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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Computational Intelligence and Neuroscience 2022年 2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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A Multilevel Intelligent Assistant for Multilevel Social Network Analysis 10

A Multilevel Intelligent Assistant for Multilevel Social Net...

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10th IEEE International Conference on Intelligent systems, IS 2020

作者： Tianxing, Man Zhukova, Nataly Tsochev, Georgi ITMO University St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences St. Petersburg Russia Bulgarian Academy of Sciences Laboratory of Telematics Sofia Bulgaria

ISBN: (纸本)9781728154565

In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts. © 2020 IEEE.

关键词： Ontology

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State synchronization in process-oriented chaincode

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Frontiers of computer Science 2019年第6期13卷 1166-1181页

作者： Lian YU Wei-Tek TSAI School of Software&Microelectronics Peking UniversityBeijing 102600China State Key Laboratory of Software Development Environment School of Computer Science&EngineeringBeihang UniversityBeijing 100191China Comp. Info.and Dec.Systems EngineeringArizona State UniversityTempeAZ 85287USA

Business processes often involve operational processes,contracts,and *** modeling of such processes must address regulation monitoring and enforcement and maintain a reliable history of data for *** study proposes modeling business processes as chaincode(CC)on permissioned blockchains(BCs).The challenges encountered by the proposed approach are state synchronizations among distributed nodes(called authnodes)and realtime *** study separates CC executions from the state management of multiple BCs and demonstrates the validity of the proposed approach with a payment authorization system at a Chinese bank.

关键词： blockchains state synchronization chaincode permissioned blockchains concurrent block building

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Understanding Merge Conflicts and Resolutions in Git Rebases

Understanding Merge Conflicts and Resolutions in Git Rebases

引用

International Symposium on software Reliability engineering (ISSRE)

作者： Tao Ji Liqian Chen Xin Yi Xiaoguang Mao Laboratory of Software Engineering for Complex Systems College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

software merging is an important activity during software development. Merge conflicts may arise and degrade the software quality. Empirical studies on software merging are helpful to understand developers' needs and the challenges of detecting and resolving conflicts. Existing studies collect merges by identifying commits that have more than one parent commit. Different from these explicit merges, rebasing branches is used to merge other changes but rewrites the evolutionary history. Hence, existing studies fail to identify implicit merges performed by rebasing branches. Consequently, the results of these studies may fail to provide comprehensive insights on software merging. In our study, we leverage the recently updated APIs of GitHub to study rebase activities in the pull requests. Our study shows that rebasing is widely used in pull requests. And our results indicate that, to resolve textual conflicts, developers adopt similar strategies shown in existing studies on explicit merges. However, in 34.2% of non-conflict rebase scenarios, developers add new changes during the rebase process. And this indicates that there are some new challenges of validating rebases. Our results provide useful insights for improving the state-of-the-art techniques on resolving conflicts and validating rebases.

关键词： Merging software quality Tools software software reliability History software development management

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Bias-corrected peaks-over-threshold estimation of the CVaR

arXiv

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arXiv 2021年

作者： Troop, Dylan Godin, Frédéric Yu, Jia Yuan Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Mathematics and Statistics Concordia University Montreal Canada Concordia Institute of Information Systems Engineering Concordia University Montreal Canada Quantact Actuarial and Financial Mathematics Laboratory Montreal Canada

The conditional value-at-risk (CVaR) is a useful risk measure in fields such as machine learning, finance, insurance, energy, etc. When measuring very extreme risk, the commonly used CVaR estimation method of sample averaging does not work well due to limited data above the value-at-risk (VaR), the quantile corresponding to the CVaR level. To mitigate this problem, the CVaR can be estimated by extrapolating above a lower threshold than the VaR using a generalized Pareto distribution (GPD), which is often referred to as the peaks-over-threshold (POT) approach. This method often requires a very high threshold to fit well, leading to high variance in estimation, and can induce significant bias if the threshold is chosen too low. In this paper, we derive a new expression for the GPD approximation error of the CVaR, a bias term induced by the choice of threshold, as well as a bias correction method for the estimated GPD parameters. This leads to the derivation of a new estimator for the CVaR that we prove to be asymptotically unbiased. In a practical setting, we show through experiments that our estimator provides a significant performance improvement compared with competing CVaR estimators in finite samples. As a consequence of our bias correction method, it is also shown that a much lower threshold can be selected without introducing significant bias. This allows a larger portion of data to be be used in CVaR estimation compared with the typical POT approach, leading to more stable estimates. As secondary results, a new estimator for a second-order parameter of heavy-tailed distributions is derived, as well as a confidence interval for the CVaR which enables quantifying the level of variability in our estimator. Copyright © 2021, The Authors. All rights reserved.

关键词： Value engineering

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Text-based automatic personality prediction: A bibliographic review

arXiv

引用

arXiv 2021年

作者： Feizi-Derakhshi, Ali-Reza Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Akan, Taymaz Ranjbar-Khadivi, Mehrdad Zafarni-Moattar, Elnaz Jahanbakhsh-Naghadeh, Zoleikha Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Software Engineering Ayvansaray University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three;pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets. © 2021, CC BY-NC-SA.

关键词： Forecasting

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Research on An Approach of ARP Flooding Suppression in Multi-Controller SDN Networks

Research on An Approach of ARP Flooding Suppression in Multi...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jinhua Du Xianming Gao Jingchao Wang Shaohua Liu Weipeng Cao Yanbo Song Shanqing Jiang School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Military Sciences Institute of Systems Engineering Beijing China Peng Cheng Laboratory Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China School of Cyber Science and Engineering Southeast University Nanjing China

ARP flooding has become an urgent problem in the deployment and application of SDN networks, which has been only effectively solved in single controller SDN networks. But it still has ARP flooding and data synchronization problems in multi-controller SDN networks. So we propose a two-stage "storing-blocking" suppression algorithm to suppress ARP flooding in multi-controller SDN networks. It stores the ARP response messages as ARP suppression entries, and uses the strong consistency algorithm to synchronize ARP suppression entries among controllers. Once an ARP request message matches the ARP suppression entries the mechanism blocks the flooding of ARP request messages, which can achieve the goal of the suppression of ARP flooding. Our experimental results show that our proposed mechanism can effectively reduce the impact of ARP flooding in multi-controller SDN networks in terms of flooding time, flooding scope, and controller overhead.

关键词： Protocols Switches Reinforcement learning Floods Synchronization

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Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

arXiv

引用

arXiv 2022年

作者： Xing, Wei W. Zhang, Ziyang Shah, Akeel A. Key Laboratory of Low-grade Energy Utilisation Technologies and Systems MOE Chongqing University Chongqing400030 China School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks is demonstrated on three data sets, particularly at the early stages of battery lifetime. Copyright © 2022, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation

IoT-ID: Robust IoT Device Identification Based on Feature Dr...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Qi Chen Yubo Song Brendan Jennings Fan Zhang Bin Xiao Shang Gao Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Telecommunications Software & Systems Group Waterford Institute of Technology The Hong Kong Polytechnic University

ISBN: (纸本)9781728181059

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

关键词： Performance evaluation Protocols Fingerprint recognition Feature extraction Robustness Classification algorithms Object recognition

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