Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring New Directions for Discovery Learning

This paper takes as its theoretical framework an approach to corpus-aided discovery learning in which the central role of corpora is seen as that of providing rich sources of autonomous learning activities of a serendipitous kind. Here the suggestion is put forward that availability of different corpora and software tools and the ability to combine these in different ways depending on the purpose of the activity may help learners develop an understanding of the patterned quality of language (probability, strength of co-occurrence restrictions, levels of contextual appropriateness), and be conducive to more appropriate use, as learners are guided not just to observe patterns, but also to develop hypotheses as to their variability. A learning experience is described, in which learners are introduced to a number of corpus tools (larger and smaller, general and specific, monolingual and bilingual corpora; two different software programmes for corpus analysis), and guided to progress from more convergent activities to autonomous browsing. Positive and negative sides of the approach are discussed, also in the light of learners' comments, and suggestions for improving the methodology and the tools currently available to learners are put forward.
A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET

The need for predictive capabilities greater than 95% with very limited false alarms are demanding requirements for reliable disruption prediction systems in tokamaks such as JET or, in the near future, ITER. The prediction of an upcoming disruption must be provided sufficiently in advance in order to apply effective disruption avoidance or mitigation actions to prevent the machine from being damaged. In this paper, following the typical machine learning workflow, a generative topographic mapping (GTM) of the operational space of JET has been built using a set of disrupted and regularly terminated discharges. In order to build the predictive model, a suitable set of dimensionless, machine-independent, physics-based features have been synthesized, which make use of 1D plasma profile information, rather than simple zero-D time series. The use of such predicting features, together with the power of the GTM in fitting the model to the data, obtains, in an unsupervised way, a 2D map of the multi-dimensional parameter space of JET, where it is possible to identify a boundary separating the region free from disruption from the disruption region. In addition to helping in operational boundaries studies, the GTM map can also be used for disruption prediction exploiting the potential of the developed GTM toolbox to monitor the discharge dynamics. Following the trajectory of a discharge on the map throughout the different regions, an alarm is triggered depending on the disruption risk of these regions. The proposed approach to predict disruptions has been evaluated on a training and an independent test set and achieves very good performance with only one tardive detection and a limited number of false detections. The warning times are suitable for avoidance purposes and, more important, the detections are consistent with physical causes and mechanisms that destabilize the plasma leading to disruptions.
Quadrature Rules for Functions with a Mid-Point Logarithmic Singularity in the Boundary Element Method Based on the x = tp Substitution

Quadrature rules for evaluating singular integrals that typically occur in the boundary element method (BEM) for two-dimensional and axisymmetric threedimensional problems are considered. This paper focuses on the numerical integration of the functions on the standard domain [-1,1], with a logarithmic singularity at the centre. The subtitution x=tp, where p(>3) is an odd integer is given particular attention, since this returns a regular integral and the domain unchanged. Gauss-Legendre quadrature rules are applied to the transformed integrals for a number of values of p. It is shown that a high values for p typically gives more accurate results.
The Compress-and-Estimate Coding Scheme for Gaussian Sources

We consider the multiterminal remote source coding problem of estimating a Gaussian signal from a bit-restricted representation of distributed linear measurements corrupted by additive white Gaussian noise. For this problem, we study the performance of the multiterminal compress-and-estimate (CE) coding scheme in which multiple remote encoders compress their measurements so as to minimize a local distortion measure which depends solely on the distribution of these measurements. In reconstruction, the decoder estimates the signal from the lossy-compressed measurements having full knowledge of the statistics of the source signal and the noisy measurements. The CE coding scheme is motivated by the scenario in which source encoders, due to their limited capabilities, operate according to a pre-determined compression strategy and cannot adapt to the sensing environment while the fusion center has full knowledge and computational capabilities. We focus, in particular, on two scenarios: the centralized observation model in which measurements are collected at a single remote encoder and the distributed observation model where measurements are provided to multiple remote sensors. In both scenarios, we investigate the performance attainable through the CE coding scheme in which the measurements are compressed according to a quadratic distortion measure and compare it to the performance of the coding scheme having full system knowledge.
Content-Aware Based Sorting Approach of Scalable Video Bit Stream

This paper proposes a scalable video stream sorting approach based on content-aware in order to improve the transmission efficiency and the video broadcast quality.According to the human subjective perception of changing scenes,the model is established to describe the dynamic characteristics of the video.Object and background motion vectors are weighted adaptively.The video packets on the frame level are tagged and sorted by the approach mentioned in this paper,which is combined with the video coding structure and the rate-distortion model.Experimental results show that the method can accurately describe the video dynamic characteristic and effectively improve the video broadcast quality and human subjective perception.This method is more significant for the complex video sports scene.
Testing for heteroskedasticity in two-way fixed effects panel data models

ABSTRACT In this paper, we propose a new method for testing heteroskedasticity in two-way fixed effects panel data models under two important scenarios where the cross-sectional dimension is large and the temporal dimension is either large or fixed. Specifically, we will develop test statistics for both cases under the conditional moment framework, and derive their asymptotic distributions under both the null and alternative hypotheses. The proposed tests are distribution free and can easily be implemented using the simple auxiliary regressions. Simulation studies and two real data analyses demonstrate that our proposed tests perform well in practice, and may have the potential for wide application in econometric models with panel data.
Diurnal Forcing and Phase Locking of Gravity Waves in the Maritime Continent

An unfiltered zonal Hovmöller depiction of rainfall in the Maritime Continent (MC) reveals remarkable spatiotemporal continuity of zonally propagating disturbances with a diurnal period, which endure over multiple days and propagate faster than the individual convective storms they coupled with. This phenomenon and its sensitivity to the Madden–Julian oscillation (MJO) during the 2011/12 Dynamics of the MJO (DYNAMO) field campaign is examined here through a well-validated, convection-permitting model simulation conducted on a large domain. We find that these disturbances are zonally propagating diurnal gravity waves excited by vigorous nocturnal mesoscale convective systems over Sumatra and Borneo. These gravity waves are diurnally phase locked: their wavelength very closely matches the distance between these two islands (~1500 km), while their particular zonal phase speed (~±17 m s−1) allows them to propagate this distance in one diurnal cycle. We therefore hypothesize that these waves are amplified by resonant interaction due to diurnal phase locking. While these zonal gravity waves decouple from convection once beyond the MC, their divergent flow signature endures well across the Indian Ocean, provoking the notion that they may influence rainfall at far remote locations. The exact controls over this zonal phase speed remain uncertain; we note, however, that it is roughly consistent with diurnal offshore-propagating modes documented previously. Further study is required to tie this down, and more generally, to understand the sensitivity of these modes to background flow strength and the geography of the MC.
Application of Hilbert Huang transform to identify the natural frequencies of steel frame

Identification of dynamic structural characteristics such as natural frequency based on measured vibration responses at site is one of the important steps in the structural investigation work of loading test and structural health monitoring. During the service stage, the dynamic characteristics can be changed due to the stiffness reduction and nonlinear of structure under extrema excitation. Therefore, the identification of instantaneous parameters of structure is very necessary in detecting and monitoring the structural deterioration continuously by the time. This paper presents the data processing method using Hilbert Huang Transform (HHT) algorithm in determining a time-dependent frequencies of steel frame. The vibration experiment for steel frame was conducted in this study. The acceleration responses in time domain were recorded and analyzed by HTT to determine the fundamental natural frequencies of frame. The instantaneous frequencies in the time domain of steel frame are the main finding in this research. The traditional data processing method using a Fast Fourier Transform (FFT) was conducted and compared with the HHT method. Keywords: Hilbert Huang Transform (HHT); steel frame; Fast Fourier Transform (FFT); natural frequency.
QualityRank : Measuring Authority of Answer in Q&A Community using Social Network Analysis

We can get answers we want to know via questioning in Knowledge Search Service (KSS) based on Q&A Community. However, it is getting more difficult to find credible documents in enormous documents, since many anonymous users regardless of credibility are participate in answering on the question. In previous works in KSS, researchers evaluated the quality of documents based on textual information, e.g. recommendation count, click count and non-textual information, e.g. answer length, attached data, conjunction count. Then, the evaluation results are used for enhancing search performance. However, the non-textual information has a problem that it is difficult to get enough information by users in the early stage of Q&A. The textual information also has a limitation for evaluating quality because of judgement by partial factors such as answer length, conjunction counts. In this paper, we propose the QualityRank algorithm to improve the problem by textual and non-textual information. This algorithm ranks the relevant and credible answers by considering textual/non-textual information and user centrality based on Social Network Analysis(SNA). Based on experimental validation we can confirm that the results by our algorithm is improved than those of textual/non-textual in terms of ranking performance.
Hyperspectral Image Classification via Compressive Sensing

Although hyperspectral technology has continued to improve over the years, it is still limited to size, weight, and power (SWaP) constraints. One major issue is the need to sample a large number of very fine spectral bands. Such prohibitively large size of hyperpsectral data creates challenges in both data archival and processing. Compressive sensing (CS) is an enabling technology for reducing the overall data processing and SWaP requirements. This paper explores the viability of performing classification for hyperspectral data on a compressively sensed band domain (CSBD) via CS instead of the original data space, without performing sparse reconstruction. In particular, the well-known restricted isometry property (RIP) and a random spectral sampling strategy are explored for hyperspectral image classification (HSIC) in CSBD. A mathematical analysis is also presented to show that the classification error can be expressed in terms of the restricted isometry constant (RIC) so that the HSIC in the original full-band data space can be achieved in CSBD provided that sufficient band-sensing conditions are met. To validate the proposed CS-HSIC a set of real hyperspectral image experiments are performed where a commonly used spectral–spatial classification algorithm based on support vector machine (SVM) and edge-preserving filters (EPFs) is implemented for a comparative study and analysis. The results clearly demonstrate the potential of CS-HSIC in future research directions.
Non-overlapping classification of hyperspectral imagery

ABSTRACT Because of high spectral resolution, hyperspectral imagery (HSI) is well suited for classifying land cover types. Spectral information and spatial context are often combined to improve classification performance. For several publicly available hyperspectral datasets, the classification accuracies reach almost 100%. However, we think this high accuracy is mainly due to heavy overlap between training and test samples generated during training and test stages. Because the training samples and test samples are randomly sampled from the same images and spatially adjacent to each other, overly optimistic results are produced. However, in real-life applications, the training samples and test samples may be collected from different locations or at different times. In order to improve the classification performance when training samples and test samples have low correlation, a low correlation sampling method and two non-overlapping classification methods are introduced. Experimental results show that, although the reduced correlation between training and test samples reduces the classification performance dramatically, the spectral-spatial combined method is still an effective way to improve the classification accuracy of HSI.
Strong coherent interference suppression based on second- order cone null steering beamforming

For active sonar systems, coherent interference, such as direct-path wave and strong multi-path, will shield the weak target echo signal, which is one of the main factors affecting the target detection performance. Null steering beamforming is an important signal processing method that suppresses strong interference. In this paper, the second-order cone programming (SOCP) method is used to perform null steering beamforming on the uniform linear array. Beam null is caused in the direction of interference to suppress strong coherent interference in the active sonar system during detection. However, there are two limitations that can causes decline of the algorithm performance. A norm-control method is proposed to limit the response near the end-fire direction of linear array, and an array manifold compensation method is introduced to solve the array manifold mismatch. Simulated and experimental data was used to evaluate the performance and verify the feasibility of the methods.
A Guide To Task Analysis: The Task Analysis Working Group

Part 1: the task analysis process. Part 2: task analysis techniques task data collection task description methods task simulation methods task behaviour assessment methods task requirements evaluation methods. Part 3: task analysis case studies balancing automation and human action through task analysis a preliminary communications system assessment a plant local panel review a staffing assessment for a local control room task simulation to predict operator workload in a command systenm task analysis of operator safety actions maintenance training a method for quantifying ultrasonic inspection effectiveness operational safety review of a solid waste storage plant a task analysis programme for a large nuclear chemical plant.
Nonparametric Data Analysis on Manifolds with Applications in Medical Imaging

Nonparametric Statistical Analysis on Manifolds in the past twenty years has been rapidly developed and applied to Medical Imaging problems. During this time period, statisticians began working more and more with nonlinear object data, regarding their observations as points on manifolds. The idea of a data analysis on abstract metric spaces goes way back to a visionary paper by Fréchet (1948), where he suggested to analyze object data on separable metric spaces rather than just on linear spaces in an effort of accommodate a large variety of elements (objects) that need to be analyzed statistically. Although standard statistical methods used for Euclidean data cannot be applied directly to such data, methodology has been developed for statistical analysis of data on manifolds. In this body of work, we focus two different medical imaging problems. The first problem corresponds to analyzing the CT scan data. In this context, we perform nonparametric analysis on the 3D data retrieved from CT scans of healthy young adults, on the Size-and-Reflection Shape Space SRΣ3,0 of k-ads in general position in 3D. This work is a part of larger project on planning reconstructive surgery in severe skull injuries which includes preprocessing and postprocessing steps of CT images. The preprocessing step, consists of the extraction the boundary of the bone structure from the CT slices while the post-processing steps consists of 3D reconstruction of the virtual skull from these bone extractions and smoothing. Next, we present our results for the Schoenbergs sample mean Size-and-Reflection Shape of k-ads in general position in R for the human skull based on these virtual reconstructions. The bootstrap distribution of the Schoenberg sample means 3D Size-and-Reflection Shape for a selected group of anatomic landmarks and pseudo-landmarks, are computed for 500 bootstrap resamples of the original 20 skulls represented by the 3 by k configurations, when k = 9 and k = 17. Finally, we report a confidence region for the Schoenberg mean configuration.
Object Detection and Displacement Integrated with RoboRealM

This paper illustrates the design and implementation of computer vision algorithms for object detection combined with object displacement. RoboRealM software is used for object detection which is interfaced to the serial communication along with ATMega328 controller and a gripper which helps in controlling the movements of the robot based on the state of output. The detection process involves perception of color, shape and size of the object which facilitates the robot to pick and place the object at a particular location. This paper incorporates supervised Artificial Intelligence techniques for better detection and movement of the object.
PROPOSITION OF AN INTELLIGENT SYSTEM FOR PREDICTIVE ANALYSIS USING MEDICAL BIG DATA

Emerging technologies such as mobile application, cloud computing, big data analytics, predictive analytics revolutionized all sectors. This is particularly true for the healthcare system as a sensitive sector. Nowadays, healthcare industry mainly depends on information technology to provide best services. A big-data revolution in healthcare starts with the vastly increased supply of health data. In fact, these new technologies are applied to improve the medical sector. This paper proposes an intelligent system that combines big-data analysis with data-mining and mobile healthcare techniques for self-monitoring. The system attempts to exploit the healthcare data through an intelligent process analysis and big data processing. This approach aims to extract useful knowledge to be used in decision making and to ensure a real-time medical monitoring.
Loss Prediction in DC-Biased Magnetic Sheets

Power losses in soft magnetic materials can be solidly assessed by the statistical theory of losses (STLs), which provides physical foundation to the concept of loss separation. The theory is, however, limited to the conventional case of symmetric hysteresis loops and cannot be straightforwardly applied for a magnetic core operating under a dc bias. We show, in this paper, that such constraint can be released by combining the STL with a simplified approach to the dynamic Preisach model. This approach leads to the more affordable static Preisach model with largely reduced computation time. In this way, the hysteresis and excess loss components, with and without dc bias, are identified and calculated starting from a minimum set of experimental data. We provide a wide-ranging experimental validation of the theory, which is applied to the behavior of the energy loss versus frequency, measured up to $f = 1$ kHz, in non-oriented and grain-oriented iron-silicon sheets and in iron-cobalt alloys, subject to different polarization bias levels.
Securing Online Banking Transaction using Predictive Approach of Hidden Markov Model

Due to a Fast growth in the electronic commerce technology, popularity of online banking and online shopping is growing day by day. While e-commerce is still gaining popularity, it also provides ground for fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. In this paper, proposed model the sequence of operation in online banking transaction processing using a Hidden Markov Model (HMM) and describe how it can be used for the detection of frauds. An HMM is initially trained with the normal behaviour of a cardholder. If current transaction is not accepted by the trained model with good probability, it is treated as fraudulent. And one time password is send to mobile of card holder.
Resource Optimization in Full Duplex Non-Orthogonal Multiple Access Systems

In this paper, we investigate a full duplex (FD) multi-user non-orthogonal multiple access (NoMA) communication system based on the optimization of received signal-to-interference-plus-noise ratio (SINR) per unit power. Since the communication system operates in the FD mode, co-channel interference (CCI) and self-interference (SI) dominate the system’s performance. Accordingly, to combat the CCI, we adopt a game-theoretic approach and propose users’ clustering algorithms and to suppress the SI, we formulate an optimization problem to maximize the power-normalized SINR (PN-SINR). While the user clustering optimization problem is constrained by: 1) the successive interference cancellation (SIC) constraint and 2) two binary constraints for the allocations of uplink (UL) and downlink (DL) users, the PN-SINR problem is constrained by: 1) total transmit power budget at the base station and UL users; 2) the fundamental condition for the implementation of successive interference cancellation in the NoMA; and 3) the minimum fairness condition for the UL users. The original PN-SINR problem is non-convex and hence is converted into an equivalent subtractive-form problem, after which we propose an iterative algorithm to find the optimal power allocation policy. Properties of all the proposed algorithms are thoroughly investigated and the numerical results are provided. Based on the channel conditions and suppression level of SI and CCI, the superiority of the proposed FD-NoMA system over half-duplex NoMA and FD orthogonal multiple access systems is verified.