检索结果-内蒙古大学图书馆

Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

Science China(Technological Sciences) 2023年第11期66卷 3129-3138页

作者： FENG ZhangCheng XU WenYing CAO JinDe YANG ShaoFu RUTKOWSKI Leszek Jiangsu Provincial Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 211189China Frontiers Science Center for Mobile Information Communication and Security Nanjing 210096China School of Computer Science and Engineering Southeast UniversityNanjing 211189China The Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447Poland AGH University of Science and Technology Krakow 30-059Poland

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their *** primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information *** utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent ***,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit ***,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： online game bandit feedback partial-decision two-point gradient estimator

来源：评论

学校读者我要写书评

暂无评论

Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

引用

作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

InfinityRand: Blockchain Non-Interactive Randomness Beacon Protocol Based on Trapdoor Verifiable Delay Function

InfinityRand: Blockchain Non-Interactive Randomness Beacon P...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Jingyu Zhang Jiejun Ou Di Lan Bojian Ma Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the prosperity of Decentralized Finance (DeFi) ecosystems and the rise of Decentralized Autonomous Organization (DAO) groups, blockchain as the underlying revolutionary theory has been attracted a lot of attention. How to achieve cryptographically unpredictable randomness in the publicly verifiable blockchain network, one of the typical collaborative systems, is a critical issue. Since Ethereum finished merging its mainnet with beacon chain, the research on randomness beacon in the blockchain field has become a hotspot. Most of the current distributed randomness beacon schemes are interactive protocols. They are constructed with Public Verifiable Secret Sharing (PVSS), leading high communication complexity O(n 2 ). In contrast, randomness beacons constructed based on Verifiable Delay Functions (VDFs) rely on the sequentiality and uniqueness of VDFs could solve this problem. This paper proposes a blockchain non-interactive randomness beacon protocol: InfinityRand (IR), which decoupled from the underlying message distribution mechanism. It could generate publicly verifiable, strongly bias-resistant, and fair random numbers. In designing InfinityRand, we also design a new trapdoor VDF scheme, which is constructed using negative wrapped convolution (NWC) based number theoretic transform (NTT) on polynomial ring. We conduct security analysis and evaluation experiments. Experiments show that InfinityRand could provide well unpredictability, leader election fairness and scalability guarantees.

关键词： Protocols Scalability Decentralized autonomous organization Voting Finance Collaboration Polynomials

来源：评论

学校读者我要写书评

暂无评论

Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

引用

Chinese Journal of Electronics 2023年第4期21卷 636-641页

作者： Hong LIU Yuling SUN Yuanyuan LI School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Modeling and simulating group behaviours have been an active research topic in the field of computer animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algorithms, a crowd path generative approach based on ABC- PSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.

关键词： Surface reconstruction Computational modeling Sociology Animation Classification algorithms Surface topography Splines (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Operator Placement for IoT Data Streaming Applications in Edge Computing Environment 22nd

Operator Placement for IoT Data Streaming Applications in ...

引用

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Sixin Tang, Bing Yang, Qing Liu, Yuanyuan School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan411201 China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou510725 China

ISBN: (纸本)9783031226762

With the rapid development of IoT applications, how to process large and continuous data streams in real time for IoT applications has become a challenge in recent years. This paper presents a model to predict the Maximum Sustainable Throughput (MST) of data streaming applications at the edge, and then proposes GACP, a constraint programming-base operator placement strategy to maximize MST using the genetic algorithm. We evaluated the proposed operator placement strategy in terms of MST, the number of edge servers in usage, and the percentage of communications within edge servers. Experimental results show that compared with the existing heuristic algorithms, the proposed GACP strategy can achieve better MST, which is 2–5 times higher than existing algorithms. © 2023, Springer Nature Switzerland AG.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Vision-language models are strong noisy label detectors 24

Vision-language models are strong noisy label detectors

引用

Proceedings of the 38th International Conference on Neural information Processing systems

作者： Tong Wei Hao-Tian Li Chun-Shu Li Jiang-Xin Shi Yu-Feng Li Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China and National Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University China and School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9798331314385

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DEFT, for adapting vision-language models. DEFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DEFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DEFT in both noisy label detection and image classification tasks. Our source code is available at https://***/HotanLee/DeFT.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

引用

International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

来源：评论

学校读者我要写书评

暂无评论

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video

Disentangling Spatio-Temporal Knowledge for Weakly Supervise...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Guiqiu Liao Matjaz Jogan Sai Koushik Eric Eaton Daniel A. Hashimoto Department of Surgery PCASO Laboratory University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania Department of Computer and Information Science University of Pennsylvania

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net.

关键词： Accuracy Weak supervision Surgery Object segmentation Object detection Streaming media Real-time systems Timing Object recognition Transient analysis

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：