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arXiv 2025年

作者： Wang, Junyi Zang, Quan Wang, Jinyu Cheng, Minquan The Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China The Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China

In this paper, we study the coded caching scheme for the (L, K, M, N) multi-user information retrieval (MIR) system, which consists of a content library containing N files, a base station (BS) with L antennas that cannot access the library, and K single-antenna users, each of which can cache at most M files from the library. The users communicate with the others assisted by the BS to decode their required files. In this paper, we focus on designing a coded caching scheme with low communication latency measured by normalized delivery time (NDT), computational complexity, and subpacketizations. When KM/N ≥ L we first simply the precoding matrix in the downlink step to an identity matrix and use the multiple-antenna placement delivery array (MAPDA), which was originally proposed for the multiple-input single-output networks, to generate several new schemes for MIR system. Compared to the existing schemes, both the theoretical and numerical analyses show that our new schemes achieve much lower computational complexity and smaller subpacketizations with the same NDT. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna arrays

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Multi-view User Preference Modeling for Personalized Text-to-Image Generation

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IEEE Transactions on Multimedia 2025年 27卷 3082-3091页

作者： Zhang, Huaiwen Wu, Tianci Wei, Yinwei Inner Mongolia University College of Computer Science China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot010021 China Shandong University School of Software Jinan China

Personalized text-to-image generation aims to synthesize images tailored to individual user preferences. Existing methods primarily generate customized content using a few reference images, which often struggle to mine user preferences from historical records, and thus fail to synthesize truly personalized content. In addition, it is difficult to directly incorporate the extracted feature of user preferences into the feature space of the generation model, since there exists a considerable gap between them. In this paper, we propose a novel multi-view personalized text-to-image generation method based on the diffusion model, named MVP-Diffusion, which learns instance- and user-level preferences from historical records and integrates them into the generation model. For instance-level user preference modeling, we employ a chain-of-thought prompting strategy to deduce preference keywords and integrate them into input prompts with the aid of a large language model. For user-level preference modeling, we construct a learnable embedding for each user to capture more comprehensive preferences by analyzing their historical records. An adaptive user preference fusion module is proposed to inject user preferences into the generation model via a set of learnable parameters. Experimental results demonstrate that the proposed method significantly enhances the personalization of the generated images compared to the other personalized text-to-image generation methods. © 1999-2012 IEEE.

关键词： Large Language Model Multi-view Personalized Text-to-image Generation

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Causal Attention Transformer for Video Text Retrieval

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IET Image processing 2025年第1期19卷

作者： Lan, Hua Lv, Chaohui School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper. © 2025 The Author(s). IET Image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Interactive computer graphics

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A Novel Wide-Band High-Gain Microstrip Quasi-Yagi Antenna Design for X-Band Applications

A Novel Wide-Band High-Gain Microstrip Quasi-Yagi Antenna De...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Xinju Wang Sentao Wang Zhuo Chen Zongsheng Gan Haowei Zhang Yutong Yin Lu Liu Peng Chen School of Ocean Information Engineering Jimei University Xiamen China School of Transportation and Civil Engineering Nantong University Nantong China Fujian Provincial Key Laboratory of Maritime Communication and Intelligent Electronic Systems Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing School of Ocean Information Engineering Jimei University Xiamen China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

A new broadband high-gain microstrip quasi-yagi antenna is proposed. With the aim of designing an end-firing antenna for X-band by utilizing the good directivity of yagi antennas. The main radiating dipole patches of this antenna adopts an additional tapered trapezoidal-like structure to optimize its radiation characteristics. In addition, the operating bandwidth and main beam gain of the antenna are effectively adjusted by placing four identical microstrip metal strip patches on the bottom surface of the dielectric substrate to act as the reflector and the guide, respectively. Simulation results show that the designed antenna operates in the frequency bands of 8.33-13.07 GHz and 13.57-13.95 GHz with relative bandwidths of 44.3% and 2.76%, respectively, and achieves a peak gain of 8.9 dBi. The antenna has a simple structural design and is suitable for X-band wireless communication systems with a wide range of applications.

关键词： Satellite antennas Dipole antennas Simulation Metals Microstrip antennas Bandwidth Radar antennas Reflector antennas Broadband antennas Microstrip

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FGCM: Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation

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IEEE Multimedia 2025年

作者： Mo, Feng Xiao, Lin Song, Qiya Gao, Xieping Song, Wenzhuo Wang, Shoujin Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China Northeast Normal University Changchun China University of Technology Sydney Sydney Australia

Multimodal recommender systems (MRSs) aim to integrate information from multiple modalities, for better capturing users' preferences. However, existing MRSs usually face the challenge of data sparsity, especially for the limited user-item interactions. It is not uncommon that a user interacts with only a few items out of millions. More importantly, the interaction information is easy to be overwhelmed, leading to poor recommendation performance. To tackle these significant gaps for further improving the performance of MRSs, we propose a Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation (FGCM). First, through the introduction of random noise into graph contrastive learning, FGCM adeptly mitigates data sparsity. Then, a modality-behavior fuser is proposed to effectively fuse interaction data and multimodal side information data by taking cooperative signals and multimodal signals as the guidance. Extensive experiments on three datasets demonstrate that our proposed approach outperforms several state-of-the-art methods. © 2025 IEEE.

关键词： Contrastive Learning

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Method for identifying vital nodes in the scientific-citation double-layer hypernetwork based on the h-index

Method for identifying vital nodes in the scientific-citatio...

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2024 International Conference on Computer Application and information Security, ICCAIS 2024

作者： Guo, Lei Liu, Wei Zhang, Xin Hu, Feng School of Computer Qinghai Normal University Qinghai Xining 810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining 810008 China

ISBN: (纸本)9781510689312

Existing studies on scientific collaboration networks and citation networks are often limited to the analysis of a single relational dimension, making it challenging to comprehensively capture the interactions between collaboration and citation relationships within multilayered networks and their characteristics in knowledge dissemination. At the same time, traditional vital node identification methods are mostly based on evaluation metrics from single-layer networks, making it difficult to comprehensively and accurately identify vital nodes in multivariate, multilayer and multidimensional relationship hypernetworks. Therefore, based on the characteristics of scientific collaboration and citation networks, and in combination with hypernetwork theory based on hypergraphs, this paper proposes the construction of a scientific-citation double-layer hypernetwork model. Subsequently, based on the concept of aggregated networks, the double-layer hypernetwork is aggregated into a weighted hypernetwork. A method for identifying vital nodes in the hypernetwork based on the h-index is proposed, where the h-index is applied to the weighted aggregated hypernetwork and combined with the K-shell algorithm and hyperdegree metric to identify vital nodes. Finally, this method is applied to the empirical analysis of the scientific-citation double-layer hypernetwork constructed from the dataset of Acta Physica Sinica from 2014 to 2024. The results, based on the analysis of network propagation dynamics, network statistical characteristics, and AUC accuracy evaluation metrics, demonstrate that the proposed identification method is accurate and effective. This study provides valuable insights into how to construct multilayer hypernetwork models based on the characteristics of real-world networks, as well as the application of multilayer hypernetworks in real networks and the identification of vital nodes. © The Authors.

关键词： h-index Hypergraph K-shell decomposition scientific-citation double-layer hypernetwork vital nodes

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Toward Comprehensive Semantic Prompt for Region Contrastive Learning Underwater Image Enhancement

Toward Comprehensive Semantic Prompt for Region Contrastive ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiao Wang Yongsheng Fu Wei Wang Wei Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater image enhancement (UIE) focuses on mitigating image quality degradation due to light absorption and scattering. However, most existing methods enhance images via a global and uniform manner, neglecting the inherent semantic information in different regions, which may cause the network to easily deviate from the region’s original color. Moreover, these methods typically rely on clear images to guide network convergence, a process constrained by the limited availability of real-world datasets, making it extremely challenging to train enhancement models for various degradations. To address these challenges, this paper introduces a semantic guidance and region contrastive constraints network (SRCNet). Initially, we propose a semantic-aware RWKV (Receptance Weighted key Value) block and a semantic prompt regularization module. These components leverage intra-target semantic correlations to preserve image details and colors within a global perceptual framework, while employing focal loss to emphasize the restoration of severely degraded regions. Subsequently, we introduce a region contrastive learning method that effectively utilizes negative samples to precisely capture features sensitive to degradation factors, thereby fostering robust feature distributions. Finally, experimental results demonstrate that our method outperforms existing state-of-the-art (SOTA) approaches.

关键词： Degradation Visualization Image color analysis Semantics Signal processing algorithms Contrastive learning Speech enhancement Signal processing Image restoration Image enhancement

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

arXiv

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arXiv 2025年

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration

arXiv

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arXiv 2025年

作者： Li, Renkai Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID. Copyright © 2025, The Authors. All rights reserved.

关键词： information leakage

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Transmission Schedule for Remote State Estimation in CPSs with Energy Harvesting and Sharing Two-Hop Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Xing, Wei Zhang, Junfeng Zong, Guangdeng Wang, Yuhan Zhao, Xudong Hainan University School of Information and Communication Engineering China Tiangong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology China

This paper investigates the problem of optimizing the transmission schedule between a sensor and a relay in cyber-physical systems to achieve accurate remote state estimation. The considered system model represents the plant as a linear time-invariant system, wherein the sensor measurements are transmitted to the remote estimator using an intelligent relay. Both the sensor and relay nodes are capable of harvesting energy from the external environment, and they are required to make transmission decisions at each time step to ensure the stability of system's operation. In addition, energy sharing between each other can yield significant advantages by capitalizing on the available battery power and maximizing energy harvesting from the external environment. We describe the problem of finding the optimal transmission schedule as a dynamic programming model, and establish structural properties on the optimal schedule of the sensor and relay nodes, more precisely, (i) for given battery levels and harvested energy, the optimal transmission strategy is to transmit the measurements when the error covariances at the relay and the remote estimator meet certain conditions, (ii) for given harvested energy and error covariances at the relay and the remote estimator, the optimal transmission strategy is a threshold-based strategy on the battery levels. © 2025 IEEE.

关键词： Dynamic programming

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