Content-Aware Depth-Adaptive Image Restoration

This work prioritizes building a modular pipeline that utilizes existing models to systematically restore images, rather than creating new restoration models from scratch. Restoration is carried out at an object-specific level, with each object regenerated using its corresponding class label information. The approach stands out by providing complete user control over the entire restoration process. Users can select models for specialized restoration steps, customize the sequence of steps to meet their needs, and refine the resulting regenerated image with depth awareness. The research provides two distinct pathways for implementing image regeneration, allowing for a comparison of their respective strengths and limitations. The most compelling aspect of this versatile system is its adaptability. This adaptability enables users to target particular object categories, including medical images, by providing models that are trained on those object classes.

Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

System logs are a common source of monitoring data for analyzing computing systems' behavior. Due to the complexity of modern computing systems and the large size of collected monitoring data, automated analysis mechanisms are required. Numerous machine learning and deep learning methods are proposed to address this challenge. However, due to the existence of sensitive data in system logs their analysis and storage raise serious privacy concerns. Anonymization methods could be used to clean the monitoring data before analysis. However, anonymized system logs, in general, do not provide adequate usefulness for the majority of behavioral analysis. Content-aware anonymization mechanisms such as PaRS preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs taken from the Taurus HPC cluster anonymized using PaRS, for behavioral analysis via recurrent neural network models.

Anomaly Detection in High Performance Computers: A Vicinity Perspective

In response to the demand for higher computational power, the number of computing nodes in high performance computers (HPC) increases rapidly. Exascale HPC systems are expected to arrive by 2020. With drastic increase in the number of HPC system components, it is expected to observe a sudden increase in the number of failures which, consequently, poses a threat to the continuous operation of the HPC systems. Detecting failures as early as possible and, ideally, predicting them, is a necessary step to avoid interruptions in HPC systems operation. Anomaly detection is a well-known general purpose approach for failure detection, in computing systems. The majority of existing methods are designed for specific architectures, require adjustments on the computing systems hardware and software, need excessive information, or pose a threat to users' and systems' privacy. This work proposes a node failure detection mechanism based on a vicinity-based statistical anomaly detection approach using passively collected and anonymized system log entries. Application of the proposed approach on system logs collected over 8 months indicates an anomaly detection precision between 62% to 81%.