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Using the SCRM method to repair a damaged planning graph for service composition

International Journal of Web Engineering and technology 2024年第4期19卷 421-442页

作者： Gao, Zhihao Zhu, Ming Li, Jing Lu, Rui College of Computer Science and Technology Shandong University of Technology Zibo China College of Engineering and Computer Science The Australian National University Canberra Australia

Software, hardware, data, and computing power can be abstracted and encapsulated as services authorised to users in a paid or free manner for on demand deployment. Service composition combines multiple existing services in a certain logical order to solve complex tasks that a single service cannot complete. Many AI approaches have been proposed to solve service composition problems. However, the network environment is dynamic, and services in a composition may disappear or change. Furthermore, the composition requirement of a user may also vary. The composition plan should adjust accordingly. This paper proposes a repairing service composition approach based on a planning graph. We repair a solution from the last layer, then search backward for missing services. Return a repaired solution until a satisfactory result is obtained. To verify the effectiveness and efficiency of our approach, experiments are carried out compared with some repair methods and replanning approaches. Experimental results indicate that our approach has an average 5.05% higher chance of finding a solution compared to the original repair method. © 2024 Inderscience Enterprises Ltd.

关键词： Web services

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Research on gesture recognition algorithm based on MME-P3D

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Mathematical Biosciences and Engineering 2024年第3期21卷 3594-3617页

作者： Jin, Hongmei He, Ning Liu, Boyu Li, Zhanli College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China

A Multiscale-Motion Embedding Pseudo-3D (MME-P3D) gesture recognition algorithm has been proposed to tackle the issues of excessive parameters and high computational complexity encountered by existing gesture recognition algorithms deployed in mobile and embedded devices. The algorithm initially takes into account the characteristics of gesture motion information, integrating the channel attention (CE) mechanism into the pseudo-3D (P3D) module, thereby constructing a P3D-C feature extraction network that can efficiently extract spatio-temporal feature information while reducing the complexity of the algorithmic model. To further enhance the understanding and learning of the global gesture movement’s dynamic information, a Multiscale Motion Embedding (MME) mechanism is subsequently designed. The experimental findings reveal that the MME-P3D model achieves recognition accuracies reaching up to 91.12% and 83.06% on the self-constructed conference gesture dataset and the publicly available Chalearn 2013 dataset, respectively. In comparison with the conventional 3D convolutional neural network, the MME-P3D model demonstrates a significant advantage in terms of parameter count and computational requirements, which are reduced by as much as 82% and 83%, respectively. This effectively addresses the limitations of the original algorithms, making them more suitable for deployment on embedded and mobile devices and providing a more effective means for the practical application of hand gesture recognition technology. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolution

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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Engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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A Federated Learning Incentive Mechanism for Dynamic Client Participation:Unbiased Deep Learning Models

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computers, Materials & Continua 2025年第4期83卷 619-634页

作者： Jianfeng Lu Tao Huang Yuanai Xie Shuqin Cao Bing Li School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan430065China College of Computer Science South-Central Minzu UniversityWuhan430074China School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China

The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and ***,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw *** that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model *** overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for *** ensures impartial model training by tailoring participation levels and payments to accommodate diverse client *** approach involves several key ***,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model ***,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation *** balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model ***,we conduct a comprehensive experimental evaluation of ENTIRE using three real *** results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.

关键词： Federated learning deep learning non-IID data dynamic client participation non-convex optimization contract

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A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

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computers, Materials & Continua 2024年第6期79卷 4297-4318页

作者： Jun Wang Changfu Si Zhen Wang Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production ***,there are more and more cyber-attacks targeting industrial control *** ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber *** intrusion detection methods still suffer from low accuracy and a high false alarm ***,it is important to build a more efficient intrusion detection *** paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing *** algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority *** approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority *** the experimental phase,the detection performance of the method is verified using two data *** results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.

关键词： Intrusion detection convolutional neural network bidirectional long short-term memory neural network multi-head self-attention mechanism

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Chasing in virtual environment:Dynamic alignment for multi-user collaborative redirected walking

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 26-46页

作者： Tianyang DONG Shuqian LV Hubin KONG Huanbo ZHANG College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310004China

Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user *** a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as *** methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and *** To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are *** algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic *** the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal ***,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent *** balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force *** The efficacy of the proposed method was evaluated using a series of simulations and live-user *** experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.

关键词： Virtual reality Multi-user redirected walking User collisions Dynamic alignment

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Cloud Computing Security: Assured Deletion

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第3期48卷 485-496页

作者： Ahmed, Sarmad Mahmood Mahmood, Baban Ahmed Department of Computer Science College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq

With the advent of cloud computing, many organizations, institutions, and individuals have chosen to store their data in the cloud as a way to compensate for limited local storage capabilities and reduce expenses. However, the process of uploading data to the cloud results in relinquishing control over it, leaving the data owner unaware of the details of its storage and location. Hence, ensuring data confidentiality and integrity has emerged as a critical concern, especially with regard to cloud employees and other potential attacks. A significant amount of investigation has been conducted on the security aspects of cloud computing, with a particular focus on permanent removals. Hence This study investigates the deployment of deletion assurance in cloud computing on two separate cloud platforms, employing strategies such as cryptographic key partitioning, cryptographic-based random writing, and link deletion within confirmed deletion scenarios. The characteristics of this approach include security safeguards like asymmetric encryption (specifically Elliptic Curve Cryptography (ECC)) and multiple hashing algorithms like hash 256 are used to fortify the security and confidentiality of data stored in the public cloud and the secrecy of files kept in the cloud is unaffected by invasions from the outside or from within an encrypted file prevents a threat from reading it and the owner may easily encrypt each file with a fresh key by managing and storing the keys using the key management center also authorized users are able to request this key thanks to secure access enforcement and In the event of assure deletion, the file is overwritten with random encryption and uploaded to the cloud, updating all copies while deleting all links and keys from the key management. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Cloud platforms

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A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

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computers, Materials & Continua 2024年第1期78卷 653-676页

作者： Haibo Li Yongbo Yu Zhenbo Zhao Xiaokang Tang College of Computer Science and Technology Huaqiao UniversityXiamen361021China

Accurate forecasting of time series is crucial across various *** prediction tasks rely on effectively segmenting,matching,and time series data *** instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction ***,these events of varying granularity frequently intersect with each other,which may possess unequal *** minor differences can result in significant errors when matching time series with future ***,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction ***,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation *** framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on *** data from a nationwide online car-hailing service in China ensures the method’s *** average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)*** other experiment is conducted on stock data froma public data *** proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained usin

关键词： Time series short-term prediction multi-granularity event alignment event matching

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YOLO-Drone:A Scale-Aware Detector for Drone Vision

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Chinese Journal of Electronics 2024年第4期33卷 1034-1045页

作者： Yutong LI Miao MA Shichang LIU Chao YAO Longjiang GUO School of Computer Science Shaanxi Normal University Key Laboratory of Modern Teaching Technology Ministry of Education College of Computer Science Sichuan University

Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention(SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss(SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset(detection track) show that our detector can achieve average precision(AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile,reducing 39.8% of the computational cost.

关键词： YOLO Costs Object oriented modeling Computational modeling Detectors Feature extraction Data models

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