检索结果-内蒙古大学图书馆

UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2022年第2期84卷 63-78页

作者： Sandescu, Cristian Dinisor, Alexandra Vladescu, Cristina-Veronica Grigorescu, Octavian Corlatescu, Dragos Dascalu, Mihai Rughinis, Razvan Dept. of Software Development CODA Intelligence SRL Romania Dept. of Informatics Technical University of Munich Germany Dept. of Computer Science University POLITEHNICA of Bucharest Romania

Cybersecurity has an immense impact on society as it enables the digital protection of individuals and enterprises against an increasing number of online threats. Moreover, the rate at which attackers discover and exploit critical vulnerabilities outperforms the vendors’ capabilities to respond accordingly and provide security patches. As such, open-source intelligence data (OSINT) has become a valuable resource, from which details on zero-day vulnerabilities can be retrieved and timely actions can be taken before the patches become available. In this paper we propose a method to automatically label articles on vulnerabilities and cyberattacks from trusted sources. Using Named Entity Recognition, we extract essential information about new vulnerabilities, such as the exploit’s public release and the environment in which the attack’s exploitation is possible. Our balanced dataset contains 1095 samples out of which 250 entries are from cybersecurity articles;the rest of the articles were crawled and annotated from the U.S. Government’s Vulnerability Database, whereas automated text augmentation techniques were also considered. Our model built on top of spaCy obtained an overall performance of 75% recall on the Exploit Available task. When considering the Attack Vector metric, the model achieved the following recalls: Network 72%, Local 78%, and Physical 92%. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Localization of Events Using Neural Networks in Twitter Data 20

Localization of Events Using Neural Networks in Twitter Data

引用

20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023

作者： Anjum, Usman Zadorozhny, Vladimir Krishnamurthy, Prashant Dept. of Computer Science University of Cincinnati United States Dept. of Informatics and Networked Systems University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9798218217495

In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods. © 2023 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Federated Digital Twin 27

Federated Digital Twin

引用

27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Vergara, Christian Bahsoon, Rami Theodoropoulos, Georgios Yanez, Wendy Tziritas, Nikos School of Computer Science University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece

ISBN: (纸本)9798350337846

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence;and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems. © 2023 IEEE.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

Visualizing Research on Explainable Artificial Intelligence for Medical and Healthcare 4

Visualizing Research on Explainable Artificial Intelligence ...

引用

4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Ali, Subhan Imran, Ali Shariq Kastrati, Zenun Daudpota, Sher Muhammad Dept of Computer Science Gjφvik2815 Norway Linnaeus University Dept. of Informatics Växjö35195 Sweden Sukkur Iba University Dept. of Computer Science Sukkur65200 Pakistan

ISBN: (纸本)9798350335316

Understanding complex machine learning and artificial intelligence models have always been challenging because these models are black-box, and often we don't know what information models rely upon to infer. Explainable Artificial Intelligence (XAI) has emerged as a new exciting field to explain and understand these machine learning models as humans can understand and improve them. In the past few years, there have been numerous research articles on explainable artificial intelligence for medical and healthcare. 1687 documents are being studied and analysed using bibliometric methods in this work. There are certain systematic reviews on the same topic, but this study is the first of its kind to use a quantitative method to analyze a large number of publications. The results of this study show that the research in this field took pace in 2011, and there have been quite many publications in the following years. We have also identified top-cited journals and articles. Through thematic analysis, we have found some important thematic areas of research in the field of XAI for medical and healthcare. The findings showed that the USA is the global leader in XAI research, followed by China and Canada at second and third place, respectively. © 2023 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

引用

27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Constructive Fuzzy Cognitive Map for Depression Severity Estimation 32

Constructive Fuzzy Cognitive Map for Depression Severity Est...

引用

32nd Medical informatics Europe Conference, MIE 2022

作者： Sovatzidi, Georgia Vasilakakis, Michael Iakovidis, Dimitris K. Dept of Computer Science and Biomedical Informatics Univ. of Thessaly Greece

ISBN: (纸本)9781643682846

Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps (FCMs) is proposed. It is applied for the estimation of the severity of depression among adolescents, based on their electroencephalogram (EEG). The introduced Constructive FCM (CFCM) utilizes features extracted by a Constructive Fuzzy Representation Model (CFRM), which conduces to detect in a more intuitive way the cause-and-effect relationships between the brain activity and depression. CFCM contributes to limiting the participation of experts, and the manual interventions in the traditional construction of FCMs, it provides an embedded mechanism for dimensionality reduction, and it constitutes an inherently interpretable approach to decision making, while being uncertainty-aware and simple to implement. The results of the experiments, using a recent publicly available dataset, demonstrate the effectiveness of the proposed framework and highlight its advantages. © 2022 European Federation for Medical informatics (EFMI) and IOS Press.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Transforming Synthesized Netlists into a Feasible & Compliant Functional Bookshelf Format 24

Transforming Synthesized Netlists into a Feasible & Complian...

引用

Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： George Kranas Kosmas Kariotis Antonios Dadaliaris George Stamoulis Athanasios Kakarountas Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece Dept. of Informatics & Telecommunications University of Thessaly Lamia Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece

来源：评论

学校读者我要写书评

暂无评论

LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

引用

Computational Intelligence in Health and Medicine (CIHM), IEEE Symposium on

作者： Paraskevi V. Tsakmaki Sotiris Tasoulis Spiros V. Georgakopoulos Vassilis P. Plagianakos dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Psychiatric disorders, including bipolar disorder, depression, and schizophrenia, pose substantial public health challenges due to the intricate nature of their diagnosis and management. Electrocardiogram (ECG) signals offer a noninvasive method to assess autonomic nervous system function, which is frequently dysregulated in these conditions. However, conventional ECG analysis techniques often require prolonged recordings and extensive preprocessing, limiting their applicability in real-time or large-scale settings. This study presents an approach leveraging Long Short-Term Memory (LSTM) networks for medical data classification, specifically for the identification of psychiatric disorders using ultra-short, raw ECG signals. Our methodology efficiently captures the temporal dynamics of ECG signals within brief windows, significantly reducing the need for extensive preprocessing and manual feature extraction. The proposed LSTM-based classifier achieved an impressive mean accuracy of 97.12% ± 0.006, underscoring its precision and efficacy in distinguishing between different psychiatric conditions. Our findings indicate that LSTM networks, with their capacity to effectively model complex temporal patterns in short ECG recordings, represent a promising advancement in the diagnostic landscape of psychiatry.

关键词： Noise Electrocardiography Schizophrenia Real-time systems Recording Reliability Public healthcare Psychiatry Long short term memory Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：