DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters

When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score.Comment: 13 pages, 5 figures, accepted by 2017 ECML PKD

The owl spreads its wings: global and international education within the local from critical perspectives

Within an era of a New Knowledge Society, assumptions abound regarding the ‘goodness' and justice of global interconnections and distributions of knowledge through international educational organizations and structures worldwide. Just as George Bush Jr. in attempting to justify the invasion of Iraq made claim to the democratic goodness of the US ‘spreading their freedoms' in the interests of an all-encompassing democratization of the world, so the assumption that sharing educational knowledge, especially an ‘all-knowing North' with a ‘helpless South' is without question for the greater good of all humanity

School-based Sexuality Education Experiences across Three Generations of Sexual Minority People

Sexual minority people face greater risk for compromised sexual health than their heterosexual peers, yet school-based sexuality education often excludes them. Little is known about whether or how sexual minority people's sexuality education experiences have varied across sociohistorical contexts of rapid social change in both sexuality education and sexual minority visibility. Semi-structured qualitative interviews were conducted among 191 sexual minority people from three distinct sociohistorical generations (ages 18-25, 34-41, and 52-59, respectively) and four geographic regions of the United States. Data were analyzed using thematic content analysis following a consensual qualitative protocol. Fifty-one participants (i.e., 27%) discussed school-based sexuality education experiences despite the lack of an explicit question in the interview protocol prompting them to do so. Four distinct yet overlapping themes emerged in participants' experiences of sexuality education: 1) Silence; 2) The profound influence of HIV/AIDS; 3) Stigma manifest through fear, shame, and prejudice; and, 4) Comparing school-based experiences to sexuality education outside of school. The presence of themes varied across groups defined by sociohistorical generation. The implications of sexuality education experiences for the wellbeing of sexual minority people are discussed

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019