Miracle-dz/UniFAR-FTUs huggingface dataset:
BROAD PHONEME CLASSIFICATION USING SIGNAL BASED FEATURES

Speech is the most efficient and popular means of human communication Speech is produced as a sequence of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme classification is achieved using features derived directly from the speech at the signal level itself. Broad phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first three formants. Features derived from short time frames of training speech are used to train a multilayer feedforward neural network based classifier with manually marked class label as output and classification accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction which is useful for applications such as automatic speech recognition and automatic language identification.
Performance Evaluation and Analysis of NVM Storage for Ultra-Light Internet of Things

With the rapid growth of semiconductor technologies, small-sized devices with powerful computing abilities are becoming a reality. As this environment has a limit on power supply, NVM storage that has a high density and low power consumption is preferred to HDD or SSD. However, legacy software layers optimized for HDDs should be revisited. Specifically, as storage performance approaches DRAM performance, existing I/O mechanisms and software configurations should be reassessed. This paper explores the challenges and implications of using NVM storage with a broad range of experiments. We measure the performance of a system with NVM storage emulated by DRAM with proper timing parameters and compare it with that of HDD storage environments under various configurations. Our experimental results show that even with storage as fast as DRAM, the performance gain is not large for read operations as current I/O mechanisms do a good job hiding the slow performance of HDD. To assess the potential benefit of fast storage media, we change various I/O configurations and perform experiments to quantify the effects of existing I/O mechanisms such as buffer caching, read-ahead, synchronous I/O, direct I/O, block I/O, and byte-addressable I/O on systems with NVM storage.
Towards a Multi-Agent E-learning Platform

In the Dynamic Content Management (DCM) system one focuses on removing the tight association between learning material to specific courses. In this system one defines a conceptual atomic unit of knowledge and builds the material by the organization of these knowledge elements from a repository. The system allows an educator to create an arbitrary collection of knowledge elements, tag them with meta-information as a single unit and link the unit with previously saved and similar aggregations. The DCM system uses idea of Concept Maps to model the relationships between the knowledge elements that can be created by the instructor or the educator. A pilot system has been implemented based on a client-server architecture. The server is programmed in Python and the client as a Java application made available through Java Web Start. In the paper we also present different design and implementation aspects of a future Multi-Agent System (MAS) based on the DCM model. The implementation of such a model of e-learning is based on JADE a FIPA compliant platform. KeywordsDCM; Knowledge Map; Learning Map; Student map; Client-Server; Python; Java; Multi-Agent Systems; FIPA; JADE
Learning to replace a human: A virtual performing agent

In this paper we describe two artworks, Recognition, an outdoor interactive installation and Instrumental a live dance performance. In both works a performing agent has learnt sequences of movement from a dancer and uses these to stand in for a human performer. The agent uses an Artificial Neural Network to learn to dance from the human dancer and can perform in the human’s stead. In Recognition the agent’s movement is used when there are no humans present in order to keep continuity of the installation. In Instrumental the agent becomes a performing partner of a live human dancer, able to recognize the dancers movement and synthesize movement sequences based on the human dancer’s movements.
Content-based search for 3D-objects

The topic of this paper is content-based 3D-object retrieval. The approach is based on feature vectors, which capture 3D-shape of a model represented as a triangle mesh. The feature vectors are invariant with respect to translation, rotation, scaling, and reflection and robust with respect to level-of-detail. Before the feature extraction, each 3D-object is transformed (normalized) into a canonical position and orientation. The search is performed in the feature vector space in which the feature vector of a query model is used as a key. Original normalization steps and feature vectors are presented in this communication.
Improving financial distress prediction via genetic programming decision tree-evidence from Taiwan

Abstract In this paper, we apply the classifiers like CART, C5.0, GP decision tree and compare with Logic model and ANN model for Taiwan listed electronic companies’s bankruptcy prediction. Our empirical results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k -fold cross validation test in out- sample. That is to say, GP decision tree model has the highest accuracy and lowest expected misclassification costs. It can provide an efficient alternative to discriminate financial distress problems in Taiwan.
Support for Flight Trajectories and Video Capture: Initial Results Using a Free Open Source Flight Simulator

Micro Aerial Vehicles (MAVs) and larger Unmanned Aerial Vehicles (UAVs) constitute a powerful tool for the modern warfighter and first responder. However, every developing technology must first be tested before being exported to the battlefield or hot spot to assure predictable behaviors. In today's digital age, computers give designers and testers unprecedented ability to quickly and easily test virtual models before physical prototypes are created and field tested. We have utilized emerging Free Open Source Software (FOSS) simulators and workbenches (e.g. FlightGear and AUV Workbench) to improve mission-driven MAV/UAV video and imagery capture and analysis capabilities. In this project, use of FlightGear to analyze flight trajectories and video capture capabilities of different UAVs is presented. FlightGear allows the free implementation of changeable flight dynamic models, weather, autopilot, GPS, and multiplayer to be included in the analysis of simulated flights. Another attractive aspect of FlightGear is its extensible and easy-to-modify implementation. Modifications are accomplished by changing or adding human readable XML files in the FlightGear subdirectories. In this effort, existing aerial vehicle models are incorporated into FlightGear to create flight trajectories and analyze video capture capabilities to determine the necessary toolset for analyzing imagery data to meet the mission needs. The limitations of the existing camera systems as well as effects of various flight parameters on mosaicking and georegistration are presented here. Sample analysis of the effect of aerial vehicle speed on the overlap of frames captured at the typical 30 frames/second frame rate, which provides a base for comparison of flight attributes, are demonstrated together with other parameters that affect mission performance. 1941 Earth and Space 2010: Engineering, Science, Construction, and Operations in Challenging Environments © 2010 ASCE
Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals who contribute to the data. Data synthesis (DS) is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional DS techniques often rely on strong assumptions on a data intruder's behaviors and background knowledge to assess disclosure risk. Differential privacy formulates a theoretical approach for strong and robust privacy guarantee in data release without having to model intruders' behaviors. In recent years, efforts have been made aiming to incorporate the DP concept in the DS process. In this paper, we examine current DIfferentially Private Data Synthesis (dips) techniques, compare the techniques conceptually, and evaluate the statistical utility and inferential properties of the synthetic data via each dips technique through extensive simulation studies. The comparisons and simulation results shed light on the practical feasibility and utility of the various dips approaches, and suggest future research directions for dips.
Métodos auto-organizáveis para segmentação de imagens

Image segmentation is one of rnost investigated problems in computer vision. Its complexity can vary according to the kind of application. In general, the goal is to divide an image into regions with similar properties. In tliis work, self-organizing methods for unsupervised classification and clustering are applied in image segmentation tasks. The first self-organizing model is the Fuzzy ART neural network and the other one is the ICA Mixture Model (ICAMM), which uses ICA method to desenhe data in each class. Beside the performance evaluation regarding the considered methods, some improvements on the segmentation results obtained by these techniques were proposed by incorporating some image preprocessing methods. Sueli methods were able to handle some questions regarding to presence of noise, image smoothing and edge enhancement, in a way that makes an image more suited to be processed by an image segmentation technique, which can become more efficient. Aiming this, a preprocessing methodology was proposed in this work that combines Sparse Code Shrinkage method for image denoise to the Sobel Edge Detector, which is applied to recover edges that were blurred by an excessive smoothing. Another original contribution of this work refers to the development of EICAMM, which was built by proposing some modifications on ICAMM, considering some limitations on the original method and analysis on liow it should be modified to become more efficient. Finally, unifying the two main contributions of this thesis, the EICAMM method was applied for segmenting some images in these original and preprocessed versions, obtained by the proposed preprocessing methodology. Such systern lias showed satisfactory image segmentation results.
Melanoma recognition using mid-level features and high-level annotations

Dermoscopic imaging is one of the most effective tools for improving the rate of early melanoma recognition. Our research focuses on three components of an automated melanoma recognition system, including lesion segmentation, representation, and classification. Our main contributions include the following: (1) We propose a new segmentation method combining graph partitioning active contours and narrow band energy. Our method provides highly competitive results compared to other segmentation algorithms on a set of over 200 images; (2) We employ mid-level features for lesion representation and evaluate various sets of feature combinations and selection schemes for these features. We also compare different sampling strategies for mid-level feature generation. In addition, we investigate the use of graph models to integrate spatial information with mid-level features. Experiments on a set of over 1,500 images show that spatial information can significantly improve melanoma recognition accuracy; (3) We formalize the problem of recognizing high-level anatomical features as a multi-instance learning model and compare it against traditional single instance learning. We further propose a newly developed learning model (with proven error bound) that can use these additional anatomical features provided by domain experts. Comparison against relevant learning models on a set of 360 lesion images shows that our method can effectively use the additional information.
A micromachined inductive sensor using folded flex-circuit structures and its wireless telemetry applications

This thesis reports a flexible, passive wireless inductive sensor with micromachined variable inductors for telemetric applications. The variable inductor is formed by folding coplanar dual spiral coil with 5-10 mm size that are microfabricated using 50-urn-thick copper-clad polyimide film commonly used for flex-circuit manufacturing. When folded, the two coils are aligned to each other where the mutual inductance depends on the gap between the aligned coils. The sensor can be combined with a variety of hydrogel materials for biomedical and chemical applications. A stimuli-responsive hydrogel element is sandwiched by the folded substrate to modulate the gap, or inductance of the device as it swells! deswells depending on the target parameter. The response of a variable inductor to the displacement of the coils is measured to be 0.40 nH/pin. A sensitivity of 71-110 ppm4tm in wireless frequency measurement is obtained using the passive resonant device that combines the variable inductor with a fixed capacitor created on the polyimide substrate. The fabricated devices are coupled with pHsensitive poly (vinyl alcohol)-poly (acrylic acid) hydrogel and a commercial wound dressing to experimentally demonstrate wireless monitoring of pH and moisture level within the dressing product, respectively. Theoretical inductive responses of the developed device obtained through finite element analysis and their comparison with the measurement result are also presented.
Ura: a universal data replication architecture

Data replication is a key building block for large-scale distributed systems to improve availability, performance, and scalability. Because there is a fundamental trade-off between performance and consistency as well as between availability and consistency, systems must make trade-offs among these factors based on the demands and technologies of their target environments and workloads. Unfortunately, existing replication protocols and mechanisms are intrinsically entangled with specific policy assumptions. Therefore, to accommodate new trade-offs for new policy requirements, developers have to either build a new replication system from scratch or modify existing mechanisms. This dissertation presents a universal data replication architecture (URA) that cleanly separates mechanism and policy and supports Partial Replication (PR), Any Consistency (AC), and Topology Independence (TI) simultaneously. Our architecture yields two significant advantages. First, by providing a single set of mechanisms that capture the common underlying abstractions for data replication, URA can serve as a common substrate for building and deploying new replication systems. It therefore can significantly reduce the effort required to construct or modify a replication system. Second, by providing a set of general and flexible mechanisms independent of any specific policy, URA enables better trade-offs than any current system can provide. In particular, URA can simultaneously provide the three PRACTI properties while any existing system can provide at most two of them. Our experimental results and case-study systems confirm that universal data replication architecture is a way to build better replication systems and a better way to build replication systems.
Computational complexity of narrow band and wide band channel simulators

Some questions such as: is it convenient to simulate channel realizations with the sum of sinusoids or with the filtering approach, are addressed in this work in order to provide a guide on which algorithms and in what situations should be used in the development of channel simulators. The conclusions presented are gathered from the comparison of the methods proposed in the literature to simulate narrowband and wideband separable channels. The comparisons are performed from the point of view of computational complexity and two metrics of approximation error. We also discuss the properties inherent to the algorithms that could determine if the proposals are suitable for channel simulation, such as synthesis complexity and their capability to approximate the conditions encountered in real channels.
Front end electronics for pixel detector of the PANDA MVD

ToPix 2.0 is a prototype in a CMOS 0.13 μm technology of the front-end chip for the hybrid pixel sensors that will equip the Micro-Vertex Detector of the PANDA experiment at GSI. The Time over Threshold (ToT) approach has been employed to provide a high charge dynamic range (up to 100 fC) with a low power dissipation (15 μW /cell). In an area of 100μm× 100μm each cell incorporates the analog and digital electronics necessary to amplify the detector signal and to digitize the time and charge information. The ASIC includes 320 pixel readout cells organized in four columns and a simplified version of the end of column readout.
JOP Reference Handbook: Building Embedded Systems with a Java Processor

This book is about JOP, the Java Optimized Processor. JOP is an implementation of the Java virtual machine (JVM) in hardware. The main implementation platform is a field-programmable gate array (FPGA). JOP began as a research project for a PhD thesis. In the mean time, JOP has been used in several industrial applications and as a research platform. JOP is a time-predictable processor for hard real-time systems implemented in Java. JOP is open-source under the GNU GPL and has a growing user base. This book is written for all of you who build this lively community. For a long time the PhD thesis, some research papers, and the web site have been the main documentation for JOP. A PhD thesis focus is on research results and implementation details are usually omitted. This book complements the thesis and provides insight into the implementation of JOP and the accompanying JVM. Furthermore, it gives you an idea how to build an embedded real-time system based on JOP.
Touch Pen Using Depth Information

Current touch pen requires the special equipments to detect a touch and its price increases in proportion to the screen size. In this paper, we propose a method for detecting a touch and implementing a pen using the depth information. The proposed method obtains a background depth image using a depth camera and extracts an object by comparing a captured depth image with the background depth image. Also, we determine a touch if the depth value of the object is the same as the background and then provide the pen event. Using this method, we can implement a cheaper and more convenient touch pen.
Formal Specification with JML

This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It is a preview of a chapter planned to appear in a book about the KeY approach and tool to the verification of Java software. JML is the dominating starting point of KeY style Java verification. However, this paper does not in any way depend on any tool nor verification methodology. Other chapters in this book talk about the usage of JML in KeY style verification. Here, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. The authors appreciate any comments or questions that help to improve the text.
APPROXIMATION CONTEXTS IN ADDRESSING GRAPH DATA STRUCTURES

xi List of Publications . xiii Acknowledgements . xiv List of Abbreviations . xv
GMSK neural network based demodulator

The pattern recognition characteristics of the Artificial Neural Networks are used to realise a real demodulator for Gaussian Minimum Shift-Keying signals, used in the GSM telecommunications. The demodulator utilizes the learning vector quantization (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronization, a pre-processing phase is organised. The demodulator prototype has been realised by implementing the pre-processing phase and the LVQ neural network on TMS320C30 digital signal processor. The demodulator has been tested according to the European Telecommunication Standard Institute recommendations.
IMPROVEMENT IN THE USABILITY OF GIS BASED SERVICES BY APPLYING SEMANTIC ONTOLOGY

After the invention of GIS Based services in the we b and the desktop areas it becomes the most widely used services over the internet and because of its tremendous capabilities of givin g accurate and most efficient information about GIS related queries of particular organizations, for users or industries these servic es are on the peak of popularity. Now a days there are lots of options available in the market who provides these GIS related functiona lities to the users such as for e.g. ArcMap, GRASS and Google map APIs etc. This system improves the user usability on geo serv ices. Purpose of this system to develop a user conv enient system related to GIS that help any normal user to handle it in its natural wa y. User do a spatial thinking in his/her mind accor ding to that some natural question where generated from this system take that questions as it is and make that questions. Filter those questions in a way that system can understand it and produce an exact resul t. Semantic concept gives a common and meaningful dformat by integrating this concept in GIS which will come to know as Geos emantic. OWL files provide well defined relationshi p between the object by mapping with real word things.
Predicting the intelligibility of cochlear-implant vocoded speech from objective quality measure

A reliable measure for predicting the intelligibility of cochlear-implant vocoded speech is desirable as it can be used to guide the development of new speech coding algorithms for cochlear implants. The present study assesses the performance of several speech quality measures in predicting the intelligibility of cochlear-implant vocoded speech (vocoded English and vocoded Mandarin Chinese). The quality measures are analyzed and correlated with the intelligibility scores of vocoded speech obtained from normal‐ hearing listeners in three experiments. The perceptual evaluation of speech quality measure (r = 0.91) and the weighted spectral slope measure (r = -0.87) are well correlated with the intelligibility score. The language effect impacts the performance of objective quality measures in predicting the intelligibility of vocoded speech.
Path analysis and probabilistic networks: analogous concepts

Network-based probabilistic techniques in artificial intelligence often mention path analysis as an ideological precursor, but the fundamental connections between the two paradigms have not been explored. An analogy between the two is presented, and it is shown that a number of computational results in the AI/expect system literature are directly obtainable from path analysis. <<ETX>>