Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Psychological research online: report of Board of Scientific Affairs' Advisory Group on the Conduct of Research on the Internet.

Abstract: As the Internet has changed communication, commerce, and the distribution of information, so too it is changing psychological research. Psychologists can observe new or rare phenomena online and can do research on traditional psychological topics more efficiently, enabling them to expand the scale and scope of their research. Yet these opportunities entail risk both to research quality and to human subjects. Internet research is inherently no more risky than traditional observational, survey, or experimental methods. Yet the risks and safeguards against them will differ from those characterizing traditional research and will themselves change over time. This article describes some benefits and challenges of conducting psychological research via the Internet and offers recommendations to both researchers and institutional review boards for dealing with them. ((c) 2004 APA, all rights reserved)

Aperiodic task scheduling for real-time systems

Abstract: This thesis develops the Sporadic Server (SS) algorithm for scheduling aperiodic tasks in real-time systems. The SS algorithm is an extension of the rate monotonic algorithm which was designed to schedule periodic tasks. This thesis demonstrates that the SS algorithm is able to guarantee deadlines for hard-deadline aperiodic tasks and provide good responsiveness for soft-deadline aperiodic tasks while avoiding the schedulability penalty and implementation complexity of previous aperiodic service algorithms. It is also proven that the aperiodic servers created by the SS algorithm can be treated as equivalently-sized periodic tasks when assessing schedulability. This allows all the scheduling theories developed for the rate monotonic algorithm to be used to schedule aperiodic tasks. For scheduling aperiodic and periodic tasks that share data, this thesis defines the interactions and schedulability impact of using the SS algorithm with the priority inheritance protocols. For scheduling hard-deadline tasks with short deadlines, an extension of the rate monotonic algorithm and analysis is developed. To predict performance of the SS algorithm, this thesis develops models and equations that allow the use of standard queueing theory models to predict the average response time of soft-deadline aperiodic tasks serviced with a high-priority sporadic server. Implementation methods are also developed to support the SS algorithm in Ada and on the Futurebus+.

Using Maximum Entropy for Text Classification

Abstract: This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety of natural language tasks, such as language modeling, part-of-speech tagging, and text segmentation. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Constraints on the distribution, derived from labeled training data, inform the technique where to be minimally non-uniform. The maximum entropy formulation has a unique solution which can be found by the improved iterative scaling algorithm. In this paper, maximum entropy is used for text classification by estimating the conditional distribution of the class variable given the document. In experiments on several text datasets we compare accuracy to naive Bayes and show that maximum entropy is sometimes significantly better, but also sometimes worse. Much future work remains, but the results indicate that maximum entropy is a promising technique for text classification.

Clustering data streams: Theory and practice

Abstract: The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm's performance on synthetic and real data streams.

A Predictive Approach to Model Selection

Abstract: This article offers a synthesis of Bayesian and sample-reuse approaches to the problem of high structure model selection geared to prediction. Similar methods are used for low structure models. Nested and nonnested paradigms are discussed and examples given.