Reviewerly/exHarmony huggingface dataset:
Discrete fractional-order Caputo method to overcome trapping in local optima: Manta Ray Foraging Optimizer as a case study

Enhancing the exploration and exploitation phases of the metaheuristic (MH) optimization algorithms is the key to avoiding local optima. The Manta ray foraging optimizer is a recently proposed MH optimizer. The MRFO showed a good performance in the simple optimization problems. However, it is trapped into the local optimum in the more elaborated ones due to the original algorithm’s low capability in exploiting the optimal solutions and its convergence. From this principle, in this work, a novel variant of the Manta ray foraging optimizer has been proposed for global optimization problems, engineering design optimization problems, and multi-threshold segmentation. In the proposed approach, the fractional calculus (FC) using Caputo fractional differ-sum operator has been adopted to enhance the manta rays movement in the exploitation phase via utilizing history dependency of FC to boost exploiting the optimal solutions via sharing the past knowledge over the optimization process. Moreover, to avoid premature convergence, the somersault factor has been adaptively tuned. The fractional-order Caputo Manta-Ray Foraging Optimizer (FCMRFO) has been proposed. The proposed algorithm’s sensitivity for the FC coefficients has been tested with ten-dimensional CEC2017 benchmarks. The scalability test of the proposed algorithm has been performed with 30, 50 and 100-dimensional CEC2017. Moreover, CEC2020 benchmarks with dimensions 5 and 20 have been applied for providing an extensive investigation, and the FCMRFO has been compared with recent state-of-the-art algorithms. Through utilizing the non-parametric statistical analysis and ranking test, the FCMRFO confirms its superiority and ability to avoid the local optimum in several cases. For the second part of the study, three constrained engineering design problems have been investigated; then, numerous natural images are applied to appraise the FCMRFO for multilevel threshold image segmentation. By performing several metrics, the FCMRFO proves its quality and efficiency compared to recent well-regarded algorithms in engineering applications and image segmentation.
Blockchain-based delegated Quantum Cloud architecture for medical big data security

Smart Healthcare systems compromise complex computations such as visualization of molecules, analysis of DNA, and therapy determination. These are considered to be complex problems that today's supercomputers are still facing. On the other hand, Quantum computing promises fast, efficient, and scalable computing resources that are sufficient to compute large and complex operations in exponential time. It is a fact that Quantum computing will adequately innovate the computation perspective. However, it is not a feasible solution yet as it is likely to be rare and highly expensive to produce. This paper presents a Quantum Cloud-as-a-service for an efficient, scalable, and secure solution for complex Smart Healthcare computations. Our novelty resides in the usage of Quantum Terminal Machines (QTM) and Blockchain technology to enhance the feasibility and security of the proposed architecture. Experimental results prove the feasibility of the architecture and the absolute security of the implemented Q-OTP encryption.
Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers

Manual searching for infrastructure damage information from large amounts of textual data requires considerable time and effort. A fast and accurate collection of damage information from such data is necessary for effective infrastructure planning. In this study, a question answering method was proposed to provide users with infrastructure damage information from textual data automatically. The proposed method relies on a natural language model called bidirectional encoder representations from transformers for information retrieval. From the 143 reports collected from the National Hurricane Center, 533 question-answer pairs were formulated. The proposed model was trained with 435 pairs and tested with the remainder. The model was also tested with 43 question-answer pairs created using earthquake-related textual data and achieved F1-scores of 90.5% and 83.6% for the hurricane and earthquake datasets, respectively.
Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing

The arrival of the fifth-generation technology standard for broadband cellular networks (5G) and beyond 5G networks (B5G) rises the speed and robustness ceiling of communicating networks and thereby empowers the rapid popularization of edge computing. Consequently, B5G-Driven edge computing allows a growing volume of data to be collected from and transmitted among pervasive edge devices for big data analytics. The collected big data becomes the driving force of artificial intelligence (AI) by training high-quality machine learning (ML) models, which is followed by severe individual privacy leakage. Federated learning(FL) is then proposed to achieve privacy-preserving machine learning by avoiding the exchange of raw data. Unfortunately, several major issues remain outstanding. Centralized processing costs significant communication resources between cloud and edge while data falsification problems persist. In addition, the private data may be reconstructed by malicious participants by exploiting the context of model parameters in FL. To solve the identified problems, we propose to integrate blockchain-enabled FL with Wasserstein generative adversarial network (WGAN) enabled differential privacy (DP) to protect the model parameters of edge devices in B5G networks. Blockchain enables decentralized FL to reduce communication costs between cloud and edge while alleviating the data falsification issues, and it also provides an incentive mechanism to alleviate the data island issue in B5G-Driven edge computing. WGAN is used to generate controllable random noise complying with DP requirements, which is then injected to model parameters. WGAN-enabled DP is able to achieve an optimized trade-off between differential privacy protection and improved data utility of model parameters. Time delay analysis is conducted to show the efficiency of the proposed model. Extensive evaluation results from simulations demonstrate superior performances from aspects of convergence efficiency, accuracy, and data utility.
Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models

Solar radiation (SR) is considered as a critical factor in determining energy management. In this research, the potential of the Bayesian averaging model (BMA) was investigated for estimating monthly SR. The inputs were monthly average temperature, wind speed, relative humidity, and sunshine duration. The BMA model was employed to estimate SR by extracting information from multiple adaptive neuro-fuzzy systems (ANFIS) and multi-layer perceptron (MLP) models. In this study, Archimedes optimization algorithm (AOA), particle swarm optimization (PSO), genetic algorithm (GA), and bat algorithm (BA) were used to tune the parameters of the ANIFS and MLP. In addition, a multitude of error indices such as root mean square error (RMSE), and Nash Sutcliff efficiency (NSE), and several graphical tools were used to investigate the accuracy of the models. The results showed the better performance of the BMA model than other models for estimating solar radiation. For example, BMA with RMSE of 6.78, MAE of 5.25, and NSE of 0.96 had the best accuracy in the training stage of the Tabriz station. On the other hand, in the testing level of Tehran station, BMA (RMSE=7.89 MJ/ m2, MAE=6.89 MJ/ m2, NSE=0.95) gave the best accuracy, and the MLP model (RMSE= 14.12 MJ/ m2, MAE=12.23 MJ/ m2, and NSE=0.77) gave the worst performance, respectively.
Cooperative multi-UAV coverage mission planning platform for remote sensing applications

This paper proposes a novel mission planning platform, capable of efficiently deploying a team of UAVs to cover complex-shaped areas, in various remote sensing applications. Under the hood lies a novel optimization scheme for grid-based methods, utilizing Simulated Annealing algorithm, that significantly increases the achieved percentage of coverage and improves the qualitative features of the generated paths. Extensive simulated evaluation in comparison with a state-of-the-art alternative methodology, for coverage path planning (CPP) operations, establishes the performance gains in terms of achieved coverage and overall duration of the generated missions. On top of that, DARP algorithm is employed to allocate sub-tasks to each member of the swarm, taking into account each UAV's sensing and operational capabilities, their initial positions and any no-fly-zones possibly defined inside the operational area. This feature is of paramount importance in real-life applications, as it has the potential to achieve tremendous performance improvements in terms of time demanded to complete a mission, while at the same time it unlocks a wide new range of applications, that was previously not feasible due to the limited battery life of UAVs. In order to investigate the actual efficiency gains that are introduced by the multi-UAV utilization, a simulated study is performed as well. All of these capabilities are packed inside an end-to-end platform that eases the utilization of UAVs' swarms in remote sensing applications. Its versatility is demonstrated via two different real-life applications: (i) a photogrametry for precision agriculture and (ii) an indicative search and rescue for first responders missions, that were performed utilizing a swarm of commercial UAVs. The source code can be found at: https://github.com/savvas-ap/mCPP-optimized-DARP
Semi-supervised multi-view graph convolutional networks with application to webpage classification

Semi-supervised multi-view learning (SML) is a hot research topic in recent years, with webpage classification being a typical application domain. The performance of SML is further boosted by the successful introduction of graph convolutional network (GCN) for learning discriminant node representations. However, there remains much space to improve the GCN-based SML technique, particularly on how to adaptively learn optimal graph structures for multi-view graph convolutional representation learning and make full use of the label and structure information in labeled and unlabeled multi-view samples. In this paper, we propose a novel SML approach named semi-supervised multi-view graph convolutional networks (SMGCN) for webpage classification. It contains a multi-view graph construction module and a semi-supervised multi-view graph convolutional representation learning module, which are integrated into a unified network architecture. The former aims to obtain optimal graph structure for each view. And the latter performs graph convolutional representation learning for each view, and provides an inter-view attention scheme to fuse multi-view representations. Network training is guided by the losses defined on both label and feature spaces, such that the label and structure information in labeled and unlabeled data is fully explored. Experiments on two widely used webpage datasets demonstrate that SMGCN can achieve state-of-the-art classification performance.
Trust Management for Internet of Things: A Comprehensive Study

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust has become increasingly apparent and received considerable scholarly attention in recent years. With the occurrence of various security incidents, data leakage accidents, and service fraud, which seriously affects the quality of service and system efficiency of IoT, trust is fast becoming a key issue in IoT system. In addition to the security and efficiency, trust further contains the reliability, attack resistance, fairness, flexibility, and incentive, resulting in a wide range of new researches from the trust management framework to the quantification method. In this work, various dimensions of trust, including definition, composition, aggregation, and computation are introduced and analyzed. We focus on comprehensive comparison of the state-of-the-art trust management researches and the related applications. Besides, we explore and discuss the important challenges, including performance bottleneck, bidirectional trust, dynamic changes of context, privacy preserving, and cross-domain issue. Some potential enabling technologies for trust management are further analyzed. The objective of this article is to comprehend the trust issue and the composition of trust management in IoT, and illustrate the difference of existing work, thereby motivating further research interest in this field.
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition

Recently, deep learning-based image classification and speech recognition approaches have made extensive use of attention mechanisms to achieve state-of-the-art recognition, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation recognition, this letter proposes a time-frequency attention mechanism for convolutional neural network (CNN)-based automatic modulation recognition. The proposed time-frequency attention mechanism is designed to learn which channel, frequency and time information is more meaningful in CNN for modulation recognition. We analyze the effectiveness of the proposed attention mechanism and evaluate the performance of the proposed models. Experiment results show that the proposed methods outperform existing learning-based methods and attention mechanisms.
Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection

Unsupervised anomaly detection aims to build models to effectively detect unseen anomalies by only training on the normal data. Although previous reconstruction-based methods have made fruitful progress, their generalization ability is limited due to two critical challenges. First, the training dataset only contains normal patterns, which limits the model generalization ability. Second, the feature representations learned by existing models often lack representativeness which hampers the ability to preserve the diversity of normal patterns. In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection. Based on the convolutional autoencoder structure, AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations. Experiments on four public multivariate time series datasets demonstrate that AMSL significantly improves the performance compared to other state-of-the-art methods. Specifically, on the largest CAP sleep stage detection dataset with 900 million samples, AMSL outperforms the second-best baseline by <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</b> %+ in both accuracy and F1 score. Apart from the enhanced generalization ability, AMSL is also more robust against input noise.
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

Deep learning methods have shown outstanding performance in many applications, including single-image super-resolution (SISR). With residual connection architecture, deeply stacked convolutional neural networks provide a substantial performance boost for SISR, but their huge parameters and computational loads are impractical for real-world applications. Thus, designing lightweight models with acceptable performance is one of the major tasks in current SISR research. The objective of lightweight network design is to balance a computational load and reconstruction performance. Most of the previous methods have manually designed complex and predefined fixed structures, which generally required a large number of experiments and lacked flexibility in the diversity of input image statistics. In this paper, we propose a dynamic residual self-attention network (DRSAN) for lightweight SISR, while focusing on the automated design of residual connections between building blocks. The proposed DRSAN has dynamic residual connections based on dynamic residual attention (DRA), which adaptively changes its structure according to input statistics. Specifically, we propose a dynamic residual module that explicitly models the DRA by finding the interrelation between residual paths and input image statistics, as well as assigning proper weights to each residual path. We also propose a residual self-attention (RSA) module to further boost the performance, which produces 3-dimensional attention maps without additional parameters by cooperating with residual structures. The proposed dynamic scheme, exploiting the combination of DRA and RSA, shows an efficient trade-off between computational complexity and network performance. Experimental results show that the DRSAN performs better than or comparable to existing state-of-the-art lightweight models for SISR.
Adversarial Attacks Against Deep Learning-Based Network Intrusion Detection Systems and Defense Mechanisms

Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove vulnerable to adversarial examples. Through these, attackers that may be oblivious to the precise mechanics of the targeted NIDS add subtle perturbations to malicious traffic features, with the aim of evading detection and disrupting critical systems. Defending against such adversarial attacks is of high importance, but requires to address daunting challenges. Here, we introduce TIKI- TAKA, a general framework for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> assessing the robustness of state-of-the-art deep learning-based NIDS against adversarial manipulations, and which <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> incorporates defense mechanisms that we propose to increase resistance to attacks employing such evasion techniques. Specifically, we select five cutting-edge adversarial attack types to subvert three popular malicious traffic detectors that employ NNs. We experiment with publicly available datasets and consider both one-to-all and one-to-one classification scenarios, i.e., discriminating illicit vs benign traffic and respectively identifying specific types of anomalous traffic among many observed. The results obtained reveal that attackers can evade NIDS with up to 35.7% success rates, by only altering time-based features of the traffic generated. To counteract these weaknesses, we propose three defense mechanisms: model voting ensembling, ensembling adversarial training, and query detection. We demonstrate that these methods can restore intrusion detection rates to nearly 100% against most types of malicious traffic, and attacks with potentially catastrophic consequences (e.g., botnet) can be thwarted. This confirms the effectiveness of our solutions and makes the case for their adoption when designing robust and reliable deep anomaly detectors.
Assessment of solar PV potential in commercial buildings

This study aims to investigate the potential of the rooftop application of solar PV in commercial buildings. It focuses on four types of commercial buildings: shopping malls, offices, hotels, and hospitals. Besides the technical aspects of the technology, the study investigated important features of building roofs such as architectural and structural details, services, maintenance, and access requirements. It examines 105 buildings with the help of remote sensing techniques. Site visits of sample buildings are also undertaken to have a detailed assessment of the relevant roof features. The examined roof restrictions have been identified and classified into five major categories. The average utilization factor has been found to be 0.53, 0.35, 0.34, and 0.23 for shopping malls, hospitals, offices, and hotels respectively. The overall UF for the studied 105 buildings has been calculated to be 0.49. Four case study buildings, one representing each of the four types of buildings, have been investigated for their roof restrictions, utilization factor, and PV output. The PV modeling results reveal an annual output of 285 kWh/m2. The modeled PV output has also been validated with the output data of an actual PV project. Finally, PV systems have been analyzed for their economic and environmental performance.
Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.
Malware Traffic Classification Using Domain Adaptation and Ladder Network for Secure Industrial Internet of Things

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep-learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this article proposes three methods based on semisupervised learning (SSL), transfer learning (TL), and domain adaptive (DA), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the classification accuracy with few labeled samples. Then, we use the DA method to solve the mismatch problem between the source domain and the target domain in the TL process. The proposed method is not only applicable to the shallow network but also to the deep neural network structure, and can achieve better classification results. Experimental results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples in IIoT. The source code for all the experiments is available at GitHub.The code of this article can be downloaded from GitHub link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yzjh/Keras-MTC-DA-Ladder</uri> .