Reviewerly/exHarmony huggingface dataset:
An improved ELM-WOA–based fault diagnosis for electric power

Due to its fast learning speed, the extreme learning machine (ELM) plays a very important role in the real-time monitoring of electric power. However, the initial weights and thresholds of the ELM are randomly selected, therefore it is difficult to achieve an optimal network performance; in addition, there is a lack of distance selection when detecting faults using artificial intelligence algorithms. To solve the abovementioned problem, we present a fault diagnosis method for microgrids on the basis of the whale algorithm optimization–extreme learning machine (WOA-ELM). First, the wavelet packet decomposition is used to analyze the three-phase fault voltage, and the energy entropy of the wavelet packet is calculated to form the eigenvector as the data sample; then, we use the original ELM model coupled with the theory of distance selection to locate faults and compared it with the SVM method; finally, the whale algorithm is used to optimize the input weight and hidden layer neuron threshold of the ELM, i.e., the WOA-ELM model, which solves the problem of the random initialization of the input weight and hidden layer neuron threshold that easily affects the network performance, further improves the learning speed and generalization ability of the network, and is conducive to the overall optimization. The results show that 1) the accuracy of selecting the data according to the fault distance is twice that of not selecting data according to it; 2) compared with the BP neural network, RBF neural network, and ELM, the fault diagnosis model based on the WOA-ELM has a faster learning speed, stronger generalization ability, and higher recognition accuracy; and 3) after optimization of the WOA, the WOA-ELM can improve 22.5% accuracy in fault detection when compared to the traditional ELM method. Our results are of great significance in improving the security of smart grid.
Modelling language for cyber security incident handling for critical infrastructures

Cyber security incident handling is a consistent methodology with which to ensure overall business continuity. However, specifically handling incidents for critical information infrastructures is challenging owing to the inherent complexity and evolving nature of the threat. Despite the number of contributions made to cyber incident handling, there is little evidence of literature that focuses on modelling activities that will enhance developers’ abilities to model incident handling processes and activities according to different views. Modelling languages of this nature should integrate essential concepts and a descriptive implementation process in order to enable developers to analyse, represent and reason about the crucial incident handling efforts required to support critical information infrastructures. The aim of this paper is, as part of the CyberSANE EU project, to develop a Cyber Incident Handling Modelling Language (CIHML) that focuses explicitly on modelling incident handling in the context of a critical information infrastructure. The work is innovative in its approach because it consolidates concepts from various domains such as security requirements, forensics, threat intelligence, critical infrastructures and cyber incident handling. The approach will allow the phases of the incident handling lifecycle to be modelled from three different views (critical information infrastructures, threat and risk analysis, and incident response). An implementation process is also proposed, which will serve as a comprehensive guide for developers in order to create these modelling views. Finally, CIHML is evaluated using a real-life scenario from the CyberSANE project to demonstrate its applicability. The incident observed had a severe impact on the overall business continuity of the context studied. The results obtained from the study show that CIHML can help critical information infrastructure operators to identify, evaluate, represent and model cyber incidents in critical information systems, in addition to providing the support required to determine the response strategies needed in order to mitigate these cyber-attacks.
Impact of Remote Work on Project Management Cases from UAE Construction Sector

The proposed study is coming at a time when companies have been facing challenges in employing an effective business model in their operational activities.In the UAE, the government proposed strict measures to curb coronavirus spread by advocating for working-at-home policies.Companies with no system architecture to adapt to the work-at-home guidelines were forced to close down for a while.The three companies under this study, being multinational companies, struggled greatly with the new directives.As a result, the management of the three companies was left with no alternative but to adopt a new strategic plan to remain stable.This study aimed at finding out the key challenges and benefits associated with virtual team technology.The result of this study is based on a combination of primary and secondary data collection.The primary data collection was obtained via interviews with officials from the three companies.On the other hand, the secondary data was obtained from the comparative analysis of three companies already employing the remote work strategy.This was coupled with the results obtained from past studies in this field.Based on the preliminary results, the new strategy, as predicted, would affect the operations of the company for a while before it is fully embraced.Further, the study recommends that the three companies continue their innovative exercise on effective alternative management strategies to run their projects during this era of coronavirus pandemic and post-COVID-19.
Detecting Unknown Vulnerabilities in Smart Contracts with Multi-Label Classification Model Using CNN-BiLSTM

Smart contracts are frequently targeted by hackers because they hold large amounts of money and cannot be modified once they are published. Existing detection methods mainly focus on known vulnerabilities with clear features and cannot deal with unknown vulnerabilities. As a consequence, proposing a method for detecting unknown vulnerabilities in smart contracts represents a significant advancement in the field of smart contract security. Aiming at this problem, based on the idea that the opcode sequences of transactions containing unknown vulnerabilities have similarities to the opcode sequences of transactions containing known vulnerabilities, we propose a novel approach for unknown vulnerability detection in smart contracts using a CNN-BiLSTM model. Our model determines whether a vulnerability is unknown by detecting the opcode sequence representing the entire execution process of a transaction. Experimental results with the opcode sequences of transactions show that the model can achieve 82.86% accuracy and 83.63% F1-score.
Portrait Map Art Generation By Asymmetric Image-to-Image Translation

Abstract The authors propose a deep neural network–based algorithm to automatically generate portrait map art (PMA), a modern art form created by British portrait artist Ed Fairburn. The authors formulate the generation of PMA as an adaptive dual-to-single image translation problem. The authors’ proposed model analyzes the appearance of one portrait and one map image using two encoder networks and utilizes their hidden encodings as representations of the portrait and map image to generate new PMA using a decoder network. An adaptive style harmonization module is proposed to fuse the two hidden encodings. Optimized by cycle-consistency constraint, the model can produce new PMA images without baselines.
Voting Ensemble-Based Model for Sentiment Classification of Hindi Movie Reviews

Lately, distinct classifiers have been seen to complement each other in classification performance. This gave rise to the belief of using an ensemble of many simple models instead of using one complex fine-tuned model for classification tasks. Movie review mining or movie review sentiment classification or sentiment analysis (SA) of movie reviews is the process of abstracting the attitude and feelings of the writer from movie reviews text, meaning thereby whether sentiment expressed in written reviews is of positive valence or negative valence. It is known that ensemble models are a promising way of solving various classification problems. These days, researchers have proposed many models for Hindi movie review mining. However, existing research investigations neglect the potency and competence of ensemble models in Hindi movie review sentiment analysis. The voting ensemble is a very popularly used ensemble technique in which many machine learning classifiers (MLCs) can be combined for yielding better predictive performance. This paper proposes a majority voting ensemble model of classifiers for doing binary sentiment classification of Hindi movie reviews. The proposed voting ensemble model has been compared with baseline models. Experimental results on our Hindi movie reviews unfold that our proposed voting ensemble model delivers better acceptable performance when compared to other stated models for sentiment classification on Hindi movie reviews.
Classification of Alteration Zones Based on Drill Core Hyperspectral Data Using Semi-Supervised Adversarial Autoencoder: A Case Study in Pulang Porphyry Copper Deposit, China

With the development of hyperspectral technology, it has become possible to classify alteration zones using hyperspectral data. Since various altered rocks are comprehensive manifestations of mineral assemblages, their spectra are highly similar, which greatly increases the difficulty of distinguishing among them. In this study, a Semi-Supervised Adversarial Autoencoder (SSAAE) was proposed to classify the alteration zones, using the drill core hyperspectral data collected from the Pulang porphyry copper deposit. The multiscale feature extractor was first integrated into the encoder to fully exploit and mine the latent feature representations of hyperspectral data, which were further transformed into discrete class vectors using a classifier. Second, the decoder reconstructed the original inputs with the latent and class vectors. Third, we imposed a categorical distribution on the discrete class vectors represented in the one-hot form using the adversarial regularization process and incorporated the supervised classification process into the network to better guide the network training using the limited labeled data. The comparison experiments on the synthetic dataset and measured hyperspectral dataset were conducted to quantitatively and qualitatively certify the effect of the proposed method. The results show that the SSAAE outperformed six other methods for classifying alteration zones. Moreover, we further displayed the delineated results of the SSAAE on the cross-section, in which the alteration zones were sensible from a geological point of view and had good spatial consistency with the occurrence of Cu, which further demonstrates that the SSAAE had good applicability for the classification of alteration zones.
Network traffic prediction model based on improved VMD and PSO‐ELM

Summary The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO‐VMD combined with multi‐scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO‐ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO‐ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high‐precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD‐PSO‐ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.
Stochastic variational inference for scalable non-stationary Gaussian process regression

Abstract A natural extension to standard Gaussian process (GP) regression is the use of non-stationary Gaussian processes, an approach where the parameters of the covariance kernel are allowed to vary in time or space. The non-stationary GP is a flexible model that relaxes the strong prior assumption of standard GP regression, that the covariance properties of the inferred functions are constant across the input space. Non-stationary GPs typically model varying covariance kernel parameters as further lower-level GPs, thereby enabling sampling-based inference. However, due to the high computational costs and inherently sequential nature of MCMC sampling, these methods do not scale to large datasets. Here we develop a variational inference approach to fitting non-stationary GPs that combines sparse GP regression methods with a trajectory segmentation technique. Our method is scalable to large datasets containing potentially millions of data points. We demonstrate the effectiveness of our approach on both synthetic and real world datasets.
Local quantum uncertainty of a two-qubit XY Heisenberg model with different Dzyaloshinskii–Moriya couplings

This study investigates the local quantum uncertainty (LQU) of a two--qubit Heisenberg XY chain with different directions of Dzyaloshinskii--Moriya (DM) interactions. The DM interaction parameters and coupling coefficient $J$ are demonstrated to be beneficial in managing correlation. The DM interaction's x--axis parameter has more influence on correlation than the DM interaction's z--axis. As a result, adjusting the direction of the DM interaction may be capable of producing a more efficient operation to improve the correlation.
A new face reconstruction technique for noisy low-resolution images using regression learning

Over the past few decades, there has been a lot of advancement in face hallucination (or super-resolution) technologies, with the position-patch-based locality constrained methods showing the most promising performance. However, these methods have not achieved up to the mark performance with noisy images as they reconstructed the high-resolution images without knowing the actual level of noise in the input LR faces. To this end, this paper proposes a Regression Learning based Locality constrained Representation (RLLcR) technique for noise robust face hallucination. In RLLcR, the Gaussian process regression learning approach and locality-constrained representation approach are integrated together to super resolve the noisy low-resolution faces. Specifically, a Gaussian process regression learning based noise level (GPRNL) prediction model is introduced that predicts the level of noise in the test images. Further, the predicted noise level is incorporated into the objective function of the proposed RLLcR technique to regularize the reconstruction error. These contributions make the proposed method robust to noise. The experiments are performed on FEI and CAS+PEAL publicly available face datasets and some real-world captured face images to demonstrate the performance of the proposed RLLcR technique. The results show improvement of 17%, 15%, 17%, 6%, 72%, 38%, 26%, 11%, and 35% on FEI dataset; 15%, 10%, 8%, 5%, 76%, 20%, 23%, 13%, and 26% on CAS+PEAL dataset in terms of PSNR, SSIM, MS-SSIM, FSIM, IFC, UQI, D-SSIM, NLPD, and BRISQUE respectively over the best compared method reported in the literature. Moreover the visual results on real-world images also reveal the superiority of the proposed RLLcR technique.
Depth Guidance and Intradomain Adaptation for Semantic Segmentation

Unsupervised domain adaptation (UDA) mitigates the domain shift between the source and target domains. Some conventional methods are based on guiding semantic segmentation across domains using depth information. Nevertheless, these methods are constrained and inadequate for the use of depth knowledge. In this work, we propose a depth-aware adaptation framework (DAF) and an intradomain adaptation (IDA) strategy to improve semantic segmentation in the context of UDA. We design a novel depth estimation network based on the channelwise attention mechanism to provide additional depth information. In our approach, DAF aims to adapt domains by exploiting the inherent correlations of semantic and depth information, where we construct a depth-aware space for the alignment between source and target domains. Furthermore, IDA bridges the gap within the target domain through a proposed depth-aware ranking strategy. This strategy is based on dividing the target domain into a subsource domain and a subtarget domain. The distribution discrepancies between these domains are then aligned. Extensive experiments on SYNTHIAb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> Cityscapes and SYNTHIAb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> Mapillary cross-domain tasks confirm that our method overperforms the conventional depth guidance methods with mean IOU values of 46.7% and 73.3% on the above two tasks, respectively.
Optical character correction of large-curvature annular sector text in polar coordinate system

Optical character recognition (OCR) of complex morphologies represented by large-curvature annular sector text (AST) is a very challenging task. A three-segment text recognition framework consisting of detection, correction and recognition is currently an effective method for dealing with complex morphological OCR. Optical character correction (OCC) is a key component in processing largecurvature AST. This paper proposes an OCC method in the polar coordinate system, which consists of control point preprocessing, polar coordinate transformation, and image remapping. The control point preprocessing is used to normalize the control points of the large curvature AST region; the polar coordinate transformation is to convert the pixels in the rectangular coordinate system to the polar coordinate system; image remapping maps the original image in polar coordinate system to polar coordinate space for re-representation. The method proposed in this paper can be used in conjunction with most detection and recognition modules and is applicable to any language type. Furthermore, the correction process consumes very little computational resources and has little impact on the speed of text detection and recognition. Experimental results show that the proposed method outperforms state-of-the-art algorithms in large curvature AST correction and recognition experiments
Timid semi–supervised learning for face expression analysis

In the last years, semi–supervised learning has been proposed as a strategy with high potential for improving machine learning capabilities. Face expression recognition may highly benefit from such a technique, as accurate labeling is both difficult and costly, whereas millions of unlabeled images with human faces are available on the Internet, but without annotations. In this paper we evaluate the benefits of semi–supervised learning in the practical scenarios of face expression analysis. Our conclusion is that better performance is indeed achievable, but by methods that put a distinct emphasis on the diversity of exploring patterns in the unlabeled data domain. The evaluation is carried on multiple tasks such as detecting Action Units on EmotioNet, assessing Action Units intensity on the spontaneous DISFA database and, respectively, recognizing expressions on static images acquired in the wild, from the RAF-DB and FER+ databases. We show that, in these scenarios, a so–called timid semi–supervised learner is more robust and achieves higher performance than standard, confident semi–supervised learners.
Union nonparallel support vector machines framework with consistency

Though there are several dozens of nonparallel support vector machines (NSVMs), little studies on general forms and characteristics of NSVMs are investigated. To fill in this gap, this paper categorizes the existing NSVMs into two types and reveals the advantages and defects of different types of them. In particular, inconsistency problems for these models are pointed out and discussed. Based on this observation, this paper further proposes and investigates a novel max–min distance-based nonparallel support vector machine (NSVM) with desired consistency. Compared with the existing methods, the proposed NSVM has the consistency of training and test and the consistency of metric. In addition, NSVM also assigns each sample an ascertained loss, which not only is able to identify if a data sample being classified correctly, but also makes NSVM completely in line with its decision rule. NSVM can be easily extended to its nonlinear version, and both of them are effectively solved through a modified proximal difference-of-convex algorithm with extrapolation algorithm. Experimental results on the artificial dataset, benchmark datasets and a real-world dataset support the advantages of the proposed model.
Benchmarking local motion planners for navigation of mobile manipulators

There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, dissimilarity of tasks and objectives, as well as uncertainties in measurements and operating environments. In this paper, we propose a simulation framework to evaluate the performance of the local trajectory planners to generate smooth, and dynamically and kinematically feasible trajectories for mobile manipulators in the same environment. We focus on local planners as they are key components that provide smooth trajectories while carrying a load, react to dynamic obstacles, and avoid collisions. We evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA) and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our software solution is applicable to any other local planners used in the Robot Operating System (ROS) framework, without additional programming effort.