Reviewerly/exHarmony huggingface dataset:
HoloCast: Graph Signal Processing for Graceful Point Cloud Delivery

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation

This paper is concerned with the problem of learning the mapping from one graph to another graph. Primarily, we focus on the issue of how to effectively learn the topology of the source graph and then decode it to form the topology of the target graph. We embed the topology of the graph into the states of nodes by exerting a topology constraint, which results in our Topology-Flow encoder. To decoder the encoded topology, we design a conditioned graph generation model with two edge generation options, which result in the Edge-Bernoulli decoder and the Edge-Connect decoder. Experimental results on the 10-nodes simple graph dataset illustrate the substantial progress of the proposed method. The MNIST digitsskeleton mapping experiment also reveals the ability of our approach to discover different typologies.
Statistics of radiation due to nondegenerate Josephson parametric down-conversion

In a process called parametric down-conversion, a dc-biased Josephson junction coupled to two microwave resonators emits photon pairs when the Josephson frequency matches the sum of the two resonance frequencies. Recent experiments have shown that such a setup permits analyzing the correlation of the radiation. Motivated by these results, we study theoretically the full counting statistics of a nondegenerate parametric oscillator below the threshold of instability. Furthermore, we analyze the second-order coherences of the radiation and discuss thermal effects on the radiation statistics. We provide results for the driving strength at which the Cauchy-Schwarz inequality is most strongly violated. Additionally, we study the impact of asymmetry in the linewidth of the modes---a distinctive property of the nondegenerate resonance effect. In particular, we find that the radiation from the mode with the larger linewidth preferably takes the total detuning, while the other mode emits photons at its resonance frequency.
Complete permutation polynomials with the form <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msup><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msup><mml:mo>−</mml:mo><mml:mi>x</mml:mi><mml:mo>+</mml:mo><mml:mi>δ</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mrow…

In this paper, several classes of complete permutation polynomials with the form (xpm−x+δ)s+axpm+bx over Fpn are proposed by the AGW criterion and determining the number of solutions of some equations. Our results also enrich constructions of known permutation polynomials.
Optical encryption method of multiple-image based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e382" altimg="si4.gif"><mml:mi>θ</mml:mi></mml:math> modulation and computer generated hologram

An optical multiple images encryption method based on θ modulation and computer generated hologram is proposed. After modulated by double random phases in Fresnel transform and θ modulation system, the images are superimposed together and encoded into a binary real value CGH. The decryption is achieved in optical spatial filtering and Fresnel transform system with spatial light modulator. Multiple images can be encrypted at once in the proposed method which has high efficiency. Except the random phase, the diffraction distance and wavelength can be used as keys to improve the security. The numerical simulation and experimental results verify the validity of proposed method in this paper.
Phase correlation functions: FFT vs. FHT

&lt;p&gt;&lt;span lang="EN-GB"&gt;The ability to process an image is a crucial skill in many measurement activities. In image processing or pattern recognition, Fast Fourier Transform (FFT) is widely used. In particular, the Phase Only Correlation (POC) method demonstrates high robustness and subpixel accuracy in pattern matching. However, there is a disadvantage in the required memory machine because of the calculation of 2D-FFT. In applications in which the use of memory is a critical element, Fast Hartley Transform (FHT) seems to be a good substitute. In this context, the use of Hartley’s transform can be of interest for apps implemented on portable systems e.g. smartphones. In this article, we present a comparison of the implementations of the phase correlation function using FFT and FHT. Particular attention is given to the analytical steps necessary to implement the POC by means of the Hartley transform.&lt;/span&gt;&lt;/p&gt;
NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC: Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of ‘Contextual Emotion Detection in Text’ as part of SemEval-2019. Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the test-set leader-board. Our code is available at https://github.com/iamgroot42/nelec
Mineralogical and Chemical Characterization of Zr-REE-Nb Ores from Khalzan Buregtei (Mongolia)—Approaches to More Efficient Extraction of Rare Metals from Alkaline Granitoids

Alkaline rocks are worldwide observed as hosts for rare metal (Zr-REE-Nb) minerals. The classification of the ore bearing rock type is challenging due to the fact that textures and mineral assemblage are obscured by post-magmatic alteration. In addition, the alteration causes fine and intricate intergrowth of the ore minerals with associated gangue. Hence, intensive comminution is necessary to liberate the ore minerals, which is one parameter hampering the economical use of this deposit type. This study provides a quantitative mineralogical investigation of the ore bearing rock suite at Khalzan Buregtei as an example of rare metal deposits. R1-R2 multication parameters are shown to be highly appropriate as quantitative mineralogical indicators based on readily available major element datasets to visualize and quantify alteration types of the ore bearing rock suite. The ore minerals were found to be associated with a cluster-forming assemblage of post-magmatic phases. Automated mineralogy was applied to quantify the textural properties of the ore mineral clusters. This finding permits efficient pre-concentration of rare metal ore at coarser particle size fraction, requiring less energy consuming comminution.
Seismicity‐Scanning Based on Navigated Automatic Phase‐Picking

Abstract We propose a new method, named Seismicity‐Scanning based on Navigated Automatic Phase‐picking (S‐SNAP), that is capable of delineating complex spatiotemporal distributions of seismicity. This novel algorithm takes a cocktail approach that combines source scanning, kurtosis‐based phasepicking, and the maximum intersection location technique into a single integrated workflow. This method is automated, detecting and locating earthquakes efficiently, comprehensively, and accurately. We apply S‐SNAP to a data set recorded by a dense local seismic array during a hydraulic fracturing operation to test this novel approach and to demonstrate its effectiveness in relation to existing methods. Overall, S‐SNAP found about 3.5 times as many high‐quality events as a template matching‐based catalogue. All events in the previous catalogue are identified with similar epicenters, depths, and magnitudes, while no false detections are found by visual inspection.
A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles

Since the 2016 U.S. presidential election, research on Hillary Clinton&amp;rsquo;s and Donald Trump&amp;rsquo;s linguistic styles has witnessed an exponential increase, with a lopsided focus on Trump in particular. This study compared Clinton&amp;rsquo;s and Trumps&amp;rsquo; campaign speeches during the general election using a corpus-based approach. Discourse analysis of the corpora was conducted using the textual analysis software AntConc 3.2.4. The results showed that Clinton used a more diverse vocabulary compared with Trump, and that both candidates stuck to their core campaign messages in their speeches. Three major differences between Clinton&amp;rsquo;s and Trump&amp;rsquo;s linguistic styles were identified: 1) Clinton was inclined towards rational discussions of public policy, while Trump was adept at appealing to voters&amp;rsquo; emotions; 2) Clinton was more positive and focused on her vision of the future, while Trump was more negative and fixated at depicting a dystopian reality; 3) Clinton aimed to find commonalities with the American people, while Trump aimed to highlight differences between himself and his opponents. By putting Clinton&amp;rsquo;s rhetoric on a par with Trump&amp;rsquo;s, this study highlighted their linguistic style differences as part of their grand campaign strategy, which could contribute to current understanding of the two candidates&amp;rsquo; rhetorical preferences, political beliefs and strategies in their 2016 campaigns.
Action recognition based on 2D skeletons extracted from RGB videos

In this paper a methodology to recognize actions based on RGB videos is proposed which takes advantages of the recent breakthrough made in deep learning. Following the development of Convolutional Neural Networks (CNNs), research was conducted on the transformation of skeletal motion data into 2D images. In this work, a solution is proposed requiring only the use of RGB videos instead of RGB-D videos. This work is based on multiple works studying the conversion of RGB-D data into 2D images. From a video stream (RGB images), a two-dimension skeleton of 18 joints for each detected body is extracted with a DNN-based human pose estimator called OpenPose. The skeleton data are encoded into Red, Green and Blue channels of images. Different ways of encoding motion data into images were studied. We successfully use state-of-the-art deep neural networks designed for image classification to recognize actions. Based on a study of the related works, we chose to use image classification models: SqueezeNet, AlexNet, DenseNet, ResNet, Inception, VGG and retrained them to perform action recognition. For all the test the NTU RGB+D database is used. The highest accuracy is obtained with ResNet: 83.317% cross-subject and 88.780% cross-view which outperforms most of state-of-the-art results.
A New Infrastructure Elasticity Control Algorithm for Containerized Cloud

In the last decade, containers have become a superior alternative to hypervisor-based virtualization. Containerization has revolutionized data centers from being an infrastructure-oriented to be application oriented. Modern cloud consumption patterns such as flash crowds require a certain amount of elasticity that is realized with controlling the amount of provisioned resources autonomously. Cloud elasticity is significant as it influences the performance of utilized resources, service level commitment, and power consumption. In this paper, an infrastructure elasticity control algorithm for a containerized cloud is proposed. The proposed algorithm augments the load balancing criterion with elasticity control. Several experiments with various metrics are carried out to examine the performance of the proposed algorithm. The results demonstrate the superiority of the proposed algorithm and the effects of elasticity across various measures.
Writer Identification Using Handwritten Cursive Texts and Single Character Words

One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.
Traffic Light Recognition Based on Binary Semantic Segmentation Network

A traffic light recognition system is a very important building block in an advanced driving assistance system and an autonomous vehicle system. In this paper, we propose a two-staged deep-learning-based traffic light recognition method that consists of a pixel-wise semantic segmentation technique and a novel fully convolutional network. For candidate detection, we employ a binary-semantic segmentation network that is suitable for detecting small objects such as traffic lights. Connected components labeling with an eight-connected neighborhood is applied to obtain bounding boxes of candidate regions, instead of the computationally demanding region proposal and regression processes of conventional methods. A fully convolutional network including a convolution layer with three filters of (1 × 1) at the beginning is designed and implemented for traffic light classification, as traffic lights have only a set number of colors. The simulation results show that the proposed traffic light recognition method outperforms the conventional two-staged object detection method in terms of recognition performance, and remarkably reduces the computational complexity and hardware requirements. This framework can be a useful network design guideline for the detection and recognition of small objects, including traffic lights.
Spliced Image Classification and Tampered Region Localization Using Local Directional Pattern

In this paper the authors have proposed a spliced image detection algorithm based on Local Directional Pattern (LDP).The output of many splicing detection techniques is either to classify spliced image from authentic images or to localize the spliced region.But the proposed algorithm has ability to classify and to localize the spliced region.First, the original image (RGB color space) is converted to Ycbcr color space.The histogram of LDP of chrominance component of suspect image is used in classification.Whereas for localization of spliced region, the chrominance component of input image is divide into overlapping blocks; then, the LDP of each block is calculated.The standard deviation of each block is used as clue to visualize the spliced region.The experimental results are calculated in terms of accuracy, specificity (true negative tare), sensitivity (true positive rate) and error rate and proves effectiveness of the proposed algorithm.The accuracy of the proposed algorithm is 98.55 %.The algorithm is also robust against post splicing image processing operation such as gaussian blur, additive white gaussian noise, JPEG compression and scaling however, previous techniques have not considered these experimental environment.
Semantic-aware neural style transfer

This study proposes a semantic-aware style transfer method for resolving semantic mismatch problems in existing algorithms. As the primary focus of this study, the consideration of semantic matching is expected to improve the quality of artistic style transfer. Here, each image is partitioned into several semantic regions for both a target photograph and a source painting. All partitioned regions of the target are then associated with one of the partitioned regions in the source according to their semantic interpretation. Given a pair of target and source regions, style is learned from the source region whereas content is learned from the target region. By integrating both the style and content components, we can successfully generate a stylized output. Unlike previous approaches, we obtain the best semantic match between regions using word embeddings. Thus, we guarantee that semantic matching is always established between the target and source. Moreover, it is unreliable to partition a painting using existing algorithms because of statistical gaps between the real photographs and paintings. To bridge such gaps, we apply a domain adaptation technique on the source painting to extract its semantic regions. We evaluated the effectiveness of the proposed algorithm based on a thorough experimental analysis and comparison. Through a user study, it is confirmed that semantic information considerably influences the quality assessment of style transfer.
Co-saliency Detection with Graph Matching

Recently, co-saliency detection, which aims to automatically discover common and salient objects appeared in several relevant images, has attracted increased interest in the computer vision community. In this article, we present a novel graph-matching based model for co-saliency detection in image pairs. A solution of graph matching is proposed to integrate the visual appearance, saliency coherence, and spatial structural continuity for detecting co-saliency collaboratively. Since the saliency and the visual similarity have been seamlessly integrated, such a joint inference schema is able to produce more accurate and reliable results. More concretely, the proposed model first computes the intra-saliency for each image by aggregating multiple saliency cues. The common and salient regions across multiple images are thus discovered via a graph matching procedure. Then, a graph reconstruction scheme is proposed to refine the intra-saliency iteratively. Compared to existing co-saliency detection methods that only utilize visual appearance cues, our proposed model can effectively exploit both visual appearance and structure information to better guide co-saliency detection. Extensive experiments on several challenging image pair databases demonstrate that our model outperforms state-of-the-art baselines significantly.
Gender Classification Based on The Non-Lexical Cues Of Emergency Calls With Recurrent Neural Networks (RNN)

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (humancomputer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls.
IMAGE QUALITY IMPROVEMENTS IN LOW-COST UNDERWATER PHOTOGRAMMETRY

Abstract. This study presents an evaluation of a cheap consumer-grade camera used for modelling a coral reef section. We evaluate the quality of a reconstructed coral reef using GoPro cameras and a high-end camera with data from an actual coral reef dataset. We also investigate components of the processing pipeline (like image quality) separate from the final results. Because our GoPro images suffer from severe chromatic aberration, we apply different image pre-processing steps to improve their quality and show its effects on the reconstructed object points. Bundle adjustment is carried out as free networks in all cases, with a follow-up rigid 3D Helmert transformation onto a geodetic control network, carried out to define the common datum and to remove the bias from the free network results.
Evaluating the Core and Full Protection Motivation Theory Nomologies for the Voluntary Adoption of Password Manager Applications

The protection motivation theory (PMT) is widely used in behavioral information security research, with multiple instantiations of the theoretical model applied in the literature.The purpose of this study is to perform a theoretical (conceptual) replication of both the core and full (PMT) nomologies in the context of voluntary password manager application use for individual home end-users.In our study, the full PMT model explained more variance than the core PMT model, but the relationships between multiple behavioral antecedents differed between the core and full PMT models, possibly due to differences in model complexity.Our findings suggest that researchers should justify the version of the PMT that they choose to use based on their research objectives with the understanding that the same variables may be significant in one version of the PMT but not significant in another version of the PMT.