A numerical algorithm for approximate multi-parametric nonlinear programming is developed. It allows approximate solutions to nonlinear optimization problems to be computed as explicit piecewise linear functions of the problem parameters. In control applications such as nonlinear constrained model predictive control this allows efficient online implementation in terms of an explicit piecewise l...

The purpose of this study is to investigate the effectiveness of response modes by item and feedback type in a webbased language learning program. The subjects of this study, 122 Korean tenth graders learning English as a foreign language, were placed into groups of four and were given a web-based language learning program consisting of two different varieties of item type (multiple choice and ...

by Parisa Rashidi, M.S. Washington State University December 2007 Chair: Diane J. Cook In current work we introduce CASAS, an adaptive smart home system that utilizes machine learning techniques in order to dynamically adapt to user advice or changes in daily routine activities. The main components of CASAS include a frequent and periodic activity miner (FPAM), a hierarchal activity model (HAM)...

The current study examined the provision of corrective feedback and learner repair following feedback in the interactional context of child-to-child conversations, particularly computer mediated, in an elementary Spanish immersion class. The relationship among error types, feedback types, and immediate learner repair were also examined. A total of 46, fifth-grade children participated in the st...

How can self-knowledge of personality be improved? What path is the most fruitful source for learning about our true selves? Previous research has noted two main avenues for learning about the self: looking inward (e.g., introspection) and looking outward (e.g., feedback). Although most of the literature on these topics does not directly measure the accuracy of self-perceptions (i.e., self-know...

Solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear (PWL) state feedback defined on a polyhedral partitioning of the state space. Even though real-time optimization is avoided, implementation of the PWL state feedback may still require a significant amount of computations. We suggest an algorithm that wil...

Measuring the quality of recommendations produced by a recommender system (RS) is challenging. Labels used for the evaluation are typically obtained from users of a RS; such explicit labels reflect true user preferences but may introduce significant biases in the evaluation process. In this paper, we investigate biases that may affect labels inferred from implicit feedback, such as clicks or ot...

Feedback is an important technique in Information Retrieval to have users provide contextual information about their search needs, with the goal of improving retrieval accuracy and achieving personalization. Relevance feedback has been studied extensively, and in recent years new types of feedback such as implicit feedback and collective feedback have attracted much research interest. However, ...

This paper proposes a new method for evaluating the quality of retrieval functions. Unlike traditional methods that require relevance judgements by experts or explicit user feedback, it is based entirely on clickthrough data. This is a key advantage, since clickthrough data can be collected at very low cost and without overhead for the user. Taking an approach from experiment design, the paper ...

This paper proposes a new method for evaluating the quality of retrieval functions. Unlike traditional methods that require relevance judgments by experts or explicit user feedback, it is based entirely on clickthrough data. This is a key advantage, since clickthrough data can be collected at very low cost and without overhead for the user. Taking an approach from experiment design, the paper p...