is challenging to extract abstract knowledge from the data
due to its massive size, sequential structure, asynchronous operation, noisy characteristics,
privacy concerns, and real time analysis requirements. Therefore, the
primary goal of this thesis is to propose theoretically grounded and practically
useful algorithms to learn from location and time stamps in sensor data. The
proposed methods are inspired by tools from geometry, topology, and statistics.
They leverage structures in the temporal and spatial data by probabilistically
modeling noise, exploring topological structures embedded, and utilizing statistical
structure to protect personal information and simultaneously learn aggregate
information. Proposed algorithms are geared towards streaming and distributed
operation for efficiency. The usefulness of the methods is argued using mathematical
analysis and empirical experiments on real and artificial datasets
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