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- Genre: Masters Thesis
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaic (PV) systems are one of the next generation's renewable energy sources for our world energy demand. PV modules are highly reliable. However, in polluted environments, over time, they will collect grime and dust. There are also limited field data studies about soiling losses on PV modules. The study showed how important it is to investigate the effect of tilt angle on soiling. The study includes two sets of mini-modules. Each set has 9 PV modules tilted at 0, 5, 10, 15, 20, 23, 30, 33 and 40°. The first set called "Cleaned" was cleaned every other day. The second set called "Soiled" was never cleaned after the first day. The short circuit current, a measure of irradiance, and module temperature was monitored and recorded every two minutes over three months (January-March 2011). The data were analyzed to investigate the effect of tilt angle on daily and monthly soiling, and hence transmitted solar insolation and energy production by PV modules. The study shows that during the period of January through March 2011 there was an average loss due to soiling of approximately 2.02% for 0° tilt angle. Modules at tilt anlges 23° and 33° also have some insolation losses but do not come close to the module at 0° tilt angle. Tilt anlge 23° has approximately 1.05% monthly insolation loss, and 33° tilt angle has an insolation loss of approximately 0.96%. The soiling effect is present at any tilt angle, but the magnitude is evident: the flatter the solar module is placed the more energy it will lose.
ContributorsCano Valero, José (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Madakannan, Arunachalanadar (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011
Description
Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.
ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis research attempts to observe, measure and visualize the communication patterns among developers of an open source community and analyze how this can be inferred in terms of progress of that open source project. Here I attempted to analyze the Ubuntu open source project's email data (9 subproject log archives over a period of five years) and focused on drawing more precise metrics from different perspectives of the communication data. Also, I attempted to overcome the scalability issue by using Apache Pig libraries, which run on a MapReduce framework based Hadoop Cluster. I described four metrics based on which I observed and analyzed the data and also presented the results which show the required patterns and anomalies to better understand and infer the communication. Also described the usage experience with Pig Latin (scripting language of Apache Pig Libraries) for this research and how they brought the feature of scalability, simplicity, and visibility in this data intensive research work. These approaches are useful in project monitoring, to augment human observation and reporting, in social network analysis, to track individual contributions.
ContributorsMotamarri, Lakshminarayana (Author) / Santanam, Raghu (Thesis advisor) / Ye, Jieping (Thesis advisor) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
Description
Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic to source trustworthiness and result importance, which, given the autonomous and uncurated nature of deep-web, have become indispensible for searching deep-web. SourceRank provides a global measure for assessing source quality based on source trustworthiness and result importance. SourceRank's effectiveness has been evaluated in single-topic deep-web environments. The goal of the thesis is to extend sourcerank to a multi-topic deep-web environment. Topic-sensitive sourcerank is introduced as an effective way of extending sourcerank to a deep-web environment containing a set of representative topics. In topic-sensitive sourcerank, multiple sourcerank vectors are created, each biased towards a representative topic. At query time, using the topic of query keywords, a query-topic sensitive, composite sourcerank vector is computed as a linear combination of these pre-computed biased sourcerank vectors. Extensive experiments on more than a thousand sources in multiple domains show 18-85% improvements in result quality over Google Product Search and other existing methods.
ContributorsJha, Manishkumar (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Ever reducing time to market, along with short product lifetimes, has created a need to shorten the microprocessor design time. Verification of the design and its analysis are two major components of this design cycle. Design validation techniques can be broadly classified into two major categories: simulation based approaches and formal techniques. Simulation based microprocessor validation involves running millions of cycles using random or pseudo random tests and allows verification of the register transfer level (RTL) model against an architectural model, i.e., that the processor executes instructions as required. The validation effort involves model checking to a high level description or simulation of the design against the RTL implementation. Formal techniques exhaustively analyze parts of the design but, do not verify RTL against the architecture specification. The focus of this work is to implement a fully automated validation environment for a MIPS based radiation hardened microprocessor using simulation based approaches. The basic framework uses the classical validation approach in which the design to be validated is described in a Hardware Definition Language (HDL) such as VHDL or Verilog. To implement a simulation based approach a number of random or pseudo random tests are generated. The output of the HDL based design is compared against the one obtained from a "perfect" model implementing similar functionality, a mismatch in the results would thus indicate a bug in the HDL based design. Effort is made to design the environment in such a manner that it can support validation during different stages of the design cycle. The validation environment includes appropriate changes so as to support architecture changes which are introduced because of radiation hardening. The manner in which the validation environment is build is highly dependent on the specifications of the perfect model used for comparisons. This work implements the validation environment for two MIPS simulators as the reference model. Two bugs have been discovered in the RTL model, using simulation based approaches through the validation environment.
ContributorsSharma, Abhishek (Author) / Clark, Lawrence (Thesis advisor) / Holbert, Keith E. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT To meet stringent market demands, manufacturers must produce Radio Frequency (RF) transceivers that provide wireless communication between electronic components used in consumer products at extremely low cost. Semiconductor manufacturers are in a steady race to increase integration levels through advanced system-on-chip (SoC) technology. The testing costs of these devices tend to increase with higher integration levels. As the integration levels increase and the devices get faster, the need for high-calibre low cost test equipment become highly dominant. However testing the overall system becomes harder and more expensive. Traditionally, the transceiver system is tested in two steps utilizing high-calibre RF instrumentation and mixed-signal testers, with separate measurement setups for transmitter and receiver paths. Impairments in the RF front-end, such as the I/Q gain and phase imbalance and nonlinearity, severely affect the performance of the device. The transceiver needs to be characterized in terms of these impairments in order to guarantee good performance and specification requirements. The motivation factor for this thesis is to come up with a low cost and computationally simple extraction technique of these impairments. In the proposed extraction technique, the mapping between transmitter input signals and receiver output signals are used to extract the impairment and nonlinearity parameters. This is done with the help of detailed mathematical modeling of the transceiver. While the overall behavior is nonlinear, both linear and nonlinear models to be used under different test setups are developed. A two step extraction technique has been proposed in this work. The extraction of system parameters is performed by using the mathematical model developed along with a genetic algorithm implemented in MATLAB. The technique yields good extraction results with reasonable error. It uses simple mathematical operation which makes the extraction fast and computationally simple when compared to other existing techniques such as traditional two step dedicated approach, Nonlinear Solver (NLS) approach, etc. It employs frequency domain analysis of low frequency input and output signals, over cumbersome time domain computations. Thus a test method, including detailed behavioral modeling of the transceiver, appropriate test signal design along with a simple algorithm for extraction is presented.
ContributorsSreenivassan, Aiswariya (Author) / Ozev, Sule (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Two-dimensional vision-based measurement is an ideal choice for measuring small or fragile parts that could be damaged using conventional contact measurement methods. Two-dimensional vision-based measurement systems can be quite expensive putting the technology out of reach of inventors and others. The vision-based measurement tool design developed in this thesis is a low cost alternative that can be made for less than $500US from off-the-shelf parts and free software. The design is based on the USB microscope. The USB microscope was once considered a toy, similar to the telescopes and microscopes of the 17th century, but has recently started finding applications in industry, laboratories, and schools. In order to convert the USB microscope into a measurement tool, research in the following areas was necessary: currently available vision-based measurement systems, machine vision technologies, microscope design, photographic methods, digital imaging, illumination, edge detection, and computer aided drafting applications. The result of the research was a two-dimensional vision-based measurement system that is extremely versatile, easy to use, and, best of all, inexpensive.
ContributorsGraham, Linda L. (Author) / Biekert, Russell (Thesis advisor) / Macia, Narciso (Committee member) / Meitz, Robert (Committee member) / Arizona State University (Publisher)
Created2011