Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of neutral stochastic switched time delay systems: An average dwell time approach

This paper considers a class of stochastic systems referred to as stochastic switched systems of neutral type with time‐varying delay, which combines switched systems with neutral stochastic systems. The systems consist of subsystems of two forms: (i) only stable subsystems and (ii) both stable subsystems and unstable subsystems. By establishing an integral inequality, the exponential stability in pth(p≥1)‐moment for such systems with only stable subsystems is first considered. Then, by using an average dwell time approach, the exponential stability in pth(p≥1)‐moment for the second form is addressed. An important finding of this study is that when the average dwell time is chosen to be sufficiently large and the total activation time of unstable subsystems is relatively small compared with that of stable subsystems, the exponential stability in pth(p≥1)‐moment for such systems can be guaranteed. Two major advantages of these new results are that the differentiability or continuity of the delay function is not required compared with the existing results in the literature, and the proposed approaches can be used to consider the case when the neutral item and the stochastic perturbation are simultaneously presented. An example is provided to verify the effectiveness and potential of the theoretic results obtained. Copyright © 2016 John Wiley & Sons, Ltd.
Dirichlet process mixture models for finding shared structure between two related data sets

A nonparametric Bayesian approach is used for the problem of learning from two related data sets. We model the shared structure between two data sets using a Dirichlet process mixture model of probabilistic canonical correlation analysers, which allows the flexibility of the mappings from shared feature to data spaces to be automatically determined from the data.
Integration of Advanced Metering Infrastructure for Mini-Grid Solar PV Systems in Off-Grid Rural Communities (SoAMIRural)

Solar energy is considered a promising source of power generation in sub-Saharan Africa due to the high sunshine in these areas. Deploying decentralised solar-powered mini-grid systems to provide access to electricity in rural areas is fraught with difficulties in accurately predicting consumption, automatic monitoring, and operation sustainability to support the socio-economic conditions of rural communities. This study proposed SoAMIRural, which integrates solar PV mini-grid and advanced metering infrastructure for rural communities. SoAMIRural was implemented and tested for a case study community in Ghana. Solar PV Selection Equation Matrix (SPSEM) and Sample Size Equation (SSE) were used to determine the sustainable demand generation capacity of 24 kVA. Load estimations and need assessments were conducted to ascertain the rural community’s electric load and priority needs. SoAMIRural was evaluated with an error margin of 5%, resulting in 95% accuracy in energy consumption threshold management and monitoring to ensure energy conservation and sustainability of the mini-grid system. This study maps out a conceptual framework for a smart solar PV mini-grid system for rural communities and its advantages in realising SDG 7 in Ghana by 2030.
Comparison of bicoherence on the ensemble of realizations and a selective evaluation of the bispectrum of the dynamics of dangerous parameters of the gas medium during fire

The object of the study is the bicoherence of the bispectrum assessment of the dynamics of dangerous parameters of the gas environment during the ignition of materials. The subject is a measure of bicoherence of the bispectrum estimation from the ensemble of realizations and selective bispectrum estimation for the dynamics of hazardous parameters of the gas environment. The practical importance of the research is the use of the measure of bicoherence of the bispectrum for the early detection of fires. The measure of bicoherence of the dynamics of hazardous parameters of the gas environment is substantiated, which allows them to be numerically compared for the studied bispectrum estimates. As such measure, it is proposed to use the integral value of bicoherence for a given frequency interval, which makes it possible to numerically compare the bicoherence of bispectrum estimates for arbitrary time intervals of the dynamics of hazardous parameters of the gas environment. On the basis of the proposed measure for the frequency range of 0.2–2 Hz, a comparison of the integral bicoherence of the bispectrum estimates was made. The numerical value of the measure was determined for three fixed time intervals of the dynamics of hazardous parameters of the environment, corresponding to the absence of ignition, the occurrence of ignition, and the subsequent burning of test materials in the laboratory chamber. According to the results of the comparison of such values, it was established that the bicoherence of the bispectrum estimation from the ensemble of realizations is the most appropriate for detecting fires. When ignited, the numerical value of the measure for all test materials is about 90°. This means that the nature of the dynamics of hazardous environmental parameters in the event of fires becomes random. In this regard, the proposed measure is recommended to be used as a test for early detection of fires.
Method of Spatio-temporal Data Outlier Detection

Depending on "k standard deviation" rule,a new Spatio-Temporal Outlier Detection(STOD) method is proposed,named as a Dual non-spatio-temporal attributes Deviation-based STO Detection(DDSTOD). For each time instant,in Spatial Neighborhoods(SN) of each feature,the rule is employed to judge whether the spatial feature is a spatial outlier,and all the spatial outliers form a spatial outlier set.In the Temporal Neighborhoods(TN) of each spatial outlier,the rule is utilized to check temporal outliers,too.All features which are outliers both in their SN and TN composite Spatio-Temporal Outliers set(STOs). Experimental results show this method is effective and feasible.
Validation of Quantitative Ultrasonography for Osteoporosis Diagnosis in Postmenopausal Women Compared to Dual-Energy X-Ray Absorptiometry (DEXA)

Introduction Bone mineral density (BMD) is an essential indicator for diagnosing osteoporosis and evaluating the success of osteoporotic treatment. Dual-energy X-ray absorptiometry (DEXA), quantitative ultrasonography (QUS), and quantitative computed tomography (QCT) are frequently used for measuring BMD. The objective of the study was to evaluate the ability of QUS to screen for osteoporosis and bone density in postmenopausal women by calibrating it against DEXA. Methods This cross-sectional study was conducted at the Department of Orthopedics and Trauma Center of the tertiary care center, Lucknow. A total of 90 patients visited this department from August 2017 to July 2018 for the present study. BMD in the same patient was evaluated by using DEXA and ultrasonography methods. Data were entered in Microsoft Excel and analyzed by using SPSS Software. Results According to linear regression analysis, T neck was found statistically significant with T QUS (p<0.001) and z QUS (p<0.001). T lumbar and T wrist were found statistically significant with T QUS (p<0.001) but not with z QUS (p>0.001). Z neck was found statistically significant with T QUS (p<0.001) and z QUS (p<0.001). Z lumbar was found statistically significant with T QUS (p<0.001) but not with z QUS (p>0.005). Z wrist was not found statistically significant with T QUS (p>0.005) or with z QUS (p>0.005). Conclusion In the present study, we found that QUS can be used as a screening tool for detecting osteoporosis by measuring BMD in contrast to DEXA. QUS also can be used to predict the DEXA values for osteoporosis and to detect osteoporosis.
High-power single emitters and low fill factor bars emitting at 808 nm

Single emitters emitting at a wavelength around 808 nm are highly-desired as pump sources of low power solid state lasers. The latest development of high-power single emitters having emitter apertures of 95 μm, 100 μm and 200 μm based on TM-polarized material are presented. The devices were characterized up to their maximum optical output powers of 15 W from 100 μm wide emitters and 22 W from 200 μm wide emitters. The operation point of these devices is set to 7 W and 10 W, respectively with wall-plug efficiencies of ~55%. Furthermore first results on 5- emitters bars operating at 40 W are presented.
Improving Acoustic Echo Cancellation by Mixing Speech Local and Global Features with Transformer

We propose MiT-Net, a novel mix-transformer neural network with a pyramid encoder operating in the time domain, for the task of acoustic echo cancellation. The MiT-Net formulates acoustic echo cancellation as a supervised speech separation problem, in which near-end speech is separated from a single microphone recording and sent to the far end, and consists of two key components. First, we apply a pyramid encoder, which adopts the coarse-to-fine structure, to extract the latent correlations between double-end signals and to fuse them in a multiscale manner. Second, we propose a mix-transformer, a combination of local and global attention in a parallel way, to leverage local and global speech information for separation. Experimental results show that the proposed method outperforms recent AEC methods in terms of objective evaluation metrics. In addition, exploring the correlation between speech local and global features by using the mix-transformer significantly improves the system performance and shows more robustness than the conventional transformer.
CF-VTON: Multi-Pose Virtual Try-on with Cross-Domain Fusion

The multi-pose virtual try-on technology aims to seamlessly fit an in-shop garment onto a reference person in various poses. This technology has attracted considerable attention from researchers due to its potential commercial and practical applications. Previous works in this field have encountered issues such as unnatural garment alignment and difficulty in preserving the person’s identity, arising from the weak mapping relationship between different feature crosses. To address these challenges, this paper proposes a novel multi-pose virtual try-on network named CF-VTON. Our approach involves predicting the "after-try-on" semantic map to guide garment alignment and try-on synthesis, warping the garment using an improved garment alignment network (GANet) to optimize unnatural alignment, synthesizing a coarse result with our proposed try-on synthesis network (TSN), and refining the output to reconstruct the virtual try-on result with rich facial identity and garment details. Qualitative and quantitative experiments demonstrate the superiority of our approach, outperforming state-of-the-art methods in an efficient manner.
Goodness of fit in Armitage-Doll Poisson models

SUMMARY We consider testing the adequacy of multistage models of cancer induction for Poisson event rates depending on age and exposure to environmental agents. The space of time and covariates is partitioned for comparison of observed and fitted event counts over regions of the partition. A test of fit based on the quadratic score statistic is proposed to assess overall residual error between observed and fitted counts. The limiting distribution of the test statistic is central chi-squared under the null hypothesis and noncentral chi-squared under local alternatives. Standardized residuals and tests of trend for individual regions of the partition are given. The method is applied to lung cancer incidence rates among British physicians grouped by age, by amount of smoking, and by decile of estimated risk. The relation of the proposed method to chi-squared frequency tests and extensions to casecontrol studies are considered.
Efficient Intelligibility Evaluation Using Keyword Spotting: A Study on Audio-Visual Speech Enhancement

We propose a new method for human speech intelligibility evaluation based on keyword spotting. In this method, participants play a stimulus and select the word they hear from a close set of alternatives. To find which sentence to use, the target word, and alternatives we mine a large set of stimuli using a phonetic dictionary and a language model. Unlike other tests, our method does not rely on specially designed sentences and can be used to evaluate in-the-wild material such as TED talks. We focus on audio-visual (AV) speech enhancement (SE) evaluation as a study case. We compared our method to a transcription task and observed that the two produce highly correlated results, albeit our task requiring substantially less participation time. We then adopted it on a large-scale evaluation of AVSE systems. Results show that keyword spotting is a suitable and efficient alternative to assess intelligibility from AV stimuli.
Improved Method of Defining Accuracy Measure for Rough Sets

In rough set theory, accuracy measure is an important numerical characterization that quantifies the imprecision of a rough set caused by its boundary region. However, the traditional accuracy measure does not take into consideration the granularity of the partition induced by an equivalence relation. This paper analyzes the limitation of the traditional accuracy measure and proposes a new kind of definition of accuracy measure based on the one proposed by Baowen Xu et al. Some of its good properties are proved. It is more suitable to measure the imprecision of rough sets and its algorithm is simpler than Xu Baowen et al’s, which are illustrated by examples.
Improvement of fast solution method for mass concrete temperature fields

An iterative method for determining variable initial values is proposed based on the symmetric successive over-relaxation preconditioned conjugated gradient(SSOR-PCG) method.The temperature value of the previous time step is regarded as the initial value of the next time step.Compared with the original approach,the solution speed increases by about 1/3.Using the iterative method,a finite element program for the temperature fields was compiled and was employed to simulate the transient temperature fields of a RCC gravity dam.The calculated results show that the present method has a high solution speed.For example,only 31 seconds are needed to solve a temperature field with 20×104 DOF once(one time step).
Image Processing Techniques for Concrete Crack Detection: A Scientometrics Literature Review

Cracks in concrete surfaces are one of the most prominent causes of the degradation of concrete structures such as bridges, roads, buildings, etc. Hence, it is very crucial to detect cracks at an early stage to inspect the structural health of the concrete structure. To solve the drawbacks of manual inspection, Image Processing Techniques (IPTs), especially those based on Deep Learning (DL) methods, have been investigated for the past few years. Due to the groundbreaking development of this field, researchers have devoted their endeavors to detecting cracks using DL-based IPTs and as a result, the techniques have given answers to many challenging problems. However, to the best of our knowledge, a state-of-the-art systematic review paper is lacking in this field that would present a scientometric analysis as well as a critical survey of the existing works to document the research trends and summarize the prominent IPTs for detecting cracks in concrete structures. Therefore, this article comes forward to spur researchers with a systematic review of the relevant literature, which will present both scientometric and critical analysis of the papers published in this research area. The scientometric data that are brought out from the articles are analyzed and visualized by using VOSviewer and CiteSpace text mining tools in terms of some parameters. Furthermore, this article elucidates research from all over the world by highlighting and critically analyzing the incarnated essence of some of the most influential papers. Moreover, this research raises some common questions as well as extracts answers from the analyzed papers to highlight various features of the utilized methods.
E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.
Modeling and calibration of pulse-modulation based ToF imaging systems

Conversely to the continuous wave indirect time-of-flight (CW-iToF) imaging scheme, pulsed modulation ToF (PM-iToF) imaging is a promising depth measurement technique for operation at high ambient illumination. It is known that non-linearity and finite charge-transfer speed impact trueness and precision of ToF systems.1–3 As pulses are no Eigenfunctions to the shutter system, this issue is especially pronounced in pulsed modulation.2, 3 Despite these effects, it is possible to find analytical expressions founded on physical observations that map scenery parameters such as depth information, reflectance and ambient light level to sensor output.3, 4 In the application, the inverse of this map has to be evaluated. In PM-iToF, an inverse function cannot be yielded in a direct manner, as models proposed in the literature were transcendental.3, 4 For a limited range an approximating linearization can be performed to yield depth information.5 To extend the usable range, recently, an alternative approach that indirectly approximates the inverse function was presented.6 This method was founded on 1D doping concentration profiles, which, however, are typically not made available to end users. Also, limitations of the 1D approximation as well as stability are yet to be explored. This work presents a calibration methodology that copes with detector insufficiencies such as finite charge transfer speed. Contrarily to the state of the art, no prior knowledge on details of the underlying devices is required. The work covers measurement setup, a benchmark of various calibration schemes and deals with issues such as overfitting or defect pixels.
Fast Online Source Steering Algorithm for Tracking Single Moving Source Using Online Independent Vector Analysis

We address the problem of separating moving sources using online independent vector analysis (IVA). To solve this problem, researchers have extended the iterative projection (IP) and iterative source steering (ISS) algorithms developed for batch auxiliary-function-based IVA (AuxIVA) to online scenarios and showed their effectiveness. However, the conventional online IP and ISS are slow because they update K × K covariance matrices for all sources, where K is the number of microphones. Here, we show that, in a target-source tracking scenario in which only one source moves, there exists an inexpensive formula for online ISS that avoids updating the full covariance matrices without changing the behavior of the algorithm. The time complexity of the proposed algorithm, which we call online source steering (OSS), is K times smaller than that of the conventional online IP and ISS for the target-source tracking task. A numerical experiment on separating a moving source demonstrates that the proposed OSS is significantly faster than the conventional online IP and ISS.
High TLR6 Expression Status Predicts a More Favorable Prognosis after Esophagectomy for Locally Advanced Thoracic Esophageal Squamous Cell Carcinoma

Most so-called “beneficial bacteria” in gut microbiota are Gram-positive, and TLR6 recognizes the peptidoglycan (PGN) present in their cell walls. We hypothesized that a high TLR6 expression status predicts a more favorable prognosis after esophagectomy. We used an ESCC tissue microarray (TMA) to examine TLR6 expression status in ESCC patients and to determine whether TLR6 expression status correlates with prognosis after curative esophagectomy. We also examined whether PGN influences the cell proliferation activity of ESCC lines. Clinical ESCC samples from 177 patients tested for the expression of TLR6 were categorized as 3+ (n = 17), 2+ (n = 48), 1+ (n = 68), or 0 (n = 44). High TLR6 expression (3+ and 2+) correlated with significantly more favorable 5-year overall survival (OS) and disease-specific survival (DSS) after esophagectomy than a lower TLR6 expression (1+ and 0). Univariate and multivariate analyses showed that TLR6 expression status is an independent prognostic factor that affects 5-year OS. PGN significantly inhibited the cell proliferation activity of ESCC lines. This is the first study to show that high TLR6 expression status predicts a more favorable prognosis in locally advanced thoracic ESCC patients after curative esophagectomy. PGN released from “beneficial bacteria” seems to have potential to inhibit the cell proliferation activity of ESCC.
Progressive Multi-Stage Neural Audio Codec with Psychoacoustic Loss and Discriminator

In this paper, we improve the efficiency of the progressive multi-stage neural audio codec (PR-Codec) by utilizing perceptually motivated training criteria. Although our baseline PR-Codec successfully reconstructs full-band signals by progressively decoding the pre-defined subband signals, transparent quality can only be guaranteed in high bit-rates. To reduce bit-rates while maintaining perceptually transparent quality, we adopt a psychoacoustic model (PAM)-based loss and propose a perceptual weighting discriminator (PWD), which enables us to synthesize and discriminate audio signals in the perceptually motivated domain. We also introduce a scalar quantization with an entropy model to further enhance the quantization efficiency. Our experimental results show that our proposed model significantly improves perceptual reconstruction quality at the expense of the waveform disparity in the time-domain, compared to our previous model.