检索结果-内蒙古大学图书馆

6th International Conference of Women in Data science at Prince Sultan University, WiDS-PSU 2023

作者： Alfardus, Asma Rawat, Danda B. Department of Electrical Engineering and Computer Science WashingtonDC20059 United States

ISBN: (纸本)9781665477239

The past decade has seen a significant increase in the automobile industry, which has come with some serious challenges and threats. Modem vehicles are now made up of complex mechanical systems, as well as sophisticated electronic devices and connections to the outside world. Various electronic devices utilize standard communication protocols, including the Controller Area Network (CAN), to establish communication with each other. Unfortunately, CAN lacks some fundamental security features, such as encryption and authentication, which makes it vulnerable to security attacks. This can lead to accidents and financial losses for the users of these vehicles. To address this issue, researchers have proposed a number of security measures, such as cryptography and Intrusion Detection Systems (IDS). This paper addresses the security vulnerabilities associated with CAN and proposes potential solutions to overcome its limitations. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Contraction Coefficients of Product Symmetric Channels 60

Contraction Coefficients of Product Symmetric Channels

引用

60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Lee, Dongmin Makur, Anuran Purdue University Department of Computer Science West LafayetteIN United States School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9798331541033

Contraction coefficients for Kullback-Leibler (KL) divergence are channel-dependent constants that are used to sharpen data processing inequalities for KL divergence into strong data processing inequalities. In this work, we present a method to analytically compute the KL contraction coefficients of product q-ary symmetric channels. Although such contraction coefficients have many applications in information theory as well as probability and statistics more broadly, deriving exact values is considered to be challenging for complex channels such as product channels. While recent discoveries have reduced the search space of the optimization problems defining contraction coefficients significantly, the problem is still far from trivial. We find that the symmetry inherent in channels such as the q-ary symmetric channel allows for further simplification, making an exact closed-form expression for contraction coefficients for KL divergence possible to derive. We begin by obtaining the KL contraction coefficients of product binary symmetric channels, and then generalize our results to larger alphabets. Our methods are not specific to product q-ary symmetric channels and can be used for wider classes of channels that satisfy certain symmetry properties. © 2024 IEEE.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Trends in One-Shot Neural Architecture Search 15

Trends in One-Shot Neural Architecture Search

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Bin Son, Seok Park, Soohyun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

Recent advances in neural architecture search (NAS) techniques have provided effective solutions for managing high-dimensional data. These methods algorithmically automate the design process of NAS, aiming to identify an optimized structure that meets target performance criteria with reduced time and resource expenditure. This paper introduces the One-shot NAS method, which offers a computationally efficient approach to exploring NAS while minimizing the associated computational costs. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Study on Partially Homomorphic Encryption 17

A Study on Partially Homomorphic Encryption

引用

17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Ryu, Jihyeon Kim, Keunok Won, Dongho Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Recently, data experts can obtain a large amount of data with the development of the Internet. When computing such data, cloud services that do not use the personal device's memory are becoming popular. However, storing sensitive data as a source in the cloud carries the risk of hijacking. To compensate for this, homomorphic encryption, which encrypts and stores sensitive data, and can safely operate in an encrypted state, is being studied. In this paper, we analyze four methods of partially homomorphic encryption among homomorphic encryption methods. We compare and analyze the key size and ciphertext size of four partially homomorphic encryptions, Paillier, ElGamal, ASHE, and Symmetria. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Disturbance Compensation for DC Motors 23

Data-Driven Disturbance Compensation for DC Motors

引用

23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Lee, Jaeho Eun, Yongsoon Dgist Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9788993215274

This paper presents applications of recently developed data-driven disturbance observer [1] for DC motor control. The proposed approach enables effective compensation for external disturbances, allowing for robust control even in the presence of noise or other interfering factors. By utilizing data-driven techniques, the disturbance observer learns the system dynamics from the behavior of the system to accurately estimate and compensate for disturbances. Experimental results demonstrate the effectiveness of the proposed data-driven observer in mitigating the impact of disturbances and improving the overall control performance of the motor system. © 2023 ICROS.

关键词： DC motors

来源：评论

学校读者我要写书评

暂无评论

3D Joist Perception, Detection, and Climbing for Hexapod Robot

3D Joist Perception, Detection, and Climbing for Hexapod Rob...

引用

IS and T International Symposium on Electronic Imaging: Intelligent Robotics and Industrial Applications using computer Vision, IRIACV 2023

作者： Li, Yibin Zakhor, Avideh Department of Electrical Engineering and Computer Science University of California Berkeley United States

Avoiding obstacles is challenging for autonomous robotic systems. In this work, we examine obstacle avoidance for legged hexapods, as it relates to climbing over randomly placed wooden joists. Our main motivation is to enable robots to navigate inside tight attic spaces of single-family residential homes in the U.S., which typically contain rows of 4, 6, or 8 inch tall joists placed 16 inches apart from each other. We formulate the task as a 3D joist detection problem, and propose a detect-plan-act pipeline using a simultaneous localization and mapping (SLAM) algorithm to generate a pointcloud and a grid map to expose high obstacles such as joists. A line detector is applied on the grid map to extract parameters of the joist, such as height, orientation, and distance;based on this information the hexapod plans a sequence of leg movements to either climb over the joist or move sideways if the joist is too high. We show that our perception and path planning modules work well on the real-world joists with different heights and orientations. © 2023, Society for Imaging science and Technology.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Compensating for Size Effect in Shear Wave Elastography Using Deep Neural Networks 23

Compensating for Size Effect in Shear Wave Elastography Usin...

引用

23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Lee, Seungyeop Hwang, Jae Youn Eun, Yongsoon Dgist Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9788993215274

In ultrasound shear wave elastography (USWE), the elasticity of a small lesion is underestimated due to the wave reflection inside the lesion. This paper proposes using a deep neural network to compensate for the size effect without explicit size information. The deep neural network corrects the underestimation of the elastic modulus. The dataset for the training process consists of 4000 images with lesions of random sizes and elastic moduli, obtained from the k-Wave MATLAB Toolbox. A mean absolute error (MAE) is calculated between the ground truth and the image generated by the generator network for 500 test datasets with the unitary elasticity background. The maximum value of MAE is 0.0082, indicating that the generator network generates images that are similar to the ground truth in size and modulus. When the unitary elasticity background is replaced by the image of the breast phantom, the neural network proves to be effective in size effect compensation. © 2023 ICROS.

关键词： Elasticity

来源：评论

学校读者我要写书评

暂无评论

Joint Access Selection and Computation Offloading in LEO Ubiquitous Edge Computing Networks: An Alternating DRL-Based Approach

引用

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1870-1886页

作者： Yang, Junyi Zhang, Yuanjun Xiao, Zhenyu Han, Zhu Beihang University School of Electronic and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the increase in users' service diversity and demand for quality of experience (QoE), the utilization of low earth orbit (LEO) satellite networks for assisted or independent offloading of computation tasks has become a promising trend. However, due to the high mobility and uneven resource distribution of LEO satellites, it is difficult to meet the requirements of low delay and low overhead by using traditional optimization algorithms or common reinforcement learning (RL) algorithms. Therefore, in this paper, we consider a scenario in which terrestrial users offload delay-sensitive (DS) computation tasks to a LEO satellite network in order to study the joint access selection and computation offloading problem. First, we analyze the characteristics of the scenario and the computation tasks, and establish a generic mathematical model. Then, based on the block descent coordinate (BCD) principle, we propose a novel algorithm of alternating Dueling DQN (ADDQN) for the joint decision-making problem, where access selection and computation offloading are performed with corresponding independent agent respectively. Comprehensive simulations show that compared with other benchmark algorithms, the proposed method not only has better convergence, but also can maximize the number of successfully completed sub-tasks and the optimization objective value meanwhile reducing the unnecessary access handovers. © 2024 IEEE.

关键词： Satellites Computational modeling Decision making Low earth orbit satellites Handover Benchmark testing Mathematical models Quality of experience Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Introduction to Quantum Multi-Agent Reinforcement Learning: Concepts and Applications 15

Introduction to Quantum Multi-Agent Reinforcement Learning: ...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cho, Yeryeong Bin Son, Seok Park, Soohyun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. This paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance. © 2024 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code

EnStack: An Ensemble Stacking Framework of Large Language Mo...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Ridoy, Shahriyar Zaman Shaon, Md. Shazzad Hossain Cuzzocrea, Alfredo Akter, Mst Shapna North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Oakland University Department of Computer Science and Engineering RochesterMI48309 United States University of Calabria IDEA Lab Rende Italy Oakland University Department of Computer Science MI United States

ISBN: (纸本)9798350362480

Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we propose a novel ensemble stacking approach that synergizes multiple pre-trained large language models (LLMs) - CodeBERT, GraphCodeBERT, and UniXcoder - to improve vulnerability detection in source code. Our method uniquely combines the semantic understanding of CodeBERT, the structural code representations of GraphCodeBERT, and the cross-modal capabilities of UniXcoder. By fine-tuning these models on the Draper VDISC dataset and integrating their predictions using meta-classifiers such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and XGBoost, we effectively capture complex code patterns that individual models may miss. The meta-classifiers aggregate the strengths of each model, enhancing overall predictive performance. Our ensemble demonstrates significant performance gains over existing methods, with notable improvements in accuracy, precision, recall, F1-score, and AUC-score. This advancement addresses the challenge of detecting subtle and complex vulnerabilities in diverse programming contexts. The results suggest that our ensemble stacking approach offers a more robust and comprehensive solution for automated vulnerability detection, potentially influencing future AI-driven security practices. © 2024 IEEE.

关键词： Decision trees

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：