Reviewerly/exHarmony huggingface dataset:
SaTYa: Trusted Bi-LSTM-Based Fake News Classification Scheme for Smart Community

This article proposes a SaTya scheme that leverages a blockchain (BC)-based deep learning (DL)-assisted classifier model that forms a trusted chronology in fake news classification. The news collected from newspapers, social handles, and e-mails are web-scrapped, prepossessed, and sent to a proposed Q-global vector for word representations (Q-GloVe) model that captures the fine-grained linguistic semantics in the data. Based on the Q-GloVe output, the data are trained through a proposed bi-directional long short-term memory (Bi-LSTM) model, and the news is classified as real-or-fake news. This reduces the vanishing gradient problem, which optimizes the weights of the model and reduces bias. Once the news is classified, it is stored as a transaction, and the news stakeholders can execute smart contracts (SCs) and trace the news origin. However, only verified trusted news sources are added to the BC network, ensuring credibility in the system. For security evaluation, we propose the associated cost of the Bi-LSTM classifier and propose vulnerability analysis through the smart check tool for potential vulnerabilities. The scheme is compared against discourse-structure analysis, linguistic natural language framework, and entity-based recognition for different performance metrics. The scheme achieves an accuracy of 99.55% compared to 93.62% against discourse structure analysis. Also, it shows an average improvement of 18.76% against other approaches, which indicates its viability against fake-classifier-based models.
Blockchain technologies for interoperation of business processes in smart supply chains

The Smart Manufacturing paradigm requires support by smart supply chains with capabilities of self-organizing and self-optimizing capabilities to accommodate uncertainties. Conventional supply chains face challenges in optimizing logistic processes and reducing overall costs of products and services. The emerging Internet of Things, Cyber-Physical Systems, and blockchain have laid the foundation to support interoperation in satisfying scalability, security, flexibility, and responsiveness of supply chains. Blockchain has shown potential in improving scalability and security. We are highly motivated to scrutinize technical obstacles of integrating blockchain into smart supply chains in the industrial Internet of Things environment. Our attempt is to integrate blockchain into Service-oriented Architecture for seamless interoperations. The system architecture and framework are presented and prototyped to enable Blockchain for the cooperation of business processes. The simulation has shown the effectiveness in improving the scalability, robustness, and adaptability of business processes in a smart supply chain.
Towards the quest to reduce income inequality in Africa: is there a synergy between tourism development and governance?

Despite the growing attention on the tourism development-income inequality nexus, a conspicuous gap in the literature is that rigorous empirical works examining how good governance moderates the relationship are hard to find. Anchoring on the trickle-down theory and the tourism-led growth hypothesis, this study fills this void in the literature based on data for 48 African countries for the period 1996–2020. We provide strong evidence robust to several specifications from the GMM estimator to show that, though unconditionally both tourism development and governance reduce income inequality in Africa, the effect of the former is amplified in the presence of good economic, political and institutional governance. Particularly, we find that control of corruption and political stability are keys for propelling Africa's tourism sector to contribute to the equalization of incomes across the continent. Policy recommendations are provided in line with SDG 10, and Aspirations 1 and 3 of Africa's Agenda 2063.
FedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition

The advancement of smartphone sensors and wearable devices has enabled a new paradigm for smart human activity recognition (HAR), which has a broad range of applications in healthcare and smart cities. However, there are four challenges, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privacy preservation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">label scarcity</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-timing</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneity patterns</i> , to be addressed before HAR can be more applicable in real-world scenarios. To this end, in this paper, we propose a personalized federated HAR framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedHAR</i> , to overcome all the above obstacles. Specially, as federated learning, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedHAR</i> performs distributed learning, which allows training data to be kept local to protect users’ privacy. Also, for each client without activity labels, in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedHAR</i> , we design an algorithm to compute unsupervised gradients under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency training</i> proposition and an unsupervised gradient aggregation strategy is developed for overcoming the concept drift and convergence instability issues in online federated learning process. Finally, extensive experiments are conducted using two diverse real-world HAR datasets to show the advantages of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedHAR</i> over state-of-the-art methods. In addition, when fine-tuning each unlabeled client, personalized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedHAR</i> can achieve additional 10% improvement across all metrics on average.
Unsupervised Domain Adaptation for Semantic Segmentation of High-Resolution Remote Sensing Imagery Driven by Category-Certainty Attention

Semantic segmentation is an important task of analysis and understanding of high-resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN)-based model shows their excellent performance in remote sensing image semantic segmentation. Most of the existing HRSI semantic segmentation methods are only designed for a very limited data domain, that is, the training and test images are from the same dataset. The accuracy drops sharply once a model trained on a certain dataset is used for cross-domain prediction due to the difference in feature distribution of the dataset. To this end, this article proposes an unsupervised domain adaptation framework based on adversarial learning for HRSI semantic segmentation. This framework uses high-level feature alignment to narrow the difference between the source and target domains at the semantic level. It uses the category-certainty attention module to reduce the attention of the classifier on category-level aligned features and increase the attention on category-level unaligned features. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in cross-domain segmentation.
BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.
Emerging digital citizenship regimes: Pandemic, algorithmic, liquid, metropolitan, and stateless citizenships

This article develops a conceptual taxonomy of five emerging digital citizenship regimes: (i) the globalised and generalisable regime called pandemic citizenship that clarifies how post-COVID-19 datafication processes have amplified the emergence of four intertwined, non-mutually exclusive, and non-generalisable new techno-politicalised and city-regionalised digital citizenship regimes in certain European nation-states' urban areas; (ii) algorithmic citizenship, which is driven by blockchain and has allowed the implementation of an e-Residency programme in Tallinn; (iii) liquid citizenship, driven by dataism – the deterministic ideology of Big Data – and contested through claims for digital rights in Barcelona and Amsterdam; (iv) metropolitan citizenship, as revindicated in reaction to Brexit and reshuffled through data co-operatives in Cardiff; and (v) stateless citizenship, driven by devolution and reinvigorated through data sovereignty in Barcelona, Glasgow, and Bilbao. This article challenges the existing interpretation of how these emerging digital citizenship regimes together are ubiquitously rescaling the associated spaces/practices of European nation-states.
Discovering Parametric Activation Functions

Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been inconsistent and task dependent, and therefore the rectified linear unit (ReLU) is still the most commonly used. This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance. Evolutionary search is used to discover the general form of the function, and gradient descent to optimize its parameters for different parts of the network and over the learning process. Experiments with four different neural network architectures on the CIFAR-10 and CIFAR-100 image classification datasets show that this approach is effective. It discovers both general activation functions and specialized functions for different architectures, consistently improving accuracy over ReLU and other activation functions by significant margins. The approach can therefore be used as an automated optimization step in applying deep learning to new tasks.
Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition

Graph convolutional networks (GCNs) have achieved promising results in skeleton-based action recognition due to their capability in analysing irregular grids with non-Euclidean geometry. Considering the fact that skeleton-based action recognition is a classification problem, the suitable graph representation in GCN-based approaches is the key for the ultimate goal of classification. Despite the practical success of GCN-based approaches for solving this problem over the past few years, learning better representation is still a challenging issue and the existing approaches fail in distinguishing similar actions. Besides, most existing GCN-based frameworks focus on modelling the spatial information and use one fixed kernel to model the temporal information. Such modelling does not pay enough attention to diversifying representations among different skeleton frames, leading to inefficiency in obtaining more discriminative temporal features for different actions and therefore, such modelling is inconvenient with the diversity of human movements. Our main concern in this work is the adaptive feature extraction of highly discriminative information for both the spatial and the temporal dimensions. To achieve that, a novel Enhanced Discriminative Graph Convolutional Network (ED-GCN) based on the attention mechanism for skeleton-based action recognition is proposed. Discriminative channel-wise features are obtained by fusing the Squeeze and Excitation (SE) module to the GCN to selectively enhance the significant features and suppress the non-significant ones. The adaptively enhanced feature map is then fused to the graph convolutional layer to improve the capability of learning better representation. For the temporal dimension inspired by temporal modelling in videos, we introduce our adaptive temporal modelling block (ATB), which is able to flexibly capture temporal structure for skeleton-based action recognition. Here, the proposed ATB is a two-stage module comprising re-calibration and motion-interaction stage, designed to learn temporal features by integrating the modelling of channel correlation and temporal evolution, respectively. Experimental results on two large-scale datasets, NTU-RGB+D and Kinetics-skeleton demonstrate the importance of discriminatively learned information and the effectiveness of the proposed ED-GCN for skeleton-based action recognition.
Active Learning for Deep Gaussian Process Surrogates

Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning for their nonstationary flexibility and ability to cope with abrupt regime changes in training data. Here, we explore DGPs as surrogates for computer simulation experiments whose response surfaces exhibit similar characteristics. In particular, we transport a DGP's automatic warping of the input space and full uncertainty quantification, via a novel elliptical slice sampling Bayesian posterior inferential scheme, through to active learning strategies that distribute runs nonuniformly in the input space—something an ordinary (stationary) GP could not do. Building up the design sequentially in this way allows smaller training sets, limiting both expensive evaluation of the simulator code and mitigating cubic costs of DGP inference. When training data sizes are kept small through careful acquisition, and with parsimonious layout of latent layers, the framework can be both effective and computationally tractable. Our methods are illustrated on simulation data and two real computer experiments of varying input dimensionality. We provide an open source implementation in the deepgp package on CRAN.
Multi-stakeholder perspectives on the impacts of service robots in urban hotel rooms

The main purpose of this exploratory research study was to determine urban hotel guest, manager, and staff perspectives on the impacts in terms of costs and benefits of the introduction of service robots in rooms. It was conducted to fill a literature gap on multi-stakeholder perspectives on the use of in-room service robots. Using a variety of data collection methods including a survey and interviews, the perspectives of stakeholder groups were found to be dissimilar. Hotel guests perceived more benefits than expenses while management views were more focused on the costs and technical aspects. Managers and financial officers primarily had a return on investment perspective on hotel robot use. The negative attitudes held by hotel managers toward service robots were related to costs of implementation and a concern that these costs could not be recouped. The technology readiness index, service robot acceptance model, and technology organization environment (TOE) framework were applied to examine guest perceptions and adoption attitudes.
Breast microscopic cancer segmentation and classification using unique <scp>4‐qubit</scp>‐quantum model

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. HIGHLIGHTS: This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.
A New Solar Power Prediction Method Based on Feature Clustering and Hybrid-Classification-Regression Forecasting

Solar generation systems are globally extending in terms of scale and number, which highlights the increasing importance of solar power forecast. In this paper, a day-ahead solar power prediction method is proposed including 1) a novel feature selecting/clustering approach based on relevancy and redundancy criteria and 2) an innovative hybrid-classification-regression forecasting engine. The proposed feature selecting/clustering approach filters out irrelevant features and partitions relevant features to two separate subsets to decrease the redundancy of features. Each of these two subsets is separately trained by one forecasting engine and the final solar power prediction of the proposed method is obtained by a relevancy-based combination of these two forecasts. The proposed forecasting engine classifies the historical data based on the learnability of its constituent regression models and assigns each class of training samples to one regression model. Each regression model predicts the outputs of the test samples that belong to its class. The effectiveness of the proposed solar power prediction method is illustrated by testing on two real-world solar farms.
A non-convex denoising model for impulse and Gaussian noise mixture removing using bi-level parameter identification

&lt;p style='text-indent:20px;'&gt;We propose a new variational framework to remove a mixture of Gaussian and impulse noise from images. This framework is based on a non-convex PDE-constrained with a fractional-order operator. The non-convex norm is applied to the impulse component controlled by a weighted parameter &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, which depends on the level of the impulse noise and image feature. Furthermore, the fractional operator is used to preserve image texture and edges. In a first part, we study the theoretical properties of the proposed PDE-constrained, and we show some well-posdnees results. In a second part, after having demonstrated how to numerically find a minimizer, a proximal linearized algorithm combined with a Primal-Dual approach is introduced. Moreover, a bi-level optimization framework with a projected gradient algorithm is proposed in order to automatically select the parameter &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Denoising tests confirm that the non-convex term and learned parameter &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; lead in general to an improved reconstruction when compared to results of convex norm and other competitive denoising methods. Finally, we show extensive denoising experiments on various images and noise intensities and we report conventional numerical results which confirm the validity of the non-convex PDE-constrained, its analysis and also the proposed bi-level optimization with learning data.&lt;/p&gt;
Demystifying TensorRT: Characterizing Neural Network Inference Engine on Nvidia Edge Devices

Edge devices are seeing tremendous growth in sensing and computational capabilities. Running state-of-the-art deep neural network (NN) based data processing on multi-core CPU processors, embedded Graphics Processing Units (GPU), Tensor Processing Units (TPU), Neural Processing Units (NPU), Deep Learning Accelerators (DLA) etc., edge devices are now able to handle heavy data computations with limited or without cloud connectivity. In addition to hardware resources, software frameworks that optimize a trained neural network (NN) model through weight clustering and pruning, weight and input-output quantization to fewer bits, fusing NN layers etc., for more efficient execution of NN inferences on edge platforms, play an important role in making machine learning at the edge (namely EdgeML) a reality. This paper is a first effort in characterizing these software frameworks for DNN inference optimizations on edge devices, especially edge GPUs which are now ubiquitously used in all embedded deep learning systems. The interactions between software optimizations and the underlying GPU hardware is carefully examined. As most NN optimization engines are proprietary softwares with undocumented internal details in the public domain, our empirical analysis on real embedded GPU platforms using a variety of widely used DNNs, provide various interesting findings. We observe tremendous performance gain and non-negligible accuracy gain from the software optimizations, but also find highly unexpected non-deterministic behaviors such as different outputs on same inputs or increased execution latency for same NN model on more powerful hardware platforms. Application developers using these proprietary software optimization engines, would benefit from our analysis and the discussed implications of our findings, with examples from real applications like intelligent traffic intersection control and Advanced Driving Assistance Systems (ADAS). There are important implications of our findings on performance modeling and prediction research too, that focus on micro-architecture modeling based application performance prediction, but should now additionally consider optimization engines that this paper examines.