检索结果-内蒙古大学图书馆

Artificial Intelligence (ICAI), International Conference on

作者： Zia Ul Islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network

arXiv

引用

arXiv 2025年

作者： Yao, Siyuan Guo, Yang Yan, Yanyang Ren, Wenqi Cao, Xiaochun School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen518107 China

Transformer-based trackers have achieved promising success and become the dominant tracking paradigm due to their accuracy and efficiency. Despite the substantial progress, most of the existing approaches tackle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been greatly overlooked, which hampers trackers’ ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack utilizes a transformer encoder to perform feature interaction between template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable or not. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, making the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods. Our code is available at https://***/ManOfStory/UncTrack. Copyright © 2025, The Authors. All rights reserved.

关键词： Object tracking

来源：评论

学校读者我要写书评

暂无评论

GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition 25

Global Feature Enhancing and Fusion Framework for Strain Gau...

引用

Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

来源：评论

学校读者我要写书评

暂无评论

Dynamic knowledge graph completion through time-aware relational message passing

Dynamic knowledge graph completion through time-aware relati...

引用

International CSI computer Conference, CSICC

作者： Amirhossein Baqinejadqazvini Saedeh Tahery Saeed Farzi Department of Software Engineering Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Artificial Intelligence Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. This paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. The proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult.

关键词： Computational modeling Message passing Neural networks Knowledge graphs Time factors Standards Context modeling

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Scheme for Time Series Prediction Using LMS Filter and ISSA-based LSTM

A Novel Hybrid Scheme for Time Series Prediction Using LMS F...

引用

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

作者： Pei Heng Li Dong Xia Chen Han Yao Huang Xiao Tong Wu Hua Wei Jiang Gen Lin Ji School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Computer and Software Engineering Nanyang Institute of Technology Nanyang China Department of Computer Science Engineering Sungkyunkwan Univeristy Suwon Korea

In the era of Internet of Everything (IoE), there is an explosive growth in data volumes and the data usually with time series characteristics. Therefore, how to deal with time series data to improve prediction accuracy remains a critical issue. In this paper, an efficient hybrid scheme combining linear and nonlinear models is proposed to greatly improve the prediction performance and simplify the hyperparameter selection process. Gradient-based least mean square (GD-LMS) is introduced for adaptive linear section, while a hyperparameter-determined long short-term memory (LSTM) is used for nonlinear section. In particular, an improved sparrow search algorithm combining opposition-based learning and Cauchy variance (ISSA) is used to construct the optimal hyperparameters for the two-layer LSTM. computer simulations with the UMass weather dataset reveal that the proposed scheme considerably outperforms three benchmarking methods in terms of data prediction accuracy and robustness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Street Light Cum Garbage System Optimal Design Based on IOT

Street Light Cum Garbage System Optimal Design Based on IOT

引用

Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Rajesh G Deepa J Ezilarasan M R Syed Fiaz A S Siva Rama Lingham N J. Jagannathan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fast and Continual Knowledge Graph Embedding via Incremental LoRA

arXiv

引用

arXiv 2024年

作者： Liu, Jiajun Ke, Wenjun Wang, Peng Wang, Jiahao Gao, Jinhua Shang, Ziyu Li, Guozheng Xu, Zijie Ji, Ke Li, Yining School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Institute of Computing Technology Chinese Academy of Sciences China College of Software Engineering Southeast University China

Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating catastrophic forgetting of old knowledge but neglect efficient learning for the emergence of new knowledge. However, in real-world scenarios, knowledge graphs (KGs) are continuously growing, which brings a significant challenge to fine-tuning KGE models efficiently. To address this issue, we propose a fast CKGE framework (FastKGE), incorporating an incremental low-rank adapter (IncLoRA) mechanism to efficiently acquire new knowledge while preserving old knowledge. Specifically, to mitigate catastrophic forgetting, FastKGE isolates and allocates new knowledge to specific layers based on the fine-grained influence between old and new KGs. Subsequently, to accelerate fine-tuning, FastKGE devises an efficient IncLoRA mechanism, which embeds the specific layers into incremental low-rank adapters with fewer training parameters. Moreover, IncLoRA introduces adaptive rank allocation, which makes the LoRA aware of the importance of entities and adjusts its rank scale adaptively. We conduct experiments on four public datasets and two new datasets with a larger initial scale. Experimental results demonstrate that FastKGE can reduce training time by 34%-49% while still achieving competitive link prediction performance against state-of-the-art models on four public datasets (average MRR score of 21.0% vs. 21.1%). Meanwhile, on two newly constructed datasets, FastKGE saves 51%-68% training time and improves link prediction performance by 1.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665468190

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. © 2022 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Chebyshev Polynomial Based Emergency Conditions with Authentication Scheme for 5G-Assisted Vehicular Fog Computing

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Al-Shareeda, Mahmood A. Gaber, Tarek Alqarni, Mohammed A. Alkinani, Monagi H. Almazroey, Alaa Atallah Almazroi, Abdulwahab Ali Southern Technical University Department of Electronic Technologies Basra Technical Institute Basra61001 Iraq Engineering and Environment University Salford School of Science Manchester United Kingdom University of Jeddah College of Computer Science and Engineering of Software Engineering Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Modern Aljazeera Company Jeddah Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah Saudi Arabia

Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle-infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, a 5G automobile network emergency situations Chebyshev polynomial using fog computing and authentication is proposed. The Chebyshev chaotic mapping algorithm uses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme has four stages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. The security analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response. The proposed method cannot execute forgery, impersonation, or replay attacks. Proposed method has lower computational, communication, and storage overhead than earlier efforts. © 2004-2012 IEEE.

关键词： Fog computing

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：