Predicting Academic Performance

This paper discussed advantages and disadvantages associated with the use of "admission tests" as predictors of performance in undergraduate studies programs. The paper analyzes performance of economics and business administration students. This performance is linked to admission tests results. The paper also analyzes aspects of performance related to (i) differential progress through time, and (ii) differences in the extent to which students have "areas of interest/ability". The paper concludes that admission tests are a usefull tool even when predictions derived from them are far from perfect.

Method for predicting pump cavitation performance

Method requires the availability of two sets of appropriate data for each pump to be analyzed. At least one set of the data must provide measurable thermodynamic effects of cavitation

Predicting Intermediate Storage Performance for Workflow Applications

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different storage configurations). To enable selecting the best configuration in a reasonable time, we design an end-to-end performance prediction mechanism that estimates the turn-around time of an application using storage system under a given configuration. This approach focuses on a generic object-based storage system design, supports exploring the impact of optimizations targeting workflow applications (e.g., various data placement schemes) in addition to other, more traditional, configuration knobs (e.g., stripe size or replication level), and models the system operation at data-chunk and control message level. This paper presents our experience to date with designing and using this prediction mechanism. We evaluate this mechanism using micro- as well as synthetic benchmarks mimicking real workflow applications, and a real application.. A preliminary evaluation shows that we are on a good track to meet our objectives: it can scale to model a workflow application run on an entire cluster while offering an over 200x speedup factor (normalized by resource) compared to running the actual application, and can achieve, in the limited number of scenarios we study, a prediction accuracy that enables identifying the best storage system configuration

Predicting Face Recognition Performance Using Image Quality

This paper proposes a data driven model to predict the performance of a face recognition system based on image quality features. We model the relationship between image quality features (e.g. pose, illumination, etc.) and recognition performance measures using a probability density function. To address the issue of limited nature of practical training data inherent in most data driven models, we have developed a Bayesian approach to model the distribution of recognition performance measures in small regions of the quality space. Since the model is based solely on image quality features, it can predict performance even before the actual recognition has taken place. We evaluate the performance predictive capabilities of the proposed model for six face recognition systems (two commercial and four open source) operating on three independent data sets: MultiPIE, FRGC and CAS-PEAL. Our results show that the proposed model can accurately predict performance using an accurate and unbiased Image Quality Assessor (IQA). Furthermore, our experiments highlight the impact of the unaccounted quality space -- the image quality features not considered by IQA -- in contributing to performance prediction errors.Comment: Submitted to TPAMI journal on Apr. 22, 2015. Decision of "Revise and resubmit as new" received on Sep. 10, 2015. At present, updating the paper to address the feedback and concerns of the two reviewers. The re-submitted paper will be uploaded as version 2 on arXi