检索结果-内蒙古大学图书馆

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Parvesh Indervati Sonia Kumari Kartik Kumar Gorakh Gupta P Rajakumar School of Computing Science & Engineering Galgotias University UP India

In the digital world, the utilization of online transactions using credit and debit cards is growing every day. However, credit or debit (CD) card fraud is one of the prime challenges confronted by customers and bankers. The financial databases are stored at various cloud servers and these types of fraud make a huge loss in the financial systems as well as security concerns in the cloud. The cloud platform provides efficient and uninterrupted access to banking databases. There are distinct security initiatives that were taken to prevent fraudulent transactions, but still, hackers try different techniques to compromise the customer's account. Currently, for online Card payments, an OTP has been sent to the customer's phone number and email-id of a customer for security during the transaction. In this article, we introduce a new approach by sending an alert message before the OTP to the card owner whenever the credit or debit card number is entered by the customer or hacker for an online transaction.

关键词： Computer hacking Databases Biometrics (access control) Phishing Biological system modeling Banking Fraud

来源：评论

学校读者我要写书评

暂无评论

Hop-level Direct Preference Optimization for Knowledge Graph Reasoning with Trees

Hop-level Direct Preference Optimization for Knowledge Graph...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tiesunlong Shen Jin Wang Xuejie Zhang Erik Cambria School of Information Science and Engineering Yunnan University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in knowledge graph question answering (KGQA) have shown promise, yet existing methods often fail to align with human reasoning patterns. This study proposes HD-PORT (hop-level direct preference optimization for knowledge graph reasoning with trees), a novel approach that combines Monte Carlo Tree Search (MCTS) with hop-level direct preference optimization (HDPO) for KGQA tasks. MCTS simulates human-like reasoning by exploring multiple inference paths in knowledge graphs, generating interpretable reasoning chains and rich hop-level preferences. HDPO then leverages these preferences for model optimization, addressing the limitations of traditional supervised learning and existing DPO methods that focus on overall solution preferences. By optimizing preferences at each reasoning step, HD-PORT more closely mirrors human problem-solving strategies. Experimental results on benchmark datasets demonstrate that HD-PORT significantly outperforms state-of-the-art methods in both accuracy and interpretability, particularly for complex, multi-hop reasoning tasks.

关键词： Monte Carlo methods Supervised learning Knowledge graphs Signal processing Cognition Question answering (information retrieval) Problem-solving Mirrors Speech processing Optimization

来源：评论

学校读者我要写书评

暂无评论

Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System 4

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, M. Xavier Prathik, A. Arun, V. Venugopal, R. Ganeshbabu, T.R. Muthulekshmi, M. School of Advanced Sciences and Languages Vit Bhopal University Department of Physics Madhya Pradesh Bhopal India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns;fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges. © 2024 IEEE.

关键词： Fisheries

来源：评论

学校读者我要写书评

暂无评论

Vision Transformers with Hierarchical Attention

引用

Machine Intelligence Research 2024年第4期21卷 670-683页

作者： Yun Liu Yu-Huan Wu Guolei Sun Le Zhang Ajad Chhatkuli Luc Van Gool Institute for Infocomm Research(I2R) A*STARSingapore 138632Singapore Institute of High Performance Computing(IHPC) A*STARSingapore 138632Singapore Computer Vision Lab ETH ZürichZürich 8092Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China

This paper tackles the high computational/space complexity associated with multi-head self-attention(MHSA)in vanilla vision *** this end,we propose hierarchical MHSA(H-MHSA),a novel approach that computes self-attention in a hierarchical ***,we first divide the input image into patches as commonly done,and each patch is viewed as a ***,the proposed H-MHSA learns token relationships within local patches,serving as local relationship ***,the small patches are merged into larger ones,and H-MHSA models the global dependencies for the small number of the merged *** last,the local and global attentive features are aggregated to obtain features with powerful representation *** we only calculate attention for a limited number of tokens at each step,the computational load is reduced ***,H-MHSA can efficiently model global relationships among tokens without sacrificing fine-grained *** the H-MHSA module incorporated,we build a family of hierarchical-attention-based transformer networks,namely *** demonstrate the superiority of HAT-Net in scene understanding,we conduct extensive experiments on fundamental vision tasks,including image classification,semantic segmentation,object detection and instance ***,HAT-Net provides a new perspective for vision *** and pretrained models are available at https://***/yun-liu/HAT-Net.

关键词： Vision transformer hierarchical attention global attention local attention scene understanding.

来源：评论

学校读者我要写书评

暂无评论

Creative Research on Automatic Detection of Flying Objects 9

Creative Research on Automatic Detection of Flying Objects

引用

9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Liu, Lu Lu, Yiyang Xu, Binrui Peng, Heyang Ren, Yuhan College of International Education SiChuan Normal University Chengdu China Faculty of Computing Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Law School SiChuan Normal University Chengdu China

ISBN: (纸本)9798350376548

With the increasing development of China's modernisation and the growing demand for national defence security, it is very important to detect unidentified flying objects in the air and eliminate potential threats to ensure national security. In order to solve the dual problems of high cost of manual inspection and poor performance of the detection model with low accuracy, this paper proposes a way to automate the detection of flying objects by using the YOLOv5 target detection model, improving the loss function CIOU-Loss to EIOU-Loss by default to speed up the convergence speed of the model, and at the same time adopting a variety of novel data enhancement methods to improve the accuracy. This paper also compares the performance of the YOLOv5 model before and after the improvement in the training and flying objects detection process to demonstrate the effectiveness of the improvement. © 2024 IEEE.

关键词： Object tracking

来源：评论

学校读者我要写书评

暂无评论

CNN-Based Broad Learning for Cross-Domain Emotion Classification

引用

Tsinghua science and Technology 2023年第2期28卷 360-369页

作者： Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 511400China Laboratory of Language Engineering and Computing Guangdong University of Foreign StudiesGuangzhou 510006China School of Computer Science and Intelligence Education Lingnan Normal UniversityGuangzhou 510006China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Soochow UniversitySuzhou 215031China

Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised *** to the domain discrepancy,an emotion classifier trained on source domain may not work well on target *** researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion ***,the problem of emotion classification for cross-domain is rarely *** this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad *** first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad ***,to take advantage of these two classifiers,we designed a co-training model to boost together for ***,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed *** experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.

关键词： cross-domain emotion classification CNN broad learning classifier co-training

来源：评论

学校读者我要写书评

暂无评论

Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems

引用

Communications in Computational Physics 2023年第2期33卷 596-627页

作者： Sidi Wu Aiqing Zhu Yifa Tang Benzhuo Lu State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China.

With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing on neural networks in solving interface *** this paper,we perform a convergence analysis of physics-informed neural networks(PINNs)for solving second-order elliptic interface ***,we consider PINNs with domain decomposition technologies and introduce gradient-enhanced strategies on the interfaces to deal with boundary and interface jump *** is shown that the neural network sequence obtained by minimizing a Lipschitz regularized loss function converges to the unique solution to the interface problem in H2 as the number of samples *** experiments are provided to demonstrate our theoretical analysis.

关键词： Elliptic interface problems generalization errors convergence analysis neural networks.

来源：评论

学校读者我要写书评

暂无评论

Exploring the tidal effect of urban business district with large-scale human mobility data

引用

Frontiers of Computer science 2023年第3期17卷 77-90页

作者： Hongting NIU Ying SUN Hengshu ZHU Cong GENG Jiuchun YANG Hui XIONG Bo LANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100086China Baidu Talent Intelligence Center Baidu Inc.Beijing 100085China Business School Imperial College LondonLondon SW72AZUK Artificial Intelligence Thrust The Hong Kong University of Science and TechnologyGuangzhou 510030China

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and *** to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business ***,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a ***,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business *** this end,we propose a data-driven and multi-dimensional framework for dynamic business district ***,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time ***,we detect and forecast the functional changes in business *** results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business ***,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business *** example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

关键词： business district trajectory functionality detection tidal effect boundary detection visiting score

来源：评论

学校读者我要写书评

暂无评论

Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity 8

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, R. Vinston Siddiqui, Shavej Ali Avilasha, B.G. Kannan, L. Mohana Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Deparment of Applied Science and Humanities Lucknow India Dayanandasagar College of Engineering Deparment of Mechanical Engineering Bangalore India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India

ISBN: (纸本)9798350376425

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments. © 2024 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

GPRS based Mobile Application for Accident Detection and Alert Notification

GPRS based Mobile Application for Accident Detection and Ale...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： Nishant Kumar Deepak Raju Ranjan School of Computing Science & Engineering Galgotias University Greater Noida

Accidents on the roads are increasing rapidly in recent years. Most fatal accidents occur among the users of two-wheelers. Timely clinical help can go a long way in saving these precious lives. This app can help the users of these automobiles and their families and loved ones to be notified of the accident from a nearby clinical hub, which can help them provide the appropriate immediate clinical care to the injured. Therefore, the utilization makes an appropriate and immediate action and delivers the report to the mobile phone of the person related to the injured person. The proposed application sends SMS to the nearest clinic, as well as to family and friends. This app can also share the accurate position of the accident, which can save more time.

关键词： Technological innovation Law enforcement Roads Market research Mobile handsets Mobile applications Automobiles

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：