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International Conference on Networking and Network Applications (NaNA)

作者： Sugang Ma Zixian Zhang Lei Zhang Yanping Chen Xiaobao Yang Lei Pu Zhiqiang Hou School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information Engineering Chang’an University Xi’an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information and Navigation Air Force Engineering University Xi’an Shaanxi China

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking.

关键词： Target tracking Feature extraction Real-time systems Robustness Object tracking Task analysis

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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An Adaptive Multi-sensor data Consistency Algorithm Based on Node Credibility 15th

An Adaptive Multi-sensor Data Consistency Algorithm Based on...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Chen, Yanping Ma, Xiao Xia, Hong Wang, Zhongmin Yv, Zhong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783030325909

By using the principle of sensor data consistency and combining the kalman filter algorithm with the bayesian algorithm, the accuracy of the algorithm is significantly improved under the condition of large data volume and complex system. An adaptive multi-sensor data consistency algorithm based on node credibility proposes the internal correlation between the sensor credibility and the data values it provides, and the final data fusion results are obtained by fusion of the optimized data through the sensor credibility. The advantages of this algorithm include: (1) the data is optimized by kalman filter, which reduces the influence of the data provided by the sensor with low reliability on the fusion result;(2) bayesian algorithm is adopted to calculate the correct probability of data values and the reliability of the sensor, which overcomes the inherent uncertainty of the sensor and improves the accuracy of data fusion. The results show that compared with the traditional data consistency algorithm, this algorithm can further improve the accuracy of fusion results and reduce the position error. © 2020, Springer Nature Switzerland AG.

关键词： Sensor nodes

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KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

arXiv

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arXiv 2021年

作者： Luo, Zhiping Xu, Wentao Liu, Weiqing Bian, Jiang Yin, Jian Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding (KGE). However, most previous KGE methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efficient contrastive learning framework for tensor decomposition based (TDB) KGE, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the performance of KGE. We evaluate our proposed method on three standard KGE datasets: WN18RR, FB15k-237 and YAGO3-10. Our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, 37.8% MRR, 28.6% Hits@1 on FB15k-237 dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset. © 2021, CC BY.

关键词： Knowledge graph

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Research on Web Service Clustering Method Based on Word Embedding and Topic Model 15th

Research on Web Service Clustering Method Based on Word Embe...

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作者： Chen, Yanping Wang, Xin Xia, Hong Wang, Zhongmin Yv, Zhong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783030325909

Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embedded topic model, which can effectively solve the problem of data sparsity. A method based on embedded words and topic models. Firstly, Wikipedia is used as an external corpus to extend API service document, and LF-LDA model is used to model its topic distribution. The corpus data is extracted from Wikipedia by wikiextractor, and the corpus is trained with Word2vec tool. The data comes from its word vector model. Use the trained Wikipedia Word Vector Model to extend API service description documents. In the extended API service documents, LF-LDA topic modeling technology is used to mine the implicit topic information and determine the optimal number of topics, so as to accurately measure the semantic similarity between API service documents. Finally, K-means algorithm is used to cluster Web services, and the validity of this method is verified by the real data of API services on the basis of programmability. The experimental results show that this method has better accuracy, recall rate and F value than the traditional Web service clustering method based on topic model algorithm. © 2020, Springer Nature Switzerland AG.

关键词： Web services

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Research on feature mining algorithm and disease diagnosis of pulse signal based on piezoelectric sensor

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Informatics in Medicine Unlocked 2021年 26卷

作者： Lin, Fan Zhang, Jincheng Wang, Zhongmin Zhang, Xiaokang Yao, Ruiling Li, Yan School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 Shaanxi China

The human pulse contains various information reflecting the internal environment of the human body. However, the classical method of pulse diagnosis in traditional Chinese medicine (TCM) has the disadvantages of relying too much on the doctor's experience and the diagnosis result is too subjective. Based on the principle of TCM pulse diagnosis, the use of photoelectric sensors to collect the pulse signals of multiple healthy people and patients with chronic diseases, and organize the detailed pulse information into a data set and analyze it with algorithms, is a solution to overcome this problem through modern technology. However, this method is still difficult to understand the patient's physiological condition in detail, and it is also difficult to explain the internal connection between abnormal pulse conditions and their physiological conditions. In the experiment, after denoising, smoothing, and eliminating the baseline drift of the subjects' pulse data, we designed two algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. The specific feature values obtained are converted into a multi-dimensional array and trained in a support vector machine (SVM) classifier. The classification accuracy is higher than the basic temporal features. Experimental results show that it is feasible to use specific feature mining algorithms for disease detection. Through analysis, this paper found the pathological characteristics reflected in the two-dimensional pulse image, discovered the internal connection between the pulse waveform characteristics of the human body and the disease, and tried to describe it through algorithms, trying to establish a method for detecting specific diseases using photoelectric signals. © 2021 The Authors

关键词： Pathological feature mining Physiological signal feature Pulse diagnosis Pulse wave analysis

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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

arXiv

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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

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23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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International Conference on data Engineering

作者： Lihui Lin Yanghui Rao Haoran Xie Raymond Y. K. Lau Jian Yin Fu Lee Wang Qing Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Lingnan University New Territories Hong Kong SAR Department of Information Systems City University of Hong Kong Kowloon Hong Kong SAR Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou China School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

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Efficient processing of top-k frequent spatial keyword queries

Research Square

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Research Square 2021年

作者： Xu, Tao Xu, Aopeng Mango, Joseph Liu, Pengfei Ma, Xiaqing Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Key Research Institute of Yellow River Civilization and Sustainable Development Henan University Kaifeng475004 China

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions. © 2021, CC BY.

关键词： Big data

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