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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Zhu, Tingting Wu, Huarui Chen, Cheng Zhu, Huaji School of Computer Science and Communication Engineering JiangSu University ZhenJiang China Big Data and Intelligence Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China Industrialization Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China

ISBN: (纸本)9781450397148

Under the background of the in-depth implementation of the Rural Revitalization Strategy, the conditions of public service facilities for the elderly have improved significantly in rural areas. However, the problem is that the asymmetry between the intelligent management and construction of the corresponding elderly care services is more prominent. It leads to the lagging management of elderly care services, poor service information, and other problems. In order to solve the above series of problems, we propose an IoT-based rural community elderly care service management system, which can effectively assist community elderly care. The designs of structure, function, and key technologies are discussed in this article. To provide more timely and accurate services for the elderly, the system can realize real-time data collection of behavior, vital signs, and the surrounding environment of the elderly, monitor and warn of abnormal situations. © 2022 Association for Computing Machinery.

关键词： Rural areas

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Impact of DG on short circuit current detected by protection 5

Impact of DG on short circuit current detected by protection

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5th International Conference on Energy engineering and Environmental Protection, EEEP 2020

作者： Gong, Jing School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China National Virtual Simulation Experimental Center for Smart City Education Beijing100044 China

DG (distributed generation) access causes the change of current detected by the protection, and then affects the sensitivity of the original protection. In order to solve the problem, the influences of DG capacity, DG access location and short-circuit location on short circuit current are analysed in this paper. When the DG capacity and grid-connected position remain unchanged, the unit current method is used to solve the short-circuit current by solving the transfer impedance. The relationship between the change of short circuit position and the impact degree of DG is discussed in detail, and the calculation formula of extreme point is derived. When the DG capacity and short circuit location remain unchanged, the impact of DG grid-connected position on the upstream and downstream current is analysed. The characteristic curve of the impact of DG position change on protection current detection is given, and the impact of DG grid-connected position on protection sensitivity is explained clearly. The conclusion provides an important reference for the selection of DG grid-connected position and the calculation of relay protection in distribution network with DG. © Published under licence by IOP Publishing Ltd.

关键词： Timing circuits

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ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Accurate and Stable Multi-Modal Landmarks in Structural Environments

ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Acc...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xingjian Luo Chenglin Pang Xuankang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In complex indoor structural scenes such as shopping centers and malls, camera pose estimation using pure point features is easy to fail due to the difficulty in extracting sufficient and stable point features from weak textures or dynamic environments. Recent works have attempted to address these challenges by introducing line features. However, the addition of line features increases the number of parameters and landmarks for BA (Bundle Adjustment), leading to efficiency reduction. This is a common issue in multi-modal SLAM (Simultaneous Localization And Mapping). To address this issue, this paper proposes a novel visual-depth-inertial odometry (ASML-VDIO) framework by combining RGB-D and IMU sensors. To improve the efficiency of BA, the proposed landmark classification method classifies 3D landmarks into accurate landmarks and other landmarks based on spatial consistency verification and depth range limitation. Then, accurate landmarks are fixed, and only other landmarks are optimized in the optimization of BA. Furthermore, to remove line features extracted from dynamic objects (pedestrian, shopping-car, etc), we propose a dynamic line removal method that combines geometric constraints and motion constraints of line features. Finally, the method is evaluated on public and author-collected datasets, showing competitive accuracy and robustness in complex indoor structural scenes while 71% speedup on optimization thread with same constraints.

关键词： Accuracy Simultaneous localization and mapping Dynamics Pose estimation Feature extraction Cameras Robustness Sensors Odometry Optimization

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Adaptive-Propagating Heterophilous Graph Convolutional Network

SSRN

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SSRN 2024年

作者： Huang, Yang Pi, Yueyang Shi, Yiqing Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China

Graph convolutional network have significant advantages in tasks dealing with graph-structured data, but most existing approaches based on graph convolutional network usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real graph-structured data. Due to the propagation mechanism of graph convolutional network, it is usually difficult to avoid the aggregation of interference information of nodes from different classes, leading to the inclusion of noise and irrelevant information in the results, thus affecting the performance of the model. We propose a new framework to address this issue, which can extend graph convolutional network to heterophilous graph-structured data. The proposed method has two main parts: Firstly, from the perspective of feature space, the heterophily of the graph-structured data is modeled so that the model can adaptively change the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information on the model. Secondly, the implicit node interaction information is captured through the feature space, which is then fused with the original interaction information to aggregate sufficient intra-class information. Extensive experiments on real-world datasets demonstrate the competitiveness of our approach among current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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Neural Dialogue Model with Retrieval Attention for Personalized Response Generation

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Computers, Materials & Continua 2020年第1期62卷 113-122页

作者： Cong Xu Zhenqi Sun Qi Jia Dezheng Zhang Yonghong Xie Alan Yang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology BeijingBeijing100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Amphenol AssembleTech HoustonTX 77070US

With the success of new speech-based human-computer interfaces,there is a great need for effective and friendly dialogue agents that can communicate with people naturally and ***,the lack of personality and consistency is one of critical problems in neural dialogue *** this paper,we aim to generate consistent response with fixed profile and background information for building a realistic dialogue *** on the encoder-decoder model,we propose a retrieval mechanism to deliver natural and fluent response with proper information from a profile ***,in order to improve the efficiency of training the dataset related to profile information,we adopt a method of pre-training and adjustment for general dataset and profile *** model is trained by social dialogue data from *** to both automatic and human evaluation metrics,the proposed model significantly outperforms standard encoder-decoder model and other improved models on providing the correct profile and high-quality responses.

关键词： Dialogue system LSTM encoder-decoder model attention mechanism

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Advances in technologies for 3D genomics research

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Science China(Life Sciences) 2020年第6期63卷 811-824页

作者： Yan Zhang Guoliang Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big DataCollege of InformaticsHuazhong Agricultural UniversityWuhan 430070China

The spatial structure of the orderly organized chromatin in the nucleus has important roles in maintaining normal cell function and in regulation of gene expression, and the high-throughput Hi-C and Ch IA-PET methods have been widely used in various biological studies for determining potential spatial genome structures and their functions. However, there are still great difficulties and challenges in three-dimensional(3D) genomics research. More efficient, economical, and unbiased approaches to studying 3D genomics need to be developed for more widespread and easier applications. Here, we review the most recent studies on new 3D genomics research technologies, such as improvements of the traditional Hi-C and Ch IA-PET methods, new approaches based on non-proximal-ligation strategies, and imaging-based methods improved in recent years. Especially, we review the CRISPR-based methods for functional validations in 3D genomics, which could be the forthcoming directions. We hope this review can show some insights into the potential improvements for future 3D genomics.

关键词： 3D genomics Hi-C Ch IA-PET FISH CRISPR

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Object Attribute Matters in Visual Question Answering

arXiv

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arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation

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Identifying data Breaches in Dark Web through Prompt Active Learning

Identifying Data Breaches in Dark Web through Prompt Active ...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Hui Xiang Yifan Wu Xuebin Wang Jinqiao Shi Can Zhao Jiapeng Zhao School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Frequent data breaches have attracted people’s attention. The dark web forums have become the crucial platform for data breach transactions. While some forums are well-organized with distinct sections, such as a dedicated area for leaks where most threads are related to data breaches, others lack clear segmentation, like Fear Forum. Hackers conduct a variety of discussions there so it is filled with tons of non-data breach information and building a data breach dataset for a classifier requires huge human resources. The content of these threads is diverse and complex, spanning various fields, and containing specific terminology in the dark web. Identifying data breach threads in these forums is extremely challenging for the model. With the influx of new threads every day the static model trained on old data may not perform well when processing new data, which also brings serious difficulty to identification. To address these challenges, we first design Prompt-based Active Learning (PAL) to guide the model to identify through prompts and select high-quality samples for annotation. Then we propose a system that combines PAL and DarkBERT. Our system can not only greatly reduce annotation costs but also continuously learn and update itself with the new informative data selected by PAL. Through rich experiments on a data breach dataset, we demonstrate that our system effectively identifies breach threads and PAL significantly outperforms other baselines. We also search for the optimal prompt templates to improve system performance.

关键词： Dark Web Costs Annotations Terminology Instruction sets System performance Active learning Semantics data breach data models

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Corrigendum to "ABUS-Net: Graph convolutional network with multi-scale features for breast cancer diagnosis using automated breast ultrasound" [Expert Syst. Appl. (2025) 273 126978] (Expert Systems With Applications (2025) 273, (S0957417425006001), (10.1016/***.2025.126978))

引用

Expert Systems with Applications 2025年 280卷

作者： Wang, Changyan Guo, Yuqing Chen, Haobo Guo, Qihui He, Haihao Chen, Lin Zhang, Qi Lab School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Institute of Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Department of Ultrasound Huadong Hospital Fudan University Shanghai China

The authors regret that the Acknowledgments information was incomplete. The correct statement of the Acknowledgments section should read: "This work was supported by the National Natural Science Foundation of China [62071285], Science and Technology Commission of Shanghai Municipality [22Y11911600], the Eastern Scholars Program from Shanghai Municipal Education Commission, and the Shanghai Technical Service Center of Science and engineering Computing, Shanghai University." The authors would like to apologise for any inconvenience caused. © 2025 Elsevier Ltd

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Topological GCN Guided Improved Conformer for Detection of Hip Landmarks from Ultrasound Images

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Huang, Tianxiang Shi, Jing Jin, Ge Li, Juncheng Wang, Jun Wang, Qian Du, Jun Shi, Jun Shanghai University Key Laboratory of Specialty Fiber Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai200444 China Shanghai Jiao Tong University School of Medicine Imaging Diagnosis Center Shanghai Children's Medical Center Shanghai200127 China

The B-mode ultrasound based computeraided diagnosis (CAD) has shown its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants within 6 months. Hip landmark detection is a feasible way for the CAD of DDH according to the Graf's method. However, existing landmark detection algorithms mainly focus on designing special models to capture the features from hip ultrasound images, but generally ignore the important spatial relations among different landmarks. To this end, a novel weakly supervised learning-based algorithm, the Topological Graph Convolutional Network (TGCN) guided Improved Conformer (TGCN-ICF), is proposed for detecting landmarks from hip ultrasound images. The TGCN-ICF includes two subnetworks: an Improved Conformer (ICF) subnetwork to generate heatmaps and constraint vectors from ultrasound images, and a TGCN subnetwork to additionally explore topological relations among hip landmarks with the guidance of class labels for further refining and improving the detection accuracy. Moreover, a new Mutual Modulation Fusion (MMF) module is developed to fully exchange and fuse the extracted feature information from the convolutional neural network (CNN) and Transformer branches in ICF. Meanwhile, a novel Mutual Supervision Constraint (MSC) strategy is designed to provide a constraint for detection of each hip landmark. The experimental results on two realworld DDH datasets demonstrate that the TGCN-ICF outperforms all the compared algorithms, suggesting its potential applications. The source code is publicly available on https://***/Tianxiang-Huang/TGCN-ICF. © 2013 IEEE.

关键词： Ultrasonic imaging

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