Reviewerly/exHarmony huggingface dataset:
Towards Accurate and Lightweight Masked Face Recognition: An Experimental Evaluation

Given the current COVID-19 pandemic, most people wear a mask to effectively prevent the spread of the contagious disease. This sanitary measure has caused a significant drop in the effectiveness of current face recognition methods when handling masked faces on practical applications such as face access control, face attendance, and face authentication-based mobile payment. Under this situation, recent efforts have been focused on boosting the performance of the existing face recognition technology on masked faces. Some solutions trying to tackle this issue fine-tune the existing deep learning face recognition models on synthetic masked images, while others use the periocular region as a naive manner to eliminate the adverse effect of COVID-19 masks. Although the accuracy of masked face recognition remains an important issue, in the last few years, the development of efficient and lightweight face recognition methods has received an increased attention in the research community. In this paper, we study the effectiveness of three state-of-the-art lightweight face recognition models for addressing accurate and efficient masked face recognition, considering both fine-tuning on masked faces and periocular images. For the experimental evaluation, we create both real and simulated masked face databases as well as periocular datasets. Extensive experiments are conducted to determine the most effective solution and state further steps for the research community. The obtained results disclose that fine-tuning existing state-of-the-art face models on masked images achieve better performance than using periocular-based models. Besides, we evaluate and analyze the effectiveness of the trained masked-based models on well-established unmasked benchmarks for face recognition and assess the efficiency of the used lightweight architectures in comparison with state-of-the-art face models.
High Energy and Thermal Neutron Sensitivity of Google Tensor Processing Units

In this article, we investigate the reliability of Google’s coral tensor processing units (TPUs) to both high-energy atmospheric neutrons (at ChipIR) and thermal neutrons from a pulsed source [at equipment materials and mechanics analyzer (EMMA)] and from a reactor [at Thermal and Epithermal Neutron Irradiation Station (TENIS)]. We report data obtained with an overall fluence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.41\times {10^{12}} {\mathrm {n/cm}}^{2}$ </tex-math></inline-formula> for atmospheric neutrons (equivalent to more than 30 million years of natural irradiation) and of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.55\times {10^{12}} {\mathrm {n/cm}}^{2}$ </tex-math></inline-formula> for thermal neutrons. We evaluate the behavior of TPUs executing elementary operations with increasing input sizes (standard convolutions or depthwise convolutions) as well as eight convolutional neural networks (CNNs) configurations (single-shot multibox detection (SSD) MobileNet v2 and SSD MobileDet, trained with COCO dataset, and Inception v4 and ResNet-50, with ILSVRC2012 dataset). We found that, despite the high error rate, most neutron-induced errors only slightly modify the convolution output and do not change the detection or classification of CNNs. By reporting details about the error model, we provide valuable information on how to design the CNNs to avoid neutron-induced events to lead to misdetections or classifications.
Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively.
Attitude to Distance Learning of Schoolchildren and Students: Subjective Assessments of Advantages and Disadvantages

Currently, during the pandemic, the forced transition to distance learning carries a number of problems. These problems affect various aspects of education, including the study of students’ attitudes to distance learning. The purpose of this research is to study the features of the subjective attitude of schoolchildren and students to distance learning. This research involved 140 secondary school students (average age M = 10.7, SD = 7.2 (66.3% men)) and 30 university students (average age M = 22.5, SD = 2.4 (20% men)). The methods used were a questionnaire, Chi-square test and Criterion φ*. Fisher angular transformation. The study showed that schoolchildren do not intend to continue studying in the distance form if they choose, with a generally positive attitude towards distance learning. The self-assessment of motivation to study in a distance format has not changed, both among schoolchildren and students. Students are more likely to have a positive attitude to distance learning than schoolchildren. The variety of choices of advantages and disadvantages of distance learning is greater among students than among schoolchildren. The perspective of this study is thus to study the factors that determine the positive and negative attitudes to distance learning.
A Simple but Efficient Ranking-Based Differential Evolution

Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.
Mitigating off-resonant error in the cross-resonance gate

Off-resonant error for a driven quantum system refers to interactions due to the input drives having non-zero spectral overlap with unwanted system transitions. For the cross-resonance gate, this includes leakage as well as off-diagonal computational interactions that lead to bit-flip error on the control qubit. In this work, we quantify off-resonant error, with more focus on the less studied off-diagonal control interactions, for a direct CNOT gate implementation. Our results are based on numerical simulation of the dynamics, while we demonstrate the connection to time-dependent Schrieffer-Wolff and Magnus perturbation theories. We present two methods for suppressing such error terms. First, pulse parameters need to be optimized so that off-resonant transition frequencies coincide with the local minima due to the pulse spectrum sidebands. Second, we show the advantage of a $Y$-DRAG pulse on the control qubit in mitigating off-resonant error. Depending on qubit-qubit detuning, the proposed methods can improve the average off-resonant error from approximately $10^{-3}$ closer to the $10^{-4}$ level for a direct CNOT calibration.
A Study on TOPSIS MCDM Techniques and Its Application

Optional selection technique similar to The Ideal So lution (TOPSIS) is a mu lti-criterion decision Is the method of analysis.This was originally Sing-Lai Hwang Created in 1981 by and Yoon, and Yoon and Yoon in 1987.In 1993Further improved by Hwang, Lai and Liu.Became TOPSIS Narrow geometric Distance from selected alternative positive optical solution (PIS) and negative ideal Based on the idea that the solution must have a very long geome tric distance (NIS) Consists of.Identify Weights for each criterion, each Every alternative and best alternative to the criteria Normalizing the marks and geometric distance between Compensation is a method of comparing the set of alternatives by calculation.The assumption of TOPSIS is The criteria increase uniformly or Decrease.Many because of parameters or Criteria scale problems often have Normalizat ion of inappropriate dimensions is common needed.Co mpensation systems such as TOPSIS Trade between criteria A llo w exchanges where bad results on one scale are good results on another scale May be denied by.It offers So much more than unpaid methods Realistic modeling form, in wh ich Alternative solutions are included or excluded In terms of tough cut-offs.Its in nuclear power plants an example of application given.
Empirical Performance Analysis of Hyperledger LTS for Small and Medium Enterprises

The massive success of blockchain technology in Bitcoin is taking the world by storm by attracting vast acceptance from both the public and private sectors. Blockchain allows digital transactions between two parties without a third party as a broker. Blockchain is now applicable beyond fintech to various other industries. Among these, Hyperledger fabric has emerged as the most popular blockchain-based open-source permissioned platform targeting business applications. It has been used in over 400 proofs-of-concept blockchain and is well proven in applications, such as supply chain, healthcare, and so on. Despite the many obvious benefits observed in blockchain-enhanced platforms, there still exist technical challenges in scalability, causing performance deficiency, which includes latency and throughput. There is an exigent need to improve the current blockchain-based applications to have the blockchain nodes be scalable without compromising the blockchain performance. In this study, we present the impact of workload variance of up to 1000 transactions with the setup of 20 blockchain nodes in the Hyperledger LTS platform. The evaluation metrics are transaction success and failure rate, throughput, and latency in the blockchain. The transaction throughput was found to be consistent with the increasing workload on a constant number of nodes. However, it showed a declining trend with an increasing number of nodes. As far as the latency, it was in tandem with the increased workload and the number of nodes. We, therefore, conclude that the LTS version is suitable for small and medium enterprises that do not scale up.
Actor-critic continuous state reinforcement learning for wind-turbine control robust optimization

The control of Variable-Speed Wind-Turbines (VSWT) extracting electrical power from the wind kinetic energy are composed of subsystems that need to be controlled jointly, namely the blade pitch and the generator torque controllers. Previous state of the art approaches decompose the joint control problem into independent control subproblems, each with its own control subgoal, carrying out separately the design and tuning of a parameterized controller for each subproblem. Such approaches neglect interactions among subsystems which can introduce significant effects. This paper applies Actor-Critic Reinforcement Learning (ACRL) for the joint control problem as a whole, carrying out the simultaneous control parameter optimization of both subsystems without neglecting their interactions, aiming for a globally optimal control of the whole system. The innovative control architecture uses an augmented input space so that the parameters can be fine-tuned for each working condition. Validation results conducted on simulation experiments using the state-of-the-art OpenFAST simulator show a significant efficiency improvement relative to the best state of the art controllers used as benchmarks, up to a 22% improvement in the average power error performance after ACRL training.
Data Physicalization

Data physicalization is a rich and vast research area that studies the use of physical artifacts to convey data. It overlaps with a number of research areas including information visualization, scientific visualization, visual analytics, tangible user interfaces, shape-changing interfaces, personal fabrication interfaces, as well as graphic design, architecture, and art. This chapter surveys academic work on data physicalization up to 2018 and also provides a broad overview of nonacademic work. It discusses how data physicalization has been used for analytical purposes, communication and education, accessibility, self-reflection and self-expression, and finally for enjoyment and meaning. It also discusses enabling technologies, reviews empirical studies, and surveys models and theories of data physicalization.
Cryptocurrency Blockchain Technology in the Digital Revolution Era

Cryptocurrency is a blockchain-based technology that is often used as a digital currency. Digital money is different from conventional money, this type of money does not have a physical form but only a block of data bound by a hash as validation. The data is spread to every cryptocurrency user who is in the environment. So that when a user makes a transaction, data mining will be carried out. Cryptocurrencies have several advantages but also have disadvantages when used as currency, from a legal point of view there is still no law governing the circulation of currency in digital form (cryptocurrency). The government must recognize digital currency so that it can be accepted by the general public as a means of payment. For now, the government has not recognized digital money (cryptocurrency) as a currency, because Cryptocurrency is a new phenomenon by some people in Indonesia. The rapid development of technology in the 4.0 revolution era, in the next few years money in physical form will be replaced by digital money, seeing the many conveniences provided by using digital money.
Prediction of Insurance Fraud Detection using Machine Learning Algorithms

In current era, people are influenced with various types of insurance such as health insurance, automobile insurance, property insurance and travel insurance, due to the availability of extensive knowledge related to insurance. People are trending to invest in such kinds of insurance, which helps the scam artist to cheat them. Insurance fraud is a prohibited act either by the client or vendor of the insurance contract. Insurance fraud from the client side is encountered in the form of overestimated claims and post-dated policies etc. Although, insurance fraud from the vendor side is experienced in the form of policies from non-existent companies and failuew to submit premiums and so on. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Random-Forest (RF), Decision-Tree (DT), Adaboost, K-Nearest Neighbor (KNN), Linear Regression (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP) to detect the insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score. The comparative results of classification algorithms conclude that DT gives the highest accuracy of 79% as compared to the other techniques. In addition to this, Adaboost shows the accuracy of 78% which is closer to the DT
The Deepfake Challenges and Deepfake Video Detection

Deepfake is a combination of fake and deep-learning technology. Deep learning is the function of artificial intelligence that can be used to create and detect deepfakes. Deepfakes are created using generative adversarial networks, in which two machine learning models exit. One model trains on a dataset and then creates the deepfakes, and the other model tries to detect the deepfakes. The forger creates deepfakes until the other model can't detect the deepfakes. Deepfakes creating fake videos, images, news, and terrorist events. When deepfake videos, and images increase on social media people will ignore to trust the truth. Deepfakes are increasingly affecting individuals, communities, organizations, security, religions, and democracy. This paper aims to investigate deepfake challenges, and to detect deepfake videos by using eye blinking. Deepfake detections are methods to detect real or deepfake images and videos on social media. Deepfake detection techniques are needed original and fake images or video datasets to train the detection models. In this study, first discussed deepfake technology and its challenges, then identified available video datasets. Following, convolutional neural networks to classify the eye states and long short term memory for sequence learning has been used. Furthermore, the eye aspect ratio was used to calculate the height and width of open and closed eyes and to detect the blinking intervals. The model trained on UADFV dataset to detect fake and real video by using eye blinking and detects 18.4 eye blinks per minute on the real videos and 4.28 eye blinks per minute on fake videos. The overall detection accuracy on real and fake videos was 93.23% and 98.30% respectively. In the future research and development needs more scalable, accurate, reliable and cross-platform deepfake detection techniques.
Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Many image encryption schemes based on compressive sensing have poor reconstructed image quality when the compression ratio is low, as well as difficulty in hardware implementation. To address these problems, we propose an image encryption algorithm based on the mixed chaotic Bernoulli measurement matrix block compressive sensing. A new chaotic measurement matrix was designed using the Chebyshev map and logistic map; the image was compressed in blocks to obtain the measurement values. Still, using the Chebyshev map and logistic map to generate encrypted sequences, the measurement values were encrypted by no repetitive scrambling as well as a two-way diffusion algorithm based on GF(257) for the measurement value matrix. The security of the encryption system was further improved by generating the Secure Hash Algorithm-256 of the original image to calculate the initial values of the chaotic mappings for the encryption process. The scheme uses two one-dimensional maps and is easier to implement in hardware. Simulation and performance analysis showed that the proposed image compression-encryption scheme can improve the peak signal-to-noise ratio of the reconstructed image with a low compression ratio and has good encryption against various attacks.
A differential evolution algorithm with the guided movement for population and its application to interplanetary transfer trajectory design

The extremely sensitive and highly non-linear search space of interplanetary transfer trajectory design brings about big challenges on global optimization. As a representative, the current known best solution (fuel consumption) of the global trajectory optimization problem designed by the European space agency is very hard to be found. To deal with this difficulty, a powerful differential evolution with the guided movement for population, named G_DE is proposed in this paper. G_DE employs a two-stage evolutionary process, which concentrates on learning global structure in the earlier process, and tends to self-adaptively probe the structures of numerous local spaces at a later stage. During these two stages, four guidance strategies related to the learning from the population distribution are proposed. The experimental test results show that G_DE can find the current known best solutions of Cassini1 and Sagas directly. For the newly proposed GTOP-X problem, G_DE has found state-of-the-art solutions for four encounter sequences, and using five gravity assists, G_DE found encounter sequences with fuel consumption as low as 1.5676 km/s. The experimental test on 10D CEC2017 problems proves G_DE has a comparable performance with recently proposed differential evolution variant.
An experimental comparison of multi-view stereo approaches on satellite images

Different methods can be applied to satellite images to derive an altitude map from a set of images. In this article we evaluate a set of representative methods from different approaches. We consider true multi-view stereo methods as well as pair-wise ones, classic methods and deep learning based ones, methods already in use on satellite images and others that were originally devised for close range imaging and are adapted to satellite imagery. While deep learning (DL) methods have taken over multi-view stereo reconstruction in the last years, this tendency has not fully reached satellite stereo pipelines that still largely rely on pair-wise classic algorithms. For the comparison, we set-up a framework that allows to interface a DL-based stereo method taken from the computer vision literature with a satellite stereo pipeline. For multi-view stereo algorithms we build on a recently proposed framework originally devised to apply Colmap method to satellite images. Methods are compared on several datasets that include sets of images taken within a few days and sets of images taken months apart. Results show that DL methods have, in general, a good generalization power. In particular, the use of the GANet DL method as the matching step in a pair-wise stereo pipeline is promising as it already performs better than the classic counterpart, even without a specific training.
RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Radar offers a promising modality for enabling gesture recognition, which is a simple and intuitive alternative to click and touch-based human–computer interface. In this article, we propose a spiking neural network (SNN)-based hand gesture recognition with frequency-modulated continuous-wave 60-GHz radar. As preprocessing, the 2-D fast Fourier transform (FFT) is performed across fast time and slow time to generate a video of range–Doppler maps, which are then processed to generate range spectrograms, Doppler spectrograms, and angle spectrograms. The spike trains are fed into an SNN to classify the gesture that has been performed. We demonstrate that with few neurons, SNNs can achieve recognition accuracies close to 99.50% comparable to their deep learning counterparts for eight dynamic gestures. Moreover, the proposed model size is 75 kB, which is substantially smaller compared to the state-of-the-art models making it memory efficient. We also demonstrate using tSNE plots that SNNs can operate with lower embedding dimensions, implying that we can realize SNN with a small compute and memory footprint.