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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Squeeze Excitation Embedded Attention U-Net for Brain Tumor Segmentation 2nd

Squeeze Excitation Embedded Attention U-Net for Brain Tumor ...

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2nd International Conference on Emerging Electronics and Automation, E2A 2022

作者： Prasanna, Gaurav Ernest, John Rohit Lalitha, G. Narayanan, Sathiya School of Electronics Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819968541

Deep learning-based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classification and segmentation of medical images. Existing architectures such as U-Net, Attention U-Net, and Attention Residual U-Net are the state-of-the-art architectures for brain tumor segmentation. However, these architectures do not address the issue of how to extract the features in channel level. In this work, we propose a new architecture called Squeeze Excitation Embedded Attention U-Net (SEEA-U-Net) which incorporates squeeze excitation network in Attention U-Net for better segmentation results. SEEA-U-Net extracts information at both spatial and channel levels. It outperforms the state-of-the-art architectures in terms of binary focal loss and Jaccard coefficient, particularly when the number of epochs is limited. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain

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Research on Network Security Access Control Based on Improved Algorithm 24

Research on Network Security Access Control Based on Improve...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Li, Shenghao Wu, Yuanyuan Chen, Yingsheng School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering China School of Science and Technology Chongqing College of Humanities China

ISBN: (纸本)9798400718137

This article proposes a computer network access isolation control method based on trusted computing. Firstly, the wavelet analysis method is used to analyze computer network access, and then the frequent IP address distance algorithm is used to identify and analyze dangerous access. Based on the characteristics of dangerous access, a network access isolation control model is established using trusted computing technology to complete the overall structural design of the computer network access isolation control system, and analyze the content of each module in the structure. By analyzing the security attributes of network access isolation control, the process of computer network access isolation control is obtained, thereby achieving the establishment and application of a computer network access isolation control system. This method can effectively prevent malicious attacks on computer networks and ensure the safe and stable operation of computer networks. Experimental results have shown that the proposed method can effectively isolate and control computer network access, improve the accuracy of isolation control, reduce the difficulty of computer network access isolation control. © 2024 Copyright held by the owner/author(s).

关键词： Computer network

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Always Provide Context: The Effects of Code Context on Programming Error Message Enhancement 2023

Always Provide Context: The Effects of Code Context on Progr...

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2nd ACM Conference on Global computing Education, CompEd 2023

作者： Santos, Eddie Antonio Prasad, Prajish Becker, Brett A. School of Computer Science University College Dublin Dublin Ireland School of Computing & Data Sciences Flame University Maharashtra Pune India

ISBN: (纸本)9798400700484

Programming error messages (PEMs) are notoriously difficult for novice programmers to utilise. Many efforts have been made to enhance PEMs such that they are reworded to explain problems in terms that novices can understand. However, the effectiveness of these efforts to enhance PEMs has been weak or inconclusive. This work seeks to determine the role that code context has on programming error message enhancement. Erroneous Java code written by novices was sampled from the Blackbox Mini dataset. The erroneous code was presented to expert raters with four different PEM variants: javac (control), Decaf - an error message enhancing IDE - and two variants generated using GPT-4: one that enhanced just the javac error message alone, and one that incorporates the code context in the prompt. We find that providing code context to LLMs increases the likelihood of correct explanations for underlying errors, produces more specific fixes for erroneous programs, and produces fixes that are more likely to be correct. In large language models, the community now has a resource that is capable of taking code context into account, to the benefit of novice programmers. © 2023 Owner/Author.

关键词： Feedback

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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Microbiologically influenced corrosion resistance enhancement of coppercontaining high entropy alloy Fe_(x)Cu_(1−x)CoNiCrMn against Pseudomonas aeruginosa

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International Journal of Minerals,Metallurgy and Materials 2024年第11期31卷 2488-2497页

作者： Yuntian Lou Weiwei Chang Yu Zhang Shengyu He Xudong Chen Hongchang Qian Dawei Zhang Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Innovation SchoolUniversity of Science and Technology BeijingFoshan 528399China School of Materials Science and Energy Foshan UniversityFoshan 528000China

To enhance the microbiologically influenced corrosion(MIC)resistance of FeCoNiCrMn high entropy alloy(HEAs),a series of Fe_(x)Cu_((1−x))CoNiCrMn(x=1,0.75,0.5,and 0.25)HEAs were *** characteristics,corrosion behavior(morphology observation and electrochemical properties),and antimicrobial performance of Fe_(x)Cu_((1−x))CoNiCrMn HEAs were evaluated in a medium inoculated with typical corrosive microorganism Pseudomonas *** aim was to identify copper-containing FeCoNiCrMn HEAs that balance corrosion resistance and antimicrobial *** revealed that all Fe_(x)Cu_((1−x))CoNiCrMn(x=1,0.75,0.5,and 0.25)HEAs exhibited an FCC(face centered cubic)phase,with significant grain refinement observed in Fe_(0.75)Cu_(0.25)CoNiCrMn *** tests indicated that Fe_(0.75)Cu_(0.25)CoNiCrMn HEA demonstrated lower corrosion current density(i_(corr))and pitting potential(E_(pit))compared to other Fe_(x)Cu_((1−x))CoNiCrMn HEAs in ***-inoculated medium,exhibiting superior resistance to ***-microbial tests showed that after 14 d of immersion,Fe_(0.75)Cu_(0.25)CoNiCrMn achieved an antibacterial rate of 89.5%,effectively inhibiting the adhesion and biofilm formation of ***,thereby achieving resistance to MIC.

关键词： microbiologically influenced corrosion Pseudomonas aeruginosa high entropy alloys antibacterial property

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Freshness Monitoring of Broccoli Using Intelligent Electronic Nose Systems for Sustainability in Food Supply Chains

Freshness Monitoring of Broccoli Using Intelligent Electroni...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Ebenezer, Brighty L. Sasithradevi, A. Baskar, Chanthini School of Electronics Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology Centre for Advanced Data Science Chennai India

ISBN: (纸本)9798331528126

Reducing food waste and enhancing sustainability in food supply chains have become increasingly critical in the global food industry. Broccoli, known for its nutrient richness, is particularly vulnerable to spoilage due to environmental factors, which results in considerable food loss. To address this issue, this paper proposes an innovative and sustainable approach to predicting broccoli freshness using an electronic nose (E-nose) system. The E-nose, equipped with gas sensors, detects volatile organic compounds (VOCs) emitted by broccoli during spoilage. The sensor data was first analyzed using unsupervised clustering algorithms such as K-Means, Gaussian Mixture Model (GMM), and Spectral Clustering, to group the broccoli samples based on freshness levels. Following this, advanced machine learning (ML) and deep learning (DL) techniques were applied to classify the samples into specific freshness categories: fresh, half-spoiled, and spoiled. K-means clustering combined with the Long Short-Term Memory (LSTM) model achieved an accuracy of 99.46%. Furthermore, hyperparameter tuning (HPT) was employed to optimize the performance of the LSTM models, improving classification accuracy to 100%. The results illustrate the effectiveness of integrating E-nose technology with AI-driven techniques for real-time, scalable freshness monitoring. This system offers a promising solution for reducing food waste and promoting sustainability in the management of perishable produce within food supply chains. © 2024 IEEE.

关键词： Electronic nose

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Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification 14

Resolution-Adaptive Representation Learning via Eliminating ...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Lu, Xing Lai, Fengshan Cao, Zhixiang Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach. © 2024 IEEE.

关键词： Contrastive Learning

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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