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Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data.
This paper presents...
In recent years, mass atrocities, terrorism, and political unrest have caused much human suffering. Thousands of innocent lives have been lost to these events. With the help of advanced technologies, we can now dream of a tool that uses machine learning and natural language processing (NLP) techniques to warn of such events. Detecting atrocities demands structured event data that contain metadata,...
Political event data have been widely used to study international politics. Previously, natural text processing and event generation required a lot of human efforts. Today we have high computing infrastructure with advance NLP metadata to leverage those tiresome efforts. TABARI -- an open source non distributed event-coding software -- was an early effort to generate events from a large corpus. It...
Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Depending on the domain, the non-conformant patterns are assigned various tags, e.g. anomalies, outliers, exceptions, malwares and so forth. Online anomaly detection aims to detect anomalies in data flowing in a streaming fashion. Such stream data is commonplace in today's cloud data centers...
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