Reviewerly/exHarmony huggingface dataset:
Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review

A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Lévy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA).
Contextual Transformation Network for Lightweight Remote-Sensing Image Super-Resolution

Current super-resolution networks typically reduce network parameters and multiadds operations by designing lightweight structures, but lightening the convolution layer is often ignored. In this work, we observe that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> convolutions occupy a high percentage of network parameters in most lightweight super-resolution networks. This motivates us to consider lightening super-resolution networks by replacing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> convolutions with lightweight convolutions, while maintaining the performance. To achieve this, we propose a lightweight convolution layer named contextual transformation layer (CTL). It can yield efficient contextual features through a context feature extraction module and enrich extracted contextual features through a context feature transformation module. Based on CTLs, we build a lightweight super-resolution network called contextual transformation network (CTN) for remote-sensing image super-resolution. Specifically, we use two CTLs to construct a contextual transformation block (CTB) for hierarchical feature learning. Interleaved with a CTB, a context enhancement module (CEM) is employed to enhance the extracted feature representations. All extracted features are processed by a contextual feature aggregation module for final remote-sensing image super-resolution. Extensive experiments are performed on a remote-sensing image super-resolution benchmark named UC Merced. Our method achieves superior results to the other state-of-the-art methods. To demonstrate the generalization ability of our CTL, we extend our CTN to two relevant tasks: natural image super-resolution and natural image denoising. Experimental results on natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, and Manga109) and natural image denoising benchmarks (i.e., SIDD and DND) further prove the superiority of our method. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BITszwang/CTNet</uri> .
Intelligent IoT Sensors: Types, Functions and Classification

This paper discusses the physicochemical effects in information, the principles of the effects of sensors, which determines the accuracy of the information received, the field of application of sensors such as monitoring, industry, etc., and also considers an example of the classification of sensors by the physical nature of primary information signals.
Blockchain Interoperability in Unmanned Aerial Vehicles Networks: State-of-the-Art and Open Issues

The breakthrough of blockchain technology has facilitated the emergence and deployment of a wide range of unmanned aerial vehicles (UAV) networks-based applications. Yet, the full utilization of these applications is still limited due to the fact that each application is operating on an isolated blockchain. Thus, it is inevitable to orchestrate these blockchain fragments by introducing a cross-blockchain platform that governs the inter-communication and transfer of assets in the UAV networks context. In this paper, we survey the literature on the state-of-the-art cross blockchain frameworks to highlight the latest advances in the field. We also provide an up-to-date review of blockchain-based UAV networks applications. Based on the outcomes of our survey, we introduce a spectrum of scenarios related to UAV networks that may leverage the potentials of the currently available cross-blockchain solutions. Finally, we identify open issues and potential challenges associated with the application of a cross-blockchain scheme for UAV networks that will hopefully guide future research directions.
Overfitting, Model Tuning, and Evaluation of Prediction Performance

Abstract The overfitting phenomenon happens when a statistical machine learning model learns very well about the noise as well as the signal that is present in the training data. On the other hand, an underfitted phenomenon occurs when only a few predictors are included in the statistical machine learning model that represents the complete structure of the data pattern poorly. This problem also arises when the training data set is too small and thus an underfitted model does a poor job of fitting the training data and unsatisfactorily predicts new data points. This chapter describes the importance of the trade-off between prediction accuracy and model interpretability, as well as the difference between explanatory and predictive modeling: Explanatory modeling minimizes bias, whereas predictive modeling seeks to minimize the combination of bias and estimation variance. We assess the importance and different methods of cross-validation as well as the importance and strategies of tuning that are key to the successful use of some statistical machine learning methods. We explain the most important metrics for evaluating the prediction performance for continuous, binary, categorical, and count response variables.
RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.
Generating Datasets with Pretrained Language Models

To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs.While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size.In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models.Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets. 1
Prediction of global solar radiation potential for sustainable and cleaner energy generation using improved Angstrom-Prescott and Gumbel probabilistic models

Predicting and improving global solar radiation models is important in Nigeria because, most government meteorological stations are unable to continuously set-up or measure this radiometric parameter in most metropolitan cities and remote villages where there is a severe need for electricity. This is because most locations are not connected to the national grid due to high cost implications. Global solar radiation (H) prediction has a great deal of benefits in adapting and implementing clean and sustainable energy infrastructure, and much more in detecting and adapting climate mitigation measures in locations affected by climate change externalities. The new Gumbel (GP) probabilistic model applied in this paper is renowned for its high prediction power and low input requirements on the coefficients of the Angstrom-Prescott (AP) model used by researchers to predict global solar radiation since 1940. This study's foremost intention was to investigate ways of generating sustainable and cleaner energy by using coefficients of the AP model in order to accelerate the greener economy in Nigeria. As such, the accuracy and suitability of 36 existing and developed empirical AP models, as well as 11 fitted GP models, were evaluated to estimate H in different climatic regions of Nigeria. The results revealed that AP models fitted with generalized datasets outperformed the 10 location-based models considered in this work and the 7 models selected from literature, however, the GP model outperformed all empirically fitted models. The model established by GP (M13) was used to optimize AP models with poor performance and those selected from literature. This study suggests that it is more realistic to apply the generalized and GP model to predict global solar radiation and generate AP estimation coefficients across Nigeria than using empirical modeling and AP estimation coefficient. This is illustrated by the high prediction accuracy, little or no instrumentation network, and the computational effort for a GP model over an instrumentation network required to generate sunshine hour datasets required to implement AP model predictions. The proposed GP model is sufficient as a valid predictive model that will promote a holistic understanding of the available solar resources in Nigeria and disseminate the application of solar photovoltaic technologies within the country.
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers

Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on deep learning cannot make full use of multiple images. Their fusion modules are incapable of adapting to an image sequence with weak temporal correlations well. To cope with these problems, we propose a novel end-to-end framework called TR-MISR. It consists of three parts: An encoder based on residual blocks, a transformer-based fusion module, and a decoder based on subpixel convolution. Specifically, by rearranging multiple feature maps into vectors, the fusion module can assign dynamic attention to the same area of different satellite images simultaneously. In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent. TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches. Extensive experiments performed on the PROBA-V Kelvin dataset demonstrate the superiority of the proposed model that provides an effective method for transformers in other low-level vision tasks.
Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment

In this article, different state-of-the-art metaheuristic algorithms are analyzed to incorporate the collision-free path-planning approach for UAVs in a disaster situation. The efficient path planning is used to identify the main cause of disaster like bushfires that are badly affecting the forest ecosystem throughout the globe. This novel approach is a first step toward a predisaster assessment and possibilities to save the survivors in minimal time. Different metaheuristic algorithms, such as PSO, GWO, WOA, BMO, and DGBCO, are compared for UAV path optimization capability. In order to test the robustness of our proposed model, four different scenarios are presented which include general environment, condense environment, maze environment, and dynamic environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for a UAV to reach the destination. The exploration and exploitation groups working simultaneously in a dynamic environment make it effective for UAV path planning using the DGBCO algorithm. Based on the parameters selected, the DGBCO algorithm outperforms other algorithms and achieves 24.5% less transportation cost and 13.3% less computational time. Hence, DGBCO can be efficiently applied for UAV path-planning optimization in any of the aforementioned environments.
Evolution of Internet of Things From Blockchain to IOTA: A Survey

Internet of Things (IoT) is the new paradigm to the scaling nature of things and their elements, interconnected, exchanging data over a network supported with nodes. The Ubiquitous use of tiny devices and embedded sensor frameworks has pushed IoT to the forefront of emerging technologies used in many applications like peer-to-peer networks, smart energy grids, home and building automation, vehicle to vehicle communication, and wearable computing devices. This massive growth and extensive use brought forth security risks that could hinder its commencement in many novel applications. The number of interconnected devices leads the way to several entry points for intruders and, along with it, security risks. The sensitive nature of the IoT applications such as health, automation, and energy grids cannot afford security risks. Traditional security mechanisms will not design or develop to secure such an emerging technology as IoT. Existing technologies have to be relied on with the non-existence of security mechanisms for this purpose. Distributed Ledger Technology (DLT) is one such technology that can reduce the security risks in IoT. The central node vulnerability that can compromise the whole system can be mitigated by eliminating the need for a central node by using the distributed ledger. Blockchain, a distributed ledger technology, has attracted tremendous attention and harnessed in itself a real-world value. However, computationally costly with limited scalability is not entirely suited for the IoT environment. IoT Application (IOTA) technology is the distributed ledger technology that can provide unlimited scalability specifically suitable for the IoT industry. This survey provides an in-depth introduction to how blockchain performs and its constraints in its nature as a generic platform for DLT. In contrast, IOTA is introduced as the technology for IoT, the next-generation blockchain overcoming blockchain’s limitations for its use in IoT.
Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset

Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.
Hybrid Nature-Inspired Optimization and Encryption-Based Watermarking for E-Healthcare

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient’s ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.
A bibliometric study on library and information science and information systems literature during 2010–2019

Purpose Studying the nature of research progress in interrelated research domains is important for evaluating the research productivity and to understand the current trends of the area of research. This study aims to examine a research domain that combines library and information science with information systems (IS). Design/methodology/approach Quartile 1 journals that cover both subject domains in SCImago were selected for the study. Bibliographic records of the publications during 2010 and 2019 were retrieved from the Scopus database. VOSviewer data visualization tool was used to perform citation, coauthorship, bibliographic coupling, cocitation and co-occurrence analysis. In addition, descriptive and inferential statistics were exploited. Findings The absence of a consistent association between the number of documents authored and the number of citations received by a researcher was an important finding of the study. The strong association of authors regardless of the different topics they researched and the trend of increasing interest on collaborative research were also highlighted. Moreover, the authors who received the highest number of citations were not always the first authors of the documents which received the most citations. The documents published in information management, information theory and IS journals attained the most citations. The journals, institutions and countries with the highest number of documents and citations were also revealed by the research. Electrical engineering departments showed a higher research productivity, while they were utilized more compared to that of other departments. IS and management, information theory, communication, information retrieval, geographic-based IS and bioinformatics were the six major research areas of the considered domain. Originality/value This is the first study related to examining the research progress in a combined subject domain using multiple aspects including, individual performance, institutional progress, geographical contribution and so on. Identifying the major research areas in the combined subject domain can also be considered a novel contribution to the field.
BANet: A Balance Attention Network for Anchor-Free Ship Detection in SAR Images

Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. However, most current ship detection networks rely too much on the anchor mechanism. These methods have low accuracy and poor generalization ability for multiscale ship detection. To solve the aforementioned problems, an anchor-free framework for multiscale ship detection in SAR images based on a balance attention network (BANet) is proposed. First, due to the diversity of scales and rotation angles of ships, deformable convolution is introduced to build a local attention module (LAM) to better obtain local information of ships and effectively enhance the robustness of the network. Second, a nonlocal attention module (NLAM) is designed to extract the nonlocal features of the SAR image, so as to balance the local features and nonlocal features acquired by the entire network. Finally, the feature pyramid network (FPN) is used to detect ships of different sizes at different scales. The detection results on three datasets demonstrate that the detection precision of our method is higher than that of all comparison methods, and this method achieves the most advanced performance.