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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

arXiv

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arXiv 2022年

作者： Jiang, Xuhao Tan, Weimin Cheng, Ri Zhou, Shili Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods. © 2022, CC BY.

关键词： Textures

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IC-CViT: Inverse-Consistent Convolutional Vision Transformer for Diffeomorphic Image Registration

IC-CViT: Inverse-Consistent Convolutional Vision Transformer...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tao Xu Ting Jiang Xiaoning Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu China

Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have been a broad focus of research in medical image registration, and CNN-based methods have made great progress. However, the results of most existing methods generally are not necessarily diffeomorphic, generating implausibly bijective mappings between images due to the interpolation and discrete representation. Furthermore, the performances of CNNs may be limited by a lack of precise comprehension of global and long-range cross-image spatial relevance. Vision Transformer (ViT) is capable of enhancing the long-distance information interaction ability to identify the semantically anatomically correspondences of medical images. Compared with CNN, ViT has weak local feature extraction ability due to less inductive bias, especially in small-scale training datasets, meaning that the samples between adjacent pixels cannot be exploited adequately. To address the above challenges, we propose a novel Inverse-Consistent Convolutional Vision Transformer (IC-CViT) network for diffeomorphic image registration. Specifically, image pairs can explicitly conduct bi-directional registration through the predicted deformation filed, generated within the diffeomorphic mappings space and restricted by the proposed inverse consistent loss term. We verify our method on two 3D brain MRI scan datasets including OASIS and LPBA40. Comprehensive results demonstrate that IC-CViT achieves state-of-the-art registration accuracy while maintaining desired diffeomorphic properties.

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction

arXiv

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arXiv 2023年

作者： Sun, Kai Zhang, Richong Mensah, Samuel Aletras, Nikolaos Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023, CC BY.

关键词： Sentiment analysis

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Workload Failure Prediction for Data centers

arXiv

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arXiv 2023年

作者： Li, Jie Wang, Rui Ali, Ghazanfar Dang, Tommy Sill, Alan Chen, Yong Department of Computer Science Texas Tech University Lubbock United States High-Performance Computing Center Texas Tech University Lubbock United States

Failed workloads that consumed significant computational resources in time and space affect the efficiency of data centers significantly and thus limit the amount of scientific work that can be achieved. While the computational power has increased significantly over the years, detection and prediction of workload failures have lagged far behind and will become increasingly critical as the system scale and complexity further increase. In this study, we analyze workload traces collected from a production cluster and train machine learning models on a large amount of data sets to predict workload failures. Our prediction models consist of a queue-time model that estimates the probability of workload failures before execution and a runtime model that predicts failures at runtime. Evaluation results show that the queue-time model and runtime model can predict workload failures with a maximum precision score of 90.61% and 97.75%, respectively. By integrating the runtime model with the job scheduler, it helps reduce CPU time, memory usage by up to 16.7% and 14.53%, respectively. © 2023, CC BY-NC-SA.

关键词： Big data

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Systematic Review on Reinforcement Learning in the field of Fintech

arXiv

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arXiv 2023年

作者： Malibari, Nadeem Katib, Iyad Mehmood, Rashid Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah21589 Saudi Arabia

Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably better than other state-of-the-art algorithms. The present work discusses the use of reinforcement learning algorithms in diverse decision-making challenges in Fintech and concludes that the organizations dealing with finance can benefit greatly from Robo-advising, smart order channelling, market making, hedging and options pricing, portfolio optimization, and optimal execution. © 2023, CC BY.

关键词： Reinforcement learning

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Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Quantum algorithms for sampling log-concave distributions and estimating normalizing constants 22

Quantum algorithms for sampling log-concave distributions an...

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Andrew M. Childs Tongyang Li Jin-Peng Liu Chunhao Wang Ruizhe Zhang Joint Center for Quantum Information and Computer Science Department of Computer Science and Institute for Advanced Computer Studies University of Maryland Center on Frontiers of Computing Studies and School of Computer Science Peking University Simons Institute and Department of Mathematics UC Berkeley Department of Computer Science and Engineering Pennsylvania State University Department of Computer Science The University of Texas at Austin

ISBN: (纸本)9781713871088

Given a convex function f : ℝd → ℝ, the problem of sampling from a distribution ∝ e-f(x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\int_{\mathbb{R}^d}e^{-f(x)}\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number κ and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error e. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity Õ(κ1/2d) and Õ(κ1/2d3/2/ε) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in κ, d, ε over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/ε1-o(1) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in ε.

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Neural transmitted radiance fields 22

Neural transmitted radiance fields

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Chengxuan Zhu Renjie Wan Boxin Shi Nat'l Eng. Research Center of Visual Technology School of Computer Science Peking University Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9781713871088

Neural radiance fields (NeRF) have brought tremendous progress to novel view synthesis. Though NeRF enables the rendering of subtle details in a scene by learning from a dense set of images, it also reconstructs the undesired reflections when we capture images through glass. As a commonly observed interference, the reflection would undermine the visibility of the desired transmitted scene behind glass by occluding the transmitted light rays. In this paper, we aim at addressing the problem of rendering novel transmitted views given a set of reflection-corrupted images. By introducing the transmission encoder and recurring edge constraints as guidance, our neural transmitted radiance fields can resist such reflection interference during rendering and reconstruct high-fidelity results even under sparse views. The proposed method achieves superior performance from the experiments on a newly collected dataset compared with state-of-the-art methods.

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