检索结果-内蒙古大学图书馆

A Novel Approach for Predictable Governance of Decentralized Autonomous Organizations Based on Parallel Intelligence

引用

IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第5期53卷 3092-3103页

作者： Ding, Wenwen Liang, Xiaolong Hou, Jiachen Li, Juanjuan Rouabah, Younes Yuan, Yong Wang, Fei-Yue Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Res Ctr Finance Computat & Digital Engn Sch Math & Engn Minist Educ Beijing 100872 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

Decentralized autonomous organizations (DAOs) have become an indispensable part of digital infrastructure in recent years. The unique organizational characteristics and functional structure empower them to become an effective tool for solving corporate governance issues, including contract risks, principal-agent dilemmas, etc. However, DAOs themselves also face a variety of governance issues. On one hand, as a new economic organization model, the existing corporate governance theories and methods are no longer fully applicable to DAOs. On the other hand, unpredictable logic vulnerabilities and code loopholes in the governance mechanism might cause devastating damage to DAOs. The parallel intelligence theory based on the ACP method (i.e., artificial systems, computational experiments, and parallel execution) is an elegant research paradigm and a practical approach tailored to solving these challenges. As such, we propose a novel parallel governance framework for DAOs based on the parallel intelligence theory and further discuss its technical methodology and implementation model. Furthermore, we construct a parallel governance system for GnosisDAO and conduct computational experiments to validate the effectiveness of its governance mechanism. The experimental results not only confirm the defects of the GnosisDAO governance mechanism but also illustrate parallel governance as a useful research direction to solve existing governance problems of DAOs.

关键词： Blockchain decentralized autonomous organizations (DAOs) GnosisDAO parallel governance parallel intelligence smart contracts

来源：评论

学校读者我要写书评

暂无评论

γ-Razor: Hardness-Aware Dataset Pruning for Efficient Neural Network Training

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2025年第3期12卷 957-971页

作者： Liu, Lei Zhang, Peng Liang, Yunji Liu, Junrui Morra, Lia Guo, Bin Yu, Zhiwen Zhang, Yanyong Zeng, Daniel D. Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Training deep neural networks (DNNs) on largescale datasets is often inefficient with large computational needs and significant energy consumption. Although great efforts have been taken to optimize DNNs, few studies focused on the inefficiency caused by the data samples with less value for model training. In this article, we empirically demonstrate that sample complexity is important for model efficiency and selecting representative samples is constructive to the model efficiency. In particular, we propose hardness-aware dataset pruning method (gamma-Razor) to select representative samples from large-scale datasets to remove the less valuable data samples for model *** -Razor is a two-stage framework that includes interclass sampling and intraclass sampling. First, we introduce the inverse self-paced learning strategy to learn hard samples and adjust their weights adaptively according to the inverse frequency of effective samples of each class. For intraclass sampling, hardness-aware cluster sampling algorithm is proposed to downsample easy samples within each class. To evaluate the performance of gamma-Razor, we conducted extensive experiments on three large-scale datasets for image classification tasks. The experimental results show that models trained with the pruned datasets show competitive performances against their counterparts trained with the original large-scale datasets in terms of robustness and efficiency. Furthermore, models trained with the pruned datasets converge faster with lower energy consumption.

关键词： Training complexity theory Computational modeling Data models Data mining Adaptation models Robustness Energy consumption Noise measurement Loss measurement Dataset pruning energy efficiency hard sample mining inverse self-paced learning (SPL)

来源：评论

学校读者我要写书评

暂无评论

An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第8期71卷 9172-9181页

作者： Yuwen, Xuan Chen, Lu Chen, Long Zhang, Hui Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 102206 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

For active visual odometry (VO), the visual information detected by the positioning camera matters. By actively controlling the gaze of the camera, the VO tends to retrieve some effective factors, such as textured objects with rich feature points. However, the active rotation of the camera can introduce more uncertainties, which may cause additional gross errors. Therefore, it is a considerable problem for the active VO to avoid the adverse effects of the active rotation while benefiting from it. To address the issue, this article proposes an improved strategy based on a robust adaptive unscented Kalman filter (RAUKF) and the relative posture of the active camera for the active VO. The pose outputted from the VO is transformed to the vehicle pose by means of the pose of the pan-tilt, and the transformed pose is treated as the measurement of the vehicle motion. Subsequently, the measurement is fed to the RAUKF to generate a refined estimation of the vehicle pose, which is then inversely transformed to obtain a more precise camera pose. Finally, the feature points cloud of the VO can be corrected according to the refined camera pose. The proposed method effectively improves the positioning accuracy of the active VO, as demonstrated through numerical and real-vehicle tests. The relative translation error and the relative rotation error of the proposed method are 1.6% and 0.0037 deg/m in average, which reduce 96.22% and 94.79% compared with the raw outputs of the active VO.

关键词： Active simultaneous localization and mapping (SLAM) outlier detection and correction robust adaptive unscented Kalman filter (RAUKF) visual odometry (VO)

来源：评论

学校读者我要写书评

暂无评论

Metacracy: A New Governance Paradigm Beyond Bounded Intelligence

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第6期11卷 7072-7085页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Kovacs, Levente Hu, Bin Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Obuda Univ Univ Res & Innovat Ctr Physiol Controls Res Ctr H-1034 Budapest Hungary Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China

来源：评论

学校读者我要写书评

暂无评论

Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A New Era of Intelligent Vehicles and Intelligent Transportation systems: Digital Twins and Parallel Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2619-2627页

作者： Wang, Ziran Lv, Chen Wang, Fei-Yue Purdue Univ Coll Engn W Lafayette IN 47907 USA Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

By March 25, our transaction has received a total of 619 original submissions. Our current average number of submissions per day (SPD) rate is 7.37. Given this SPD, I would like to expand our editorial board to includ...

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometry-Guided Neural Implicit Surface Reconstruction

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第6期11卷 7898-7908页

作者： Li, Keqiang Wu, Huaiyu Zhao, Mingyang Fang, Qihang Yang, Jian Zhang, Ao Shen, Zhen Xiong, Gang Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Multiview 3-D reconstruction holds considerable promise across a wide applications in social manufacturing. Conducting in-depth research on precise and robust multiview 3-D reconstruction holds the potential to significantly empower the domain of social manufacturing. Recently, there has been a burgeoning interest in the domain of neural implicit surfaces learning through volume rendering for the purpose of multiview reconstruction without 3-D supervision. Conventional approaches often overlook explicit multiview geometry constraints, resulting in shortcomings in generating consistent surface reconstructions and recovering fine details. To solve this, we propose geometry-guided neural implicit surface (GG-NeuS), a geometry-guided neural implicit surfaces learning method for multiview surface reconstruction. Our model places a stronger emphasis on maintaining geometry consistency, significantly enhancing the quality of reconstruction. First, we enforce multiview geometry constraints on the surface points by locating the zero-level set of signed distance function (SDF). Second, we incorporate normal cues, predicted by general-purpose monocular estimators, to substantially recover fine geometric details. Additionally, we introduce a voxel-based surface reconstruction methodology that strikes an optimal balance between training time and reconstruction quality. Through comprehensive qualitative and quantitative experiments and analyses, we demonstrate that GG-NeuS successfully reconstructs fine-grained surface details and achieves superior surface reconstruction quality than state-of-the-art approaches.

关键词： Surface reconstruction Image reconstruction Rendering (computer graphics) Three-dimensional displays Image color analysis Accuracy Surface treatment Explicit geometry constraints multiview images neural implicit learning surface reconstruction

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2023年第8期34卷 3966-3978页

作者： Hu, Guangzheng Zhu, Yuanheng Zhao, Dongbin Zhao, Mengchen Hao, Jianye Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Huawei Noahs Ark Lab Beijing 100085 Peoples R China

Communicating agents with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited bandwidth communication. If the bandwidth is fully occupied, some agents are not able to send messages promptly to others, causing decision delay and impairing cooperative effects. Recent related work has started to address the problem but still fails in maximally reducing the consumption of communication resources. In this article, we propose an event-triggered communication network (ETCNet) to enhance communication efficiency in multi-agent systems by communicating only when necessary. For different task requirements, two paradigms of the ETCNet framework, event-triggered sending network (ETSNet) and event-triggered receiving network (ETRNet), are proposed for learning efficient sending and receiving protocols, respectively. Leveraging the information theory, the limited bandwidth is translated to the penalty threshold of an event-triggered strategy, which determines whether an agent at each step participates in communication or not. Then, the design of the event-triggered strategy is formulated as a constrained Markov decision problem and reinforcement learning finds the feasible and optimal communication protocol that satisfies the limited bandwidth constraint. Experiments on typical multi-agent tasks demonstrate that ETCNet outperforms other methods in reducing bandwidth occupancy and still preserves the cooperative performance of multi-agent systems at the most.

关键词： Bandwidth Protocols Reinforcement learning Task analysis Optimization Communication networks Multi-agent systems Event trigger limited bandwidth multi-agent communication multi-agent reinforcement learning (MARL)

来源：评论

学校读者我要写书评

暂无评论

A Novel Scenarios Engineering Methodology for Foundation Models in Metaverse

引用

IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第4期53卷 2148-2159页

作者： Li, Xuan Tian, Yonglin Ye, Peijun Duan, Haibin Wang, Fei-Yue Peng Cheng Lab Dept Math & Theories Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China

Foundation models are used to train a broad system of general data to build adaptations to new bottlenecks. Typically, they contain hundreds of billions of hyperparameters that have been trained with hundreds of gigabytes of data. However, this type of black-box vulnerability places foundation models at risk of data poisoning attacks that are designed to pass on misinformation or purposely introduce machine bias. Moreover, ordinary researchers have not been able to completely participate due to the rise in deployment standards. This study introduces the theoretical framework of scenarios engineering (SE) for building accessible and reliable foundation models in metaverse, namely, "SE-enabled foundation models in metaverse." Particularly, the research framework comprises a six-layer architecture (infrastructure layer, operation layer, knowledge layer, intelligence layer, management layer, and interaction layer), which can provide controllability, trustworthiness, and interactivity for the foundation models in metaverse. This creates closed-loop, virtual-real, and human-machine environments that provides the best indices and goals for the foundation models, which allows us to fully validate and calibrate the corresponding models. Then, examples of use cases from the automotive industry are listed to provide transparency on the possible use and benefits of our approach. Finally, the open research topics of related frameworks are discussed.

关键词： Foundation models knowledge automation management metaverse parallel intelligence scenarios engineering (SE)

来源：评论

学校读者我要写书评

暂无评论

MixGradient: A gradient-based re-weighting scheme with mixup for imbalanced data streams

引用

NEURAL NETWORKS 2023年第1期161卷 525-534页

作者： Peng, Xinyu Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Tsinghua Univ Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

A challenge for contemporary deep neural networks in real-world problems is learning from an imbalanced data stream, where data tends to be received chunk by chunk over time, and the prior class distribution is severely imbalanced. Although many sophisticated algorithms have been derived, most of them overlook the importance of gradient information. From this perspective, the difficulty of learning from imbalanced data streams lies in the fact that the gradient estimated on an uneven class distribution is not informative enough to reflect the critical pattern of each class. To this end, we propose to assign higher weights on the training samples whose gradients are close to the gradient of corresponding typical samples, thus highlighting the important samples in minority classes and suppressing the noisy samples in majority classes. Such an idea can be combined with Mixup, which exploits the interpolation information of data to further compensate for the information of sample space that the typical samples do not provide and expand the role of the proposed re -weighting scheme. Experiments on artificially induced long-tailed CIFAR data streams and long-tailed MiniPlaces data stream show that the resulting method, termed MixGradient, boosts the generalization performance of DNNs under different imbalance ratios and achieves up to 10% accuracy improvement.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Deep learning Imbalanced data streams Sample gradient Typical samples Mixup

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：