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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Yubing Bai Xinhang Song Weijie Li Sixian Zhang Shuqiang Jiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology CAS Suzhou

The goal of visual object navigation for an agent is to find the target objects accurately. Recent works mainly focus on the feature of embedding, attempting to learn better features with different variants, such as object distribution and graph representations. However, some typical navigation problems in complex environments, such as partially known and obstacle problems, may not be effectively addressed by previous feature embedding methods. In this paper, we propose a framework with a long-short objective policy, where the hidden states are classified according to the navigation objectives at that moment and separately rewarded. Specifically, we consider two objectives: the long-term objective is to go closer to the target, and the short-term objective is for obstacle avoidance and exploration. To alleviate the effect of long-term and short-term alternation, we build a state memory and propose an adjustment gate to update the state memory. Finally, all past hidden states are reweighted and combined for action prediction with an action-boosting gate. Experimental results on RoboTHOR show that the proposed method can significantly outperform the state-of-the-art.

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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Remaining Useful Life Prediction of Bearing via a Double Attention-Based Deep Neural network 19

Remaining Useful Life Prediction of Bearing via a Double Att...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yongkang Pan, Donghui Zhang, Haifeng Zhong, Kai Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China

ISBN: (纸本)9798350346558

Predicting the remaining useful life (RUL) of bearings is important for the secure running and reducing maintenance costs of rotating machinery. In this article, a novel feature extraction and RUL prediction framework of bearing is proposed by utilizing time-frequency representations (TFRs) and deep neural network with double attention (DA-DNN). Firstly, TFRs are employed to extract the degradation features associated with RUL prediction, which can effectively cope with the non-stationary properties of bearing vibration data. The reduced dimensional TFRs are then used as the DA-DNN model input. Afterwards, DA-DNN is developed by employing the double attention structure to extract the key degradation features more efficiently. The dual-attention structure has ability to capture the degradationrelated features from two aspects of channel attention and spatial attention, which enables the network to focus on high RUL-relevant information. Finally, the experimental results demonstrate the effectiveness of DA-DNN, which shows higher performance in terms of prediction accuracy compared to other deep learning (DL) methods. © 2022 IEEE.

关键词： Deep neural networks

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Community Evolution Tracking Based on High-Order Neighbor Consideration and Node Change Identification 19th

Community Evolution Tracking Based on High-Order Neighbor C...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Zhang, Yunan Wang, Chaohui Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Community evolution tracking is widely used in complex network analysis, which analyzes and identifies how communities evolve over time based on dynamic community detection. However, the current incremental dynamic community detection method has the phenomenon of ’concept drift’, which leads to inaccurate tracking of community evolution. Although some studies on dynamic community detection have proposed to perform global community detection on the network by timing or when the error information accumulates to a certain extent, this method will lead to a sudden change in community division, which will interrupt the evolution sequence of some communities and bring great difficulties to the continuous tracking of community evolution. In addition, the current community evolution tracking methods match the communities of two adjacent snapshots, and it is difficult to capture the fine-grained evolution process within the community. This paper proposes a community evolution tracking algorithm based on high-order neighbor consideration and node change identification. Firstly, based on an incremental update strategy that considers high-order neighbors, we adaptively expand the scope of incremental updates to reduce the accumulation of error information. Secondly, through the node change identification strategy to mine fine-grained community evolution events. Experiments show that our method can effectively track the evolution of communities in dynamic networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Community evolution Complex network Dynamic network Evolution tracking

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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UGCM-LU: A Unified Stream and Batch Graph computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Hong Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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Super-Broadband Rectifier with Expanded Input Power Range for Microwave Power Transmission 5

Super-Broadband Rectifier with Expanded Input Power Range fo...

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Chen, Yan Liu, Xin Li, Yingsong Huang, Zhixiang The Fifth Research Institute of Miit Guangzhou511370 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A super-broadband rectifier with expended input power range(IPR) for wireless power transfer (WPT) and radio frequency energy harvesting (RFEH) is proposed and analyzed. Meanwhile, using a simple impedance compression network consisting of quadruple microstrip lines (QMLICN) with the topology of the dickson charge pump, high power conversion efficiency (PCE) and super-broadband impedance matching are achieved. In addition, the IPRs of the rectifier are also extended. The simulation results indicate that an IPR of 12 - 25 dBm with a frequency range of 0.4 - 2.5 GHz (fractional bandwidth over 144%), the PEC exceeds 50% are realized, while at an input power of 19 dBm, the rectifier reaches a peak PCE of 74.2%. The rectifier covers the GSM 900, GSM 1800, 3G 2100 and WIFI 2400 bands, which design would have significant potential applications for the development of microwave power transmission systems. © 2022 IEEE.

关键词： Energy transfer

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