检索结果-内蒙古大学图书馆

4th International Conference on Artificial Intelligence, Automation and High Performance computing, AIAHPC 2024

作者： Yu, Fahong Chen, Meijia Center of Intelligent Computing and Security Research Shanwei Institute of Technology China Shanwei Institute of Technology China

ISBN: (纸本)9798400710049

Path optimization problem is a classical issue in urban infrastructure. Heuristic strategies may not provide immediate qualitative solutions to complex problems instantly. This paper gave a novel reinforcement learning algorithm, in which the path planning was considered as in which route planning is considered as a process of route development with an encoder and a decoder used to generate routes. A multi-headed strategy was used to investigate spatiotemporal relationships in the encoder. The decoder was designed to generate solutions by optimizing reward and acquiring transition state, which enhances reward and display state transitions. The research results showed that the proposed method could get more efficiency and robustness comparing with those traditional strategies. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

research and Practice of Value-added Assessment for Cultivating Cybersecurity Talents in Higher Vocational Colleges 24

Research and Practice of Value-added Assessment for Cultivat...

引用

3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Meijia Yu, Fahong Shanwei Institute of Technology Guangdong Shanwei China Center of Intelligent Computing and Security Research Shanwei Institute of Technology Guangdong Shanwei China

ISBN: (纸本)9798400712692

To achieve quality value added cultivation of network security professionals, the course 'Network security' in higher vocational education establishes a quality evaluation model that better cultivates network security professionals in line with industry demand. The core objectives and specific contents of the evaluation were defined, and the model measured the level of knowledge mastery, practical skills and professional ethics of students in network security and calculated a quality value-added process of talent cultivation. The study analyzes how much knowledge, skills and abilities a school of students adds to its value between the entrance and exit doors. The value-added evaluation result was presented, and the effect of quality of talent cultivation in higher vocational education was verified by practice and application in cultivating cybersecurity talents. We proposed some optimization suggestions on teaching evaluation that aim to enhance teaching evaluation and provide guidance to the development of quality and value-added evaluation in higher vocational education talent cultivation at the same time. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Apprentices

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review: The Utilization of Artificial Intelligence in Forensic Odontology 11

A Systematic Review: The Utilization of Artificial Intellige...

引用

11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Supriyadi, Muhamad Rodhi Samah, Azurah Bte A. Majid, Hairudin Abdul Muliadi, Jemie Universiti Teknologi Malaysia National Research and Innovation Agency Bandung Indonesia Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Research Center for Artificial Intelligent and Cyber Security National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

The use of Artificial Intelligence (AI) in forensic odontology signifies a significant change, with the potential to improve the precision, effectiveness, and dependability of forensic investigations mostly focused on teeth and jawbones. This systematic review seeks to summarize the present state of AI applications in forensic odontology. It will analyze research conducted up to 2023 to assess the methods used, the results obtained, and the potential future developments in this field. By conducting a thorough and systematic search across well-known databases, employing specific keywords associated with artificial intelligence and forensic odontology, we successfully located pertinent papers that specifically address estimating age, determining gender, and detecting mandibular third molars. The results of our research indicate a growing interest in utilizing deep learning models, specifically convolutional neural networks, for the analysis of dental images and the automated interpretation of dental characteristics. These models surpass traditional methods by enabling quicker and more precise forensic examinations. In conclusion, AI has the ability to greatly transform forensic odontology by improving the accuracy of diagnoses and increasing operational efficiency. These models have the potential to be effective tools for identifying victims of large disasters and can provide valuable assistance in medico-legal scenarios. © 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

Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

arXiv

引用

arXiv 2025年

作者： Yang, Linyao Luo, Shi Cheng, Xi Yu, Lei Research Center for Data Hub and Security Zhejiang Lab Hangzhou311121 China Research Center for Intelligent Manufacturing Computing Zhejiang Lab Hangzhou311121 China

Digital twin technology is a transformative innovation driving the digital transformation and intelligent optimization of manufacturing systems. By integrating real-time data with computational models, digital twins enable continuous monitoring, simulation, prediction, and optimization, effectively bridging the gap between the physical and digital worlds. Recent advancements in communication, computing, and control technologies have accelerated the development and adoption of digital twins across various industries. However, significant challenges remain, including limited data for accurate system modeling, inefficiencies in system analysis, and a lack of explainability in the interactions between physical and digital systems. The rise of large language models (LLMs) offers new avenues to address these challenges. LLMs have shown exceptional capabilities across diverse domains, exhibiting strong generalization and emergent abilities that hold great potential for enhancing digital twins. This paper provides a comprehensive review of recent developments in LLMs and their applications to digital twin modeling. We propose a unified description-prediction-prescription framework to integrate digital twin modeling technologies and introduce a structured taxonomy to categorize LLM functionalities in these contexts. For each stage of application, we summarize the methodologies, identify key challenges, and explore potential future directions. To demonstrate the effectiveness of LLM-enhanced digital twins, we present an LLM-enhanced enterprise digital twin system, which enables automatic modeling and optimization of an enterprise. Finally, we discuss future opportunities and challenges in advancing LLM-enhanced digital twins, offering valuable insights for researchers and practitioners in related fields. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

A New Dual-Population Evolutionary Algorithm Leveraging Objective-Constraint Relationships for Constrained Multi-Objective Optimization

SSRN

引用

SSRN 2025年

作者： Ye, Jialu Tan, Chaogui Xia, Yizhang Hou, Zhanglu Liu, Yuan Zou, Juan Hunan Engineering Research Center of intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan National Applied Mathematics Center China Xiangtan University China

Population co-evolution strategies are widely used to handle constrained multi-objective optimization problems (CMOPs). However, existing coevolutionary algorithms oversimplify population collaboration and are rigid in evolving the auxiliary population, ignoring the importance of maintaining population diversity, especially for CMOPs with irregular feasible regions. To tackle this issue, we propose a new dual-population evolutionary algorithm, denoted as AACMO, which attempts to mine the relational features between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF) to better adapt to CMOPs with various constraints. Specifically, the method is categorized into two phases: a learning phase and an evolving phase. First, the main population (MP) aims to quickly learn the relationships between the UPF and the CPF by leveraging the advantages of different operators, namely, genetic algorithm operators and differential evolution operators, during the learning phase. Secondly, two auxiliary strategies are designed to collaborate with the MP during the evolving phase. These strategies are capable of employing a more effective co-evolution method to assist the MP in maintaining diversity, based on the degree of alignment between the learned UPF and CPF at the end of the learning phase. The effectiveness of AACMO is confirmed through comparative analysis with eight state-of-the-art algorithms across four CMOP benchmark suites. © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning for Robust Android Malware Familial Classification

引用

IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review: The Utilization of Artificial Intelligence in Forensic Odontology

A Systematic Review: The Utilization of Artificial Intellige...

引用

International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Muhamad Rodhi Supriyadi Azurah Bte A Samah Hairudin Abdul Majid Jemie Muliadi National Research and Innovation Agency Universiti Teknologi Malaysia Bandung Indonesia Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Research Center for Artificial Intelligent and Cyber Security National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

关键词： Accuracy Systematics Forensics Disasters Lips Transforms Teeth Dentistry Artificial intelligence Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

引用

IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time 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：