检索结果-内蒙古大学图书馆

23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Tang, Xinyu Lv, Hongtao Gao, Yingjie Wu, Fan Liu, Lei Cui, Lizhen School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University Shandong Research Institute of Industrial Technology Jinan China

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3/2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5/4;when it falls below 2, the 3+γ lower bound becomes (Equation presented). © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Autonomous agents

来源：评论

学校读者我要写书评

暂无评论

Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks

Deep Bayesian Image Set Classification Approach for Defense ...

引用

2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Mirnateghi, Nima Shah, Syed Afaq Ali Bennamoun, Mohammed Edith Cowan University Center for Ai and Machine Learning Australia The University of Western Australia School of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350382204

Deep learning has become an integral part of various pattern recognition and computer vision systems in recent years due to its outstanding achievements in object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to being fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in physical-world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image-set-based adversarial defense approach. Image set classification has shown exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defense framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defense methods and single-shot recognition task. The empirical results demonstrate superior performance on the CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets. Our code is available at https://***/ai-voyage/***. © 2023 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-view Stereo 12th

Adaptive Cost Aggregation in Iterative Depth Estimation for...

引用

12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiang Bai, Xiao Wang, Chen School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China

ISBN: (纸本)9783031463075

The deep multi-view stereo (MVS) approaches generally construct 3D cost volumes to regularize and regress the depth map. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we presented an multi-stage iterative depth map estimation method for MVS. In our network, the cost volume is iteratively processed by lightweight 2D convolution based GRU modules, and the multi-stage coarse-to-fine structure is adopted to speed up the depth estimation process. To further improve the 3D reconstruction quality, we make improvements from two different perspectives of adaptive cost aggregation: a view-adaptive weighting module is proposed to account for the occlusion problem in cost volume fusion, and a spatial-adaptive deformable geometric feature encoding module is introduced to the cost volume feature encoding before feeding into GRUs for stronger modeling capability. Experiments on the DTU dataset demonstrated the effectiveness of the proposed network in accuracy with remarkable efficiency performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

引用

1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Deep Semi-Supervised Learning with Fine-Grained Dynamic Weights and Pseudo-Label Constraints

Deep Semi-Supervised Learning with Fine-Grained Dynamic Weig...

引用

2023 International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2023

作者： Li, Kuan Lian, Qianzhi Gao, Can Dongguan University of Technology School of Cyberspace Security Dongguan China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350311297

Semi-supervised learning is comonly trained using a large amount of unlabeled data and a small amount of labeled data. Existing works have achieved excellent results through consistent regularization methods. However, when designing the loss function, they set a loss function for labeled and unlabeled data separately, ignoring the connection between labeled and unlabeled data. Furthermore, unlabeled data all share the same weight, but the weights of different unlabeled data should be different. Based on these observations, a pair loss is proposed in this paper to strengthen the connection between labeled and unlabeled data;in addition, a dynamic weight is designed for each unlabeled data based on EMA (Exponential Moving Average) when calculating the loss function. Finally, a deep semi-supervised learning method called DWPC is proposed by combining dynamic weighting and pair loss. Experimental results on several datasets show that the proposed method improves performance in many scenarios. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Detached and Interactive Multimodal Learning 24

Detached and Interactive Multimodal Learning

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Fan, Yunfeng Xu, Wenchao Wang, Haozhao Liu, Junhong Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Hubei China School of Software Beihang University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798400706868

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://***/fanyunfeng-bit/DI-MML. © 2024 Owner/Author.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Efficient Large-Scale Constraint Optimization Problem Solving by Integrating Machine Learning and Automated Reasoning 2

Advancing Efficient Large-Scale Constraint Optimization Prob...

引用

2nd International Workshop on Spatio-Temporal Reasoning and Learning, STRL 2023

作者： Kong, Shufeng School of Software Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Cornell University Ithaca United States

来源：评论

学校读者我要写书评

暂无评论

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

引用

China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

来源：评论

学校读者我要写书评

暂无评论

Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification 24

Category-Prompt Refined Feature Learning for Long-Tailed Mul...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Yan, Jiexuan Huang, Sheng Mu, Nankun Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over *** code is available at https://***/jiexuanyan/CPRFL. © 2024 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Research on intelligent access control technology of face recognition model based on parameter sharing and dense connection

引用

International Journal of Computational Intelligence Studies 2023年第3-4期12卷 255-270页

作者： Xu, Yonghua School of Computer Engineering Jinling Institute of Technology Jiangsu Nanjing211169 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China

This study proposes a laboratory intelligent facial recognition system based on improved CNN, which significantly improves the accuracy of facial recognition by optimising the portrait recognition algorithm, improving CNN calculation and large parameter scale, and utilising perspective projection to improve portrait effect and sample utilisation. The experimental results show that the recognition rate has been improved by 10%, the CPU usage rate is less than 100%, and the model parameters have been reduced by more than 95%. This system can effectively and accurately recognise faces, making it suitable for embedded facial recognition devices. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Face recognition

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：