检索结果-内蒙古大学图书馆

2024 International Conference on Cloud and Network Computing, ICCNC 2024

作者： Dai, Minghui Dou, Chenglong Wu, Yuan Qian, Liping Lin, Bin Su, Zhou Shen, Xuemin Sherman University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China Zhejiang University of Technology College of Information Engineering Hangzhou China Dalian Maritime University Department of Communication Engineering Dalian China Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350367805

The recent development of unmanned aerial vehicles (UAVs) technology has been envisioned as a promising paradigm to cater for the growing maritime activities. However, the increasing growth of marine services poses challenges for processing maritime data. In this paper, we propose a surface computing paradigm in maritime networks, in which multi-Task sensed by UAVs can be offloaded to multiple beacon stations. Multiple UAVs can process their workloads locally or offload to surface computing. Taking the system welfare and energy consumption into consideration, we present an optimization problem to determine the selection of beacon stations and the offloading decision, with the objective of maximizing the system welfare. We propose a hybrid auction and convex optimization approach to address the formulated problem. Finally, simulation results demonstrate the effectiveness of our proposed algorithms compared to several baselines. © 2024 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework 39

DCHM: Dynamic Collaboration of Heterogeneous Models Through ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance Beijing Technology and Business University Beijing China Department of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)157735897X

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models;however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Location and Reward Privacy-Preserving based Secure Task Allocation in Mobile Crowdsensing

引用

IEEE Transactions on Mobile Computing 2025年

作者： Li, Zhetao Shi, Weifan Choi, Young-June Sekiya, Hiroo Deng, Qingyong Jinan University College of Information Science and Technology Guangzhou510632 China Ajou University Department of Software Suwon443749 Korea Republic of Chiba University Graduate School of Advanced Integration Science Chiba263-8522 Japan Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Laboratory of Multisource Information Mining and Security Guilin541004 China

Online multi-task allocation has become an essential research topic in Mobile Crowdsensing (MCS). Most existing studies merely focus on minimizing the total distance that workers need to travel, but ignore considering the total task rewards, which could lead to a reduction in the willingness of workers to complete tasks. In this paper, to incentivize workers to participate in tasks and protect their privacy, we propose a Location and Reward Privacy-Preserving based Secure Task Allocation(LRPP-STA) scheme. First, we design a secure distance computation method to obtain the distance from the workers to the tasks under location privacy preserving. Second, considering fixed reward for the task, we propose a Fixed Rewarding Secure Task Allocation(FR-STA) scheme, where a secure utility calculation method is proposed to calculate the encrypted utility of the worker upon completing tasks under rewards privacy preserving, along with the path planning for workers to maximize the total utility of the system through an Extended Maximum-Utility Flow model(EMUF). Third, considering the situation of dynamic task reward adjusted by requesters based on the supply and demand relationship as well as the urgency of the task, we propose a Dynamic Rewarding Secure Task Allocation(DR-STA) scheme to optimize the task allocation for workers while improving requesters satisfaction. Finally, we theoretically analyze the security of location and reward privacy-preserving scheme, and conduct extensive experiments with real-world datasets to verify that the secure task allocation scheme is effective in improving the total utility of workers compared to other baseline online tasking schemes. © 2002-2012 IEEE.

关键词： Privacy by design

来源：评论

学校读者我要写书评

暂无评论

AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

引用

24th International Arab Conference on information technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

来源：评论

学校读者我要写书评

暂无评论

Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

引用

Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

来源：评论

学校读者我要写书评

暂无评论

Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

引用

38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scalar optical hopfions

引用

eLight 2022年第1期2卷 284-290页

作者： Chenhao Wan Yijie Shen Andy Chong Qiwen Zhan School of Optical‑Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Optoelectronics Research Centre University of SouthamptonSouthampton SO171BJUK Department of Physics Pusan National UniversityGeumjeong‑GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Hopfions are three-dimensional(3D)topological states discovered in field theory,magnetics,and hydrodynamics that resemble particle-like objects in physical *** inherit the topological features of the Hopf fibration,a homotopic mapping from unit sphere in 4D space to unit sphere in 3D *** we design and demonstrate dynamic scalar optical hopfions in the shape of a toroidal vortex and expressed as an approximate solution to Maxwell’s *** lines correspond to disjoint and interlinked loops forming complete ring tori in 3D *** Hopf invariant,product of two winding numbers,is determined by the topological charge of the poloidal spatiotemporal vortices and toroidal spatial vortices in toroidal *** hopfions provide a photonic testbed for studying topological states and may be utilized as high-dimensional information carriers.

关键词： Hopfion Toroidal vortex Spatiotemporal vortex Wave packet Topology Topological charge

来源：评论

学校读者我要写书评

暂无评论

GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

引用

arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting

arXiv

引用

arXiv 2025年

作者： Wang, Weiyan Shi, Xingjian Shu, Ruiqi Gao, Yuan Chen, Rui Ray Wang, Kun Xu, Fan Xue, Jinbao Li, Shuaipeng Tao, Yangyu Wang, Di Wu, Hao Huang, Xiaomeng TEG Tencent China Boson AI United States Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore Department and Computer and Science University of Science and Technology of China China

In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose BeamVQ, a novel probabilistic framework to realize iterative self-training with new self-ensemble strategies, achieving better physical consistency and generalization on extreme events. Following any base forecasting model, we can encode its deterministic outputs into a latent space and retrieve multiple codebook entries to generate probabilistic outputs. Then BeamVQ extends the beam search from discrete spaces to the continuous state spaces in this field. We can further employ domain-specific metrics (e.g., Critical Success Index for extreme events) to filter out the top-k candidates and develop the new self-ensemble strategy by combining the high-quality candidates. The self-ensemble can not only improve the inference quality and robustness but also iteratively augment the training datasets during continuous self-training. Consequently, BeamVQ realizes the exploration of rare but critical phenomena beyond the original dataset. Comprehensive experiments on different benchmarks and backbones show that BeamVQ consistently reduces forecasting MSE (up to 39%), enhancing extreme events detection and proving its effectiveness in handling data scarcity. Our codes are available at https://***/easylearningscores/BeamVQ. © 2025, CC BY.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

引用

Data Driven Control and Learning systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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：