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International Conference on Computing and Networking Technology (ICCNT)

作者： Lijin Shaji Suji Pramila R Computer Applications Noorul Islam Center for Higher Education Thucklay Tamil Nadu India Computer Science and Engineering Mar Baselios Institute of Technology and Science Kothamangalam Kerala India

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated; thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used.

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Causal discovery with endogenous context variables 24

Causal discovery with endogenous context variables

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wiebke Günther Oana-Iuliana Popescu Martin Rabel Urmi Ninad Andreas Gerhardus Jakob Runge German Aerospace Center (DLR) Institute of Data Science Jena Germany and Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany and Technische Universität Dresden Faculty of Computer Science Dresden Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany and Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany and Technische Universität Dresden Faculty of Computer Science Dresden Germany

ISBN: (纸本)9798331314385

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables - precipitation can make a dry soil wet - leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method.

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FCGSM: Fast Conjugate Gradient Sign Method for Adversarial Attack on Image Classification 6th

FCGSM: Fast Conjugate Gradient Sign Method for Adversarial ...

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6th International Conference on Innovative Computing, IC 2023

作者： Xia, Xiaoyan Xue, Wei Wan, Pengcheng Zhang, Hui Wang, Xinyu Zhang, Zhiting School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Maanshan243032 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819922864

Deep neural network is sensitive to adversarial samples that crafted by adding imperceptible perturbations to original images, and many methods of generating adversarial samples have emerged. Although existing methods based on gradient direction have good attack performance, some ill-conditioned issues may reduce their performance on occasion. In this paper, we propose a novel attack method based on three-terms conjugate gradient direction, which is effectively for improving this limitation, and its is named as fast conjugate gradient sign method (FCGSM). The proposed method FCGSM can jump from the local maximum during the process of finding the maximum value of loss function, thus generating more adversarial samples than the SOTA methods APGD and ACG. Experiments conducted on two benchmark datasets show that the FCGSM works well in attacking deep neural network-based classification models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Revolutionizing Prostate Cancer Diagnosis: Harnessing the Potential of Transfer Learning for MRI-Based Classification 4

Revolutionizing Prostate Cancer Diagnosis: Harnessing the Po...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Trivedi, Sanjay Dave, Harsh Nilaykumar, Patel Khush Dalal, Pankaj Sigma University Department of Computer Engineering Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gjpiasr Surgeon Department of Surgery Gujrat Anand India Smt.B.K.Shah Medical Institute and Research Center Gujarat Vadodara India Charotar University of Science and Technology Changa India Sigma University Computer Science and Applications Gujrat Vadodara India

ISBN: (纸本)9798350300888

This study introduces a novel framework that harnesses the potential of transfer learning for MRI -based classification in prostate cancer diagnosis. The study explores the challenges of accurately classifying prostate cancer from MRI images and proposes a methodology that integrates pre-trained deep neural networks with a large, annotated dataset. The framework demonstrates superior performance compared to conventional methods, achieving higher Accuracy, Precision, Recall, Fl-Score. The approach exhibits robustness across different MRI scanners and patient populations, highlighting its potential for real-world clinical applications. Additionally, this study emphasizes the interpretability of the model through visualization techniques, aiding clinicians in understanding the underlying pathology and making informed treatment decisions. Overall, this research contributes to the revolutionization of prostate cancer diagnosis, offering improved accuracy and efficiency in clinical settings and paving the way for advanced computer-aided diagnostic systems. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Fault Injection Method for Evaluating Single Event Soft Errors in Deep Neural Networks

A Fault Injection Method for Evaluating Single Event Soft Er...

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International Conference on Radiation Effects of Electronic Devices (ICREED)

作者： He Lyu Bo Mei Pengchao Xue Bo Wang Rigen Mo Qifei Yuan Wu Gao School of Computer Science and Technology Northwestern Polytechnical University Xi'an China China Aerospace Components Engineering Center China Academy of Space Technology Beijing China

ISBN: (数字)9798331549299

ISBN: (纸本)9798331549305

Single Event Effects (SEEs) pose significant reliability challenges to deep neural networks (DNNs), particularly in aerospace environments characterized by radiation exposure. This paper introduces a comprehensive fault injection (FI) framework for systematic vulnerability assessment and mitigation of SEEs in DNN architectures. We propose a Progressive Bit Search (PBS) technique to efficiently identify sensitive layers within DNNs, combined with Triple Modular Redundancy (TMR) for hardening critical layers. Experimental results on YOLOv5 demonstrate that our approach improves fault injection evaluation efficiency by over tenfold while enhancing SEE hardening capability by more than 24 times, with a resource cost increase of less than 10%. This work addresses the critical gap between software-based FI simulations and hardware-accurate reliability assessment, providing a practical solution for radiation-hardened DNN design in aerospace applications.

关键词： YOLO Fault diagnosis Radiation effects Systematics Costs Radiation hardening (electronics) Prevention and mitigation Redundancy Artificial neural networks Reliability engineering

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LLM for Question Generation and Validation to Enhance School Level Student Assessment

LLM for Question Generation and Validation to Enhance School...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Adithya D Varma Sruthy Anand Akshay Krishnan Thekkedath Kiran P Niranjan Prasanth Sethuraman N Rao Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Recent advancements in large language models (LLMs) have accelerated the growth of several AI-based solutions in the daily lives of people, including in the field of education. Using LLM in education can potentially automate various aspects of the teaching-learning process. This work explores how LLMs can assist educators in generating questions for student assessment. Using sophisticated prompt engineering, a system has been developed to generate multiple levels of questions from a single topic, enabling teachers to evaluate and curate a variety of questions attuned to the diverse needs of learners. This innovative approach diverges from conventional ICT-based techniques that primarily rely on keyword matching and superficial metrics. The system generates questions, taking into account the semantic meaning and contextual relevance. Furthermore, LLM evaluation metrics have been applied to assess the methods used for the generation of questions, and the results are presented accordingly.

关键词： Measurement Pervasive computing Social networking (online) Large language models Semantics Education Taxonomy computer architecture Question generation Prompt engineering

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A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

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computers, Materials & Continua 2022年第7期72卷 1123-1137页

作者： Xiaorui Zhang Jie Zhou Wei Sun Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.

关键词： Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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IM-Diff: Implicit Multi-Contrast Diffusion Model for Arbitrary Scale MRI Super-Resolution

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4200-4212页

作者： Liu, Lanqing Zou, Jing Xu, Cheng Wang, Kang Lyu, Jun Xu, Xuemiao Hu, Zhanli Qin, Jing The Hong Kong Polytechnic University Centre for Smart Health School of Nursing 999077 Hong Kong Nanyang Technological University School of Mechanical and Aerospace Engineering 639798 Singapore Harvard Medical School Department of Psychiatry Brigham and Women's Hospital BostonMA02115 United States South China University of Technology Guangdong Engineering Center for Large Model and GenAI Technology School of Computer Science and Engineering Guangzhou510640 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Shenzhen518172 China CAS SIAT-PolyU Multi-modal Medical Molecular Imaging Joint Laboratory 999077 Hong Kong

Diffusion models have garnered significant attention for MRI Super-Resolution (SR) and have achieved promising results. However, existing diffusion-based SR models face two formidable challenges: 1) insufficient exploitation of complementary information from multi-contrast images, which hinders the faithful reconstruction of texture details and anatomical structures;and 2) reliance on fixed magnification factors, such as 2× or 4×, which is impractical for clinical scenarios that require arbitrary scale magnification. To circumvent these issues, this paper introduces IM-Diff, an implicit multi-contrast diffusion model for arbitrary-scale MRI SR, leveraging the merits of both multi-contrast information and the continuous nature of implicit neural representation (INR). Firstly, we propose an innovative hierarchical multi-contrast fusion (HMF) module with reference-aware cross Mamba (RCM) to effectively incorporate target-relevant information from the reference image into the target image, while ensuring a substantial receptive field with computational efficiency. Secondly, we introduce multiple wavelet INR magnification (WINRM) modules into the denoising process by integrating the wavelet implicit neural non-linearity, enabling effective learning of continuous representations of MR images. The involved wavelet activation enhances space-frequency concentration, further bolstering representation accuracy and robustness in INR. Extensive experiments on three public datasets demonstrate the superiority of our method over existing state-of-the-art SR models across various magnification factors. © 2013 IEEE.

关键词： Image enhancement

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