Reviewerly/exHarmony huggingface dataset:
A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.
Multiclass imbalanced and concept drift network traffic classification framework based on online active learning

The complex problems of multiclass imbalance, virtual or real concept drift, concept evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network traffic classification tasks. In this paper, we propose a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFoal), which includes a configurable supervised learner for the initialization of a network traffic classification model, an active learning method with a hybrid label request strategy, a label sliding window group, a sample training weight formula and an adaptive adjustment mechanism for the label cost budget based on a periodic performance evaluation. In addition, a novel uncertain label request strategy based on a variable least confidence threshold vector is designed to address the problems of a variable multiclass imbalance ratio or even the number of classes changing over time. Experiments performed based on eight well-known real-world network traffic datasets demonstrate that MicFoal is more effective and efficient than several state-of-the-art learning algorithms.
Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems

Aquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-dimensional (3D) HR image acquisition typically requests long scan time and, results in small spatial coverage and low signal-to-noise ratio (SNR). Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach scale-specific projection between LR and HR images, thus these methods can only deal with fixed up-sampling rates. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the LR image and the HR image are represented using the same implicit neural voxel function with different sampling rates. Due to the continuity of the learned implicit function, a single ArSSR model is able to achieve arbitrary and infinite up-sampling rate reconstructions of HR images from any input LR image. Then the SR task is converted to approach the implicit voxel function via deep neural networks from a set of paired HR and LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.
Exploiting Stacked Autoencoders for Improved Sentiment Analysis

Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a genetic algorithm (GA), stacked autoencoder (SAE), and support vector machine (SVM) for fine-grained sentiment analysis. Features are extracted using continuous bag-of-words (CBOW), and then input into the SAE. In the proposed GA(SAE)-SVM, the hyperparameters of the SAE algorithm are optimized using GA. The features extracted by SAE are input into the SVM for final classification. A comparison is performed with a random search and grid search for parameter optimization. GA optimization is faster than grid search, and selects more optimal values than random search, resulting in improved accuracy. We evaluate the performance of the proposed model on eight benchmark datasets. The proposed model outperformed when compared to the baseline and state-of-the-art techniques.
Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum; the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend; and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization; for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing

In this study, to address the current high early-detection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.
Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.Chest X-ray is one of the most widely used medical imaging tests worldwide to diagnose and manage heart and lung diseases. In this study, we developed a computer-based tool to predict patients’ age from chest X-rays. The tool precisely estimated patients’ age from chest X-rays. Furthermore, in patients with heart failure and those admitted to the intensive care unit for cardiovascular disease, elevated X-ray age estimated by our tool was associated with poor clinical outcomes, including readmission for heart failure or death from any cause. With further testing, our tool may help clinicians to predict outcomes in patients with heart disease based on a simple chest X-ray.
A survey on computationally efficient neural architecture search

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve this major limitation of NAS, improving the computational efficiency is essential in the design of NAS. However, a systematic overview of computationally efficient NAS (CE-NAS) methods still lacks. To fill this gap, we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods, together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities. The remaining challenges and open research questions are also discussed, and promising research topics in this emerging field are suggested.
Deep hybrid model for single image dehazing and detail refinement

Deep learning technologies have been applied in Single Image Dehazing (SID) tasks successfully. However, most SID algorithms seldom consider to refine image details during dehazing. Therefore, there exist some detail-loss regions in dehazed results. To solve this issue, we design a deep hybrid network to improve dehazing performance and remedy the loss of details. Different from existing algorithms that usually ignore detail refinement and adopt a unified framework to remove haze, we propose to treat dehazing and detail refinement as two separate tasks, so that each task could be solved via different ways. Particularly, we design two sub-networks with a multi-term loss function. First, for removing haze effectively, we introduce the Squeeze-and-Excitation (SE) to design a haze residual attention sub-network, which is used to reconstruct the dehazed image. Second, as for remedying details, we take the previous dehazed image as the input to a detail refinement sub-network, where the image details can be enhanced via multi-scale contextual information aggregation. Through the joint training of two sub-network, the haze can be removed clearly and the image details can be preserved well. Moreover, the detail refinement sub-network can be detached into other existing dehazing methods to improve their model performances. Extensive experiments also verify the superiority of our proposed network against recently proposed state-of-the-arts.
<scp>U‐Pb</scp> Saturn: New <scp>U‐Pb</scp>/<scp>Pb‐Pb</scp> Data Reduction Software for <scp>LA‐ICP‐MS</scp>

U‐Pb Saturn is new freeware for U‐Pb LA‐ICP‐MS data reduction. It has been developed to provide easy interaction with and visualisation of LA‐ICP‐MS U‐Pb datasets, and allows fast and reliable data reduction of hundreds of data‐points. Saturn offers dynamic graphic interfaces to quickly view, evaluate, and plot U‐Pb and Pb‐Pb isotope data. It operates online (or offline), giving the freedom to change parameters and reprocess data at any stage of data acquisition. The main interface allows the user to: (1) choose the best statistics for drift correction, (2) include/exclude offset factors and (3) apply (or not) Pb c corrections in different modes. Signal intensities are displayed in a separate graphic interface that allows users to interact with the time‐resolved signal of individual spot analyses. All graphic windows are interactive; any modification to data treatment (e.g., inclusion or exclusion of analyses of reference material, or modification of the time‐resolved signal windows) is instantaneously updated on the data tables. Saturn is particularly attractive for beginners in LA‐ICP‐MS U‐Pb geochronology as it is non‐commercial, easy to install, and very interactive. Coding information and a version of the software can be accessed at http://www.air.ufop.br .
Improved block chain system for high secured IoT integrated supply chain

The incredibly complex supply chains in today's world face significant problems in terms of accountability and reliability. Blockchain technology could be able to solve these problems by offering a tamper-proof audit trail of supply chain activities & data on a product lifecycle, but it could resolve the issue of information's inherent lack of reliability. Current Reputation systems offer a practical solution to the confidence issue. Moreover, it is based on a small number of observations, lack granularity or mechanization have considerable overhead, current reputational methods are not suitable for blockchain-based supply chain operations. In this research, we present TrustChain, a three-layered system for managing trust that tracks connections between supply chain actors and automatically calculates trust and reputation scores based on those connections using a consortium blockchain with Internet of Things (IoT). Trustchain would be novel because it supports reputation rankings that distinguish between supply chain participants & goods, allowing the assignment of product-specific reputations for the same participant, (a) the reputation design that assesses the performance of commodities and the trustworthiness of entities based on several observations of supply chain events, (b) the use of smart contracts for transparent, impactful, safe, and automated computation of reputation rating, and (c) reputation scores that differentiate between supply chain members and services.
Robust zero-watermarking scheme based on a depthwise overparameterized VGG network in healthcare information security

As healthcare information technology has rapidly evolved, securely storing and transmitting medical data online and successfully protecting patient privacy are currently the research focus in the healthcare information field. To better protect the security of medical data, this paper introduces deep neural network and convolutional block attention module (CBAM) into the study of watermarking techniques and proposes a medical image zero-watermarking scheme based on depthwise overparameterized VGG (DO-VGG). First, we extract the high-dimensional abstract feature information of medical images using the pretrained DO-VGG model. Then, the construction of the zero-watermarking scheme utilizes the mean-perceptual hashing algorithm, which can efficiently resist both common and geometric attacks. Meanwhile, using the improved logistic mapping to encrypt the watermarking image effectively improves the security of the scheme. Experimental results indicate that all NC values of the proposed scheme are maintained above 0.8 under various degrees of attacks, which has good robustness and invisibility. The proposed scheme can satisfy the special requirements of medical image integrity and effectively protect the private information of medical images.
Fusion of Multispectral Remote-Sensing Data through GIS-Based Overlay Method for Revealing Potential Areas of Hydrothermal Mineral Resources

Revealing prospective locations of hydrothermal alteration zones (HAZs) is an important technique for mineral prospecting. In this study, we used multiple criteria inferred from Landsat-8 OLI, Sentinel-2, and ASTER data using a GIS-based weighted overlay multi-criteria decision analysis approach to build a model for the delineating of hydrothermal mineral deposits in the Khnaiguiyah district, Saudi Arabia. The utilized algorithms revealed argillic, phyllic, and propylitic alteration characteristics. The HAZs map resulted in the identification of six zones based on their mineralization potential, providing a basis for potential hydrothermal mineral deposit assessment exploration, which was created by the fusion of mineral bands indicators designated very low, low, moderate, good, very good, and excellent and covers 31.36, 28.22, 20.49, 10.99, 6.35, and 2.59%. Based on their potential for hydrothermal mineral potentiality, the discovered zones match gossans related to sulfide mineral alteration zones, as demonstrated by previous studies.
Finite-time synchronization of multi-scroll hyperchaotic system and its application in image encryption

In this paper, an image algorithm using finite time synchronization is designed based on multi-scroll hyperchaotic system. A new ring-cutting scrambling is used in the algorithm to scramble the pictures in a relatively simple way. In the end, the encryption algorithm is completed by plane diffusion. In particular, the bit-plane algorithm needs to restore four bit-planes to get plaintext which can greatly reduce the deciphering ability. Experimental results show that the proposed algorithm can better resist violent attacks and cropping attacks through histogram, correlation coefficient red, and robustness tests.
Perspective: Present and Future of Virtual Reality for Neurological Disorders

Since the emergence of Virtual Reality technology, it has been adopted in the field of neurology. While Virtual Reality has contributed to various rehabilitation approaches, its potential advantages, especially in diagnosis, have not yet been fully utilized. Moreover, new tides of the Metaverse are approaching rapidly, which will again boost public and research interest and the importance of immersive Virtual Reality technology. Nevertheless, accessibility to such technology for people with neurological disorders has been critically underexplored. Through this perspective paper, we will briefly look over the current state of the technology in neurological studies and then propose future research directions, which hopefully facilitate beneficial Virtual Reality studies on a wider range of topics in neurology.