Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmission performance of transparent multi-wavelength optical cross-connected networks

The transmission performance of regular two-connected multi-hop transparent optical networks in uniform traffic under hot-potato, single-buffer deflection routing schemes is presented. The Manhattan Street (MS) network and ShuffleNet (SN) are compared in terms of bit error rate (BER) and packet error rate (PER) both analytically and by simulation. We implement a novel strategy of analysis, in which the transmission performance evaluation is linked to the traffic randomness of the networks. Amplifier spontaneous emission (ASE) noise, and device-induced crosstalk severely limit the characteristics of the network, such as propagation distance, sustainable traffic, and bit-rate. Our results indicate that under the same load the BER performance of single-buffer deflection routing is worse than hot-potato. However, at BER=10/sup -9/ single-buffer has a higher throughput than hot-potato. It is shown that the feasibility of deflection routing in transparent networks with MS and SN topologies heavily depends on the power coupling coefficient of the routing space switch used in each node.
Correcting Algorithm of DFT Spectrum of Pulsed Radar Signal Based on Cubic Convolution Interpolation

In radar signal reconnaissance digital receiver based on DFT (discrete Fourier transform), because there existing fence effect, measuring frequency algorithm based on peak-searching will produce greatly error, so the correcting to DFT spectrum of pulsed radar signal is necessary. Firstly the fact of pulsed radar signal is composed of countless single sine signals is derived and the reason that peak-searching algorithm produces error is also analyzed. Then the interpolating kernel function and interpolating function of correcting algorithm of single sine signal based on cubic convolution are proposed and shape parameter is determined via simulation. On the basis of correcting algorithm of single sine signal based on cubic interpolation, a novel correcting and detecting of pulsed radar signal DFT spectrum is given at last.
Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations

Mobile phones are arguably one of the most prolific sources of large-scale human mobility data. The availability of this data has generated a massive body of research focused on understanding the dynamics and patterns of human mobility. However, it is increasingly evident that additional value can be derived from such data. This paper proposes a novel approach for understanding the attributes of mobile users by analyzing calling behavior derived from field survey data, in combination with call detail records (CDRs). Our survey reveals distinctive traits in calling behavior that correspond to user attributes. Analysis results demonstrate that frequent call locations, the variability in call time distributions, and the locations from which calls are made around midday are all keys to distinguishing gender. In addition, the location of calls initiated during the morning hours is a key to analyzing income levels for males.
Compensation of Loudspeaker–Room Responses in a Robust MIMO Control Framework

A new multichannel approach to robust broadband loudspeaker-room equalization is presented. Traditionally, the equalization (or room correction) problem has been treated primarily by single-channel methods, where loudspeaker input signals are prefiltered individually by separate scalar filters. Single-channel methods are generally able to improve the average spectral flatness of the acoustic transfer functions in a listening region, but they cannot reduce the variability of the transfer functions within the region. Most modern audio reproduction systems, however, contain two or more loudspeakers, and in this paper we aim at improving the equalization performance by using all available loudspeakers jointly. To this end we propose a polynomial based MIMO formulation of the equalization problem. The new approach, which is a generalization of an earlier single-channel approach by the authors, is found to reduce the average reproduction error and the transfer function variability over a region in space. Moreover, pre-ringing artifacts are avoided, and the reproduction error below 1000 Hz is significantly reduced with an amount that scales with the number of loudspeakers used.
A Portfolio of Dilemmas: Experimental Evidence on Choice Bracketing in a Mini-Trust Game

Bracketing is a mental procedure about how people deal with multiple tasks. If a decision maker handles all the tasks at the same time, it is called broad bracketing. If she handles the tasks separately, e.g., one or a few tasks each time, it is called narrow bracketing. This paper experimentally investigates the effect of broad versus narrow bracketing in the context of a mini-trust game. The result shows that, in the narrow bracketing treatment, the investor (first mover) is more likely to place trust on others, but the receiver (second mover) is less likely to fulfill the trust under the same condition. The effect is partly conditional on beliefs in others' behavior.
Genetic Algorithm for Hybrid Flow-Shop Scheduling with Parrel Batch Processors

In classical flow-shop scheduling problem, eachprocessor can process one job at a time. However, in practice, there may be many processors that can process jobs batch by batch. We call these processors batch processors. If the processing time of a batchis equal to the largest processing time among its members, we call such a batch processor parallel batch processor. In this paper, we study the hybrid flow-shop problem in which the processors are parrel batch processors. This problem is obviously NP-hard. Therefore, we propose a genetic algorithm in this work.
Cooperative collision avoidance for multi-vehicle systems using reinforcement learning

Collision avoidance is a fundamental problem in navigation. In this paper, we present a novel method of cooperative movement planning to examine how two vehicles can orchestrate their movements so as to avoid collisions and subsequently return to their intended paths. Movement planning in this research is solved by regarding it as a decision process. When the vehicles are at risk of a collision, the system determines appropriate steering motions for both vehicles at each time step, so that they can cooperatively change course to avoid collisions and return to their original course when the risk is averted. Reinforcement learning is applied to solve this decision-making task. States of the system are described in terms of the vehicles' position and orientation and actions are defined considering the kinematic constraints of the vehicles. In reinforcement learning, an approximate value function is iteratively developed according to certain rules to evaluate state-action combinations of the system. Appropriate motions are selected by the system after calculating the approximate value of possible target states, which also satisfy the requirement of the smoothness of paths, as well as the distances between, and velocities of, both vehicles. The method of least squares is applied in the iterative mechanism to update the approximate value function given a scoring technique for a collection of state samples featuring continuous state space and action space. This paper summarizes the concept and methodologies used to implement an online cooperative collision avoidance system. Different scenarios are tested to assess the performance of the proposed algorithm.
Spots segmentation in SAR images for remote sensing of environment

The paper proposes an algorithm to segment spots in synthetic aperture radar (SAR) images in order to support environmental remote monitoring. This approach consists of isolating dark areas that may have originated from oil pollution, thus achieving the aim of our investigation. The proposed algorithm combines a region growing approach and a multiscale analysis employed by an undecimated wavelet transform to localize dark areas in the sea. The undecimated wavelet applied to SAR images smooths the speckle noise while enhancing edges, thus providing a better result for the proposed segmentation algorithm that is achieved by a modified region growing approach. The minmax scheme is used to provide post processing of the segmented image. The algorithms were tested on real SAR images of oil spills.
Plastic Modification of Anti-Saccades: Adaptation of Saccadic Eye Movements Aimed at a Virtual Target

Saccades allow us to visually explore our environment. Like other goal-directed movements, their accuracy is permanently controlled by adaptation mechanisms that, in the laboratory, can be induced by systematic displacement of the “real” visual target during the saccade. However, in an anti-saccade (AS) task, the target is “virtual” because gaze has to be shifted away from the “real” visual target toward its mentally defined mirror position. Here, we investigated whether the brain can adapt movements aimed at a virtual target by trying, for the first time, to adapt AS. Healthy human volunteers produced leftward AS during three different exposure phases in which a visual target provided feedback after the AS. In the adaptation condition, the feedback target appeared after completion of the AS response at a location shifted outward from final eye position (immediate non-veridical feedback). In the two control conditions, adaptation was prevented by delaying (800 ms) the shifted feedback target (delayed-shift) or by providing an immediate but veridical feedback at the mirror position of the visual target (no-shift). Results revealed a significant increase of AS gain only in the adaptation condition. Moreover, testing pro-saccades (PS) before and after exposure revealed a significant increase of leftward PS gain in the adaptation condition. This transfer of adaptation supports the hypotheses of a motor level of AS adaptation and of a visual level of AS vector inversion. Together with data from the literature, these results also provide new insights into adaptation and planning mechanisms for AS and for other subtypes of voluntary saccades.
Multiple-exemplar discriminant analysis for face recognition

Face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis(LDA). LDA is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e., the sample mean of the class. We present a multiple-exemplar discriminant analysis (MEDA) where each class is represented using several exemplars or even the whole available sample set. The proposed approach produces improved classification results when tested on a subset of FERET database where LDA is ineffective.
Intravenous glargine and regular insulin have similar effects on endogenous glucose output and peripheral activation/deactivation kinetic profiles.

OBJECTIVE To compare the effects of intravenously administered long-acting insulin analog glargine and regular human insulin on activation and deactivation of endogenous glucose output (EGO) and peripheral glucose uptake. RESEARCH DESIGN AND METHODS In this single-center, randomized, double-blind, crossover euglycemic glucose clamp study, 15 healthy male volunteers (aged 27 +/- 4 years, BMI 24.2 +/- 0.7 kg/m(2) [mean +/- SE]) received a primed continuous intravenous infusion of 40 mU/m(2) of insulin glargine or regular human insulin on 2 different study days in a randomized order. Euglycemia was maintained at 90 mg/dl using a simultaneous variable intravenous infusion of 20% dextrose containing D-[3-(3)H]glucose. EGO and peripheral glucose disposal kinetics were determined during a 4-h insulin infusion activation period and a 3-h deactivation period. RESULTS The results demonstrated no significant difference in activation or deactivation kinetics with respect to EGO and peripheral glucose disposal between insulin glargine and regular human insulin when given intravenously. The mean +/- SE time required for 50% suppression of EGO after insulin infusion was 73 +/- 23 min for regular insulin and 57 +/- 20 min for insulin glargine (NS). The mean maximum rate of glucose disposal was 10.10 +/- 0.77 and 9.90 +/- 0.85 mg. kg(-1). min(-1) for regular insulin and insulin glargine, respectively (NS). The mean time required for 50% suppression of incremental glucose disposal rate (GDR), defined as the time required for activation from the basal glucose disappearance rate (R(d)) to half-maximum insulin-stimulated R(d), was 32 +/- 5 and 42 +/- 10 min for regular insulin and insulin glargine, respectively (NS). The time required for deactivation from maximum insulin-stimulated GDR to half-maximum GDR after cessation of insulin infusion was 63 +/- 5 and 57 +/- 4 min for regular insulin and insulin glargine, respectively (NS). CONCLUSIONS Activation and deactivation kinetics of EGO and peripheral glucose uptake as well as absolute disposal rate are similar between regular human insulin and insulin glargine when administered intravenously. Thus, the various biological actions of these insulin preparations when given subcutaneously are completely due to their different absorption kinetics.
Node-centric RDF Graph Visualization

RDF, visualization, Resource Description Framework, graph, browser, nodecentric This paper describes a node-centric technique for visualizing Resource Description Framework (RDF) graphs. Nodes of interest are discovered by searching over literals. Subgraphs for display are constructed by using the area around selected nodes. Wider views are created by sorting and displaying nodes based on the number of incoming and outgoing arcs.
The QT21/HimL Combined Machine Translation System

This paper describes the joint submission of the QT21 and HimL projects for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). The submission is a system combination which combines twelve different statistical machine translation systems provided by the different groups (RWTH Aachen University, LMU Munich, Charles University in Prague, University of Edinburgh, University of Sheffield, Karlsruhe Institute of Technology, LIMSI, University of Amsterdam, Tilde). The systems are combined using RWTH’s system combination approach. The final submission shows an improvement of 1.0 BLEU compared to the best single system on newstest2016.
ARLEQUIN: an integrated Java application

In the six years since Java was offered to the developer community, it has proven to be an extraordinary product of recent achievements in computer science. But Java has the reputation, especially among non-java users, to be slow and non-well suited for the development of large computer codes with intensive numerical calculations. This is the main reason for the hesitations of high-performance computer code developers to adopt it, even though the well-known qualities of this language (to build homogeneous applications - GUI, command language, numerical code - and the “write once, run anywhere” advantages) are well established.
Identification of ECG signal pattern changes to reduce the incidence of Ventricular Tachycardia false alarms

The paper focuses on the reduction of the false alarms in the Intensive Care Units (ICU). Five alarm types were analyzed in this study: Asystole, Extreme Bradycardia, Extreme Tachycardia, Ventricular Tachycardia and Ventricular Flutter/Fibrillation. Most of the analyzed alarm types rely on the quality of the heart rate estimation. The false alarm reduction algorithms analyzed in this paper use the quality estimate of the arterial blood pressure signal from which the heart rate is estimated and additionally the results of heart beat detection in two ECG signals are analyzed before making the final decision about the true or false alarm type. The most attention in this paper is focused on the correct detection of Ventricular Tachycardia alarms. The decision about the true or false alarm is made according to RR interval variation and changes of QRS complex shape features. A subset of sample entries data of the Physionet/CinC Challenge 2015 is used to test the proposed algorithm modifications. The false alarm detection according to the RR interval variation gave 49% TPR, 49% TNR (score 34.82) for the Phase I Entries data set and 46% TPR, 51% TNR (score 34.59) for the Phase II Entries data set. The VT alarm detection algorithm based on the features related to the the ECG waveform shape has increased the VT score for Phase I Entries data set to 41.98.
Multidimensional Human Capital, Wages and Endogenous Employment Status in Ghana

Previous studies of labor market outcomes such as employment and wages have mostly been limited to investigating the impact of formal schooling only and, as a consequence, have seldom considered skills or alternative routes to acquiring skills, such as adult literacy programs, or other types of education. This paper examines these issues for Ghana, by estimating the joint effects of formal schooling, literacy and numeracy skills, and adult literacy programs on employment and wage outcomes. Wage and employment status equations are estimated jointly, allowing employment status to be endogenous. Substantial returns to basic cognitive skills are established, while the education system - especially the lower levels of formal education - is found to be relatively successful in creating these skills. At the same time the results hint at there being substantial returns to skills other than basic literacy and numeracy. These skills appear to be produced mostly from technical and vocational education and training and at higher levels of formal education. Adult literacy participants are less likely to be economically inactive and more likely to be self-employed, hinting at the income-generating activities component of these programs having indirect effects on wages through its effect on labor market participation, especially for females, individuals with no formal education, and in urban areas.
Accelerating time series subsequence matching on the Intel Xeon Phi many-core coprocessor

The problem of time series subsequence matching occurs in a wide spectrum of subject areas. Currently Dynamic Time Warping (DTW) is the best similarity measure but despite various existing speedup techniques it is still computationally expensive. Due to this reason science community is trying to accelerate DTW calculation by means of parallel hardware. There are implementations of DTW-based subsequence matching on GPU and FPGA but there none for accelerators based on the Intel Many Integrated Core architecture. This paper presents a parallel algorithm for time series subsequence matching based on DTW distance adapted to the Intel Xeon Phi coprocessor. The experimental results on synthetic and real data sets are presented and confirm the efficiency of the algorithm.
Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy

Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences. Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data. So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance. Motivated by this, we propose Harmony, a practical, accurate and efficient system for collecting and analyzing data from smart device users, while satisfying LDP. Harmony applies to multi-dimensional data containing both numerical and categorical attributes, and supports both basic statistics (e.g., mean and frequency estimates), and complex machine learning tasks (e.g., linear regression, logistic regression and SVM classification). Experiments using real data confirm Harmony's effectiveness.
Preventing Cache-Based Side-Channel Attacks in a Cloud Environment

Cloud computing is a unique technique for outsourcing and aggregating computational hardware needs. By abstracting the underlying machines cloud computing is able to share resources among multiple mutually distrusting clients. While there are numerous practical benefits to this system, this kind of resource sharing enables new forms of information leakage such as hardware side-channels. In this paper, we investigate the usage of CPU-cache based side-channels in the cloud and how they compare to traditional side-channel attacks. We go on to demonstrate that new techniques are necessary to mitigate these sorts of attacks in a cloud environment, and specify the requirements for such solutions. Finally, we design and implement two new cache-based side-channel mitigation techniques, implementing them in a state-of-the-art cloud system, and testing them against traditional cloud technology.
A Local Difference coding algorithm for face recognition

Stimulated by the popular Local Binary Pattern (LBP) coding, we propose a new operator, i.e. the Local Difference (LD) coding. Different from other local coding approaches, the LD coding extracts direction information of both first-order and second-order difference. We apply the LD coding in feature extraction for face recognition, and obtain a very low dimensional feature in comparison with the classic operator such as the Uniform LBP (U-LBP). Experimental results show that the feature obtained with the LD operator could increase or maintain the recognition rate with much lower number of feature dimension.
The multi-paradigmatic character of contemporary educational action research: a promising perspective or an underlying threat?

Abstract From the very first moment action research started to be used in the social research field until now, it has presented significant variations. Action research looks like a mosaic of theoretical and methodological approaches, named differently from time to time and/or from place to place: for example, practitioner inquiry, practitioner research, teacher research, participatory action research, and so forth. The basic aims of this article are to find the reasons for this and to discuss the implications of the phenomenon. Starting with the first issue, we could ask ourselves: why do we today find so many and considerable variations in action research approaches in the literature? The evident reason is because action research draws upon many theoretical paradigms that – in some circumstances – are not only different, but also contradict each other. Hammersley states characteristically that action research draws upon positivism, pragmatism, hermeneutics, critical theory and postmodernism! After such claims, we can easily wonder: how can such a multi-paradigmatic approach compose cohesive methodological proposals? We can also pose another crucial question: although we can easily understand that this multi-paradigmatic nature is a real advantage which gives action research a pluralistic essence, is it really ‘a strong indicator of the power of action research to reinvent itself according to local needs’ as Somekh stated, or might it at the same time conceal serious risks?