Within the last few decades there has been a push to identify students who have or who are at-risk for learning disabilities as early as possible. Much of this recent focus is related to research showing the positive long-term benefits of early education for the general population and children in poverty, as well as to educational theory about early educational interventions. However, little to no research has been conducted on the long-term effects of age of first service provision for students with learning disabilities. Whether students with learning disabilities are doing better academically in high school or graduating high school at higher rates based on when they are identified or when they received services is yet to be known. This study analyzed data collected from families and schools for 2,000 youth with learning disabilities from the National Longitudinal Transition Study 2 (NLTS2), a study that investigated a nationally representative sample of approximately 12,000 students with disabilities. The present study used latent variable structural equation modeling (SEM) to investigate the effects of age of first service provision on high school educational achievement and high school graduation in order to better understand the long-term effects of the age of intervention for students with learning disabilities. Contrary to what was hypothesized, the age a student first received services for a learning disability did not statistically significantly affect his or her grades in high school or likelihood of graduating from high school. The age a student first received services for a learning disability was statistically significantly and positively related to standardized achievement tests in high school; however, the direction of causation was counter to what was hypothesized. Students who received services at a later age performed better on high school standardized achievement tests. An important limitation of these data is that measures of a student's cognitive abilities or the severity of a student's learning disability were not available for use in these analyses. Further limitations and possible implications of these findings are discussed. / text

The purpose of this study was to examine and compare the effectiveness of teacher-directed instruction (i.e., teacher-directed instruction without using an iPad, TDI) and iPad-assisted instruction (IAI) on the word identification and oral reading fluency of elementary school students with reading learning disabilities (RLD), who have reading goals on their individual education plans (IEPs). Four 5th grade students with RLD participated in the study. An alternating treatments design combined with a multiple baseline design across the participants was applied. Visual analysis indicated that a moderate experimental effect from TDI and IAI on word identification and oral reading fluency was present for all four students when the baseline and intervention phases were compared. Specifically, regarding word identification, the percentage of non-overlapping data (PND) and non-overlap of all pairs (NAP) indicated that TDI and IAI are effective reading instructional procedures according to single-case research design standards. The finding was also supported by a Tau-U analysis that suggests both TDI and IAI demonstrated a large effect on improving word identification. Regarding oral reading fluency, however, the results were mixed; Tau-U indicates there was a large and significant effect from TDI and IAI for three of the four students in terms of increasing their oral reading fluency. Although data analysis indicates that TDI and IAI demonstrate moderate evidence in improving word identification and oral reading fluency, there was no clear differentiation found between the two treatments. A social validity questionnaire that examined student perspectives about intervention showed the students' positive views on their intervention experience and revealed their perspectives that intervention was helpful in building their reading skills. The second social validity questionnaire that asked the students about their reading perspectives indicated that the intervention increased their positive attitudes toward their reading (e.g., reading is a source of excitement and interest, reading is fun). / text

<p>In this thesis, a full review on what are the minimum requirements needed to perform an Authentication System is explained. While building the system we have in consideration the users of it, the security needed for each of the resources that must be accessed by the users and what methods can be applied to access to these resources.</p><p>In basics, an Authentication System is built when we need to keep track to who is entering on an organization, the bigger the organization is and the more information must be keep  safe the more complex the system will be.</p><p>Although there are other methods, I tried to keep it easy and understandable for all the possible readers. With this, the reader will understand the basics that he need to keep in mind when implementing such a system like this. The organization in mind for the system is a University that consist between twenty two thousand (22.000) and twenty five thousand (25.000) users.</p>

Probabilistic models have been successfully applied for a wide variety of problems, such as but not limited to information retrieval, computer vision, bio-informatics and speech processing. Probabilistic models allow us to encode our assumptions about the data in an elegant fashion and enable us to perform machine learning tasks such as classification and clustering in a principled manner. Probabilistic models for bio-acoustic data help in identifying interesting patterns in the data (for instance, the species-specific vocabulary), as well as species identification (classification) in recordings where the label is not available. The focus of this thesis is to develop efficient inference techniques for existing models, as well as develop probabilistic models tailored to bioacoustic data. First, we develop inference algorithms for the supervised latent Dirichlet allocation (LDA) model. We present collapsed variational Bayes, collapsed Gibbs sampling and maximum-a-posteriori (MAP) inference for parameter estimation and classification in supervised LDA. We provide an empirical evaluation of the trade-off between computational complexity and classification performance of the inference methods for supervised LDA, on audio classification (species identification in this context)as well as image classification and document classification tasks. Next, we present novel probabilistic models for bird sound recordings, that can capture temporal structure at different hierarchical levels, and model additional information such as the duration and frequency of vocalizations. We present a non-parametric density estimation technique for parameter estimation and show that the MAP classifier for our models can be interpreted as a weighted nearest neighbor classifier. We provide an experimental comparison between the proposed models and a support vector machine based approach, using bird sound recordings from the Cornell Macaulay library. / Graduation date: 2011 / Access restricted to the OSU Community at author's request from Dec. 30, 2010 - Dec. 30, 2011

Renewable energy generation will play an important role in solving the climate change problem. With renewable electricity generation increasing, there will be some significant changes in electric power systems, notably through smaller generators embedded in the distribution network. Historically insignificant volumes of Embedded Generation (EG) mean that traditionally it has been treated by the transmission system operator as negative load, with its impact on the dynamic behaviour of power systems neglected. However, with the penetration level increasing, EG would start to influence the dynamics and stability of the transmission network. Hence the dynamic behaviour of distribution network cannot be neglected any more. In most cases, a detailed distribution network model is not always available or necessary for the study of transmission network dynamics and stability. Thus a dynamic equivalent model of the distribution network that keeps its essential dynamic behavior, is required. Most existing dynamic equivalencing methods are based on the assumption that the detailed information of the complete power system is known. Dynamic equivalencing methods based on coherency of the machines have been applied to transmission networks but cannot be applied to distribution networks due to their radial structure. Hence an alternative methodology has been developed in this project to derive the dynamic equivalent model of the distribution network using system identification, without the detailed information of the distribution network necessarily known. Case studies have been accomplished in PSS/E on a model of the Scottish transmission network with the distribution network in Dumfries and Galloway. Embedded generation with a certain penetration level in either conventional generation or DFIG wind generation has been added to the model of the distribution network. The dynamic equivalent models of the distribution network are compared with the original distribution network model using a series of indicators. A constant power model has also been involved in the comparison to illustrate the advantage of using the dynamic equivalent to represent the distribution network. The results suggest that a proper dynamic equivalent model derived using this methodology may have better agreement to the original power system dynamic response than constant power equivalent. A discussion on factors that influence the performance of the dynamic equivalent model, is given to indicate the proper way to use this methodology. The major advantage of the dynamic equivalencing methodology developed in this project is that it can potentially use the time series obtained from measurements to derive the dynamic equivalent models without knowing detailed information on the distribution network. The derived dynamic equivalent, in a simple spate-space form, can be implemented in commercial simulation tools, such as PSS/E.

A frequent problem in physics and engineering is that of determining a mathematical model for the dynamic performance of a system. It is particularly useful to be able to make measurements which enable such a model to follow changes in the system dynamics in the course of normal operation. Linear control theory, although now being replaced by a more general approach, can still form the basis for such system analysis. Cross correlating signals from a linear process can give a great deal of information about the process dynamics without injecting any test disturbances, or, when test signals are possible, cross correlation can be used to recover dynamic information in the presence of considerable background noise. The use of specially constructed test signals can make cross correlation a powerful technique in the identification of dynamic systems.

This research was an investigation of the relationship between the cognitive processes labeled successive, simultaneous, and planning proposed by Luria and Das (Das, Kirby & Jarman, 1979) and the solution of visual metaphors and the achievement of 6th grade elementary age students from metropolitan schools. A battery of information processing tasks based on previous research with the Luria-Das model was administered to 191 6th grade students, approximately half of whom were Anglo and half of whom were Hispanic. In addition, the Metaphoric Triads Task (MTT), a measure of comprehension of visual metaphors, was administered to the same group and achievement data from the Iowa Test of Basic Skills gathered on the same students. Confirmatory factor analysis was used to determine whether the Luria-Das model or another model fit the data; and how the information processing variables related to each other, to the Metaphoric Triads task, and to the measures of achievement. A three factor solution consistent with previous research was obtained when confirmatory factor analysis was used to fit the Luria-Das model to the data. For the Anglo group, the factor structures of all three factors were consistent with the Luria-Das model of successive, simultaneous, and planning processes. For the Hispanic group, the factor structure of the simultaneous and planning factors was consistent with previous research, but the factor structure of the successive factor was not the same as that obtained in previous research. The relationship between the academic achievement scores for the whole group and the factors was consistent with the results of previous research with the exception of Spelling. Results of the current research indicate that group administration of the battery can produce results consistent with the Luria-Das model, but further research that includes minority populations is needed. The analysis of the performance of the Anglo and Hispanic groups on the Metaphoric Triads Task indicated that the two groups performed approximately the same but may have employed different processes to solve the metaphors. The results of this study provide important data about information processing styles of 6th grade students that can be useful in educational planning.

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