Reviewerly/exHarmony huggingface dataset:
SF-GAN: Face De-Identification Method Without Losing Facial Attribute Information

This paper discusses the face de-identification without losing facial attribute information and provides a solution called SF-GAN (Secret Face Generative Adversarial Network). The proposed model aims to realize face de-identification effectively and generate visually reasonable images while retaining the facial attribute information of original images, such as facial expression, gender, hairstyle and wearing glasses or not, as much as possible. To optimize the face de-identification, we construct a variety of external mechanisms to balance the influence of multiple factors on the effect of face de-identification. The SF-GAN differs from the face replacement and face swapping because the later two fails actually in the privacy protection. As for the multi-attribute retention, we use the shallow face attribute information and deep face attribute information, and adopt different processing strategies for different face attribute information based on the uniqueness of each kind of face attribute information. Finally, we train and test the model on two high-definition datasets Celeba-HQ and FFHD. Compared with existing face de-identification methods, the proposed model proved to perform better in protecting the facial privacy.
Sign Language Recognition from Digital Videos Using Deep Learning Methods

In this paper, we investigate the state-of-the-art deep learning methods for sign language recognition. In order to achieve this goal, Capsule Network (CapsNet) is proposed in this paper, which shows positive result. We also propose a Selective Kernel Network (SKNet) with attention mechanism in order to extract spatial information. Sign language as an important means of communications, the problems of recognizing sign language from digital videos in real time have become the new challenge of this research field. The contributions of this paper are: (1) The CapsNet attains the accuracy of overall recognition up to 98.72% based on our own dataset. (2) SKNet with attention mechanism is able to achieve the best recognition accuracy 98.88%.
Reactive Navigation in Crowds for Non-Holonomic Robots With Convex Bounding Shape

This letter describes a novel method for nonholonomic robots of convex shape to avoid imminent collisions with moving obstacles.The method's purpose is to assist navigation in crowds by correcting steering from the robot's path planner or driver.We evaluate its performance using a custom simulator which replicates real crowd movements and corresponding metrics which quantify agents' efficiency and the robot's impact on the crowd and count collisions.We implement and evaluate the method on the standing wheelchair Qolo.In our experiments, it drives in autonomous mode using on-board sensing (LiDAR, RGB-D camera and a system to track pedestrians) and avoids collisions with up to five pedestrians and passes through a door.
Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models

Fitting ellipses from unrecognized data is a fundamental problem in computer vision and pattern recognition. Classic least-squares based methods are sensitive to outliers. To address this problem, in this paper, we present a novel and effective method called hierarchical Gaussian mixture models (HGMM) for ellipse fitting in noisy, outliers-contained, and occluded settings on the basis of Gaussian mixture models (GMM). This method is crafted into two layers to significantly improve its fitting accuracy and robustness for data containing outliers/noise and has been proven to effectively narrow down the iterative interval of the kernel bandwidth, thereby speeding up ellipse fitting. Extensive experiments are conducted on synthetic data including substantial outliers (up to 60%) and strong noise (up to 200%) as well as on real images including complex benchmark images with heavy occlusion and images from versatile applications. We compare our results with those of representative state-of-the-art methods and demonstrate that our proposed method has several salient advantages, such as its high robustness against outliers and noise, high fitting accuracy, and improved performance.
A novel multi-view clustering approach via proximity-based factorization targeting structural maintenance and sparsity challenges for text and image categorization

Multi-view data contains a set of features representing different perspectives associated with the same data and this phenomenon can be commonly observed in real-world applications. Multi-view clustering in terms of text and image data faces substantial challenges such as Structure-preserving and Sparsity. Existing methods do not conserve the structure of data space and the recent improvements have earmarked only the local layout. Preserving the local structure of data space is not sufficient to handle sparsity in these data. In this paper, we propose a novel clustering approach, called Proximity-based Multi-View Non-negative Matrix Factorization (PMVNMF), which utilizes both the local and global structure of data space conjointly to handle sparsity in real-world multimedia (text and image) data. For each view, the 1-step and 2-step transition probability matrices as the first-order and second-order proximity matrices are constructed to uncover their respective latent local and global geometric structures. Then, view-specific proximity matrices as an integration of the above two types of proximity matrices are constructed. Eventually, Non-negative Matrix Factorization (NMF) is explored via graph regularization and consensus regularization, to consider the obtained integrated graph structures as well as to disclose the indistinct common structure shared by all representations. The algorithm can capture elementary structure of data space and is robust to sparse data. We conduct experiments on six real-world datasets including two text and four image datasets; and compare the performance of the proposed algorithm with eight baseline approaches. Six evaluation metrics including accuracy, f-score, precision, recall, NMI, and entropy are employed to evaluate the performance of algorithm. The results show the outperformance of proposed algorithm over baselines.
An overlapping clustering approach for precision, diversity and novelty-aware recommendations

Recommender systems aim to provide users with recommendations of quality. New evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of applications, including the ecommerce, due to their ability to improve online revenues. High recommendation diversity allows a higher chance to satisfy the users' needs. However, in a large market of users and products, the scalability of the system is questionable because of the required computing resources. We present a scalable evolutionary clustering algorithm that allows to target two objectives. The proposed solution balances between the recommendation accuracy and coverage by making an overlapped clustering. In our approach, we use a Genetic Algorithm to assign each user to a main cluster from which he gets his recommendations and to secondary clusters as a candidate neighbor. The performance comparison of our algorithm against classic well-known approaches, such as k-NN based Collaborative Filtering, showed a significant improvement.
A novel focal-loss and class-weight-aware convolutional neural network for the classification of in-text citations

We argue that citations, as they have different reasons and functions, should not all be treated in the same way. Using the large, annotated dataset of about 10K citation contexts annotated by human experts, extracted from the Association for Computational Linguistics repository, we present a deep learning–based citation context classification architecture. Unlike all existing state-of-the-art feature-based citation classification models, our proposed convolutional neural network (CNN) with fastText-based pre-trained embedding vectors uses only the citation context as its input to outperform them in both binary- (important and non-important) and multi-class (Use, Extends, CompareOrContrast, Motivation, Background, Other) citation classification tasks. Furthermore, we propose using focal-loss and class-weight functions in the CNN model to overcome the inherited class imbalance issues in citation classification datasets. We show that using the focal-loss function with CNN adds a factor of [Formula: see text] to the cross-entropy function. Our model improves on the baseline results by achieving an encouraging 90.6 F1 score with 90.7% accuracy and a 72.3 F1 score with a 72.1% accuracy score, respectively, for binary- and multi-class citation classification tasks.
Time to Change the CVSS?

According to its creators, the Common Vulnerability Scoring System (CVSS) "provides a way to capture the principal characteristics of a vulnerability ... reflecting its severity ... to help organizations properly assess and prioritize their vulnerability management processes." <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> However, the CVSS scoring algorithm is not justified, either formally or empirically. According to the specification, using CVSS directly as a risk score is a mistake. Yet some compliance bodies recommend such misuse explicitly, for example, the U.S. government and the global payment card industry. The CVSS is suggested for misuse by federal civilian departments and agencies via National Institute of Standards and Technology (NIST) guidance (for example, Special Publications 800-115, page 7-4 and 800-40r3, page 4). PCI Data Security Standard requires such misuse in, for example, the regulation on Approved Scanning Vendors (v3). But even those organizations that use CVSS to "properly assess and prioritize their vulnerability management processes" are using unreliable output. We argue that the current CVSS version is still inadequate for both its intended purpose and as a proxy for risk to a vulnerable system. This article will summarize the ways the CVSS formula is unjustified and the ways it fails to properly inform vulnerability management before suggesting a way forward to reduce these problems.
Region-based dropout with attention prior for weakly supervised object localization

Weakly supervised object localization (WSOL) methods utilize the internal feature responses of a classifier trained only on image-level labels. Classifiers tend to focus on the most discriminative part of the target object, instead of considering its full extent. Adversarial erasing (AE) techniques have been proposed to ameliorate this problem. These techniques erase the most discriminative part during training, thereby encouraging the classifiers to learn the less discriminative parts of the object. Despite the success of AE-based methods, we have observed that the hyperparameters fail to generalize across model architectures and datasets. Therefore, new sets of hyperparameters must be determined for each architecture and dataset. The selection of hyperparameters frequently requires strong supervision (e.g., pixel-level annotations or human inspection). Because WSOL is premised on the assumption that such strong supervision is absent, the applicability of AE-based methods is limited. In this paper, we propose the region-based dropout with attention prior (RDAP) algorithm, which features hyperparameter transferability. We combined AE with regional dropout algorithms that provide greater stability against the selection of hyperparameters. We empirically confirmed that the RDAP method achieved state-of-the-art localization accuracy on four architectures, namely VGG-GAP, InceptionV3, ResNet-50 SE, and PreResNet-18, and two datasets, namely CUB-200-2011 and ImageNet-1k, with a single set of hyperparameters.
Multi-Gate Attention Network for Image Captioning

Self-attention mechanism, which has been successfully applied to current encoder-decoder framework of image captioning, is used to enhance the feature representation in the image encoder and capture the most relevant information for the language decoder.However, most existing methods will assign attention weights to all candidate vectors, which implicitly hypothesizes that all vectors are relevant.Moreover, current self-attention mechanisms ignore the intra-object attention distribution, and only consider the inter-object relationships.In this paper, we propose a Multi-Gate Attention (MGA) block, which expands the traditional self-attention by equipping with additional Attention Weight Gate (AWG) module and Self-Gated (SG) module.The former constrains the attention weights to be assigned to the most contributive objects.The latter is adopted to consider the intra-object attention distribution and eliminate the irrelevant information in object feature vector.Furthermore, most current image captioning methods apply the original transformer designed for natural language processing task, to refine image features directly.Therefore, we propose a pre-layernorm transformer to simplify the transformer architecture and make it more efficient for image feature enhancement.By integrating MGA block with pre-layernorm transformer architecture into the image encoder and AWG module into the language decoder, we present a novel Multi-Gate Attention Network (MGAN).The experiments on MS COCO dataset indicate that the MGAN outperforms most of the state-of-the-art, and further experiments on other methods combined with MGA blocks demonstrate the generalizability of our proposal.
Formation of the scientist image in modern conditions of digital society transformation

Abstract The publication considers factors that are influencing formation of scientists image especially: availability to inform scientist or scientific organization about the registration, scientometric indices, use of global identifiers to improve accuracy in calculating indicators, publication of papers in journals with high impact factor, publications in resources that provide visibility in global information space, involvement in global communications system, level of competence. Specialists in various fields of science developed a number of practical recommendations for various techniques and tools that can be used and are helpful to create and to make, both personal image and image of the organization, institution, firm, etc. Also, main directions of using digital technologies to create the image of scientists are identified and substantiated. Scientists formulated recommendations to make their own image using digital systems based on analysis of scientific literature and personal experience: author’s digital identifier ORCID, profiles in international scientometric systems, saved publications in electronic libraries, profiles in social and scientific electronic networks, etc.
Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

As quantum computers are becoming real, they have the inherent potential to significantly impact many application domains. In this paper we outline the fundamentals about programming quantum computers and show that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from quantum humanities is discussed, hinting which applications in this domain can already be used in the field of (quantum) machine learning. Finally, a collaborative platform for solving problems with quantum computers – that is currently under construction – is presented.
Self-paced data augmentation for training neural networks

Data augmentation is widely used for machine learning; however, an effective method to apply data augmentation has not been established even though it includes several factors that should be tuned carefully. One such factor is sample suitability, which involves selecting samples that are suitable for data augmentation. A typical method that applies data augmentation to all training samples disregards sample suitability, which may reduce classifier performance. To address this problem, we propose the self-paced augmentation (SPA) to automatically and dynamically select suitable samples for data augmentation when training a neural network. The proposed method mitigates the deterioration of generalization performance caused by ineffective data augmentation. We discuss two reasons the proposed SPA works relative to curriculum learning and desirable changes to loss function instability. Experimental results demonstrate that the proposed SPA can improve the generalization performance, particularly when the number of training samples is small. In addition, the proposed SPA outperforms the state-of-the-art RandAugment method.
Human Age And Gender Classification using Convolutional Neural Network

Pattern recognition and automatic classification are very active research areas, their main objectives are to develop intelligent systems able to achieve efficiently learning and recognizing objects. An essential section of these applications is attached to biometrics, which is used for security purposes in general. The facial modality as a fundamental biometric technology has become increasingly important in the field of research. The goal of this work is to develop a gender prediction and age estimation system based on convolutional neural networks for a face image or a real-time video. In this paper, three CNN network models were created with different architecture (the number of filters, the number of convolution layers ...) validated on IMDB and WIKI dataset, the results obtained showed that CNN networks greatly improve the performance of the system as well as the accuracy of the recognition.
RiftNet: Radio Frequency Classification for Large Populations

Radio transmitters can be identified by their unique radio frequency (RF) fingerprints. Hardware differences and manufacturing variations between different RF devices impart the unique RF fingerprints to their transmitted waveforms, which can be exploited to accurately distinguish them. Most previous work has focused on using expert-defined physics-based features to distinguish between 100 or less individual devices, with more recent trends turning to machine learning, and deep learning in particular. In this paper, we present a deep learning approach that scales to 10,000 devices, across different signal types, with upwards of 95% accuracy, which we call RiftNet (Resampling in Frequency and Time Network). RiftNet is particularly suited to pulling out generic signal features that may be relevant to other RF machine learning tasks, beyond just fingerprinting,
A Traceable Online Will System Based on Blockchain and Smart Contract Technology

In recent years, with the rapid levels of economic development, there have been more and more problems in property inheritance and distribution. In today’s society, people still have many taboos when writing a will. Writing a will not only involves various laws and regulations but also costs a lot of money and time, which can be daunting. However, with the development of the Internet, blockchain technology has gradually been applied to many applications. Blockchain technology uses consensus algorithms to ensure consistency and records transaction information in blocks to ensure the effectiveness of transactions. In this paper, we use the cryptography mechanism to propose an online will system based on blockchain and smart contract technology. The architecture considers effectiveness and cost reduction. By combining this with blockchain technology, will assets are saved in blocks, which provides comprehensive will security and non-tamperable security protection. In addition, combined with a smart contract, it realizes the method of automatic property distribution. At the same time, this mechanism also proposes an arbitration solution when there are disputes over wills, and ensures the integrity of data, public verifiability, unforgeability, nonrepudiation, irreversibility of information, and the ability to resist counterfeiting attacks.
Perceptual Quality Evaluation of Hazy Natural Images

Haze is an intrusion element that disrupts color fidelity and contrast of outdoor natural images, affecting their perceptual quality. The differential characteristics of hazy images compared to other natural images restrict the generalization of existing image quality assessment (IQA) algorithms. At the same time, efficient IQA algorithms for predicting the perceptual quality of naturally hazed images have not been proposed in the literature due to lack of a relevant dataset. To address this, we build the IIT-JMU Hazy Image Dataset comprising of 1000 high-definition hazy natural images consisting of diverse categories such as landscape, forests, roads, seascapes, and cityscapes, along with their subjective quality ratings. We present an analysis of existing natural-scene-statistics-based IQA algorithms on hazy natural images. In this article, we propose a convolutional-neural-network-based quality assessment algorithm for hazy natural images along with an IQA metric called deep learning-based haze perceptual quality evaluator (DLHPQE). The proposed DLHPQE efficiently predicts the perceptual quality of hazy natural images without a reference. Our results demonstrate that the DLHPQE outperforms existing state-of-the-art no-reference IQAs in terms of several performance parameters such as Pearson linear correlation coefficient, Spearman rank-order correlation coefficient, Kendall's rank-order correlation coefficient, and root-mean-square error.
A Blockchain-Based Energy Trading Scheme for Electric Vehicles

An energy-trading system is essential for the successful integration of Electric vehicles (EVs) into the smart grid. Existing systems merely focus on making optimal decisions while others depend on anonymization to achieve EVs drivers' privacy which is not enough because they can be identified from visited locations. In this paper, leveraging blockchain technology, we propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. To preserve privacy, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.