检索结果-内蒙古大学图书馆

Behavior Trees and State Machines in Robotics Applications

IEEE Transactions on software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Fast Implementation of Eta-T pairing on FPGAs

Fast Implementation of Eta-T pairing on FPGAs

引用

2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Aslam, Waseem Raza, Asim University of Central Punjab Department of Software Engineering Lahore Pakistan University of Central Punjab Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Key distribution as a core feature of any security system is one of the challenging tasks in an online transaction. Pairing is used to share the key between the users as an answer to the underlying security problem. Diffie-Hellman key exchange protocol computes pairing in two rounds whereas Eta-T is the most efficient and widely used pairing algorithm in the field of elliptic curve cryptosystems. After careful analysis of existing Eta-T pairing techniques, this work proposes a modification from serial computation scheme to parallel for better overall performance. Parallel Karatsuba multiplier and Most Significant reduction algorithm is used to speed up and optimize the pairing algorithm. Consequently, proposed pairing accelerator calculates Eta-T pairing in just 36.4μs and takes only 3597 clock cycles with maximum clock frequency of 98.7 MHz on Virtex-II. Further, by increasing the number of coefficients processed per cycle up to 6, this work shows reduction in the calculation time and clock cycles of Eta-T pairing up to 26.16μs and 1997 respectively. It is also shown that calculation time starts increasing after reaching D = 14. Similarly, it is shown that the fastest calculation of Eta-T pairing in characteristics three over the field F397 uses significantly less clock cycles than previous known implementations at the cost of less clock frequency. © 2024 IEEE.

关键词： Electric clocks

来源：评论

学校读者我要写书评

暂无评论

A Reputation Layered Coding Based Storage Strategy for Consortium Blockchain 22

A Reputation Layered Coding Based Storage Strategy for Conso...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ziqian Pan, Maolin Yu, Yang Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9798331509712

The improvement of consensus algorithms has greatly enhanced the performance of consortium blockchain, making it possible to be applied in large-scale network scenarios such as finance, healthcare and supply chain management. However, better performance often results in a heavier storage burden on the nodes. A storage strategy based on erasure code is a good solution, capable of reducing the storage consumption of each block to O(1). However, in large-scale consortium blockchain, this storage strategy encounters issues like poor performance and limited system dynamicity. To address the issues associated with the application of coding-based storage strategies in large-scale consortium blockchain, this paper proposes a storage strategy based on reputation-layered coding, named Level-Store. Level-Store first partitions the nodes into different storage units based on their reputations, then allocates the storage tasks of different block segments to different storage units for encoded storage. By calculating reasonable storage schemas for each storage unit, the data availability is ensured. Experiment shows that compared to existing coding-based storage strategies, Level-Store significantly improves system performance and dynamicity. It demonstrates notable advancements in key metrics such as space overhead, time consumption for storage and retrieval, the number of nodes and chunks affected by re-encoding, and the time consumption for re-encoding. Additionally, the system's security and fairness have also been enhanced. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Efficient Detection of Selfish Mining Attacks on Large-Scale Blockchain Networks 24

Efficient Detection of Selfish Mining Attacks on Large-Scale...

引用

24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Erfan, Fatemeh Bellaiche, Martine Halabi, Talal Polytechnique Montréal Department of Computer and Software Engineering MontréalQC Canada Université Laval Department of Computer Science QuébecQC Canada

ISBN: (纸本)9798350365658

Selfish mining attacks pose a significant and ongoing security threat to blockchain networks, including major platforms like Bitcoin and Ethereum. Understanding and effectively countering these attacks is crucial for maintaining the stability and integrity of these networks. This attack strategy involves a miner or a group of miners seeking to gain an advantage by delaying the immediate broadcasting of their mined blocks to the network. The selfish miners potentially mine more blocks in secret, increasing their rewards at the expense of other miners and the stability of the blockchain. Research has mainly focused on detecting this attack by analyzing data related to blocks and forks. This paper presents a new approach to efficiently detect selfish mining attacks in large-scale networks by analyzing various network indicators. To achieve this, we simulate a Bitcoin network and generate a dataset consisting of several features of individual miners and the overall network. We select the most reliable indicators by analyzing network features and reducing feature dimensions. Then, we employ random forest classification (RFC) to classify benign and selfish network behaviors. This approach not only achieves enhanced accuracy (99.96%), surpassing state-of-the-art methods utilizing deep learning, but also significantly reduces computational, storage, and temporal complexities. In doing so, it fortifies blockchain security more efficiently and accurately. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

引用

computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of COVID-19 in Chest Radiographs: Leveraging Machine Learning Approaches

引用

IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 572-578页

作者： Batool, Raheela Raza, Ghulam Musa Khalid, Usman Kim, Byung-Seo Department of Business Administration Pusan National University Korea Republic of Department of Software & Communication Engineering Hongik University Sejong Korea Republic of Department of Computer Science Comsats University Islamabad Sahiwal Pakistan

The World Health Organization (WHO) has designated the COVID-19 pandemic a global health emergency, prompting responses all over the world. The fatality rate is between 2% and 5%, and millions of people around the world have been infected. While the WHO recommends tests, resource-intensive testing has motivated the development of CNN technology for automated identification. Research employing machine learning models shows great accuracy in classifying X-ray and CT images for COVID-19 detection. These models include denseNet201, resnet50V2, inceptionv3, mobile net, and custom CNNs. The interpretation of chest X-rays has come a long way, yet there are still obstacles to overcome. In this paper, we present a way for using a machine learning model to categorize chest X-ray pictures into normal, COVID-19, viral pneumonia, and lung opacity, demonstrating the model's efficacy in assisting medical diagnosis, especially in time-sensitive situations like COVID-19. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

引用

Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

An end-to-end contrastive deep-learning framework for remote physiological signal measurement 3

An end-to-end contrastive deep-learning framework for remote...

引用

3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Wu, Bingjie Zhou, Menghan Liu, Wei Wang, Xingyao Zheng, Xingjian Xie, Yiping Luo, Chaoqi Zhen, Liangli Singapore Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Electrical Engineering Southwest Jiaotong University Chengdu China

Heart rate measurements based on remote physiological signals could significantly facilitate health monitoring in daily life. However, the ground-truth labels of the physiological signals are expensive and hard to collect. In this paper, we present a contrastive self-supervised learning framework to extract discriminative remote physiological features by leveraging periodic signal priors without ground-truth labels in the pre-training stage. Specifically, a ranking loss and a contrastive learning loss are constructed to extract knowledge with resampling of the video clips. In addition, data augmentation and ensemble learning strategies are designed to fine-tune the pre-trained model and fuse the results to improve the heart rate measurement. Our final solution achieves the 1st place in track 1 of the 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge. © 2024 Copyright for this paper by its authors.

关键词： contactless heart rate measurement Remote photoplethysmography RePSS self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Self-adaptive label filtering learning for unsupervised domain adaptation

引用

Frontiers of computer science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source *** of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating *** overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative ***,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

来源：评论

学校读者我要写书评

暂无评论

Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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：