检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wickramasuriya, Shanika L. Bandara, Kasun Hewamalage, Hansika Perera, Maneesha Department of Econometrics and Business Statistics Monash University Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia School of Computer Science & Engineering University of New South Wales Sydney Australia Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia

Forecast reconciliation is a procedure for forecasting multivariate time series with linear aggregation constraints to ensure that forecasts satisfy the same set of constraints. Most state-of-the-art reconciliation methods reconcile the forecasts using linear mappings to project the base forecasts onto a coherent subspace. They also use assumptions such as the forecasts are unbiased and residuals are jointly covariance stationary. In this study, we propose a non-linear forecast reconciliation approach by using machine learning methods, relaxing the assumptions of traditional linear reconciliation approaches. We introduce a novel loss function incorporating non-linear mappings to obtain a set of coherent forecasts from the individual base forecasts. To obtain the weights of the non-linear mapping between the base forecasts, we train a feed-forward neural network with the proposed loss function using transformed fitted values of the base models as inputs to the network. We evaluate the proposed methodology against state-of-the-art linear reconciliation approaches on multiple benchmark datasets. The results indicate that the proposed method works well when there is a reasonable number of observations in comparison to the number of bottom-level and non-bottom level series. © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Noisy Image Segmentation Algorithm Amalgamating Weighted Local information

SSRN

引用

SSRN 2024年

作者： Huang, Chengquan Lei, Huan Chen, Yang Cai, Jianghai Qin, Xiaosu Peng, Jialei Zhou, Lihua Zheng, Lan Engineering Training Center Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang550025 China

The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image segmentation. Therefore, we propose a new noisy image segmentation algorithm based on weighted local information for interval type-2 enhanced possibilistic fuzzy C-means clustering. Firstly, a new possibilistic fuzzy weighted local information factor, which amalgamates the mutually guided image filtering, is devised to control the relationship between mutually guided image filtering and the original image utilizing the absolute difference image between the original image and mutually guided image filtering. Additionally, two weighted sum functions are also introduced into the new possibilistic fuzzy weighted local information factor to calculate the greyscale differences between the current pixel and its neighborhood pixels. Secondly, the distance of the objective function is corrected using mutually guided image filtering to obtain the local features and global information of the image. Finally, this paper introduces the new possibilistic fuzzy weighted local information factor into the interval type-2 possibilistic fuzzy C-means clustering algorithm and proposes a new interval type-2 enhanced possibilistic fuzzy C-means clustering algorithm with local information and mutually guided image filtering constraints to improve noisy image segmentation results. Through extensive experiments on noisy synthetic images and noisy real images, the proposed algorithm can achieve higher performance and more accurate segmentation results compared with several existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

arXiv

引用

arXiv 2024年

作者： Li, Yicong Sun, Xiangguo Chen, Hongxu Zhang, Sixiao Yang, Yu Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD4072 Australia Department of Systems Engineering and Engineering Management Nanyang Technological University Singapore999077 Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

引用

6th IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Hardware Trojan Detection Based on Ensemble Learning 23

A Deep Learning Approach for Hardware Trojan Detection Based...

引用

Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Yinan Yao Chen Dong Zhengye Xie Yuqing Li Xiaodong Guo Yang Yang Xiaoding Wang College of Computer and Data Science Fuzhou University China Maynooth International Engineering College Fuzhou University China School of Computing and Information Systems Singapore Management University Singapore College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

In recent years, the application of integrated circuits in various fields has become more and more extensive. The attackers utilize the hidden dangers of the industrial chain to design and launch hardware Trojan attacks and their attack patterns and attack surfaces are also evolving, which has become an emerging security threat in the production of modern integrated circuits. Traditional detection methods have made it difficult to deal with hardware Trojans in a timely and effective manner, so new detection methods are imminent. In this paper, a deep learning-based hardware Trojan detection method to improve the accuracy of hardware Trojan detection to some extent is proposed. The paper designs a convolutional neural network (TextCNN), a bidirectional long and short-term memory network (Bi-LSTM), and the integrated learning of these two single models (TextCNN+Bi-LSTM) as the hardware Trojan detection scheme. The experimental results show that the hardware Trojan detection method based on deep learning proposed in this paper has advantages in terms of detection accuracy and robustness.

关键词： chip security ensemble learning hardware Trojan

来源：评论

学校读者我要写书评

暂无评论

Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

引用

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

来源：评论

学校读者我要写书评

暂无评论

Efficient Graph-Based Intrusion Detection for CAN Bus Security with Minimal Training data

Efficient Graph-Based Intrusion Detection for CAN Bus Securi...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Abul Ala Walid Maloy Kumar Devnath Bakhtiar Muiz Md Mehedi Hassan Melon Pronob Kumar Barman Pronoy Kumar Barman Department of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of Information Systems University of Maryland Baltimore County MD USA Department of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of Computer in Data Science Pacific States University Los Angeles CA USA Department of Statistics Jagannath University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

In this study, we explore the efficacy of using a graph-based intrusion detection system (GBIDS) for Controller Area Network (CAN) bus security with minimal training data. Traditional machine learning models often require extensive data for training, typically around 70%, to achieve high accuracy and reliability. However, our approach demonstrates that even with significantly reduced training data—specifically, using just 30% of the available 70% of data (21% of total data)—we can maintain high levels of accuracy, precision, recall, and F1 score. This minimal data training is facilitated by on-the-fly training capabilities akin to edge computing, allowing for real-time adaptation and learning. Our results indicate that it is not always necessary to rely on the conventional 70% training data benchmark to ensure robust CAN bus security. By leveraging advanced graph-based algorithms and edge computing techniques, we achieve efficient and effective intrusion detection with reduced data requirements, thus offering a promising solution for real-time CAN bus security in resource-constrained environments.

关键词： Training Accuracy Training data Intrusion detection Machine learning Real-time systems Controller area networks Security Reliability Edge computing

来源：评论

学校读者我要写书评

暂无评论

Learning Eco-Driving Strategies at Signalized Intersections

arXiv

引用

arXiv 2022年

作者： Jayawardana, Vindula Wu, Cathy MIT Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society CambridgeMA02139 United States

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits. Copyright © 2022, The Authors. All rights reserved.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms

Successful Integration of IoT and Blockchain Technologies Us...

引用

Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： C. Calarany M Indumathy P. Senthilraja D. Suganya V Gopi Vardhan Yelisetty Shanmukh Rahul Department of Computer Science Tagore Government Arts and Science College Puducherry India Department of Information Technology Rajiv Gandhi College of Engineering & Technology Kirumampakkam India Department of Computer Science K.S.Rangasamy College of Technology Tamil Nadu India Department of Computer Science Rajiv Gandhi College of Engineering & technology Kirumampakkam Data science and business systems SRM Institute of Science and Technology Kattankulathur India

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

引用

International Conference on data engineering

作者： Jianshun Zhang Fang Wang Sheng Qiu Yi Wang Jiaxin Ou Junxun Huang Baoquan Li Peng Fang Dan Feng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute Bytefrance Inc.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Key- Value Stores (KVS) implemented with log- structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV- separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB).

关键词： data engineering Compaction Indexes Optimization

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：