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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Su, Piaoyi Dong, Xiwang Zhao, Qin Li, Qingdong Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Beijing Institute of Control and Electronics Technology Beijing100038 China

ISBN: (纸本)9789811939976

This literature investigate the dynamic formation tracking problem of multiple Lagrange systems with linear nonautonomous leader over directed topology. In order to estimate the leader system, a distributed control scheme is proposed firstly by constructing adaptive distributed observer. Secondly, this paper design a nonlinear function to decrease the influence from the leader’s bounded unknown input. Then, the dynamic formation tracking problem of multiple Euler-Lagrange systems with nonlinear uncertainties and disturbances is solved by distributed tracking algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Design and Analysis of NICS Based Web Attack Detection for Advanced Intrusion Detection System 3rd

Design and Analysis of NICS Based Web Attack Detection for A...

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3rd Iberoamerican Knowledge Graph and Semantic Web Conference and the 2nd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2021

作者： Shandilya, Shishir Kumar School of Data Science and Forecasting Devi Ahilya University MP Indore India School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9783030913045

Worldwide Web has been the most prominent data sources since its inception, and several technologies have emerged around it and finally amalgamated with it. With the decades of progression and advancements, web is still structuring and welcoming latest technologies. However, cyber threats are also raising and becoming the concern. Since last decade, the defensive methods in Cyber Security domain have also transformed from conventional pattern/rule-based response systems to more active defensive systems. The active defensive methods are now equipped with Artificial Intelligence and Machine Learning techniques which are capable enough to learn and respond quickly to an entirely new cyber-attack. However, these techniques needs training and sometimes fail to perform in case of incomplete or inaccurate training data. Nature-inspired Cyber Security attempts to deliver robust solution to this problem as they are fundamentally tolerant to the missing, incomplete and inaccurate data. While the development both in Worldwide Web and Cyber Security is still continuing, the researchers are working on advanced security measures to make the worldwide web safe in all respects. This paper focuses on the design and analysis of NICS-based web attack detection system in cooperation with the existing Intrusion Detection System (IDS), by experimenting with the methods and procedures of Nature-inspired Cyber Security to generate adaptive responses against advanced cyber-attacks. The proposed method is tested and validated on multiple attack scenarios and achieved better results in early detection of suspicious activities of web attacks for IDS. © 2021, Springer Nature Switzerland AG.

关键词： Cybersecurity

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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SKETCH2MANGA: Shaded Manga Screening from Sketch with Diffusion Models

SKETCH2MANGA: Shaded Manga Screening from Sketch with Diffus...

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IEEE International Conference on Image Processing

作者： Jian Lin Xueting Liu Chengze Li Minshan Xie Tien-Tsin Wong School of Computing and Information Sciences Saint Francis University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Data Science and Artificial Intelligence Monash University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty in generating shaded high-frequency screentones of high-quality. Classic manga screening approaches generally require user input to provide screentone exemplars or a reference manga image. Recent deep learning models enable automatic generation by learning from a large-scale dataset. However, the state-of-the-art models still fail to generate high-quality shaded screentones due to the lack of a tailored model and high-quality manga training data. In this paper, we propose a novel sketch-to-manga framework that first generates a color illustration from the sketch and then generates a screentoned manga based on the intensity guidance. Our method significantly outperforms existing methods in generating high-quality manga with shaded high-frequency screentones.

关键词： Deep learning Image color analysis Training data Entertainment industry Diffusion models Animation data models

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Expanding and Squeezing Differential Expressed Genes for Insect Fitness Prediction 34

Expanding and Squeezing Differential Expressed Genes for Ins...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： Sun, Zhiyong Khan, Md Ayshik Rahman Yang, Yan Ahmed, Khandaker Asif Hossain, Md Zakir Biological Data Science Institute Australian National University Canberra Australia La Trobe University Melbourne Australia Australian Centre for Disease Preparedness Commonwealth Scientific and Industrial Research Organisation Melbourne Australia School of Electrical Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

The study of differential expressed genes (DEG) and making predictions from it is crucial for the advancements in numerous domains such as medical diagnostics and pharmaceutical and agricultural developments as it provides valuable insights into the underlying mechanisms of these sectors. Despite machine learning being one of the most indispensable tools for this, a persistent challenge arises from the high dimensionality of DEG data, where the number of features was notably larger than the number of available samples which would lead to overfitting. Therefore, to combat this critical issue we designed a data dimension adjustment method for DEG data called (DDA-DEG) which selects the important features and resamples the data to improve model generalization. The DDA-DEG method utilizes a GA-based (genetic algorithm) method for feature selection and a CVAE-based (conditional variational autoencoder) method for resampling. In our comprehensive evaluation, we compare the performance of DDA-DEG against four machine learning methods - Random Forest (RF), Support Vector Regression (SVR), Single Layer Feed-forward Neural Network (SLFN), and Extreme Learning Machine (ELM). Alongside GA and CVAE, PCC, PIMP and Robust Regression was also used for feature selection and RO, SMOTER and SMOGN for resampling. But as, GA and CVAE came out on top, the further experiments were continued with them. The results of our extensive experiments reveal that RF exhibits the best RMSE amongst the four methods with raw data (RMSE = 0.1509). Furthermore, applying DDA-DEG leads to a significant improvement in SVR’s performance, reducing its RMSE from 0.1576 with raw data to 0.095 with feature selection and 0.093 with resampling. This significant improvement in results with DDA-DEG undeniably demonstrates the effectiveness of our proposed method in overcoming overfitting and improving predictive performance. Copyright © 2023 Sun, Khan, Yang, Ahmed and Hossain.

关键词： Genes

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Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies 3

Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies

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3rd International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Durgadevi, P. Vijayalakshmi, S. Sabbharwal, Munish Galgotias University School of Computing Science and Engineering India Computer Science and Engineering Galgotias College of Engineering and Technology India Christ University Department of Data Science India

ISBN: (纸本)9781665438117

In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies;clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. © 2021 IEEE.

关键词： Magnetic resonance imaging

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CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion

arXiv

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arXiv 2024年

作者： Hsu, Chih-Chung Ni, Chih-Chien Lee, Chia-Ming Kang, Li-Wei Institute of Data Science Miin Wu School of Computing National Cheng Kung University Tainan Taiwan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

Hyperspectral imaging, capturing detailed spectral information for each pixel, is pivotal in diverse scientific and industrial applications. Yet, the acquisition of high-resolution (HR) hyperspectral images (HSIs) often needs to be addressed due to the hardware limitations of existing imaging systems. A prevalent workaround involves capturing both a high-resolution multispectral image (HR-MSI) and a low-resolution (LR) HSI, subsequently fusing them to yield the desired HR-HSI. Although deep learning-based methods have shown promising in HR-MSI/LR-HSI fusion and LR-HSI super-resolution (SR), their substantial model complexities hinder deployment on resource-constrained imaging devices. This paper introduces a novel knowledge distillation (KD) framework for HR-MSI/LR-HSI fusion to achieve SR of LR-HSI. Our KD framework integrates the proposed Cross-Layer Residual Aggregation (CLRA) block to enhance efficiency for constructing Dual Two-Streamed (DTS) network structure, designed to extract joint and distinct features from LR-HSI and HR-MSI simultaneously. To fully exploit the spatial and spectral feature representations of LR-HSI and HR-MSI, we propose a novel Cross Self-Attention (CSA) fusion module to adaptively fuse those features to improve the spatial and spectral quality of the reconstructed HR-HSI. Finally, the proposed KD-based joint loss function is employed to co-train the teacher and student networks. Our experimental results demonstrate that the student model not only achieves comparable or superior LR-HSI SR performance but also significantly reduces the model-size and computational requirements. This marks a substantial advancement over existing state-of-the-art methods. The source code is available at https://***/ming053l/CSAKD. © 2024, CC BY.

关键词： Image fusion

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FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

arXiv

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arXiv 2024年

作者： He, Jialuo Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adversarial attacks, which can impact model robustness and fairness. Personalized FL attempts to provide some relief by customizing models for individual clients. However, it falls short in addressing server-side aggregation vulnerabilities. We introduce a novel method called FedAA, which optimizes client contributions via Adaptive Aggregation to enhance model robustness against malicious clients and ensure fairness across participants in non-identically distributed settings. To achieve this goal, we propose an approach involving a Deep Deterministic Policy Gradient-based algorithm for continuous control of aggregation weights, an innovative client selection method based on model parameter distances, and a reward mechanism guided by validation set performance. Empirically, extensive experiments demonstrate that, in terms of robustness, FedAA outperforms the state-of-the-art methods, while maintaining comparable levels of fairness, offering a promising solution to build resilient and fair federated systems. Our code is available at https://***/Gp1g/FedAA. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Enabling Technologies for Web 3.0: A Comprehensive Survey

arXiv

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arXiv 2023年

作者： Hassan, Md Arif Jamshidi, Mohammad Manh, Bui Duc Chu, Nam H. Nguyen, Chi-Hieu Hieu, Nguyen Quang Nguyen, Cong T. Hoang, Dinh Thai Nguyen, Diep N. Van Huynh, Nguyen Alsheikh, Mohammad Abu Dutkiewicz, Eryk School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University United Kingdom Faculty of Science and Technology University of Canberra Australia

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Web

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Expanding and Squeezing Differential Expressed Genes for Insect Fitness Prediction 44

Expanding and Squeezing Differential Expressed Genes for Ins...

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44th International Conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Sun, Zhiyong Khan, Ayshik Rahman Yang, Yan Ahmed, Khandaker Asif Hossain, Md Zakir Biological Data Science Institute Australian National University Canberra Australia La Trobe University Melbourne Australia Australian Centre for Disease Preparedness Commonwealth Scientific and Industrial Research Organisation Melbourne Australia School of Electrical Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9781713893622

关键词： Genes

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