检索结果-内蒙古大学图书馆

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

Full-scale field-free spin-orbit switching of the CoPt layer grown on vicinal substrates

arXiv

引用

arXiv 2023年

作者： Yongming, Luo Mengfan, Liang Zhongshu, Feng Haoran, Chen Nan, Jiang Jianhui, Chen Mingyue, Yuan Jingcang, Zhang Yifeng, Cheng Lu, Sun Ru, Bai Xiaohe, Miao Ningning, Wang Yizheng, Wu Renchao, Che Center for Integrated Spintronic Devices Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Research Center for Intelligent Computing Platforms Zhejiang laboratory Hangzhou311121 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Instrumentation and Service Center for Physical Sciences Westlake University Hangzhou310024 China

A simple, reliable and field-free spin orbit torque (SOT)-induced magnetization switching is a key ingredient for the development of the electrical controllable spintronic devices. Recently, the SOT induced deterministic switching of the CoPt single layer has attracts a lot of interests, as it could simplifies the structure and add new flexibility in the design of SOT devices, compared with the Ferromagnet/Heavy metal bilayer counterparts. Unfortunately, under the field-free switching strategies used nowadays, the switching of the CoPt layer is often partial, which sets a major obstacle for the practical applications. In this study, by growing the CoPt on vicinal substrates, we could achieve the full-scale (100% switching ratio) field-free switching of the CoPt layer. We demonstrate that when grown on vicinal substrates, the magnetic easy axis of the CoPt could be tilted from the normal direction of the film plane;the strength of Dzyaloshinskii–Moriya interaction (DMI) would be also be tuned as well. Micromagnetic simulation further reveal that the field-free switching stems from tilted magnetic anisotropy induced by the vicinal substrate, while the enhancement of DMI help reducing the critical switching current. In addition, we also found that the vicinal substrates could also enhance the SOT efficiency. With such simple structure, full-scale switching, tunable DMI and SOT efficiency, our results provide a new knob for the design SOT-MRAM and future spintronic devices. © 2023, CC BY.

关键词： Binary alloys

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based lightweight trusted data interaction scheme for cross-domain IIoT

引用

Digital Communications and Networks 2024年

作者： Fengqi Li Yingjie Zhao Kaiyang Zhang Hui Xu Yanjuan Wang Deguang Wang School of Railway Intelligent Engineering Dalian Jiaotong University Dalian 116028 China Dalian Key Laboratory of Blockchain Technology and Application Dalian 116028 China Yunnan Key Laboratory of Service Computing Kunming 650221 China

To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a solution, current techniques still suffer from issues related to efficiency, security, and privacy. Our research aims to address these challenges by proposing a lightweight, trusted data interaction scheme based on blockchain, which reduces redundant interactions among entities. We enhance the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm to support lightweight distributed consensus in large-scale IIoT scenarios. Introducing a composite digital signature algorithm and incorporating veto power minimizes resource consumption and eliminates ineffective consensus operations. The experimental results show that, compared with PBFT, our scheme reduces latency by 27.2%, thereby improving communication efficiency and resource utilization. Furthermore, we develop a lightweight authentication technique specifically for cross-domain IIoT, leveraging blockchain technology to achieve distributed collaborative authentication. The performance comparisons indicate that our method significantly outperforms traditional schemes, with an average authentication latency of approximately 151 milliseconds. Additionally, we introduce a trusted federated learning (FL) algorithm that ensures comprehensive trust assessments for devices across different domains while protecting data privacy. Extensive simulations and experiments validate the reliability of our approach.

关键词： Blockchain Industrial internet of things Federated learning Cross-domain Authentication

来源：评论

学校读者我要写书评

暂无评论

The behavioral sign of account theft: Realizing online payment fraud alert 29

The behavioral sign of account theft: Realizing online payme...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Cheng Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (纸本)9780999241165

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1%). © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-Based Multi-Cloud Storage Data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing Data centers Costs Blockchains Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Multi-body structure information enhancement GCN for 3D human pose estimation in videos

引用

Applied Soft computing 2025年 180卷

作者： Hanghang Zhou Yumei Zhang Jinli Ma Honghong Yang Xiaojun Wu School of Computer Science Shaanxi Normal University Xi’an China Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an China

To alleviate the problem of depth ambiguity in 3D human pose estimation, we propose Multi-body Structure information enhancement Graph Convolutional Network (MS-GCN), which exploits neighborhood subgraph structure to distinguish similar nodes and reduces the risk of over-smoothing of graph convolutional networks. Specifically, MS-GCN comprises the Structure Weight information enhancement GCN (SWGCN) and the Structural Semantic information excitation GCN (SSGCN), which capture global and local interactions among nodes. In the SWGCN, we propose a data-enhanced method. Here, an adaptive distance perception method is proposed to dig the multi-body structure weight information of neighborhood subgraphs, which contains the nonlinear dynamic characteristics of human motion. Then, the proposed multi-head cross-talk graph attention explores a variety of possible associations between node neighbors based on content and structure representations. In the SSGCN, we propose a Neighborhood Subgraph Encoder (NSE) to aggregate structural semantic information, which provides sufficient feature information for pose estimation. Besides, we adopt a Gated Recurrent Unit Pose Lifting (GRUPL) layer to improve the accuracy of the final result. Finally, we conduct extensive experiments on two popular benchmarks, e.g., Human3.6M and HumanEva-I, where the proposed model outperforms a number of recent state-of-the-art approaches with fewer input frames and model size.

关键词： 3D human pose estimation Graph convolutional networks Multi-body structure information Neighborhood subgraph encoder

来源：评论

学校读者我要写书评

暂无评论

An enumeration of 1-perfect ternary codes

arXiv

引用

arXiv 2021年

作者： Shi, Minjia Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

MSC Codes 94B25We study codes with parameters of the ternary Hamming (n=(3m−1)/2, 3n−m, 3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n − m + 1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4;we find that there are 93241327 equivalence classes of such codes. Copyright © 2021, The Authors. All rights reserved.

关键词： Equivalence classes

来源：评论

学校读者我要写书评

暂无评论

Icd: A New Interpretable Cognitive Diagnosis Model for intelligent Tutor Systems

SSRN

引用

SSRN 2022年

作者： Qi, Tianlong Ren, Meirui Guo, Longjiang Li, Xiaokun Li, Jin Zhang, Lichen Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China Xi’an Key Laboratory of Culture Tourism Resources Development and Utilization Xi’an710062 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd Harbin150090 China

Cognitive diagnosis plays a crucial role in intelligent Tutor Systems, which aims to diagnose learners' cognitive states according to learners' observable records, such as exercise records. However, this paper observes that the existing cognitive diagnosis models still have rooms for performance improvements: (1) they ignore the interaction between knowledge concepts;(2) they ignore the quantitative relation between exercises and concepts. In order to solve the aforementioned problems, this paper proposes a new interpretable cognitive diagnosis model named ICD based on a novel neural network designed by us, which can conduct answer records with different lengths. The neural network can fully utilize the interaction among concepts, the quantitative relation between exercises and concepts, and considers bias replacement by slip and guess, which makes the ICD more interpretable. The paper has conducted extensive experiments on four real publicity datasets, and the results show that both the performances and the interpretability of ICD are better than the latest state-of-the-art CDMs such as RCD, NCDM, and CDGK, and the classical CDMs such as DINA and MIRT. © 2022, The Authors. All rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Two-stream Hierarchical Similarity Reasoning for Image-text Matching

arXiv

引用

arXiv 2022年

作者： Chen, Ran Wang, Hanli Wang, Lei Kwong, Sam The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China DeepBlue Academy of Sciences Shanghai200336 China The Department of Computer Science City University of Hong Kong Hong Kong

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Survey on service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

arXiv

引用

arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

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：