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The Minimal Search Space for Conditional Causal Bandits

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arXiv 2025年

作者： Simoes, Francisco N.F.Q. Feigenbaum, Itai Dastani, Mehdi van Omen, Thijs Department of Information and Computing Sciences Utrecht University Netherlands Department of Computer Science Lehman College Graduate Center City University of New York United States

Causal knowledge can be used to support decision-making problems. This has been recognized in the causal bandits literature, where a causal (multi-armed) bandit is characterized by a causal graphical model and a target variable. The arms are then interventions on the causal model, and rewards are samples of the target variable. Causal bandits were originally studied with a focus on hard interventions. We focus instead on cases where the arms are conditional interventions, which more accurately model many real-world decision-making problems by allowing the value of the intervened variable to be chosen based on the observed values of other variables. This paper presents a graphical characterization of the minimal set of nodes guaranteed to contain the optimal conditional intervention, which maximizes the expected reward. We then propose an efficient algorithm with a time complexity of O(|V |+ |E|) to identify this minimal set of nodes. We prove that the graphical characterization and the proposed algorithm are correct. Finally, we empirically demonstrate that our algorithm significantly prunes the search space and substantially accelerates convergence rates when integrated into standard multi-armed bandit algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

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PSMiner: A Pattern-Aware Accelerator for High-Performance St...

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Enhanced Detection of APT Vector Lateral Movement in Organizational Networks Using Lightweight Machine Learning

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computers, Materials & Continua 2025年第4期83卷 281-308页

作者： Mathew Nicho Oluwasegun Adelaiye Christopher D.McDermott Shini Girija Research and Innovation Center Rabdan AcademyAbudhabiP.O.Box 114646United Arab Emirates UQ Cyber Research Centre The University of QueenslandBrisbaneQLD 4027Australia Department of Computer Science Bingham UniversityAbuja961105Nigeria School of Computing Robert Gordon UniversityAberdeenAB107QBUK College of Technological Innovation Zayed UniversityDubaiP.O.Box 144534United Arab Emirates

The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming *** Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense *** AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and *** review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant ***,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter *** have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for ***,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT *** the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset ***,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct *** the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltrat

关键词： Intrusion detection lateral movement machine learning advanced persistent threats

Accurately Estimating Unreported Infections using Information Theory

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arXiv 2025年

作者： Cui, Jiaming Adhikari, Bijaya Haddadan, Arash Ul Haque, A.S.M. Ahsan Vreeken, Jilles Vullikanti, Anil Prakash, B. Aditya College of Computing Georgia Institute of Technology United States Department of Computer Science University of Virginia United States Department of Computer Science Virginia Tech United States Department of Computer Science The University of Iowa United States Amazon United States CISPA Helmholtz Center for Information Security Germany

One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly. © 2025, CC BY-NC-ND.

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APP: Enabling Soft Real-Time Execution on Densely-Populated Hybrid Memory System 23

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APP: Enabling Soft Real-Time Execution on Densely-Populated ...

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zheng-Wei Wu Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University and Mohamed bin Zayed University of Artificial Intelligence Department of Computer Science and NTU High Performance and Scientific Computing Center National Taiwan University

ISBN: (纸本)9798350323481

Memory swapping was considered slow and evil, but swapping to Ultra Low-Latency storage like Optane has become a promising solution to save power and cost, helping densely-populated edge server to overcome its DRAM capacity bottleneck. However, the lack of integration between CPU scheduling and memory paging causes soft real-time tasks running on edge servers to miss deadlines under heavy memory multiplexing. We propose APP (Adaptive Page Pinning), lightweight protection of working set memory to ensure meeting soft real-time task deadlines without starving other non-real-time tasks. Experiments show that APP alleviates thrashing in memory-intensive tasks and upholds soft real-time task deadlines.

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

CLINICAL INSPIRED MRI LESION SEGMENTATION

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arXiv 2025年

作者： Yan, Lijun Wang, Churan Zhong, Fangwei Wang, Yizhou School of Software and Microelectronics Peking University China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology Peking University China School of Artificial Intelligence Beijing Normal University China

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the subsequence feature, i.e. the difference between post contrast-enhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

Message digest and blockchain based chaotic ordered cyber secured cloud of things for smart health care

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-20页

作者： Munnangi, Ashok Kumar Maruthapillai, Kumaresan Rajeyyagari, Sivaram Sekaran, Ramesh Ravi, Vinayakumar Al Mazroa, Alanoud Andhra Pradesh Vijayawada520007 India Karnataka Bangalore562112 India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia P.O. Box 84428 Riyadh11671 Saudi Arabia

Over previous years, the Internet of Things (IoT) made to connect multifaceted things to the internet. It has led to a digital disturbance when by alternating method is utilized. It led to digital interruption through exchanging methods of expertise is used. Numerous industries, such as Smart Health Care (SHC) transport as well as smart grids have metamorphosed this time smarter than still using a cloud of things. Conversely, cybersecurity assaults endanger confidentiality, and so on. Through the widespread consumption of IoT expertise at SHC, novel security as well as privacy methods ought to be mentioned. MDC-BCO cyber secured technique together at storage and recovery is obtainable for SHC scheme using Cloud. The entire technique is divided into three phases. They are the sign in stage, device authentication stage, and device communication stage. The primary, sign is carried out by using Message Digest Crypto for every IoT tool through a cloud server (CS), guarantee scalable and manageable IoT structural design. Subsequently, authentication is carried out for every registered tool by Blockchain-based Chaotic Order. The encryption is performed by the device private key to verify the authentication to ensure network latency and security. Lastly, secured device communication is performed for each authorized user or device by using Chaotic Order. Secure data communication between the devices (i.e. patient and hospital or patient and doctors) is achieved by reducing end-to-end delay to make relationship requests among tools. Outcomes obtained as of simulations exposed improved achievement of the proposed technique compared to conventional blockchain-based techniques. From the experimental analysis, the MDC-BCO minimizes network latency, delay, and memory consumption by 24%, 16%, and 14% and improves security by 6% with MHEALTH dataset as compared to existing works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Cyber attacks

Clinical Inspired MRI Lesion Segmentation

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Clinical Inspired MRI Lesion Segmentation

IEEE International Symposium on Biomedical Imaging

作者： Lijun Yan Churan Wang Fangwei Zhong Yizhou Wang School of Software and Microelectronics Peking University China Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University China School of Artificial Intelligence Beijing Normal University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the subsequence feature, i.e. the difference between post contrastenhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications.

关键词： Image segmentation Pathology Sensitivity Magnetic resonance imaging Brain tumors Breast Lesions Medical diagnosis Standards Diseases