Reviewerly/exHarmony huggingface dataset:
Multi-Swarm Algorithm for Extreme Learning Machine Optimization

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine-cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.
Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%).
From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.
The Recognition of Handwritten Digits Using Neural Network Technology

In this paper, we compared the quality of processing handwritten digits by a fully connected feedforward neural network and a convolutional neural network. Examples of training and use of both a fully connected neural network and a convolutional neural network are given. The ways of image segmentation and finding the boundaries of handwritten digits for their further analysis are considered. Algorithms affecting the accuracy of learning, the speed of learning are considered. The general principles of the structure and principles of operation of a feed-forward neural network and a convolutional neural network are presented.
Just Add Data: automated predictive modeling for knowledge discovery and feature selection

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.
Self-adaptive Bat Algorithm With Genetic Operations

Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA's efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness

Aiming at the difficulty in obtaining a complete Bayesian network (BN) structure directly through search-scoring algorithms, authors attempted to incorporate expert judgment and historical data to construct an interpretive structural model with an ISM-K2 algorithm for evaluating vaccination effectiveness (VE). By analyzing the influenza vaccine data provided by Hunan Provincial Center for Disease Control and Prevention, risk factors influencing VE in each link in the process of “Transportation—Storage—Distribution—Inoculation” were systematically investigated. Subsequently, an evaluation index system of VE and an ISM-K2 BN model were developed. Findings include: (1) The comprehensive quality of the staff handling vaccines has a significant impact on VE; (2) Predictive inference and diagnostic reasoning through the ISM-K2 BN model are stable, effective, and highly interpretable, and consequently, the post-production supervision of vaccines is enhanced. The study provides a theoretical basis for evaluating VE and a scientific tool for tracking the responsibility of adverse events of ineffective vaccines, which has the value of promotion in improving VE and reducing the transmission rate of infectious diseases.
A Survey of Emergencies Management Systems in Smart Cities

The rapid urbanization process in the last hundred of years has deeply changed the way we live and interact with each other.As most people now live in urban areas, cities are experiencing growing demands for more efficient and sustainable public services that may improve the perceived quality of life, specially with the anticipated impacts of climatic changes.In this already complex scenario with increasingly overcrowded urban areas, different types of emergency situations may happen anywhere and anytime, with unpredictable costs in human lives and properties damages.In order to cope with often unexpected and potentially dangerous emergencies, smart cities initiatives have been developed in different cities, addressing multiple aspects of emergencies detection, alerting, and mitigation.In this scenario, this article surveys recent smart city solutions for crisis management, proposing definitions for emergenciesoriented systems and classifying them according to the employed technologies and provided services.Additionally, recent developments in the domains of Internet of Things, Artificial Intelligence and Big Data are also highlighted when associated to the management of urban emergencies, potentially paving the way for new developments while classifying and organizing them according to different criteria.Finally, open research challenges will be identified, indicating promising trends and research directions for the coming years.
Parameter Conjugate Gradient with Secant Equation Based Elman Neural Network and its Convergence Analysis

Abstract Elman neural network (ENN) is one of the local recursive networks with a feedback mechanism. The parameter conjugate gradient method is a promising alternative to the gradient descent method, due to its faster convergence speed that results from searching for the conjugate descent direction with an adaptive step size (obtained by Wolfe conditions). However, there are still some challenges such as how to avoid the sawtooth phenomenon in gradient algorithms to improve the learning accuracy of the second‐order curvature of an objective function. As such, this paper presents a novel parametric conjugate gradient method that is based on the secant equation for training ENN in an effective way. Strict proof of the theoretical convergence of the proposed algorithm is provided in detail. In particular, the weak convergence and strong convergence of the algorithm, as well as the monotonicity of the error function are proved. Except for the theoretical analysis, the three numerical experiments have been conducted by applying the algorithm to three problems of classification, regression, and function approximation on nine real‐world datasets. The experimental results have demonstrated the feasibility of the proposed algorithm and the correctness of this theoretical analysis.
Advances in the quantum internet

A deep dive into the quantum Internet's potential to transform and disrupt.
DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems

The rapid development of Internet of Things (IoT) stimulates the innovation for the health-related devices such as remote patient monitoring, connected inhalers and ingestible sensors. Simultaneously, with the aid of numerous equipments, a great number of collected data can be used for disease prediction or diagnosis model establishment. However, the potential patient data leak will also bring privacy and security issues in the interaction period. To deal with these existing issues, we propose a decentralized, efficient, and privacy-enhanced federated edge learning system called DEEP-FEL, which enables medical devices in different institutions to collaboratively train a global model without raw data mutual exchange. Firstly, we design a hierarchical ring topology to alleviate centralization of the conventional training framework, and formulate the ring construction as an optimization problem, which can be solved by an efficient heuristic algorithm. Subsequently, we design an efficient parameter aggregation algorithm for distributed medical institutions to generate a new global model, and the total amount of data transmitted by N nodes is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2/N$</tex-math></inline-formula> times that of traditional algorithm. In addition, data security among different medical institutions is enhanced by adding artificial noise to the edge model. Finally, experimental results on three medical datasets demonstrate the superiority of our system.
Collaborative representation learning for nodes and relations via heterogeneous graph neural network

Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly suitable for characterizing real-world complex systems. In recent years, due to their strong capability of capturing rich semantics, heterogeneous graph neural networks (HGNNs) have proven to be a powerful technique for representation learning on heterogeneous graphs. However, most of the existing HGNNs only focus on learning node representations and ignore the learning of relation representations, which are complementary to node representations. To address this limitation, we propose a new HGNN model with Collaborative Representation Learning for Nodes and Relations (named CoNR) for link prediction task in this paper. Collaborative learning means that node representations and relation representations participate in and affect each other’s learning process. Specifically, node representations are obtained through a delicate two-step attention mechanism incorporating relation representations that can hierarchically aggregate information within one relation and across different relations. For relation representations, a relation encoder based on node information is designed to encode node representations into relation representations. Therefore, in this framework, node representations and relation representations are mutually updated in a layer-wise manner and work together to facilitate the downstream tasks better. Extensive experimental results on different datasets show the excellent performance of the proposed CoNR.
A deeper look into cybersecurity issues in the wake of Covid-19: A survey

This study analyzed the Coronavirus (COVID-19) crisis from the angle of cyber-crime, highlighting the wide spectrum of cyberattacks that occurred around the world. The modus operandi of cyberattack campaigns was revealed by analyzing and considering cyberattacks in the context of major world events. Following what appeared to be substantial gaps between the initial breakout of the virus and the first COVID-19-related cyber-attack, the investigation indicates how attacks became significantly more frequent over time, to the point where three or four different cyber-attacks were reported on certain days. This study contributes in the direction of fifteen types of cyber-attacks which were identified as the most common pattern and its ensuing devastating events during the global COVID-19 crisis. The paper is unique because it covered the main types of cyber-attacks that most organizations are currently facing and how to address them. An intense look into the recent advances that cybercriminals leverage, the dynamism, calculated measures to tackle it, and never-explored perspectives are some of the integral parts which make this review different from other present reviewed papers on the COVID-19 pandemic. A qualitative methodology was used to provide a robust response to the objective used for the study. Using a multi-criteria decision-making problem-solving technique, many facets of cybersecurity that have been affected during the pandemic were then quantitatively ranked in ascending order of severity. The data was generated between March 2020 and December 2021, from a global survey through online contact and responses, especially from different organizations and business executives. The result show differences in cyber-attack techniques; as hacking attacks was the most frequent with a record of 330 out of 895 attacks, accounting for 37%. Next was Spam emails attack with 13%; emails with 13%; followed by malicious domains with 9%. Mobile apps followed with 8%, Phishing was 7%, Malware 7%, Browsing apps with 6%, DDoS has 6%, Website apps with 6%, and MSMM with 6%. BEC frequency was 4%, Ransomware with 2%, Botnet scored 2% and APT recorded 1%. The study recommends that it will continue to be necessary for governments and organizations to be resilient and innovative in cybersecurity decisions to overcome the current and future effects of the pandemic or similar crisis, which could be long-lasting. Hence, this study's findings will guide the creation, development, and implementation of more secure systems to safeguard people from cyber-attacks.
Measurement-device-independent one-step quantum secure direct communication

The one-step quantum secure direct communication (QSDC) ( Sci. Bull. 67 , 367 (2022)) can effectively simplify QSDC’s operation and reduce message loss. For enhancing its security under practical experimental condition, we propose two measurement-device-independent (MDI) one-step QSDC protocols, which can resist all possible attacks from imperfect measurement devices. In both protocols, the communication parties prepare identical polarization-spatial-mode two-photon hyperentangled states and construct the hyperentanglement channel by hyperentanglement swapping. The first MDI one-step QSDC protocol adopts the nonlinear-optical complete hyperentanglement Bell state measurement (HBSM) to construct the hyperentanglement channel, while the second protocol adopts the linear-optical partial HBSM. Then, the parties encode the photons in the polarization degree of freedom and send them to the third party for the hyperentanglement-assisted complete polarization Bell state measurement. Both protocols are unconditionally secure in theory. The simulation results show the MDI one-step QSDC protocol with complete HBSM attains the maximal communication distance of about 354 km. Our MDI one-step QSDC protocols may have potential applications in the future quantum secure communication field.
BSMD:A blockchain-based secure storage mechanism for big spatio-temporal data

As more and more mobile devices and IoT terminals are connected to the Internet, a huge amount of spatio-temporal data is generated. In order to cope with the pressure of storing and computing massive amounts of data locally and to share data to fully utilize its value, data owners usually store data in cloud servers. However, the data owner will lose physical control of big spatio-temporal data while the big spatio-temporal data is stored on the cloud, and it will be at the risk of being tampered with and deleted. Therefore, this paper designs a blockchain-based secure storage mechanism, BSMD, which adopts an on-chain and off-chain cooperative storage model to alleviate the shortage of blockchain storage capacity. The updatable subvector commitment designed in this paper is used to construct the on-chain and off-chain secure authentication protocol which ensures the consistency of on-chain and off-chain data and makes the protocol have the capability of batch processing. Finally, the correctness and security of the proposed protocol are proved, and the performance of the protocol is analyzed.
Cyber Security Threats on Digital Banking

Digital banking is the new form of banking that involves using the internet and mobile applications and excludes the use of pen and paper. One of the main issues that are associated with the form of banking is the presence of several forms of security risks. These risks are caused due to criminal activities of hackers and fraudsters who aim to steal people's money. Therefore, an efficient security system that involves multiple verifications, authentication processes, and data encryption are needed to combat such a threat. There has been much other work about this topic by other authors. By analyzing the finding from all of these sources, the STUDY has found that cyber security threats are a major problem in digital banking. Most people are not even aware or concerned about this matter. The research has used a theoretical analysis method involving secondary information sources. The research has also been able to bring out some hypotheses about this topic which it has also been able to support by using the statements and graphical charts given by other authors in their previous works about this topic. The research topic is interesting and can be used to do further research in the future.