检索结果-内蒙古大学图书馆

第41届中国控制会议

作者： Changyou Ma Aimin Wu Wenzhuo Ma Ke Chen Yun Liu Xiaoning Liang Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University College of Big Data & Intelligent Engineering Chongqing College of International Business and Economics College of Artificial Intelligence Southwest University XLY Salvationdata Technology INC.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of the Internet,malicious web pages including phishing,spamming and malware are gradually increasing,resulting in endangering network *** address this challenging issue,a novel malicious URL recognition method based on multi-feature fusion and machine learning is *** specifically,three classic features(URL,HTML and JavaScript code features) and two presented effective features(HTTP request and Text features) are first extracted from URL ***,several machine learning models are adopted to train binary classification model with the selected discriminant ***,we use ten-fold cross-validation accuracy to evaluate the performance of different classification *** results verify that the selected features are conductive for malicious URLs detection and the random forest model trained using the presented hybrid feature set performs better than other classification techniques.

关键词： Malicious URL detection multi-feature fusion machine learning features

来源：评论

学校读者我要写书评

暂无评论

Metalenses phase characterization by multidistance phase retrieval

引用

Light(Science & Applications) 2024年第9期13卷 1898-1907页

作者： Bowen Liu Jialuo Cheng Maoxiong Zhao Jin Yao Xiaoyuan Liu Shaohu Chen Lei Shi Din Ping Tsai Zihan Geng Mu Ku Chen State Key Laboratory of Surface Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and Department of PhysicsFudan University200433 ShanghaiChina Department of Electrical Engineering City University of Hong KongKowloonHong Kong SARChina State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongKowloonHong Kong SARChina Institute for Nanoelectronic Devices and Quantum Computing Fudan University200438 ShanghaiChina Collaborative Innovation Center of Advanced Microstructures Nanjing University210093 NanjingJiangsuChina Shanghai Research Center for Quantum Sciences 201315 ShanghaiChina Centre for Biosystems Neuroscienceand NanotechnologyCity University of Hong KongKowloonHong Kong SARChina Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua University518071 ShenzhenGuangdongChina

Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential *** further optimize the performance of metalens,it is necessary to characterize the phase modulation of the *** this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and *** findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently *** system enables the analysis of focal length and aberration by utilizing the computed phase *** extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the ***,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working *** system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.

关键词： phase system distribution

来源：评论

学校读者我要写书评

暂无评论

Cross-Store Next-Basket Recommendation 24

Cross-Store Next-Basket Recommendation

引用

24th IEEE International Conference on data Mining, ICDM 2024

作者： Ma, Liang-Chen Li, Ya Mai, Zi-Feng Liang, Fei-Yao Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model. © 2024 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

引用

28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Joint Location and Power Optimization for Secure Communications in Multi-UAVs Enabled Cellular Networks

Joint Location and Power Optimization for Secure Communicati...

引用

2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Deng, Fei Qian, Kaiguo Shen, Shikai Hong, Sunyan Yang, Bin Li, Xuanxuan He, Jun College of Information Engineering Kunming University Kunming650214 China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan Kunming650214 China School of Computer and Information Engineering Chuzhou University Chuzhou239000 China Kunming University Office of Science and Technology Kunming650214 China

ISBN: (纸本)9798350327380

This paper investigates joint location and power optimization for secure communication in a multi-unmanned acrial vehicles (U A V s) enabled cellular network consisting of interference link, eavesdropping link, cellular link and D2D link. To this end, we first derive the expression of secure rate. Then, we formulate the maximization of secure rate as a constrained optimization problem. Finally, we solve it using the method of exhaustion. © 2023 IEEE.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Review on application progress of federated learning model and security hazard protection

引用

Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

来源：评论

学校读者我要写书评

暂无评论

Don’t Ignore the Drive of Curiosity: Rethinking Subtleties Between Universality of Commonsense knowledge and Excellence of Large Language Models

引用

SN Computer Science 2024年第6期5卷 1-14页

作者： Wang, Chao Chen, Tao Liu, Jingping School of Future Technology Shanghai University Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Commonsense reasoning is one of the abilities necessary for artificial intelligence to be as intelligent as humans. However, how to make AI understand commonsense has been a problem that has plagued artificial intelligence for more than 60 years. Existing efforts focus more on the means of knowledge acquisition and strive to enrich the capacity of commonsense knowledge (CSK) bases and dimensions of CSK through advanced methods. Unfortunately, this exuberance has obscured a general consideration of CSK, such as how to follow human habits to obtain the most representative knowledge we need to understand the world. In this paper, this representative knowledge is referred to as core CSK. The influence of core CSK is extensive, and it constitutes almost the fundamental element of human life and the most fundamental cognition of the world. Harnessing human curiosity to find solutions to the above problems is an effective and straightforward route. Specifically, we focus on a special corpus to mine core CSK, namely, why-questions. For example, we can harvest “the sky is blue” from “why is the sky blue?”. To this end, we propose a novel method to extract CSK from why-questions, which mainly consist of two modules. The first is a question classification module used to determine whether a question contains CSK. In this module, we propose a classifier based on a one-sided bootstrapping method and design several informative features for the classifier. The second is a crowdsourcing module used to improve the quality of the extracted commonsense. We conduct extensive experiments, and the experimental results show that our method effectively mines CSK from question corpora. Furthermore, statistical analysis demonstrates the feasibility of this curiosity-driven approach, implying that we provide a basic idea for collecting core CSK. Remarkably, today’s outstanding large language models do not have such simple knowledge summarization capabilities, demonstrating the barrier between

关键词： Classification Core Commonsense knowledge Crowdsourcing Curiosity Psychologically

来源：评论

学校读者我要写书评

暂无评论

Social inequity of park accessibility in Taiyuan:highlighting the unfair layout of parks in second-tier cities of China and the relative role of contributors

引用

地球空间信息科学学报（英文版） 2024年第4期27卷 1118-1140页

作者： Jing He Fu Ren Jiaxin Dong Huihui Zhang Wenhao Yan Jiping Liu School of Resource and Environmental Sciences Wuhan UniversityWuhanChina School of Resource and Environmental Sciences Wuhan UniversityWuhanChina Key Laboratory of Geographic Information Systems Ministry of EducationWuhan UniversityWuhanChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Guangdong Provincial Department of Natural Resources Land and Resources Technology CenterGuangzhouChina Research Center of Geospatial Big Data Application Chinese Academy of Surveying and MappingBeijingChina

The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a ***,the social equity of parks warrants further *** paper proposes a fine-grained comprehensive evaluation framework that combines geographic accessibility models,geo-statistical analysis,and machine learning algorithms to explore social inequity in Taiyuan,*** this framework,gini coefficient and lorentz curve express spatial equality,accessibility shows spatial equity,and ridge regression model handles the interdependence of variables with different dimensions to quantify the relative effects of local participants on changes in park *** this basis,the imbalance between vulnerable groups and park supply is analyzed to further understand the core concept of social *** highlight serious spatial inequality in all three types of parks allocation of six urban areas,especially in commu-nity *** actual access level of people to parks is also stratified by their demographic and socioeconomic characteristics,revealing the social inequity in access to *** distribution is indeed not conducive to some social vulnerable groups,whose contradiction between supply and demand is highly prominent in urban-rural junctions and new urban *** paper also confirms the unfair layout of public facilities can be observed in second-tier cities of China by highlighting the social inequity of parks in *** findings of this work have profound implications for urban planning and sustainable development.

关键词： Parks social inequity spatial equality accessibility social vulnerable groups Taiyuan

来源：评论

学校读者我要写书评

暂无评论

Learning Nonconvex Tensor Representation with Generalized Reweighted Sparse Regularization for Hyperspectral Anomaly Detection

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14718-14737页

作者： Yang, Jing Zhao, Jianbin Chen, Lu Geng, Haijun Zhang, Pei Shanxi University School of Automation and Software Engineering Taiyuan 030031 China Shanxi University Institute of Big Data Science and Industry Key Laboratory of Evolutionary Science Intelligence of Shanxi Province Taiyuan 030006 China Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan 716000 China

Tensor representation can sensitively perceive the inherent prior structure of hyperspectral images, showing broad prospects in hyperspectral anomaly detection (HAD). However, current models are still limited in effectively representing background and anomaly components therein. First, the depiction of background priors relies on multiple separate regularizers with lenient convex surrogate measures, leading to suboptimal background representation. Second, treating all entries in anomaly component equally diminishes the ability to identify anomaly targets. To address these challenges, we propose a novel tensor representation model termed as NTCTV-GRS, which is characterized by two innovative regularizers. Particularly, for background component, a nonconvex tensor correlated total variation (NTCTV) regularizer is developed. It effectively integrates low-rankness and smoothness priors of the background in one unified regularizer. More importantly, it fully considers the physical difference of singular values through a rigorous nonconvex surrogate, ensuring robust background representation. For anomaly component, a generalized reweighted sparse (GRS) regularizer is formulated by assigning the optimal weight distribution for all entries, which more effectively detects and pinpoints anomaly targets. Comparative experiments on five public datasets are extensively conducted and verify the superiority of the proposed method over existing methods in terms of diverse evaluation metrics. © 2008-2012 IEEE.

关键词： generalized reweighted sparse regularization Hyperspectral anomaly detection nonconvex optimization nonconvex tensor correlated total variation tensor representation

来源：评论

学校读者我要写书评

暂无评论

A Review-aware Graph Contrastive Learning Framework for Recommendation

arXiv

引用

arXiv 2022年

作者： Shuai, Jie Sun, Peijie Zhang, Kun Hong, Richang Li, Yong Wu, Le Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University China

Most modern recommender systems predict users’ preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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：