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Using NSGA-Ⅲ for optimising biomedical ontology alignment

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CAAI Transactions on Intelligence Technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

Deep representation learning for road detection using siamese network

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arXiv 2019年

作者： Liu, Huafeng Han, Xiaofeng Li, Xiangrui Yao, Yazhou Huang, Pu Tang, Zhenming School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI ROAD demonstrate the effectiveness of our proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Roads and streets

Multi-agent game abstraction via graph attention neural network

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arXiv 2019年

作者： Liu, Yong Wang, Weixun Hu, Yujing Hao, Jianye Chen, Xingguo Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University Tianjin University NetEase Fuxi AI Lab Noah's Ark Lab Huawei Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Genetic algorithms

Resource allocation in cognitive wireless powered communication networks under outage constraint 4

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Resource allocation in cognitive wireless powered communicat...

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Li, Qun Wang, Longqi Xu, Ding Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538683392

We investigate a multi-carrier cognitive wireless powered communication network (CWPCN), where the primary user (PU) is wirelessly powered by harvesting energy from the information signals from the secondary user (SU). The PU is protected by complying with the outage constraint which requires that a minimum rate is satisfied by the PU for a given outage probability. Both offline and online algorithms are proposed to jointly optimize time, subcarrier and power allocation to maximize the SU rate under the outage constraint. Simulation results are presented to verify the proposed algorithms. It is shown that the proposed algorithms outperform the benchmark algorithm with zero-outage constraint. It is also shown that the online algorithm achieves almost the same performance that the offline algorithm achieves. © 2018 IEEE.

关键词： Outages

Secure communications for primary users in cognitive radio networks with collusive eavesdroppers

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Secure communications for primary users in cognitive radio n...

作者： Xu, Ding Li, Qun Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to noncollusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Cognitive radio

Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

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Localization of Deep Inpainting Using High-Pass Fully Convol...

International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

Sequential online prediction in the presence of outliers and change points: An instant temporal structure learning approach

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arXiv 2019年

作者： Liu, Bin Qi, Yu Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing Jiangsu210023 China College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310058 China

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration to the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, on the basis of the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter value of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

Temporal data fusion at the edge

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arXiv 2019年

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 & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

—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. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things