1,720,989 research outputs found
Extended B-ALIF: Improving Anomaly Detection with Human Feedback
No full textAnomaly Detection is a task in engineering aiming at identifying deviations from expected patterns in data. Data-driven approaches have emerged in past recent years due to the fact that a model of complex system may be hard or impossible to be derived in many scenarios. Moreover, unsupervised approaches have been particularly appealing for practitioners and scientists given the typical unavailability of tagged data. Such approaches are often integrated in frameworks, like Decision Support Systems, that assist domain experts and operators in the monitoring task. Human presence, by providing a limited amount of feedback, can be leveraged as a valuable source of information to iteratively enhance detection performance. In this work we introduce Extended B-ALIF, a framework designed to incrementally select and integrate expert feedback into the Extended Isolation Forest anomaly detection model. This study extends Bayesian Active Learning Isolation Forest (B-ALIF), which originally proposed the same theoretical principles for another anomaly detection model, the Isolation Forest
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
Fault Identification Enhancement with Reinforcement Learning (FIERL)
No full textThis letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control input design. This formulation is very general, and most existing AFD literature can be viewed through this lens. By recognizing this separation, PFD methods can be leveraged to provide components that make efficient use of the available information, while the control input is designed in order to optimize the gathering of information. The core contribution of this work is FIERL, a general simulation-based approach for the design of such control strategies, using Constrained Reinforcement Learning (CRL) to optimize the performance of arbitrary passive detectors. The control policy is learned without the need of knowing the passive detector inner workings, making FIERL broadly applicable. However, it is especially useful when paired with the design of an efficient passive component. Unlike most AFD approaches, FIERL can handle fairly complex scenarios such as continuous sets of fault modes. The effectiveness of FIERL is tested on a benchmark problem for actuator fault diagnosis, where FIERL is shown to be fairly robust, being able to generalize to fault dynamics not seen in training
Active Learning-based Isolation Forest (ALIF): Enhancing anomaly detection with expert feedback
No full textThe detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical. The definition of anomaly varies depending on the domain; however, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to overcome the lack of labels often fails to catch domain-specific anomalies as they rely on general definitions of outliers. This paper suggests a novel approach to address this problem, Active Learning-based Isolation Forest (ALIF), reducing the number of required labels and tuning the detector to the definition of anomaly provided by the user. The proposed approach is particularly appealing in scenarios where users can interact and provide feedback to the anomaly detector. Smart monitoring software embedded with anomaly detection capabilities commonly relies on unsupervised models, lacking a way to adjust its prediction: ALIF is able to enhance the capabilities of such systems by exploiting user feedback during common operations. ALIF is a lightweight modification of the popular Isolation Forest that proved superior performance compared to other state-of-the-art algorithms in a multitude of real anomaly detection datasets
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
- …
