Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy

Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the features using Weighted Principal Component Analysis (PCA). The purpose of PCA is to reduce the dimensionality of the data space to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between variables. The aim of this paper is to show feature weighting effect on increasing the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 92.1% and average recall become 97.7% respectively.
MITIGATION OF HARMONICS AND INTERHARMONICS IN ADJUSTABLE SPEED DRIVES

This paper provides an efficient way to mitigate the interharmonics appearing in the input side of the adjustable speed drives which are created by an imbalance in the motor current. The adjustable speed drive consists of VSI and diode rectifier. The present work examines how unbalance in the motor current distorts the inverter dc side current and this appears as interharmonic distortion in the input currents of the AC side. C-type filter and AC choke are designed for the proposed method for mitigation of this harmonics and interharmonics. The proposed method is simulated using MATLAB/ SIMULINK software. For Power Quality (PQ) studies it is necessary to analyse harmonic and interharmonic current produced by Voltage Source Inverter fed variable speed drives. Adjustable Speed Drives (ASDs) are generally used as variable speed control devices in industrialized and some housing appliance. An ASD is a frequency converter, that changes an input supply frequency to an output of desired frequency. The block diagram of Voltage source Inverter fed variable speed drive is shown in Fig.1. AC voltage is converted into DC voltage using an uncontrolled bridge rectifier. A DC link is connected after the rectifier to remove ripples in the DC voltage and gives a constant DC output voltage. This DC voltage is converted into AC voltage of variable magnitude and frequency using a PWM inverter for the speed control of induction motor. The main causes of interharmonics are over modulation of the inverter, if there is any interference created at the output of the inverter then it will propagate as interharmonics at the rectifier AC side. The unequal single phase load distribution in three phase lines is another reason for the appearance of interharmonics. That is, if the load imbalance occurs, over modulation of the inverter is the primary cause for the interharmonics in variable speed drives.
Probabilistic Unfolding Approach

It is known that any selection statement, e.g., if-statement, in any application is associated with some probability which could either be predetermined by user input or chosen at runtime. This type of processing can be regarded as a computation node whose computation time is represented by a random variable. This paper focuses on any iterative application (containing loops) re ecting those uncertainties. Such an application can then be transformed to a probabilistic dataow graph. This paper introduces two timing models, the time-invariant and time-variant models, to characterize the nature of these applications. Furthermore, for the time-invariant model, we propose a means of selecting a minimum rate-optimal unfolding factor which guarantees the best schedule length. We also propose a good estimation for choosing an unfolding factor for a graph under the time-variant model. Experiments show that using our selection scheme results in an iteration period close to the theoritical iteration bound of the experimental graph.
In order to make spatial statistics computationally feasible, we need to forget about the covariance function

Gaussian random fields (GRFs) are the most common way of modeling structured spatial random effects in spatial statistics. Unfortunately, their high computational cost renders the direct use of GRFs impractical for large problems and approximations are commonly used. In this paper, we compare two approximations to GRFs with Matérn covariance functions: the kernel convolution approximation and the Gaussian Markov random field representation of an associated stochastic partial differential equation. We show that the second approach is a natural way to tackle the problem and is better than methods based on approximating the kernel convolution. Furthermore, we show that kernel methods, as described in the literature, do not work when the random field is not smooth. Copyright © 2011 John Wiley & Sons, Ltd.
iLocScan: harnessing multipath for simultaneous indoor source localization and space scanning

Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a "curse" and hence take great efforts to cope with it. Consequently, each sensor only obtains the information about the direct path; this necessitates a networked sensing system (hence higher system complexity and deployment cost) with at least three sensors to actually locate a source. In this paper, we deem multipath a "bless" and thus innovatively exploit the power of it. Essentially, with minor knowledge of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with only one hand-held device. At the same time, the extra information provided by multipath can help to at least partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems. To demonstrate these ideas, we implement a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan offers satisfactory results on both source localization and space scanning.
The professional identities of teacher educators in three further education colleges: an entanglement of discourse and practice

Abstract Initial teacher education programmes have been identified as crucial to meeting the twin policy aims of professionalising the further education (FE) workforce and achieving improved learner outcomes, yet college-based teacher educators are underrepresented in published research and commentary. Drawing on a case study of teacher educators employed by three FE colleges in England, this paper argues that the contested and politicised nature of the FE sector presents a unique set of circumstances that distinguishes this population from other members of their professional group and severely restricts the identities available to them. Through a thematic discursive analysis of documentation, observation and interview data, it is argued that FE positions teacher educator identity through political governance, through the business practices of colleges and through the sector’s historical relationships with vocational and higher education. Within this distinctive context, teacher educators experience competing identities of ‘qualified and credible’, ‘teacher’, ‘different from others’, ‘part of FE’ and ‘employee’ that are entangled with the dominant discourses of English further education. After discussing the implications of these findings for a professional profile of teacher educators, the paper concludes that teacher educators are better understood as a heterogeneous occupational group in order to avoid obscuring professional concerns linked to policy landscapes.
Predicting Reassignments of Bug Reports - An Exploratory Investigation

Bug tracking systems play an important role in the software development process since they allow users to report bugs they have encountered. Unfortunately, the information provided in the bug reports may contain errors since the reporter is not always acquainted with the technical nature of the software project. It remains a manual process left to the bug triager to correct reports with erroneous information. Consequently, this results in a delay of the resolution time of a bug. In this study, we propose to use data-mining techniques to make early predictions of which particular reported bugs are assigned to the incorrect component. This way, bug triagers are assisted in their task of identifying reported bugs where the "component" field contains an incorrect value. By using open-source cases like Mozilla and Eclipse, we demonstrate the possibility of predicting which particular bug is likely to be reassigned to a different "component".
On the Ergodic Capacity of Frequency Selective MIMO Systems Equipped with MMSE Receivers: An Asymptotic Approach

This paper is devoted to the study of the ergodic capacity of frequency selective MIMO systems equipped with a MMSE receiver when the channel state information is available at the receiver side and when the second order statistics of the channel taps are known at the transmitter side. As the expression of this capacity is rather complicated and difficult to analyse, it is studied in the case where the number of transmit and receive antennas converge to +oo at the same rate. In this asymptotic regime, the main results of the paper are related to the design of an optimal precoder in the case where the transmit antennas are correlated. It is shown that the left singular eigenvectors of the optimum precoder coincide with the eigenvectors of the mean of the channel taps transmit covariance matrices, and its singular values are solution of a certain maximization problem.
Data access microarchitectures for superscalar processors with compiler-assisted data prefetching

The performance of superscrdar processors is more sensitive to the memory system delay than their single-issue predecessors. This paper examines alternative data access microarchitectures that effectively support compilerassisted data prefetching in superscalar processors. In particular, a prefetch buffer is shown to be more effective than increasing the cache dimension in solving the cache pollution problem. All in all, we show that a small data cache with compiler-assisted data prefetching can achieve a performance level close to that of an ideal cache.
Spatial Analysis of Incidence Rates : A Bayesian Approach

Spatial models have been used in many fields of science where the data are collected in different locations, i.e. each observation is associated to a point in space. In particular, the analysis of spatial dispersion of the risk of occurrence of a certain event is in general performed via maps of incidence, where a set of areas is shaded according to the values of a variable of interest. The goal of this mapping is to infer the geographic distribution of the rates thus identifying areas of higher or lower incidence. In this work, maps of incidence rates will be constructed using a Bayesian approach. In particular, situations where the geographic units have small populations will be studied. The variability of the rates due to the differences among areas of underlying risk is separated from that due to random fluctuations via appropriate prior distributions. Some combinations of covariates will also be evaluated in order to check for possible influence of social factors on the incidence rates. The risks will be estimated via Markov chain Monte Carlo (MCMC) methods and we select the most appropriate model among those analised via the Deviance information criterion (DIC). Key-words: Spatial analysis, Bayesian inference, MCMC, WinBUGS, Gibbs sampler, conditionally autoregressive models.
User validation of image quality assessment algorithms

Even with modern graphics hardware, it is still not possible to achieve high fidelity renderings of complex scenes in real time. However, as these images are produced for human observers we exploit the fact that the human eye is good, but not that good. In particular, it is possible to render parts of an image at high quality and the rest of the scene at lower quality without the user being aware of this difference. Image quality assessment algorithms, such as the Daly model, provide a measure of the perceptual quality difference between image pairs. This paper presents a psychophysical evaluation of an image quality metric and investigates how such models can be developed to rapidly determine the parts of the scene with the most noticeable perceptual difference
Author Profiling of Twitter Users: Notebook for PAN at CLEF 2015

In this paper, we focused on profiling authors on age, gender, and five personality traits. The corpus consists of anonymized twitter posts categorized into 4 different languages. Our proposed approach was to use a combination of tfidf, function words, stylistic features, and text bigrams, and used an SVM for each task.
Discriminant Minimization Search for Large-Scale RF-Based Localization Systems

In large-scale fingerprinting localization systems, fine-grained location estimation and quick location determination are conflicting concerns. To achieve finer grained localization, we have to collect signal patterns at a larger number of training locations. However, this will incur higher computation cost during the pattern-matching process. In this paper, we propose a novel discriminant minimization search (DMS)-based localization methodology. Continuous and differentiable discriminant functions are designed to extract the spatial correlation of signal patterns at training locations. The advantages of the DMS-based methodology are threefold. First, with through slope of discriminant functions, the exhaustive pattern-matching process can be replaced by an optimization search process, which could be done by a few quick jumps. Second, the continuity of the discriminant functions helps predict signal patterns at untrained locations so as to achieve finer grained localization. Third, the large amount of training data can be compressed into some functions that can be represented by a few parameters. Therefore, the storage space required for localization can be significantly reduced. To realize this methodology, two algorithms, namely, Newton-PL and Newton-INT, are designed based on the concept of gradient descent search. Simulation and experiment studies show that our algorithms do provide finer grained localization and incur less computation cost.
VR Based Adaptive Simulation in Estimation of Whiplash Injuries

VR as a source of controllable visual and audio stimulations, which initiate and induce different kinematical, psychological, cognitive, and emotional reactions from the subject, is more than adequate state-of-the-art technology for assessment of whiplash injuries. A series of kinematical tests, which are integrated in Whiplash Injury Advanced Control Software (WIACS) package, provide assessment of subject’s head-neck Range Of Motion (ROM). This statistically relevant ROM information, with some other psychological and cognitive tests, provides solid background for making reliable estimation of whiplash injuries. The scene adaptation in subject’s audio-visual stimulation is performed according to the updated Subject’s Aggregated Knowledge Database, in order to stimulate the subject with the most suitable set of stimulations which take into account subject’s disorders and constraints. At the end of the article, the concept of advanced adaptive control of stimulations of subject is proposed, which is based on Fuzzy Logic Controller and Subject’s Aggregated Knowledge Database.
A remote memory access infrastructure for global address space programming models in FPGAs

We are proposing a shared-memory communication infrastructure that provides a common parallel programming interface for FPGA and CPU components in a heterogeneous system. Our intent is to ease the integration of reconfigurable hardware into parallel programming models like Partitioned Global Address Space (PGAS). For this purpose, we introduce a remote memory access component based on Active Messages that implements the core API of the Berkeley GASNet communication library, and a simple controller that manages communication and synchronization for custom FPGA cores. We demonstrate how these components deliver a simple and easily configurable communication mechanism between distributed memories in a multi-FPGA system with processors as well as custom hardware nodes.
SAR Minimum-Entropy Autofocus Using an Adaptive-Order Polynomial Model

A new algorithm is presented for autofocus in synthetic aperture radar imaging. Entropy is used to measure the focus quality of the image, and better focus corresponds to smaller entropy. The phase response of the focus filter is modeled as a specially designed polynomial, and the coefficients of this polynomial are adjusted in sequence to minimize the entropy of the image. Because the order of this polynomial is adaptive, this algorithm applies more widely than the minimum-entropy algorithms with a fixed-order polynomial model
Collaborative grasp planning with multiple object representations

Grasp planning based on perceived sensor data of an object can be performed in different ways, depending on the chosen semantic interpretation of the sensed data. For example, if the object can be recognized and a complete 3D model is available, a different planning tool can be selected compared to the situation in which only the raw sensed data, such as a single point cloud, is available. Instead of choosing between these options, we present a framework that combines them, aiming to find consensus on how the object should be grasped by using the information from each object representation according to their confidence levels. We show that this method is robust to common errors in perception, such as incorrect object recognition, while also taking into account potential grasp execution errors due to imperfect robot calibration. We illustrate this method on the PR2 robot by grasping objects common in human environments.
Insensitive Job Assignment With Throughput and Energy Criteria for Processor-Sharing Server Farms

We study the problem of stochastic job assignment in a server farm comprising multiple processor-sharing servers with various speeds and finite buffer sizes. We consider two types of assignment policies: without jockeying, where an arriving job is assigned only once to an available server, and with jockeying, where a job may be reassigned at any time. We also require that the underlying Markov process under each policy is insensitive. Namely, the stationary distribution of the number of jobs in the system is independent of the job size distribution except for its mean. For the case without jockeying, we derive two insensitive heuristic policies: One aims at maximizing job throughput, and the other trades off job throughput for energy efficiency. For the case with jockeying, we formulate the optimal assignment problem as a semi-Markov decision process and derive optimal policies with respect to various optimization criteria. We further derive two simple insensitive heuristic policies with jockeying: One maximizes job throughput, and the other aims at maximizing energy efficiency. Numerical examples demonstrate that, under a wide range of system parameters, the latter policy performs very close to the optimal policy. Numerical examples also demonstrate energy/throughput tradeoffs for the various policies and, in the case with jockeying, they show a potential of substantial energy savings relative to a policy that optimizes throughput.
Notice of Violation of IEEE Publication PrinciplesRealtime Planar Shape Deformation

Interactive space deformation is a powerful approach for editing raster images, vector graphics, geometric models and animated characters. This breadth of possibilities has led to an abundance of methods seeking to improve interactive deformation with real-time computation and intuitive use. Our goal is to make the design and control of deformations simpler by allowing the user to work freely with the most convenient combination of handle types. We construct three handle types: points, skeletons, and cages, points are quick to place and easy to manipulate, bones make some directions stiff, and cage is easy to control bulging and thinning in regions of interest. We provide planar shape deformation method based on Moving Least Squares and Mean Value Coordinate using linear functions including similarity and rigid transformations. These deformations are realistic and give the user the impression of manipulating real-world objects. For each of these techniques, we provide simple closed-form solutions that yield fast deformations, which can be performed in real-time. We demonstrate successful real-time deformation of planar shape.
Robust LMS-based compressive sensing reconstruction algorithm for noisy wireless sensor networks

Wireless sensor networks (WSNs) show immense promise in many applications, such as environmental monitoring and remotely metering. Compressive sensing (CS) is a novel signal processing that has been envisioned as a useful regime to address the energy and scaling constraints in WSNs. CS is able to move the burden of sensory nodes to central cloud/server. However, prevailing CS reconstruction algorithms are vulnerable to noise. In this paper, we exploit the natural noise-tolerance property of least mean square (LMS) adaptive filter and propose a greedy-LMS algorithm for CS reconstruction. When SNR is 48dB, greedy-LMS algorithm achieves 16% and 47% higher successful rate than BPDN and OMP, respectively. In addition, the computational complexity of greedy-LMS is competitive with OMP.
Phraseology as indicator for translation quality assessment of museum texts: A corpus-based analysis

Abstract Phraseological competence, although traditionally left somewhat in the background in research on translation quality assessment, refers to a high level of knowledge of a language, and is therefore worthy of greater attention. Some studies, which are exceptions to this neglect, suggest that the level of phraseological quality of a text is directly related to its overall quality. This paper assesses these 2 types of quality in 14 original and translated texts from a parallel electronic corpus from museums and art centers in the city of New York, the aim being to test whether there is a correlation between phraseological and overall quality. In absolute terms, the results of the phraseological assessment are usually seen to be better than those from the overall assessment. On comparing the two analyses, and despite generally negative results and a few notable exceptions, there is a tendency toward this correlation.
High resolution reduced complexity synchronization in spread spectrum systems

The estimation of several unknown channel amplitudes and taps in a multipath environment is an important problem when designing receivers both for communication and navigation purposes. A popular approach for these estimation problems are methods solving the maximum likelihood (ML) problem for each dimension separately like the expectation maximization (EM) approach and the more sophisticated space alternating generalized expectation maximization (SAGE) algorithm. However, both methods require a high computational complexity when used in spread spectrum systems due to long spreading sequences. Therefore, we apply methods for decreasing the computational complexity before executing the iterative estimation algorithms. The respective performance is assessed by means of computer simulations for a UMTS scenario which show a negligible degradation in estimation accuracy despite a substantial complexity reduction.