Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical power network decomposition for parallel computations

Introduces an electrical power network decomposition methodology for the network equation solution by block-iterative methods implemented on parallel computers. The proposed methodology is based on the building up of a fixed number of subnetworks by aggregating network nodes to a given number of so-called seed nodes. The aggregation method is controlled by a node ranking criterion based on the weights assigned to the network nodes reflecting the magnitude of their electrical coupling. The resulting decompositions are usually conducive to well-conditioned iterative processes, with acceptable computational load balancing, and relatively low communication requirements for the parallel solution of the network equations. Results of computational tests performed on medium-size real networks are presented. <<ETX>>
Dynamic CAA and peer supported learning in interactive classrooms

Advances in interactive classroom technology are opening up exciting, new ways of engaging students with CAA during face-to-face teaching. Project LOLA (Live and On-Line Assessment) is a 3-year national teaching fellowship project started in 2001, which has been exploring how one-way group response systems and two-way classroom communication systems can be used most effectively. The PRS (Personal Response System) is a popular group response system used as a means of promoting greater interactivity in the classroom, but there are many limitations in its standard use. These limitations are addressed in this paper and a system called RxShow, which overcomes some of them described. A significant benefit of RxShow is its integrated delivery with the option for dynamic display of results, which encourages “peer supported learning”. Assessment of students by the lecturer and feedback to the lecturer from students become more lively and interesting. Results from student evaluations of the improved approach are extremely positive.
Researching in Shallow Parsing Based on Function Invariance of Maximum Likelihood Estimation

Abstract. Shallow parsing is a new strategy of language processing in the domain of natural language processing recently years. It is not focus on the obtaining of the full parsing tree but requiring of the recognition of some simple composition of some structure. It separated parsing into two subtasks: one is the recognition and analysis of chunks the other is the analysis of relationships among chunks. In this essay, some applied technology of shallow parsing is introduced and a new method of it is experimented.
Integrating Assessment with Learning: What Will It Take to Make It Work?

Improving education is a priority for all countries. Increasing the level of educational achievement brings benefits to the individual, such as higher lifetime earnings, and to society as a whole, both in terms of increased economic growth and lower social costs such as health care and criminal justice costs (Gritz & MaCurdy, 1992; Hanushek, 2004; Levin, 1972; Tyler, Murnane, & Willett, 2000). Indeed, the total return on investments in education can be well over $10 for every $1 invested (Schweinhart et al., 2005). This means that even loosely focused investments in education are likely to be cost-effective. Given public skepticism about such long-term investments, however, and given too the reluctance of local, state, and federal governments to raise taxes, there is a pressing need to find the most cost-effective ways of improving student achievement.
Texture preserving despeckling of SAR images using GMRFs

Basic textures as they appear especially in high resolution SAR imagery are affected by multiplicative speckle noise and should be preserved by despeckling algorithms. Sharp edges between different regions also must be preserved. To despeckle images the authors use a maxirmum a posteriori (MAP) estimation of the cross section choosing between different prior models. The proposed approach uses a GMRF model for textured areas and allows an adaptive neighbourhood system for edge preservation between uniform areas. In order to obtain the best possible texture reconstruction an expectation maximization algorithm is used to estimate the texture parameters and that provide the highest evidence. The borders between homogeneous areas are determined using a stochastic region-growing algorithm. In this way, they perform a MAP estimate of the image reconstructing either texture of separated regions of uniform backscatter.
Implementation of the direct windowed DHT parallel algorithm

Hardware realizations of an algorithm of discrete Hilbert transform (DHT) are made mainly with using the FFT because of its higher resolution [1] [4] in comparison to the direct DHT based on unwindowed impulse response (IR). On the other hand, as it is shown previously in [5] [6] the appropriate use of weight windows for IR provides a higher resolution of the direct DHT in comparison to the DHT method with FFT using. In this work a parallel algorithm and appropriate filter structure for the direct windowed DHT which are faster than the algorithms and structures based on the FFT are presented. The work of the algorithm and filter is illustrated by examples of phase transitions capturing of noisy signals.
A computer simulation system of the electric activity in brains

Several aspects in the numerical analysis system of electroencephalography (EEG) are discussed in this paper. Based on improved vector flow snakes and dynamic template method, the boundaries of different region in MRI image are extracted. Using the boundary element method, forward computation of EEG is investigated and acceptable results are obtained. The paper also proposes a method based on LORETA-FOCUSS to estimate the current density inside the brain with high spatial resolution. From simulated experiments, it is turned out that the proposed techniques yield satisfactory results.
JUnit Recipes: Practical Methods for Programmer Testing

When testing becomes a developer's habit good things tend to happen--good productivity, good code, and good job satisfaction. If you want some of that, there's no better way to start your testing habit, nor to continue feeding it, than with JUnit Recipes. In this book you will find one hundred and thirty seven solutions to a range of problems, from simple to complex, selected for you by an experienced developer and master tester. Each recipe follows the same organization giving you the problem and its background before discussing your options in solving it. JUnit-the unit testing framework for Java-is simple to use, but some code can be tricky to test. When you're facing such code you will be glad to have this book. It is a how-to reference full of practical advice on all issues of testing, from how to name your test case classes to how to test complicated J2EE applications. Its valuable advice includes side matters that can have a big payoff, like how to organize your test data or how to manage expensive test resources.
On the use of neural networks for automatic vowel recognition

An approach to face the problem of speaker-independent vowel recognition using artificial neural networks (ANN) is presented. The approach lies in algorithms and techniques for training the ANN with backpropagation rule and then for testing it with the spectral representations of vowels obtained by linear predictive coding (LPC). Speech patterns consist in 12 LPC spectral components obtained from the stationary zones of the vowels and they compose a speech database collected in laboratory conditions with computer noise from a number of speakers. A training set and a testing set of vowel patterns were designed. Network topology is a multilayer perceptron (MLP) with a variable number of processing units in the hidden layer. Three types of MLP were tested and the results on vowel recognition rate are presented under this framework.
Itanium Architecture for Software Developers

From the Publisher: This unique description of the Itanium-based application architecture helps the software development community use the power of Intel's new line of Itanium processors. Developers will understand how to create high-performance software by tapping the unique features of the architecture and corresponding compilers. Provided is a clear explanation of how the massively parallel Itanium architecture meets demand for increased throughput by Internet-based applications and e-commerce. Also included are descriptions of salient features of the Itanium processor's application software architecture. Unique concepts that help the developer to understand how Itanium architecture differs from traditional architectures are highlighted. Walter Triebel is the author of 17 books on microprocessors, digital logic, and memory including The 8088 and 8086 Microprocessors. He teaches computer architecture at Farleigh Dickinson University. He lives in Wayne, New Jersey.
A Selection Theory and Methodology for Heterogeneous Supercomputing

In this paper, a methodology for mapping algorithms onto heterogeneous suite of supercomputers is presented. A n approach for selecting an optimal suite of computers for solving problems with diverse computational requirements, called Heterogeneous Optimal Selection Theory (HOST), is presented. HOST is an extension t o Augmented Optimal Selection Theory in two ways: It incorporates heterogeneous parallelism embedded in the tasks, and it reflects the costs associated in using various fine grain mapping strategies at individual machine level. The proposed mapping methodology is based on the Cluster-M programming paradigm. For the mapping purpose, the input format, assumed in HOST, is modeled in terms of Hierarchical Cluster-M specification and representation. For a given problem, a hj'ierarchical Cluster-M specification is generated t o indicate the execution of concurrent tasks at different stizges of the computation. This specification is then mapped onto the Hierarchical ClusterM representation ofthe underlying heterogeneous suite of supercomputers.
The employment of separated women in Europe: Individual and institutional determinants

A surface traveling mobile apparatus which moves by an endless track belt installed to be wrapped around a case from end to end, traveling on a surface by the rotation of wheels driven by a power source is provided. The surface traveling mobile apparatus includes vacuum chambers installed to be attached to the sides of the wheels, a vacuum tube for connecting the vacuum chambers to each other, a vacuum pump formed in part of the vacuum tube, the vacuum pump for inhaling and exhausting air, and suction mechanisms installed along the endless track belt at predetermined intervals, valve driven by contacting the wheels, and vacuum attached to the surface by the vacuum pump inhaling and exhausting air. Also, a cleaning apparatus including a spray unit for spraying water or wash liquid to the surface when the surface traveling mobile apparatus moves, and a rotating brush for washing the surface using the sprayed liquid, are provided. Accordingly, the surface traveling apparatus and the cleaning apparatus using the same can freely travel a plane, an inclined plane, a vertical plane, and a ceiling and thus, can clean areas which are not easily cleaned by people, such as windows or high buildings and high ceilings.
Applying protection principles for controlling distributed generation

Abstract In a distribution system, it is essential to maintain the voltage variation within a specified limit for satisfactory operation of connected customers' equipment. Normally, this goal is achieved by controlling the operation of compensating devices, such as load tap changing transformers, shunt capacitors, series capacitors, shunt reactors and static VAr compensators. However, technical and regulatory developments are encouraging a greater number of small generator units, known as distributed generation (DG), and this has the potential to significantly affect voltage control systems. This paper presents an adaptive voltage control technique that incorporates DG systems into the voltage control system. The control scheme uses on-load tap changing transformer (OLTC) and DG for voltage corrections, both are driven by advanced line drop compensators (LDC). At the substation, the LDC is employed to control step-up or step-down decisions of the OLTC, while another LDC will be used at the DG connection point to set DG parameters. Also, for a more cost-effective system, voltage control action coordination is proposed using magnitude and time grading. The control approach is tested on a modified distribution system with load variations that are stochastic in time and location. The results show that the integration of these magnitude and time grading protection principles have considerably reduced the DG energy required to achieve the desired control.
An algorithm for reducing loss rate of high-speed TCP

Some high-speed protocols such as HSTCP have been proposed to improve the ability of bandwidth utilization in high-speed networks. However, the increased scalability of high-speed TCP leads to many dropped packets in a single loss event in drop tail environment. In addition, there exists burstiness on short time scales that may cause lots of packets loss. In this paper, we analyze the problem of packet loss, and then propose ACWAP (Adaptive Congestion Window Adjustment plus Pacing) algorithm to reduce the loss rate of high-speed TCP. Along with pacing algorithm for avoiding burstiness on short time scales, ACWAP uses delay information to estimate the network state and adaptively changes the increase parameter to 1 before congestion to reduce the number of dropped packets. Many simulation results show our proposed algorithm can reduce the number of dropped packets in a single loss event, alleviate synchronized loss phenomena and improve the RTT unfairness while keeping the advantages of high-speed TCP.
Advanced Analytics with Spark

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder
Otantik Liderliğin Demografik Özellikler Açısından İncelenmesi

Otantik liderlik, orgut icerinde ortak bir guc olusturarak, ekip calismasini on planda tutarak, etige, bagliliga ve ortak degerlere onem vererek, olumlu ciktilarin olusumuna katki saglayabilen pozitif bir liderlik turudur. Otantik liderligi aciklamayi ve otantik liderligin dort boyutu olan oz farkindaligin, iliskisel seffafligin, karar almada bilginin dengeli degerlendirilmesinin, icsellestirilmis ahlak anlayisinin demografik ozelliklerle iliskisini ortaya koymayi amaclayan bu arastirmada, Gaziantep, Adiyaman ve Kilis illerinde faaliyette bulunan hastanelerde calisan 513 kisi ile anket uygulamasi gerceklestirilmistir. Elde edilen verilerin analizleri icin SPSS programindan yararlanilmis olup, arastirmaya katilan calisanlarin otantik liderlik algilarinin demografik ozelliklerine gore farklilik gosterip gostermedigini tespit edebilmek adina T-Testi ve ANOVA analizleri (tek yonlu varyans analizi) uygulanmistir. Arastirma sonuclarina gore; otantik liderlik boyutlarinin (oz farkindalik, iliskisel seffaflik, karar almada bilginin dengeli degerlendirilmesi ve icsellestirilmis ahlak anlayisi), calisanlarin cinsiyetlerine, yaslarina, gorev yaptiklari hastanelere, hastanelerdeki pozisyonlarina, gorev yaptiklari hastanelerin bulunduklari illere gore; otantik liderlik boyutunun (oz farkindalik) ise egitim durumlarina gore istatistiksel olarak anlamli farkliliklar gosterdigi tespit edilmistir.
Simulation of Asynchronous Instruction Pipelines

This paper presents the ARAS simulator with which asynchronous instruction pipelines can be modelled, simulated and displayed. ARAS allows one to construct instruction pipelines by preparing various configuration files. Using these files and a number of benchmark programs, performance of the instruction pipelines can be obtained. The performance of asynchronous instruction pipelines can also be compared to synchronous case. Thus, one can decide the optimal design for instruction pipelines in asynchornous or synchronous cases and explore the deisng space of asynchronous instruction pipeline architectures. Read complete abstract on page 2.
Dépôt de films nanométriques en pulvérisation cathodique radiofréquence

Cet article traite du depot de films nanometriques en pulverisation cathodique radiofrequence, reserve il y a encore quelques temps aux domaines spatial et aeronautique. Desormais, ce procede se propage a d’autres secteurs d’activites tels que la decoration ou le biomedical. L'article s’attache tout d'abord aux decharges basse temperature, basse pression : decharges diode continue et decharges radiofrequence (decharges excitees par des champs electriques haute frequence). Les parametres de depot sont ensuite passes en revue : les mecanismes de pulverisation, la puissance, la pression et la distance cible-substrat.
The creation and management of knowledge: what can we learn from applying the principles of economics?

This paper attempts to assess the role of orthodox Economics in the increasingly prominent area of Knowledge Management. More specifically, it serves three purposes: first, it explores the definitions and understanding of knowledge, knowledge creation and knowledge management from a variety of different academic perspectives; second, in addition to the acquisition of knowledge and by reference to a neighbouring concept – the acquisition of skills – it investigates whether the mainstream principles of Economics as an analytical discipline can be usefully deployed as tools to add to a better understanding of the application of the creation and management of knowledge. Although the Economics of Knowledge Management is seen as an embryonic area for development, it can be shown that the orthodox economic analysis of the components of knowledge management can produce constructive outcomes. It follows that the third purpose of the paper is an attempt to inspire others to join the field.
Novel methods for semantic and aesthetic multimedia retrieval

In the internet era, computerized classification and discovery of image properties (objects, scene, emotions generated, aesthetic traits) is of crucial importance for the automatic retrieval of the huge amount of visual data surrounding us. But how can computers see the meaning of an image? Multimedia Information Retrieval (MMIR) is a research field that helps building intelligent systems that automatically recognize the image content and its characteristics. In general, this is achieved by following a chain process: first, low-level features are extracted and pooled into compact image signatures. Then, machine learning techniques are used to build models able to distinguish between different image categories based on such signatures. Such model will be finally used to recognize the properties of a new image. Despite the advances in the field, human vision systems still substantially outperform their computer-based counterparts. In this thesis we therefore design a set of novel contributions for each step of the MMIR chain, aiming at improving the global recognition performances. In our work, we explore techniques from a variety of fields that are not traditionally related with Multimedia Retrieval, and embed them into effective MMIR frameworks. For example, we borrow the concept of image saliency from visual perception, and use it to build low-level features. We employ the Copula theory of economic statistics for feature aggregation. We re-use the notion of graded relevance, popular in web page ranking, for visual retrieval frameworks. We explain in detail our novel solutions and prove their effectiveness for image categorization, video retrieval and aesthetics assessment.
Body Sensor Network: A Modern Survey & Performance Study in Medical Perspect

As because of modern emerging technologies, low power integrated circuits and wireless communication has enabled a new generation of sensors network. The incorporation of these sensors networks in Health care is very popular and plays a vital role in breath breaking situations. The deployment of monitoring hardware incorporated with various wireless standards plays a key role in regard to interoperability, invasion privacy, sensors validation data consistency and interference related issues. The goal of our paper is to make a comparative study in realm of modern wireless trends such as Bluetooth, Wi-fi, Zigbee and Wibree and related facets. Index Terms– Wireless Body area network, Zigbee, Wi-fi, Bluetoooth
Visual Learning in Surveillance Systems

1.1 Motion Detection 3 1.2 Motion Tracking 5 1.3 Multiple Camera Surveillance Systems 6 1.4 Camera Calibration 8 1.4. 1 Single Camera Calibration 8 1.4. 2 Multiple Camera Calibration 9 1.5 Learning from observation in surveillance systems 10 1.5. 1 Problem statement 10 1.5. 2 Previous work 11
Digital Image Watermarking Based on Multiband Wavelet Transform

Digital image watermarking is an important tool for protecting the secret information. In this paper a Multiband based watermarking technique is proposed. The proposed scheme is based on, to decompose the image and get multiple sub-bands in one level decomposition. It will improve the quality of watermarked image and robustness of the watermark. This method is more secure and robust to attacks. The experimental results are observed with the proposed work in terms of PSNR.
The design methodology for the multivariable fuzzy controller and its learning rule

This paper puts forwards a new approach for the systematic design of a multivariable fuzzy control system. The concepts of sliding-mode control and control meta-knowledge are adopted to construct a general-purpose multivariable fuzzy controller, which has the following characteristics: the generalization and simplicity of the control rules; and adaptive ability. Moreover, based on fuzzy cell mapping, the controller can be formally represented, so the proposed adaptive scheme is built on the rigorous mathematical analysis. Finally, the simulation results demonstrate the availability of the proposed approach.
Efficient Keyword Based Text Mining of Documents in Cloud Environment

-As we know we have to spend more time to search as well as to read the research and it takes more than two to three hours to read a single research paper, so it is necessary to use new search engine which provide best result because it is based on fastest reading algorithm. To give better verification of the research paper it will be very helpful. The traditional data mining assumes that information mined should always in the relational database form but in many cases information is available in the form of Natural Languages. The proposed work is thus based on the clustering and text mining in cloud environment. Clustering is widely studied data mining problem in text domain.