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19th International Conference on Web Information Systems and applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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analysis on the causes of massive stranding of Yellow Sea green tide on Lianyungang and Rizhao coasts in 2022

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Journal of Oceanology and Limnology 2024年第3期42卷 816-830页

作者： Lingjuan WU Juan HUANG Yi DING Guiyan LIU Shuai HUANG Song GAO Chao YUAN Jiangling XU Ping WU Rui HUANG Ruobing WEN Jie MEI North China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesQingdao 266061China Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China East China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesShanghai 200136China North China Sea Ecological Center Ministry of Natural ResourcesQingdao 266033China

In 2022,Yellow Sea green tide caused by Ulva prolifera reached a historic minimum and the coastal areas of Shandong Peninsula were less ***,the largest amount of seaweed biomass has been washed ashore on Lianyungang and Rizhao coasts since *** studied the development pattern of Yellow Sea green tide in 2022,and analyzed the key environmental factors on the growth and drifting,then discussed the possible reasons that resulted in the massive stranding of green tide biomass in Lianyungang and *** show under the combined influence of the east to southeast winds and currents with shoreward anomalies,green tide drifted to the coastal waters between Shandong and Jiangsu provinces and the distribution areas located westward compared with previous years(2008–2021).Floating *** rafts from the coastal waters of Binhai and Sheyang drifted continuously into the coastal waters of Lianyungang and Rizhao,providing important supplements for Yellow Sea green *** green tide in 2022 distributed close to the coastal waters,the abundant nutrients might support their continuous high growth *** addition,the amount of rainfall around Shandong Peninsula from late June to early July were significantly higher than in previous years,which might promote the development of green tide to some extent.

关键词： Yellow Sea green tide stranding biomass development pattern environmental factor

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Robust Control For Connected Automated Vehicle Platoon With Multiple-Predecessor Following Topology Considering Communication Loss

SSRN

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SSRN 2024年

作者： Yang, Lei Sun, Zhanbo Liu, Yafei Chen, Linbin School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

The paper presents a robust control method for effectively managing uncertainties and communication loss in a connected automated vehicle (CAV) platoon under the multiple-predecessor following (MPF) topology. The proposed approach incorporates uncertainties in vehicle dynamics, such as vehicle parameters and environmental resistances, into the closed-loop platoon system to enhance the robustness of the platoon controller. The impacts of communication loss are analyzed specifically for the MPF topology, considering potential disruptions in information flow among different numbers and locations of predecessors in a CAV platoon. A novel formulation of desired spacing, suitable for the CAV platoon with MPF topology under communication loss, is then developed based on the constant time headway (CTH) policy. Furthermore, the paper derives and proves the sufficient and necessary conditions for the local stability of the proposed robust platoon controller using Kharitonov's theorem. The sufficient conditions for string stability are also discussed through frequency-domain analysis. These conditions establish the stability region for the robust controller of a CAV platoon with varying locations and numbers of unconnected predecessors. Simulation experiments are conducted to demonstrate that the stability region of the controller diminishes as the number of unconnected predecessors increases, with the greatest impact observed when the communication with the nearest connected predecessor is lost. Additionally, the control performance is affected by uncertain dynamics and the range of time headway, resulting in a significant reduction in the stability region. The findings highlight the importance of fine-tuning control parameters within the stability region guided by the derived stability conditions to ensure both local and string stability of CAV platoons. © 2024, The Authors. All rights reserved.

关键词： Topology

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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CAT: A Simple yet Effective Cross-Attention Transformer for One-Shot Object Detection

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Journal of Computer Science & technology 2024年第2期39卷 460-471页

作者：林蔚东邓玉岩高扬王宁刘凌峤张磊王鹏 School of Computer Science Northwestern Polytechnical UniversityXi’an710000China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi'an710000China School of Computer Science The University of AdelaideAdelaideSA 0115Australia

Given a query patch from a novel class,one-shot object detection aims to detect all instances of this class in a target image through the semantic similarity ***,due to the extremely limited guidance in the novel class as well as the unseen appearance difference between the query and target instances,it is difficult to appropriately exploit their semantic similarity and generalize *** mitigate this problem,we present a universal Cross-Attention Transformer(CAT)module for accurate and efficient semantic similarity comparison in one-shot object *** proposed CAT utilizes the transformer mechanism to comprehensively capture bi-directional correspondence between any paired pixels from the query and the target image,which empowers us to sufficiently exploit their semantic characteristics for accurate similarity *** addition,the proposed CAT enables feature dimensionality compression for inference speedup without performance *** experiments on three object detection datasets MS-COCO,PASCAL VOC and FSOD under the one-shot setting demonstrate the effectiveness and efficiency of our model,e.g.,it surpasses CoAE,a major baseline in this task,by 1.0%in average precision(AP)on MS-COCO and runs nearly 2.5 times faster.

关键词： one-shot object detection Transformer attention mechanism

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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IoTPass: IoT data Management System for Processing Time-series data

IoTPass: IoT Data Management System for Processing Time-seri...

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International Conference on Advanced Cloud and big data (CBD)

作者： Zehua Nie Can Su Yichen Mao Kaigui Bian School of Computer Science Peking University National Engineering Laboratory for Big Data Analysis and Application

ISBN: (纸本)9798350309720

With the increased popularity of IoT devices and applications, the amount of time-series IoT data has exploded and the need for efficient processing and analysis of time-series IoT data has also emerged. In this paper, we present, IoTPass, an IoT data management system for processing time-series data. IoTPass employs the microservice architecture to facilitate the development and deployment of functions, and adopts the Object model to structure the data and facilitate data analysis and visualization afterwards. IoTPass also adopt Revert-RPC (Remote Procedure Call) communication technique to avoid the performance degradation problem in service call processes and supports both synchronous and asynchronous service invocation modes. Experimental results demonstrate that IoTPass system meets the requirement of processing time-series IoT data in real time, which shows the effectiveness and scalability of the system.

关键词： Degradation data analysis databases Scalability Microservice architectures data visualization data models

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PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation

arXiv

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arXiv 2024年

作者： Guo, Yuhan Shao, Hanning Liu, Can Xu, Kai Yuan, Xiaoru Key Laboratory of Machine Perception Ministry of Education School of AI Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China University of Nottingham United Kingdom

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and it often requires many trials and errors to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what have been explored and how the prompt changes impact the output image, yet little research attention has been paid to the visual analysis of such process to support users. We propose the Image Variant Graph, a novel visual representation designed to support comparing prompt-image pairs and exploring the editing history. The Image Variant Graph models prompt differences as edges between corresponding images and presents the distances between images through projection. Based on the graph, we developed the PrompTHis system through co-design with artists. Besides Image Variant Graph, PrompTHis also incorporates a detailed prompt-image history and a navigation mini-map. Based on the review and analysis of the prompting history, users can better understand the impact of prompt changes and have a more effective control of image generation. A quantitative user study with eleven amateur participants and qualitative interviews with five professionals and one amateur user were conducted to evaluate the effectiveness of PrompTHis. The results demonstrate PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process. © 2024, CC BY.

关键词： Image analysis

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of Computer Science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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