Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable machine learning algorithms on a big data infrastructure

Two currently popular topics in computer science are machine learning and big data. Often the two are combined to obtain powerful machines with learning capabilities or high throughput data analysis programs among others. This research analyses which machine learning techniques qualify to be efficiently implemented on a scalable big data infrastructure. Several machine learning algorithms are analyzed and modified to scale on a multi-processor machine. Furthermore this thesis investigates the scalability potential of an existing image segmentation pipeline, used for cancer diagnostics, containing an artificial neural network. The neural network is implemented according to one of the proposed scalable algorithms on a 64 CPU, 256 thread PowerPC-7 cluster with 64 CPU's capable of running 256 threads. While suffering from a large overhead penalty, the pipelines run time is still reduced greatly and show excellent scalability. This scalability allows for greater input sets with equal execution times by expanding the platforms resources. This provides an opening for future research in improving the pipelines diagnostics capability.
Modem constellation identification: a performance comparison of two methods

Two algorithms are described for determining the signal constellation quadrature amplitude modulation in use on a (QAM) modulator, one based on Radon transform ideas and the other based on histogramming received signal radius information. The detection performances of the two specific algorithms are compared as a function of the signal-to-disturbance power ratio and the number of samples available. Generally speaking, it is shown that, when the carrier frequency is known with reasonable accuracy, the Radon transform method performs much better than the specific radius- or magnitude-only method tested, in the sense that for the same signal-to-disturbance ratio the former can reliably determine the constellation size and orientation with about one-tenth of the data samples required by the latter. It should be noted, however, that neither of the two algorithms has been optimized, nor have the comparisons taken into account other issues such as the details of particular applications, the relative computational requirements, and the added complexity needed by the Radon transform method to deal with uncertainties in the carrier frequency. <<ETX>>
Face Texture Reconstruction for Illumination Variant Face Recognition

In illumination variant face recognition, existing methods extracting face albedo as light normalized image may lead to loss of extensive facial details, with light template discarded. To improve that, a novel approach for realistic facial texture reconstruction by combining original image and albedo image is proposed. First, light subspaces of different identities are established from the given reference face images; then by projecting the original and albedo image into each light subspace respectively, texture reference images with corresponding lighting are reconstructed and two texture subspaces are formed. According to the projections in texture subspaces, facial texture with normal light can be synthesized. Due to the combination of original image, facial details can be preserved with face albedo. In addition, image partition is applied to improve the synthesization performance. Experiments on Yale B and CMUPIE databases demonstrate that this algorithm outperforms the others both in image representation and in face recognition. Keywords—texture reconstruction, illumination, face recognition, subspaces
Process management for library multimedia development service

Purpose – The purpose of this paper is to highlights the complex issue of developing management processes for learner centered library media production service. The work provides a practical and theoretical perspective on management tools and systems for this emerging library service.Design/methodology/approach – The research was drawn from a body of peer reviewed literature covering theoretical and practical library and business management practices. The author tempered this knowledge with first‐hand development and implementation experience of formal process management tools for a student‐centered library multimedia production center.Findings – The research offers a new theoretical perspective for viewing the emerging media rich, collaborative nature of library work. In addition, a diagramming strategy based on Human Interaction Theory is described. The tool can be used to foster the development of management processes in multimedia development centers and other library service units.Originality/value –.
DYNAMIC STOCHASTIC EARLY DISCOVERY: A NEW CONGESTION CONTROL TECHNIQUE TO IMPROVE NETWORKS PERFORMANCE

The present paper proposes the Dynamic Gentle Random Early Detection (DGRED) algorithm for early stage congestion detection at the router buffer. Generally, the proposed DGRED algorithm depends on the stability of the average queue length at a specic level between allocated minimum and maximum threshold values, with the aim to improve the network performance. The DGRED algorithm is simulated and compared with the most known Active Queue Management Early Detection (RED) algorithm and two of its variants, namely, Gentle RED and Adaptive GRED. This comparison was con- ducted based on different performance measures, such as mean queue length, throughput, average queuing delay, packet loss, and dropping probability for packets. The comparison aimed to identify the algorithm that offers better performance measurement results under either non-congestion or congestion situation at the router buffers. The acquired results show that the proposed algorithm contributes in providing lesser queue length, delayed queuing, and packet loss probability compared with the existing algorithms when high packet arrival probability appears, that is, ( > 0 :63) . Furthermore, DGRED generates
Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from Demonstration

Learning from Demonstration is an important technology for the new wave of robots that are envisioned to work side-by-side with workers in factories as well as social robots. Most available techniques for learning from demonstration rely on the existence of a training set of demonstrations that is assumed to be pre-segmented and is usually processed in batch. Another problem with most available methods is the need to set the model complexity used to model the motion. In this paper, we propose a solution to both problems based on incremental piecewise linear segmentation of the motion using an extension of the SWAB algorithm. Evaluation experiments show that the proposed method is able to generate motion models with adequate complexity without the need for model comparison methods and assuming incremental streaming of demonstrations rather than the availability of the complete training set in advance. The proposed method is applicable not only to robot learning from demonstration but to other industrial applications in which an accurate model of time variant data needs to be built from streaming input.
KIRCHHOFF: an educational software for learning the basic principles and methodology in electrical circuits modeling

An interactive courseware package designed to be used as an auxiliary instructional tool by lower division students is described. The pedagogical goal of KIRCHHOFF is to foster mastery of basic methodological skills in the study of lumped electrical circuits. For a given circuit layout, proposed by the teacher, this is verified when the circuit has been systematically labeled and when a correct, complete and nonredundant set of algebraic and integro-differential equations has been written down by the student. KIRCHHOFF, a symbolic exercising tool, provides graphic guidance in such a process and, when needed, gives the learner suitable corrective explanations. >
Specifying and Verifying Heap Space Allocation with JML and ESC/Java2

An imprinter for imprinting a plurality of fields on a document having at least two independently mounted print rollers for respectively imprinting the two fields where both print rollers are in printing position when the print rollers are moved in a predetermined direction with respect to the print bed during imprinting whereby interaction between the print rollers is minimized as they imprint at their respective fields on the document. At least one of the print rollers is preloaded and set to a minimum distance from the print bed so that the preloaded print roller raises from the print bed as raised characters on the printing plate are encountered whereby automatic compensation is provided for characters of varying height associated with the preloaded print roller.
Adaptation Algorithm of Geometric Graphs for Robot Motion Planning in Dynamic Environments

This study proposes an adaptive graph algorithm for collision-free motion planning of articulated robots in dynamic environments. For this purpose, deformations of the configuration space were analyzed according to the changes of the workspace using various simulations. Subsequently, we adopted the principles of gas motion dynamics in our adaptation algorithm to address the issue of the deformation of the configuration space. The proposed algorithm has an adaptation mechanism based on expansive repulsion and sensory repulsion, and it can be performed to provide the entire adaptation using distributed processing. The simulation results confirmed that the proposed method allows the adaptation of the roadmap graph to changes of the configuration space.
New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques

A data-driven approach to the modeling of turbulent and transitional flows is proposed in this work, with the goal of developing more robust and accurate closure models. The key idea is to (i) infer the functional form of deficiencies in known closure models by applying inverse problems to computational and experimental data, (ii) use machine learning to reconstruct the improved functional forms, and (iii) to inject the improved functional forms in simulations to obtain more accurate predictions. The inverse modeling step, on its own, can yield valuable insight to the modeler, essentially converting data to information. The machine learning step is a tool to convert information into modeling knowledge. Representative examples are used to describe the methodology and to demonstrate its viability. The first example investigates the modeling of a non-equilibrium turbulent boundary layer, and the second involves the modeling of bypass transition to turbulence. Evidence from these problems emphasizes the utility of the proposed approach in offering new routes to closure modeling in general computational physics disciplines.
ECG Based Human Authentication using Wavelets and Random Forests

The electrocardiogram(ECG) is an emerging novel biometric for human identification. It can be combined in a multi-modal biometric identification system or used alone for authentication of subjects. His primary application can be in health care systems where the ECG is used forhealth measurements. It does furthermore, better than any other biometrics measures, deliver the proof of subject’s being alive as extra information which other biometrics cannot deliver as easily. The main purpose of this study is to present a novel personal authentication approach for human authentication based on their ECG signal s. We present a methodology for identity verification that quantifies the minimum number of heartbeats required to authenticate an enrolled individual. The cardiac signals were used to identify a total of 80 individuals obtained from four ECG databases from the Physionet database (MIT -BIH, ST-T, NSR, PTB) and an ECG database collected from20 student volunteers from Paris Est University.Feature extraction was performed by using Discrete Wavelet Transform (DWT). Wavelets have proved particularly effective for extracting discriminative features in ECG signal classi fication. The Random Forest was then presented for the ECG signals authentication. Preliminary experimental results ind icate that the system is accurate and can achieve a low false negative rate, low false positive rate and a 100% subject recognition rate for healthy subjects with the reduced set of features.
Detecting and Dealing with Outliers in Univariate and Multivariate Contexts.

Because multivariate statistics are increasing in popularity with social science researchers, the challenge of detecting multivariate outliers warrants attention. Outliers are defined as cases which, in regression analyses, generally lie more than three standard deviations from Yhat and therefore distort statistics. There are, however, some outliers that do not distort statistics when they are on the mean of Yhat lines. In univariate analyses, finding outliers can be accomplished using Casewise Diagnostics in the Statistical Package for the Social Sciences (SPSS) version 9.0, which as a three standard deviation default that can be changed easily by the researcher. In bivariate and multivariate analyses, finding outliers more than three standard deviations from Yhat is not as easy. Casewise Diagnostics will detect outliers of "Y"; however, in multivariate analyses, statistics can be distorted by a case lying within the arbitrary three standard deviations because it is said to be exerting so much influence or leverage on the regression line that, in fact, the regression line is distorted. There are two popular ways of detecting this leverage, through distance and influence calculations. The most popular statistic for detecting outliers using distance calculations is Mahalanobis. Several other ways of detecting leverage in multivariate cases are available in SPSS 9.0. Once a researcher has identified a case as being a possible outlier, then the choices are to find out if there has been an error in recording the data, or if the outlier is truly an outlier. it can be argued that there are always going to be outliers in the population as a whole, and this is an argument for keeping the score, because it reflects something natural about the general population. If the researcher decides to drop the case, then the researcher should report it and offer reasons why. (Contains 10 figures, 3 tables, and 20 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
A Review of some P2P-IPTV Applications

This paper reviews concepts and algorithms of some popular existing IPTV applications which popular on the Internet. There are presented based on the experience of a real system deployed by PPLive, CoolStreaming, Sopcast and UUSee. The performance and technological challenges are concerned and described. However, the comparison of different P2P video streaming are measured and presented based on the standard measurement. Lastly, the advantages and disadvantages of the different IPTV applications are also summarized.
Data-driven Facial Animation Using Sketch Interface

Creating stylistic facial animation is one of the most important problems in character animation. Traditionally facial animation is created manually by animators or captured using motion capture systems. But this process is very difficult and labor-intensive. In this work, we present an intuitive, easy-to-use, sketch-based user interface system that facilitates the process of creating facial animation and key-frame interpolation method using facial capture data. The user of our system is allowed to create expressive speech facial animation easily and rapidly. 키워드 : 얼굴 애니메이션, 스케치 인터페이스, 데이터기반 애니메이션, 애니메이션 Keyword : facial animation, sketch interface, data-driven animation, animation
Synchronization Analysis of Autonomous Microwave Circuits Using New Global-Stability Analysis Tools

A new spectral-balance technique for the global- stability analysis of autonomous circuits is presented in this paper. This technique relies on the introduction of measuring probes into the circuit and it allows a simple determination of both bifurcation diagrams and bifurcation loci as a function of any suitable parameter. Through the proposed algorithms, this kind of analysis can be easily added to any existing software, since it is performed externally to the harmonic-balance (HB) calculation. Due to its local nature, it also allows an easy selection of the bifurcation parameters, which spreads the simulation possibilities. Both periodic and quasi-periodic regime simulations are possible, and bifurcations are detected in both operating modes. The synchronization phenomenon in injected oscillators and frequency dividers is also analyzed in detail for an accurate prediction of the operating bands. The simulation techniques are illustrated by means of their application to a cubic nonlinearity oscillator. They are then used for the stability analysis of a monolithic microwave integrated circuit (MMIC) divider by two operating in the millimetric range. A very good agreement has been obtained with the experimental results.
Causal Inference Lecture Notes: Covariate Adjustments in Randomized Experiments

In our previous discussion of classical approaches to randomized experiments, we did not talk about the situations in which we know more about experimental units. Indeed, typically such covariate information is available to experimenters. For example, in social science experiments, the demographic information about subjects may be known to researchers. Here, we consider how we may take an advantage of such information in randomized experiments.
Parallel process diagnosis based on qualitative models

This paper presents a method for process diagnosis based on a relational process model. To overcome the exponential complexity of the modelling and diagnostic tasks, three main results are described. First, the modelling complexity is reduced by building the overall system model from subsystem models in a component-oriented way. Second, a solution of the diagnostic problem based on relational algebra is presented and successively improved by exploiting the inherent structure of the diagnostic problem. The third idea is to decompose the diagnostic problem and to solve the resulting subproblems in parallel on a multicomputer system. A communication scheme which ensures the correct solution of the diagnostic problem is given. The theoretical results are illustrated by a diagnostic problem occurring in a power station.
A 9.8-11.5 GHz quadrature ring oscillator for optical receivers

A 9.8-11.5 GHz quadrature ring oscillator for use in the data clock recovery circuit of optical receivers has been realized in a BiCMOS technology with 30 GHz cut-off frequency. The circuit implementation of the oscillator uses active inductors which provide isolation between the oscillator and cascaded circuits such as buffers and flip-flops. Carrier to noise ratios better than 94 dBc/Hz at 2 MHz offset are measured with 75 mW dissipation and 2.7 V supply voltage. The realized quadrature oscillator achieves a state of the art oscillation frequency over transistor cut-off frequency ratio of 0.38.
Editorial: Testing practice and research

Comprehensive testing of modern communication systems often requires large and complex test suites, which have to be maintained throughout the system life cycle. Industrial experience, with those written using the standardized Testing and Test Control Notation (TTCN-3), has shown that this maintenance is a non-trivial task and its burden can be reduced by means of appropriate concepts and tool support. To this aim, Motorola has collaborated with the University of Gottingen to develop TRex, an open-source TTCN-3 development environment, which notably provides suitable metrics and refactorings to enable the assessment and automatic restructuring of test suites. This article presents concepts like metrics and refactoring for the quality assurance of TTCN-3 test suites and their implementation provided by the TRex tool. These means make it far easier to construct and maintain TTCN-3 tests that are concise and optimally balanced with respect to maintainability quality characteristics. Copyright © 2008 John Wiley & Sons, Ltd. This article is an extended version of the paper ‘TRex—The Refactoring and Metrics Tool for TTCN-3 Test Specifications’ by Paul Baker, Dominic Evans, Jens Grabowski, Helmut Neukirchen, and Benjamin Zeiss, which was originally presented at TAIC PART 2006 (Testing: Academic & Industrial Conference—Practice And Research Techniques).
Automatic visual inspection of wood surfaces

A prototype software system for visual inspection of wood defects has been developed. The system uses a hierarchical vector connected components (HVCC) segmentation which can be described as a multistage region-growing type of segmentation. The HVCC version used in experiments uses RGB color vector differences and Euclidean metrics. The HVCC segmentation seems to be very suitable for wood surface image segmentation. Geometrical, color and structural features are used in classification. Possible defects are classified using combined tree-kNN classifier and pure kNN-classifier. The system has been tested using plywood boards. Preliminary classification accuracy is 85-90% depending on the type of defect. <<ETX>>
THE POTENTIAL OF MOVING PICTURES DOES PARTICIPATORY VIDEO ENABLE LEARNING FOR LOCAL INNOVATION

Learning is essential for local innovation and enhancing the ability of the rural clients to discover new solutions to prevailing challenges. Equally, the growing complexities of the challenges in the theatre of agriculture and rural development require multi-actor learning process. Participatory communication through face-to-face interaction remains an important approach to support local people’s innovation capacity. Is there any mean other than face-to-face interaction that enables learning for innovations? Video has been used for several decades, however, in most cases instrumentally as a mass media for expert information dissemination. In recent years the interest in the alternative use of video, mostly known as participatory video, has grown. This study attempts to understand the potential of participatory video to support learning for local innovation by reviewing available literature about the cases of participatory video in the field of agriculture and natural resource management. A deductive coding approach was employed in order to identify the potentials of participatory video. The documented cases we found in the literature suggest that participatory video has a substantial role for both vertical and horizontal flow of local knowledge and information in a multi-actor setting. It creates a ‘safe space’ for communication where different actors are able to articulate their perceptions. What follows, actors get an opportunity for reciprocal learning process. Participatory video facilitates communication for the marginalized segment of developing nations in Asia and Africa to represent their knowledge and skills and to link these to other knowledge bodies such as scientific, formal, managerial and bureaucratic. Participatory video stimulates reflection and experimentation by creating new impetus for learning within and across stakeholder (actor) groups. Nevertheless, potentials of participatory video depend on careful analysis of social competencies of facilitators, institutional ambience and role of intermediaries and facilitating organizations. We also proposed future research angles on these issues.