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5th International Conference on computer science and Management Technology, ICCSMT 2024

作者： Yang, Yanhua Zhan, Xinrong College of Computer Engineering Jimei University Fujian Xiamen China School of Data and Computer Science Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400709999

Service organized all kinds of services to one for the *** the cloud environment, the services and related QoSs (Quality of Services) in every cloud may be *** this paper, how to compose those services together in the multiple clouds environment is the *** use a SCDQN (Service Composition based a Deep-Q-Network) method, and consider the execution time, privacy level, cost and availability of the service and the service request in the multiple *** show that SCDQN has a lower value in the execution time and cost while satisfying privacy level and availability requirement. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： DQN

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FSM: A Fine-grained Splitting and Merging Framework for Dual-balanced Graph Partition 50th

FSM: A Fine-grained Splitting and Merging Framework for Dual...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Liu, Chengjun Peng, Zhuo Zheng, Weiguo Zou, Lei School of Data Science Fudan University China Wangxuan Institute of Computer Technology Peking University China

Partitioning a large graph into smaller subgraphs by minimizing the number of cutting vertices and edges, namely cut size or replication factor, plays a crucial role in distributed graph processing tasks. However, many prior works have primarily focused on optimizing the cut size by considering only vertex balance or edge balance, leading to significant workload imbalance and consequently hindering the performance of downstream tasks. Therefore, in this paper, we address the dual-balanced graph partition problem that minimizes the cut size while simultaneously guaranteeing both vertex and edge balance. We propose a lightweight effective twophase framework, namely fine-grained splitting and merging (FSM), which decomposes the graph into more and smaller partitions and then merges them. FSM offers the flexibility of integrating with various state-of-the-art single-balanced techniques. We develop two efficient algorithms Fast Merging and Precise Merging to enable trade-offs between computational efficiency and partitioning quality. Experimental results on large real-world graphs demonstrate that FSM achieves state-of-the-art cut size while maintaining dual balance. The runtime for downstream tasks PageRank, connected component, and diameter estimation, can be reduced by a large proportion, up to 9.43%, 11.35%, and 17.94%, respectively. © 2024, VLDB Endowment. All rights reserved.

关键词： Merging

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Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

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Frontiers of computer science 2022年第4期16卷 131-146页

作者： Guoshuai Zhou Xiuxia Tian Aoying Zhou School of Computer Science and Technology ShangHai University of Electric PowerShanghai200090China School of Data Science and Engineering East China Normal UniversityShanghai200062China

Image forgery detection remains a challenging *** the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further *** the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected *** method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery ***,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be ***,the dual feature matching is used to match the features and locate the forgery ***’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high *** experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.

关键词： image copy-move forgery passive detection self-selected sub-images pulse coupled neural network(PCNN) dual feature matching

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Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation 38

Beware of Road Markings: A New Adversarial Patch Attack to M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Hangcheng Wu, Zhenhu Wang, Hao Han, Xingshuo Guo, Shangwei Xiang, Tao Zhang, Tianwei College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Beijing University of Posts and Telecommunications China College of Computer Science Chongqing University China

Monocular Depth Estimation (MDE) enables the prediction of scene depths from a single RGB image, having been widely integrated into production-grade autonomous driving systems, e.g., Tesla Autopilot. Current adversarial attacks to MDE models focus on attaching an optimized adversarial patch to a designated obstacle. Although effective, this approach presents two inherent limitations: its reliance on specific obstacles and its limited malicious impact. In contrast, we propose a pioneering attack to MDE models that decouples obstacles from patches physically and deploys optimized patches on roads, thereby extending the attack scope to arbitrary traffic participants. This approach is inspired by our groundbreaking discovery: various MDE models with different architectures, trained for autonomous driving, heavily rely on road regions when predicting depths for different obstacles. Based on this discovery, we design the Adversarial Road Marking (AdvRM) attack, which camouflages patches as ordinary road markings and deploys them on roads, thereby posing a continuous threat within the environment. Experimental results from both dataset simulations and real-world scenarios demonstrate that AdvRM is effective, stealthy, and robust against various MDE models, achieving about 1.507 of Mean Relative Shift Ratio (MRSR) over 8 MDE models. The code is available at this Github Repo. © 2024 Neural information processing systems foundation. All rights reserved.

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Towards Assessing Cycleway Pavement Surface Roughness Using an Action Camera with IMU and GPS 11

Towards Assessing Cycleway Pavement Surface Roughness Using ...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Baig, Muhammad Hassam Antonio Ayala Garcia, Jeziel Qureshi, Waqar Shahid Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Data Science Institute University of Galway Galway Ireland

ISBN: (纸本)9789897587450

This paper introduces an autonomous and cost-effective method for assessing cycleway pavement roughness, using an action camera equipped with high-resolution sensors including an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS). The methodology utilizes simplified quarter car model for bicycles, without manual intervention, to calculate International Roughness Index (IRI) for cycleway surface quality evaluation. It utilizes our novel approach to determine stable section from which average acceleration orientation vector is computed. For analysis we propose a corrected-roughness index (CRI), which is a quantized version of IRI. Experiments conducted on asphalt cycleways in Ireland revealed strong correlations between vehicle vibration and surface roughness. Results further demonstrate the consistency of the proposed model across different bikes through comparative analysis. Observations indicate bias in vibration data, influenced by different tire sizes and the mechanical features of the bicycles. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Asphalt pavements

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The Generalized Fixed Point Iteration Method for AVE

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Journal of Mathematical Research with Applications 2024年第6期44卷 837-849页

作者： Changfeng MA Jing KANG School of Data and Computer Science Xiamen Institute of Technology College of Mathematics and Informatics Fujian Normal University

In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute value equation(AVE). The proposed method not only includes SOR-like method, FPI method, MFPI method and so on, but also generates some special versions. Some convergence conditions of the proposed method with different iteration error norms are presented. Furthermore, methods corresponding to other splitting methods are studied in detail. The effectiveness and feasibility of the proposed method are confirmed by some numerical experiments.

关键词： absolute value equations fixed point iteration convergence

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NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORATIVE FILTERING: THEORY AND PRACTICE 12

NEURON-ENHANCED AUTOENCODER MATRIX COMPLETION AND COLLABORAT...

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12th International Conference on Learning Representations, ICLR 2024

作者： Fan, Jicong Chen, Rui Zhang, Zhao Ding, Chris H.Q. School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science & Information Engineering Hefei University of Technology China

Neural networks have shown promising performance in collaborative filtering and matrix completion but the theoretical analysis is limited and there is still room for improvement in terms of the accuracy of recovering missing values. This paper presents a neuron-enhanced autoencoder matrix completion (AEMC-NE) method and applies it to collaborative filtering. Our AEMC-NE adds an element-wise autoencoder to each output of the main autoencoder to enhance the reconstruction capability. Thus it can adaptively learn an activation function for the output layer to approximate possibly complicated response functions in real data. We provide theoretical analysis for AEMC-NE as well as AEMC to investigate the generalization ability of autoencoder and deep learning in matrix completion, considering both missing completely at random and missing not at random. We show that the element-wise neural network has the potential to reduce the generalization error bound, the data sparsity can be useful, and the prediction performance is closely related to the difference between the numbers of variables and samples. The numerical results on synthetic data and five benchmark datasets demonstrated the effectiveness of AEMC-NE in comparison to many baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Collaborative filtering

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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CredAct: Privacy-Preserving Activity Verification for Benefits Schemes in Self-Sovereign Identity 6

CredAct: Privacy-Preserving Activity Verification for Benefi...

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Mukta, Rahma Paik, Hye-Young Lu, Qinghua Kanhere, Salil S. Unsw School of Computer Science and Engineering Sydney Australia Csiro Data61 Sydney Australia

ISBN: (纸本)9798350316742

We propose CredAct, a user activity verification designed with data minimisation to protect privacy. Many Benefits Schemes, such as discount offers, loyalty programs, and incentive systems, require verification of user activity (e.g., buying healthy food, step counts) in their business processes. These service providers can collect a large amount of users' personal information, and often users do not have fine-grained control over the scope of data disclosure. In CredAct, we propose a Self-Sovereign Identity based framework implemented on blockchain that enables users participating in a benefits scheme to minimise data sharing during the submission and verification of data. We use a smart contract-based function along with a Zero-Knowledge Proof cryptographic commitment scheme, that forces the entities involved in the business process to collect or disclose only the required (minimum) data to fulfill the intended purpose. The evaluation shows that the system is feasible with minimal operational overheads compared to traditional cryptographic techniques. We also perform a qualitative privacy and security analysis considering relevant threats to CredAct. © 2024 IEEE.

关键词： Differential privacy

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