检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 51st

LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Co...

引用

51st International Conference on Very Large data Bases, VLDB 2025

作者： Ye, Junhao Li, Jiahui Chen, Lu Mao, Yuren Gao, Yunjun Li, Tianyi Zhejiang University China Zhejiang University Zhejiang Key Laboratory of Big Data Intelligent Computing China Aalborg University Denmark

Selecting a good execution plan can significantly improve the query efficiency of Spark SQL. Several machine learning-based techniques have been proposed to select good execution plans for DBMS, but none of them perform well on Spark SQL due to the following issues.(1) Limited compatibility with Spark SQL: these approaches rely on physical operator enumeration, while Spark SQL doesn’t support it;(2) Unreliable cost estimation: they often select execution plans with poor performance due to inaccurate cost estimation;(3) Time-consuming plan enumeration: they take much time to generate a large number of candidate execution plans in Spark *** overcome these issues, in this paper, we propose LEAP, the first learned query optimizer tailored for Spark SQL, which can be integrated seamlessly into Spark SQL and solves the compatibility issue. Also, to avoid the unreliable cost value estimation, LEAP selects execution plans with an estimation-free method, which directly performs comparisons between the plans. Furthermore, LEAP employs an efficient progressive plan enumeration algorithm with pruning techniques to find better plans with fewer enumerations. Extensive experiments on three public benchmarks show the effectiveness of LEAP. It reduces the end-to-end execution time of the native optimizer by up to 54% and other learned methods by up to 94%. © 2025, VLDB Endowment, All rights reserved.

关键词： Cost benefit analysis

来源：评论

学校读者我要写书评

暂无评论

MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents 13th

MPPQA: Structure-Aware Extractive Multi-span Question Answer...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhou, Bihan Ren, Haopeng Cai, Yi Lian, Zetao Zhu, Pinli Zeng, Yushi South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Procedural documents, which provide procedural knowledge with step-by-step instructions to help achieve specific goals, have shown growing popularity in both industry workflows and daily routines. Particularly, answering prerequisite associated questions facilitates thorough preparation for procedures and supports the execution of the subsequent operations. However, most prevailing researches in procedural knowledge primarily concentrate on the task of entity state tracking or "What if" reasoning, typically formulated as single-span extraction or multiple-choice question answering. Due to the limitation of single-span extraction format or disregard for leveraging the structural procedural knowledge within procedural document, existing question answering methods still struggle to accurately answer multi-span prerequisite associated questions. In this paper, we explore the problem of prerequisite associated question answering task to extract multi-span style answers from the procedural documents, and propose a key procedure concentrated question answering model SAPE integrated with structure-aware module. Additionally, a new dataset MPPQA is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model.

关键词： Structural procedural knowledge Prerequisite associated question Multi-span question answering

来源：评论

学校读者我要写书评

暂无评论

Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

引用

Science China(Technological Sciences) 2024年第8期67卷 2579-2593页

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Institute of Artificial IntelligenceDonghua UniversityShanghai 201620China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of TechnologyGuangzhou 510006China

Supplier selection is an important business activity in order to realize the purchasing function in supply chain *** supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and ***,all the participants have raised concerns about the increased transparency in supplier ***,this study proposes a transparent supplier selection method by considering the engagement of *** this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for ***,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making ***,a case study of supplier selection in purchasing vibration acceleration sensors is *** result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and *** this way,the transparency of supplier selection is enhanced.

关键词： blockchain supplier selection supply chain transparency Bayesian BWM group decision-making

来源：评论

学校读者我要写书评

暂无评论

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science big data 1

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Ea...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xiao, Yunpeng Zhang, Qunqing Zhao, Wanjing Li, Xufeng Peng, Jinhua Mo, Haonan Zhu, Haipeng Tang, Fei Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350358780

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Granular Differential Evolution Algorithm for Multi-Agent Task Allocation 9

Multi-Granular Differential Evolution Algorithm for Multi-Ag...

引用

9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Gao, Peng Liu, Qun Jiang, Haihuan Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350330076

The multi-agent task allocation presents a fundamental challenge in the field of multi-agent systems, especially in uncertain environments. Although extensive research has been conducted on the multi-agent task allocation in deterministic environments, discussions around the multi-agent task allocation in uncertain environments are relatively scarce. In reality, uncertain data is more common in practical decision-making processes. To address the multi-agent task allocation problem in uncertain environments, this study frames it as a noisy optimization problem and proposes a novel Multi-Granular Differential Evolution (MGDE) algorithm to solve it. MGDE combines the powerful differential evolution (DE) with the granular-ball computing which has high robustness in noise. The proposed MGDE is compared with other three state-of-the-art algorithms on 12 scenarios encompassing 6 agent and task quantity combinations and 2 uncertainty levels. Experimental results demonstrate the superior performance of MGDE. © 2023 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Research on Collaborative Storage of data Based on IPFS and Blockchain 7

Research on Collaborative Storage of Data Based on IPFS and ...

引用

7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Han, Boyuan Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China

ISBN: (纸本)9798350370782

The advent of the digital economy has necessitated secure and efficient storage solutions for large-scale data. Blockchain technology, characterized by its decentralization and immutability, offers unique advantages for protecting data privacy. However, as the scale of on-chain data processing expands, blockchain faces significant challenges in terms of storage costs and efficiency. This research introduces the IPFS-BC system, a novel collaborative storage solution that integrates the InterPlanetary File System (IPFS) with blockchain. This system harnesses IPFS' s capability to manage large-scale data and employs blockchain smart contracts to facilitate automated interactions between the IPFS and blockchain systems, thereby ensuring data integrity and security. The IPFS-BC system combines the blockchain's transaction record layer with IPFS's storage solution to achieve decentralized data storage and enhanced efficiency. Experimental validation demonstrates that this system effectively reduces the storage load on the blockchain, improves storage performance, and maintains data security and integrity. This study not only provides a new solution for large-scale data storage but also lays a practical foundation for further integration and interaction between blockchain and IPFS. © 2024 IEEE.

关键词： Storage efficiency

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Object Detection Based On Label Constrained data Augmentation 9

Few-Shot Object Detection Based On Label Constrained Data Au...

引用

9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Shi, Yan Wang, Guoyin Liu, Qun Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350330076

The majority of object detection methods typically depend on a significant quantity of annotated data, while few-shot object detection (FSOD) endeavors to identify novel classes of objects using a limited number of training instances. However, the limited number of samples leads to the problem of disparate data distributions between the source and target domains, which makes the generalization ability of the detector usually weak. In this paper, we combine data augmentation with fine-tuning to design a pseudo-labeled constrained model called LCDA, aiming to obtain high-quality pseudo-labels to effectively enrich the training examples. Furthermore, we leverage the pre-trained CLIP model to enhance the quality of pseudo-labels by restricting the category information as well as the designed bounding box consistency criterion. The experimental outcomes demonstrate that our model outperforms the existing models on two public datasets across various shot scenarios. The average enhancement of our method on different shots is 1.2AP, 1.6AP, 2.3AP, 2.7AP, and 4.2AP. We also validate the performance of the model in real applications of the USV dataset which shows an improvement of 1.9AP over the baseline methods. All demonstrate the effectiveness of our model. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

引用

Machine Intelligence Research 2023年第1期20卷 121-144页

作者： Guo-Yin Wang Dong-Dong Cheng De-You Xia Hai-Huan Jiang Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Big Data and Intelligent Engineering Yangtze Normal UniversityChongqing 408100China

Swarm intelligence has become a hot research field of artificial *** the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper *** a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm *** a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different ***-population swarm intel-ligence is inspired by biological *** further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm ***,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human *** have introduced human intelligence into computing systems and proposed human-machine hybrid swarm *** addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this *** also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other ***,we discuss future research directions and key issues to be studied in swarm intelligence.

关键词： Swarm intelligence single-population multi-population human-machine hybrid multi-granularity

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：