Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Planning in Urban Area Using Local Features of the Road System

In this paper is proposed a tool for path planning in urban area. The proposed method can anticipate travel time for vehicles even for future dates and hours or can decide whether to revise the current path based on the local features of the road system ahead. Travelling time and waiting time are analyzed on several features of the road system, like pedestrian crossing, traffic lights, and roundabouts. These features are typical for cities in Europe, built around medieval settlements. The method can also be expanded to include other features as traffic accidents, meteorological conditions, cultural or sport activities which will perturb the normal traffic conditions. Finally, a simulation is performed to evaluate the proposed method using the A-star graph searching algorithm.
Quality of Experience for Multimedia

Description: Based on a convergence of network technologies, the Next Generation Network (NGN) is being deployed to carry high quality video and voice data. In fact, the convergence of network technologies has been driven by the converging needs of end–users. The perceived end–to–end quality is one of the main goals required by users that must be guaranteed by the network operators and the Internet Service Providers, through manufacturer equipment. This is referred to as the notion of Quality of Experience (QoE) and is becoming commonly used to represent user perception. The QoE is not a technical metric, but rather a concept consisting of all elements of a user's perception of the network services. The authors of this book focus on the idea of how to integrate the QoE into a control–command chain in order to construct an adaptive network system. More precisely, in the context of Content–Oriented Networks used to redesign the current Internet architecture to accommodate content–oriented applications and services, they aim to describe an end–to–end QoE model applied to a Content Distribution Network architecture. Head of several executive national and international positions, he was the founder of the Network Control Research activity at UPEC with extensive international academic and industrial collaborations. His general area of research is in adaptive real–time control for high–speed new generation dynamic wired/wireless networks in order to maintain acceptable Quality of Service/Experience for added–value services. He is an active member of the IEEE Communications Society and has held several offices including leadership positions in IEEE Communications Society Technical Committees. His research focuses on routing algorithms, quality of service, quality of experience, and wireless sensor networks, as well as bio–inspired artificial intelligence approaches. His work has been published in several international conferences and journals and he serves on several TPCs. research focuses on QoE aspects, QoS adaptive control/command mechanisms, wired routing, as well as bio–inspired artificial intelligence approaches.
遞迴式類神經網路語言模型應用額外資訊於語音辨識之研究 (Recurrent Neural Network-based Language Modeling with Extra Information Cues for Speech Recognition) [In Chinese]

The goal of language modeling (LM) attempts to capture the regularities of natural languages. It uses large amounts of training text for model training so as to help predict the most likely upcoming word given a word history. Therefore, it plays an indispensable role in automatic speech recognition (ASR). The N-gram language model, which determines the probability of an upcoming word given its preceding N-1 word history, is most prominently used. When N is small, a typical N-gram language model lacks the ability of rendering long-span lexical information. On the other hand, when N becomes larger, it will suffer from the data sparseness problem because of insufficient training data. With this acknowledged, research on the neural network-based language model (NNLM), or more specifically, the feed-forward NNLM, has attracted considerable attention of researchers and practitioners in recent years. This is attributed to the fact that the feed-forward NNLM can mitigate the data sparseness problem when estimating the probability of an upcoming word given its corresponding word history through mapping them into a continuous space. In addition to the feed-forward NNLM, a recent trend is to use the recurrent neural network-based language model (RNNLM) to construct the language model for ASR, which can make efficient use of the long-span lexical information inherent in the word history in a recursive fashion. In this thesis, we not only investigate to leverage extra information relevant to the word history for RNNLM, but also devise a dynamic model estimation method to obtain an utterance-specific RNNLM. We experimentally observe that our proposed methods can show promise and perform well when compared to the existing LM methods on a large vocabulary continuous speech recognition (LVCSR) task.
Influence of Tasks Duration Variability on Task-Based Runtime Schedulers

In the context of HPC platforms, individual nodes nowadays consist of heterogenous processing resources such as GPU units and multicores. Those resources share communication and storage resources, inducing complex co-scheduling effects, and making it hard to predict the exact duration of a task or of a communication. To cope with these issues, runtime dynamic schedulers such as starpu have been developed. These systems base their decisions at runtime on the state of the platform and possibly on static priorities of tasks computed offline. In this paper, our goal is to quantify performance variability in the context of HPC heterogeneous nodes, by focusing on very regular dense linear algebra kernels, such as Cholesky and LU factorizations. We therefore first concentrate on the evaluation of the individual block-size kernels variability. Then, we analyze the impact of this variability at the scale of a full application on a dynamic runtime scheduler such as starpu, in order to analyze whether the strategies that have been designed in the context of MapReduce applications to cope with stragglers could be transferred to HPC systems, or if the dynamic nature of runtime schedulers is enough to cope with actual performance variations, even in presence of task dependencies.
Black hole in a uniform magnetic field

Using the fact that a Killing vector in a vacuum spacetime serves as a vector potential for a Maxwell test field, we derive the solution for the electromagnetic field occurring when a stationary, axisymmetric black hole is placed in an originally uniform magnetic field aligned along the symmetry axis of the black hole. It is shown that a black hole in a magnetic field will selectively accrete charges until its charge becomes $Q=2{\mathrm{Bb}}_{0}J$, where ${B}_{0}$ is the strength of the magnetic field and $J$ is the angular momentum of the black hole. As a by-product of the analysis given here, we prove that the gyromagnetic ratio of a slightly charged, stationary, axisymmetric black hole (not assumed to be Kerr) must have the value $g=2$.
Investigating the Potential Incorporation of Multimodal Biometrics into Fully Automated Entry-Exit System for the European Union

In this paper a cost model with accompanying calculations about the multimodal biometrics application within the automated entry-exit system of the European Union is presented. fusion among facial, fingerprint and fingervein features of the travelers is considered expecting to produce higher verification/identification accuracy and improving the level of reliability of border crossings. The model suggests significant cost savings based on the difference from the expected investment in current infrastructure, its operation, maintenance and support from one hand and the reduction of the number of border officers on the other. It has been verified with sample data for the past two phases of the Smart Borders initiative where close values are obtained between the predicted and already registered financial results.
The Sign Language Analyses (SLAY) Database

This paper describes the construction and input analysis of the Sign Language Analyses (SLAY) Database, both as it currently stands and as a guide to further expansion of the project. SLAY contains condensed crosslinguistic grammatical information from signed languages. It was designed so that the framework of the database can be expanded indefinitely to include investigations of new questions. It differs from similar projects (such as the World Atlas of Linguistic Structure (Haspelmath et al., 2005)) in that it focuses exclusively on signed languages and modality-specific grammatical questions.
Research status of operational environment partitioning and path planning for multi - robot systems

There is a lot of applications for multi-robot systems instead of single robot systems. Therefore, coordination algorithms have become popular in the field of robotics for two decades. This paper analyzes and summarizes the current research status of coordinated algorithms for multi-robot systems, from perspectives of partitioning the environment, and path planning.
M5-BRANES AND WILSON SURFACES

We investigate the M5-brane description of the Wilson surface operators in six-dimensional (2,0) superconformal field theory from AdS/CFT correspondence. We consider the Wilson surface operators in high-dimensional representation, whose description could be M5-brane string soliton solutions in AdS(7) x S' background. We construct such string soliton solutions from the covariant M5-brane equations of motion and discuss their properties. The supersymmetry analysis shows that these solutions are half-BPS. We also discuss the subtle issue on the boundary terms.
A feature matching and fusion-based positive obstacle detection algorithm for field autonomous land vehicles

Positive obstacles will cause damage to field robotics during traveling in field. Field autonomous land vehicle is a typical field robotic. This article presents a feature matching and fusion-based algorithm to detect obstacles using LiDARs for field autonomous land vehicles. There are three main contributions: (1) A novel setup method of compact LiDAR is introduced. This method improved the LiDAR data density and reduced the blind region of the LiDAR sensor. (2) A mathematical model is deduced under this new setup method. The ideal scan line is generated by using the deduced mathematical model. (3) Based on the proposed mathematical model, a feature matching and fusion (FMAF)-based algorithm is presented in this article, which is employed to detect obstacles. Experimental results show that the performance of the proposed algorithm is robust and stable, and the computing time is reduced by an order of two magnitudes by comparing with other exited algorithms. This algorithm has been perfectly applied to our autonomous land vehicle, which has won the champion in the challenge of Chinese “Overcome Danger 2014” ground unmanned vehicle.
MatrixMap: Programming abstraction and implementation of matrix computation for big data analytics

The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel programming framework for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed in-memory data structure called bulk key matrix and a programming interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with context-aware data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.
Effective user management with high strength crypto –key in dynamic group environment in cloud

Cloud Clusters consists of various collections of files which are being accessed by multiple users of Cloud. The users are managed as a group and the association of the user to a particular group is dynamic in nature. Every group has a manager who handles the membership of a user to a particular group by issuing keys for encryption and decryption. Due to the dynamic nature of a user he/she may leave the group very frequently. But an attempt can be made by the user who has recently left the group to access a file maintained by that group. Key distribution becomes a critical issue while the behavior of the user is dynamic. Existing techniques to manage the users of group in terms of security and key distribution has been investigated so that to arrive at an objective to identify the scopes to increase security and key management scheme in cloud. The usage of various key combinations to measure the strength of security and efficiency of user management in dynamic cloud environment has been investigated.
A Hybrid Approach for Computing Semantic Similarity of Concepts in Knowledge Graphs

This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g. path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.
Model-mediated teleoperation with improved stability

Model-mediated teleoperation has been developed to improve both transparency and stability in teleoperation. It uses local model of remote environment to provide non-delayed force feedback rather than using delayed force signals from slave side and thus is robust to arbitrary time delay. However, updating parameters in the local model may cause sudden force change during the operation. Meanwhile, the undesirable deep penetration or overlarge contact force may occur on the slave side due to the modeling error. Both of them will jeopardize the system stability. In this article, we propose a novel force-based model updating algorithm, which restrains the abrupt force caused by parameter updating. The update efficiency has been greatly improved by comparing with the existing solution; meanwhile, it ensures a stable human–machine interaction at the same time. Then, a new adaptive impedance controller that restricts both overlarge force and penetration is introduced. The obtained results on a one-degree of freedom contact experiment with a delay of 5 s demonstrate the superiority of proposed approaches in comparison with state-of-the-art methods.
Statistics in Research and Development

What are statistics? describing the sample describing the population testing and estimation - one sample testing and estimation - two samples testing and estimation - qualitative data testing and estimation - assumptions statistical process control detecting process changes investigating the process - an experiment why was the experiment not successful? some simple but effective experiments adapting the simple experiments improving a bad experiment analysis of variance an introduction to Taguchi technique. Appendices: The sigma notation notation and formulae sampling distributions copy of computer print-out from a multiple regression programme partial correlation significance tests on effect estimates from a p-2-n factorial experiment.
Active Control of Complexity Growth in Naming Games: Hearer's Choice

How do linguistic conventions emerge among a population of individuals? A shared lexicon can self-organize at this level through local interactions between individuals, as this has been modelled in the Naming Games computational framework. However, the dynamics of the convergence process towards this shared convention can differ a lot, depending on the interaction scenario. Infants, who acquire social conventions really fast, control actively the complexity of what they learn, often following a developmental pathway. Adults also adapt the complexity of their linguistic input when speaking to language beginners. We show here that such active learning mechanism can improve considerably the speed of language formation in Naming Game models. We compare two scenarios for the interactions: the speaker exherts an active control, or the hearer does. The second scenario shows faster dynamics, with more robustness.
EXPRESSIONS FOR VALUES OF THE GAMMA FUNCTION

This paper presents expressions for gamma values at rational points with the denominator dividing 24 or 60. These gamma values are expressed in terms of 10 distinct gamma values and rational powers of $\pi$ and a few real algebraic numbers. Our elementary list of formulas can be conveniently used to evaluate, for example, algebraic Gauss hypergeometric functions by the Gauss identity. Also, algebraic independence of gamma values and their relation to the elliptic K-function are briefly discussed.
Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image.
El esquema general de la decoherencia como punto de partida para un enfoque basado en valores medios

Según el enfoque ortodoxo, la decoherencia se produ ce en un sistema físico, como una partícula o un conjunto de partículas: el fenóm eno consiste en la diagonalización de la matriz reducida que representa el estado de tal sistema, y explica su carácter clásico. En este trabajo argumentaremos que, en muchos ejemp los concretos, dicho enfoque resulta insuficiente. Por lo tanto, si la decoheren cia pretende dar cuenta de la clasicidad, debe ser redefinida ya no como la diagonalización d e la matriz reducida, sino como la desaparición de los términos de interferencia del v alor medio de un subespacio de observables. Esta nueva definición amplía el domini o de aplicación de la teoría y da cuenta de los casos que quedan sin explicación en e l enfoque ortodoxo.
Rheology of metallocene polyethylene-based nanocomposites: Influence of graft modification

Several compounds were prepared by melt compounding a metallocene linear low density polyethylene with a graft-modified polyethylene and an organically modified clay in different proportions in a twin screw extruder. X-ray diffraction, transmission electron microscopy, and rheology were used to characterize the extent of intercalation of the silicate galleries. Incorporation of minimum weight fraction of 2% clay and 50% maleated polyethylene was found to introduce radical changes in the rheological behavior of the nanocomposites. Broadening peaks in x-ray diffraction indicated increasing dispersion of silicate galleries into polymer as a function of graft modification. With higher incorporation of maleated polyethylene, possible networking was seen with solidlike response beginning to appear at low frequencies. Flow activation energy was found to decrease with incorporation of clay. Relaxation spectra determined using a parsimonious model were found to extend to higher relaxation time scales with exfoliat.
Analysis of Backsheet and Rear Cover Reflection Gains for Bifacial Solar Cells

Bifacial solar cells are known to increase module power and performance. Due to their active rear side additional gains are possible from internal reflections. Existing models to analyze cell-to-module (CTM) gains need to be extended. We analyze reflection within modules with bifacial cells and establish a system and a nomenclature for gains resulting from internal reflection. Transmission through the cell, subsequent reflection on module cover layers and a second absorption of light in the solar cell leads to additional gains in the range of 0.5 – 0.8% for bifacial cells in modules with reflecting rear cover materials (i.e. white backsheets) under STC conditions. We present models to calculate gains and perform measurements on bifacial modules with different backsheets and covers. Cover coupling gains for bifacial cells are higher compared to monofacial cells (20%rel in modules with white backsheets).
Analysis of Radio resource management strategies to guarantee quality of service for CDMA systems

The paper considers the Connection Admission Control (CAC) which is a key resource management procedure above all in next generation wireless networks, by proposing two different approaches to solve this issue. The first method is based on the definition of a Fuzzy system its input and output, whereas the second is based on the Effective Bandwidth concept. A comparison between the two methods is performed in order to analyse pros and cons of the above mentioned methods. The two algorithms are applied to a particular case study i.e., a return link via satellite based on CDMA. Nevertheless the algorithms are totally general thus they are suitable for every CDMA-based system.