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A Survey on Event Tracking in Social Media Data Streams

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Big Data Mining and Analytics 2024年第1期7卷 217-243页

作者： Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Ocean College Jiangsu University of Science and TechnologyZhenjiang 212100China university Executive Office Edinburgh Napier UniversityEdinburghEH114BNUK

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are generated.As such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks.Event tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real time.Moreover,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking algorithms.In this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks.We introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event evolution.Finally,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream methods.Finally,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

关键词： Event Detection(ED) event propagation event evolution social networks

Editorial for Special Issue on Brain-inspired Machine Learning

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Machine Intelligence Research 2022年第5期19卷 347-349页

作者： Zhao-Xiang Zhang Bin Luo Jin Tang Shan Yu Amir Hussain Institute of Automation Chinese Academy of Sciences Anhui university edinburgh napier university

The recent years have witnessed significant progress on artificial intelligence(AI).Undoubtedly,one of the essential elements that contribute to the prosperity of current AI is brain-inspired machine learning.By introducing structural principles and operational mechanisms of the human brain into the design of computing algorithms and devices.

关键词： artificial computing introducing

Design and Grasping Force Modeling for a Soft Robotic Gripper with Multi-stem Twining

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Journal of Bionic Engineering 2023年第5期20卷 2123-2134页

作者： Yu Shan Yanzhi Zhao Hongnian Yu Changlei Pei Zhaopeng Jin Yue Sun School of Yanshan university Qinhuangdao066004China School of Computing Engineering and the Built EnvironmentEdinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

To improve the grasping power of soft robots,inspired by the scene of intertwined and interdependent vine branches safely clinging to habitats in a violent storm and the phenomenon of large grasping force after being entangled by aquatic plants,this paper proposes a soft robotic gripper with multi-stem twining.The proposed robotic gripper can realize a larger contact area of surrounding or containing object and more layers of a twining object than the current twining gripping methods.It not only retains the adaptive advantages of twining grasping but also improves the grasping force.First,based on the mechanical characteristics of the multi-stem twining of the gripper,the twining grasping model is developed.Then,the force on the fiber is deduced by using the twining theory,and the axial force of the gripper is analyzed based on the equivalent model of the rubber ring.Finally,the torsion experiments of fibers and the grasping experiments of the gripper are designed and conducted.The torsion experiment of fibers verifies the influence of a different number of fiber ropes and fiber torque on the grasping force,and the grasping experiment reflects the large load of the gripper and the high adaptability and practicability under different tasks.

关键词： Soft robotic gripper Multi-stem twining Adaptability Grasping force model

A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

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Computer Systems Science & Engineering 2023年第7期46卷 57-72页

作者： Maha Sabir Jawad Ahmad Daniyal Alghazzawi Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburgh EH105DYUK

The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.

关键词： Autoencoder cybersecurity deep learning intrusion detection IoT

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Guest Editorial

Digital Communications and Networks 2022年第6期8卷 865-867页

作者： Honghao Gao Ramon J.Duran Barroso Zhiyuan Tan School of Computer Engineering and Science Shanghai UniversityChina Universidad de Valladolid Spain School of Computing Edinburgh Napier UniversityUK

Special issue on Collaborative computing to information fusion for5GIoTsocialdata With the development of 5G IoT technology,a large amount of social data is generated every day.How to handle these social data has become a critical problem.Information Fusion with Collaborative Computing aims to help academia and industry understand data,information and resources.Thus,in this special issue,we selected the following highquality papers from open submissions.A summary is outlined below.

关键词： handle problem IoT

Cryptanalysis of 2D-SCMCI Hyperchaotic Map Based Image Encryption Algorithm

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Computer Systems Science & Engineering 2023年第8期46卷 2401-2414页

作者： Mohammed S.Alshehri Sultan Almakdi Mimonah Al Qathrady Jawad Ahmad Department of Computer Science Najran UniversityNajran55461Saudi Arabia Department of Information Systems Najran UniversityNajran55461Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.

关键词： Cryptanalysis hyperchaotic map image encryption chosen-plaintext attack cryptosystem

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

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Computer Systems Science & Engineering 2023年第2期44卷 1309-1324页

作者： Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

In project management,effective cost estimation is one of the most cru-cial activities to efﬁciently manage resources by predicting the required cost to fulﬁll a given task.However,ﬁnding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artiﬁcial neural network(ANN).An artiﬁcial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

关键词： Software cost estimation neural network backpropagation forward neural networks software effort estimation artiﬁcial neural network

Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

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Journal of Pharmaceutical Analysis 2022年第2期12卷 193-204页

作者： Umer Saeed Syed Yaseen Shah Jawad Ahmad Muhammad Ali Imran Qammer H.Abbasi Syed Aziz Shah Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK School of Computing Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK School of Computing Edinburgh Napier UniversityEdinburghEH114BNUK James Watt School of Engineering University of GlasgowGlasgowG128QQUK

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.

关键词： Artificial intelligence Non-invasive healthcare Machine learning Non-contact sensing COVID-19

EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

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Computers, Materials & Continua 2022年第3期70卷 4619-4633页

作者： Saadullah Farooq Abbasi Harun Jamil Wei Chen School of Computing Edinburgh Napier UniversityEdinburgh EH105DTUK Center for Intelligent Medical Electronics Department of Electronic EngineeringFudan University200433China Department of Computer Engineering Jeju National UniversityJejusiJeju Special Self-Governing Province63243Korea

Sleep stage classification can provide important information regarding neonatal brain development and maturation.Visual annotation,using polysomnography(PSG),is considered as a gold standard for neonatal sleep stage classification.However,visual annotation is time consuming and needs professional neurologists.For this reason,an internet of things and ensemblebased automatic sleep stage classification has been proposed in this study.12 EEG features,from 9 bipolar channels,were used to train and test the base classifiers including convolutional neural network,support vector machine,and multilayer perceptron.Bagging and stacking ensembles are then used to combine the outputs for final classification.The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage(wake,active sleep,and quiet sleep)classification,respectively.The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min.The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.

关键词： Internet of things machine learning convolutional neural network artificial intelligence