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5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Xing, Yuyi Huang, Guimin Guo, Qingkai Deng, Nanxiao School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

The study and application of multimodal emotion recognition have gained significant popularity in recent years, representing one of the challenging tasks in the field of affective computing. We propose a multimodal speech emotion recognition model that utilizes multiple acoustic and textual information layers. This model incorporates transcribed textual data to complement speech data and enable accurate emotion recognition. In the unimodal model, we employ AlexNet, BiGRU, and HuBERT to extract multi-layer acoustic feature information from speech, and the RoBERTa encoder to extract text features. Additionally, we perform fusion between speech and text by utilizing the co-attentive mechanism to extract complementary information across modalities and eliminate inter-modality noise. This process ultimately enhances the emotional representation of the target modality. Finally, the fused features are utilized to predict the emotion category. Our model achieved a weighted sentiment recognition accuracy of 77.41% and an unweighted accuracy of 78.66%. © 2023 SPIE.

关键词： Emotion Recognition

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A personalized test paper generation model for English examination based on knowledge tracking 5

A personalized test paper generation model for English exami...

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5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Shao, Xiuli Huang, Guimin Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses. © 2023 SPIE.

关键词： Students

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Modal Analysis of an Endfire Traveling Wave Antenna with High Gain and High Efficiency

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IEEE Transactions on Antennas and Propagation 2025年

作者： Pan, Ya Liu, Juhua Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Guangzhou510006 China

In this communication, we propose and analyze an endfire traveling wave antenna with high gain and high efficiency. The antenna is composed of an offset double-side parallel strip line that is loaded by a set of closely spaced T-shaped patches. The structure supports an improper leaky wave mode that is evolved from the TEM mode in the parallel transmission line, and a common and a differential proper surface wave modes that are evolved from the corrupted-conductor-like surface wave mode in a single conductor line with T-shaped patches loaded. When fed at one end, the leaky wave mode and the differential surface wave mode can be excited. With their phase constants close to the wavenumber in free space, the two modes contribute to endfire radiation wherein the surface wave mode plays the dominant role. A prototype of the antenna is fabricated and measured. The antenna with a size of 4.65×0.43×0.03λ03 achieves a high gain of 15.1 dBi and a peak efficiency of 95.8%, with a 3 dB gain bandwidth of 18.5%. © 1963-2012 IEEE.

关键词： Directional patterns (antenna)

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Uncover the Body: Occluded Person Re-identification via Masked Image Modeling 12th

Uncover the Body: Occluded Person Re-identification via Ma...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Xu, Kunlun Peng, Yuxin Zhou, Jiahuan Wangxuan Institute of Computer Technology Peking University Beijing100871 China National Key Laboratory for Multimedia Information Processing Peking University Beijing100871 China

ISBN: (纸本)9783031463044

Person re-identification (ReID) has attracted tremendous attention and achieved significant progress on holistic data where the whole body of a pedestrian is completely presented. However, in a more realistic scenario where pedestrians are partially occluded, the discriminative ability of the existing ReID methods is severely limited since the visual information of the pedestrians becomes noisy and unreliable. To alleviate this issue, current solutions mostly pay more attention to visible body parts for extracting fine-grained features. Nevertheless, different occluded parts on different images of the same pedestrian always result in inaccuracy matching. In this paper, we propose an Uncover the Body Network (UBN) which exhibits the ability to remove the occlusion and attempt to restore the full body of a pedestrian. The proposed UBN can alleviate the noise brought by occlusions and extract more robust feature representations. To achieve this, we propose a MIM (Masked Image Modeling) based method for its powerful representation of partial images to the whole. Instead of randomly masking the images, we propose a Mask Prediction Module (MPM) to readily locate the occluded patches, and an occlusion-guided masking strategy is adopted to facilitate the learning. Extensive experimental results on both the occluded and holistic ReID benchmarks have demonstrated the superiority of UBN against the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

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A Compact Quasi-Magnetic-Electric Circularly Polarized Antenna with Reconfigurable Patterns

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Liu, Qinghe Liu, Juhua Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems The School of Electronics and Information Technology Guangzhou510006 China

A novel compact quasi-magnetic-electric circularly polarized (CP) antenna with reconfigurable patterns is proposed. The antenna, featuring a compact structure and a simple feeding mechanism, can generate four endfire CP radiation beams covering the entire azimuth plane. The antenna is composed of four quasi-magnetic-electric elements arranged in rotational symmetry. Each element consists of two layers of staggered trapezoidal shorted metals with a compact size of 0.51 λ0 × 0.16 λ0 × 0.05 λ0 (λ0 denotes the free space wavelength at 900 MHz) and is designed to provide an endfire CP beam with a single feed. By integrating four such elements and switching the excited elements through the feeding ports, the antenna can obtain four endfire CP radiation patterns in different directions. The prototype antenna has a compact size of 0.54 λ0 × 0.54 λ0 × 0.05 λ0. The operational bandwidth of the fabricated antenna, constrained by |S11| © 2025 IEEE.

关键词： Circular polarization

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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Reputation based Adaptive Federated Learning in Medical Scenarios 9

Reputation based Adaptive Federated Learning in Medical Scen...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Liu, Tianrui Wang, Yingxiaochun Wang, Xinlei Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence. © 2024 IEEE.

关键词： Federated learning

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction 9

Infringement Detection and Traceability Scheme Based on Ciph...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Wang, Xinlei Liu, Tianrui Wang, Yingxiaochun Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller's address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy. © 2024 IEEE.

关键词： Differential privacy

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