A Variational Autoencoder (VAE) is an extension of the traditional autoencoder that incorporates probabilistic modeling. VAEs aim to generate new data points by learning the underlying probability distribution of the input data. Unlike a standard autoencoder, which produces a deterministic encoding, a VAE produces a probabilistic encoding. This probabilistic encoding allows for sampling different points in the latent space, enabling the generation of diverse and realistic data samples.

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ICeTEA: Mixture of Detectors for Metric-Log Anomaly Detection

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Anomaly detection is essential for identifying unusual system behaviors and has wide-ranging applications, from fraud detection to system monitoring. In web servers, anomalies are typically detected using two types of data: metrics (numerical indicators of performance) and logs (records of system events). While correlations between metrics and logs in real-world scenarios highlight the need for joint analysis, which is termed the “metric-log anomaly detection” problem, it has not been fully explored yet due to inherent differences between metrics and logs. In this paper, we propose ICeTEA, a novel system for metric-log anomaly detection that integrates three detectors: a metric-log detector based on a multimodal Variational Autoencoder (VAE), and two individual metric and log detectors. By leveraging the ensemble technique to combine outputs of these detectors, ICeTEA enhances the effectiveness and robustness of metric-log anomaly detection. Case studies demonstrate two key functionalities of ICeTEA: data visualization and rankings of contributions to anomaly scores. Experiments demonstrate that our proposed ICeTEA accurately detects true anomalies while significantly reducing false positives.

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Video Generation From Text

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Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called “gist,” are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs.

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