1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
RoamML Platform: Enabling Distributed Continual Learning for Disaster Relief Operations
No full textMachine learning offers a promising avenue for improving the efficiency and effectiveness of decision-making in disaster recovery and relief efforts. These operations face significant hurdles due to the large volumes of data, intermittent connectivity, and infrastructure limitations. In this paper, we present the RoamML Platform, a sophisticated modular implementation of the RoamML framework, designed specifically to address these challenges and enable efficient distributed machine learning. We advocate for a foundational principle that "the transmission of the ML model itself is usually more efficient than the costly transfer of large datasets", leading to a more adaptable training regime. The platform orchestrates the activities of the RoamML model along with its related metadata, collectively referred to as the "RoamML Agent", while faithfully observing the Data Gravity principle to guarantee thorough model training. We extensively validated the platform through a simulated disaster recovery scenario employing the Mininet-WiFi emulator. Our results highlight the benefits of integrating the RoamML framework, including enhanced ML performance and significant bandwidth savings
A Machine Learning Operations Platform for Streamlined Model Serving in Industry 5.0
No full textMachine Learning (ML) plays an increasingly important role in many Big Data applications in Industry 5.0: predictive maintenance, zero defect manufacturing, process and/or supply chain optimization, etc. However, the dynamic and high-stakes nature of the manufacturing environment requires ML models to be maintained through continuous monitoring, periodical reevaluation, and possible retraining to ensure they remain accurate and relevant to the actual context. In addition, to match the desired performance (as well as security and safety) requirements ML models need to be executed in different locations along the edge-to-Cloud continuum (and possibly migrated in case of need), on dedicated serving runtimes that suit the specific needs of the use case. To address these issues, we realized an MLOps platform that is capable of managing ML models through their entire lifecycle and enabling their deployment in different ML serving runtimes. More specifically, the initial experimental evaluation presented in the paper focuses on Bento Yatai and TorchServe serving runtimes. It demonstrates that our platform is capable of effectively running ML models on both runtimes and provides a comparative evaluation at both the quantitative and qualitative levels
RoamML: Distributed Machine Learning at the Tactical Edge
No full textMachine learning is an effective methodology for enabling real-time data-driven decision-making in tactical scenarios, but its application in such scenarios raises many challenges due to data volume, unpredictable connectivity, and infrastructural challenges in edge environments. Furthermore, the need to perform training operations on remote powerful computing nodes might not be suited for tactical edge networks that often lack high bandwidth links, thus causing critical delays in assessing relevant information for decision-making. To overcome these challenges and enable machine learning at the tactical edge, this paper presents RoamML, a novel distributed continual learning approach tailored explicitly for the tactical edge. Built upon the foundational principle that “moving the model is usually much cheaper than transferring extensive datasets”, RoamML seamlessly performs training operations by traversing network nodes, according to the data gravity concept. As RoamML encounters new data at each node, it continually trains on the encountered data to ensure that RoamML maintains an up-to-date and accurate model without transferring data to a centralized entity. Experimental results comparing RoamML with a baseline centralized machine learning solution show the potential of the proposed approach, which is capable of closely matching the accuracy of the baseline method
An MLOps Framework for GAN-based Fault Detection in Bonfiglioli’s EVO Plant
No full textIn Industry 5.0, the scarcity of data on defective components in smart manufacturing leads to imbalanced data sets. This imbalance poses a significant challenge to the develop ment of robust Machine Learning (ML) models, which typically require a rich variety of data for effective training. The imbal ance not only restricts the models’ accuracy but also their ap plicability in diverse industrial scenarios. To tackle this issue, our research delves into the capabilities of Deep Generative Models, with a special focus on Generative Adversarial Networks, for the generation of synthetic data. This approach is aimed at rectify ing dataset imbalances, thereby enhancing the training process of ML models. We demonstrate how synthetic data can substan tially bolster the performance and reliability of ML models in industrial settings. Furthermore, the paper presents an innova tive MLOps pipeline and architecture, meticulously designed to incorporate Deep Generative Models (DGMs) into the entire ML development cycle. This solution is automated and goes beyond mere automation; it is self-optimizing and capable of making necessary corrections, specifically engineered to address the dual challenges of data imbalance and scarcity, thus enabling more precise and dependable ML applications in smart manufacturing
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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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