Reviewerly/exHarmony huggingface dataset:
Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition

Although deep learning-based face recognition techniques have achieved amazing performance in recent years, low-resolution (LR) face recognition remains challenging. In this letter, we address this problem by proposing an identity-aware face super-resolution network to recover identity information of LR faces. To learn identity-aware features effectively, the identity features are explicitly disentangled to two orthogonal components: the magnitude and angle of features that project identity features to a hypersphere space. We show that the magnitude of features is related to the quality of a face. The proposed approach shows its superiority on recovering identity-related textures which are beneficial to recover identity information for recognition. Extensive experiments demonstrate the effectiveness of the proposed algorithm in LR face recognition.
A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices

How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model's training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> ultilayered-and-randomized <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> actor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a ‘one-iteration’ training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.
Color Image Encryption Algorithm Based on Hyper-Chaos and DNA Computing

In this study, a novel technique using a hyper chaotic dynamical system and DNA computing has been designed with high plaintext sensitivity. In order to reduce cost, a selection procedure using tent map has been employed for generating different key streams from the same chaotic data obtained from the iterations of chaotic dynamical system. After separating the three channels from the input color image, they are both confused and diffused. First of all, these channels are diffused on a decimal level. Then they are permuted. Further, DNA encoding is performed upon these channels. Moreover, DNA level diffusion is performed to further increase the degree of randomness in the image. Lastly, the DNA encoded image is converted into decimal to get the final cipher image. Both the experimental results and security analysis strongly demonstrate the robustness of the proposed scheme. A comparison of the proposed scheme has also been made with other recently developed schemes to show that this scheme outperforms the others in terms of computational cost, time and memory efficiency. Additionally, with the large key space, the proposed scheme can resist any brute force, plaintext and statistical attacks, therefore it is a good fit for the real world applications of the image security.
Privileges and Immunity Certification During the COVID-19 Pandemic

This Viewpoint explains the ethics of implementing licensing or certification of coronavirus immunity as basis for permitting public activities, anticipates challenges to effectively implementing such a system, and proposes potential policy solutions.
Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics

The integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.
Nature-inspired computation and swarm intelligence: a state-of-the-art overview

Abstract Nature-inspired algorithms can be flexible and efficient for solving optimization problems. There are a wide spectrum of nature-inspired algorithms in the literature, and most of such algorithms are based on swarm intelligence. This chapter provides an overview of some widely used algorithms for optimization. Their main characteristics will be discussed in comparison with traditional algorithms such as gradient-based algorithms. Some open problems concerning swarm intelligence and nature-inspired computation will be highlighted.
Distributed multi-target search and tracking using the PHD filter

This paper proposes a distributed estimation and control algorithm that enables a team of mobile robots to search for and track an unknown number of targets. These targets may be stationary or moving, and the number of targets may vary over time as targets enter and leave the area of interest. The robots are equipped with sensors that have a finite field of view and may experience false negative and false positive detections. The robots use a novel, distributed formulation of the Probability Hypothesis Density (PHD) filter, which accounts for the limitations of the sensors, to estimate the number of targets and the positions of the targets. The robots then use Lloyd's algorithm, a distributed control algorithm that has been shown to be effective for coverage and search tasks, to drive their motion within the environment. We utilize the output of the PHD filter as the importance weighting function within Lloyd's algorithm. This causes the robots to be drawn towards areas that are likely to contain targets. We demonstrate the efficacy of our proposed algorithm, including comparisons to a coverage-based controller with a uniform importance weighting function, through an extensive series of simulated experiments. These experiments show teams of 10–100 robots successfully tracking 10–50 targets in both 2D and 3D environments.
Comparative Analysis of Gaussian Filter, Median Filter and Denoise Autoenocoder

Images can be enhanced and denoised with the help of filters. In this paper, we use a Gaussian filter, a Median Filter and a Denoising Auto encoder for noise removal. Gaussian filter is a linear type of filter which is based on Gaussian function. But the median filter is a non-linear type of filter. It preserves edge while removing noise. Deep Convolutional neural network (CNN) is able to handle Gaussian denoising at a certain noise level. We compare these three types of noise removers with the help of four types of evaluation techniques. We use time performance, Peak signal-to-noise ratio (PSNR), Structure Similarity (SSIM) and Normalization mean square error (NMSE) evaluation techniques for finding the best filter for removing noise from the image on different situations. We found that sometimes a Gaussian filter is better and sometimes the median filter is better depending on the iteration of the filter. Sometimes a denoise autoencoder is also better but it takes more time with respect to a Gaussian filter and a median filter. When we consider only the time parameter, then the Median filter gives better results in less time in comparison to a Gaussian filter and a denoise autoencoder filter.
Exploring deep learning approaches for Urdu text classification in product manufacturing

From last decade, machine learning (ML) techniques have been used for Urdu text processing. Due to lack of language resources, potential of deep learning (DL) models have not been exploited yet for Urdu text document classification. A text document has more noise, redundant information, and large vocabulary than short text like tweets. This study is the systematic comparison of four well-known DL models. We also compare DL models with four ML models. We also explore the various text preprocessing techniques. Experimental results show that CNN outperforms the others. Further, single-layer architecture of LSTM and BiLSTM performs better than multiple-layers architecture.
An online auction mechanism for time-varying multidimensional resource allocation in clouds

Multidimensional resource allocation is a hot topic in cloud computing, but current cloud platforms support only fixed resource allocation, that is, the user resource requirements are consistent throughout the usage period, which may cause a waste of resources and reduce the revenue of resource providers. Therefore, time-varying multidimensional resource allocation and the corresponding pricing mechanism represent a new challenge in cloud computing. We address the problem of online time-varying multidimensional resource allocation and pricing in clouds. Specifically, (1) we propose a novel integer programming model for the time-varying multidimensional resource allocation problem and (2) we design a truthful online auction mechanism for resource allocation in a competitive environment. For the resource allocation algorithm, we propose a waiting period strategy and dominant-resource-based strategy to improve the social welfare and resource utilization. Simultaneously, a payment pricing algorithm based on critical value theory is proposed. Finally, we prove that the mechanism is truthful and individual rationality. Compared with existing research, our approach is characterized by high social welfare, high resource utilization and short execution time.
Improving physical activity recognition using a new deep learning architecture and post-processing techniques

This paper proposes a Human Activity Recognition system composed of three modules. The first one segments the acceleration signals into overlapped windows and extracts information from each window in the frequency domain. The second module detects the performed activity at each window using a deep learning structure based on Convolutional Neural Networks (CNNs). The first part of this structure has several layers associated to each sensor independently and the second part combines the outputs from all sensors in order to classify the physical activity. The third module integrates the window-level decision in longer periods of time, obtaining a significant performance improvement (from 89.83% to 96.62%). These are the best classification results on the PAMAP2 dataset with a Leave-One-Subject-Out (LOSO) evaluation.
RGBD-Dog: Predicting Canine Pose from RGBD Sensors

The automatic extraction of animal 3D pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images, based on 2D labelling of joint positions. However, due to the difficult nature of obtaining training data, no ground truth dataset of 3D animal motion is available to quantitatively evaluate these approaches. In addition, a lack of 3D animal pose data also makes it difficult to train 3D pose-prediction methods in a similar manner to the popular field of body-pose prediction. In our work, we focus on the problem of 3D canine pose estimation from RGBD images, recording a diverse range of dog breeds with several Microsoft Kinect v2s, simultaneously obtaining the 3D ground truth skeleton via a motion capture system. We generate a dataset of synthetic RGBD images from this data. A stacked hourglass network is trained to predict 3D joint locations, which is then constrained using prior models of shape and pose. We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images. Finally, despite our training set consisting only of dog data, visual inspection implies that our network can produce good predictions for images of other quadrupeds - e.g. horses or cats - when their pose is similar to that contained in our training set.
General multi-view semi-supervised least squares support vector machines with multi-manifold regularization

Multi-view semi-supervised learning achieves great success in recent years which leverages information among labeled and unlabeled multi-view data to improve the generalization performance. So far two classical two-view semi-supervised learning methods are multi-view Laplacian support vector machines (MvLapSVM) and multi-view Laplacian twin support vector machines (MvLapTSVM). But they can only deal with two-view classification problems and cannot deal with general multi-view classification problems. They both solve the quadratic programming problems (QPPs) so that their time complexity is quite high. In this paper, we formulate general multi-view Laplacian least squares support vector machines (GMvLapSVM) and general multi-view Laplacian least squares twin support vector machines (GMvLapTSVM) which solve linear equations as compared to QPPs in MvLapSVM and MvLapTSVM. They can handle the general multi-view classification problems by combining multiple different views in a non-pairwise way. The disagreement among different views is considered as a regularization term in the objective function to explore the consensus information. Multi-manifold regularization is adopted for multi-view semi-supervised learning. Combination weights for all view in the norm regularization terms are adopted to exploit complementarity information among distinct views. Finally, an efficient alternating algorithm is proposed for optimization. Experiments are performed on various real world datasets, which give state-of-the-art generalization performance.
Neuroevolution-based autonomous robot navigation: A comparative study

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.
LH-ABSC: A Lightweight Hybrid Attribute-Based Signcryption Scheme for Cloud-Fog-Assisted IoT

One of the best ways to deal with the massive data generated by the Internet of Things (IoT) is storing them in the cloud. However, outsourced storage raises some security and privacy issues, such as data leaking and illegal access. The attribute-based signcryption (ABSC) is one of the most promising approaches which can ensure the confidentiality and authenticity of data simultaneously. Nonetheless, it not only inherits the fine-grained access control but also the heavy computational cost which is intolerable for most resource-limited IoT devices. In this article, we propose lightweight hybrid-policy ABSC (LH-ABSC), a lightweight ABSC scheme which adopts ciphertext-policy encryption (CPABE) and key-policy attribute-based signature (KPABS). Ciphertext-policy attribute-based signature leads the decision making that who can decrypt to the data owners directly. Meanwhile, the signature is related with data owners' attribute set which can be used to testify the authenticity of data. In particular, LH-ABSC has constant signature size and satisfies public verification which is deeply important for IoT devices. Moreover, LH-ABSC outsources most computing overhead to fog nodes, including signature, verify, and decryption. Comprehensive theoretical analyses, such as confidentiality, unforgeability, and verifiability, are provided. Also, the selective chosen ciphertext security, the selective chosen message security, and signers anonymity are achieved.
Watermarking Deep Neural Networks in Image Processing

Publishing/sharing pretrained deep neural network (DNN) models is a common practice in the community of computer vision. The increasing popularity of pretrained models has made it a serious concern: how to protect the intellectual properties of model owners and avert illegal usages by malicious attackers. This article aims at developing a framework for watermarking DNNs, with a particular focus on low-level image processing tasks that map images to images. Using image denoising and superresolution as case studies, we develop a black-box watermarking method for pretrained models, which exploits the overparameterization of the DNNs in image processing. In addition, an auxiliary module for visualizing the watermark information is proposed for further verification. Extensive experiments show that the proposed watermarking framework has no noticeable impact on model performance and enjoys the robustness against the often-seen attacks.
Economic viability of bitcoin mining using a renewable-based SOFC power system to supply the electrical power demand

One of Bitcoin’s most significant problems is its seemingly insatiable use of electricity. In the present research, along with providing the required power for Bitcoin (BTC) mining, a solid oxide fuel cell (SOFC) system fed either by natural gas or biogas as a renewable source of energy is used to supply the electricity demand. Thermodynamic modeling for the fuel cell system is applied to determine the required biogas or natural gas. For the proposed cases (grid-based, natural gas-fed SOFC and biogas fed SOFC), various scenarios depending on the Bitcoin price and mining difficulty are proposed. Also, the economic viability for each scenario in several countries is investigated and compared. Results indicate more profitability for grid-based mining in Bitcoin prices up to $20,000, but as the Bitcoin price increases SOFC based mining operations achieve reasonable profitability. It is shown that Iran, Russia, and China with cumulative cash flows of $87,300, $77,200 and $70,500 respectively, are the best countries to mine BTC using grid electricity while Iran, Canada and Russia are the best countries using a natural gas-fed SOFC system. While the profitability of SOFC-based mining is lower than grid-based mining, the latter method compensates with better sustainability and lower environmental costs.
Gradient Shielding: Towards Understanding Vulnerability of Deep Neural Networks

Deep neural networks (DNNs) have been widely adopted but they are vulnerable to intentionally crafted adversarial examples. Various attack methods against DNNs have been proposed, yet there still lacks theoretical explanation of adversarial examples. In this paper, we aim to understand adversarial examples from the attacking process and we assume adding perturbations to the key/sensitive regions of the image could fool image classification DNNs. We propose gradient shielding to verify the assumption which ignores insensitive information during generating adversarial examples. Specifically, we propose interactive gradient shielding (IGS) method which selects sensitive regions and then applies gradient-based attack. To remove region selection, we propose adaptive gradient shielding (AGS) method which ignores insensitive gradients automatically. We conduct extensive experiments to evaluate the performance and the results also corroborate our perspective. With this method, we won the first place in IJCAI-AAAC 2019 Non-targeted Adversarial Attack competition.
Variational Adaptive Kalman Filter With Gaussian-Inverse-Wishart Mixture Distribution

In this article, a new variational adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution is proposed for a class of linear systems with both partially unknown state and measurement noise covariance matrices. The state transition and measurement likelihood probability density functions are described by a Gaussian-inverse-Wishart mixture distribution and a Gaussian-inverse-Wishart distribution, respectively. The system state vector together with the state noise covariance matrix and the measurement noise covariance matrix are jointly estimated based on the derived hierarchical Gaussian model. Examples are provided to demonstrate the effectiveness and potential of the developed new filtering design techniques.