Miracle-dz/UniFAR-FTUs huggingface dataset:
INTEGRATING BWM AND ARAS UNDER HESITANT LINGUISTIC ENVIRONMENT FOR DIGITAL SUPPLY CHAIN FINANCE SUPPLIER SECTION

In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.
Forward-Backward Generative Adversarial Networks for Anomaly Detection

Generative adversarial network (GAN) has established itself as a promising model for density estimation, with its wide applications to various problems. Of particular interest in this paper is the problem of anomaly detection which involves identifying events that do not conform to expected patterns in data. Recent application of GANs to the task of anomaly detection, resort to their ability for learning probability distributions of normal examples, so that abnormal examples or outliers are detected when they reside in very low-probability regimes. Existing GAN methods often suffer from the bad cycle-consistency problem, which yields the large reconstruction error so that the anomaly detection performance is degraded. In order to alleviate this, we present a model that consists of a forward GAN and backward GAN, each of which has an individual discriminator, that are coupled by enforcing feature matching in two discriminators. We show that our forward-backward GANs (FBGANs) better captures the data distribution so that the anomaly detection performance is improved over existing GAN-based methods. Experiments on MNIST an KDD99 datasets demonstrate that our method, FBGANs, outperforms existing state-of-the-art anomaly detection methods, in terms of the area under precision recall curve (AUPR) and F1-score.
Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm.

Background In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient. Methods In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization. Results The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed. Conclusions Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.
Asymptotic Analysis of Surface-Plasmon Whispering-Gallery Resonators for Large Q-factor

Large Q-factor resonators are essential components in most electronic and photonic systems; that requirement is even more pertinent to the next generation of highly compact nano-photonic integrated circuits. Surface-Plasmon waves combined with Whispering-Gallery modes have the potential to satisfy the dual requirements of small geometry and large Q. A quasi-analytic formulation for SPWG large Q resonator modes is presented in this paper and is demonstrated to be a convenient yet satisfactory procedure to compute device characteristics.
Recognition Systems for Forensic Application

This paper demonstrates how an author recognition system could be benchmarked, as a prerequisite for admission in court. The system used in the demonstration is the FEDERALES system, and the experimental data used were taken from the British National Corpus. The system was given several tasks, namely attributing a text sample to a specific text, verifying that a text sample was taken from a specific text, and verifying that a text sample was produced by a specific author. For the former two tasks, 1,099 texts with at least 10,000 words were used; for the latter 1,366 texts with known authors, which were verified against models for the 28 known authors for whom there were three or more texts. The experimental tasks were performed with different sampling methods (sequential samples or samples of concatenated random sentences), different sample sizes (1,000, 500, 250 or 125 words), varying amounts of training material (between 2 and 20 samples) and varying amounts of test material (1 or 3 samples). Under the best conditions, the system performed very well: with 7 training and 3 test samples of 1,000 words of randomly selected sentences, text attribution had an equal error rate of 0.06% and text verification an equal error rate of 1.3%; with 20 training and 3 test samples of 1,000 words of randomly selected sentences, author verification had an equal error rate of 7.5%. Under the worst conditions, with 2 training and 1 test sample of 125 words of sequential text, equal error rates for text attribution and text verification were 26.6% and 42.2%, and author verification did not perform better than chance. Furthermore, the quality degradation curves with slowly worsening conditions were not smooth, but contained steep drops. All in all, the results show the importance of having a benchmark which is as similar as possible to the actual court material for which the system is to be used, since the measured system quality differed greatly between evaluation scenarios and system degradation could not be predicted easily on the basis of the chosen scenario parameters.
Fingerprint Minutiae Extraction

The most widely used method for fingerprint recognition is based on minutiae matching. A critical step in such systems is to automatically and reliably extract minutiae from the input fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction to obtain more reliable estimation of minutiae locations. The objective of this paper is to present a fingerprint minutiae extraction method. It includes two main steps: fingerprint image enhancements and minutiae extraction. Two methods are adopted for Image Enhancement: the first one is based on Fourier Transform and the other is Histogram Equalization. Minutiae extraction process includes Binarization, Thinning, Minutiae Detection, and finally removing false minutiae. The validity of adopted algorithms is tested on fingerprint images of FVC2002 database. A good ratio of true minutiae detection is achieved.
Use of a modified endotracheal tube for self-expandable metallic Y-shaped airway stent deployment without rigid bronchoscope or fluoroscopic guidance.

Background Self-expandable metallic Y-shaped airway stents (SEMYS) are commonly used in the management of airway stenosis and fistulae caused by thoracic neoplasms. Methods A new technique using a slightly modified regular endotracheal tube has been developed for the deployment of SEMYS with flexible bronchoscopy alone. The technique and devices are described. Results To date, successful deployment of SEMYS with this method has been carried out successfully in 17 out of 20 patients without major complications while the other 3 required conversion to rigid bronchoscopy because of limited pharyngeal cavity space, massive hemorrhage and severe cicatrization of the airway, respectively. Conclusions This simplified deployment technique with the modified endotracheal tube enables safe, simple and fast insertion of SEMYS in a regular bronchoscopy suite, which may benefit the vast less privileged institutions where SEMYS are necessary but rigid bronchoscopy and fluoroscopy are not available. The skill of the bronchoscopist, cautious selection of patients and effective coordination of the operating team are crucial for the procedure.
Imputation aided analysis of the association between autoimmune diseases and the MHC

The Major Histocompatibility Complex (MHC) is a genomic region in chromosome 6 which has been consistently found to be associated with the risk of developing virtually all common autoimmune diseases. Although its importance in disease pathogenesis has been known for decades, efforts to disentangle the roles of the classical human leukocyte antigens (HLA) and other variants responsible for the susceptibility to disease have often met with limited success, owing to the complex structure and extreme heterogeneity of the region. In this thesis, I interrogate the MHC for association with three common autoimmune diseases, ankylosing spondylitis, psoriasis and multiple sclerosis, with the aim of confirming the previously-reported associations and of identifying novel ones. To do so, I employ a systematic, joint analysis of single nucleotide polymorphism (SNP) and HLA allele data, in a logistic regression framework, using a recently developed algorithm to predict the HLA alleles for samples where such information is unavailable. To ensure the reliability of the analysis, I apply stringent quality control procedures and integrate over the uncertainty of the HLA allele predictions. Moreover, I resolve the haplotype phase of individuals from the HapMap project to create reliable reference panels, used in both HLA prediction and in quality control procedures. By directly testing HLA subtypes for association with the disease, the power to detect such associations is increased. I present the results of the analysis on the three disease phenotypes and discuss the evidence for important novel findings amongst both SNPs and HLA alleles in two of the diseases. In the final part of this thesis, I introduce a novel, model-based approach to detect inconsistencies in the data and show how it can be used to flag problematic SNPs which conventional quality control procedures may fail to identify.
Quantitative studies of water and sanitation utilities: a benchmarking literature survey

This paper performs a literature update of quantitative studies of water and sanitation services (WSS), with an emphasis on tracking the benchmarking literature. There are 190 studies which use cost or production functions to evaluate the performance of WSS utilities. The studies examine: (1) the scale, scope or density economies of utilities in a particular country or region; (2) the influence of ownership on efficiency; (3) the existence and power of incentives associated with different governance systems (including external regulation); and (4) performance assessment (benchmarking). In addition, this paper presents patterns regarding quantitative methods adopted over time, as well as some major trends in results.
SWOG S 0800 ( NCI CDR 0000636131 ) : addition of bevacizumab to neoadjuvant nab-paclitaxel with dose-dense doxorubicin and cyclophosphamide improves pathologic complete response ( pCR ) rates in inflammatory or locally advanced breast cancer

SWOG S0800, a randomized open-label Phase II clinical trial, compared the combination of weekly nab-paclitaxel and bevacizumab followed by dose-dense doxorubicin and cyclophosphamide (AC) with nab-paclitaxel followed or preceded by AC as neoadjuvant treatment for HER2-negative locally advanced breast cancer (LABC) or inflammatory breast cancer (IBC). Patients were randomly allocated (2:1:1) to three neoadjuvant chemotherapy arms: (1) nab-paclitaxel with concurrent bevacizumab followed by AC; (2) nab-paclitaxel followed by AC; or (3) AC followed by nab-paclitaxel. The primary endpoint was pathologic complete response (pCR) with stratification by disease type (non-IBC LABC vs. IBC) and hormone receptor status (positive vs. negative). Overall survival (OS), event-free survival (EFS), and toxicity were secondary endpoints. Analyses were intent-to-treat comparing bevacizumab to the combined control arms. A total of 215 patients were accrued including 11 % with IBC and 32 % with triple-negative breast cancer (TNBC). The addition of bevacizumab significantly increased the pCR rate overall (36 vs. 21 %; p = 0.019) and in TNBC (59 vs. 29 %; p = 0.014), but not in hormone receptor-positive disease (24 vs. 18 %; p = 0.41). Sequence of administration of nab-paclitaxel and AC did not affect the pCR rate. While no significant differences in OS or EFSwere seen, a trend favored the addition of bevacizumab for EFS (p = 0.06) in TNBC. Overall, Grade 3–4 adverse events did not differ substantially by treatment arm. The addition of bevacizumab to nab-paclitaxel prior to dose-dense AC neoadjuvant chemotherapy significantly improved the pCR rate compared to chemotherapy alone in patients with triple-negative LABC/IBC and was accompanied by a trend for improved EFS. This suggests reconsideration of the role of bevacizumab in high-risk triplenegative locally advanced breast cancer.
ON THE SHRINKAGE OF PASTE IN MORTAR CONTAINING SATURATED LIGHTWEIGHT AGGREGATE

Recently, there has been an increase in the desire to use saturated lightweight aggregates (LWA) to reduce shrinkage and shrinkage cracking in low w/c concrete. The use of saturated LWA as a method to provide internal curing involves the partial replacement of normal weight aggregate with saturated LWA. The saturated LWA provides additional water that can replenish the paste to compensate for the effects of self-desiccation. This paper uses Pickett’s model to determine the shrinkage of the cement paste portion of a mortar containing different volumes of saturated LWA. The results of the study indicate that while the inclusion of saturated LWA reduces the stiffness of the mortar, the reduction in shrinkage of the paste due to the supply of additional water is more crucial in reducing the overall shrinkage of the mortar. The size of the pores emptied due to self-desiccation is calculated using results from internal relative humidity measurements and are compared with mercury intrusion porosimetry test data. Similar pore sizes are observed with both methods.
Postprocessed time-delay interferometry for

1550-7998=20 High-precision interpolation of LISA phase measurements allows signal reconstruction and formulation of time-delay interferometry (TDI) combinations to be conducted in postprocessing. The reconstruction is based on phase measurements made at approximately 10 Hz (for a 1 Hz signal bandwidth) at regular intervals independent of the TDI delay times. Interpolation introduces an error less than 1 10 8 with continuous data segments as short as 2 s in duration. The 10 Hz sampling rate represents an increase from the 2 Hz sampling rate needed for the original implementation of TDI. The advantages of this technique include increased flexibility of the data analysis and significantly simplified hardware.
Seismic section image detail enhancement method based on wavelet transform

Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. First, the image is wavelet decomposed to obtain a low frequency structural component and a series of high frequency texture detail components; Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail; Finally, the processed high and low frequency components are reconstructed by wavelet can obtained the seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.
FOR INLAND WATERWAYS : Applying Concept Design Method – Practice Approach – Case by Case Design

The PIANC INCOM WG 141 has undertaken a great review on guidelines and practice examples as well as analyzed methods for detailed design. International standards as well as practice examples show a wide variety of design cases. One main reason for the differences is the great variety in shipping traffic. Furthermore a river is a complex system influenced by its varying bathymetry and currents to mention just a few aspects. So it is not appropriate to give just “one” design waterway dimension. Instead a design method was developed leading to three recommended design steps: “Concept Design Method”, “Practice Approach” and “Case by Case Design”. This approach is illustrated by three examples of fairway design: Canal, Free Flowing River, Lock Approach will show the applicability of the method.
Question Answering on SQuAD 2 . 0

The task of Reading Comprehension can be described as follows: from a context (passage / paragraph) and question pair, the goal is to answer the question using information from the context. This paper aims at solving the sub-task of Extractive Question Answering. In this setting, the answer is a segment (span) of the context. Our goal is to achieve good performance on the updated version of the Stanford Question Answering Dataset (SQuAD 2.0) without the use of Pretrained Context Embedding (non-PCE). In this paper, we built our QA system on top of the BiDirectional Attention Flow (BiDAF) [1] model, and further explored several ideas to improve on this baseline. We specifically considered existing approaches to enrich the word embedding (including character embedding and tag features) and incorporated a co-attention mechanism. Moreover, we explored several novel ideas such as a distance-aware loss and a segment-based aggregation approach. We were able to improve the baseline EM, F1 score from 55, 58 to 61, 65, respectively. For the methods that did not work well, we carry out in-depth analysis and discuss ways to improve these approaches.
The JHU/KyotoU Speech Translation System for IWSLT 2018

This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system’s parameters is important for training on small datasets.
SERIES SWP 2014 / 10 ! ! Food Demand Elasticities for Australia

There is renewed interest in robust estimates of food demand elasticities at a disaggregated level not only to analyse the impact of changing food preferences on the agricultural sector, but also to establish the likely impact of pricing incentives on households. Using data drawn from two national Household Expenditure Surveys covering the periods 1998/99 and 2003/04, and adopting an Almost Ideal Demand System approach that addresses the zero observations problem, this paper estimates a food demand system for 15 food categories for Australia. The categories cover the standard food items that Australian households demand routinely. Own-price, crossprice and expenditure elasticity estimates of the Marshallian and Hicksian types have been derived for all categories. The parameter estimates obtained in this study represent the first integrated set of food demand elasticities based on a highly disaggregated food demand system for Australia, and all accord with economic intuition.
An Efficient Feature Extraction Method for Tuberculosis detection using Chest Radiographs

Screening is widely used for the detection of tuberculosis; it’s difficult for a radiologist to interpret correctly the disease. It’s possible to improve this task by using CAD system (Computer-Aided diagnosis) which highlights the areas most likely to contain bacterial cells. In this paper we compare three feature extraction methods: Local Binary Pattern (LBP), Histogram of Gradients (HOG) and Tamura (LHT) and proposed LHTGF feature extraction methods (LBP, HOG, Tamura and Gabour filter) for Tuberculosis detection using chest radiographs. When compared with existing methods, our proposed method LHTGF yields better performance in detecting tuberculosis. All methods are based on features extracted from a local window and on a multiclass SVM classifier with fast search.
Rough Image Based Ensemble of Convolutional Neural Networks for Object Recognition

Object recognition is one of the challenging computer vision problem due to the difficulty in deriving features for the effectual classification in different viewing directions, different lighting conditions, and differently colored objects. A set of feature extraction techniques for the effectual classification of a given object will not successfully classify a different object. Recently convolutional neural networks (CNNs) with more number of convolution layers and more number of filters are widely used for object classification, which has more than 10 million learning parameters. Due to these tremendous number of learning parameters, the results of CNNs are not still optimal. Hence this paper presents a rough image based ensemble of CNNs for effectual object recognition. The results on the benchmarking dataset shows promising results.
Secure Energy-Efficient Transmission for SWIPT Intelligent Connected Vehicles With Imperfect CSI

In this paper, the secure energy-efficient transmission problem for an artificial-noise (AN)-aided simultaneous wireless information and power transfer (SWIPT) and vehicle-to-infrastructure (V2I) communication system with the imperfect channel state information (CSI) is studied. The secure energy efficiency problem is formulated with the imperfect CSI, where the constraints contain the minimum secrecy rate request of the legitimate vehicle (LV), the minimum energy harvesting (EH) request, the maximum transmit power, and the power splitting (PS) value. By exploiting the maximum ratio transmission (MRT) scheme and the norm-bounded matrix theory, we convert the original intractable problem to a fractional optimization one with three variables. Then, a two-level optimization algorithm is presented for the nonconvex fractional optimization problem. By introducing an auxiliary slack variable, the first level problem can be solved via one-dimensional optimization algorithm. The second level fractional optimization problem was furhther converted into an integer optimization one by the Dinkelbach method, and the closed-form solutions of the transmit power and the AN power are provided. Simulation results are given to verify the performance of our presented algorithm.