检索结果-内蒙古大学图书馆

IEEE Conference on Global Communications (GLOBECOM)

作者： Zejun Liu Chao Qiu Xiaoxu Ren Xiaofei Wang Zehui Xiong Haipeng Yao Dusit Niyato College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts’ information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare.

关键词： Analytical models Fluids Uncertainty Computational modeling Heuristic algorithms Refining Transforms Global communication Artificial intelligence Information entropy

来源：评论

学校读者我要写书评

暂无评论

Oversampling Multi-Label data Based on Natural Neighbor and Label Correlation

SSRN

引用

SSRN 2023年

作者： Liu, Bin Zhou, Ao Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing School of Computer Science of Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

In multi-label learning, addressing the class imbalance issue is of paramount importance. Oversampling methods are preferred as they offer a more general solution independent of the model choice, i.e., they alleviate the imbalance of datasets by augmenting instances in the pre-processing step. Existing neighbor-based oversampling methods employ an empirical number of neighbors (k=5), to identify the local region for new instances creation. However, a single fixed k value cannot fit all labels, because every label usually has its own distinct distribution and complexity. Furthermore, the label assignment for synthetic instances usually depends on the statistics of individual labels within the corresponding neighborhood, ignoring the informative correlation among labels. To overcome these limitations, we propose an oversampling method called Multi-Label Oversampling with Natural neighbor and label Correlation (MLONC). Our approach offers three main advantages: 1) the adaptive number of neighbors for each label related to the data complexity is obtained via natural neighbor detection;2) it encourages generating more instances proximate to the decision boundary of highly imbalanced labels, and diminishes the impact of outliers;3) exploitation of label correlation in label assignment enhances the quality of the synthetic instances. Experimental results demonstrate the effectiveness of MLONC under various base classifiers. © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Movable Antenna-Assisted Integrated Sensing and Communication Systems

arXiv

引用

arXiv 2025年

作者： Jiang, Chengjun Zhang, Chensi Huang, Chongwen Ge, Jianhua Niyato, Dusit Yuen, Chau The State Key Lab. of Integrated Service Networks Xidian University Xi’an China China The College of Computing and Data Science Nanyang Technological University Singapore The School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Blockchain and Machine Learning Integration for Safe data Sharing in Finance and HR

Blockchain and Machine Learning Integration for Safe Data Sh...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sivakumar, T.B. Prathiba, E. Sharma, Shweta Prasad, Rashmi Saket Ramaratnam, M.S. Krishnamoorthy, V. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Mailam Engineering College Department of Artificial Intelligence and Data Science Mailam India Shambhunath Institute of Engineering & Technology Department of Business Administration Prayagraj India D Y Patil Pgdm Institute Akurdi Pune India Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Management Studies Kanchipuram India Kongu Engineering College Department of Management Studies Perundurai Erode India

ISBN: (纸本)9798350360660

Modern finance and HR businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and HR industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Goal-Modeling Privacy-by-Design Patterns for Supporting GDPR Compliance

Goal-Modeling Privacy-by-Design Patterns for Supporting GDPR...

引用

18th International Conference on Software Technologies, ICSOFT 2023

作者： Al-Obeidallah, Mohammed Ghazi Piras, Luca Iloanugo, Onyinye Mouratidis, Haralambos Alkubaisy, Duaa Dellagiacoma, Daniele Department of Software Engineering Al Ain University Abu Dhabi United Arab Emirates School of Computing Middlesex University London United Kingdom School of Computing Robert Gordon University Aberdeen United Kingdom Institute for Analytics and Data Science University of Essex Colchester United Kingdom College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Centre for Secure Intelligent and Usable Systems University of Brighton Brighton United Kingdom

ISBN: (纸本)9789897586651

The introduction of the European General data Protection Regulation (GDPR) has imposed obligations on organisations collecting data in the EU. This has been beneficial to citizens due to rights reinforcement achieved as data subjects. However, obligations heavily affected organisations, and their privacy requirements analysts, having issues with interpreting and implementing GDPR principles. This paper proposes visual GDPR Patterns supporting analysts through Privacy-by- Design (PbD) and GDPR compliance analysis. In order to achieve that, we extended a requirements modeling tool, SecTro, which is used to assist analysts in creating visual requirements models. Specifically, we extended SecTro with novel visual GDPR patterns representing GDPR principles. We evaluated the patterns in a healthcare case study. The evaluation results suggest that the GDPR patterns can help analysts in PbD modeling analysis, by representing GDPR principles and considering relevant ready-to-use alternatives, towards achieving GDPR compliance. © 2023 by SCITEPRESS-Science and Technology Publications, Lda.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Swarm Dynamic Spectrum Access for Internet-of-Things

Swarm Dynamic Spectrum Access for Internet-of-Things

引用

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Wang, Yingjie Shen, Bowen Li, Feng Lam, Kwok-Yan College of Computing and Data Science Nanyang Technological University Singapore School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore

ISBN: (纸本)9798350368369

With the rapid advancement of wireless communication technologies, the scarcity of available spectrum resources has become increasingly pronounced. Dynamic Spectrum Access (DSA) emerges as a promising solution to address this challenge. Traditional DSA methods based on Q-learning emphasize autonomous learning by individual nodes, whereas more recent approaches incorporating Federated Learning (FL) introduce collaborative learning among nodes but remain reliant on a central server. In this paper, we propose a novel DSA scheme based on Swarm Learning (SL), which enables a fully decentralized, distributed machine learning paradigm by establishing a blockchain-based peer-to-peer network. This approach capitalizes on the strengths of SL, facilitating cooperative learning among multiple nodes to enhance DSA performance. By allowing IoT terminals to share model parameters within a blockchain framework, the proposed scheme mitigates the vulnerabilities associated with centralized servers. Simulation results demonstrate that the SL-based DSA scheme not only surpasses the access efficiency of FL-based methods but also obviates the necessity of a central aggregation server. Furthermore, the fully decentralized architecture enhances the auditability of system data, thereby bolstering user privacy protection. © 2025 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Futures Quantitative Investment With Heterogeneous Continual Graph Neural Network

Futures Quantitative Investment With Heterogeneous Continual...

引用

IEEE International Conference on data Mining (ICDM)

作者： Zhizhong Tan Min Hu Bin Liu Guosheng Yin School of Computer Science and Engineering Macau University of Science and Technolog China School of Finance Southwestern University of Finance and Economics China Center of Statistical Research School of Statistics Southwestern University of Finance and Economics China School of Computing and Data Science The University of Hong Kong Hong Kong China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

It is challenging to predict futures prices with traditional econometric models as it necessitates a comprehensive consideration of both historical observations and correlations among various futures. Spatial-temporal graph neural networks (STGNNs) offer a promising approach for modeling such complex spatio-temporal data. Nevertheless, the direct application of STGNNs to high-frequency futures data remains a challenge, as traders must account for both short- and long-term characteristics when making decisions. To capture these distinct timeframes, we leverage additional label information by devising four heterogeneous tasks: price regression, price gap regression, price moving average regression, and change-point detection. To make full use of these labels, we train the model in a continual manner. Traditional continual GNNs define the gradient of losses as parameter importance to overcome the catastrophic forgetting (CF) issue, while this is unsuitable for our heterogeneous tasks with losses in distinct spaces. Therefore, we propose to calculate the parameter importance with mutual information between the original observations and the extracted features. The empirical results based on 49 varieties of commodity futures demonstrate that our model achieves superior performance compared to the leading approaches, highlighting the effectiveness of our approach in modeling the intricate dynamics of the futures market.

关键词： Correlation Biological system modeling Predictive models Feature extraction Graph neural networks data models data mining Econometrics Mutual information Investment

来源：评论

学校读者我要写书评

暂无评论

Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

引用

computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： A. Babisha G. Umarani Srikanth D.R. Angel Kiruba R Sundar Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing.

关键词： Sign language Visualization Translation Annotations Generative Pre-trainer transformer Transformers Feature extraction Multilingual Optimization Videos

来源：评论

学校读者我要写书评

暂无评论

Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

引用

International Conference on Image Information Processing

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the “APTOS 2019 Blindness Detection” dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model’s performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment.

关键词： Deep learning Training Diabetic retinopathy Visual impairment Transfer learning Blindness Information processing

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：