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IAENG International Journal of computer science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Service Function Chain Deployment with Intrinsic Dynamic Defense Capability

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5418-5432页

作者： Wang, Ran Cai, Lundan Wu, Qiang Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanyang Technological University School of Computer Science and Engineering Singapore

The Service Function Chain (SFC) leverages Network Function Virtualization (NFV) and software-Defined Networking (SDN) for flexible deployment, creating customized service chains tailored to specific applications. As NFV and SDN technologies play crucial roles in the SFC implementation, any security risk that arises in an NFV/SDN network can potentially pose a threat to SFC. Thus, SFC becomes vulnerable to network security attacks. To address this, intrinsic security technologies, including moving target defense and mimic defense, offer proactive protection against both known and unknown threats. It is expected to break through traditional security protection mechanisms such as "enhanced", "plug-in"and "passive"defense. This paper proposes an intrinsic dynamic defense architecture to equip SFC with active defense capabilities, shifting from passive reactive mechanism based on prior knowledge to an active defense against various attacks. The architecture comprises two models and five modules, including a sub-pool partitioning algorithm that enhances heterogeneity across sub-pools by splitting the heterogeneous replica pool into several sub-pools among replica VNFs. To meet Quality of Service (QoS) requirements like latency, cost, and security, we formulate a multi-objective optimization problem with three objectives: latency, cost, and defense success rate. Following that, we propose a dynamic Deep Reinforcement Learning (DRL)-based deployment algorithm. This algorithm selects appropriate VNFs based on heterogeneity and historical information, improving SFC and VNF security against external attacks. Extensive experiments validate that our architecture significantly enhances network security, provided that this improvement comes at the expense of limited cost and latency. © 2002-2012 IEEE.

关键词： Quality of service

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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More and Larger Auxiliary Feature-Guided Spatial-Temporal Super-Resolution for Rendered Sequences 17th

More and Larger Auxiliary Feature-Guided Spatial-Temporal S...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Zheng, Lijie Liang, Xiao School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9789819609628

The post-processing rendered sequences improves the quality of the sequences and shortens the time of the rendering phase. However, most of the current post-processing methods for sequences are suitable for video. Directly transferring these methods to rendered sequences cannot obtain high-quality results. To address this problem, we propose an end-to-end spatial-temporal super-resolution network for rendered sequences, which improves rendering efficiency by simultaneously implementing frame interpolation (FI) and super-resolution. In the FI task, accurately inferring results that closer to the real motion state of the target frames is the key and difficult point to ensure the generation effect. For this issue, we design an auxiliary feature-guided interpolation (AFGI) module. By introducing the auxiliary features corresponding to the target frames, AFGI module provides the real motion state of the target frames to the network. In the part of aggregating contextual information, we propose a weighted aggregation upsampling (WAUpS) module. Aggregation is selectively performed based on the correlation between incoming information and the current frame. WAUpS module effectively prevents irrelevant information from affecting the super-resolution results, which is a problem with the direct aggregation methods used previously. At the same time, WAUpS module combines the upsampled features with the corresponding high-resolution auxiliary features. This addition provides the output with rich detail textures and other key information, further improving the overall processing quality. Experimental results show that compared with state-of-the-art (SOTA) methods, our method not only obtains high-quality rendered sequences processing results, but also effectively improves the rendering efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Rendering (computer graphics)

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Instruction selection optimization for VLIW architecture based on classification node merging

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Journal of Supercomputing 2025年第7期81卷 1-19页

作者： Liu, Fangjun Zhang, Huifu Hu, Yonghua Xie, Anxing Cao, Wei Mo, Shangfeng School of Computer Science and Engineering Hunan University of Science and Technology XiangTan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology XiangTan411201 China

Traditional instruction selection methods fail to fully exploit the very long instruction word (VLIW) architecture’s efficient scalar instructions. We propose an optimized instruction selection method based on classification node merging. This method abstracts scalar intermediate instructions into nodes and divides them into associated and similar ones according to operational semantics. Combined with the VLIW architecture’s instruction set features and guided by the instruction cost model, the nodes are merged and optimized to select efficient instructions. We design an instruction generation framework that swiftly converts the low level virtual machine intermediate representation into the instruction sequence of the target architecture. The experimental results show that, compared with the instruction sequence generated by M7002-cc, the instruction sequence generated with the solution proposed in this paper exhibits an average execution performance that is up to 54% higher on the FT-M7002 hardware platform. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Real time systems

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Blockchain-enabled IoT access control model for sharing electronic healthcare data

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Multimedia Tools and Applications 2025年第10期84卷 8127-8148页

作者： Ilyas, Benkhaddra Kumar, Abhishek Ali, Setitra Mohamed Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Chandigarh University Gharuan Mohali Punjab140413 India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Deploying the Internet of Things (IoT) in the transfer of enormous medical data often promotes challenges with the security, confidentiality, and privacy of the user’s sensitive data. In addition, the access control for prohibiting unauthorized access has been facing challenges with single-point-of-failure, collusion, and other security issues. To address these challenges associated with IoT access control, this research proposes a Blockchain-enabled IoT access control for sharing Electronic health records (EHR). The proposed authentic access control scheme transfers the data to the authenticated user without discrepancies. The proposed technique utilizes the hyperledger composer fabric blockchain that improves the transparency and traceability of the network, and the intelligent contract keeps track of the information, which reduces the possibility of unauthorized access. The proposed access control scheme validates the request effectively and transmits the data efficiently. The effectiveness of the proposed access control scheme is evaluated by analyzing the blocks in terms of metrics such as responsiveness of 185.47 secs, 95% Genuine User Detection (GUD), 96.50% Privacy, and 3.50% Information loss, for 100 blocks and proposed access control scheme works more efficiently in transmitting the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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