检索结果-内蒙古大学图书馆

KGDR-EGs: Knowledge graph enhanced deep recommendation method for E-government services

Expert Systems with Applications 2025年 292卷

作者： Mian Wu Chunlei Fu Penghui Tang Yijia Fan Zihe Li School of Big Data and Software Engineering Chongqing University Chongqing401331 PR China

Government portals provide a wide range of services that empowers citizens and enterprises to carry out their interactions with government more conveniently and at lower cost. However, faced with the information overload resulting from a substantial number of E-government services, how to filter out the intended services from substantial E-government services and recommend them to users is challenging for government portals. We propose the Knowledge Graph Enhanced Deep Recommendation Method for E-government services (KGDR-EGs). We first obtain user feature vectors from user interaction data based on deep matrix factorization. Then we propose a general approach for constructing an E-government services knowledge graph automatically from multi-source and heterogeneous E-government services data. Based on the generated E-government service knowledge graph, we design a scoring function to embed the entity context information and integrate it with entity description information to obtain service feature vectors. Finally, the proposed algorithm combines the knowledge graph and recommendation by joint learning to predict the scores of each pair of users and services, and recommend service with higher scores to users. The experimental results on a real-world dataset indicate that KGDR-EGs is more effective than several existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

引用

arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

来源：评论

学校读者我要写书评

暂无评论

Weak Signal Detection based on Beta Divergence

Weak Signal Detection based on Beta Divergence

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Jiahao Liu Haoxuan Liu Wenqiang Pu Rui Zhou Ming-Yi You Qingjiang Shi Science and Technology on Communication Information Security Control Laboratory Jiaxing China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Software Engineering Tongji University Shanghai China

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. Information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Target detection based on improved YOLOv7-tiny

Target detection based on improved YOLOv7-tiny

引用

IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Chenye Xi Chenxi Liu Renghui Liu Hangning Liu Southampton Ocean Engineering Joint Institute Harbin Engineering University Harbin Heilongjiang Province China Big Data College Yunnan Agricultural University Kunming Yunnan Province China Software College Shanxi Agricultural University Jinzhong Shanxi Province China

As deep learning technologies continually advance, enhanced performance metrics are being realized in the realm of object detection. To address the challenge of ship detection in optically captured imagery set against intricate terrestrial and maritime backdrops, this study introduces a refined detection paradigm founded on YOLOv7-tiny. Initially, the backbone network juncture of the baseline model is supplanted by the streamlined MobileNetV3 architecture. Subsequently, a weighted Bi-directional Feature Pyramid Network (BiFPN) is integrated at the feature amalgamation stage to facilitate cross-scale coherence and weighted attribute fusion. Lastly, anchor frames optimization is achieved via the K-Means technique, and the CIOU loss function is supplanted by the EIOU loss. Evaluated on the Seaships (7000) dataset, the augmented model exhibits a detection precision of 98.3%, a enhancement of 0.2% relative to its predecessor. Concurrently, the computational and parametric overheads are curtailed to 6.9GFLOPs and 4.18M, representing reductions of 47% and 31%, respectively. Thus, the optimized YOLOv7-tiny framework offers a computationally efficient yet highly accurate solution for ship detection tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AWEsome-Cache: Dependency-Free Rule-Caching for Arbitrary Wildcard Patterns in TCAM

AWEsome-Cache: Dependency-Free Rule-Caching for Arbitrary Wi...

引用

International Conference on Network Protocols

作者： Zeyu Luan Qing Li Zutao Zhang Yong Jiang Meng Chen Yu Wang Kejun Li Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China Software College of Northeastern University Northeastern University Shenyang China Department of Big Data and Software Engineering Chongqing University Chongqing China

Ternary Content Addressable Memory (TCAM) is a specialized high-speed memory that enables fast parallel lookups for both exact-match rules and wildcard-match rules. TCAM has become a standard hardware component in software-Defined Networking (SDN) switches to implement flow tables for packet classification. However, limited TCAM storage capacity poses a significant scalability challenge for SDN to enforce fine-grained policy-based forwarding. To this end, TCAM-based rule-caching systems are proposed by combining TCAM with Random Access Memory (RAM). Specifically, TCAM caches heavy-hitting rules to capture packets from hot flows, while RAM accommodates the complete ruleset for other cache-miss packets. However, previous rule-caching systems either failed to eliminate cross-rule dependencies or restricted their applications to prefix rules only. In this work, we propose AWEsome-Cache, a unifying framework to fundamentally eliminate cross-rule dependencies for wildcard rules with arbitrary matching patterns. The rationale behind AWEsome-Cache is to concretize a minimum number of wildcard bits in the best-match rule, thereby pruning its overlapping match fields with all direct dependent rules. AWEsome-Cache also develops replacement algorithms during TCAM updates to adapt to dynamic traffic locality. Experiments with prefix and non-prefix rules show that AWEsome-Cache outperforms baselines in achieving a comparable cache-hit rate but requiring 75.9% less TCAM occupancy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques

Early Fault Detection in Rolling Element Bearings Using Sign...

引用

International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg DE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-to-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method.

关键词： Vibrations Fault diagnosis Low voltage Smart cities Rolling bearings Signal processing Motors Robots Monitoring Investment

来源：评论

学校读者我要写书评

暂无评论

Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

引用

Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

StyleAU: StyleGAN based Facial Action Unit Manipulation for Expression Editing

StyleAU: StyleGAN based Facial Action Unit Manipulation for ...

引用

IEEE International Joint Conference on Biometrics (IJCB)

作者： Yanliang Guo Xianxu Hou Feng Liu Linlin Shen Lei Wang Zhen Wang Peng Liu Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Qualcomm Technologies Inc

Facial expression editing has a wide range of applications, such as emotion detection, human-computer interaction, and social entertainment. However, existing expression editing methods either fail to allow for fine-grained editing, resulting in unnatural and unrealistic facial expressions, or generate artifacts and blurs, leading to poor image quality. In this paper, we propose a novel framework called StyleAU, which is based on StyleGAN and facial action units, to address these problems. Our framework leverages the pre-trained StyleGAN prior knowledge to enable action unit editing of the face in the StyleGAN latent space, allowing precise expression editing. In addition, we use an encoder to extract multi-scale content features to achieve high-fidelity image reconstruction. Our approach qualitatively and quantitatively outperforms competing methods for action unit manipulation and expression editing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

arXiv

引用

arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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：