Reviewerly/exHarmony huggingface dataset:
Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems

Nowadays, medical diagnosis based on machine learning is an essential, active, and interdisciplinary research area. Making smart diagnosis and decision support systems have a profound impact on healthcare informatics. Integrating machine learning classifier systems into computer-aided diagnosis systems promotes the early detection of diseases, which results in more effective treatments and prolonged survival. In this chapter, we address popular diagnosis problems using an evolutionary machine learning approach which performs feature weighting and tuning the parameters of support vector machines (SVMs) simultaneously. A new and powerful metaheuristic called salp swarm algorithm is combined with SVM for this task. The designed SSA-SVM approach shows several merits compared to other SVM-based frameworks with well-regarded algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).
Real-Time Hand Gesture Recognition Model Using Deep Learning Techniques and EMG Signals

Gesture recognition has multiple applications in medicine, engineering and robotics. It also allows us to develop new and more natural approaches to human-machine interaction. Real-time hand gesture recognition consists of identifying, with no perceivable delay, a given gesture performed by the hand at any moment. In this paper, we propose a model for real-time hand gesture recognition. The proposed model takes as input electromyographic (EMG) signals measured on the forearm, using the commercial sensor Myo Armband. We use an autoencoder for automatic feature extraction, and an artificial feed-forward neural network for classification. The proposed model can recognize the same 5 gestures as the proprietary recognition system of the Myo Armband, achieving an average recognition accuracy of 85.08% ± 15.21%, with an average response time of 3 ± 1 ms. The proposed model is general, which implies that it can recognize the gestures from any user, even when his\her data are not included in the training dataset. Finally, for reproducing this work, we make publicly available the code that implements the proposed model.
A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.
Fast Image Restoration With Multi-Bin Trainable Linear Units

Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks. Such approaches generally employ very deep architectures, large number of parameters, large receptive fields and high nonlinear modeling capacity. In order to obtain efficient and fast image restoration networks one should improve upon the above mentioned requirements. In this paper we propose a novel activation function, the multi-bin trainable linear unit (MTLU), for increasing the nonlinear modeling capacity together with lighter and shallower networks. We validate the proposed fast image restoration networks for image denoising (FDnet) and super-resolution (FSRnet) on standard benchmarks. We achieve large improvements in both memory and runtime over current state-of-the-art for comparable or better PSNR accuracies.
Multi-Layer RNN-based Short-term Photovoltaic Power Forecasting using IoT Dataset

Photovoltaic power fluctuation in daytime is one of critical problems for the efficient and stable operation of the smart grid. To respond the PV power fluctuation resulting from weather change, the short-term PV power forecasting algorithm using multi-layer RNN is proposed in this paper. It consists of multiple RNN layers using power and meteorological data which are collected by on-site IoT (Internet of Things) sensors. Experimental results showed that the accuracies of the short-term PV power prediction of 5 minutes and 1 hour later using 3 RNN layers with 12 time-step were 98.02% and 96.58% based on the normalized RMSE, respectively. These experimental results confirmed that the proposed short-term prediction algorithm using multi-layer RNN model was applicable to respond the short-term PV power fluctuation.
Survey of 3D modeling using depth cameras

Three-dimensional (3D) modeling is an important topic in computer graphics and computer vision. In recent years, the introduction of consumer-grade depth cameras has resulted in profound advances in 3D modeling. Starting with the basic data structure, this survey reviews the latest developments of 3D modeling based on depth cameras, including research works on camera tracking, 3D object and scene reconstruction, and high-quality texture reconstruction. We also discuss the future work and possible solutions for 3D modeling based on the depth camera.
DAuth: A Decentralized Web Authentication System using Ethereum based Blockchain

Over the past decade, a lot of evolution has happened in the field of security specifically authentication system. The most commonly used authentication service we use now is OAuth 2.0 based authentication. In this method, we are dependent on a 3rd party authentication service provider to which we need to trust. Though this model is used extensively nowadays, studies show that it is still vulnerable to several hacks. In addition to that, the 3rd party authentication provider has total control over the user data to which they can leak or modify at their will. Thus the use of OAuth 2.0 based protocol has raised security and privacy concerns. In this paper, blockchain and its use cases are studied and an alternative way of authentication service has been proposed based on Ethereum Blockchain called DAuth. Furthermore, a prototype has been developed which enables user authentication on the site. DAuth proposes to enhance transparency and user control in transactions which involves identity management.
Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images

Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications.
A Hybrid Cryptographic Technique for File Storage Mechanism Over Cloud

The cloud is a radical platform that conveys dynamic, virtualization asset pools and high convenience. Since distributed computing lays on web, security issues like information security, protection, secrecy, and verification are experienced. In order to get rid of these, an assortment of mechanisms and encryption algorithms are utilized in various blends. On the comparable terms, we made utilization of hybrid encryption with the utilization of crossbreed cryptographic calculations to upgrade the security of information or data file on cloud. We plan at scrutinizing different incorporation of encryption algorithms, in view of various execution constraints to reason a hybrid calculation which can anchor information more effectively on cloud.
Security Analysis of Discrete-Modulated Continuous-Variable Quantum Key Distribution over Seawater Channel

We investigate the optical absorption and scattering properties of four different kinds of seawater as the quantum channel. The models of discrete-modulated continuous-variable quantum key distribution (CV-QKD) in free-space seawater channel are briefly described, and the performance of the four-state protocol and the eight-state protocol in asymptotic and finite-size cases is analyzed in detail. Simulation results illustrate that the more complex is the seawater composition, the worse is the performance of the protocol. For different types of seawater channels, we can improve the performance of the protocol by selecting different optimal modulation variances and controlling the extra noise on the channel. Besides, we can find that the performance of the eight-state protocol is better than that of the four-state protocol, and there is little difference between homodyne detection and heterodyne detection. Although the secret key rate of the protocol that we propose is still relatively low and the maximum transmission distance is only a few hundred meters, the research on CV-QKD over the seawater channel is of great significance, which provides a new idea for the construction of global secure communication network.
An ensemble based on neural networks with random weights for online data stream regression

Abstract Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on neural network with random weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm’s response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift, and much faster updating times compared to the existing available approach. Additionally, the use of design of experiments as an effective tool for hyperparameter tuning is demonstrated.
Securing PIN‐based authentication in smartwatches with just two gestures

Summary Smartwatches are becoming increasingly ubiquitous as they offer new capabilities to develop sophisticated applications that make daily life easier and more convenient for consumers. The services provided include applications for mobile payment, ticketing, identification, access control, etc. While this makes modern smartwatches very powerful devices, it also makes them very attractive targets for attackers. Indeed, PINs and Pattern Lock have been widely used in smartwatches for user authentication. However, such authentication methods are not robust against various forms of cybersecurity attacks, such as side channel, phishing, smudge, shoulder surfing, and video‐recording attacks. Moreover, the recent adoption of hardware‐based solutions, like the Trusted Execution Environment (TEE), can mitigate only partially such problems. Thus, the user's security and privacy are at risk without a strong authentication scheme in place. In this work, we propose 2GesturePIN, a new authentication framework that allows users to authenticate securely to their smartwatches and related sensitive services through solely two gestures. 2GesturePIN leverages the rotating bezel or crown, which are the most intuitive ways to interact with a smartwatch, as a dedicated hardware. 2GesturePIN improves the resilience of the regular PIN authentication method against state‐of‐the‐art cybersecurity attacks while maintaining a high level of usability.
Determinants of blockchain adoption in the public sector: An empirical examination

What are the factors that influence blockchain adoption in the public sector? This paper uses the diffusion of innovation theory to examine leading adopters of blockchain at the national government level. Six factors for blockchain adoption were tested using logistic regression: cybersecurity, control of corruption, e-government development, government effectiveness, political stability, and democratic participation. The analysis shows that cybersecurity, government effectiveness, and political stability are significant predictors. High levels of cybersecurity and government effectiveness increases the likelihood of countries to adopt blockchain. Paradoxically, higher degree of political stability decreases the likelihood of early blockchain adoption.
Robust nonnegative matrix factorization with local coordinate constraint for image clustering

Nonnegative matrix factorization (NMF) has attracted increasing attention in data mining and machine learning. However, existing NMF methods have some limitations. For example, some NMF methods seriously suffer from noisy data contaminated by outliers, or fail to preserve the geometric information of the data and guarantee the sparse parts-based representation. To overcome these issues, in this paper, a robust and sparse NMF method, called correntropy based dual graph regularized nonnegative matrix factorization with local coordinate constraint (LCDNMF) is proposed. Specifically, LCDNMF incorporates the geometrical information of both the data manifold and the feature manifold, and the local coordinate constraint into the correntropy based objective function. The half-quadratic optimization technique is utilized to solve the nonconvex optimization problem of LCDNMF, and the multiplicative update rules are obtained. Furthermore, some properties of LCDNMF including the convergence, relation with gradient descent method, robustness, and computational complexity are analyzed. Experiments of clustering demonstrate the effectiveness and robustness of the proposed LCDNMF method in comparison to several state-of-the-art methods on six real world image datasets.
Semi-supervised Classification of Fraud Data in Commercial Auctions

Given the magnitude of monetary transactions at auction sites, they are very attractive to fraudsters and scam artists. Shill bidding (SB) is a severe fraud in e-auctions, which occurs during the bidding period and is driven by modern-day technology and clever scammers. SB does not produce any obvious evidence, and it is often unnoticed by the victims. The lack of availability of training datasets for SB and the difficulty in identifying the behavior of sophisticated fraudsters hinder research on SB detection. To safeguard consumers from dishonest bidders, we were incentivized to investigate semi-supervised classification (SSC) for the first time, which is the most suitable approach to solving fraud classification problems. In this study, we first introduce two new SB patterns, and then based on a total of nine SB patterns, we build an SB dataset from commercial auctions and bidder history data. SSC requires the labeling of a few SB data samples, and to this end, we propose an anomaly detection method based on data clustering. We addressed the skewed class distribution with a hybrid data sampling method. Our experiments in training several SSC models show that using primarily unlabeled SB data with a few labeled SB data improves predictive performance when compared to that of supervised models.
The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information

Adversarial attacks for image classification are small perturbations to images that are designed to cause misclassification by a model. Adversarial attacks formally correspond to an optimization problem: find a minimum norm image perturbation, constrained to cause misclassification. A number of effective attacks have been developed. However, to date, no gradient-based attacks have used best practices from the optimization literature to solve this constrained minimization problem. We design a new untargeted attack, based on these best practices, using the well-regarded logarithmic barrier method. On average, our attack distance is similar or better than all state-of-the-art attacks on benchmark datasets (MNIST, CIFAR10, ImageNet-1K). In addition, our method performs significantly better on the most challenging images, those which normally require larger perturbations for misclassification. We employ the LogBarrier attack on several adversarially defended models, and show that it adversarially perturbs all images more efficiently than other attacks: the distance needed to perturb all images is significantly smaller with the LogBarrier attack than with other state-of-the-art attacks.
Pre‐training interventions to counteract seductive details in virtual reality training programs

Abstract We propose that (a) certain aspects of virtual reality (VR) training programs are seductive details, (b) these seductive details prompt trainee distraction and/or cognitive overload, and (c) pre‐training interventions can benefit learning from VR training programs by targeting these mechanisms. In Study 1, we apply a meta‐cognitive strategy pre‐training intervention, which targets distraction, and a habituation pre‐training intervention, which targets cognitive overload. Habituation had no effect, whereas meta‐cognitive strategies worsened learning. Qualitative results indicated the meta‐cognitive strategy intervention prompted trainees to become more cognitively engaged in the distracting seductive details of the VR training program. In Study 2, we tested an alternative pre‐training intervention, attentional advice, to reduce distraction and increase learning. The attentional advice pre‐training intervention was successful, as trainees demonstrated greater learning when provided attentional advice. Together, all proposals were supported. VR can contain seductive details that cause distraction, and attentional advice can improve learning by reducing distraction.
Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, {\itshape {M}} $ \geq $ 6.0. Variable EPA method

Strong earthquake-prone areas recognition ( ≥ 6.0) in the Caucasus is performed by means of the new "Barrier-3" pattern recognition algorithm.The obtained result is compared with potentially high seismicity zones recognized previously using the "Cora-3" pattern recognition algorithm.It is proposed to define an interpretation of the integral recognition result by the "Barrier-3" and "Cora-3" algorithms as a fuzzy set of recognition objects in the vicinity of which strong earthquakes may occur in the Caucasus.
An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning

In machine learning, performance is of great value. However, each learning process requires much time and effort in setting each parameter. The critical problem in machine learning is determining the hyperparameters, such as the learning rate, mini-batch size, and regularization coefficient. In particular, we focus on the learning rate, which is directly related to learning efficiency and performance. Bayesian optimization using a Gaussian Process is common for this purpose. In this paper, based on Bayesian optimization, we attempt to optimize the hyperparameters automatically by utilizing a Gamma distribution, instead of a Gaussian distribution, to improve the training performance of predicting image discrimination. As a result, our proposed method proves to be more reasonable and efficient in the estimation of learning rate when training the data, and can be useful in machine learning.
Quaternary Hermitian Linear Complementary Dual Codes

The largest minimum weights among quaternary Hermitian linear complementary dual codes are known for dimension $2$. In this paper, we give some conditions for the nonexistence of quaternary Hermitian linear complementary dual codes with large minimum weights. As an application, we completely determine the largest minimum weights for dimension $3$, by using a classification of some quaternary codes. In addition, for a positive integer $s$, a maximal entanglement entanglement-assisted quantum $[[21s+5,3,16s+3;21s+2]]$ codes is constructed for the first time from a quaternary Hermitian linear complementary dual $[26,3,19]$ code.
How Agile Is Hybrid Agile? An Analysis of the HELENA Data

Context: Many researchers advocate “tailoring” agile methods to suit a project’s or company’s specific environment and needs. This includes combining agile methods with more traditional “plan driven” practices. Objective: This study aims to assess to what extent projects actually combine agile and traditional practices. Method: Data from the HELENA survey of nearly 700 projects were examined to assess how many projects combine agile methods and traditional methods, and also to what extent they used different software development practices. Results: The data show that, overall, two-thirds of the projects in the survey combine agile and traditional methods to some extent. However, projects that combine agile and traditional methods are significantly less likely to use agile practices than projects that solely use agile methods. Conclusions: We hypothesize that the mindset of an organization, rather than technical necessity, determines whether a project will adopt a hybrid vs. purely agile approach.
A Practical Approach for SAR Image Despeckling Using Deep Learning

In this paper, we propose a deep learning based practical approach to despeckle synthetic aperture radar (SAR) images without using ground truth (despeckled images). Conventionally, the learning based approaches need the despeckled images for the training. However, in real scenarios, it is often difficult to acquire the ground truth. To this end, we demonstrate a technique to restore the speckled images using the available SAR data only. We modify the UNet architecture in order to train it with the given speckled images. We 1) introduce an extra residual connection in each Conv-Block of UNet, and 2) replace the transpose convolution with parameter free bi-linear interpolation. We show the training of this modified network in the mean-squared error (MSE) sense. Experiments have been conducted on ImageNet dataset as well as on the real SAR images. The results are validated both qualitatively as well as quantitatively and compared with state-of-the-art-approaches.