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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Bao, Haizhou Huo, Yiming School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China University of Victoria Department of Electrical and Computer Engineering Victoria Canada

ISBN: (纸本)9798350325959

This paper discusses the application of Cellular Vehicle-to-Everything (C-V2X) communications, specifically focusing on the integration of non-orthogonal multiple access (NOMA) in platoon technology within the V2X network. These systems have the potential to significantly enhance a variety of critical, content-oriented applications that extend beyond 5G and Internet of Things (IoT) infrastructures. Crucially, we argue that optimized communication at the edge of the network can effectively mitigate network congestion, augment delivery efficiency, and optimize energy use. Nonetheless, constraints inherent to vehicular networks, such as limited backhaul bandwidth and ever-changing topology, can compromise the reliability and latency of multimedia services in C-V2X networks, thereby affecting overall user experience. To address the mobility-related challenges of the topology, we introduce an intelligent driver model (IDM)-based platoon paradigm, and subsequently propose a NOMA-based platoon transmission model designed specifically for C-V2X networks. This NOMA-based transmission approach is intended to enhance vehicle-to-vehicle communications within the platoon, with the objective of optimizing throughput. Our simulation outcomes affirm the effectiveness of our proposed model in improving throughput while reducing response latency. © 2023 IEEE.

关键词： 5G Cellular Vehicular-to-Everything (C-V2X) Internet-of-Things (IoT) Non-Orthogonal Multiple Access (NOMA) Platoon

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A New Parameters Estimation Method for Near-Field Cyclostationary Sources

A New Parameters Estimation Method for Near-Field Cyclostati...

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2022 International Conference on Electronic Information Engineering and computer science, EIECS 2022

作者： Yu, Binbin Wang, Liguo The School of Computer Science and Technology Beihua University Jilin China

ISBN: (纸本)9781510663312

An improved MUSIC method is extended to solve the parameters estimation of cyclostationary sources. The method constructs two cyclic moment matrices from the data received by the centro-symmetric uniform linear array, and uses the propagation operator to estimate the noise subspace, then estimates the parameters by the root music method. In contrast with the traditional ROOT-MUSIC, although the accuracy is slightly worse, it has low computing complexity. Furthermore, the method is applicable to the case of additive Gaussian noise environment, whether white noise or color noise. © 2023 SPIE.

关键词： Parameter estimation

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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An improved Harris Hawk optimization algorithm and its application to Extreme Learning Machine 3

An improved Harris Hawk optimization algorithm and its appli...

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3rd International Conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Liu, Ziliang Chen, Hongwei School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9798350331578

The Harris Hawk optimization (HHO) algorithm is an excellent swarm intelligence optimization algorithm which has the advantages of high efficiency in finding the best, ease of implementation and wide application. It also has some disadvantages such as the possibility of convergence too fast and the tendency to fall into local optima. This paper combines an improved escape energy update approach and the leader update operator of the Salp Swarm Algorithm to improve the HHO, named IMHHO. The experiments show that the improvements have improved the algorithm's ability to find the best. IMHHO was also used in the parameter optimization of the Extreme Learning Machine, which also enables the ELM to find the right weights and bias values and to regress the data more accurately. © 2023 IEEE.

关键词： Optimization

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A Real-Time 3D Perception Method with Multi-resolution Voxel Space 6

A Real-Time 3D Perception Method with Multi-resolution Voxel...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhu, Haowei Shi, Jinlong Wang, Chenggen Lu, Jiawen Jiangsu University of Science and Technology School of Computer Zhenjiang China

ISBN: (纸本)9798350325485

3D perception is the key technology of intelligent equipment to understand the world. We propose an online 3D perception method from RGBD sequences which consists of 3D instance segmentation, camera pose optimization, 3D reconstruction, and refinement modules. Firstly, we perform 3D instance segmentation by mapping and fusing 2D instance segmentation in 3D space. Secondly, we obtain accurate camera poses by adopting a frame-To-frame optimization method and local-To-global strategy. Thirdly, we reconstruct 3D scenes by a multi-resolution implicit structure based on voxels with semantic information. Finally, we render refined implicit surfaces by an improved rendering method. Experiments show that our method has better 3D perception results in a variety of complex environments compared with similar methods. Our code is available: https://***/3D-Vision-Lab-of-JUST/Real-Time-3D-Perception. © 2023 IEEE.

关键词： Cameras

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Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships 24

Enhancing Change Impact Prediction by Integrating Evolutiona...

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18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2024

作者： Zhou, Daihong Zhang, Jiyue Yu, Ping Guo, Wunan School of Computer Science and Information Engineering Shanghai Institute of Technology China Huawei Technologies Co. Ltd China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (纸本)9798400710476

Background: Changes on source code may propagate to distant code entities through various relationships, making related changes obligatory. Identifying change impacts is challenging due to the complexity of how changes spread. Although association rules are widely used for change impact prediction, they rely solely on historical co-changes, which limits their accuracy when entities rarely or never co-change. Aims: This study explores the integration of evolutionary coupling with software change relationships among changed code entities to enhance the state-of-the-art association rule mining technique, TARMAQ. Method: We integrate evolutionary coupling with 12 types of software change relationships, such as structural dependencies and code clones, to better capture associated changes. Results: Analyzing thousands of commits from six open-source systems, we observed: (1) Incorporating software change relationship analysis significantly improves TARMAQ's prediction recall and mean average precision (MAP), (2) The top-5 predictions exhibit notable increasing in precision, recall, F1-score, and MAP, and (3) Based on our implementation, the integrated method is practically applicable. Conclusions: Combining evolutionary coupling and software change relationships can improve the recall and prioritization of impact predictions in association rule-based techniques. © 2024 ACM.

关键词： Prediction models

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General-Special Neighborhood-Enhanced Graph Contrastive Learning for Recommendation 5

General-Special Neighborhood-Enhanced Graph Contrastive Lear...

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5th International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Yang, Zezheng Xia, Xiaoling DongHua University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350359930

Graph Neural Networks (GNNs) have gained widespread application in the field of recommendation systems due to their flexibility and effectiveness in modeling graph-structured data. GNN enable the collection of high-dimensional information from user-item interaction graphs through multiple layers of convolution and information propagation, facilitating a holistic modeling of users and items. However, GNN-based models face the challenge of over-smoothing due to sparsity, which hinders their ability to learn optimal node representations. To address this issue, graph contrastive learning algorithms have been introduced, yielding promising results. Yet, these graph contrastive learning-based methods tend to overlook the historical *** tackle this problem, we propose a novel method called General and Specific neighborhood-enhanced Graph Contrastive Learning (GS-GCL). This method approaches node neighbor discovery and contrastive learning from both general and specific perspectives. We observe that in GNN even-pass information propagation encodes homogenous information that can be extracted as the general neighbor representation of the central nodes. Furthermore, we calculate the cosine similarity of a node's historical interactions to select the most similar nodes as its specific neighbors. To validate the effectiveness of our method, we conducted extensive experiments on three publicly available datasets. The results demonstrate significant improvements compared to the currently most effective method NCL method, particularly achieving a remarkable 14.14% enhancement on the Amazon-Books dataset. Our code repository can be found at: https://***/big-maomi/GS-GCL © 2023 IEEE.

关键词： Recommender systems

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction 9th

When Alignment Makes a Difference: A Content-Based Variatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Ren, Jianyu Zhang, Ruoqian School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9783031466601

Click-Through Rate (CTR) prediction is a core task in recommendation systems. Despite VAE-based models have shown promising accuracy performance, they are still weak in supporting cold-start CTR prediction due to limited personal interactions. To this end, this paper proposes a content-based variational CTR model, which jointly models content information and interactions behaviors in a shared probability space via variational inference. Specifically, a three-step scheme is designed to fully utilize content information for the improved ability of preference modeling toward cold-start users. First, our method adopts VAE to model user preferences from personal interactions by probabilistic distributions, instead of a fixed embedding vector for representing the user’s interest. Then, we transform content information into variational probabilistic distribution to model the implicit preferences of cold-start users. Finally, a variational alignment strategy is applied to maximize the similarity between variational preference distributions obtained from interactions behaviors and content information respectively, so that the interest of the cold user can be recovered. Besides, we adopt a self-attention mechanism to reasonably balance the importance of latent features for CTR prediction. Experiments on two public real datasets show the effectiveness of the proposed approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Hybrid attention network for Semantic segmentation 4

Hybrid attention network for Semantic segmentation

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Yang, Yin Yang, Juan Wang, Ronggui School of Computer Science Hefei University of Technology Hefei China

ISBN: (纸本)9781510666412

Deep neural networks have made significant improvements in pixel -level semantic segmentation. However, the existing semantic segmentation algorithm still faces the problem of weighing between the accuracy and calculation cost of the segmentation. In response to this issue, this article proposes a hybrid network structure (HAM), looking for a balance point in the calculation accuracy and calculation speed. In this method, we construct a dual -attention module. The role of this module is to guide high -level characteristics through underlying characteristics to obtain more context information. Among them, the shape flow branch retains low -level space details, and semantic flow branches capture senior context information. These two branches are fused to strengthen information dissemination between different levels, thereby achieving higher segmentation accuracy. Experiments on the dataset show that this method achieves higher accuracy and speed under relatively small parameters. Compared with other real-time semantic segmentation methods, our network has achieved good compromise between parameters, speed, and accuracy. © 2023 SPIE.

关键词： Semantic Segmentation

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