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arXiv 2023年

作者： Zhou, Chengmin Wang, Chao Hassan, Haseeb Shah, Himat Huang, Bingding Fränti, Pasi Machine Learning Group School of Computing University of Eastern Finland JoensuuFI-80100 Finland College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Machine Learning Group School of Computing University of Eastern Finland JoensuuFI-80100 Finland College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Blockchain industry Chengdu University of Information Technology Chengdu610225 China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen518118 China

Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot’s motions, data-efficiency in reinforcement learning (RL) training, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding

arXiv

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arXiv 2025年

作者： Ng, Dianwen Zhou, Kun Chao, Yi-Wen Xiong, Zhiwei Ma, Bin Chng, Eng Siong MiroMind Singapore College of Computing & Data Science Nanyang Technological University Singapore Tongyi Speech Lab Alibaba Group Singapore

Achieving high-fidelity audio compression while preserving perceptual quality across diverse audio types remains a significant challenge in Neural Audio Coding (NAC). This paper introduces MUFFIN, a fully convolutional Neural Psychoacoustic Coding (NPC) framework that leverages psychoacoustically guided multi-band frequency reconstruction. Central to MUFFIN is the Multi-Band Spectral Residual Vector Quantization (MBS-RVQ) mechanism, which quantizes latent speech across different frequency bands. This approach optimizes bitrate allocation and enhances fidelity based on psychoacoustic studies, achieving efficient compression with unique perceptual features that separate content from speaker attributes through distinct codebooks. MUFFIN integrates a transformer-inspired convolutional architecture with proposed modified snake activation functions to capture fine frequency details with greater precision. Extensive evaluations on diverse datasets (LibriTTS, IEMOCAP, GTZAN, BBC) demonstrate MUFFIN’s ability to consistently surpass existing performance in audio reconstruction across various domains. Notably, a high-compression variant achieves an impressive SOTA 12.5 Hz rate while preserving reconstruction quality. Furthermore, MUFFIN excels in downstream generative tasks, demonstrating its potential as a robust token representation for integration with large language models. These results establish MUFFIN as a groundbreaking advancement in NAC and as the first NPC system. Speech demos and codes are available. © 2025, CC BY-NC-SA.

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Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI

arXiv

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arXiv 2023年

作者： Deo, Yash Bonazzola, Rodrigo Dou, Haoran Xia, Yan Wei, Tianyou Ravikumar, Nishant Frangi, Alejandro F. Lassila, Toni School of Computing and School of Medicine University of Leeds Leeds United Kingdom Leeds United Kingdom Alan Turing Institute London United Kingdom Electrical Engineering and Cardiovascular Sciences Department KU Leuven Leuven Belgium Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom

Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse events such as haemorrhagic stroke (resulting from the rupture of aneurysms in blood vessels). However, MRAs are not acquired routinely, hence, an approach to synthesise blood vessel segmentations from more routinely acquired MR contrasts such as T1 and T2, would be useful. We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI. We propose a two-phase multi-objective learning approach, which captures both global and local features. It uses learned local attention maps generated by dilating the segmentation labels, which forces the network to only extract information from the T2 MRI relevant to synthesising the CoW. Our synthetic vessel segmentations generated from only T2 MRI achieved a mean Dice score of 0.79 ±0.03 in testing, compared to state-of-the-art segmentation networks such as transformer U-Net (0.71±0.04) and nnU-net(0.68±0.05), while using only a fraction of the parameters. The main qualitative difference between our synthetic vessel segmentations and the comparative models was in the sharper resolution of the CoW vessel segments, especially in the posterior circulation. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Incremental Feature Selection for Dynamic Incomplete data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles

arXiv

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arXiv 2022年

作者： Liu, Xiaoyan Dong, Zehui Xu, Zhiwei Liu, Siyuan Tian, Jie College of Data Science and Application Inner Mongolia University of Technology Huhhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer Science New Jersey Institute of Technology NJ07102 United States

Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks’ weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the limitation of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters, leading to the drawback of a single point of failure and scaling issues for increasing V2X network size. Meanwhile, in intelligent transport scenarios, data collected from onboard sensors are redundant, which degrades the performance of aggregation. To tackle these problems, we explore a novel idea of decentralized data processing and introduce a federated learning framework for in-network vehicles, C-DFL(Consensus based Decentralized Federated Learning), to tackle federated learning on connected vehicles and improve learning quality. Extensive simulations have been implemented to evaluate the performance of C-DFL, that demonstrates C-DFL outperforms the performance of conventional methods in all cases. © 2022, CC BY.

关键词： Vehicle to Everything

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Approximate relation between black hole perturbation theory and numerical relativity

引用

Physical Review D 2023年第12期108卷 124046-124046页

作者： Tousif Islam Gaurav Khanna Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics and Center for Computational Research University of Rhode Island Kingston Rhode Island 02881 USA

We investigate the interplay between numerical relativity (NR) and adiabatic point-particle black hole perturbation theory (ppBHPT) in the comparable mass regime for quasicircular nonspinning binary black holes. Specifically, we reassess the α−β scaling technique, previously introduced by Islam et al. [Surrogate model for gravitational wave signals from nonspinning, comparable-to large-mass-ratio black hole binaries built on black hole perturbation theory waveforms calibrated to numerical relativity, Phys. Rev. D 106, 104025 (2022)], as a means to effectively match ppBHPT waveforms to NR waveforms within this regime. In particular, α rescales the amplitude and β rescales the time (and hence the phase). Utilizing publicly available long NR data (SXS:BBH:2265 [SXS Collaboration, Binary black-hole simulation SXS:BBH:2265 (2019)]) for a mass ratio of 1∶3, encompassing the final ∼65 orbital cycles of the binary evolution, we examine the range of applicability of such scalings. We observe that the scaling technique remains effective even during the earlier stages of the inspiral. Additionally, we provide commentary on the temporal evolution of the α and β parameters and discuss whether they can be approximated as constant values. Consequently, we derive the α−β scaling as a function of orbital frequencies and demonstrate that it is equivalent to a frequency-dependent correction. We further provide a brief comparison between post-Newtonian (PN) waveforms and the rescaled ppBHPT waveform at a mass ratio of 1∶3 and comment on their regime of validity. Finally, we explore the possibility of using PN theory to obtain the α−β calibration parameters and still provide a rescaled ppBHPT waveform that matches NR.

关键词： Gravitational waves

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Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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A Generative Adversarial Active Learning Method for Effective Outlier Detection

A Generative Adversarial Active Learning Method for Effectiv...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Mohamed Jaward Bah Ji Zhang Ting Yu Feng Xia Zhao Li Shuigeng Zhou Hongzhi Wang Big Data Intelligence Research Center Zhejiang Lab Hangzhou China University of Southern Queensland School of Sciences Toowoomba Australia Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Zhejiang University China Fudan University School of Computer Science Shanghai China Harbin Institute of Technology Massive Data Computing Lab China

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.

关键词： Learning systems Games Benchmark testing Generative adversarial networks Generators Labeling Task analysis

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Dengue Dashboard for Forecasting the Future Trend of Dengue Cases in Pahang

Dengue Dashboard for Forecasting the Future Trend of Dengue ...

引用

Software Engineering & Computer Systems and International Conference on Computational science and Information Management (ICSECS-ICOCSIM), International Conference on

作者： Muhammad Khairul Syafiq Mustafa Junaida Sulaiman Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computing College of Computing and Applied Sciences Centre For Data Science & Artificial Intelligence Soft Computing & Intelligent Systems (SPINT) Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (数字)9781665414074

ISBN: (纸本)9781665414081

The study is concerned about the rising number of dengue cases in Pahang. As a preventive solution, an analytical dashboard is proposed to forecast the trend of dengue cases in Pahang for the next two years using Auto Regressive Integrated Moving Average (ARIMA) model. Due to its efficiency to solve linear problems, ARIMA model is a popular model used by the researchers and economists to forecast a time-series data. The dashboard contains various information of dengue cases for instances the total number of dengue cases in Pahang, total number of dengue cases by district, a map that shows the green zone and the red zone which indicates the risk of the dengue infection in that area and a graph that displays the forecasted trend of dengue cases in Pahang from 2021 to 2023 using the best- fitted ARIMA model. The dashboard and the forecasted data trend of dengue is generated to assist JKN Pahang to plan a preventive measure regarding the rising number of dengue cases in thefuture.

关键词： Temperature Rain Scientific computing Meteorological factors Weather forecasting Predictive models Market research

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