Everaldo Aguiar

A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

Many school districts have developed successful intervention programs to help students graduate high school on time. However, identifying and prioritizing students who need those interventions the most remains challenging. This paper describes a machine learning framework to identify such students, discusses features that are useful for this task, applies several classification algorithms, and evaluates them using metrics important to school administrators. To help test this framework and make it practically useful, we partnered with two U.S. school districts with a combined enrollment of approximately 200,000 students. We together designed several evaluation metrics to assess the goodness of machine learning algorithms from an educators perspective. This paper focuses on students at risk of not finishing high school on time, but our framework lays a strong foundation for future work on other adverse academic outcomes.

Private and oblivious set and multiset operations

Privacy-preserving set operations and set intersection in particular are a popular research topic. Despite a large body of literature, the great majority of the available solutions are two-party protocols and are not composable. In this work we design a comprehensive suite of secure multi-party protocols for set and multiset operations that are composable, do not assume any knowledge of the sets by the parties carrying out the secure computation, and can be used for secure outsourcing. All of our protocols have communication and computation complexity of O(m log m) for sets or multisets of size m, which compares favorably with prior work. Furthermore, we are not aware of any results that realize composable operations. Our protocols are secure in the information theoretic sense and are designed to minimize the round complexity.

An Overview of Issues and Recent Developments in Cloud Computing and Storage Security

The recent rapid growth in the availability and popularity of cloud services allows for convenient on demand remote storage and computation. Security and privacy concerns, however, are among the top impediments standing in the way of wider adoption of cloud technologies. That is, in addition to the new security threats that emerge with the adoption of new cloud technology, a lack of direct control over one’s data or computation demands new techniques for service provider’s transparency and accountability. The goal of this chapter is to provide a broad overview of recent literature covering various aspects of cloud security. We describe recently discovered attacks on cloud providers and their countermeasures, as well as protection mechanisms that aim at improving privacy and integrity of client’s data and computations. The topics covered in this survey include authentication, virtualization, availability, accountability, and privacy and integrity of remote storage and computation.

Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time

Several hundred thousand students drop out of high school every year in the United States. Interventions can help those who are falling behind in their educational goals, but given limited resources, such programs must focus on the right students, at the right time, and with the right message. In this paper, we describe an incremental approach that can be used to select and prioritize students who may be at risk of not graduating high school on time, and to suggest what may be the predictors of particular students going off-track. These predictions can then be used to inform targeted interventions for these students, hopefully leading to better outcomes.

Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator

The collection and analysis of student-level data is quickly becoming the norm across school campuses. More and more institutions are starting to use this resource as a window into better understanding the needs of their student population. In previous work, we described the use of electronic portfolio data as a proxy to measuring student engagement, and showed how it can be predictive of student retention. This paper highlights our ongoing efforts to explore and measure the valence of positive and negative emotions in student reflections and how they can serve as an early warning indicator of student disengagement.

ALIVE: a multi-relational link prediction environment for the healthcare domain

An underlying assumption of biomedical informatics is that decisions can be more informed when professionals are assisted by analytical systems. For this purpose, we propose ALIVE, a multi-relational link prediction and visualization environment for the healthcare domain. ALIVE combines novel link prediction methods with a simple user interface and intuitive visualization of data to enhance the decision-making process for healthcare professionals. It also includes a novel link prediction algorithm, MRPF, which outperforms many comparable algorithms on multiple networks in the biomedical domain. ALIVE is one of the first attempts to provide an analytical and visual framework for healthcare analytics, promoting collaboration and sharing of data through ease of use and potential extensibility. We encourage the development of similar tools, which can assist in facilitating successful sharing, collaboration, and a vibrant online community.