Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Robust Learning with Educational Data Mining.

Volume Nine | Winter 2014 Abstract Many university leaders and faculty have the goal of promoting learning that connects across domains and prepares students with skills for their whole lives. However, as assessment emerges in higher education, many assessments focus on knowledge and skills that are specific to a single domain. Reworking assessment in higher education to focus on more robust learning is an important step towards making assessment match the goals of the context where it is being applied. In particular, assessment should focus on whether learning is robust (Koedinger, Corbett, & Perfetti, 2012), whether learning occurs in a way that transfers, prepares students for future learning, and is retained over time; and also on skills and meta–competencies that generalize across domains. By doing so, we can measure the outcomes that we as educators want to create, and increase the chance that our assessments help us to improve the outcomes we wish to create. In this article, we discuss and compare both traditional test–based methods for assessing robust learning, and new ways of inferring robustness of learning while the learning itself is occurring, comparing the methods within the domain of college genetics. AUTHORS Ryan S. Baker, Ph.D. Teachers College, Columbia University
Cooperation and modularity for classification through neural network techniques

We study modularity in the frame of neural network systems on two real-size applications. The cooperation of performant modules is used to improve recognition and rejection rates of handwritten digits coming from postal zip-codes. From a multi-class problem in multi-font character recognition, we have designed 49 neural submodules (one per class), and different cooperation schemes are studied and compared. The relation between the quality of the expert system and the efficiency of the cooperation scheme is shown. <<ETX>>
Multipath Search Coding of Stationary Signals with Applications to Speech

This paper deals with the application of multipath search coding (MSC) concepts to the coding of stationary memoryless and correlated sources and of speech signals at a rate of one bit per sample. We have made use of three MSC classes: 1) codebook coding (vector quantization), 2) tree coding, and 3) trellis coding. This paper explains the performances of these coders and compares them both with those of conventional coders and with rate-distortion bounds. Figs. 2 and 3 demonstrate the potentials of MSC coding strategies. The paper reports also on results of MSC coding of speech, where both the strategy of adaptive quantization and of adaptive prediction were included in coder design.
3D gaze estimation based on facial feature tracking

A 3D gaze estimation and tracking algorithm based on facial feature tracking is presented in this paper. Firstly, we used the Active Shape Model (ASM) to extract facial feature points with a stereo camera. Then, the full 3D pose of head is estimated by comparing the feature points of current pose with initial head pose. After that, the center of eyeball is obtained based on a 3D eye model which is related to head pose and the middle point of eye corners. Thereby, optical axis was computed as 3D vectors through the center of eyeball to the center of pupil. Finally, visual axis was gotten by adding an angle to optical axis. Here, in our system, a one-time personal calibration is used to determine this angle. The experimental results show the accuracy of our gaze tracking system achieves less than 3 degree.
Intelligent PID Control and the Bifucation Behavior of Hydrogenerator Set

Considering of the highly nonlinear, time-variable and complex dynamics and the easy variance of hydrogenerator set systems structure and parameters, a nonlinear dynamical model is presented, which can be analyzed by the bifurcation theory of nonlinear differential equations and simulated in computer. By using the genetic algorithm, the PID parameters of hydrogenerator set intelligent system are optimized and the control performances are improved. The algebra criterion about the existence of bifurcation is employed to study the bifurcation behavior of the nonlinear model which takes the PID parameters as the bifurcation ones. The simulations show that a supercritical bifurcation is likely to exist in hydrogenerator set system and the bifurcation will occur for the higher values of PID parameters. Theory and simulation results of this paper can be considered as good suggest for intelligent PID control system of hydrogenerator set system.
Reliability Analysis Method Based on Support Vector Machines Classification and Adaptive Sampling Strategy

For probabilistic design problems with implicit limit state functions encountered in practical application, it is difficult to perform reliability analysis due to the expensive computational cost. In this paper, a new reliability analysis method which applies support vector machine classification(SVM-C) and adaptive sampling strategy is proposed to improve the efficiency. The SVM-C constructs a model defining the boundary of failure regions which classifies samples as safe or failed using SVM-C, then this model is used to replace the true limit state function,thus reducing the computational cost. The adaptive sampling strategy is applied to select samples along the constraint boundaries. It can also improves the efficiency of the proposed method. In the end, a probability analysis example is presented to prove the feasible and efficient of the proposed method.
Clarifying the 'logical framework' as a tool for planning and managing development projects

The ‘logical framework’ is a tool for project preparation which has been adopted by many agencies funding development programmes in the third world, yet its diffusion has been barely noticed in the academic literature. This paper argues that, although one of the framework's strengths is its simplicity, that is deceptive, and that the potential of the tool requires attention to some ambiguities. These are examined, some suggestions are made, and a modified framework is proposed. Finally some criticisms of the framework are reviewed.
Weighted-Bit-Flipping-Based Sequential Scheduling Decoding Algorithms for LDPC Codes

Low-density parity-check (LDPC) codes can be applied in a lot of different scenarios such as video broadcasting and satellite communications. LDPC codes are commonly decoded by an iterative algorithm called belief propagation (BP) over the corresponding Tanner graph. The original BP updates all the variable-nodes simultaneously, followed by all the check-nodes simultaneously as well. We propose a sequential scheduling algorithm based on weighted bit-flipping (WBF) algorithm for the sake of improving the convergence speed. Notoriously, WBF is a low-complexity and simple algorithm. We combine it with BP to obtain advantages of these two algorithms. Flipping function used in WBF is borrowed to determine the priority of scheduling. Simulation results show that it can provide a good tradeoff between FER performance and computation complexity for short-length LDPC codes.
FEM and BEM parallel processing: theory and applications – a bibliography (1996‐2002)

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.
A contention-alleviated static keeper for high-performance domino logic circuits

The domino logic circuit is popular in high performance VLSI chips. A feedback keeper is usually used to overcome the charge sharing problem in the domino circuit. In this paper, a contention-alleviated static keeper (CASK) is proposed. The key idea is to reduce the contention between the pull-down network and the feedback keeper by lowering the gate voltage of the keeper. By using the CASK technique, better performance can be achieved, in addition to maintained noise robustness, with smaller sizing factor. By tuning the gate-bias voltage to the feedback PMOS, the proposed keeper is suitable for domino circuits with a range of supply voltage. Additionally, the CASK has been demonstrated to enhance the performance of low-power low swing domino circuits, and the results show that delay and power can be reduced by 26% and 11%, respectively.
Multi-Feature Bio-Inspired Model for Scene Classification

A multi-feature bio-inspired model for scene image classification (MFBIM) is presented in this work; it extends the hierarchical feedforward model of the visual cortex. Firstly, each of three paths of classification uses each image property (i.e. shape, edge or color based features) independently. Then, BPNN assigns the category of an image based on the previous outputs. Experiments show that the model boosts the classification accuracy over the shape based model. Meanwhile, the proposed approach achieves a high accuracy comparable to other reported methods on publicly available color image dataset.
An extension of regression trees to generate better predictive models

For situations where the data are drawn from reasonably homogeneous populations, traditional methods such as multiple regression typically yield insightful analyses. For situations where the data are drawn from more heterogeneous populations, decision tree approaches, such as Classification and Regression Trees (CART) and Generalized, Unbiased, Interaction, Detection, and Estimation (GUIDE), are more likely to recognize idiosyncratic subpopulations and interactions automatically. In contrast to CART, however, GUIDE yields models with better predictive performance for each subpopulation. This article extends the idea of GUIDE to handle analysis of covariance-type problems. This article compares GUIDE modeling to various decision tree methods and to multiple regression. The article identifies and discusses the relative advantages and disadvantages of multiple regression, CART, and GUIDE. GUIDE produces quality or reliability models that exhibit greater predictive accuracy than multiple regression or CART for complex, highly diverse populations. Also, GUIDE is readily applicable to many other areas, such as repairability and maintainability settings involving both qualitative and quantitative variables. A small case study of an engineered wood product, medium-density fiberboard, is presented to illustrate the application of GUIDE. Accepted in 2005 for a special issue on Reliability co-edited by Hoang Pham, Rutgers University; Dong Ho Park, Hallym University, Korea; and Richard Cassady, University of Arkansas.
Evidence and circularity in multimodal discourse analysis

In the context of rapid theoretical development in multimodal discourse analysis, and of its growing interdisciplinary influence, it is crucial that those working in the field give due consideration to methodological rigour. The corpus-based approach described here offers a means of addressing some key methodological issues. Firstly, this approach provides a check on over- and under-interpretation and also reveals a more nuanced picture of data about specific genres than might be derived from even the closest observation of individual instances. Thus it helps avoid pitfalls associated with relying on hand-picked examples. Secondly, the semi-automated implementation of a multilayered annotation scheme, which separates the representation of layout from rhetorical structure, supports the empirical investigation of a variety of research questions, while minimizing the influence of the analyst on the data by delaying interpretation insofar as possible until it becomes unavoidable. This article illustrates the corpus-based approach through a contrastive case study of one very visual genre, product packaging, with data taken from two locales, Taiwan and the UK. In so doing, issues of the selection of texts for inclusion and corpus design are addressed and the principles and practicalities involved in data preparation are discussed. Consideration is also given to the types of question which such an approach enables us to explore. In addition, since the data analyzed here are drawn from different languages and cultures, the present study sheds light on some issues of interest from the perspective of localization. Finally, some benefits of the approach are suggested, among which not least is that a stronger basis for the critique of designs in turn supports identification of opportunities for their improvement. This is not possible when the analysis is itself circular.
Fermionic functional renormalization group approach to Bose–Einstein condensation of dimers

Fermionic functional renormalization group (f-FRG) is applied to describe Bose-Einstein condensation (BEC) of dimers for a two-component fermionic system with attractive contact interaction. In order to describe the system of dimers without introducing auxiliary bosonic fields (bosonization), we propose a new exact evolution-equation of the effective action in f-FRG with an infrared regulator for the fermion vertex. Then we analyze its basic properties in details. We show explicitly that the critical temperature of the free Bose gas is obtained naturally by this method without bosonization. Methods to make systematic improvement from the deep BEC limit are briefly discussed.
High Compression Rate Text Summarization

This thesis focuses on methods for condensing large documents into highly concise summaries, achieving compression rates on par with human writers. While the need for such summaries in the current age of information overload is increasing, the desired compression rate has thus far been beyond the reach of automatic summarization systems. The potency of our summarization methods is due to their in-depth modelling of document content in a probabilistic framework. We explore two types of document representation that capture orthogonal aspects of text content. The first represents the semantic properties mentioned in a document in a hierarchical Bayesian model. This method is used to summarize thousands of consumer reviews by identifying the product properties mentioned by multiple reviewers. The second representation captures discourse properties, modelling the connections between different segments of a document. This discriminatively trained model is employed to generate tables of contents for books and lecture transcripts. The summarization methods presented here have been incorporated into large-scale practical systems that help users effectively access information online. Thesis Supervisor: Regina Barzilay Title: Associate Professor
Use of filter vectors in hyperspectral data analysis

We report recent progress using a filter vector technique to analyze the data from a hyperspectral image. The filter vector technique finds the optimal filter vectors for demixing the complex patterns found in the hyperspectral image. The method has the potential to be implemented in real time since it is fully parallel. Computation of the filter vectors for a given family of known species vectors is fast and direct and improved algorithms for developing of the algorithm which may be updated as conditions change is possible. Advantages of using the filter vector techniques over the technique of pattern matching will be discussed. The portable hyperspectral images for low light spectroscopy (PHILLS) instrument has been used on a number of depolyments in the last year. Typically, the instrument files on the Naval Research Laboratory's P-3 Orion aircraft. Currently, the PHILLS instrument records over 1000 wavelength bands between UV and near IR. Results from a number of deployment and test situations is shown.
Remote walkthrough over mobile networks using 3-D image warping and streaming

A mobile walkthrough environment based on 3-D superview warping and view compensation streaming is presented. In this environment, 3-D views are rendered on the remote clients using 3-D oversized reference views and compensated on a view-on-demand basis by the graphically rendered views from the server. The view compensations (hole residuals and the depth map) are coded using a layered joint source-channel coding with unequal error corrections to protect the significant packets on noisy and erasure mobile channels. The new compensated view will then serve as a reference view for the subsequent rendering on the clients. This environment enables numerous mobile virtual applications, such as remote m-gaming, remote m-design, m-learning, m-CAD/CAM, etc., scalable to different network capabilities.
Forest classification using spectrometer and SAR data

This work deals with automatic classification of forest areas using remote sensing imagery. The authors compare the discrimination ability of two complementary sensors, a SAR sensor and a spectrometer. A Gaussian maximum likelihood classifier was used in all classification experiments. The hyperspectral data alone gave fairly good results for classification of tree species. The results for SAR data alone were not convincing. Joining the two data set in a simple fusion experiment improved the results obtained significantly for data from a single sensor, and also allowed a classification of tree species and height simultaneously.
A fully pipelined programmable real-time (3/spl times/3) image filter based on capacitive threshold-logic gates

A novel programmable 2-D image filter architecture with a (3/spl times/3) window size is presented, which is based on Capacitive Threshold-Logic (CTL) gates. The filter architecture is fully pipelined, and data processing is highly parallelized in order to take advantage of the high-speed building blocks used in the design. A fixed-coefficient processing core has been designed and fabricated using conventional 1.2 micron double-polysilicon CMOS technology to evaluate the performance of the proposed architecture. The chip core area (1.8 mm/spl times/1.5 mm) compares very favorably with other image filter chips using a similar technology. Extensive post-layout simulations have shown that the chip will allow a maximum clock frequency of about 100 MHz. With this data throughput capability, the programmable image filter will be able to process high-resolution (1024/spl times/1024) pixel images at a rate of 50 frames per second.
ABITS: learning more about students through intelligent educational software

Purpose – One of the greatest challenges facing any intelligent tutoring system is being able to adapt its behaviour based on the student's current knowledge level, ability, needs and wishes within a course. This paper aims to present a framework of BDI agents within an agent‐based intelligent tutoring system (ABITS). Design/methodology/approach – A conceptual discussion approach is taken.Findings – The agents provide the core reasoning ability. In particular, the paper demonstrates how the system sources and refines a particular set of commonly available data. Also shows how these data are incorporated into the agents' belief set so that they may adapt their behaviour to support individual students.Originality/value – Provides a framework that can improve learning procedures for future users of ABITS.
An improved lookahead instruction prefetching

A new lookahead instruction prefetching mechanism is proposed in this paper. Though significant performance improvement can be obtained by improving both the cache miss ratio and average access time for successfully prefetched blocks, most conventional prefetching mechanisms improve only one out of the two factors. To achieve balanced improvement of the two factors, a lookahead prefetching scheme that fetches multiple blocks for a prefetch request and adopts prefetch on miss mechanism is proposed. Performance evaluation is carried out through the trace-driven simulation and the proposed prefetch scheme reduces 32%/spl sim/56% of the memory access delay time of the cache system that does not perform any prefetching.
Neural networks for statistical recognition of continuous speech

In recent years there has been a significant body of work, both theoretical and experimental, that has established the viability of artificial neural networks (ANN's) as a useful technology for speech recognition. It has been shown that neural networks can be used to augment speech recognizers whose underlying structure is essentially that of hidden Markov models (HMM's). In particular, we have demonstrated that fairly simple layered structures, which we lately have termed big dumb neural networks (BDNN's), can be discriminatively trained to estimate emission probabilities for an HMM. Recently simple speech recognition systems (using context-independent phone models) based on this approach have been proved on controlled tests, to be both effective in terms of accuracy (i.e., comparable or better than equivalent state-of-the-art systems) and efficient in terms of CPU and memory run-time requirements. Research is continuing on extending these results to somewhat more complex systems. In this paper, we first give a brief overview of automatic speech recognition (ASR) and statistical pattern recognition in general. We also include a very brief review of HMM's, and then describe the use of ANN's as statistical estimators. We then review the basic principles of our hybrid HMM/ANN approach and describe some experiments. We discuss some current research topics, including new theoretical developments in training ANN's to maximize the posterior probabilities of the correct models for speech utterances. We also discuss some issues of system resources required for training and recognition. Finally, we conclude with some perspectives about fundamental limitations in the current technology and some speculations about where we can go from here. >
Toward a neurolexicology: A method for exploring the organization of the mental lexicon by analyzing electrophysiological signals

This article analyzes the organization of the mental lexicon based on neurophysiological data. The neuroscience literature has devoted many studies to the semantic processing of words. However, the research remains specific to certain categories, studied separately, and does not address the lexicon as a system. In order to provide further insight into the neuronal organization of the lexicon, we conducted an EEG-based semantic decision experiment using words from eight categories (four living and four nonliving categories) as the material. A data-analysis method (correspondence analysis or CA) commonly used in computational linguistics was applied to the electrophysiological signals. The results revealed a two-factor structure: an ontological organization separating the living from the nonliving, and an organization with a human referential structured by proximity to the person. A comparison of the ERP-CA and the linguistic-CA data revealed organizational analogies. Lastly, a tomography software (Loreta®) was used to estimate the cerebral sources associated with the ERP signals.