Reviewerly/exHarmony huggingface dataset:
Histogram-Based CRC for 3D-Aided Pose-Invariant Face Recognition

Traditional Collaborative Representation-based Classification algorithms for face recognition (CRC) usually suffer from data uncertainty, especially if it includes various poses and illuminations. To address this issue, in this paper, we design a new CRC method using histogram statistical measurement (H-CRC) combined with a 3D morphable model (3DMM) for pose-invariant face classification. First, we fit a 3DMM to raw images in the dictionary to reconstruct the 3D shapes and textures. The fitting results are used to render numerous virtual samples of 2D images that are frontalized from arbitrary poses. In contrast to other distance-based evaluation algorithms for collaborative (or sparse) representation-based methods, the histogram information of all the generated 2D face images is subsequently exploited. Second, we use a histogram-based metric learning to evaluate the most similar neighbours of the test sample, which aims to obtain ideal result for pose-invariant face recognition using the designed histogram-based 3DMM model and online pruning strategy, forming a unified 3D-aided CRC framework. The proposed method achieves desirable classification results that are conducted on a set of well-known face databases, including ORL, Georgia Tech, FERET, FRGC, PIE and LFW.
Statistical Properties of an Unassisted Image Quality Index for SAR Imagery

The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich the M estimator to extend its capabilities.
Using Highway Connections to Enable Deep Small‐footprint LSTM‐RNNs for Speech Recognition

Long short-term memory RNNs (LSTMRNNs) have shown great success in the Automatic speech recognition (ASR) field and have become the state-ofthe- art acoustic model for time-sequence modeling tasks. However, it is still difficult to train deep LSTM-RNNs while keeping the parameter number small. We use the highway connections between memory cells in adjacent layers to train a small-footprint highway LSTM-RNNs (HLSTM-RNNs), which are deeper and thinner compared to conventional LSTM-RNNs. The experiments on the Switchboard (SWBD) indicate that we can train thinner and deeper HLSTM-RNNs with a smaller parameter number than the conventional 3-layer LSTM-RNNs and a lower Word error rate (WER) than the conventional one. Compared with the counterparts of small-footprint LSTMRNNs, the small-footprint HLSTM-RNNs show greater reduction in WER.
Influence of Augmented Reality on the Achievement and Attitudes of Ambiguity Tolerant/Intolerant Students

This paper aims to investigate the influence of augmented reality technology on the achievement and attitudes of student tolerance for ambiguity. Seventy-eight undergraduate students at King Faisal University participated in the experiment, in two groups according to their learning style (tolerant or intolerant of ambiguity). The findings revealed that augmented reality technology helps ambiguity-tolerant students improve achievement and form positive attitudes toward the use of augmented reality in learning more than it does those who are ambiguity intolerant. The study promotes taking account of students’ learning styles and personal characteristics when designing interactive learning environments, to provide the best learning experience for their preferences.
Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.
The Malaise of the Soul at Work: The Drive for Creativity, Self-Actualization, and Curiosity in Education

Franco “Bifo” Berardi tells us that the current transformation of every domain of social life into economy has led to “the subjugation of the soul to work processes.” There is a newfound love of work and, consequently, writes Berardi, “no desire, no vitality seems to exist anymore outside of the economic enterprise.” Concerned as it once was with “fostering the soul,” and concerned as it now (almost exclusively) is with preparing students for the job market, what role might education have in Berardi’s musings? Can “creativity,” “self-actualization” and “curiosity,” which are so valued in education, still speak and help foster a sense of the soul that exceeds economy and work? In this paper, I explore these questions by providing an account of what is at stake in Berardi’s conception of the soul and its subjugation to work. I draw on his ideas to critically appraise the seemingly-benign aspirations of the “creative class,” a term coined to signal a new era for human flourishing afforded by shifts towards personal creativity in the digital economy. I then move to consider how, in particular, the ubiquitous promotion of “creativity” and “curiosity” in education tends to replicate a form of learning that puts the soul to work, as it were, promoting a narrow, self-enterprising subject with a frantic instrumental orientation towards the world. I conclude with a discussion on how demands on learners, to be always curious, innovative, nimble and open to discovery lead to panic, collapse and depression: the diseases of the soul.
Reduced-reference image quality metric based on statistic model in complex wavelet transform domain

A new Reduced-Reference (RR) image quality metric based on statistical models in the complex wavelet transform domain is proposed. The magnitude and the relative phase information of the complex wavelet coefficients is modeled by using probability density function, and a strategy based on the information criterion is proposed to optimally approximate the distribution. To further improve the accuracy of the metric, a comparison of the candidate models is studied, and the inverse Gaussian distribution and the wrapped Cauchy distribution are selected to model the magnitude and the relative phase distributions, respectively. The Kullback–Leibler divergence between the distributions of the reference image and the distorted one serves as the RR feature to measure the distortion. Finally, a generalized regression neural network is employed to map the RR feature into an objective score. Experimental studies confirmed that the proposed RR image quality metric is quality-aware and highly correlated with the human visual system.
Recursive Binary Neural Network Training Model for Efficient Usage of On-Chip Memory

We present a novel deep learning model for a neural network that reduces both computation and data storage overhead. To do so, the proposed model proposes and combines a binary-weight neural network (BNN) training, a storage reuse technique, and an incremental training scheme. The storage requirements can be tuned to meet the desired classification accuracy, storing more parameters on an on-chip memory, and thereby reducing off-chip data storage accesses. Our experiments show 4-6× reduction in weight storage footprint when training binary deep neural network models. On the FPGA platform, this results in a reduced amount of off-chip accesses, enabling our model to train a neural network in 14× shorter latency, as compared to the conventional BNN training method.
Pedagogical talent: psychological and pedagogical solutions for identification, selection, and support

А. А. Федоров, Е
A post-quantum light weight 1-out-n oblivious transfer protocol

Security is a key concern in any IoT deployment, particularly if we have to take into consideration future attacks facilitated by the use of quantum computers. Therefore, in this paper, we present a post-quantum lightweight 1-out-n oblivious transfer (OT) protocol, based on the NTRU cryptographic primitive. Compared to the OT scheme proposed by Chou and Orlandi in LATINCRYPT 2015, our protocol is more suitable for deployment in heterogeneous and distributed environment, due to the reusability of encoded data set. Findings from the performance evaluation indicate that the proposed protocol outperforms that of Chou and Orlandi [13] protocol, in terms of computation and communication costs.
ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images

Minimally invasive surgical and diagnostic systems are commonly used in clinical practices. However, the accuracy and robustness of these systems depend heavily on computer based processes such as tracking, detecting or segmenting clinically meaningful regions of interest, which are significantly affected by the inherent specular reflections that appear on the organs’ surfaces. Restoration of the acquired data for clinical purposes still presents challenges because of the high texture and color variations across the image. In this work, we propose a novel fully-automated solution for endoscopic image restoration, which we call ReTouchImg. Our approach is designed as a two-step scheme. The first is a detection step that is based on the synergy of a set of color variations and gradient information conditions. For the second step, we introduce an inpainting process which is based on graph data structures for recovering the missing information. We exhaustively evaluate our approach on real endoscopic datasets and compare it against some works from the body of literature. We also demonstrate that our solution deals with complex cases such as strong illumination variation and large affected areas through a careful quantitative evaluation of a range of numerical results.
A New Image Registration Algorithm Based on Evidential Reasoning

Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.
Discrimination-Aware Integration for Person Re-Identification in Camera Networks

In this paper, we propose a novel method named Discrimination-Aware Integration (DAI) for person re-identification (re-ID) in camera networks, which not only integrates multiple re-ID models but also adaptively learns integration weights for different feature dimensions. To avoid the tough selection of deep models, we employ different data sources to train the same re-ID model for learning features from different views, and then, we obtain multiple features for each pedestrian image. To effectively integrate these features, the proposed DAI learns integration weights for each feature dimension according to their importance. Finally, the features extracted from different re-ID models are integrated with the learned integration weights to form the final representation for the pedestrian images. We evaluate the performance of the proposed DAI on three public large-scale person re-ID datasets, i.e., Market1501, CUHK03, and DukeMTMC-reID, and the experimental results show that the proposed DAI outperforms the state-of-the-art results.
OBSERVING THE INUNDATED AREA USING LANDSAT-8 MULTITEMPORAL IMAGES AND DETERMINATION OF FLOOD-PRONE AREA IN BANDUNG BASIN

Flood is the most frequent hydro-meteorological disaster in Indonesia. Flood disasters in the Bandung basin result from increasing population density, especially in the Citarum riverbank area, accompanied by land use changes in upstream of the Citarum catchment area which has disrupted the river’s function. One of the basic issues that need to be investigated is which areas of the Bandung basin are prone to flooding. This study offers an effective and efficient method of mapping flood-prone areas based on flood events that have occurred in the past through the use of historical remote sensing image data. In this research, Landsat-8 imagery was used to observe the inundated area in the Bandung basin in the past (2014–2018) using an improved algorithm, the modified normalized water index (MNDWI). The results of the study show that MNDWI is the appropriate parameter to be used to detect flooded areas in the Bandung basin area that have heterogeneous land surface conditions. The flood-prone area was determined based on flood events for 2014 to 2018, identified as inundated areas in the images. The estimation of the flood-prone area in the Bandung basin is 11,886.87 ha. Most of the flood-prone areas are in the subdistricts of Rancaekek, Bojongsoang, Solokan Jeruk, Ciparay, Cileunyi, Bale Endah and Cikancung. This area geographically or naturally is a water habitat area. Therefore, if the area will be used for residential, this will have consequences that flood will always be a threat to the area.
Addressing Skewness in Iterative ML Jobs with Parameter Partition

Computational skewness is a significant challenge in multi-tenant data-parallel clusters that introduce dynamic heterogeneity of machine capacity in distributed data processing. Previous efforts to addressing skewness mostly focus on batch jobs based on the assumption that processing time is linearly dependent on the size of partitioned data. However, they are illsuited for iterative machine learning (ML) jobs, which (1) exhibit a non-linear relationship between the size of partitioned parameters and processing time within each iteration, and (2) show an explicit binding relationship between input data and parameters for parameter update. In this paper, we present FlexPara, a parameter partition approach that leverages the non-linear relationship and provisions adaptive tasks to match the distinct machine capacity so as to address the skewness in iterative ML jobs on data-parallel clusters. FlexPara first predicts task processing time based on a capacity model designed for iterative ML jobs without the linear assumption. It then partitions parameters to parallel tasks through proactive parameter reassignment. Such reassignment can significantly reduce network transmission cost incurred by input data movement due to the binding relationship. We implement FlexPara in Spark and evaluate it with various ML jobs. Experimental results show that compared to hash partition, FlexPara speeds up the execution by up to 54% and 43% in private and NSF Chameleon clusters, respectively.
Library Services Navigation: Improving the Online User Experience

While the discoverability of traditional information resources is often the focus of library website design, there is also a need to help users find other services such as equipment, study rooms, and programs. A recent assessment of the Brigham Young University Library website identified nearly two hundred services. Many of these service descriptions were buried deep in the site, making them difficult to locate. This article will describe a web application that was developed to improve service discovery and to help ensure the accuracy and maintainability of service information on an academic library website.
Outlier detection for questionnaire data in biobanks

Biobanks increasingly collect, process and store omics with more conventional epidemiologic information necessitating considerable effort in data cleaning. An efficient outlier detection method that reduces manual labour is highly desirable.We develop an unsupervised machine-learning method for outlier detection, namely kurPCA, that uses principal component analysis combined with kurtosis to ascertain the existence of outliers. In addition, we propose a novel regression adjustment approach to improve detection, namely the regression adjustment for data by systematic missing patterns (RAMP).Application to epidemiological record data in a large-scale biobank (Tohoku Medical Megabank Organization, Japan) shows that a combination of kurPCA and RAMP effectively detects known errors or inconsistent patterns.We confirm through the results of the simulation and the application that our methods showed good performance. The proposed methods are useful for many practical analysis scenarios.
Exploring the Recent Trends of Paraphrase Detection

This study is to examine paraphrase detection (PD) for diagnostic purposes.Which is defined as the capability to find and discover the similarity between sentences that are written in a natural language?Where detecting similar sentences written in natural language is extreme importance and it is very essential for computer software used in plagiarism detection, Q and A automated systems, text mining, authorship authentication and text recapitulation.The goal of paraphrase detection is to detect whether two statements have the identical semantic or not.There is hundreds of empirical research in this direction.This study will focus on the discussion of recent studies of the PD methods and will categorize them in two categories, supervised learning and unsupervised learning.Also will give an idea about text similarity, machine learning and deep learning approaches.The performance of the selected researches is assessed by how accurate the F-measures are in detecting paraphrase in Microsoft Research Paraphrase Corpus (MSPR).
Mapping Hydrothermal Alteration Targets from Landsat 8 OLI/TIRS and Magnetic Data Using Digital Image Processing Techniques in Garoua, North Cameroon

A geological based remote sensing study was carried out over the Poli group in Garoua (northern Cameroon), renowned for its hydrothermally occurring mineralizations (Gold and Uranium).In this study, Landsat 8 OLI/TIRS coupled with magnetic data were used in order to realize a regional litho-structural mapping of hydrothermal alteration targets, required for mineral exploration.Hence, the enhancement of both Landsat 8 and magnetic data was achieved via digital image processing (DIP) techniques notably Band rationing (BR), Principal Component Analysis (PCA), Horizontal gradient (HG), and lineament extraction algorithms.Based on reference band ratio combinations (Sultan's and Kaufmann's) and Crosta Technique (PCA analysis), hydrothermally altered minerals such as hydroxyl-bearing minerals and iron oxides were identified.The PCA map was shown to better illustrate hydrothermally altered areas than Band ratios.Thus, a classification map emphasizing on mineral targets was obtained from the PCA map.Besides, the CET grid analysis tool (horizontal gradient) from Geosoft software 8.4 and the line algorithm (pan band 8) from Geomatica 2013 allowed the extraction of subsurface and surface lineaments respectively.The resulting lineaments showed a predominant E-W trending direction, which suggests a major tectonic event having prevailed within the area.Other directions include NE-SW/ENE-WSW, and N-S.The generation of a target exploration map, via ArcGIS 10.4.1, was performed by the overlay of hydrothermal alteration mapping and structural mapping.New regional maps, providing a useful insight for further mining exploration, were obtained.
Accelerated Optimization with Orthogonality Constraints

We develop a generalization of Nesterov's accelerated gradient descent method which is designed to deal with orthogonality constraints. To demonstrate the effectiveness of our method, we perform numerical experiments which demonstrate that the number of iterations scales with the square root of the condition number, and also compare with existing state-of-the-art quasi-Newton methods on the Stiefel manifold. Our experiments show that our method outperforms existing state-of-the-art quasi-Newton methods on some large, ill-conditioned problems.
The Mw 6.0 South Napa earthquake of August 24, 2014—Observations of surface faulting and ground deformation, with recommendations for improving post-earthquake field investigations

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88).Horizontal coordinate information is referenced to the World Geodetic System 1984 datum (WGS84).The use of InSAR was invaluable for identifying and mapping secondary traces with small displacements, and for delineating the overall details of the extensive rupture.InSAR data also highlighted other areas with possible ground deformation-some of which are found coincident with previously mapped fault traces, whereas others are in areas where no faults were previously mapped.Several of these regions had no visible ground deformation, whereas others did produce features that were inconsistent with tectonic faulting, so care must be taken not to over interpret the InSAR data without careful, corroborating field investigations.
Non‐local weighted fuzzy energy‐based active contour model with level set evolution starting with a constant function

In the traditional active contour models, global region-based methods fail to segment images with intensity inhomogeneity, and local region-based methods are sensitive to initial contour. In this study, a novel fuzzy energy-based active contour model is proposed to segment medical images, which are always corrupted by intensity inhomogeneity. In order to deal with intensity inhomogeneity, a local energy term is first constructed by substituting a non-local weight for Gaussian kernel widely used in traditional local region-based models. Second, the defined adaptive force can drive the level set function to adaptively increase or decrease according to image intensity information. Therefore, the initial contour can be initialised as a constant function, which eliminates the problem caused by contour initialisation. Moreover, the proposed active contour model is a convex function. Thus, the problem, resulting from optimising a non-convex functional in the traditional active contour models, can be avoided. Experimental results validate the superiorities and effectiveness of the proposed model for image segmentation with comparisons of those yielded by several state-of-the-art techniques.