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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Ling Gao, Pingping Guo, Kun Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

ISBN: (纸本)9798331520861

With deep learning's advancement in recent years, heterogeneous graph neural networks (HGNNs) have drawn a lot of interest. They are a group of deep learning models specifically designed for handling heterogeneous graph data, in which nodes and edges are of various types and all nodes are assumed to have either direct or indirect associations. A metaschema, which serves as a thorough and unified blueprint for the heterogeneous graph, elegantly connects varied node types to other heterogeneous nodes via multiple heterogeneous edges with rich sematincs. However, many existing methods that utilize meta-schema to mine heterogeneous information often focus on the different meta-relationships inside the schema, limiting the meta-schema to describing only the local relational semantics. To better utilize meta-schema for efficiently integrating different heterogeneous information, we organize them into a hierarchical structure(i.e., hierarchical meta-schema), with all types of nodes distributed across different layers. The layers are interconnected through heterogeneous relationships. The target type is placed at the final layer and others are arranged hierarchically in the front according to the heterogeneous relationships defined in the meta-schema. During information aggregation, according to the hierarchical meta-schema, the information of each layer is gradually aggregated to the target type on the final layer from far to near via the attention mechanism. Moreover, metapaths regularly begin and end with the same type and help to define complex structures and semantic. Therefore, we also consider the semantic information of neighbors based on metapaths within the proposed hierarchical meta-schema to enhance the complex semantics among nodes of the same-type that may be lacking in the meta-schema. Finally, we proposed a meta-path and meta-schema hierarchical aggregation for heterogeneous graph neural network model, named HGNN-MMHA. And we may allow the meta-schema to not

关键词： Graph neural networks

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An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1376-1387页

作者： Fang, Jinsheng Lin, Hanjiang Zeng, Kun School of Computer Science and Engineering Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://***/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Echo state network based symbol detection in chaotic baseband wireless communication

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Digital Communications and Networks 2023年第6期9卷 1319-1330页

作者： Huiping Yin Chao Bai Haipeng Ren Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an710048China Xi'an Key Laboratory of Intelligent Equipment Xi'an Technological UniversityXi'an710021China

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting ***,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold *** unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)*** contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using *** proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative *** this approach,the calculation complexity is reduced compared to the previous ESN-based *** proposed method achieves better BER performance compared to the previous *** reason for this superior result is ***,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was ***,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information *** results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

关键词： Chaotic baseband wireless communication system(CBWCS) Inter-symbol interference(ISI) Echo state network(ESN) Threshold prediction

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural Computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting

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Journal of Shanghai Jiaotong university(Science) 2023年第6期28卷 822-830页

作者：高锦程赵宜升陈加法陈忠辉 Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information EngineeringFuzhou UniversityFuzhou 350108China

Aiming at excessive users existing in a pico base station(PBS)in the multi-layer heterogeneous networks,the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this *** deploying a relay node with energy harvesting function,the data of some users in the PBS can be transferred to an adjacent idle *** bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization *** objective is to maximize the energy eficiency of the whole heterogeneous networks under the constraints of the user's minimum data rate and energy *** suboptimal solution is obtained by using the particle swarm optimization(PSO)algorithm and quantum-behaved particle swarm optimization(QPSO)*** results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth *** addition,the time complexity of the adopted methods is relatively low.

关键词： energy harvesting heterogeneous networks energy efficiency

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Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System

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Journal of Shanghai Jiaotong University (Science) 2023年 1-12页

作者： He, Ximei Zhao, Yisheng Xu, Zhihong Chen, Yong Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading, a resource allocation method for unmanned aerial vehicle (UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing (MEC) system is proposed. The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group. The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation. The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing. By jointly optimizing users’ offloading time, users’ transmitting power, and the hovering position of the UAV, the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency. The suboptimal solution is obtained by adopting the differential evolution algorithm. Simulation results show that, compared with the resource allocation method based on genetic algorithm and the without user cooperation method, the proposed method has higher computation efficiency. © 2023, Shanghai Jiao Tong University.

关键词： Resource allocation

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Distracted driving behaviour recognition based on transfer learning and model fusion

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 159-168页

作者： Luo, Guantai Xiao, Wanghui Chen, Xinwei Tao, Jin Zhang, Chentao -HIT Research Institute of Engineering and Technology Quanzhou Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian China Department of Instrumental and Electrical Engineering Xiamen University Fujian Xiamen China

In the recognition of distracted driving behaviour, traditional manual feature extraction is subjective and complex;single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images, respectively. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by stacking method, using 5-fold cross-validation method to reduce over-fitting. Experimental results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition. © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Instance Reweighting Adversarial Training Based on Confused Label

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intelligent Automation & Soft Computing 2023年第8期37卷 1243-1256页

作者： Zhicong Qiu Xianmin Wang Huawei Ma Songcao Hou Jing Li Zuoyong Li Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou511442China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger *** probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision ***,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our *** tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during ***-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial *** of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth ***-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger *** demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.

关键词： Reweighting adversarial training adversarial example boundary closeness confused label

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Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

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intelligent Automation & Soft Computing 2023年第8期37卷 1753-1771页

作者： Qi Liu Jing Li Xianmin Wang Wenpeng Zhao School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510002China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core *** methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic *** tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense ***,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding ***,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor ***,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target *** carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

关键词： Semi-supervised learning attention mechanism feature augmentation consistency regularization

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