Reviewerly/exHarmony huggingface dataset:
AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification

Extreme learning machines (ELMs) has been theoretically and experimentally proved to achieve promising performance at a fast learning speed for supervised classification tasks. However, it does not perform well on imbalanced binary classification tasks and tends to get biased toward the majority class. Besides, since a large amount of training data with labels are not always available in the real world, there is an urgent demand to develop an efficient semi-supervised version of ELM for imbalanced binary classification tasks. In this article, owing to the distinct insensitivity of area under the ROC curve (AUC) to both class skews and changes of class distributions, we focus the study on integrating AUC maximization into the ELM framework to tackle with imbalanced binary classification tasks well. By demystifying the AUC metric with the ELM framework, we develop a new AUC-based ELM called AUC-ELM for imbalanced binary classification, which essentially is revealed to be equivalent to an ELM on another transformed data space. Accordingly, its semi-supervised version called SAUC-ELM is also developed. Both AUC-ELM and SAUC-ELM have the distinctive merits: 1) they share the advantage of ELM in both generalization capability and training efficiency, and further uniquely tailored for imbalanced binary classification tasks and 2) in contrast to the existing imbalanced variants of ELM, such as class-specific cost regulation ELM and semi-supervised ELM, they have fewer parameters to tune, thereby reducing the computational cost for model selection. Experiments on a heap of datasets show that both AUC-ELM and SAUC-ELM outperform the other comparative methods in terms of both classification performance and training speed.
A new audio augmented reality interaction and adaptation model for museum visits

Augmented Reality is a recent impressive technology applied in many areas. However, in tourism and especially in museums, where visual contact with exhibitions is fundamental, augmenting reality with graphical objects, or using graphical applications, can distract visitors’ attention from the main subject. Also, having a classical experience with no personalization or adaptation can make a negative sensation for the whole experience. In this paper, we introduce an adaptive gesture-based audio augmented reality system, in which content personalization is a key design for the interaction experience. The evaluation has been conducted in a real environment which was the Musée des arts et Métiers in Paris. This evaluation has confirmed the ease of use, as well as the usefulness and enjoyment impact of our model.
Refine reconstruction and verification of meso-scale modeling of three-dimensional five-directional braided composites from X-ray computed tomography data

This paper proposes a statistical approach to generate the refine meso-scale representative volume element of three-dimensional (3D) five-directional braided composites considering the fabric compaction and the axial yarn torsion. The method firstly extracts the regions of interest to obtain the reference model (R-model) from the X-ray computed tomography (Micro-CT) data. Then, the geometric parameters of the collected yarns of R-model are statistically analyzed based on the scanning tomograms. Consequently, the meso-scale statistical model (S-model) are reconstructed after being fitted with the appropriate function. Moreover, for the validation of S-model, the traditional ideal model (I-model) and the experimental tests are considered. The results show that the proposed S-model is capable of accurately presenting the effects of the fabric compaction and yarn torsion on the mechanical behaviors of 3D braided composites, validated by the elastic constants and the damaged modes.
A Survey of Missing Data Imputation Using Generative Adversarial Networks

Recently, many deep learning models for missing data imputation have been studied. One of the most popular models is Generative Adversarial Networks (GANs), which generate plausible fake data through adversarial training. In this paper, we take a look at the architecture, objective of a generator and a discriminator, training method and loss function. After that, we can see what improvements have been made to each model. Moreover, we can easily compare several GAN-based models for missing data imputation.
Application of IoT in Smart Grid: Challenges and Solutions

The prevailing development in energy grids and emersion of new energy players along with the advent of the Internet of Things (IoT) lead available energy systems (e.g., smart electricity grid) toward “energy internet” concept. The expansion of an electrical power grid, because of its unique features, has caused this system to be converted to the central core of the energy ecosystem over the past decades. This process has also been preserved in the IoT in such a way that the presence of this technology increases the ability of evolutionary development in the power industry. IoT applications in smart grid (IoT-SG) have lots of advantages such as expenditure reduction, save of time, and smartness of grid equipment. Nevertheless, the disadvantages pertaining to IoT-SG should not be neglected. For instance, one of the most significant drawbacks and ahead challenges of IoT-SG is security and big data issues. In IoT-SG, each connected device can be a probable port to the IoT's infrastructure with personal data. Concerns about security and data privacy are indispensable, but with the entrance of complexity, safety weaknesses, and probable vulnerabilities, in cases such as interoperability and autonomous decision-making, possible risks of IoT have reached new levels. This paper mostly focuses on the IoT-SG investigations, advantages and ahead challenges, plus effective solutions to these challenges are being discussed. Furthermore, according to some key challenges such as safety and big data, general conclusions for confronting and dealing with these challenges have been made.
What is this URL's Destination? Empirical Evaluation of Users' URL Reading

Common anti-phishing advice tells users to mouse over links, look at the URL, and compare to the expected destination, implicitly assuming that they are able to read the URL. To test this assumption, we conducted a survey with 1929 participants recruited from the Amazon Mechanical Turk and Prolific Academic platforms. Participants were shown 23 URLs with various URL structures. For each URL, participants were asked via a multiple choice question where the URL would lead and how safe they feel clicking on it would be. Using latent class analysis, participants were stratified by self-reported technology use. Participants were strongly biased towards answering that the URL would lead to the website of the organization whose name appeared in the URL, regardless of its position in the URL structure. The group with the highest technology use was only minorly better at URL reading.
Security and Privacy of QR Code Applications: A Comprehensive Study, General Guidelines and Solutions

The widespread use of smartphones is boosting the market take-up of dedicated applications and among them, barcode scanning applications. Several barcodes scanners are available but show security and privacy weaknesses. In this paper, we provide a comprehensive security and privacy analysis of 100 barcode scanner applications. According to our analysis, there are some apps that provide security services including checking URLs and adopting cryptographic solutions, and other apps that guarantee user privacy by supporting least privilege permission lists. However, there are also apps that deceive the users by providing security and privacy protections that are weaker than what is claimed. We analyzed 100 barcode scanner applications and we categorized them based on the real security features they provide, or on their popularity. From the analysis, we extracted a set of recommendations that developers should follow in order to build usable, secure and privacy-friendly barcode scanning applications. Based on them, we also implemented BarSec Droid, a proof of concept Android application for barcode scanning. We then conducted a user experience test on our app and we compared it with DroidLa, the most popular/secure QR code reader app. The results show that our app has nice features, such as ease of use, provides security trust, is effective and efficient.
Domain-Specialized Cache Management for Graph Analytics

Graph analytics power a range of applications in areas as diverse as finance, networking and business logistics. A common property of graphs used in the domain of graph analytics is a power-law distribution of vertex connectivity, wherein a small number of vertices are responsible for a high fraction of all connections in the graph. These richly-connected, hot, vertices inherently exhibit high reuse. However, this work finds that state-of-the-art hardware cache management schemes struggle in capitalizing on their reuse due to highly irregular access patterns of graph analytics. In response, we propose GRASP, domain-specialized cache management at the last-level cache for graph analytics. GRASP augments existing cache policies to maximize reuse of hot vertices by protecting them against cache thrashing, while maintaining sufficient flexibility to capture the reuse of other vertices as needed. GRASP keeps hardware cost negligible by leveraging lightweight software support to pinpoint hot vertices, thus eliding the need for storage-intensive prediction mechanisms employed by state-of-the-art cache management schemes. On a set of diverse graph-analytic applications with large high-skew graph datasets, GRASP outperforms prior domain-agnostic schemes on all datapoints, yielding an average speed-up of 4.2% (max 9.4%) over the best-performing prior scheme. GRASP remains robust on low-/no-skew datasets, whereas prior schemes consistently cause a slowdown.
Robust Few-Shot Learning for User-Provided Data

Few-shot learning (FSL) focuses on distilling transferrable knowledge from existing experience to cope with novel concepts for which the labeled data are scarce. A typical assumption in FSL is that the training examples of novel classes are all clean with no outlier interference. In many realistic applications where examples are provided by users, however, data are potentially noisy or unreadable. In this context, we introduce a novel research topic, robust FSL (RFSL), where we aim to address two types of outliers within user-provided data: the representation outlier (RO) and the label outlier (LO). Moreover, we introduce a metric for estimating robustness and use it to investigate the performance of several advanced methods to FSL when faced with user-provided outliers. In addition, we propose robust attentive profile networks (RapNets) to achieve outlier suppression. The results of a comprehensive evaluation of benchmark data sets demonstrate the shortcomings of current FSL methods and the superiority of the proposed RapNets when dealing with RFSL problems, establishing a benchmark for follow-up studies.
Sparse Regularization-Based Fuzzy <i>C</i>-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.
Patch Based Video Summarization With Block Sparse Representation

In recent years, sparse representation has been successfully utilized for video summarization (VS). However, most of the sparse representation based VS methods characterize each video frame with global features. As a result, some important local details could be neglected by global features, which may compromise the performance of summarization. In this paper, we propose to partition each video frame into a number of patches and characterize each patch with global features. Instead of concatenating the features of each patch and utilizing conventional sparse representation, we formulate the VS problem with such video frame representation as block sparse representation by considering each video frame as a block containing a number of patches. By taking the reconstruction constraint into account, we devise a simultaneous version of block-based OMP (Orthogonal Matching Pursuit) algorithm, namely SBOMP, to solve the proposed model. The proposed model is further extended to a neighborhood based model which considers temporally adjacent frames as a super block. This is one of the first sparse representation based VS methods taking both spatial and temporal contexts into account with blocks. Experimental results on two widely used VS datasets have demonstrated that our proposed methods present clear superiority over existing sparse representation based VS methods and are highly comparable to some deep learning ones requiring supervision information for extra model training.
Construction of <i>Z</i>-Complementary Code Sets With Non-Power-of-Two Lengths Based on Generalized Boolean Functions

<i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> -complementary code set (ZCCS) plays a major role in modern communication systems. It has applications in multi-carrier code-division multiple access (MC-CDMA) scheme, channel estimation in multiple-input and multiple-output (MIMO) systems, etc. In this letter, we propose a construction of q-ary ZCCS of a length of the form 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + 2 using generalized Boolean functions (GBFs). The constructed ZCCS is optimal with respect to the theoretical upper bound. Based on the authors' knowledge, all the existing constructions of ZCCSs based on GBFs are limited to the lengths of power-of-two only.
Tooth Detection and Segmentation with Mask R-CNN

In this paper, we expound the application of Mask RCNN on automatic tooth detection and segmentation. Mask R-CNN is a recently proposed surprising algorithm for object detection and semantic segmentation. This paper aims at detecting and segmenting tooth only. We show that Mask RCNN also has a good segmentation effect in complex and crowded teeth structures. We use the pixel accuracy (PA) to evaluate the results.
Corpus based Machine Translation System with Deep Neural Network for Sanskrit to Hindi Translation

Sanskrit language is the mother of almost all Indian languages. The main requirement in Sanskrit domain is to translate the life-transforming stories (epics), Vedas etc. to make them available in other languages, for public at large. In the field of machine translation system there is need to develop a machine translation system which translate Sanskrit language to Hindi. So, the main focus of this work is to propose a new corpus-based translation system for Sanskrit to Hindi translation where Bhagvad Gita – the song of the lord is used as an input data. In this work, Deep neural network is used for training where input data is passed to neural network after data analysis and processing which then performs auto-tuning that help to make this model better. Target text is prepared using this proposed model and achieves better BLEU Score and Word Error Rate.
Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features

In spite of the recent advancements in the field of deep learning based techniques for facial expression recognition, the efficiency of the state-of-the-art recognition methods in the wild scenarios, remains a challenge. The main reason behind the less efforts made for handling wild scenarios is two-folds: very less and varying levels of cues available to identify the distinguishable patterns of features (spatial and temporal) and non-availability of a big dataset to train a deep learning model. Recently, a huge dataset called AffectNet is introduced in the literature providing enough base to apply a deep learning model to train. This paper proposes an efficient combination of hand crafted and deep learning features for facial expression recognition in the wild. We use facial landmark points as hand-crafted features and XceptionNet for the deep learned features. We experiment with XceptionNet and Densenet propose the use of XceptionNet as it performs better compared to DenseNet, when applied on wild scenarios. The proposed fusion of the hand-crafted and XceptionNet features outperforms the state-of-the-art methods for facial expression recognition in the wild.
MRI denoising using progressively distribution-based neural network

Magnetic Resonance (MR) images often suffer from noise pollution during image acquisition and transmission, which limits the accuracy of quantitative measurements from the data. Noise in magnitude MR images is usually governed by Rician distribution, due to the existence of uncorrelated Gaussian noise with zero-mean and equal variance in both the real and imaginary parts of the complex K-space data. Different from the existing MRI denoising methods that utilizing the spatial neighbor information around the pixels or patches, this work turns to capture the pixel-level distribution information by means of supervised network learning. A progressive network learning strategy is proposed via fitting the distribution of pixel-level and feature-level intensities. The proposed network consists of two residual blocks, one is used for fitting pixel domain without batch normalization layer and another one is applied for matching feature domain with batch normalization layer. Experimental results under synthetic, complex-valued and clinical MR brain images demonstrate great potential of the proposed network with substantially improved quantitative measures and visual inspections.
NeurReg: Neural Registration and Its Application to Image Segmentation

Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignment, and motion analysis. Popular registration tools such as ANTs and NiftyReg optimize an objective function for each pair of images from scratch which is time-consuming for large images with complicated deformation. Facilitated by the rapid progress of deep learning, learning-based approaches such as VoxelMorph have been emerging for image registration. These approaches can achieve competitive performance in a fraction of a second on advanced GPUs. In this work, we construct a neural registration framework, called NeurReg, with a hybrid loss of displacement fields and data similarity, which substantially improves the current state-of-the-art of registrations. Within the framework, we simulate various transformations by a registration simulator which generates fixed image and displacement field ground truth for training. Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature. Extensive experimental results validate the effectiveness of the proposed NeurReg framework based on various metrics: the endpoint error (EPE) of the predicted displacement field, mean square error (MSE), normalized local cross-correlation (NLCC), mutual information (MI), Dice coefficient, uncertainty estimation, and the interpretability of the segmentation. The proposed NeurReg improves registration accuracy with fast inference speed, which can greatly accelerate related medical image analysis tasks.
Vulnerability of classifiers to evolutionary generated adversarial examples

This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.
Detecting Stealthy Domain Generation Algorithms Using Heterogeneous Deep Neural Network Framework

Distinguishing malicious domain names generated by various domain generation algorithms (DGA) is critical for defending a network against sophisticated network attacks.In recent years, stealthy domain generation algorithms (SDGA) have been proposed and revealed significantly stronger stealthiness comparing to the traditional character-based DGA.Existing state-of-the-art detection schemes are not effective enough for detecting SDGA.In this paper, we exploit the character-level characteristics of the SDGA domain names and propose a heterogeneous deep neural network framework (HDNN) for detecting SDGA.HDNN employs a proposed improved parallel CNN (IPCNN) architecture with multisizes of convolution kernel for extracting multi-scale local features from a domain name.The framework also contains a proposed self-attention based bidirectional long short term memory (SA-Bi-LSTM) architecture which can extract the bidirectional global features with attention mechanism from a domain name.Besides that, the focal loss function is introduced to mitigate the imbalance of the sample quantity in the training phase.The benchmark experiments are carried out based on the database composed of the collected benign domain names, real-world DGA and SDGA ones.Compared to the 6 influential deep-learning-based DGA detection schemes, the proposed scheme has achieved state-of-the-art detection results on SDGAs, and also achieved state-of-the-art results on binary and multiclass classification for traditional DGAs.