Miracle-dz/UniFAR-FTUs huggingface dataset:
Musical Activity in The Music Learning Process Through Children Songs in Primary School Level

This study aims to analyze and describe the musical activity in the process of learning music through children songs in primary school. A qualitative approach in this research is made by focusing on the field research method. Data were collected by observation techniques, interviews, and documentation studies. Data analysis was done by following certain stages respectively, i.e. data collection, data reduction, data display, conclusion drawing, and verification. The results showed that musical activities implemented in three primary schools consist of (1) listening to music; (2) singing; (3) playing musical instruments; (4) moving to follow the music; and (5) reading music. However, forms, types, and variations of the musical activities taking place in these schools are different, depending on the schools’ policy, schools’ abilities, and music teachers’ abilities in teaching the music subject.
Field Guide to Dynamical Recurrent Networks

From the Book: The idea for this book emerged as the two of us were organizing the 1996 Neural Information Processing Systems (NIPS) workshop on recurrent networks. Many of the telephone calls between us turned to students and research. We both bemoaned the fact that we were handing large stacks of journal papers to our potential graduate students, in some cases, scaring them off to other advisors. Since we both were dealing with terminal masters students whom we would be lucky if they stuck around for a whole year, we needed introductory material that was self-contained, accessible, and devoid of distractionsa book that a first-year graduate student, or upper-division undergrad, could read and become productive in a short period of time. Unfortunately, there were no such books available at that time. Hertz, Krogh, and Palmers textbook serves well in getting students up to speed on back propagation, Hopfield networks, and Boltzmann machines. However, it did little justice to our research area, dynam-ical recurrent networks. Sometime before the workshop, we decided to fill this void. From the conception of this project, we both agreed that this book was not going to be yet another edited collection of papers. We wanted a coherent presentation of the field. One difficulty for students has been the arbitrary nomenclature and mathematical notation used to describe recurrent networks. Though not insurmountable, it does act as a speed bump, reducing the valuable time that they could be spending elsewhere. Like any other young field, we have several researchers with competing viewpoints. One problem with edited volumes is that the author of a chapter will have a biased view of the world. We decided to reduce this bias by assigning multiple authors to chapters. The goal of the assignments was to ensure a balanced presentation of the material, one that would be coming directly from the experts in that field. We also encouraged the authors to tie their chapters with other chapters in the book. Though difficult at times, it transforms the text from a collection of papers into a coherent book. The book is organized into five sections. The first section presents the range of dynamical recurrent network (DRN) architectures that will be used in the book. With these architectures in hand, we turn to examine their capabilities as computational devices. The third section presents several training algorithms for solving the network loading problem. Next, we look back and temper our enthusiasm by acknowledging the limitations of DRNs. The final section deals with applications of recurrent networks. The material in this book assumes a basic understanding of neural networks. The reader, we assume, has read an introductory textbook covering multilayered perceptrons and back-propagation. As such, this book would be ideal for a second course in neural networks.
Interleaved FDMA: equalization and coded performance in mobile radio applications

"Interleaved frequency-division multiple-access" (IFDMA) has previously been introduced as a new spread-spectrum multiple-access scheme for mobile communications. In this paper, performance results for uncoded and coded mobile radio transmission taking into account different equalization techniques are presented. As a result, equalization reduces the effects of intersymbol interference significantly. Uncoded IFDMA applying optimum equalization performs even better than uncoded multicarrier code-division multiple-access applying maximum likelihood multi-user detection. Moreover, in the case of coded IFDMA transmission coding gains up to 6.8 dB at a bit-error-rate of 10/sup -4/ are achieved using a standard convolutional code.
Robust railway crew schedules

On an intensively used network as the Dutch railway network, the problem of crew reschedul- ing is hard. Currently at NS, when disruptions occur there is no quantitative support for dispatchers to reschedule the duties. However, the manual approach of the dispatchers is not effective nor efficient. Therefore, operations researchers developed several methods that can support dispatchers in decision making. One of them, an approach using Column Generation, is very promising and is extensively tested in this thesis together with an heuristic approach that mimics the manual approach of the dispatchers and an agent-based approach. To test the three methods several disruption cases are constructed and combined with the schedules, either with or without reserve duties. After the comparison of the methods, the set of schedules is extended with some additional schedules. One of them is a schedule that is constructed as emergency schedule that can be used during extreme situations. Besides schedules, also different sets of stand-by duties are proposed. These new sets can be divided into two categories, where one is based on a location perspective and the other is based on a time perspective. Finally, we extended the scenarios such that a more realistic presentation of the practical situation, where multiple serious disruptions a day occur, is obtained. From the analysis it becomes clear the method based on Column Generation is in general the best approach. However, the agent-based approach performs particular well on smaller instances, sometimes even better than the Column Generation approach. The heuristic ap- proach does not perform well. Only rescheduling a schedule like the emergency schedule with the heuristic provides reasonable results. Apart from the emergency schedule there is very little difference in robustness between the schedules for all rescheduling methods. Robustness of a schedule is measured as the way schedules can be adapted after disruptions. Reserves make the process of rescheduling more easy, but the Column Generation approach performs also good without reserves. So the use of a good rescheduling method seems to contribute more to a robust railway crew schedule than changing the properties of a schedule.
Statistical Compression Cache Designs

On-chip caches are essential as they bridge the growing speed-gap between off-chip memory and processors. To this end, processing cores are sacrificed for more cache space in the chip's real estate, possibly affecting the cache access time and power dissipation. An alternative to increase the effective cache capacity without enlarging its size is cache compression. However, the compression and decompression processes required add complexity and latency; especially decompression lies in the critical memory access path. Prior work focuses on methods that target lower decompression latency by sacrificing important gains in compressibility. On the other hand, this thesis focuses on cache designs that exploit more advanced compression methods, i.e., statistical compression. The thesis first contributes with an abstract value-aware cache model, which shows that applications often exhibit value locality, and establishes that ideally, by storing each appeared value exactly once, important compression opportunities open up. Motivated by this, the thesis proposes SC^2, a Huffman-based statistical compression cache design. The thesis tackles the problem of statistics acquisition by building a sampling mechanism in hardware. It finds that value locality is rather stable over long time-periods, hence code generation can be offloaded in software. Then it builds the support for compression and decompression in hardware, deals with practical issues such as cache space management, and finally makes a detailed exploration of statistical compression in the last-level cache. Unfortunately, this approach cannot be straightforwardly applied to data types that contain semantically well-defined data fields. Among such types, the thesis focuses on the common double-precision floating-point data and explores a different avenue to extract value locality by considering the different fields (sign, exponent and mantissa) in isolation. Contrary to prior observations, it is shown that the mantissa exhibits significant value locality if it is further partitioned. Then a novel statistical compression method, called FP-H, tailored for cache compression is proposed. Finally, the thesis makes the observation that none of the compressed cache designs, including state of the art, are hitherto always better than others. Hence the thesis establishes HyComp, a practical cache design that adopts hybrid compression for the first time where one out of data-type specific compression methods is selected through heuristics. HyComp offers robust compressibility across applications that manipulate diverse data types, without affecting decompression but only slightly impacting compression latency.
A 1.5-V-100-μW ΔΣ modulator with 12-b dynamic range using the switched-opamp technique

The design and implementation of a very low supply voltage/low power /spl Delta//spl Sigma/ modulator is presented. It is based on the switched-opamp technique, which allows low voltage operation with a standard process and without voltage multiplication. The design methodology is illustrated with a second-order single-loop /spl Delta//spl Sigma/ modulator. The chip is implemented in a 0.7-/spl mu/m CMOS process with standard threshold voltages. The power supply is 1.5 V and the power dissipation is only 100 /spl mu/W. The measured dynamic range in the speech bandwidth of 300-3400 Hz is 12 b. The total harmonic distortion (THD) is lower than -72 dB.
Application and evaluation of DNA computing simulation by list based processing

DNA computing methods based on complementary characteristics and massive parallelism facilitate the solution of complex large-scale problems such as NP-complete problems. In this paper, we design a computer simulation system that corresponds to a basic DNA computing algorithm using list processing. Using the proposed simulation system we are able to predict a scale problem of biochemical calculations. The simulation system also treats any practical engineering complex network problem.
Classification of images on the basis of the properties of informative regions.

A new algorithm is reported that simulates the mechanisms of biological vision in performing classification of traffic sign images, which can increase the recognition rate of signs up to 96% under real viewing conditions. The work also made a significant contribution towards a better understanding of human vision behaviour. Part of the work was carried out in collaboration with Russian scientists on the development of perception models of human vision. This collaboration resulted in an award from the European Commission for Tele-imaging in Medicine (TIME) (CN/ASIA-IT&C/009(91450)).
Non-photorealistic computer graphics: modeling, rendering, and animation

In the past decade, the field of non-photorealistic computer graphics (NPR) has developed as the product of research marked by diverse and sometimes divergent assumptions, approaches, and aims. This book is the first to offer a systematic assessment of this work, identifying and exploring the underlying principles that have given the field its cohesion. In the course of this assessment, the authors provide detailed accounts of today's major non-photorealistic algorithms, along with the background information and implementation advice you need to put them to productive use. As NPR finds new applications in a broadening array of fields, Non-Photorealistic Computer Graphics is destined to be the standard reference for researchers and practitioners alike.
Language and Speech Architecture for Prototype 2

This invention is a scaleable system to perform exact matching or similarity matching between a large store of reference temporal media sequences and a query target temporal media sequence. The system is not limited to finding exact matching media segments but also can find media segments that are similar. One kind of similarity between media segments is the similarity between a long commercial and a short commercial, where the short commercial is formed by sub-segments of the longer commercial. Another kind of similarity of two media segment is when they depict three-dimensional actions that are similar and imaged from similar viewpoints. Given a reference media segment, a multitude of features are computed in a consistent way from either predetermined or media content-dependent key intervals. These features are stored in segment index tables along with identifiers of the corresponding reference media segments. At search time, again, a multitude of features is computed from each key interval and now the features are used to search the segment index table for identifiers of stored media segments. If the key intervals are similar, many if not all, features computed from the key intervals point to the same reference media identifier.
A simple key agreement protocol

We propose a simple key agreement protocol(SKA) that provides key establishment with authentication over an insecure channel using only a human memorable password. The SKA is based on Diffie-Hellman scheme and has many of desirable security attributes: it resists off-line dictionary attacks mounted by either passive or active adversaries over network, allowing low-entropy passwords to be used safely. It also offers perfect forward secrecy, which protects past sessions and passwords against future compromise. Besides, it is secure against an adversary who captured a host's password file. Since user passwords are stored in a verifier form, the adversary cannot use it directly to compromise security. The proposed scheme here shows that it is more efficient in computational time and communication overhead over the existing schemes.
Tracking Vehicles in traffic Surveillance Video

We present a system for detecting and tracking vehicles in surveillance video. Our algorithm uses a simple motion model to determine salient regions in a sequence of video frames. Similar regions are associated between frames and clustered to yield coherent final tracks. The entire process is automatic and uses computation time that scales according to the size of the input video sequence.
Energy Efficient MIMO Wireless Communication Systems Performance Analysis

Multiple Input Multiple Output (MIMO) systems with multiple antenna elements at both Transmitter and Receiver ends are an efficient solution for future wireless communications systems. They provide high data rates by exploiting the spatial domain under the constraints of limited bandwidth and transmit power. Space-Time Block Coding (STBC) is a MIMO transmit strategy which exploits transmit diversity and high reliability. The proposed work presents a comprehensive performance analysis of orthogonal space-time block codes (OSTBCs) with transmit antenna selection under uncorrelated Rayleigh fading channel employing Alamouti’s code. The transmitted symbols belong to BPSK, QPSK and Quadrature amplitude modulation (QAM). The objective of this paper is to analyze the performance of MIMO wireless systems in Rayleigh channel using various modulations schemes such as BPSK, QPSK, 16-QAM, and 64-QAM
Back-propagation fuzzy system as nonlinear dynamic system identifiers

The authors develop a training algorithm, similar to the backpropagation algorithm for neural networks, to train fuzzy systems to match desired input-output pairs. The key ideas in developing this training algorithm are to view a fuzzy system as a three-layer feedforward network, and to use the chain rule to determine gradients of the output errors of the fuzzy system with respect to its design parameters. It is shown that this training algorithm performs an error backpropagation procedure: hence, the fuzzy system equipped with the backpropagation training algorithm is called the backpropagation fuzzy system (BP FS). An online initial parameter choosing method is proposed for the BP FS, and it is shown that it is straightforward to incorporate linguistic if-then rules into the BP FS. Two examples are presented which demonstrate (1) how the fuzzy system learns to match an unknown nonlinear mapping as training progresses and (2) that performance is improved by incorporating linguistic rules. <<ETX>>
Integrating Queueing Network and ACT-R Cognitive Architectures

Adaptive Control of Thought-Rational (ACT-R) and Queueing Network (QN) are two complementary but isolated cognitive architectures. The research reported in this paper aims to integrate the two architectures and benefit from their advantages so as to enhance cognitive modeling capabilities. The new combined architecture, named ACTR-QN, represents ACT-R as a QN whose servers are ACT-R modules and buffers and run the corresponding ACT-R functions. Task-specific knowledge and parameters are defined with ACT-R syntaxes. ACTR-QN provides real-time visualization of mental information processing in addition to ACT-R’s text output traces and is verified with 20 tasks that have been modeled by ACT-R. The steps and benefits of further integration are discussed.
Detection of abnormalities in the signal averaged electrocardiogram: a subspace system identification approach

This paper addresses the detection and classification of low amplitude signals within the QRS complex of the signal-averaged electrocardiogram. The raw data is used to fit a state-space model using the N4SID algorithm and the residual from the model are then used for detection. The fundamental assumption behind the state-space model is that the residuals are a white noise process. Therefore, anything that cannot be modeled with the state-space model will show up in the residuals as flow amplitude signal+noise. Compared to typical residuals, the low amplitude signal behaves as influential observations and can be treated as outliers. Diagnostic tests and analysis on the residuals will then lead to detection and classification of abnormalities in the intra-QRS complex. Residual analysis in this paper includes whiteness and Gaussian tests, statistical process control, and the use of a tracking signal. The end result is a tool to aid the physician in diagnosing the heart condition of a patient.
Controlling chaos in buck converters

A dc-dc buck converter controlled by naturally sampled, constant-frequency pulse width modulation in continuous conduction mode gives rise to a great variety of behaviors, depending on the values of the parameters of the circuit. This paper reexamines the buck converter studied by Fossas and Olivar [1996] and analyzes a method of controlling chaos; that is to convert chaotic oscillations into ones with a periodic time dependence.
Rigid object motion estimation from intensity images using straight-line correspondences

Determining three-dimensional (3D) rigid object motion from intensity images is a central problem in computer vision. This thesis presents a computational approach of 3D rigid object motion estimation using straight line correspondences. Based on the rigidity assumption, a linear algorithm and a nonlinear algorithm of motion estimation are developed. With six or more line correspondences over three frames, the nonlinear algorithm first solves for the rotations, then eliminates the rotations from the original motion and computes the translations. The linear algorithm requires at least thirteen straight line correspondences over three frames to recover motion. A set of intermediate parameters is first estimated, and motion parameters are then determined from these intermediate parameters. Also discussed in this thesis are techniques of straight edge extraction and straight line matching used for obtaining line correspondences of significant edges with high accuracy for motion estimation. The algorithms of motion estimation have also been extended to two applications. The first extension considers the problem of camera location determination from lines and points, and the second, the problem of 3D rigid object motion estimation from image corners. Experimental results on synthetic as well as real image data are given.
Center weighted median filters and their applications to image enhancement

The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the central value of each window, is studied. This filter can preserve image details while suppressing additive white and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM filter can outperform the median filter. Some relationships between CWM and other median-type filters, such as the Winsorizing smoother and the multistage median filter, are derived. In an attempt to improve the performance of CWM filters, an adaptive CWM (ACWM) filter having a space varying central weight is proposed. It is shown that the ACWM filter is an excellent detail preserving smoother that can suppress signal-dependent noise as well as signal-independent noise. >
Estimating The Number Of Daily Patient Applications By Using Artificial NeuralNetworks

This study is aiming at estimating the patient volumes of hospitals by using artificial neural networks. In order to train the artificial neural network models in this study, historical patient applications data from a Turkish hospital were used. All patient applications counted as daily numbers during three years and dependent variable of our study (patient_count) is derived. A different approach used in this study and instead of a single independent variable (which is time), four different time periods were used as input variables of the artificial neural network models. These input variables were day of month, day of week, month, and year. Several artificial neural network models have been generated and compared with each other by their predictive performance measures. The best predictive artificial neural network architecture has an estimation accuracy of 94.22 percent. This artificial neural network model has an input layer with four neurons, an output layer with one neuron, and only one hidden layer with nineteen neurons. The arithmetic mean of patient application in a day is 755.93 (S.d.=486.60). Mean error of the artificial neural network model is -0.047 and mean absolute error is 105.64. The linear correlation between the actual values and the predicted values of the number of patients is 0.918. Keywords: artificial neural networks, decision support systems, modeling, estimation, hospital management.
FLOVA: A four-issue VLIW geometry processor with SIMD instructions and lighting acceleration unit

This paper describes a VLIW (very long instruction word) geometry processor called FLOVA (FLOating-Point VLIW Architecture) which was developed to accelerate the geometry stage of 3D graphics. FLOVA executes four instructions in one cycle and supports 136 instructions including 35 SIMD (single instruction multiple data) instructions to accelerate the geometry stage. Special features to accelerate transformation and lighting operations in 3D graphics geometry stage are described. FLOVA can calculate the power value of two floating-point numbers in only four clock cycles with a negligible loss of accuracy, compared to over 150 clock cycles in other processors.