Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Nonlinear Manifold Detection Approach for Software Defect Prediction

Rapid development of software technology has influence on substantial industrial growth. Wide application of software in business related matters leads to development of reliable and defect free software system which is a challenging task. It requires development of effective techniques for prediction of software defects at early stage. For complexities in manual prediction of defects, automated techniques have come into effect. They are basically based on learning of pattern from earlier versions of software development and finding out the defects from the current version. Considerable impact of these techniques on industrial growth by predicting defects in software system attracted researchers in this field.In-spite of many studies performed by applying these techniques, desirable performance level and accurate defect prediction still remains a challenging task. For solving this problem, a hybrid technique based on Nonlinear Manifold Detection Techniques (Nonlinear MDTs) and machine learning for prediction of defects has been proposed in this paper. A new hybrid Nonlinear Manifold Detection (Nonlinear MD) Model has been applied for selecting and optimizing the features of software datasets that have been processed using Decision Tree (DT) and Random Forest (RF) classifications. Finally, a comparison and statistical evaluation of the experimental results obtained using new hybrid Nonlinear MD Model-DT have been made by Friedman test followed by Wilcoxon Sign rank test. The statistical outcome revealed that the proposed new hybrid Nonlinear MD Model-DT classification is better result oriented and more accurate in software defect prediction.
Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

In this paper, we present an algorithm to estimate the relative acoustic transfer function (RTF) of a target source in wireless acoustic sensor networks (WASNs). Two well-known methods to estimate the RTF are the covariance subtraction (CS) method and the covariance whitening (CW) approach, the latter based on the generalized eigenvalue decomposition. Both methods depend on the use of the noisy correlation matrix, which, in practice, has to be estimated using limited and (in WASNs) quantized data. The bit rate and the fact that we use limited data records therefore directly affect the accuracy of the estimated RTFs. Therefore, we first theoretically analyze the estimation performance of the two approaches in terms of bit rate. Second, we propose a rate-distribution method by minimizing the power usage and constraining the expected estimation error for both RTF estimators. The optimal rate distributions are found by using convex optimization techniques. The model-based methods, however, are impractical due to the dependence on the true RTFs. We therefore further develop two greedy rate-distribution methods for both approaches. Finally, numerical simulations on synthetic data and real audio recordings show the superiority of the proposed approaches in power usage compared to uniform rate allocation. We find that in order to satisfy the same RTF estimation accuracy, the rate-distributed CW methods consume much less transmission energy than the CS-based methods.
Chronic Wound Healing Assessment System Based on Different Features Modalities and Non-Negative Matrix Factorization (NMF) Feature Reduction

Identification and treatment of chronic wounds (CWs) are considered economic and social challenges, especially with respect to bedridden and elderly persons. CWs do not follow a predictive course of healing within a particular period. Their treatment and management costs are very high. Also, CWs decrease quality of life for patients, which cause severe pain and discomfort. In this paper, we proposed a comprehensive wound healing assessment framework based on current appearance, texture, and prior visual appearance analysis to handle different types of CWs depending on extracting various tissue types. The framework provided an accurate evaluation tool for the CW healing process depending on extracting and fusing significant features from the CWs RGB images. Non-negative matrix factorization (NMF) was used to retrieve the most significant features to reduce computation time. The gradient boosted trees (GBT) classifier was used to classify different tissue types. Finally, the healing assessment of the CWs depended on calculating the improvement in the area of the necrotic eschar, slough, granulation, and healing epithelial tissues. The framework was trained and tested using 377 RGB images from Medetec wound database and national pressure ulcer advisory panel website. The proposed system achieved an average accuracy of 96% for tissue classification, which helps in obtaining an accurate CW healing assessment. This result can be considered as a promising result when compared to the other state-of-the-art techniques.
Embedding for Out of Vocabulary Words Considering Contextual and Morphosyntactic Information

Word embeddings effectively represent the similarity between words through mapping to a low-dimensional latent space. However, the adoption of pre-trained word embeddings for NLP (Natural Language Processing) tasks fails to treat OOV (Out of Vocabulary) words in a productive way. Typically, OOV words are assigned to the UNK token which tends to fail in the presence of numerous OOV words. The paper proposes an embedding method for OOV words which exploits both word spells and contextual information to extract desired information. To relax the misrepresentation problem with OOV words, we develop a function that adds a context vector to a character-based model. It is demonstrated that the context-reinforced embedding method shows a better performance in several NLP tasks.
Performance Enhancement of Thin Film Solar Cell Using Two-Dimensional Plasmonic Grating in Rear Electrode

An effective scheme is proposed and investigated to improve the performance of thin film solar cells using a two-dimensional (2-D) plasmonic grating structure at the rear electrode or back metal contact. The proposed light trapping scheme replaces the conventional flat rear electrode by a corrugated electrode with a square mesh of periodic grooves forming a 2-D plasmonic grating. The grating surface diffracts the light and increases the path for reflected light. Further, the energy of surface plasmons excited inside the grooves gets coupled to the absorber layer. An efficient light trapping scheme and surface plasmons resonance conjointly increase the absorption in the absorber layer of solar cell, which results as enhanced efficiency. Maximum contribution of light path enhancement and surface plasmons is achieved by optimizing the periodicity, width, and depth of the grooves. The proposed design is compared with the conventional simple solar cell as well as with the solar cell having the rear electrode with 1-D plasmonic grating structure to demonstrate the effectiveness of the scheme. Finally, the optimized design is proposed which exhibits 92% absorption, 43% enhanced light path and more than 200% increase in the short circuit current as well as conversion efficiency.
A Visibility Restoration Framework for rainy images by using Lo gradient minimization and Bilateral Filtering

In this paper we proposed an algorithm for the visibility restoration of color rainy images. Rain drops affect the visual impact on image so that the visibility and intensity of the captured images are very low. We use a L0 gradient minimization technique for removing the rain pixels. L0 gradient minimization technique globally control the non zero gradient, so that low amplitude and insignificant detail are diminished but salient edges of the images are preserved. First we apply this refining illumination techniques on our rainy image for removing the rain and Secondly we apply a bilateral filtering for smoothing the color image for human perception. The quantitative results of proposed algorithm and other existing rainy algorithms for color rainy images are obtained in terms peak to peak signal to noise ratio (PSNR), universal image quality index(UIQI) and structure similarity index measurement (SSIM) and on different color rainy images from databases. The visual quality of the proposed algorithm also better than other existing rainy algorithms. Finally, the simulation result shows that the proposed algorithm is highly effective and efficient for visibility restoration of rainy images.
Outage Minimized Resource Allocation for Multiuser OFDM Systems With SWIPT

This paper considers a downlink multiuser orthogonal frequency division multiple (OFDM) system with simultaneous wireless information and power transfer (SWIPT), where the users decode information and harvest energy using the same received downlink signals. In particular, both power splitting (PS) and time switching (TS) SWIPT modes are considered for coordinating information transferring and energy harvesting. For multiuser information transmission, OFDM access (OFDMA) and time division multiple access (TDMA) are adopted for PS-SWIPT and TS-SWIPT, respectively. The outage performance of such a multiuser OFDM system with SWIPT is investigated. Particularly, we optimize the subcarrier allocation, power allocation and PS ratio for PS-SWIPT, and the power allocation and time allocation for TS-SWIPT, aiming at minimizing the fraction of outage over all users under the minimum harvest energy constraint along with the peak and total transmit power constraints. Suboptimal heuristic algorithms are obtained based on the Lagrange duality method. The outage performances of PS-SWIPT and TS-SWIPT are compared under various system parameter settings. We show that the outage performance of TS-SWIPT is better than that of the PS-SWIPT, except when the minimum required harvested energy is large, the total transmit power limit is small, or the number of users is small.
Cooperative spectrum sensing method for MC-CDMA systems in cognitive radio networks

The development of wireless communication networks has made spectrum management more compulsory. Different systems such as Multi-carrier code division multiple access (MC-CDMA) have to be adapted for cognitive radio technology to solve this problem. In this article, cooperative spectrum sensing technique, which is one of the spectrum sensing techniques, is adapted to MC-CDMA technology. This method can be used to decrease the mathematical density of the obtained spectral characteristics or to get better the probability of detection. Cognitive radio has made it possible to make spectrum perception more effective and reduce the possibility of false perception. The aim is to increase the likelihood that an unauthorized user will be able to identify the presence of a licensed user by cooperating with each other. The theoretical value and the simulated result are compared and then the results are interpreted according to the changing signal-to-noise ratios (SNRs).
Facial Makeup Transfer Combining Illumination Transfer

To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software. The makeup effects could present on the user’s input image in real time, with an only single reference image. The input image and reference image are divided into three layers by facial feature points landmarked: facial structure layer, facial color layer, and facial detail layer. Except for the above layers are processed by different algorithms to generate output image, we also add illumination transfer, so that the illumination effect of the reference image is automatically transferred to the input image. Our approach has the following three advantages: 1) Black or dark and white facial makeup could be effectively transferred by introducing illumination transfer; 2) Efficiently transfer facial makeup within seconds compared to those methods based on deep learning frameworks, and; 3) Reference images with the air-bangs could transfer makeup perfectly.
Deep Learned Optical Multiplexing for Multi-Focal Plane Microscopy

To obtain microscope images at multiple focal planes, the distance between the objective and sample can be mechanically adjusted. Images are acquired sequentially at each axial distance. Digital refocusing with a light-emitting diode (LED) array microscope allows elimination of this mechanical movement. In an LED array microscope, the light source of a conventional widefield microscope is replaced with a 2-dimensional LED matrix. A stack of images is acquired from the LED array microscope by sequentially illuminating each LED and capturing an image. Previous work has shown that we can achieve digital refocusing by post-processing this LED image stack. Though mechanical scanning is eliminated, digital refocusing with an LED array microscope has low temporal resolution due to the acquisition of multiple images. In this work, we propose a new paradigm for multi-focal plane microscopy for live imaging, utilizing an LED array microscope and deep learning. In our deep learning approach, we look for a single LED illumination pattern that allows the information from multiple focal planes to be multiplexed into a single image. We jointly optimize this LED illumination pattern with the parameters of a post-processing deep neural network, using a training set of LED image stacks from fixed, not live, Dugesia japonica planarians. Once training is complete, we obtain multiple focal planes by inputting a single multiplexed LED image into the trained post-processing deep neural network. We demonstrate live imaging of a D. japonica planarian at 5 focal planes with our method.
Evidence against a general positive eddy feedback in atmospheric blocking

The eddy straining mechanism of Shutts (1983; S83) has long been considered a main process for explaining the maintenance of atmospheric blocking. As hypothesized in S83, incoming synoptic eddies experience a meridional straining effect when approaching a split jetstream, and as a result, enhanced PV fluxes reinforce the block. A two-layer QG model is adopted here as a minimal model to conduct mechanism-denial experiments. While transient eddies' forcing is clearly critical to the formation and maintenance of a block, using a large ensemble, the authors demonstrate that the straining of generic eddies does not maintain blocks, thus challenge the idea of eddy straining serving as a positive feedback for the blocks. These results indicate that specific configurations of the eddy field are required for the maintenance stage. The authors also remark on the main supporting evidence in S83: the second-order induced flow is sensitive to the location of the wavemaker.
Neutral 
B
-meson mixing from full lattice QCD at the physical point

We calculate the bag parameters for neutral $B$-meson mixing in and beyond the Standard Model, in full four-flavor lattice QCD for the first time. We work on gluon field configurations that include the effect of $u$, $d$, $s$, and $c$ sea quarks with the highly improved staggered quark (HISQ) action at three values of the lattice spacing and with three $u/d$ quark masses going down to the physical value. The valence $b$ quarks use the improved nonrelativistic QCD action and the valence light quarks, the HISQ action. Our analysis was blinded. Our results for the bag parameters for all five operators are the most accurate to date. For the Standard Model operator between ${B}_{s}$ and ${B}_{d}$ mesons we find ${\stackrel{^}{B}}_{{B}_{s}}=1.232(53)$ and ${\stackrel{^}{B}}_{{B}_{d}}=1.222(61)$. Combining our results with lattice QCD calculations of the decay constants using HISQ quarks from the Fermilab/MILC Collaboration and with experimental values for ${B}_{s}$ and ${B}_{d}$ oscillation frequencies allows determination of the Cabibbo-Kobayashi-Maskawa (CKM) elements ${V}_{ts}$ and ${V}_{td}$. We find $|{V}_{ts}|=0.04189(93)$, $|{V}_{td}|=0.00867(23)$, and $|{V}_{ts}|/|{V}_{td}|=0.2071(27)$. Our results agree well (within $2\ensuremath{\sigma}$) with values determined from CKM unitarity constraints based on tree-level processes (only). Using a ratio to $\mathrm{\ensuremath{\Delta}}{M}_{s,d}$ in which CKM elements cancel in the Standard Model, we determine the branching fractions $\mathrm{Br}({B}_{s}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}})=3.81(18)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}$ and $\mathrm{Br}({B}_{d}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}})=1.031(54)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}10}$. We also give results for matrix elements of the operators ${R}_{0}$, ${R}_{1}$, and ${\stackrel{\texttildelow{}}{R}}_{1}$ that contribute to neutral $B$-meson width differences.
GASP – XX. From the loose spatially resolved to the tight global SFR–mass relation in local spiral galaxies

Exploiting the sample of 30 local star-forming, undisturbed late-type galaxies in different environments drawn from the GAs Stripping Phenomena in galaxies with MUSE (GASP), we investigate the spatially resolved star formation rate–mass ($\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$) relation. Our analysis includes also the galaxy outskirts (up to >4 effective radii, re), a regime poorly explored by other Integral Field Spectrograph surveys. Our observational strategy allows us to detect H α out to more than 2.7re for 75 per cent of the sample. Considering all galaxies together, the correlation between the $\rm \Sigma _{SFR}$ and $\rm \Sigma _\ast$ is quite broad, with a scatter of 0.3 dex. It gets steeper and shifts to higher $\rm \Sigma _\ast$ values when external spaxels are excluded and moving from less to more massive galaxies. The broadness of the overall relation suggests galaxy-by-galaxy variations. Indeed, each object is characterized by a distinct $\rm \Sigma _{SFR}$ –$\rm \Sigma _\ast$ relation and in some cases the correlation is very loose. The scatter of the relation mainly arises from the existence of bright off-centre star-forming knots whose $\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$ relation is systematically broader than that of the diffuse component. The $\rm \Sigma _{SFR}$–$\rm \Sigma _{tot \, gas}$ (total gas surface density) relation is as broad as the $\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$ relation, indicating that the surface gas density is not a primary driver of the relation. Even though a large galaxy-by-galaxy variation exists, mean $\rm \Sigma _{SFR}$ and $\rm \Sigma _\ast$ values vary of at most 0.7 dex across galaxies. We investigate the relationship between the local and global SFR–M* relation, finding that the latter is driven by the existence of the size–mass relation.
Permutation Inference with a Finite Number of Heterogeneous Clusters

I introduce a simple permutation procedure to test conventional (non-sharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjusted permutation test is easy to implement in practice and performs well at conventional levels of significance with at least four treated clusters and a similar number of control clusters. It is particularly robust to situations where some clusters are much more variable than others.
Geometric and obstacle scattering at low energy

Abstract We consider scattering theory of the Laplace Beltrami operator on differential forms on a Riemannian manifold that is Euclidean at infinity. The manifold may have several boundary components caused by obstacles at which relative boundary conditions are imposed. Scattering takes place because of the presence of these obstacles and possible non-trivial topology and geometry. Unlike in the case of functions eigenvalues generally exist at the bottom of the continuous spectrum and the corresponding eigenforms represent cohomology classes. We show that these eigenforms appear in the expansion of the resolvent, the scattering matrix, and the spectral measure in terms of the spectral parameter λ near zero, and we determine the first terms in this expansion explicitly. In dimension two an additional cohomology class appears as a resonant state in the presence of an obstacle. In even dimensions the expansion is in terms of λ and log λ. The theory of Hahn holomorphic functions is used to describe these expansions effectively. We also give a Birman-Krein formula in this context. The case of one-forms with relative boundary conditions has direct applications in physics as it describes the scattering of electromagnetic waves.
Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS

DNS over TLS (DoT) and DNS over HTTPS (DoH) encrypt DNS to guard user privacy by hiding DNS resolutions from passive adversaries. Yet, past attacks have shown that encrypted DNS is still sensitive to traffic analysis. As a consequence, RFC 8467 proposes to pad messages prior to encryption, which heavily reduces the characteristics of encrypted traffic. In this paper, we show that padding alone is insufficient to counter DNS traffic analysis. We propose a novel traffic analysis method that combines size and timing information to infer the websites a user visits purely based on encrypted and padded DNS traces. To this end, we model DNS sequences that capture the complexity of websites that usually trigger dozens of DNS resolutions instead of just a single DNS transaction. A closed world evaluation based on the Alexa top-10k websites reveals that attackers can deanonymize at least half of the test traces in 80.2% of all websites, and even correctly label all traces for 32.0% of the websites. Our findings undermine the privacy goals of state-of-the-art message padding strategies in DoT/DoH. We conclude by showing that successful mitigations to such attacks have to remove the entropy of inter-arrival timings between query responses.
Exploring Electronic Cheque Clearing Systems Acceptance by Jordanian Commercial Banks: A Theoretical Integration of Technology Acceptance Model, User Satisfaction and Delone and Mclean Model

This study provides a structure for understanding ECCS success. The study argues that using user satisfaction as surrogate indicator for measuring the success of ECCS has some theoretical difficulties. In similar vein, using the technology acceptance model (TAM) alone may not be sufficient to adequately capture the full meaning of effectiveness or the success of these. It posits that ECCS success is a joint function of system and information characteristics and acceptance. The study population consists of ECCS users from the central bank of Jordan and 24 commercial banks in Jordan. A convenience sample was drawn from ECCS users from the central bank of Jordan and 11 commercial banks, whereas 304 valid questionnaires were collected out of 420 distributed questionnaires. Collected data were statistically analyzed using SPSS package to investigate the relationship between dependent variable ECCS users satisfaction and independent variables: ECC system quality, ECCS information quality, ECCS perceived ease of use and ECCS perceived usefulness. On the other hand, the relationship significance between the dependent variable ECC system success and using ECCS users satisfaction as an independent variable was investigated. The main study conclusions were that ECCS success is positively associated with ECCS users satisfaction which is positively associated with the independent variables mentioned earlier. The major study recommendation is that the business sector should pay attention to the major role of users attitudes in determining the success of information systems application. The importance of future investigation on the perceived value of ECCS by banks customers is also emphasized.