Topic
Class (philosophy)
About: Class (philosophy) is a research topic. Over the lifetime, 821 publications have been published within this topic receiving 28000 citations.
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TL;DR: In this paper , a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL) via assigning developers and allocating issue types in the bug triage process.
12 citations
Journal Article•
TL;DR: A (combinatorial) channel is a set of pairs of words describing all the possible input-output channel situations, and the concept "maximal error-detecting capability" of a given language is introduced with respect to a certain class of channels.
Abstract: A (combinatorial) channel is a set of pairs of words describing all the possible input-output channel situations. We introduce the concept "maximal error-detecting capability" of a given language, with respect to a certain class of channels, which is simply a maximal channel for which the given language is error-detecting. The new concept is intended to address formally the question of "finding the largest amount of errors that a language can detect". We focus on rational channels (those described by finite transducers) and on regular languages, and consider the problem of computing maximal error-detecting capabilities of a given regular language for various classes of rational channels. We also discuss briefly the concept "maximal error-correcting capability" of a formal language.
12 citations
01 Jan 2011
TL;DR: In this article, the authors present the results of un estudio cualitativo con el objetivo of ver como this modelo apoya el avance linguistico.
Abstract: Amplios estudios sobre la e nsenanza Basada en Contenidos ( e BC) evidencian la efectividad de este modelo en el desarrollo de la lengua y conocimiento de contenido; sin embargo, hay poca explicacion sobre el por que de esta efectividad. Ampliando un estudio anterior, este articulo presenta los resultados de un estudio cualitativo con el objetivo de ver como este modelo apoya el avance linguistico. Los datos de las entrevistas y diarios de los participantes revelaron que el uso del material autentico que era significativo, interesante y relevante a las necesidades actuales y futuras de los estudiantes, la activacion del conocimiento previo y la metodologia especifica utilizada en clase demostro ser de gran utilidad al ayudar a los estudiantes a desarrollar el idioma
12 citations
TL;DR: In this article , the authors used wearable EEG, VR, and machine learning to classify seven different types of hazards (e.g., fall and slip/trip) in an immersive virtual reality (VR) environment.
12 citations
TL;DR: In this article , the concept of spectrum of a user and a class of users is introduced for multi-class classification of Ethereum users based on their past behavior, and a metric capable of measuring the similarity degree between the spectrum of user and the one of a class is proposed.
Abstract: Abstract Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior.
12 citations