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2023 International Conference on Electronic, Electrical and Information Technology, EEIT 2023

作者： Guo, Ziyu Kong, Weiyang Liu, Yubao Sun Yat-Sen University Guang Zhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Anomaly detection in time series data (e.g., sensor data) is becoming a fundamental research problem that has various applications. Due to the complex inter-sensor relationships, it is challenging to detect anomalous events such as system faults and attacks hidden the high-dimensional time series. Recent advancements in deep learning approaches such as Graph Neural Networks (GNN) have greatly improved anomaly detection performance in time series data. However, existing methods usually use separate components to capture spatial and temporal dependence relationship and ignore the heterogeneities in spatial-temporal data which motivates us to capture them together. In this paper, we propose a novel approach STGDNN (short for Spatial-Temporal Graph Deviation Neural Networks) that learns the spatial-temporal dependence relationship together in structure learning of graph neural networks. In addition, we use the learned spatial-temporal graph structure and attention weights to explain the detected anomalies. The experiments on three real world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation compared with state-of-the-art methods. © Published under licence by IOP Publishing Ltd.

关键词： Anomaly detection

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A Lightweight Neural Network Localization Algorithm for Structureless Wireless Sensor Networks 13th

A Lightweight Neural Network Localization Algorithm for Stru...

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13th China Conference on Wireless Sensor Networks, CWSN 2019

作者： Gao, Rong Yang, Zhongheng Wu, Hejun Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789811517846

This paper studies distributed range-based localization in arbitrarily deployed wireless ad hoc networks. Existing range-based localization approaches depend on specially deployed anchors or require dense network deployment. Our algorithm is a distributed paradigm that only requires local information of each node. Therefore, it is applicable to the resource-limited embedded sensors. Specifically, our algorithm performs a three-stage optimization through coarse-grained, middle-grained, and fine-grained levels. We designed an efficient but accurate neural network to learn the hidden relations between the distances of nodes and their positions. Simulations show that our proposed algorithm works in many more types of network deployments than the existing approaches. Furthermore, our algorithm achieves the highest localization accuracy on average. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Global image correlation filter with H-D fusion mechanism for visual tracking 9

Global image correlation filter with H-D fusion mechanism fo...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Yi, Yang Chen, Jiaqian Liang, Da'An Tian, Guixiong School of Data and Computer Science Sun Yat-sen University School of Information Science !!!Xinhua College of Sun Yat-sen University @@@Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9781450387835

The discriminative correlation filter has remarkable robustness under various circumstances in the visual tracking of videos. Nevertheless, there are some challenges for the further improvement of the tracking performance, including confusion between the tracked target, complex background, and underfitting because of a lack of training samples. The main contribution of the study is developing a new framework which combines with global image sampling enable to significantly augment the negative samples without the need of corruptting those positive samples. Besides we propose a fusion mechanism by combining two image patch representations in accordance with their confidence scores. Obtained results show that the proposed method is an effective scheme in leveraging the complementary properties of deep and hand-crafted features. Based on the conducted experiments on several benchmarks such as OTB2013, TB-50, and TB-100 datasets, it is concluded that the proposed approaches can achieve better result compared to the state-ofthe-art methods. © 2020 ACM.

关键词： Computer vision

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Design and implementation of a Cesium-based 3D visualization platform for the Yellow River dam bank

Design and implementation of a Cesium-based 3D visualization...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Sun, Huaiyuan Zhang, Lei Dang, Lanxue Liu, Yang Xie, Yi Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan Universfiity Kaifeng475001 China Zhong Ke Langfang Institute of Spatial Information Applications Langfang065000 China

ISBN: (纸本)9781510664616

Flood control is one of the most important operations of Yellow River control. The dam bank danger in flood season every year has the characteristics of time-varying, multi-dimension and complex situation, which poses a certain threat to people's life and property safety. For timely, flood control engineering data quickly and implement management interface of information visualization, effectively reduce the labor intensity of artificial working risk, this paper puts forward a kind of based on three-dimensional library of Cesium Huang Heba shore the realization method of 3D visualization platform adopted the aerial drones to create dam shore entity 3D model, Combined with WebGIS technology and front-end technology, the system's 3D expression is realized. Through interface design and system function development, the system has three-dimensional model display,with the functions of dam bank cruise and measurement tools, the platform has strong sense of reality and good interaction effect, which realizes the three-dimensional visual display of the Madu control project in Zhengzhou, henan Province. © 2023 SPIE.

关键词： Flood control

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Split and Attentive-Aggregated Learnable Shift Module for Video Action Recognition 20

Split and Attentive-Aggregated Learnable Shift Module for Vi...

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3rd International Conference on Machine Learning and Machine Intelligence, MLMI 2020

作者： Wu, Xiao Ji, Qingge School of Data and Computer Science Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9781450388344

Existing approaches for video action recognition using convolutional neural network (CNN) usually suffer from the trade-off between accuracy and complexity. On the one hand, the 2D CNNs have difficulty in modeling the long-term temporal dependencies though they are computationally cheap. On the other hand, 3D CNNs have the ability to perceive temporal cues however lead to a high computational cost. In this paper, we propose a generic building block named Split and Attentive-aggregated Learnable Shift Module (SALSM) which has capacity of modeling spatiotemporal representations while maintain the complexity of the 2D CNN. Specifically, we split the input tensor into multiple groups, and conduct adaptive shift operations by applying the learnable shift kernels for different channels of each group along time dimension, so that the spatiotemporal information from neighboring frames can be mingled with 2D convolutions. The output feature maps of each group are integrated together with attention mechanism. With SALSM plugged in, the 2D CNN is enhanced to handle temporal information and become a highly efficient spatiotemporal feature extractor with little parameters and computational cost. We conduct ablation experiments to verify the effectiveness of our method, and our proposed SALSM achieves competitive or even better results over the state-of-the-art methods on several benchmark datasets. © 2020 ACM.

关键词： Convolutional neural networks

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A Dynamic Acceleration Method for Remote Sensing Image processing Based on CUDA 12th

A Dynamic Acceleration Method for Remote Sensing Image Proce...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Zuo, Xianyu Zhang, Zhe Qiao, Baojun Tian, Junfeng Zhou, Liming Zhang, Yunzhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China National Cultural Heritage Administration Beijing100010 China

ISBN: (纸本)9783030727918

The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as big data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. Therefore, how to take fully advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. Effectiveness of the proposed DAA method in accelerate remote sensing algorithm with point operations were verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Graphics processing unit

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data analysis Platform of Cement Ontology Based on Rough Concept Lattice Model 4th

Data Analysis Platform of Cement Ontology Based on Rough Con...

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4th International Conference on Innovative Computing, IC 2021

作者： Xu, Hongsheng Jiang, Shengli Zhang, Yi Luoyang Normal University Luoyang China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang China

ISBN: (纸本)9789811642579

Enterprise production and management big data can promote the modernization of enterprise management, which has very realistic economic benefits and far-reaching social significance in the aspects of transforming management mechanism, effectively reducing cost, speeding up technological progress and enhancing enterprise competitiveness. This paper proposes a cement ontology data analysis platform based on rough concept lattice model, including: product analysis based on cement ontology, association rule mining, knowledge management, personalized recommendation, and decision support. The analysis and mining platform based on big data helps the company to reconstruct the strategic policy, realize the data speaking as the basis, and let the data drive become the core of the enterprise decision. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Character-Level Street View Text Spotting Based on Deep Multisegmentation Network for Smarter Autonomous Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 297-308页

作者： Zhang, Chongsheng Tao, Yuefeng Du, Kai Ding, Weiping Wang, Bin Liu, Ji Wang, Wei Henan Provincial Key Laboratory of Big Data Analysis and Processing Laboratory of the Yellow River Cultural Heritage Henan University Kaifeng475001 China School of Information Science and Technology Nantong University Nantong226019 China School of Electrical Engineering Zhejiang University Hangzhou310058 China Big Data Lab Baidu Inc. Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Urban scenes are full of street entities with sign boards. Therefore, in autonomous driving, street view text spotting techniques will play a significant role in the precise understanding of surrounding scenes during driving, because texts contained in the images usually provide important clues for accurate image understanding, while it is often ambiguous for existing computer vision algorithms to understand scene images without texts. In this work, we propose a Multi-Segmentation network for character-level scene Text Detection (MSTD). The MSTD introduces a densely connected atrous spatial pyramid pooling module to enlarge the receptive field of the feature extraction layer, so as to localize long as well as large-sized text instances. Moreover, it devises a double segmentation subnetwork to utilize two independent but inherently complementary losses to co-optimize the network and increase the reliability of the confidence scores in predicting the text/nontext areas. With the character instances detected by the MSTD, one can easily perform scene text spotting with classic object recognition networks such as ResNet and DenseNet. We carried out extensive experiments on nine scene text datasets to demonstrate the outstanding performance of the MSTD on character-level and line-level text instance localization and scene text recognition, where the MSTD significantly outperforms the state-of-the-art scene text detection methods and the sequence-to-sequence-learning-based scene text recognizers. © 2020 IEEE.

关键词： Character recognition

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analysis of big data processing Based on Mapreduce and Convolution Neural Network 4th

Analysis of Big Data Processing Based on Mapreduce and Convo...

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4th International Conference on Innovative Computing, IC 2021

作者： Xu, Hongsheng Wang, Lan Zhang, Yi Luoyang Normal University Luoyang China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang China

ISBN: (纸本)9789811642579

This paper first describes the principle of convolution neural network. Convolution neural network (CNN) is proposed to deal with pattern recognition problem on the basis of neural cognitive machine. Convolution NN is a multi-layer neural network, which each layer is composed of multiple two-dimensional planes, and each plane is composed of multiple independent neurons. In this paper, the principle and design idea of MapReduce based on large-scale data are described in detail. The paper presents analysis of big data processing by MapReduce and Convolution Neural Network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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A Causal Graph for Learning Gender Debiased Word Embedding 9

A Causal Graph for Learning Gender Debiased Word Embedding

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Feng, Yanxia Rao, Yanghui Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9798350330076

Word embedding learning is a powerful technique to represent words' rich semantics as low-dimensional vectors, but it may encode harmful social biases. Such biases can leave negative impacts on downstream applications that rely on word embeddings, e.g., text classification and sentiment analysis. In this work, we design a novel approach to debias word embeddings using a causal graph that separates gender and semantic information within words. Our method is theoretically interpretable and can effectively alleviate the issue of gender bias from words in real-world corpora without destructing their semantic meanings. We perform a comprehensive comparison for our proposed approach with existing state-of-the-art techniques using a range of evaluation metrics and tasks, and demonstrate that our method can not only mitigate bias mixed in word embeddings, but also improve their performance on downstream tasks. © 2023 IEEE.

关键词： Embeddings

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