检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第2期5卷 839-853页

作者： Moniruzzaman, Md Yassine, Abdulsalam Benlamri, Rachid Department of Electrical and Computer Engineering Lakehead University Thunder BayONP7B 5E1 Canada Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada University of Doha for Science and Technology Doha24449 Qatar

The proliferation of electric vehicles (EVs) and the advancement of vehicle-to-everything energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This article proposes a novel federated reinforcement learning system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of proof of state of charge as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Competition Dynamics in the Meme Ecosystem

引用

ACM Transactions on Social Computing 2023年第3-4期6卷 1-19页

作者： Ford, Trenton W. Krohn, Rachel Weninger, Tim Integrated Science Center College of William and Mary 540 Landrum Dr WilliamsburgVA23185 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Moench Hall Terre HauteIN47803 United States Department of Computer Science and Engineering 380 Fitzpatrick Hall University of Notre Dame Notre DameIN46556 United States

Creating and sharing memes is a common modality of online social interaction. Because of the prevalence of memes, an abundance of research focuses on understanding how memes are shared and perceived within their online social environment and in what ways they differ from other modalities of online communication. In the present work, we present findings that suggest a relationship between how memes are introduced, shared, and diminish in online social systems and the behavior of biological species within an ecosystem. Framed by this perspective, we borrow ecological methodologies and concepts like resource availability, speciation, and competition to study human attention, creativity, and sharing dynamics (respectively) over a large collection of memes shared on Reddit. Specifically, we find that the population of memes has scaled almost exactly with the total amount of content created over the past decade. We find a consequence of limited human attention in the face of a growing number of memes is that the diversity of memes has decreased at the community level, albeit slightly, in the same period. A further consequence is that the average life span of memes has decreased dramatically, which is further evidence of an increase in competition and a decrease in a meme species’ primary resource: human attention. From this work, we have found reasonable preliminary support for linking memes to species and, thus, meme research to ecological research. We believe that future research should work to strengthen this relationship and transfer more methodologies to meme and information research from ecology. © 2023 Copyright held by the owner/author(s)

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: AutoML for Nonstationary Data

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2456-2457页

作者： Cheng, Ran Escalante, Hugo Jair Tu, Wei-Wei Rijn, Jan N. Van Wang, Shuo Yang, Yun Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Instituto Nacional de Astrofísica Óptica y Electrónica Computer Science Department Puebla72840 Mexico 4Paradigm Inc. Beijing100089 China RA Leiden2300 Netherlands The University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom Yunnan University National Pilot School of Software Kunming650504 China

The five papers in this special section address different aspects of automated machine learning (AutoML) from fundamental algorithms to real-world applications. Developing high-performance machine learning models is a difficult task that usually requires expertise from data scientists and knowledge from domain experts. To make machine learning more accessible and ease the labor-intensive trial-and-error process of searching for the most appropriate machine learning algorithm and the optimal hyperparameter setting, AutoML was developed and has become a rapidly growing area in recent years. AutoML aims at automation and efficiency of the machine learning process across domains and applications. Nowadays, data is commonly collected over time and susceptible to changes, such as in Internet-of-Things (IoT) systems, mobile phone applications and healthcare data analysis. It poses new challenges to the traditional AutoML with the assumption of data stationarity. Interesting research questions arise around whether, when and how to effectively and efficiently deal with non-stationary data in AutoML. © 2020 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

引用

2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： BaniMustafa, Ahmed Bulkrock, Olla Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350353488

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-the-art review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation. © 2024 IEEE.

关键词： Information use

来源：评论

学校读者我要写书评

暂无评论

Automated Programming Assignment Marking Tool 7

Automated Programming Assignment Marking Tool

引用

7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Vimalaraj, Hareeni Thenuwara, T.B.K.P. Wijekoon, Vinuri Udara Sathurjan, Thavaraja Reyal, Shyam Kuruppu, Thilmi Anuththara Tharmaseelan, Janani Department of Computer Science and Software Engineering Malabe Sri Lanka Department of It Malabe Sri Lanka

ISBN: (纸本)9781665421683

Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student's ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation 6

Empirical Study on Mispronunciation Detection for Tajweed Ru...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Alsahafí, Yousef S. Asad, Muhammad Department of Information technology Computing and Information technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350373875

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics. © 2024 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

引用

computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

来源：评论

学校读者我要写书评

暂无评论

Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

引用

30th International Conference on Computational and Experimental engineering and sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Fractal Pattern Identification from Wearable Inertial and Electromyographic Signals Data during Walking 25

Fractal Pattern Identification from Wearable Inertial and El...

引用

25th International Conference on computer and Information Technology, ICCIT 2022

作者： Rahman, Sam Matiur Al Mamun, Md. Abdullah Ali, Md. Asraf Department of Software Engineering Dhaka1341 Bangladesh Department of Computer Science Dhaka1229 Bangladesh

ISBN: (纸本)9798350346022

Acceleration, angular velocity and electromyographic (EMG) signal at the lower limb muscles, specially over both leg's Tibialis Anterior muscles are highly non-stationary, even if no perturbing influences can be identified during walking at any speed. This study analyzed the fractal dynamics (i.e., complexity of gait time series) in the walking gait time series of four types of signals obtained from wearable sensors such as IMUs (inertial measurement units), i.e., accelerometer signals which represents the acceleration experienced by the body, gyroscope signals which is the angular velocity, and magnetometer signals which is magnetic field vector, and Electromyographic (EMG) signal from both leg's Tibialis Anterior muscles. Gait time series from twenty-two healthy participants were analyzed while they performed walking at their comfortable speed. The scaling exponents (i.e., α-values) of the gait dynamics were accomplished by evaluating their fluctuation through detrended fluctuation analysis (DFA), which is most common and widely used non-linear technique for any non-stationary time series. DFA (the scaling exponents α) results established an anti-persistent in EMG and acceleration signal, less persistent pattern in angular velocity and persistent (i.e., long-range or fractal-like correlations) in magnetometer signal. This fractal complexity or noise patterns obtained from the EMG and inertial signals might provide new approaches for assessing and forecasting sudden injury risk during walking. © 2022 IEEE.

关键词： Muscle

来源：评论

学校读者我要写书评

暂无评论

TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot 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：