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A relation prediction method based on PU learning

A relation prediction method based on PU learning

引用

International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Gao-Jing Peng Ke-Jia Chen Shijun Xue Bin Liu Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China

This paper studies relation prediction in heterogeneous information networks under PU learning context. One of the challenges of this problem is the imbalance of data number between the positive set P (the set of node pairs with the target relation) and the unlabeled set U (the set of node pairs without the target relation). We propose a K-means and voting mechanism based technique SemiPUclus to extract the reliable negative set RN from U under a new relation prediction framework PURP. The experimental results show that PURP achieves better performance than comparative methods in DBLP co-authorship network data.

关键词： Reliability Artificial neural networks Clustering algorithms Algorithm design and analysis Prediction methods Sun

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CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

引用

15th International Conference on intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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A Point Cloud Registration Method for Substations Based on an Improved SAC-IA Algorithm

A Point Cloud Registration Method for Substations Based on a...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Lei Chen, Yiping Zong, Haonan Wang, Lulu Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

This paper proposes a point cloud registration method for substations based on an improved SAC-IA algorithm. This method optimizes the SAC-IA algorithm by filtering the randomly selected point pairs to ensure that the triangles formed by the selected points in the model and scene point clouds have certain similarities. Furthermore, the registration error judgment of the SAC-IA algorithm has been improved. In addition to using the Euclidean distance between the registered model and scene point clouds as the judgment standard, this method also considers the SHOT difference and average curvature difference. Successful registration is only considered when all three standards are satisfied. The experimental research shows that the time performance of this method is twice as high as that of the traditional SAC-IA algorithm on average, and the recognition accuracy is improved in the recognition results. © 2023 IEEE.

关键词： Electric substations

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs 26

Path Planning based on Reinforcement Learning with Improved ...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ding, Qun Xu, Xiaolong Gui, Wenming Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China

ISBN: (纸本)9798350331684

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs. © 2023 IEEE.

关键词： Reinforcement learning

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A Point Cloud Completion Method for Industrial Component Based on Multiple Encoders

A Point Cloud Completion Method for Industrial Component Bas...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Lei Chen, Ran Huang, Wei Chen, Yiping Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

In real-world scenarios, laser-scanned point clouds often suffer from missing data points due to occlusion and other factors, significantly impacting subsequent tasks. We propose a novel point cloud completion method based on self-attention mechanisms and multi-layer perceptrons to address this issue, building upon the PCN model. Our experimental evaluations on the PCN dataset demonstrate notable enhancements in the completion effectiveness and evaluation metrics compared to the baseline model. Additionally, focusing on the point cloud completion context for industrial components, we introduce an approach for constructing an industrial component point cloud dataset. This method leverages 3D mesh models to swiftly generate complete point clouds with uniform distribution and devoid of noise while simulating the incomplete point clouds obtained from real-world scanning scenarios. The final results fully exhibit the feasibility of this scheme, which is a strong candidate for future industrial component point cloud completion. © 2023 IEEE.

关键词： Signal encoding

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A Social-Aware Mode Selection Algorithm for D2D Communications

A Social-Aware Mode Selection Algorithm for D2D Communicatio...

引用

International Conference on Communications, Signal processing, and Systems, CSPS 2018

作者： Lu, Wei-feng Lin, Wei-jun Chen, Si-guang Yang, Li-jun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China College of IoT Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

ISBN: (纸本)9789811362637

Aiming at mode selection in D2D communication, a mode selection strategy based on social-aware was proposed. In addition to physical connection status, users’ social relationships also served as an observation element. Through relationship between users, the closeness of user relationship can be judged, and then through users’ reference, the similarity between users can be obtained. In this way, by combining physical connection with social relationships, users’ aggregate utility was acquired. According to the physical connection quality and the social relationship strength, taking maximizing users’ aggregate utility as the goal, a random algorithm was designed to match users. Simulation results show that mode selection based on social perception can effectively improve users’ aggregate utility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Aggregates

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Complex Substation Scene Segmentation Method Based on Optimized Straight Line Detection

Complex Substation Scene Segmentation Method Based on Optimi...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Hao Liu, Chuang Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

This paper proposes a scene segmentation method based on optimized line detection to address the difficulty of segmenting spatially independent equipment in complex substation scenes. The method first employs the RANSAC method to simulate the ground plane and extract the ground point cloud. Then, it projects the 3D point cloud scene onto a 2D plane to obtain a 2D image of the substation. Straight line segments of the power line are extracted from the image using the edge detection algorithm and an improved line detection algorithm. The straight line segments that do not belong to the power line are filtered out using the surrounding features of the 3D point corresponding to the 2D point on the line. The power line's straight line segment is extended using the region-growing algorithm. Finally, the power line clustering is completed using European clustering based on the three-dimensional coordinates of the two ends of the power line, thereby completing the scene segmentation. Experimental results show that the proposed method can effectively segment substation equipment. Compared to traditional segmentation methods, this method has an average power line identification accuracy of 14% higher. © 2023 IEEE.

关键词： Edge detection

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Robustness May be at Odds with Stability in Adversarial Training based Feature Selection?

Robustness May be at Odds with Stability in Adversarial Trai...

引用

IEEE International Conference on data Mining (ICDM)

作者： Yue Liu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

As an important stage in machine learning pipeline, feature selection techniques are mainly used to improve the generalization performance and training efficiency of machine learning model, but few works have focused on the robustness of machine learning models from the perspective of feature selection when dealing with adversarial attacks. In this paper, we propose the adversarial training (AT) based feature selection framework, i.e. AT based feature selection, to improve the robustness of machine learning model built on the feature selection result, which is inspired by using adversarial training to improve the robustness of deep learning model. AT based feature selection framework is the combination of adversarial training with some traditional feature selection algorithm, which can be divided into AT in-processing and AT post-processing feature selection. On the other hand, stability is also a very important property for feature selection. Then we experimentally analyze the relationship between robustness and stability of AT based feature selection, especially theoretically analyze the stability of $\ell_processing$ regularized AT in-processing feature selection algorithm in two different adversarial training forms. Our experimental results on benchmark data sets show that AT based feature selection algorithm is effective to improve the robustness of machine learning model, however, obtain lower stability than corresponding feature selection model without AT.

关键词： Training Deep learning Machine learning algorithms Pipelines Benchmark testing Feature extraction Stability analysis

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MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning

MDPFL:A Multiple Differential Privacy Protection Method base...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Xinyu Liu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users’ original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved.

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Temporal data fusion at the edge

Temporal data fusion at the edge

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Yang, Linfu Liu, Bin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728152097

As an enabler technique, data fusion has gained great attention in the context of Internet of things (IoT). In traditional settings, data fusion is done at the cloud server. So the data to be fused should be transferred from the sensor nodes to the cloud server before data fusion. Such an application mode of data fusion inherits disturbing concerns from the cloud computing framework, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption. We take into account how to do temporal data fusion at the edge to bypass the above issues. We present a Gaussian process based temporal data fusion (GPTDF) method targeted for the problem of sequential online prediction at the edge. The GPTDF method fits the edge computing framework and thus inherits desirable properties from edge computing, such as privacy-preserving, low latency between data capture and computation, and tiny bandwidth consumption. Through a real-data experiment using archived traffic datasets from the Caltrans Performance Measurement System (PeMS), we demonstrate that the application of GPTDF can provide more timely and accurate real-time predictions at the network edge. © 2019 IEEE.

关键词： Internet of things

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