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HA-FGOVD: Highlighting Fine-Grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection

IEEE Transactions on Multimedia 2025年 27卷 3171-3183页

作者： Ma, Yuqi Liu, Mengyin Zhu, Chao Yin, Xu-Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-grained category rather than focus on their fine-grained attributes, e.g., colors or materials, thus failed to identify objects specified with certain attributes. Despite being pretrained on large-scale image-text pairs with rich attribute information, their latent feature space does not highlight these fine-grained attributes. In this paper, we introduce HA-FGOVD, a universal and explicit method that enhances the attribute-level detection capabilities of frozen OVD models by highlighting fine-grained attributes in explicit linear space. Our approach uses a LLM to extract attribute words in input text as a zero-shot task. Then, token attention masks are adjusted to guide text encoders in extracting both global and attribute-specific features, which are explicitly composited as two vectors in linear space to form a new attribute-highlighted feature for detection tasks. The composition weight scalars can be learned or transferred across different OVD models, showcasing the universality of our method. Experimental results show that HA-FGOVD achieves state-of-the-art performance on the FG-OVD benchmark and demonstrates promising generalization on the OVDEval benchmark, suggesting that our method addresses significant limitations in fine-grained attribute detection and has potential for broader fine-grained detection applications. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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A novel feature selection method based on adaptive search particle swarm optimization

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Neural Computing and Applications 2025年第12期37卷 7767-7783页

作者： Han, Fei Wang, Yi-Huai Li, Fan-Yu School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China

As an effective method for data dimensionality reduction, feature selection could improve the classification accuracy and reduce the computational cost when dealing with high-dimensional data. Feature selection is essentially a complex optimization search problem. Among many optimization algorithms, particle swarm optimization (PSO) has been widely used due to its good global search ability and easy to implement. However, most feature selection methods based on PSO ignore the correlations between the features of data, which may lead to more redundant features, and the feature selection methods are prone to fall into local minima. This study proposes an improved feature selection method based on adaptive search particle swarm optimization (AS-BPSO-FS). On one hand, AS-BPSO-FS is designed to consider the feature correlation according to the feature correlation information. The particle position adaptive update strategy selects features based on the correlation coefficient between features, ensuring that features with higher correlation are more likely to be culled, so as to obtain a subset of features with less redundancy. On the other hand, AS-BPSO-FS identifies particles trapped in local minima by calculating the update time of individual and global optimal positions, and uses an adaptive particle neighborhood search strategy to help particles escape from local minima. The AS-BPSO-FS has been tested on 10 UCI data and compared with some state-of-the-art feature selection methods. The results verify that the proposed method could obtain feature subsets with better classification performance and less redundancy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Feature Selection

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Individualized Driving Intention Prediction With Inverse Reinforcement Learning

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8125-8139页

作者： Liu, Siqi Li, Xinyang Chen, Jiansheng Guo, Chenghao Wu, Jiehui Luo, Qifeng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers' awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers' actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver's historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios. © 2000-2011 IEEE.

关键词： Advanced driver assistance systems

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A computational offloading algorithm for cloud-edge collaboration in smart agriculture

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International Journal of Simulation and Process Modelling 2024年第2期21卷 121-129页

作者： Li, Feng Li, Yiyuan Pan, Yuqing School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang China

To solve the problem that the massive amount of information and real-time processing in the IoT system puts pressure on the computing resources of the whole system, the industry often adopts the computation offloading algorithm for cloud-edge collaboration. However, at this stage, conventional computation offloading algorithms frequently fail to account for dynamic conflicts between terminal tasks and offloaded tasks, and the results of offloading are inefficient. To that purpose, this paper introduces the task volume prediction model, the response time model and the power consumption model to represent the whole computing process of agricultural activities from various angles. On that basis, the DQN algorithm is used to solve the model’s mixed-integer nonlinear optimisation issue. Experiments show that the algorithm proposed in this paper can increase the offloading accuracy and the speed and accuracy of cloud-edge collaborative computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： smart agriculture cloud-edge collaboration computation offloading deep reinforcement learning

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Low-profile,low sidelobe array antenna with ultrawide beam coverage for UAV communication

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Chinese Journal of Aeronautics 2025年第1期38卷 444-454页

作者： Mei LI Zhiliang SHANG Lin PU Ming-Chun TANG Lei ZHU School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering University of MacaoMacao SAR 999078China

This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground *** array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low ***,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source ***,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating *** consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation *** indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer *** concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.

关键词： Array antenna Broad beam Fan beam Low sidelobe level Chebyshev synthesis UAV communication

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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A secure double spectrum auction scheme

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Digital communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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Sentiment analysis algorithm based on word embedding in text mining

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International Journal of Speech Technology 2025年第1期28卷 141-151页

作者： Hu, Qiong School of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing210000 China

This study proposes a sentiment analysis algorithm model that combines word embedding and machine learning algorithms to address issues such as data shortage, low accuracy, and limited coverage. The proposed model uses the continuous bag-of-words word embedding method for word analysis and incorporates convolutional neural network, long and short-term memory neural network model, and attention mechanism to enhance the traditional word embedding *** research results obtained using the English review dataset and the hotel review dataset, which contain an average of 10,000 entries, show that in the performance comparison of hotel review sentiment analysis tests, the highest value of the model loss function used in this study is 0.92. The accuracy, recall, and F1 value of the model are 90.56%, 91.65%, and 92.36%, respectively, which are 5.89%, 7.00%, and 6.74% higher than the lowest values in the convolutional neural network, long short term memory neural network, and convolutional long short term memory neural network. In the performance comparison for different emotional tendencies, the algorithm model used in this study achieved various indicator values of 94.68%, 93.86%, and 94.68% in sentiment analysis, respectively. The model with attention mechanism has a higher accuracy, with an accuracy of 96.4%. This shows that the algorithmic model used in the study is able to improve the accuracy and effectiveness of sentiment analysis to some extent. The research model is particularly suitable for sentiment analysis tasks in scenarios such as product reviews and hotel evaluations on e-commerce platforms, and can extract key emotional information from massive user reviews. This provides strong support for user sentiment analysis and data-driven decision-making in industries such as e-commerce and tourism. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Attention mechanisms Convolutional neural networks Long short term memory neural network Sentiment analysis Text mining

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Resource allocation optimization for SWIPT full-duplex relaying networks with practical energy harvester

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 39-46页

作者： Xu Siyang Song Xin Zhu Jiahui School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China

In this paper,the resource allocation optimization for the simultaneous wireless information and power transfer(SWIPT)full-duplex(FD)relaying networks is investigated,in which the power-constrained relay scavenges energy from the source signal and assists information transmission by FD *** into account non-linear energy harvesting(EH)hardware circuit characteristics of the relay,the information rate maximization problem is developed by jointly optimizing the time-switching(TS)factor and transmission powers of the source in two different ***,the formulated optimization is highly non-convex and difficult to *** cope with this problem,the primary problem is decomposed into two sub-problems with respect to the TS factor and transmission *** solving these two sub-problems,the final sub-optimal solutions can be obtained by alternating *** results prove that the optimization of both TS factor and transmission powers can effectively enhance the information rate for the considered networks.

关键词： resource allocation optimization full-duplex relaying networks energy harvesting information rate

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