检索结果-内蒙古大学图书馆

Sensors, Electronics and computer Engineering (ICSECE), IEEE International Conference on

作者： Rui Qiao Xin Wang Xiaoben Zhong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Yunnan College of Business Management Anning China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness.

关键词： Law Collaborative filtering Market research User experience Sensor systems Servers Security Reliability Springs Intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

Improving Visual Grounding with Multi-Modal Interaction and Auto-Regressive Vertex Generation

SSRN

引用

SSRN 2024年

作者： Qin, Xiaofei Li, Fan He, Changxiang Wang, Lin Zhang, Xuedian School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Science University of Shanghai for Science and Technology Shanghai200093 China Shanghai Key Laboratory of Modern Optical System Shanghai200093 China Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education Shanghai200093 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201210 China

We propose a concise and consistent network focusing on multi-task learning of Referring Expression Comprehension (REC) and Segmentation (RES) within Visual grounding (VG). To simplify the model architecture and achieve parameter sharing, we reconstruct the Visual grounding task as a floating-point coordinate generation problem based on both image and text inputs. Consequently, rather than separately predicting bounding boxes and pixel-level segmentation masks, we represent them uniformly as a sequence of coordinate tokens and output two corner points of bounding boxes and polygon vertices autoregressively. To improve the accuracy of point generation, we introduce a regression-based decoder. Inspired by bilinear interpolation, this decoder can directly predict precise floating-point coordinates, thus avoiding quantization errors. Additionally, we devise a Multi-Modal Interaction Fusion (M2IF) to address the imbalance between visual and language features in the model. This module focuses visual information on regions relevant to textual descriptions while suppressing the influence of irrelevant areas. Based on our model, Visual grounding is realized through a unified network structure. Experiments conducted on three benchmark datasets (RefCOCO, RefCOCO+, RefCOCOg) demonstrate that the proposed unified network outperforms or is on par with many existing task-customized models. Codes are available at https://***/LFUSST/MMI-VG. © 2024, The Authors. All rights reserved.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

引用

IEEE Transactions on Affective computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

来源：评论

学校读者我要写书评

暂无评论

DUNE: Improving Accuracy for Sketch-INT Network Measurement systems

arXiv

引用

arXiv 2022年

作者： Wei, Zhongxiang Tian, Ye Chen, Wei Gu, Liyuan Zhang, Xinming The Anhui Key Laboratory of High Performance Computing School of Computer Science and Technology University of Science and Technology of China Anui Hefei230026 China

In-Band Network Telemetry (INT) and sketch algorithms are two representative methodologies for measuring network traffics in real time. To combine sketch with INT and to keep their advantages, the "reconstructing sketch at end-host" approach, which uses INT to send small pieces of switch sketch (i.e., sketchlet) to end-host for reconstructing an identical sketch, is a promising direction. However, we reveal that the naive column sketchlet is not efficient, and inaccuracies arise because of the invalid and stale measurement data in the end-host reconstructed sketch. In this paper, we present an innovative sketch-INT measurement system named DUNE. DUNE is lightweight by following the "reconstructing the sketch at end-host" approach, and to improve the measurement accuracy, we make two innovations: First, we design a novel sketchlet named scatter sketchlet that is more efficient in transferring measurement data by allowing a switch to select individual sketch buckets to add to sketchlet;Second, we develop data structures for tracing "freshness" of the sketch buckets, and present algorithms for smartly selecting buckets that contain valuable measurement data to send to end-host. We theoretically prove that our proposed methods are superior comparing with the existing solution, and implement a prototype on the commodity Barefoot Tofino switches. We conduct extensive experiments on DUNE, and the evaluation results show that the system considerably improves measurement accuracies at negligible costs. In particular, with less than 0.36% loss of the packet forwarding rate, DUNE avoids up to 60% errors in the end-host reconstructed sketch. Copyright © 2022, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Region-aware neural graph collaborative filtering for personalized recommendation

引用

International Journal of Digital Earth 2022年第1期15卷 1446-1462页

作者： Shengwen Li Renyao Chen Chenpeng Sun Hong Yao Xuyang Cheng Zhuoru Li Tailong Li Xiaojun Kang State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China School of Future Technology China University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information System China University of GeosciencesWuhanPeople’s Republic of China

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information ***,personalized recommendation models based on graph structure have advanced greatly in predicting user ***,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized *** study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user *** approach first characterizes the relationships between items and users with a user-item-region ***,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and ***,the model fuses region and item vectors to infer user *** on real-world dataset results show that introducing region entities improves the accuracy of personalized *** study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

关键词： Collaborative filtering neural graph collaborative filtering geographical region personalized recommendation graph convolution networks

来源：评论

学校读者我要写书评

暂无评论

Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments

Achieving Privacy-Preserving and Scalable Graph Neural Netwo...

引用

IEEE International Conference on Communications (ICC)

作者： Yanli Yuan Dian Lei Chuan Zhang Ximeng Liu Zehui Xiong Liehuang Zhu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Guangdong Provincial Key Laboratory Novel Security Intelligence Technologies Guangdong China College of Mathematics and Computer Science Fuzhou University Fuzhou China School of Information Systems Singapore Management University Singapore Information System and Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. However, given the increasing privacy concerns about GNNs models and graph data, as well as the need to quickly generate embeddings for new nodes in real-world applications, a critical issue in this emerging paradigm is to ensure the security and scalability of GNN predictions. In this paper, we propose a privacy-preserving and scalable GNN prediction scheme, named PS-GNN, to address the privacy issues in cloud environments. Specifically, PS-GNN utilizes a customized array structure to store graph data and employs secret sharing to preserve the confidentiality of both the GNN model and graph data. Besides, the scalability of PS-GNN is achieved by aggregating feature information from local node neighborhoods in parallel. Through a detailed analysis, we demonstrate the security of PS-GNN. Extensive experiments on real-world datasets demonstrate that PS-GNN outperforms existing schemes in terms of computational and communication overhead, and reaches state-of-the-art performance on large graphs.

关键词： Privacy Data privacy Scalability Computational modeling Predictive models Graph neural networks Data models

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Feature Selection based on Mutual Information

Privacy-Preserving Feature Selection based on Mutual Informa...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Ningning Wu Hongwei Li Hanxiao Chen Xilin Zhang Yingzhe Wang School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi China

In the context of collaborative data analysis or machine learning tasks (such as biomedical image segmentation for tumor region definition) involving multiple parties, secure feature selection protocols enable the parties to jointly select the most informative features while preserving their privacy from being exposed to others. However, current secure feature selection methods have several limitations: (1) they leak intermediate results during feature selection raises serious privacy concerns; (2)they cannot cover various scenarios comprehensively. To this end, we present a secure feature selection framework based on mutual information, which improves the privacy-preserving data preprocessing work. Specifically, the division protocol performs low-complexity interactive computations, reducing the time cost to $578^{th}$ of the original; besides, our logarithm protocol converts shared values through Beaver's triple, and increases the efficiency by up to 118 times. We conduct extensive experimental evaluations on diverse real-world datasets, and the results demonstrate that our framework achieves up to 28.5% accuracy improve-ment, thus effectively enhancing the privacy protection work of machine learning data preprocessing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Object Tracking by Jointly Utilizing Template Updating and Relocation Mechanisms⋆

SSRN

引用

SSRN 2023年

作者： Wen, Jia Ren, Kejun School of Information Science and Engineering Yanshan University Qinhuangdao066099 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province China

The Siamese network-based tracker has achieved competitive performance in the field of single target tracking because of its excellent tracking speed and tracking accuracy. When faced with target deformations, most Siamese trackers rely on fixed template features from the initial frame, severely constraining the tracker’s performance. To mitigate this situation, we introduce a novel tracking framework(SiamTAR) that allows adaptive template updates based on past tracking information. In addition, we design a feature refinement module which can capture the target information more accurately at the full scale on the search image through an integrated attention mechanism, thus enabling the tracker to pay less attention to redundant information. Notably, we introduce a relocalization mechanism to solve the problems of random drift of the tracking box and inaccurate scale estimation in online tracking. Extensive experiments on GOT-10K, UAV123, OTB100 and DTB70 datasets show that the tracker achieves good tracking results and real-time performance. © 2023, The Authors. All rights reserved.

关键词： Target tracking

来源：评论

学校读者我要写书评

暂无评论

DynamicNet: Efficient Federated Learning for Mobile Edge computing With Dynamic Privacy Budget and Aggregation Weights

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Li, Zheng Duan, Mingxing Yu, Siyang Yang, Wenjing School of Information Science and Engineering Hunan University Changsha China School of Computer Science and Electronic Engineering Hunan University Hunan China Hunan University of Finance and Economics Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

Mobile Edge computing (MEC) has been successfully applied to smart homes, mobile devices, and other electronic products, offering reduced communication latency and alleviating pressure on cloud services. The integration of Federated Learning (FL) and MEC provides customized services for enhancing user experiences. However, there are still gaps in combining these technologies: FL is susceptible to membership inference attacks, risking user privacy;MEC faces limitations in energy and computational resources, restricting FL performance. In order to address these issues, we propose a novel approach, named DynamicNet, which incorporates Privacy Budget Regulation (PBR) and Dynamic Aggregation Weights (DAW). PBR addresses privacy leaks in edge devices by assessing local model privacy, dynamically adjusting privacy budget for subsequent use, ensuring desired level of privacy protection while minimizing noise impact on global model. DAW tackles MEC-related performance limitations by generating synthetic data, computing optimal weights for edge devices using this synthetic data, and aggregating parameters accordingly. Experimental results on two mainstream datasets demonstrate: 1) PBR preserves participant data privacy while enhancing global model convergence speed and accuracy;2) DAW adapts well to heterogeneous federated environments, ensuring high global model accuracy even when some edge device datasets are of inferior quality. IEEE

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-0534

引用

Research in Astronomy and Astrophysics 2023年第10期23卷 255-266页

作者： Chen-Chen Miao Victoria Blackmon Wei-Wei Zhu Dong-Zi Li Ming-Yu Ge Xiao-Peng You Maura McLaughlin Di Li Na Wang Pei Wang Jia-Rui Niu M.Cruces Jian-Ping Yuan Jun-Tao Bai D.J.Champion Yu-Tong Chen Ming-Min Chi P.C.C.Freire Yi Feng Zhen-Ye Gan M.Kramer Fei-Fei Kou Yu-Xi Li Xue-Li Miao Ling-Qi Meng Chen-Hui Niu Sheng-Nan Sun Zhong-Yi Sun H.M.Tedila Shuang-Qiang Wang Qing-Dong Wu Jing-Bo Wang Zhi-Gang Wen Shen Wang Ya-Biao Wang Cheng-Jie Wang Meng-Yao Xue You-Ling Yue Mao Yuan Ju-Mei Yao Wen-Ming Yan Ru-Shuang Zhao Lei Zhang De Zhao National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China West Virginia University 1500 University AveMorgantownWV 26506USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Cahill Center for Astronomy and Astrophysics California Institute of Technology1216 E California BoulevardPasadenaCA 91125USA Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China Max-Planck Institut für Radioastronomie Auf dem Hügel 69D-53121 BonnGermany School of Computer Science Fudan UniversityShanghai 200433China Tencent Youtu Lab Shanghai 200233China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Institute of Optoelectronic Technology Lishui UniversityLishui 323000China Guizhou Normal University Guiyang 550001China

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M⊙. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar(MSP) using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), the Green Bank Telescope(GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarization angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5 ×10^(3)km(assuming an orbital inclination angle of 90°) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere.

关键词： (stars:)pulsars:general (stars:)pulsars:individual(J1720-0534) (stars:)binaries:eclipsing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：