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arXiv 2021年

作者： Huang, Rubing Cui, Chenhui Towey, Dave Sun, Weifeng Lian, Junlong The Faculty of Information Technology Macau University of Science and Technology Taipa999078 China The School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China The School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo315100 China The School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Adaptive Random Testing (ART) is an enhancement of Random Testing (RT), and aims to improve the RT failure-detection effectiveness by distributing test cases more evenly in the input domain. Many ART algorithms have been proposed, with Fixed-Size-Candidate-Set ART (FSCS-ART) being one of the most effective and popular. FSCS-ART ensures high failure-detection effectiveness by selecting the next test case as the candidate farthest from previously-executed test cases. Although FSCS-ART has good failure-detection effectiveness, it also faces some challenges, including heavy computational overheads. In this paper, we propose an enhanced version of FSCS-ART, Vantage Point Partitioning ART (VPP-ART). VPP-ART addresses the FSCS-ART computational overhead problem using vantage point partitioning, while maintaining the failure-detection effectiveness. VPP-ART partitions the input domain space using a modified Vantage Point tree (VP-tree) and finds the approximate nearest executed test cases of a candidate test case in the partitioned sub-domains - thereby significantly reducing the time overheads compared with the searches required for FSCS-ART. To enable the FSCS-ART dynamic insertion process, we modify the traditional VP-tree to support dynamic data. The simulation results show that VPP-ART has a much lower time overhead compared to FSCS-ART, but also delivers similar (or better) failure-detection effectiveness, especially in the higher dimensional input domains. According to statistical analyses, VPP-ART can improve on the FSCS-ART failure-detection effectiveness by approximately 50% to 58%. VPP-ART also compares favorably with the KDFC-ART algorithms (a series of enhanced ART algorithms based on the KD-tree). Our experiments also show that VPP-ART is more cost-effective than FSCS-ART and KDFC-ART. Copyright © 2021, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

arXiv

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arXiv 2020年

作者： Pian, Weiguo Wu, Yingbo Qu, Xiangmou Cai, Junpeng Kou, Ziyi Automotive Collaborative Innovation Center Chongqing University China School of Big Data & Software Engineering Chongqing University China Department of Computer Science and Engineering University of Notre Dame United States

Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhiying Xiong Qilin Fan Kai Wang Xiuhua Li Xu Zhang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University Chongqing China School of Computer Science and Technology Harbin Institute of Technology Weihai China Research Institute of Cyberspace Security Harbin Institute of Technology Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China Department of Computer Science University of Exeter Exeter UK

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.

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Research on water quality prediction based on SARIMA-LSTM: A case study of beilun estuary 21

Research on water quality prediction based on SARIMA-LSTM: A...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Rui Xiong, Qingyu Yi, Hualing Wu, Chao Ye, Jianxin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data AND Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781728120584

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability. © 2019 IEEE.

关键词： Long short-term memory

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NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation

arXiv

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arXiv 2024年

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Chen, Zhen Wang, Yi Li, Chenxin He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2024, CC BY.

关键词： Decoding

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Multi-Grained Selection and Fusion for Fine-Grained Image Representation

Multi-Grained Selection and Fusion for Fine-Grained Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianrong Jiang Hongxing Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.

关键词： Feature extraction Image retrieval Image representation Semantics Visualization Image classification Task analysis

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Heterogenous Multi-Source Fusion for Ship Trajectory Complement and Prediction with Sequence Modeling

Heterogenous Multi-Source Fusion for Ship Trajectory Complem...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Changmeng Zheng Qi Peng Xuemiao Xu Key Laboratory of Big Data and Intelligent Robot School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Effectively monitoring ships and discovering abnormal ship trajectory in time is necessary for marine traffic supervision. The basic work of discovering the ship's abnormal trajectory is to predict the ship's navigation dynamically. Previous works in ship trajectory prediction are basically concern on single-source data, for example, the AIS data. These methods ignore the relations between different sources which may improve the performance of predicting ship trajectory. We propose a neural sequence model based on heterogeneous multisource fusion for ship trajectory completion and prediction. Our method makes better utilization of AIS, GPS and ARPA radar information to predict ship trajectory precisely. We construct a dataset which contains about 8 million ship trajectory samples and the experiments demonstrate that our multi-source fusion model gains promising results.

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Diminished Mitigating Effect of Vegetation on Surface Urban Heat Islands in Large Cities

SSRN

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SSRN 2023年

作者： Deng, Xiangyi Yu, Wenping Shi, Jinan Huang, Yajun Li, Dandan He, Xuanwei Zhou, Wei Xie, Zunyi Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical Science Southwest University No. 2 Tiansheng Road Beibei District Chongqing400715 China Yibin Academy of Southwest University Sichuan Yibin644000 China Chongqing Engineering Research Center for Remote Sensing Big Data Application Southwest University Chongqing400715 China Software Engineering Institute Pass College of Chongqing Technology and Business University No. 593 Jiaotong Road Hechuan District Chongqing401520 China College of Geography and Environmental Science Henan University Kaifeng475004 China School of Environment The University of Queensland BrisbaneQLD4072 Australia

Given the increasing severity of the surface urban heat island (SUHI) phenomenon, urban residents face heightened heat stress. Consequently, mitigating the effects of SUHI becomes critically important to improve urban livability. Previous studies often overlooked the influence of city size on SUHI, focusing on the correlational between SUHI and drivers and sidelining the synergistic effects of multiple factors on SUHI. To address this issue, we introduced a novel method, "geographical convergent cross-mapping", coupled with the random forest and Shapley value theory. Our results indicated that: Globally, SUHI exhibited a decreasing trend during day and night, with significant spatiotemporal disparities, and in cities of varying sizes, the cooling capacity of vegetation for SUHI showed significant variation. Locally, in arid cities spanning 150-200 km², vegetation contributed to cooling SUHI by up to 61%, but this effect lessened in larger cities. Notably, in equatorial cities larger than 200 km², this capability dropped drastically during summer, from 49% to 7%. Long-term trends showed an increase in daytime SUHI in equatorial regions and large cities along the southeastern coasts of China and the United States. This study provides key insights for precise urban climate adaptation and sustainable planning in cities of different sizes. © 2023, The Authors. All rights reserved.

关键词： Vegetation

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Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs

arXiv

引用

arXiv 2023年

作者： Chen, Aodong Xu, Fei Han, Li Dong, Yuan Chen, Li 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 Software Engineering 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 Peng Cheng Laboratory Huazhong University of Science and Technology China

GPUs have become the defacto hardware devices for accelerating Deep Neural Network (DNN) inference workloads. However, the conventional sequential execution mode of DNN operators in mainstream deep learning frameworks cannot fully utilize GPU resources, even with the operator fusion enabled, due to the increasing complexity of model structures and a greater diversity of operators. Moreover, the inadequate operator launch order in parallelized execution scenarios can lead to GPU resource wastage and unexpected performance interference among operators. In this paper, we propose Opara, a resource- and interference-aware DNN Operator parallel scheduling framework to accelerate DNN inference on GPUs. Specifically, Opara first employs CUDA Streams and CUDA Graph to parallelize the execution of multiple operators automatically. To further expedite DNN inference, Opara leverages the resource demands of operators to judiciously adjust the operator launch order on GPUs, overlapping the execution of compute-intensive and memory-intensive operators. We implement and open source a prototype of Opara based on PyTorch in a non-intrusive manner. Extensive prototype experiments with representative DNN and Transformer-based models demonstrate that Opara outperforms the default sequential CUDA Graph in PyTorch and the state-of-the-art operator parallelism systems by up to 1.68× and 1.29×, respectively, yet with acceptable runtime overhead. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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