检索结果-内蒙古大学图书馆

3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Acharya, Shreya Sornalakshmi, K. Paul, Bidisha Singh, Anshul Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

This project proposes a simple and smart question answering system using Named Entity Recognition (NER) and Bidirectional Encoder Representations from Transformers (BERT) for communication purposes. Communication is a major factor that eases our day-to-day work. In this smart Question answering system, the system will create a customized dataset for the user based on the answers given by the user. Using NER, only the selective answers are taken, i.e., the most important answers, and then updated in the dataset. Based on that dataset, if a person then asks the system a question, the system will use BERT and CDQA (closed domain question answering) to fetch the best answer for the question asked. The resulting system will be an interactive one. © 2022 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Research on performance optimization of virtual data space across WAN

引用

Frontiers of Computer science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance computing Virtual data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

引用

IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

来源：评论

学校读者我要写书评

暂无评论

Customer Churn Prediction Using Machine Learning Approaches

Customer Churn Prediction Using Machine Learning Approaches

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Srinivasan, R. Rajeswari, D. Elangovan, G. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603 203 India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603 203 India

ISBN: (纸本)9798350334357

Customer Churn (CC) is a major issue and important concerns for large organizations and businesses alike. Telecom industries are attempting to improve methods to predict possible customer churn due to the immediate impact on revenue, particularly in the telecom sector. This paper discusses the various ML algorithms used to construct the churn model that helps telecom operators to predict customers who are likely to churn. The experimental results are compared to predict the best model among various techniques. As a result, the use of the Random Forest combined with SMOTE-ENN outperforms best result than other in terms of Fl-score. According to our analysis, the maximum prediction is 95 percent based on Fl-score. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Winning with Losses: The Surprising Success of Negative Strategies in Social Interaction Behavior

引用

Physical Review Letters 2024年第16期133卷 167401页

作者： Joel Weijia Lai Kang Hao Cheong Institute for Pedagogical Innovation Research and Excellence School of Physical and Mathematical Sciences College of Computing and Data Science (CCDS)

In the vast network of social interactions and behaviors, it is common to find certain schemes that, on their own, might appear counterproductive to societal progress. When observed in isolation, these schemes often seem to hinder more than they help. However, due to society’s complexity, the hidden potential of combining these seemingly detrimental schemes often goes unnoticed. Here, we investigate two such social behaviors, reputation and reciprocity, and their role in explaining Darwin’s survival of the fittest, examining how these fundamental principles govern individual interactions and shape broader social dynamics. We outline the dynamics of these two social behaviors and underline the importance of combined strategies in enhancing group welfare and contributing to interdisciplinary research in social physics.

关键词： Animal behavior, social interactions & emergent phenomena Ecological networks Chaos & nonlinear dynamics

来源：评论

学校读者我要写书评

暂无评论

Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

引用

Computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

来源：评论

学校读者我要写书评

暂无评论

Real-Time Double-Layer Graph Attention Networks for Parathyroid Detection

Real-Time Double-Layer Graph Attention Networks for Parathyr...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Wanling Lu, Wenhuan Sun, Qian Chen, Fei Wang, Bo Zhao, Wenxin Tianjin University College of Intelligence and Computing Tianjin China Fuzhou University College of Computer and Data Science Fuzhou China Fujian Medical University Union Hospital Fuzhou China

ISBN: (纸本)9798350386226

Since parathyroid glands (PG) regulate the body's calcium levels and significantly impact human health, developing methods for their automatic detection during endoscopic thyroid surgery is of utmost clinical significance. However, existing parathyroid detection works suffer from color variations, target deformation, blur, and lighting effects in intricate surgical environments. To address the above shortcomings, in this paper, we propose a novel double-layer graph attention network for PG detection, which explicitly facilitates local augmentation via key visual features (e.g., texture and shape) identification and global interactions. It can robustly combat image blur and better differentiate the PG targets and background parts, thus improving the detection precision. Furthermore, we observe most prior works fail to deeply understand the spatial relation among targets and unavoidably suffer from false or missed detection, which is heavily due to total ignorance or insufficient utilization of depth information, especially under lighting variations and occlusions. To fill the gap, we propose a depth relation augmentation component to adaptively capture the prominent relative positional relations between targets based on depth information and incorporate it into the proposed GNN framework, significantly deepening spatial understandings and naturally enhancing generalizability. Due to lacking a thyroid endoscopy surgery benchmark for evaluating this task, we meticulously established a novel dataset from 838 actual surgeries conducted (via the fully laparoscopic thoracic-breast approach) at the Fujian Medical University Union Hospital. Extensive experiments show that our framework achieves superior PG detection accuracy compared to current state-of-the-art counterparts while keeping real-time efficiency. © 2024 IEEE.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Explore Statistical Properties of Undirected Unweighted Networks from Ensemble Models 27th

Explore Statistical Properties of Undirected Unweighted Netw...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Xunda Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9783031783975

Complex network theory has been widely demonstrated as a powerful tool in modeling and characterizing various complex systems. In the past, complex network theory has focused on the behaviors as well as the characteristics of the network nodes and edges. However, with the continuous evolution of society, traditional graph theory faces challenges due to the emergence of extermely large network structures. Recently, complex network method based statistics has attracted much attention. The new approach effectively manages very large networks and uncovers their intrinsic properties. In this paper, we present a complex network analysis model for undirected, unweighted networks based on a statistical analysis approach. This model is inspired by the ensemble model in thermostatistical physics. Based on the established mathematical model, we derive physical measures that reflect the intrinsic properties of the network, including Entropy, Free Energy, Temperature, and so on. In the experimental part, we first explored the mathematical characterization of these metrics. Then, we observed the performance of various network categories under the same metric. Finally, we applied these measures to the field of graph classification. Extensive experiments demonstrate the effectiveness and superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Undirected graphs

来源：评论

学校读者我要写书评

暂无评论

MG-Net: Learn to Customize QAOA with Circuit Depth Awareness 38

MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

引用

2024 IEEE International Conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Buddhawar, Gaurav Dave, Dharin Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Dept. of Computer Science and Engineering Surat India Pune India Flame University School of Computing and Data Sciences Pune India

ISBN: (纸本)9798331533311

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model's capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos. © 2024 IEEE.

关键词： Convolutional neural 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：