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PERRY: A Flexible and Scalable Data Preprocessing System for "ML for Networks" Pipelines
Get PDFThe integration of machine learning techniques into networking research has catalyzed significant advancements in areas such as traffic classification, intrusion detection, and quality of experience (QoE) estimation. This progress has been fueled by remarkable developments in deep learning, leading to state-of-the-art models in various domains, leveraging powerful neural networks, encoders, transformers, and language model architectures.Developing these complex ML-based models relies heavily on the data pre-processing module to extract features from the raw network data (e.g., packet traces) and add labels to different data points. Different model specifications require extracting disparate sets of features. Currently, there is a tight coupling between the data pre-processing and model training modules in the ML pipelines used for developing ML artifacts for networking. Specifically, the pre-processing modules are only suited to extract a limited set of features (e.g., extract time series features) that are suitable for specific downstream model specifications (e.g., LSTM). Consequently, researchers exploring new learning models for different networking problems end up spending a significant amount of their time developing custom data pre-processing modules, impeding the pace of innovation.This thesis focuses on decoupling data pre-processing from model training in ML pipelines for networking. Specifically, we present the design and implementation of PERRY, a flexible data pre-processing module for networking that can extract a wide range of (high-quality) features at scale that can be consumed by disparate model specifications for model training. PERRY offers an intuitive user interface that allows developers to express their data pre-processing intents. More concretely, PERRY supports three distinct classes of features: packet content, time series, and aggregate statistics. For each class, it lets the user specify different parameters. For instance, the user can express which set of fields (e.g., timestamp, number of bytes, etc.) to use for time series features and at what granularity (e.g., per packet, burst, or flow). Similarly, it lets the user select which set of aggregate features to extract and at what granularity.To scale the pre-processing tasks, PERRY leverages state-of-the-art data analytics and storage tools—making the best use of limited computing and storage resources. Specifically, it decomposes the pre-processing task at flow-level granularity. Such decomposition offers horizontal scalability offered by existing tools without compromising the semantic integrity of the extracted features. Further, to minimize wasteful data processing, it offers a hybrid schema that aims to strike a balance between expressiveness and scale. Specifically, this schema only exposes a subset of popular features to the user, offering pointers to raw data. Such an approach ensures that only a subset of features is extracted for network traffic, and more complex features are dynamically extracted from a subset of network traffic on demand. By decoupling data pre-processing and model training in ML pipelines for networking, PERRY lowers the threshold for developing new ML models in networking. PERRY represents a step forward in simplifying and enhancing data processing in networking research and opens new possibilities for future innovations in the field
PERRY: A Flexible and Scalable Data Preprocessing System for "ML for Networks" Pipelines
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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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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