Miracle-dz/UniFAR-FTUs huggingface dataset:
Minds In Play: Computer Game Design as a Context for Children''s

Originally published in Contemporary Psychology: APA Review of Books, 1996, Vol 41(12), 1221. This article reviews the book “Minds in Play: Computer Game Design as a Context for Children's Learning,” by Yasmin B. Kafai (See record 1994-98802-000). The reviewer briefly examines selected chapters of the text and concludes the review with praise for the author's work. (PsycINFO Database Record (c) 2006 APA, all rights reserved)
Noise cancellation techniques in speech signals (DLPNC)

A new noise cancellation technique "delayed linear prediction noise cancellation" (DLPNC) was described by the author (see ibid., p.WM9.25.1-25.4, 1992) in a prevoius paper. In this technique, the current clean speech sample is predicted as a linear combination of the past M noisy speech samples delayed by one pitch period T. That is why this technique is named "delayed linear prediction noise cancellation". In this article, the DLPNC technique is compared with two well-known noise cancellation techniques, Wiener noise cancellation, formulated by Sambur (1979) and adaptive noise cancellation proposed by Widrow et. al. (1975) and modified by Sambur (1978). The quality of the processed speech was judged by informal perceptual listening tests throughout this work. The criteria being the quality of the processed speech and the computational load the preliminary results reported in this paper indicate that DLPNC ranks close to WNC and ANC in its noise cancellation capabilities. <<ETX>>
Computational models of early language acquisition and the role of different voices

Babies learn words from the speakers in their environment. This project / thesis was inspired by the question how babies can discover words in the speech signal. This signal is continuous, unlabelled, and not divided into shorter units that coincide with sound- or word-boundaries. So far, research on infants' input concentrated on the main caregiver, usually the mother. The assumption was that the main caregiver provides most of the information babies need to learn their native language. The role of other speakers as largely been unknown. This project used computational models to simulate the language acquisition process. This allowed for full control over the input and over all processes inside the simulated baby's mind. Computer models learned words from real speech, without intervening processed that described the continuous signal in terms of single sounds or words. It turned out that these models could learn words and that we can even simulate babies in experiments successfully with such a model. Additional studies revealed that hearing many speakers, both men and women, can help the word learning process. Hearing variable input led to very successful word learning overall. Especially in tricky conditions, such as noise or encountering yet another unknown speaker, the models who learned from many speakers usually fared better. In conclusion, variability and different voices in the speech signal can be very valuable when learning words.
Video Object Tracking and Segmentation Using Artificial Neural Network for Surveillance System

This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems the survey concludes with a discussion of possible future directions.
Novel machine learning and correlation network methods for genomic data

Author(s): Song, Lin | Advisor(s): Horvath, Stefan | Abstract: Both correlation and mutual information (MI) are common co-expression measures. MI has a major advantage to measure non-linear relationships. However, it is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. We provided a comprehensive comparison between mutual information and correlation in 8 empirical data sets and in simulations. We confirmed close relationships between MI and correlation in all data sets. The biweight midcorrelation, a robust form of correlation, outperformed MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often led to modules superior to MI and maximal information coefficient (MIC) in terms of gene ontology enrichment. In addition, we proposed the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. Overall, our results indicated that MI networks could be safely replaced by correlation networks for stationary co-expression data.Sample classification, especially disease status prediction, is an important area of investigation for gene expression studies. Many machine learning methods, i.e. predictors, have been developed to tackle this problem. We proposed a novel bootstrap aggregated (bagged) GLM predictor randomGLM (RGLM) that shares superior accuracy and good interpretability. RGLM incorporates several elements of randomness and instability, such as random subspace method, optional interaction terms and forward feature selection. The prediction performances of various predictors were evaluated on hundreds of genomic data sets, the UCI machine learning benchmark data and simulations. RGLM often outperformed alternative methods including random forests and penalized regression models (ridge regression, elastic net, lasso) in both binary and continuous outcome predictions. Further, RGLM provides variable importance measures that can be used to define a ``thinned" ensemble predictor (involving few features) retaining excellent predictive accuracy.RGLM has won the 2012 COPD Improver Challenge, in which we aimed to predict the chronic obstructive pulmonary disease (COPD) status based on gene expression data. We outlined how RGLM compared with random forest on the COPD data set, and discussed potential reasons for the superior performance of RGLM in this sub-challenge.
Structured Information Extraction from Medical Texts in Bulgarian

Abstract This paper presents an approach for Information Extraction (IE) from Patient Records (PRs) in Bulgarian. The specific terminology and lack of resources in electronic format are some of the obstacles that make the task of current patient status data extraction in a structured format quite challenging. The usage of N-grams, collocations and words’ distances allows us to cope with this problem and to extract automatically the attribute-value pairs with relatively high precision.
Alterations in Heart Rate, Blood Lactate Accumulation and Perceived Exertion at Lactate Threshold as a Consequence of Exercise Training in the Elderly

OBJECTIVE. The purpose of this investigation was to examine the alterations in heart rate, blood lactate accumulation and perceived exertion (RPE) at lactate threshold (LT) as a consequence of exercise training in elderly. METHODS. Thirty-seven elderly aged>65 yr. , served as training group (n=20) and control group (n=17). All participants performed the submaximal bench stepping test before and after the twelve-week intervention in order to determine the metabolic equivalents (METs), heart rate, lactate concentration and RPE at LT. The subjects in the training group performed the bench stepping exercise at LT for 20 minutes or more every day during the intervention. RESULTS. After the intervention, METs and heart rate at LT significantly increased compared with those at baseline level in the training group (p<0.01), whereas neither RPE nor lactate concentration at LT changed significantly. Furthermore the increase of heart rate at LT significantly correlated with the increase of METs at LT or heart rate at LT for baseline level (p<0.01). CONCLUSION. The heart rate at LT may increase in association with the increase of the aerobic capacity especially in individuals with lower fitness level and /or at the beginning of the training program, in contrast to RPE and lactate concentration at LT.
An investigation into the potential of a corpus-influenced syllabus for primary English literacy education in Japan

The research presented in this thesis investigates the feasibility of a corpus-influenced syllabus for primary literacy education in Japan. It achieves this with reference to two aspects of the context within which such an initiative might be developed. One is the cultural context; that is,the demands of primary ELT in Japan. Therefore this research explores policy makers’ and teachers’views, the texts frequently used in primary ELT classrooms, and some aspects of teacher training. The other focus is from a linguistic viewpoint, concerned with the identification of linguistic features which pupils need to learn for the development of their English literacy. This thesis describes an innovative method for identifying such features. The cultural context was investigated by means of three surveys, the first of which was used to inform the choice of texts to include in the corpus. The surveys reveal a lack of attention to literacy teaching and teacher education in primary ELT in Japan, but also point to some potential for syllabus development. The research offers support for a corpus-influenced syllabus for teaching English literacy, while concluding that there is a need for incorporating it into teacher education and developing teaching methodologies which suit the pedagogic context of the Japanese primary school classroom.
Acoustic segmentation and phonetic classification in the SUMMIT system

Recently, the authors initiated a project to develop a phonetically-based spoken-language-understanding system called SUMMIT. In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering, their approach attempts to express the speech knowledge within a formal framework using well-defined mathematical tools. In the authors' system, features and decision strategies are discovered and trained automatically, using a large body of speech data. The authors describe those parts of the system dealing with acoustic segmentation and phonetic classification and document its current performance. <<ETX>>
Function modeling: improved raster analysis through delayed reading and function raster datasets

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space, often limiting the types of analyses that can be performed. To address this issue, we have developed Function Modeling. Function Modeling is a new modeling framework that streamlines the raster modeling process by utilizing lazy reading methodologies. To assess the efficacy of Function Modeling, we compared the processing time and storage space required to execute six simulation using our newly developed methodology and conventional modeling techniques. Our findings indicate that Function Modeling substantially reduces both processing time and storage space when compared to conventional modeling. Outside of simulations, we have used Function Modeling to characterize the impacts of fuel treatments on soil erosion given fire disturbance, estimate basal area, trees, and tons of above ground biomass per acre, identify locations in need of forest management, calculate forest residuals given multiple management prescriptions, and integrate spatially explicit delivery cost models with forest residual estimates in a fraction of the time and storage space it would take to perform similar analysis using conventional modeling methodologies. Overall, Function Modeling significantly improves how raster models are processed. To facilitate the use of Function Modeling, we built an object oriented . NET library called RMRS Raster Utility. RMRS Raster Utility is free, readily available, has an intuitive user interface, and directly plugs into Environmental Science Research Institute (ESRI)'s software as an ESRI add-in.
Large Scale Data Access: Architectures and Performance

In this work we consider the problems related to the publishing of very large amounts of data to a big set of data consumers over a distributed environment. Different points of view of this problem have been considered, both methodological, algorithmic and technological. Some of the requirements for such systems are high availability, scalability, fast and low latency access to the data. Such a system has to be designed by starting considering the appropriate architectures, and then through reasoning about resource location algorithms and data transferring optimization techniques. The most important aspects of the performances of such a system are then analyzed through a simulation based approach, built around a case study: the publishing of data taken from the BaBar High Energy Physics experiment. The conclusions highlight the fact that the main objectives have been reached, and that such techniques have been successfully deployed.
Health changes in Hispanic older adults in a Spanish arthritis self -management education program

OF THE DISSERTATION HEALTH CHANGES IN HISPANIC OLDER ADULTS IN A SPANISH ARTHRITIS SELF MANAGEMENT EDUCATION PROGRAM by Helen Z. Comely Florida International University, 2003 Miami, Florida Professor Douglas H. Smith, Major Professor Arthritis is the most common chronic condition affecting older people and is a major cause of limited activity. Arthritis education programs in English have demonstrated a positive impact on health but these programs have not reached the Hispanic communities where arthritis is the leading cause of disability. Minorities, such as Hispanics, have traditionally been reluctant to pursue self-help programs, and have been identified as an under-served population in terms of medical care. This study examined the effectiveness of one community health adult education program targeting Hispanic older adults with arthritis, the Spanish Arthritis Self Management Education Program (SASMEP), by evaluating changes in the participants’ general health, pain, disability, self-efficacy, health perceptions, frequency of physician visits, and exercise. A pre and post control group experimental design and analyses of covariance were used to determine the pre and post differences in health status and health behaviors for a group participating in the SASMEP and a group who did not using gender and age as covariates. A repeated measures design was also used, and repeated measures analyses of variance and post hoc tests were done on health status and health behavior data collected pre, post and one-year post education to determine long-term differences. Results indicated the participants’ health status significantly improved in general health, significantly decreased in pain, and significantly decreased in arthritic disability immediately following the education. Self-efficacy and health perceptions increased for both groups but not significantly. The participants’ health behaviors showed significantly fewer physician visits and significantly increased time spent performing stretching and strengthening exercise and time spent performing aerobic exercise. No group differences were found in the frequency of arthritis physician visits. The improvements seen immediately after the SASMEP participation were not reflected in the post one-year scores. No significant differences were found for the participants’ health status or health behaviors one year following the education. Health status and health behaviors did not return below baseline scores after one year suggesting the participants’ health, although not improved, did not deteriorate. Therefore, the SASMEP education provided short-term health benefits for older Hispanic adults with arthritis, but not long-term health benefits.
Text, graphics, and multimedia materials employed in learning a computer-based procedural task

Text, Graphics, and Multimedia Materials Employed In Learning A Computer-Based Procedural Task Kari Christine Carlson Coffindaffer The present research study investigated the interaction of graphic design students with different forms of software training materials. Four versions of the procedural task instructions were developed (A) Traditional Textbook with Still Images, (B) Modified Text with Integrated Still Images, (C) Onscreen Modified Text with Silent Onscreen Video and (D) Onscreen Narrated Video for four computer tasks. Two research questions guided the study: Research Question 1: Are there any significant differences in student learning of a computer-based procedural task due to the format of the training materials? Research Question 2: Do individual differences in prior knowledge and spatial abilities make a significant difference in student learning? This study included quantitative research methods. The population for the study consisted of sophomore and junior graphics students enrolled in the Computer Applications in Graphics (GRAPHICS 1150) course in the Department of Graphics Technology at a Fairmont State University and Pierpont Community and Technical College during the 2009 spring semester. One section had 8 participants and the other 11 (N=19). The six instruments used to collect data for this study were a Prior Knowledge pre-test, the MRT (a Mental Rotations Test), and 4 counterbalanced graphics image-manipulation tasks. The findings indicate that high spatial ability and high prior knowledge positively affected student’s scores on the graphics imagemanipulation tasks and that Training Condition B (Modified Text with Integrated Still Images) was a positive contributor to test scores on the counterbalanced image manipulation tasks. The participants experienced shorter task completion times for any task trained with the Modified Text with Integrated Still Images materials. Text, Graphics and Multimedia iii
Implementation of Explicit Density-Based Unstructured CFD Solver for Turbomachinery Applications on Graphical Processing Units

For the aerodynamic design of multistage compressors and turbines Computational Fluid Dynamics (CFD) plays a fundamental role. In fact it allows the characterization of the complex behaviour of turbomachinery components with high fidelity.Together with the availability of more and more powerful computing resources, current trends pursue the adoption of such high-fidelity tools and state-of-the-art technology even in the preliminary design phases. Within such a framework Graphical Processing Units (GPUs) yield further growth potential, allowing a significant reduction of CFD process turn-around times at relatively low costs.The target of the present work is to illustrate the design and implementation of an explicit density-based RANS coupled solver for the efficient and accurate numerical simulation of multi-dimensional time-dependent compressible fluid flows on polyhedral unstructured meshes. The solver has been developed within the object-oriented OpenFOAM framework, using OpenCL bindings to interface CPU and GPU and using MPI to interface multiple GPUs.The overall structure of the code, the numerical strategies adopted and the algorithms implemented are specifically designed in order to best exploit the huge computational peak power offered by modern GPUs, by minimizing memory transfers between CPUs and GPUs and potential branch divergence occurrences. This has a significant impact in terms of the speedup factor and is especially challenging within a polyhedral unstructured mesh framework. Specific tools for turbomachinery applications, such as Arbitrary Mesh Interface (AMI) and mixing-plane (MP), are implemented within the GPU context.The credibility of the proposed CFD solver is assessed by tackling a number of benchmark test problems, including Rotor 67 axial compressor, C3X stator blade with conjugate heat transfer and Aachen multi-stage turbine. An average GPU speedup factor of approximately S ≃ 50 with respect to CPU is achieved (single precision, both GPU and CPU in 100 USD price range). Preliminary parallel scalability test run on multiple GPUs show a parallel efficiency factor of approximately E ≃ 75%. Copyright © 2015 by ASME
Speech signal processing using a time-frequency distribution of Cohen's class

It is well known that the standard methods for spectral estimation of speech signals, such as spectrograms, suffer from trade-offs between time and frequency resolution. These methods usually have difficulty isolating closely spaced formants and tracking rapidly changing spectral peaks and fundamental frequency (pitch). The authors use a time-frequency distribution with compound kernel function of Cohen's class to visualize these temporal features in both the time and frequency domains simultaneously. It shows the time-varying characteristics of the speech signal more precisely. <<ETX>>
Scheduling real-time messages in packet-switched networks

In a real-time network, it is not practical to have one centralized scheduler manage all the network resources, e.g., the transmission links and buffer space. Instead, each node has its own scheduler which manages the various resources at that node. In an ideal case, the schedulers are completely independent, and the well-known scheduling and analysis techniques developed for single-node systems can be used, thus greatly simplifying the real-time network design. However the use of independent schedulers may lead to buffer overruns or missed deadlines and hence, network failure. This thesis addresses the problems that arise in scheduling real-time messages in a packet-switched network that has multiple schedulers and has limited buffer space. In our development of the schedulers and the mechanisms for synchronization between the schedulers, we address the following issues: the complexity of the scheduler, the complexity of the synchronization mechanism, the scheme for admission control, the achievable utilization of the network, the ability of the scheduler to meet diverse real-time requests, and the robustness of the scheduler under unpredictable conditions such as temporary overload.