Reviewerly/exHarmony huggingface dataset:
Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems

In recent years, several multi-method and multi-operator-based algorithms have been proposed for solving optimization problems. Generally, their performance is better than other algorithms that based on a single operator and/or algorithm. However, they do not perform consistently well over all the problems tested in the literature. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple differential evolution operators, with more emphasis placed on the best-performing operator. The performance of the proposed algorithm is tested by solving 10 problems with 5, 10, 15 and 20 dimensions taken from CEC2020 competition on single objective bound constrained optimization, with its results outperforming both single operator-based and different state-of-the-art algorithms.
Effects of an immersive virtual reality‐based classroom on students’ learning performance in science lessons

Abstract The increased availability and development of immersive technologies have given students growing opportunities to engage in different educational subjects. However, there is a lack of empirical research exploring the educational influence of using Immersive Virtual Reality (IVR) in science classrooms. To address this gap, this study developed a series of IVR‐based science lessons for middle‐school students and further examined these lessons’ effects on learning performance. Our quasi‐experimental approach employed a pretest and posttest to measure academic achievement and questionnaires to measure engagement and technology acceptance. A total of 90 sixth‐grade students from two classes were randomly assigned to the experimental and control groups. The experimental group engaged with the science lessons using Head‐Mounted Displays, whereas the control group learned the same material through traditional teaching methods. The results revealed that the experimental group obtained significantly higher academic achievement and engagement scores (cognitive, behavioral, emotional and social) than the control group. Moreover, the experimental group had a high level of technology acceptance for IVR usage in classrooms. Our study provides empirical evidence for IVR’s use in science education. Furthermore, it also sheds light on how to develop and implement an IVR‐based classroom for formal educational purposes. Practitioner Notes What is already known about this topic? Virtual Reality (VR) technology has been applied to different educational subjects and there is some empirical evidence indicating its potential benefits for students’ learning. VR is considered to have potential for educational uses in classrooms. Many studies have applied non‐immersive VR to science subjects, but few have focused on the use of Immersive VR (IVR) in everyday classrooms. What this paper adds We developed and implemented an IVR‐based teaching platform containing a set of systematic science lessons that students can engage in with Head‐Mounted Displays in classrooms. The results revealed that adopting IVR science lessons in the classroom can improve students’ academic achievement and engagement. Implications for practice and/or policy This study contributes to identifying the positive influences of an IVR‐based classroom on students’ science academic achievement and engagement. The results provide practical insights into developing and implementing an IVR‐based classroom for educational purposes. This study offers a concrete example of using IVR in middle‐school classrooms for formal science education.
RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise

Imbalanced classification is an important task in supervised learning, and Synthetic Minority Over-sampling Technique (SMOTE) is the most common method to address it. However, the performance of SMOTE deteriorates in the presence of label noise. Current generalizations of SMOTE try to tackle this problem by either selecting some samples in minority class as seed samples or combining SMOTE with a certain noise filter. Unfortunately, the former approach usually introduces extra parameters difficult to be optimized, and the latter one relies heavily on the performance of certain specific noise filter. In this paper, a self-adaptive robust SMOTE, called RSMOTE, is proposed for imbalanced classification with label noise. In RSMOTE, relative density has been introduced to measure the local density of every minority sample, and the non-noisy minority samples are divided into the borderline samples and safe samples adaptively basing their distinguishing characteristics of relative density. In addition, we reweigh the number that needs to be generated by every minority samples based on its chaotic level. Furthermore, we generate new samples within in the borderline area and safe area respectively to enhance the separability of the boundary. RSMOTE does not rely on any specific noise filter nor introduce any extra parameters. The experimental results demonstrate that the proposed approach performs better than the comparison methods in terms of several metrics, including Precision, Recall, Area Under the Curve (AUC), F1-measure, and G-mean. The implementation of the proposed RSMOTE in programming language Python is available at https://github.com/syxiaa/RSMOTE.
Shapley-Lorenz eXplainable Artificial Intelligence

Explainability of artificial intelligence methods has become a crucial issue, especially in the most regulated fields, such as health and finance. In this paper, we provide a global explainable AI method which is based on Lorenz decompositions, thus extending previous contributions based on variance decompositions. This allows the resulting Shapley-Lorenz decomposition to be more generally applicable, and provides a unifying variable importance criterion that combines predictive accuracy with explainability, using a normalised and easy to interpret metric. The proposed decomposition is illustrated within the context of a real financial problem: the prediction of bitcoin prices.
Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor-critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.
Wireless Image Retrieval at the Edge

We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize the accuracy of the retrieval task under power and bandwidth constraints over the wireless link. Due to the stringent delay constraint of the underlying application, sending the whole image at a sufficient quality is not possible. We propose two alternative schemes based on digital and analog communications, respectively. In the digital approach, we first propose a deep neural network (DNN) aided retrieval-oriented image compression scheme, whose output bit sequence is transmitted over the channel using conventional channel codes. In the analog joint source and channel coding (JSCC) approach, the feature vectors are directly mapped into channel symbols. We evaluate both schemes on image based re-identification (re-ID) tasks under different channel conditions, including both static and fading channels. We show that the JSCC scheme significantly increases the end-to-end accuracy, speeds up the encoding process, and provides graceful degradation with channel conditions. The proposed architecture is evaluated through extensive simulations on different datasets and channel conditions, as well as through ablation studies.
An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals

Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.
OpenCitations, an infrastructure organization for open scholarship

OpenCitations is an infrastructure organization for open scholarship dedicated to the publication of open citation data as Linked Open Data using Semantic Web technologies, thereby providing a disruptive alternative to traditional proprietary citation indexes. Open citation data are valuable for bibliometric analysis, increasing the reproducibility of large-scale analyses by enabling publication of the source data. Following brief introductions to the development and benefits of open scholarship and to Semantic Web technologies, this paper describes OpenCitations and its data sets, tools, services, and activities. These include the OpenCitations Data Model; the SPAR (Semantic Publishing and Referencing) Ontologies; OpenCitations’ open software of generic applicability for searching, browsing, and providing REST APIs over resource description framework (RDF) triplestores; Open Citation Identifiers (OCIs) and the OpenCitations OCI Resolution Service; the OpenCitations Corpus (OCC), a database of open downloadable bibliographic and citation data made available in RDF under a Creative Commons public domain dedication; and the OpenCitations Indexes of open citation data, of which the first and largest is COCI, the OpenCitations Index of Crossref Open DOI-to-DOI Citations, which currently contains over 624 million bibliographic citations and is receiving considerable usage by the scholarly community.
A hybrid feature selection method based on information theory and binary butterfly optimization algorithm

Feature selection is the problem of finding the optimal subset of features for predicting class labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization Algorithm (S-bBOA) is a nature-inspired algorithm for solving the feature selection problems. The evidence shows that S-bBOA has a better performance in exploration, exploitation, convergence, and avoidance of getting stuck in local optimal compared to other optimization algorithms. However, S-bBOA does not consider redundancy and relevancy of features. This paper proposes Information Gain binary Butterfly Optimization Algorithm (IG-bBOA), to overcome the S-bBOA constraints firstly. IG-bBOA maximizes both the classification accuracy and the mean of the mutual information between features and class labels. In addition, IG-bBOA also tries to minimize the number of selected features and is used within a three-phase proposed method called Ensemble Information Theory based binary Butterfly Optimization Algorithm (EIT-bBOA). In the first phase, 80% of irrelevant and redundant features are removed using Minimal Redundancy-Maximal New Classification Information (MR-MNCI) feature selection. In the second phase, the best feature subset is selected using IG-bBOA. Finally, a similarity based ranking method is used to select the final features subset. The experimental results are conducted using six standard datasets from UCI repository. The findings confirm the efficiency of the proposed method in improving the classification accuracy and selecting the best optimal features subset with minimum number of feature in most cases.
Component Divide-and-Conquer for Real-World Image Super-Resolution

In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring the utility of guiding SR model with low-level image components. DRealSR establishes a new SR benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation. In general, the targets of SR vary with image regions with different low-level image components, e.g., smoothness preserving for flat regions, sharpening for edges, and detail enhancing for textures. Learning an SR model with conventional pixel-wise loss usually is easily dominated by flat regions and edges, and fails to infer realistic details of complex textures. We propose a Component Divide-and-Conquer (CDC) model and a Gradient-Weighted (GW) loss for SR. Our CDC parses an image with three components, employs three Component-Attentive Blocks (CABs) to learn attentive masks and intermediate SR predictions with an intermediate supervision learning strategy, and trains an SR model following a divide-and-conquer learning principle. Our GW loss also provides a feasible way to balance the difficulties of image components for SR. Extensive experiments validate the superior performance of our CDC and the challenging aspects of our DRealSR dataset related to diverse real-world scenarios. Our dataset and codes are publicly available at https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution .
Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users' daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.
Learning to Drop: Robust Graph Neural Network via Topological Denoising

Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along the edges of the given graph. Despite their success, however, the existing GNNs are usually sensitive to the quality of the input graph. Real-world graphs are often noisy and contain task-irrelevant edges, which may lead to suboptimal generalization performance in the learned GNN models. In this paper, we propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of GNNs by learning to drop task-irrelevant edges. PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. To take into consideration the topology of the entire graph, the nuclear norm regularization is applied to impose the low-rank constraint on the resulting sparsified graph for better generalization. PTDNet can be used as a key component in GNN models to improve their performances on various tasks, such as node classification and link prediction. Experimental studies on both synthetic and benchmark datasets show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing

Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. Despite its great potential, a main challenge of FL is that the training data are usually non-Independent, Identically Distributed (non-IID) on the clients, which may bring the biases in the model training and cause possible accuracy degradation. To address this issue, this paper aims to propose a novel FL algorithm to alleviate the accuracy degradation caused by non-IID data at clients. Firstly, we observe that the clients with different degrees of non-IID data present heterogeneous weight divergence with the clients owning IID data. Inspired by this, we utilize weight divergence to recognize the non-IID degrees of clients. Then, we propose an efficient FL algorithm, named CSFedAvg, in which the clients with lower degree of non-IID data will be chosen to train the models with higher frequency. Finally, we conduct simulations using publicly-available datasets to train deep neural networks. Simulation results show that the proposed FL algorithm improves the training performance compared with existing FL protocol.
A Worldwide Journey through Distance Education—From the Post Office to Virtual, Augmented and Mixed Realities, and Education during the COVID-19 Pandemic

Surprisingly, distance education is quite an old concept. Its origins date back to the first correspondence-based course, which took place via the postal service in Boston, USA, in the 18th century. Rapid technological developments, especially in video and audio streaming, have increased the availability of such courses and moved learning into the virtual world. Due to the ongoing COVID-19 pandemic, we are witnessing an accelerated revolution in the learning process, as nearly all forms of education have been shifted online. Will this have a destructive effect on the human psyche? Is humanity sufficiently aware and ready for such a dramatic change? Will we return to physical in-classroom studies, or is remote distance education set to become the new norm? In particular, in medicine, computer science, fine arts, or architectural design, such a rapid change in the way students learn can be quite challenging. In this paper, we provide an overview of the history of distance learning, taking into account teachers’ and students’ points of view in both secondary and higher education.
Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy

Intelligent data-driven machinery fault diagnosis methods have been popularly developed in the past years. While fairly high diagnosis accuracies have been obtained, large amounts of labeled training data are mostly required, which are difficult to collect in practice. The promising collaborative model training solution with multiple users poses high demands on data privacy due to conflict of interests. Furthermore, in the real industries, the data from different users can be usually collected from different machine operating conditions. The domain shift phenomenon and data privacy concern make the joint model training scheme quite challenging. To address this issue, a federated transfer learning method for fault diagnosis is proposed in this article. Different models can be used by different users to enhance data privacy. A federal initialization stage is introduced to keep similar data structures in distributed feature extractions, and a federated communication stage is further implemented using deep adversarial learning. A prediction consistency scheme is also adopted to increase model robustness. Experiments on two real-world datasets suggest the proposed federated transfer learning method is promising for real industrial applications.
Iterative quantum amplitude estimation

We introduce a new variant of Quantum Amplitude Estimation (QAE), called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only based on Grover's Algorithm, which reduces the required number of qubits and gates. We provide a rigorous analysis of IQAE and prove that it achieves a quadratic speedup up to a double-logarithmic factor compared to classical Monte Carlo simulation. Furthermore, we show with an empirical study that our algorithm outperforms other known QAE variants without QPE, some even by orders of magnitude, i.e., our algorithm requires significantly fewer samples to achieve the same estimation accuracy and confidence level.