检索结果-内蒙古大学图书馆

Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第1期20卷 495-505页

作者： Zhang, Ang Min, Zhe Zhang, Zhengyan Yang, Xing Meng, Max Q-H Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China UCL Ctr Med Image Comp London WC1E 6BT England UCL Wellcome EPSRC Ctr Intervent & Surg Sci London WC1E 6BT England Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Res Inst Shenzhen 518172 Peoples R China

Registration is highly demanded in many real-world scenarios such as robotics and automation. Registration is challenging partly due to the fact that the acquired data is usually noisy and has many outliers. In addition, in many practical applications, one point set (PS) usually only covers a partial region of the other PS. Thus, most existing registration algorithms cannot guarantee theoretical convergence. This article presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets (PSs) are aligned and the anisotropic positional noise is considered. The high-dimensional point sets typically consist of the positional vectors and normal vectors. On one hand, with the normal vectors, the proposed method is more robust to noise and outliers, and the point correspondences can be found more accurately. On the other hand, incorporating the registration into the BCPD framework will guarantee the algorithm's theoretical convergence. Our contributions in this article are three folds. First, the problem of rigidly aligning two general PSs with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm's iterations are given in closed-form or with iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD.

关键词： Bayes methods Hidden Markov models Convergence Probabilistic logic inference algorithms Covariance matrices Three-dimensional displays Rigid point set registration correspondence estimation anisotropic positional error variational Bayesian inference

来源：评论

学校读者我要写书评

暂无评论

The Affordances of Multivariate Elo-Based Learner Modeling in Game-Based Assessment

引用

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2023年第2期16卷 152-165页

作者： Ruiperez-Valiente, Jose A. Kim, Yoon Jeon Baker, Ryan S. Martinez, Pedro A. Lin, Grace C. Univ Murcia Dept Informat & Commun Engn Murcia 30100 Spain Univ Wisconsin Madison Dept Curriculum & Instruct Madison WI 53706 USA Univ Penn Grad Sch Educ Philadelphia PA 19104 USA MIT MIT Educ Arcade Cambridge MA 02139 USA

Previous research and experiences have indicated the potential that games have in educational settings. One of the possible uses of games in education is as game-based assessments (GBA), using game tasks to generate evidence about skills and content knowledge that can be valuable. There are different approaches in the literature to implement the assessment machinery of these GBA, all of them having strengths and drawbacks. In this article, we propose using multivariate Elo-based learner modeling, as we believe it has a strong potential in the context of GBA for three aims: first, to simultaneously measure students competence across several knowledge components in a game;second, to predict task performance;and finally, to estimate task difficulty within the game. To do so, we present our GBA Shadowspect, which is focused on solving geometry puzzles, and we depict our implementation using data collected from several high schools across the USA. We obtain high-performing results (AUC of 0.87) and demonstrate that the model enables analysis of how each student's competency evolves after each puzzle attempt. Moreover, the model provides accurate estimations of each task's difficulty, enabling iterative improvement of the game design. This study highlights the potential that multivariate Elo-based learner modeling has within the context of GBA, sharing lessons learned, and encouraging future researchers in the field to consider this algorithm to build their assessment machinery.

关键词： Games inference algorithms Task analysis Machinery Behavioral sciences Computational modeling Context modeling Educational technology game-based assessment (GBA) K-12 education knowledge inference learning analytics

来源：评论

学校读者我要写书评

暂无评论

Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 4735-4747页

作者： Rosales-Perez, Alejandro Garcia, Salvador Herrera, Francisco Ctr Invest Matemat AC Dept Comp Sci Monterrey 66629 Mexico Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class;however, real-world problems may have an uneven class distribution. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyperparameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters. This optimization problem is solved using an evolutionary algorithm (EA) at the upper level and sequential minimal optimization (SMO) at the lower level. These two methods work in a nested fashion, that is, the optimal support vectors help guide the search of the hyperparameters, and the lower level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets.

关键词： Optimization Support vector machines Costs Kernel Training Search problems inference algorithms Bilevel optimization (BLO) data preprocessing evolutionary algorithms (EAs) imbalanced classification support vector machines (SVMs)

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Belief Propagation on Image Segmentation Pyramid

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 4432-4442页

作者： Yan, Tingman Yang, Xilian Yang, Genke Zhao, Qunfei Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Dept Automat Shanghai 200240 Peoples R China Ningbo Artificial Intelligence Inst Ningbo 315012 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

The Markov random field (MRF) for stereo matching can be solved using belief propagation (BP). However, the solution space grows significantly with the introduction of high-resolution stereo images and 3D plane labels, making the traditional BP algorithms impractical in inference time and convergence. We present an accurate and efficient hierarchical BP framework using the representation of the image segmentation pyramid (ISP). The pixel-level MRF can be solved by a top-down inference on the ISP. We design a hierarchy of MRF networks using the graph of superpixels at each ISP level. From the highest/image to the lowest/pixel level, the MRF models can be efficiently inferred with constant global guidance using the optimal labels of the previous level. The large texture-less regions can be handled effectively by the MRF model on a high level. The advanced 3D continuous labels and a novel support-points regularization are integrated into our framework for stereo matching. We provide a data-level parallelism implementation which is orders of magnitude faster than the best graph cuts (GC) algorithm. The proposed framework, HBP-ISP, outperforms the best GC algorithm on the Middlebury stereo matching benchmark.

关键词： Image segmentation inference algorithms Labeling Belief propagation Three-dimensional displays Costs Computational modeling Markov random field belief propagation stereo matching

来源：评论

学校读者我要写书评

暂无评论

Finding the Source in Networks: An Approach Based on Structural Entropy

引用

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2023年第1期23卷 1-25页

作者： Zhang, Chong Guo, Qiang Fu, Luoyi Ding, Jiaxin Cao, Xinde Long, Fei Wang, Xinbing Zhou, Chenghu Shanghai Jiao Tong Univ Dongchuan Rd 800 Shanghai 200240 Peoples R China Xinhua News Agcy Sanyuan SL 17 Beijing 100077 Peoples R China Chinese Acad Sci Datun Rd 11 Beijing Peoples R China

The popularity of intelligent devices provides straightforward access to the Internet and online social networks. However, the quick and easy data updates from networks also benefit the risk spreading, such as rumor, malware, or computer viruses. To this end, this article studies the problem of source detection, which is to infer the source node out of an aftermath of a cascade, that is, the observed infected graph GN of the network at some time. Prior arts have adopted various statistical quantities such as degree, distance, or infection size to reflect the structural centrality of the source. In this article, we propose a new metric that we call the infected tree entropy (ITE), to utilize richer underlying structural features for source detection. Our idea of ITE is inspired by the conception of structural entropy [21], which demonstrated that the minimization of average bits to encode the network structures with different partitions is the principle for detecting the natural or true structures in real-world networks. Accordingly, our proposed ITE based estimator for the source tries to minimize the coding of network partitions brought by the infected tree rooted at all the potential sources, thus minimizing the structural deviation between the cascades from the potential sources and the actual infection process included in GN. On polynomially growing geometric trees, with increasing tree heterogeneity, the ITE estimator remarkably yields more reliable detection under only moderate infection sizes, and returns an asymptotically complete detection. In contrast, for regular expanding trees, we still observe guaranteed detection probability of ITE estimator even with an infinite infection size, thanks to the degree regularity property. We also algorithmically realize the ITE based detection that enjoys linear time complexity via a message-passing scheme, and further extend it to general graphs. Extensive experiments on synthetic and real datasets confirm the superior

关键词： Source detection structural entropy graph theory inference algorithms

来源：评论

学校读者我要写书评

暂无评论

Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第3期45卷 3476-3491页

作者： Bing, Zhenshan Lerch, David Huang, Kai Knoll, Alois Tech Univ Munich Dept Informat D-80333 Munich Germany Sun Yat Sen Univ Shenzhen 510275 Guangdong Peoples R China

In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The concept of meta-reinforcement learning (meta-RL) has been recently proposed to enable agents to learn similar but new skills from a small amount of experience by leveraging a set of tasks with a shared structure. Due to the task representation learning strategy with few-shot adaptation, most recent work is limited to narrow task distributions and stationary environments, where tasks do not change within episodes. In this work, we address those limitations and introduce a training strategy that is applicable to non-stationary environments, as well as a task representation based on Gaussian mixture models to model clustered task distributions. We evaluate our method on several continuous robotic control benchmarks. Compared with state-of-the-art literature that is only applicable to stationary environments with few-shot adaption, our algorithm first achieves competitive asymptotic performance and superior sample efficiency in stationary environments with zero-shot adaption. Second, our algorithm learns to perform successfully in non-stationary settings as well as a continual learning setting, while learning well-structured task representations. Last, our algorithm learns basic distinct behaviors and well-structured task representations in task distributions with multiple qualitatively distinct tasks.

关键词： Task analysis Training Robots Adaptation models Multitasking inference algorithms Gaussian mixture model Meta-reinforcement learning task inference task adaptation robotic control

来源：评论

学校读者我要写书评

暂无评论

Dynamic Tsetlin Machine Accelerators for On-Chip Training Using FPGAs

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Mao, Gang Rahman, Tousif Maheshwari, Sidharth Pattison, Bob Shao, Zhuang Shafik, Rishad Yakovlev, Alex Newcastle Univ Sch Engn Microsyst Res Grp Newcastle Upon Tyne NE1 7RU England IIT Jammu Jammu 181221 Jammu & Kashmir India

The increased demand for data privacy and security in machine learning (ML) applications has put impetus on effective edge training on Internet-of-Things (IoT) nodes. Edge training aims to leverage speed, energy efficiency and adaptability within the resource constraints of the nodes. Deploying and training Deep Neural Networks (DNNs)-based models at the edge, although accurate, posit significant challenges from the back-propagation algorithm's complexity, bit precision trade-offs, and heterogeneity of DNN layers. This paper presents a Dynamic Tsetlin Machine (DTM) training accelerator as an alternative to DNN implementations. DTM utilizes logic-based on-chip inference with finite-state automata-driven learning within the same Field Programmable Gate Array (FPGA) package. Underpinned on the Vanilla and Coalesced Tsetlin Machine algorithms, the dynamic aspect of the accelerator design allows for a run-time reconfiguration targeting different datasets, model architectures, and model sizes without resynthesis. This makes the DTM suitable for targeting multivariate sensor-based edge tasks. Compared to DNNs, DTM trains with fewer multiply-accumulates, devoid of derivative computation. It is a data-centric ML algorithm that learns by aligning Tsetlin automata with input data to form logical propositions enabling efficient Look-up-Table (LUT) mapping and frugal Block RAM usage in FPGA training implementations. The proposed accelerator offers 2.54x more Giga operations per second per Watt (GOP/s per W) and uses 6x less power than the next-best comparable design.

关键词： Training Field programmable gate arrays inference algorithms Heuristic algorithms Hardware Artificial neural networks Computer architecture Learning automata Computational modeling System-on-chip Edge training coalesced tsetlin machines dynamic tsetlin machines embedded FPGA machine learning accelerator on-chip learning logic-based-learning

来源：评论

学校读者我要写书评

暂无评论

Monte Carlo Planning in Hybrid Belief POMDPs

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第8期8卷 4410-4417页

作者： Barenboim, Moran Shienman, Moshe Indelman, Vadim Technion Israel Inst Technol Technion Autonomous Syst Program TASP IL-32000 Haifa Israel Technion Israel Inst Technol Dept Aerosp Engn IL-32000 Haifa Israel

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online partially observable Markov decision processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.

关键词： Planning Random variables Monte Carlo methods inference algorithms Markov processes Data models History Planning under uncertainty autonomous agents

来源：评论

学校读者我要写书评

暂无评论

A Networked Radar Resource Management Approach Utilizing Target Priority and Maneuver for Cooperative Air Defense Fire Control Radars

引用

IEEE ACCESS 2023年 11卷 136279-136291页

作者： Tuncer, O. Cirpan, Hakan Ali ASELSAN AS Def Syst Technol Div TR-06830 Ankara Turkiye Istanbul Tech Univ Dept Elect & Commun Engn TR-34469 Istanbul Turkiye

Modern air defense systems utilize active electronically scanned array multi-function phased array fire control radars, which provide multiple radar functions simultaneously with the help of a radar resource manager. As air defense systems become network-centric, those connected systems benefit from sharing radar resources to improve system effectiveness by reducing target tracking load, increasing target search capability, and improving target tracking quality. This improvement can be achieved by utilizing adaptive mutual target tracking while sharing radar resources in networked fire control radars. This paper proposes a networked radar resource management approach utilizing target priority and target maneuver in a networked air defense system consisting of cooperative fire control radars that adaptively adjust target tracking rates. Simulation results are presented for sample scenarios consisting of two cooperative fire control radars. Comparative numerical results indicate decreased utilization of tracking resources, improved track initiation time, and increased search capacity of the air defense network with an overall increase in the tracking qualities for high-priority maneuvering targets to be engaged.

关键词： Radar tracking Adaptive systems Target tracking Radar detection Atmospheric modeling Resource management inference algorithms Adaptive tracking cooperative radar systems resource management

来源：评论

学校读者我要写书评

暂无评论

Generalized Unitary Approximate Message Passing for Double Linear Transformation Model

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1524-1538页

作者： Mo, Linlin Lu, Xinhua Yuan, Jide Zhang, Chuanzong Wang, Zhongyong Popovski, Petar Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Nanyang Inst Technol Coll Informat Engn Nanyang 473000 Peoples R China Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

The double linear transformation model Y = AXB + W plays an important role in a variety of science and engineering applications, where X is estimated through known transformation matrices A and B from the noisy measurement Y. Decoupling X from Y is a formidable task due to the high complexity brought by the multiplication of the unknown matrix (vector) with the transformation matrix (M-UMTM). Unitary approximate message passing (UAMP) has been verified as a low complexity and strong robustness solution to the M-UMTM problems. However, it has only been used to tackle the problems with a single linear transformation matrix. In this work, we develop a generalized algorithm, namely, generalized double UAMP (GD-UAMP) for the target model, which not only inherits the low complexity of AMP, but also enhances robustness by employing double unitary transformation. As a generalized algorithm, GD-UAMP can be applied to address the generalized Bayesian inference problem, i.e., the arbitrary prior probability of X and likelihood function of Z, where Z = AXB is the noiseless measurement. We verify the feasibility of the proposed algorithm in the channel estimation problem for various wireless communication systems. Numerical results demonstrate that the proposed algorithm can perfectly fit different scenarios and showcase superior performance compared with benchmarks.

关键词： Signal processing algorithms Approximation algorithms Message passing Complexity theory inference algorithms Mathematical models Bayes methods Approximate message passing (AMP) channel estimation message passing Reconfigurable intelligent surface (RIS) unitary transformation

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：