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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yushan Xie Lijun Ouyang Dongping Xiong Xiaozhi Zhang School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Due to the lengthy acquisition time required for magnetic resonance imaging (MRI), researchers have proposed various methods to shorten this time. However, these methods often overlook the non-local similarity within the images, resulting in the inability to reconstruct images with clear structures. To address this issue, we present a Graph Channel Attention U-Net (GCA-Unet).The input to GCA-Unet consists of three images with different resolutions. Each encoder is specifically trained on input images of varying scales, as well as feature information from the downsampled preceding decoder. The architecture employs a Multi-Head Graph Attention Module (MGAM) to capture non-local features within the images. In the final layer of the decoder, we output the reconstructed image. Given that the input comprises images of different scales, we also introduce a Multi-Scale Feature Fusion Module (MFFM) to effectively integrate features across multiple scales. Extensive experiments conducted on brain and knee datasets demonstrate that our proposed GCA-Unet significantly outperforms other MRI reconstruction methods across various sampling rates. These results validate the effectiveness of GCA-Unet in reconstructing images with clear structures.

关键词： Computer vision Image resolution Magnetic resonance imaging Machine learning Computer architecture Reconstruction algorithms Decoding Image reconstruction

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SDN control plane security: Attacks and mitigation techniques 2021

SDN control plane security: Attacks and mitigation technique...

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4th International Conference on Networking, Information Systems and Security, NISS 2021

作者： Fatima, Kiran Zahoor, Kanwal Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women

ISBN: (纸本)9781450388719

Traditional networks are complex and hard to manage due to many reasons such as manual configuration requirements of dedicated devices, lack of flexibility and a non-dynamic approach. To overcome these limitations and to meet the challenges of modern networks a new networking paradigm software Defined Networking (SDN) has been introduced. SDN presents a centralized and completely dynamic environment which provides flexibility and programmability in networks. It enables the network to be controlled centrally and intelligently using multiple software applications. SDN being in its infancy, brings along new challenges. Standardization of various interfaces, scalability, compatibility of contrasting or divergent networks, and vulnerability issues are few of them. This paper discusses various vulnerabilities and possible attacks on every layer of an SDN and focuses on control plane attacks. Further, it presents a comprehensive survey on numerous attacks on the brain of an SDN i.e. the control plane along with existing solutions. The study concludes that despite the challenges that are worth debating, SDN has many characteristics that make it an ideal candidate for future networks. © 2021 ACM.

关键词： Attacks on control plane SDN security software defined network (SDN) Vulnerabilities of SDN

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Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models

Synthetic Minority Over-sampling Technique for Student Perfo...

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International Conference on Computer and Information Technology (ICCIT)

作者： Khaled Mahmud Sujon Rohayanti Hassan Nusrat Jahan Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology(CUET) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study explores the impact of the Synthetic Minority Oversampling Technique (SMOTE) on predictive accuracy in student performance prediction using both linear (Logistic Regression, LR) and ensemble (Random Forest, RF) models. We compare the performance of these models, using a real-time dataset from Universiti Teknologi Malaysia (UTM). While previous studies have examined these models separately, this research uniquely investigates the simultaneous application of SMOTE to both. Our results show that LR’s accuracy improves from 91% to 95% with SMOTE, while RF maintains a consistent 98% accuracy. These findings demonstrate that SMOTE significantly enhances the performance of linear models in handling imbalanced data, providing useful insights for curriculum development in higher education. This research contributes to the literature by guiding higher education institutions in selecting the most effective predictive models.

关键词： Radio frequency Analytical models Logistic regression Accuracy Computational modeling Education Predictive models Data models Real-time systems Information technology

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Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization

arXiv

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arXiv 2024年

作者： Wang, Zeyi Liu, Songbai Chen, Jianyong Tan, Kay Chen College of Computer and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularly in scenarios involving constraints. In this study, we employ a large language model (LLM) to enhance evolutionary search for solving constrained multiobjective optimization problems. Our aim is to speed up the convergence of the evolutionary population. To achieve this, we finetune the LLM through tailored prompt engineering, integrating information concerning both objective values and constraint violations of solutions. This process enables the LLM to grasp the relationship between well-performing and poorly performing solutions based on the provided input data. Solution’s quality is assessed based on their constraint violations and objective-based performance. By leveraging the refined LLM, it can be used as a search operator to generate superior-quality solutions. Experimental evaluations across various test benchmarks illustrate that LLM-aided evolutionary search can significantly accelerate the population’s convergence speed and stands out competitively against cutting-edge evolutionary algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Cross Device Federated Intrusion Detector for Early Stage Botnet Propagation in IoT

Cross Device Federated Intrusion Detector for Early Stage Bo...

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Annual IEEE Systems Conference

作者： Angela Grace Famera Raj Mani Shukla Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio Computing and Information Science Anglia Ruskin University Cambridge

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

A botnet is an army of zombified computers infected with malware and controlled by malicious actors to carry out tasks such as Distributed Denial of Service (DDoS) attacks. Billions of Internet of Things (IoT) devices are primarily targeted to be infected as bots since they are configured with weak credentials or contain common vulnerabilities. Detecting botnet propagation by monitoring the network traffic is difficult as they easily blend in with regular network traffic. The traditional machine learning (ML) based Intrusion Detection System (IDS) requires the raw data to be captured and sent to the ML processor to detect intrusion. In this research, we examine the viability of a cross-device federated intrusion detection mechanism where each device runs the ML model on its data and updates the model weights to the central coordinator. This mechanism ensures the client’s data is not shared with any third party, terminating privacy leakage. The model examines each data packet separately and predicts anomalies. We evaluate our proposed mechanism on a real botnet propagation dataset called MedBIoT. Overall, the proposed method produces an average accuracy of 71%, precision 78%, recall 71%, and f1-score 68%. In addition, we also examined whether any device taking part in federated learning can employ a poisoning attack on the overall system.

关键词： Data privacy Accuracy Federated learning Botnet Intrusion detection Telecommunication traffic Data models

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Smart UML - Assignment Management Tool for UML Diagrams 4

Smart UML - Assignment Management Tool for UML Diagrams

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4th International Conference on Advancements in computing, ICAC 2022

作者： Weerasinghe, D.N.H. Thiwanka, K.A.T. Jayasith, H.B.C. Onella Natalie, P.A.D. Samantha Rajapaksha, U.U. Karunasena, Anuradha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system's stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most Computer science-related students get assessed in UML modelling, diagram plagiarism isn't considered in most exams since it's a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage. © 2022 IEEE.

关键词： Unified Modeling Language

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ALGIN: Adaptive Local and Global Interests Network for Click-Through Rate Prediction

ALGIN: Adaptive Local and Global Interests Network for Click...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yingjia Xiang Xuanyu Lu Wei Zhou Junhao Wen School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing China University of Geosciences Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

User behavior sequence modeling represents users' interests and preferences, which has a crucial impact on click-through rate (CTR) prediction models and recommendation systems. In practical scenarios, multiple types of interactive behaviors usually constitute users' complex interests and preferences. However, existing CTR prediction models based on multi-type interactive behavior modeling have two limitations: (1) they cannot handle noise in users' global interests, resulting in the inability to model accurate global interest preferences; (2) they struggle to depict users' local interest patterns and cannot model the complex patterns embedded in multi-behavior sequences. To overcome these limitations, an Adaptive Local and Global Interests Network For Click-Through Rate Prediction (ALGIN) is proposed, which effectively models global and local interests within multi-behavior interaction sequences through a unified framework. Firstly, ALGIN employs a Fourier Global Interest Modeling module to eliminate noise in the behavioral sequence; secondly, ALGIN designs a Multi-Scale Local Interest Extraction module to capture behavioral patterns within multi-type interactive sequences; finally, an Item-driven Interest Selector is used to integrate users' local interests with their global interests. Experiments on multiple public datasets demonstrate that the ALGIN model outperforms the current state-of-the-art (SOTA), and ablation experiments confirm the effectiveness of each module within the model.

关键词： Adaptation models Adaptive systems Fourier transforms Accuracy Convolution Noise Predictive models Recommender systems

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High-Frequency Conditional Diffusion Probabilistic Model for Under-sampled MRI Reconstruction

High-Frequency Conditional Diffusion Probabilistic Model for...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Ziwei Zhu Lijun Ouyang Dongping Xiong Xiaozhi Zhang School of Electrical Engineering University of South China Hengyang China School of Computing Software University of South China Hengyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The magnetic resonance imaging (MRI) is extensively used in medical clinical diagnosis due to its excellent performance in medical imaging. However, the disadvantage of slow imaging speed has hindered the further application of MRI in medical clinical diagnosis. Diffusion probabilistic models (DPM) have achieved impressive results in image restoration due to the extraordinary ability to generate highly realistic images. However, DPM needs to be set up with a long number of diffusion steps to obtain excellent results, leading to the training and sampling process to consume a lot of time. This paper presents a high-frequency conditional diffusion Probabilistic Model (HFC-DDPM), which defines the diffusion and inference process in wavelet domains rather than image domain. On this basis, the Wave-DiffNet, a network for solving MRI reconstruction tasks is presented, which recovers the high-frequency and low-frequency components of MR image by diffusion model and encoder-decoder structure, respectively. Notably, to prevent conflicts between frequencies caused by optimizing objectives at different stages, the Frequency Cross-attention Alignment module is designed to align and normalize the frequencies at each iteration of the denoising process. Our model has been evaluated on the MICCAI Grand Challenge dataset and has significantly improved the quality and speed of MRI image reconstruction.

关键词： Training Wavelet domain Magnetic resonance imaging Frequency-domain analysis Computational modeling Diffusion models Brain modeling Clinical diagnosis Image reconstruction Medical diagnostic imaging

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Cross-modal Frequency-aware Transformer for Multimodal Medical Image Fusion

Cross-modal Frequency-aware Transformer for Multimodal Medic...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Lamei Wang Lijun Ouyang Dongping Xiong Xiaozhi Zhang School of Computing/Software University of South China Hengyang China School of Electrical Engineering University of South China Hengyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Fusing multimodal medical images seeks to amalgamate their unique informational strengths, thereby enhancing the precision of clinical diagnostics and the accuracy of surgical navigation. The resulting image quality in multimodal fusion is contingent upon the characteristics derived from individual modalities and the strategies employed for integrating this diverse data. Nevertheless, current deep learning-driven fusion techniques, although adept at harnessing the semantic aspects of each imaging mode, struggle to differentiate between the beneficial low-frequency and high-frequency details within each modality. This limitation hinders their ability to capture comprehensive global insights and to achieve dynamic, context-aware fusion. To address these issues, we introduce an innovative fusion framework that utilizes Fourier transforms and incorporates a cross-modal frequency-aware transformer. This framework includes a frequency-aware cross-attention mechanism for adaptive feature fusion across modalities by exchanging keys and queries between spatial and frequency domains. It also computes cross-attention scores to facilitate the integration of spatial and frequency data. Additionally, we employ a hybrid approach of gradient preservation and transformers for feature extraction, capturing both local specifics and global information. Our model surpasses existing methods assessments, markedly improving fused image quality.

关键词： Image quality Fourier transforms Navigation Frequency-domain analysis Semantics Surgery Transformers Spatial databases Image fusion Medical diagnostic imaging

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CRAYM: neural field optimization via camera RAY matching 24

CRAYM: neural field optimization via camera RAY matching

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

作者： Liqiang Lin Wenpeng Wu Chi-Wing Fu Hao Zhang Hui Huang College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computing Science Simon Fraser University

ISBN: (纸本)9798331314385

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives.

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