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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

Event-aided Semantic Scene Completion

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arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Denden, Mohsen Jemmali, Mahdi Boulila, Wadii Soni, Mukesh Khan, Faheem Ahmad, Jawad Department of Computer and Information Technologies College of Telecommunication and Information CTI Riyadh Saudi Arabia Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia Department of CSE University Centre for Research and Development Chandigarh University Mohali Punjab- India Department of Computer Engineering Gachon University Seongnam-si South Korea School of Computing Engineering and the Built Environment Edinburgh Napier University U.K

Edge computing emerges as a pivotal model in the era of next-generation consumer electronics and the emerging challenges of multimodal data-driven decision-making. Specifically, edge computing offers an open computing architecture for the vast and diverse consumer multimodal data generated by mobile computing and Internet of Things (IoT) technologies. While edge computing is instrumental in optimizing latency and bandwidth control in processing consumer multimodal data, the viability of employing edge resources is complicated by high service costs and the complexities of managing multimodal data diversity. This study introduces an innovative optimization method for distributing multimodal data on edge storage, considering both the I/O (input/output) speed and the overall distribution cost. The core part of our approach is the deployment of intelligent algorithms that ensure equitable data distribution across storage servers, thus eliminating unused space and reducing extra costs. Given the complexity of this NP-hard (non-deterministic polynomial-time) challenge, our study reveals a unique model incorporating an edge-broker component in combination with novel algorithms. The proposed algorithms aim to harmonize data distribution and reduce resource allocation expenses in a multimodal edge environment. Our proposed approach achieves excellent results, highlighting the efficacy of the proposed algorithms in several parameters such as makespan, cost, multimodal data security, and total processing time. Empirical tests reveal that the BCA (Best Clustering Algorithm) performs best, achieving a minimum load balancing rate of 92.2%, an average variance of 0.04, and an average run time of 0.56 seconds. IEEE

关键词： Edge computing

Unbiased Parameter Estimation for Bayesian Inverse Problems

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

A novel channel model and optimal power control schemes for mobile mmWave two-tier networks

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TechRxiv

TechRxiv 2022年

作者： Ghosh, Joydev Haci, Huseyin Kumar, Neeraj Al-Utaibi, Khaled A. Sait, Sadiq M. So-In, Chakchai School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Department of Computer Science & Engineering Thapar Institute of Engineering & Technology Punjab India Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science College of Computing Khon Kaen University Thailand

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements. © 2022, CC BY.

关键词： Femtocell

Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy

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arXiv 2023年

作者： Song, Hyunwoo Yang, Shuojue Wu, Zijian Moradi, Hamid Taylor, Russell H. Kang, Jin U. Salcudean, Septimiu E. Boctor, Emad M. Department of Computer Science Whiting School of Engineering The Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Whiting School of Engineering The Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modality in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker (PM) method, where the ultrasound/photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument. Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principle of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics (i.e., 1.15 ± 0.29 mm from the experimental evaluation). The result is also comparable with our previous approach (i.e., 1.05 ± 0.37 mm), and the proposed method can be implemented with a normal white light stereo camera and doesn’t require highly accurate localization of the PM. Conclusion: The proposed arc-to-line based frame registration algorithm that transform between the camera and the TRUS imaging frames can allow simple yet efficient integration of commercial US/PA imaging system into the laparoscopic surgical setting, and contribute to the image-guided surgical intervention applications. © 2023, CC BY.

关键词： Ultrasonic imaging

The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis

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The Scientific Progress and Prospects of Artificial Intellig...

International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Puput Dani Prasetyo Adi Dian Lestari Sandy Vikki Ariyanto Agung Purnomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta Indonesia Faculty of Medicine Universitas Airlangga Surabaya Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical school affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years.

关键词：

Generative AI Powered Forensic Device

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Generative AI Powered Forensic Device

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Vanita G. Kshirsagar Digvijay G. Bhosale Shubhangi Suryawanshi Anita Sachin Mahajan Pramod Patil Jyotsna Vilas Barpute Prashant G. Ahire Rahul A. Patil Automation and Robotics Dr. D. Y. Patil Institute of Technology Pune India Artificial Intelligence and Data Secience Dr. D. Y. Patil Institute of Technology Pune Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pune India Computer Engineering Ajeenkya D. Y. Patil School of Engineering Pune India Computer Engineering Dr. D.Y. Patil Institute of Technology Pune India Computer Science and Engineering Symbiosis Institute of Technology Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Crime is a dominant concern in contemporary society. It is crucial to prevent it and deliver justice promptly. Forensic science is critical for criminal investigations and maintaining law and order. The traditional tactics utilized in this field are frequently based on manual procedures, which are both time-consuming and prone to error, rendering them ineffectual in providing prompt and dependable justice. Utilizing automation and advanced technologies for forensic science and crime scene investigation is now essential. This paper suggests a portable gadget that utilizes Internet of Things (IOT), Machine Learning and Deep Learning to predict, gain understanding, and possibly recognize offenders. This gadget is designed to reconstruct fingerprints, detect weapons and recognize suspicious activities. It utilizes algorithms and technologies like autoencoders, GANs, SSD and YOLOv8. We talk about the benefits of merging these technologies into one easy-to-use device, which will offer data integration, instant data processing, and improved forensic analysis. The article offers information about automating important areas to improve investigative processes and enhance the criminal justice system's efficiency and effectiveness in the future.

关键词： Cloud computing Automation Forensics Weapons Prevention and mitigation Fingerprint recognition Internet of Things Image reconstruction Testing Videos

Adaptive Fuzzy Sliding Mode Control With Logarithmic Sliding Surface for Active Suspension Systems

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Adaptive Fuzzy Sliding Mode Control With Logarithmic Sliding...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Andika Aji Wijaya Fitri Yakub Winda Astuti Mahmud Iwan Solihin S. Syafiie Department of Mechanical Engineering University of Business and Technology (UBT) Jeddah Saudi Arabia Department of Electronic Systems Engineering University of Technology Malaysia (UTM) Kuala Lumpur Malaysia Department of Computer Engineering BINUS ASO School of Engineering Automotive and Robotics Program Bina Nusantara University (BINUS) Jakarta Indonesia Department of Mechanical and Mechatronics Engineering UCSI University Kuala Lumpur Malaysia Department of Chemical and Materials Engineering Faculty of Engineering-Rabigh King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper introduces a hybrid control strategy that integrates the unique features of natural logarithm sliding mode control (lnSMC) and fuzzy logic systems. The lnSMC control approach ensures that the vibration amplitude remains within a bound, as determined by the parameters of the lnSMC switching function. Furthermore, the fuzzy logic system is employed to approximate the nonlinearities and uncertainties inherent in the suspension system components, such as the spring and damper. The stability of the closed-loop system is analyzed using the Lyapunov stability theorem, guaranteeing convergence within a finite time. To validate the effectiveness of the proposed controller, a simulation study was carried out using a quarter-car suspension model, considering the system’s nonlinearities, parameter uncertainties, and various external disturbances. The results indicate that the proposed controller significantly improves ride comfort while maintaining suspension deflection and road holding within critical safety limits.

关键词： Fuzzy logic Vibrations Uncertain systems Uncertainty Roads Switches Stability analysis Vehicle dynamics Springs Sliding mode control