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3rd International Conference on Computing, communication, Perception and Quantum technology, CCPQT 2024

作者： Wang, Liang Zhang, Jun Shi, Wenzheng Wang, Shiguo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798331528386

In contrast to the single intelligent reflecting surface (S-IRS) systems, improved performance is attainable by employing double-IRS (D-IRS) in a wireless-communication system. Nevertheless, precise channel estimation is crucial in D-IRS assisted wireless communication systems for achieving significant performance enhancements. In some specialized communication scenarios, obstacles may block the communication links between IRS1 and IRS2, thereby diminishing the potential gains of the D-IRS system. To tackle these challenges, this article introduces a novel D-IRS method with maximal a posterior probability rule (DMAP) for a multi-input single-output (MISO) uplink communication where the line-of-sight (LOS) link between the base station (BS) and the users are ignored. Furthermore, the BS-IRS links are harnessed as a common-link for decomposing the complex cascaded channels and IRSs are kept always "ON" in the channel estimation protocol. Finally, optimization for the phase configuration of IRSs is constructed to ensure better estimation accuracy. Experimental results show that DMAP significantly reduces pilot overhead and exceeds baseline performance levels. © 2024 IEEE.

关键词： channel estimation DMAP double-IRS (D-IRS) multiple-input single-output (MISO) single intelligent reflecting surface (S-IRS)

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Exploration of Quantum Computing in Multi-Die FPGA Routing Problems 9

Exploration of Quantum Computing in Multi-Die FPGA Routing P...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Tian, Haochang Zhang, Xu Wang, Chengkai Ji, Weiqing Kou, Mingyang Yao, Hailong School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798331509453

This paper delves into the innovative application of quantum computing to solve complex multi-die FPGA (FieldProgrammable Gate Array) routing challenges. Leveraging the Quantum Approximate Optimization Algorithm (QAOA), we propose a novel approach that integrates classical routing techniques with quantum computing methodologies. This integration specifically utilizes QAOA to enhance the efficiency and effectiveness of the routing process. This hybrid approach partitions the routing task into intra-FPGA and inter-FPGA components, optimizing each segment through the appropriate computational techniques. Extensive experiments conducted on quantum computing simulators, alongside trials on actual quantum hardware, demonstrate the theoretical potential of quantum computing in enhancing the efficiency of multi-die FPGA routing solutions. Our findings suggest that despite the current limitations of quantum hardware, such as noise and error rates, quantum computing holds promise for advancing EDA methodologies. This paper contributes to the field by pioneering the integration of quantum algorithms with EDA techniques, offering a novel perspective on future applications of quantum computing in complex routing problems. © 2024 IEEE.

关键词： Quantum computers

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HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in Parallel Computing Programs 20th

HASLB: Huge Page Allocation Strategy Optimized for Load-Bal...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin Zhang, Wenzhe School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628292

Equitable resource allocation is currently considered as a key enabler for achieving complete parallelism in parallel computing. Optimized memory management mechanisms are crucial for load balance in parallel tasks. In some cases, the performance of Linux’s huge page management mechanism deteriorates when handling parallel tasks due to the nonhomogeneous allocation of memory resources among parallel processes. This research introduces HASLB, a huge page allocation strategy optimized for parallel programs. HASLB evaluates the status of parallel processes based on the count of huge pages acquired by each process and the number of page faults it encountered. Following the results of the analysis, huge pages are allocated using a method conducive to load balance. The problems in the Linux transparent huge page mechanism are optimized through this strategy, which limits the greedy allocation mechanism in some situations. HASLB achieves a reasonable allocation of memory resources among parallel processes with low overhead, benefiting the load balance of parallel computing programs. In addition, contiguous memory would be protected because of limiting the greedy allocation of huge pages. The parallel benchmark experiments show a maximum performance improvement of 6.4% compared to Linux in the BFS benchmark. In the IS benchmark program, huge page consumption exhibits a 9% reduction. © IFIP International Federation for Information Processing 2025.

关键词： Memory management

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Community Topic Propagation Prediction-Based Malicious User Detection Scheme in Overlapping Community Networks 6

Community Topic Propagation Prediction-Based Malicious User ...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Deng, Yang Liu, Jian Gu, Ke Chen, Dongdong Li, Yongkang Jiang, LuYao Changsha University of Science & Technology School of Computer and Communication Engineering ChangSha China

ISBN: (纸本)9798350388374

In this paper, we propose a malicious user detection scheme for overlapping communities based on community topic propagation prediction. First, we construct a malicious topic classification model based on topic text feature vectors. At the same time, based on the community attributes of users obtained from overlapping community detection, we construct user attribute vectors and calculate the fit between users and topics, based on which we construct a topic propagation prediction model. Secondly, we combine the malicious topic classification model and topic propagation prediction model to construct the overlapping community malicious user detection model. Finally, experiments verify that our scheme can effectively identify malicious users. © 2024 IEEE.

关键词： Prediction models

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Multi-Stage Interaction and Multi-Scale Reconstruction: A Hybrid Attention Network for Skin Lesion Image Segmentation 10

Multi-Stage Interaction and Multi-Scale Reconstruction: A Hy...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Wei Luo, Yuewen Wang, Xin Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331520861

Accurate segmentation of skin lesion areas is significant for the diagnosis and analysis of skin diseases. Due to the irregular and blurred boundaries between healthy skin and lesion areas and sometimes the interference of hair and pigmentation deposits, it is still a challenging task to accomplish accurate segmentation. To solve the above problems, a hybrid attention network (SISR-HANet) based on stage interaction and scale reconstruction is proposed. The network contains three main modules: The Multi-Scale Global Perception (MSGP) module perceives global features through multi-scale receptive fields, thus compensating for the lack of global contextual information. The Multi-Stage Spatial Reconstruction (MSSR) module utilizes the cross-mixing of features from different stages to adaptively generate feature masks corresponding to each stage for reconstructing the features in each stage, thus eliminating the semantic differences between feature maps at different scales. The Channel-Optimized Inter-Group Aggregation (COGA) module helps the model recover more detailed features during the up-sampling process by optimizing and regrouping the channel information. To evaluate the effectiveness of SISR-HANet, we conducted experimental validation on the public datasets ISIC2017, ISIC2018, and PH2. The experimental results show that the DSC scores of these datasets are improved by 1.58%, 1.53%, and 1.34%, respectively, compared with the suboptimal networks. © 2024 IEEE.

关键词： Image reconstruction

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Analyzing Public Disputation of Autonomous Driving technology: A Study Based on Data Mining 4

Analyzing Public Disputation of Autonomous Driving Technolog...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Lv, Lehao Chen, Yuan Ma, Fanshu Li, Yongwei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350349252

With the rapid development of autonomous driving technology, public's opinion of this technology shows diversification and complexity. The discussion about autonomous driving online has a large quantity and diverse perspectives, which causes the traditional data analysis methods hard to catch and understand the whole spectrum of these data. This paper explores how to use text mining techniques effectively, especially the Latent Dirichlet Allocation (LDA) model, to analyze and understand people's attitude and viewpoint towards autonomous driving technology. By calculating the perplexity, coherence, Jensen-Shannon (JS)divergence and other related parameter indicators of the LDA model, striving to obtain the text mining model with best performance, in order to get valuable information faster and more efficiently. © 2024 IEEE.

关键词： Autonomous driving data mining Latent Dirichlet Allocation

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BEFNet: A Hybrid CNN-Mamba Architecture for Accurate Skin Lesion Image Segmentation

BEFNet: A Hybrid CNN-Mamba Architecture for Accurate Skin Le...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Wei Luo, Yuewen Wang, Xin Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350386226

Accurate image segmentation of skin lesions is crucial for the detection and treatment of skin cancer. Based on the modern state space model Mamba, a novel hybrid CNN-Mamba network (BEFNet) is proposed. Specifically, BEFNet introduces Visual State Space (VSS) blocks as the main encoder for extracting and aggregating global information. To improve the expressiveness and utilization of the features, the Adjacent Feature Interaction Module (AFIM) is proposed, and the information lost during the channel interaction process is compensated by the Knowledge Retrospective Module (KRM). To compensate for Mamba's lack of attention to local detail information, an auxiliary encoder is constructed based on CNN blocks, and a Boundary Optimization Auxiliary Module (BOA) is designed to obtain richer boundary detail information. Finally, a Refined Feature Guidance Module (RFGM) was designed to use the enhanced boundary information as a priori information to guide feature fusion. We conducted experiments on three publicly available skin cancer datasets, and the experimental results demonstrate that BEFNet has competitive performance on the task of skin lesion image segmentation. © 2024 IEEE.

关键词： Image segmentation

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A Dam Deformation Prediction Model Based on SVM-RFE 3rd

A Dam Deformation Prediction Model Based on SVM-RFE

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Cai, Shuo Zhang, Jie Gao, Huixin School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China

ISBN: (纸本)9789819624676

Dams play a pivotal part in national economic development, and to protect the operation of dams, monitoring and prediction of dam deformation are crucial. In response to the fact that the commonly used HST (hydrostatic, season, time) model for predicting dam deformation in the past cannot explain the influence of environmental variables well, the measured temperature was used to replace the harmonic function in the HST model as the temperature component, and the lagged water levels were added as an influencing factor. Then, the feature set was filtered using the Recursive Feature Elimination (RFE) algorithm, which reduces the dimensionality of the original feature set to the same level as the HST model to alleviate the computational burden of the model and the Support Vector Machine (SVM) was used to build the model. Aiming at the sensitivity of SVM model prediction accuracy to parameters, the Particle Swarm Optimization algorithm was introduced to find the best combination of parameters for SVM, and the SVM-RFE dam deformation prediction model was established. The measured data of Shuibuya concrete-faced rockfill dam verified the validity of the model. The final analysis indicate that compared with the traditional model, the improved SVM-RFE dam deformation prediction model obviously improves the prediction performance, reflecting the superiority of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harmonic functions

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Exploring Stable Meta-Optimization Patterns via Differentiable Reinforcement Learning for Few-Shot Classification 24

Exploring Stable Meta-Optimization Patterns via Differentiab...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Han, Zheng Zhu, Xiaobin Yang, Chun Zhou, Hongyang Qin, Jingyan Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400706868

Existing few-shot learning methods generally focus on designing exquisite structures of meta-learners for learning task-specific prior to improve the discriminative ability of global embeddings. However, they often ignore the importance of learning stability in meta-training, making it difficult to obtain a relatively optimal model. From this key observation, we propose an innovative generic differentiable Reinforcement Learning (RL) strategy for few-shot classification. It aims to explore stable meta-optimization patterns in meta-training by learning generalizable optimizations for producing task-adaptive embeddings. Accordingly, our differentiable RL strategy models the embedding procedure of feature transformation layers in meta-learner to optimize the gradient flow implicitly. Also, we propose a memory module to associate historical and current task states and actions for exploring inter-task similarity. Notably, our RL-based strategy can be easily extended to various backbones. In addition, we propose a novel task state encoder to encode task representation, which fully explores inner-task similarities between support set and query set. Extensive experiments verify that our approach can improve the performance of different backbones and achieve promising results against state-of-the-art methods in few-shot classification. © 2024 ACM.

关键词： Contrastive Learning

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