1,721,127 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
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Embedding Security into Systems After Their Design
Get PDFSecurity is rarely designed into systems and architectures from the beginning. Typically, security enters into the design process only after applications are built and security issues arise. While security is often dependent on specific use cases, decades of development provide an opportunity to synthesize common security needs into a set of critical features and embed them into the core underlying systems.Please don't get this dissertation through ProQuest. Copies are freely available from the UC Santa Cruz library and the University of California's open access eScholarship initiative (see escholarship.org) and my website: capelis.d
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Dynamic performance enhancement of scientific networks and systems
Get PDFLarge distributed storage systems such as High Performance Computing (HPC) systems used by national or international laboratories must deliver performance for demanding scientific workloads that pose challenges from scale, complexity, and dynamism. Ideally, data is placed in locations and network routes are set to optimize performance, but the aforementioned challenges of large scientific systems and networks inhibits adaptive performance tuning on a large scale.If the data is not placed or routed with the potential future access patterns, the system risks having popular data stored on a slow file system. This can cause a steep drop in performance when access demand shifts to that data. Ideally, determining when and how to move data around in anticipation of future demand spikes can prevent steep drops in performance, and we refer to these drops as performance bottlenecks. We can leverage the fact that workloads shift through time, creating access patterns that can be used to forecast demand. Past accesses can be used to reveal important information when determining future accesses.An existing solution for determining where to move the data is to use access logs to create system performance models that can predict how the system reacts to fluctuations in demand. The drawback is that the search space of how to build an effective model is massive. The more there are locations where the data can be stored to larger the search space becomes. Additionally, applying the model to a system can potentially negatively impact any performance benefits of dynamically moving the data around the system. If a model is not set up correctly, the added overhead of using the model on the system may overshadow the benefit of applying the model on a target system. Ideally, the models created by the system engineers place and route the data to optimize performance, but workloads shift can be hard to predict using traditional methods such as heuristics.This dissertation applies and enhances online learning to predict and accelerate the performance of data movements and accesses in large scale storage systems. We focus on large scientific systems and networks like the CERN EOS, Pacific Northwest National Laboratory’s (PNNL) BlueSky system and the Caltech Tier 2 system. These systems allow for us to stress test our models with real data. Additionally it allows us to collect information about how accesses are distributed and executed on real systems. From the collected information we are able to create models based on real workloads. We are also able to reduce the training overhead of our models by determining which features may not bring added information to a model during training, and removing them. We developed a methodology to select features over time that is resilient to data collection noise added by new data collected from the system.Access logs created by system engineers often track hundreds of metrics describing its daily operation, and we must sift through these metrics to discover the most relevant metrics for the modeling task at hand. To explore different feature selection techniques, we have developed WinnowML, a system of automatically determining the most relevant feature subset for a specific modeling methodology when modeling system performance over time. We use WinnowML to determine what combination of existing techniques allow us to get a stable selected subset of features which will not vary significantly over time while keeping a low prediction error. Using the created list of features, system analysts can determine what features should be used when modeling an aspect of their system over time. Using WinnowML lowered the resulting mean absolute error by 13.6\% on average compared to the closest performing approach such as L1-regularization or Principal Component Analysis (PCA).To optimize the placement of data, we developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Additionally to optimize the network routing decisions, we developed Diolkos, a tool that dynamically reroutes data flow in response to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy and Diolkos determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout and routing decisions to mitigate or prevent them. Using WinnowML to determine which features to use when training our Geomancy tool, the predicted data layouts of Geomancy offered benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11\% to 30\%. It managed to free up resources that other workloads running concurrently could use. We then moved on to tackle the data transfer overhead in scientific networks. There exist several techniques that reroute data within scientific networks while improving network performance, leveraging latent parallel data transfer capabilities. The issue with these techniques is that they require a central authority, which may add computational and network overhead to the system. We propose a decentralized rerouting technique that exists at the switch level. When we applied Diolkos to the Caltech Tier 2 network, we found that our most accurate model, a dense model with one hidden layer trained using all the ports, increases throughput of switches up to 49\% compared to the best performing heuristic approach, exponentially weighted moving average, and up to 28\% when using a traditional controller
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Improving File Management Through Provenance And Rich Metadata
Get PDFModern high end computing systems store hundreds of petabytes of data and have billions of files, as many files as the internet of only a few years ago. Even modern personal computers store numbers of files that would be massive for the largest mainframe computers of forty years ago. The quantities of data in modern computing have long since overwhelmed anyone's ability to manage it manually, and the forty year old tools currently in use for file finding and management are reaching the limits of scale. In an environment like this, secure, effective, and efficient search algorithms and automatic file management become a necessity, not a nicety.This dissertation addresses the question of how users can better find and manage files by taking advantage of advances in file systems. We focus on a multi-user scientific computing environment, but many of the techniques we describe are effective and advantageous at desktop scale as well. We begin with an empirical description of the problem, drawn from user studies and our statistical analysis of scientific data, in order to better understand the problem domain. We then describe a new technique for studying provenance in scientific systems, and a technique to synthesize system level provenance from existing traces. We describe our novel algorithm designed to provide importance ranking for file system search by leveraging provenance, and discuss the relationship between ranking and access prediction. And finally, we show how rich metadata can be used to improve file management by automatically generating expressive, unique file names.Modern file management must be automatic and scalable, allowing users and file systems to focus on what each does best. By exploiting richer information such as provenance and semantic metadata, file systems can offer sophisticated new capabilities to ease the burdens of users, making file systems easier to use, navigate, and understand
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