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A Hybrid Solution for Secure Privacy-Preserving Cloud Storage & Information Retrieval

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Human-centric computing and Information sciences 2023年 13卷

作者： Kumar, Ankit Aljrees, Turki Hsieh, Sun-Yuan Singh, Kamred Udham Singh, Teekam Raja, Linesh Samriya, Jitendra Kumar Mundotiya, Rajesh Kumar Department of Computer Engineering & Applications GLA University UP Mathura India College of Computer Science and Engineering University of Hafr Al-Batin Eastern Province Saudi Arabia Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models and International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Manipal University Jaipur Jaipur India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India School of Computer Science University of Petroleum and Energy Studies Dehradun India

Cloud storage is an emerging archetype used by businesses for data storage. Clients require easy access to data in the cloud, which helps clients with limited computing power move their high-value, high-risk principle jobs to the cloud. The primary concern of this study is towards flawless verifying, checking data bundles, and determining modules to carry out the project. As discussed at several research platforms, each client is concerned about data storage and retrieval. Data security is critical in cloud computing, and thus monitoring has evolved into a vigilance that abstracts the monitoring process. Request made by clients for security planning is always computationally exclusive. As a result, each customer is concerned as to their verified condition. This paper will highlight a secure cloud communication method for client data to address the afore-mentioned issues. Synchronization creates secret hare keys, combined with a model-encrypted data packet sent to the cloud. These secure data are then sent over a public network, subject to monitoring to avert data leakage to an unauthorized party. They lack the cohesion to outsource data storage to the cloud deliberately. As an outcome of this model's distribution, the perception of authorized data owners would be changed regarding cloud storage up to 97.6%, addressing the trust issues between data owners and cloud ace affiliations © 2023, Human-centric computing and Information *** Rights Reserved.

关键词： Cryptography

Deep Learning for Camera Calibration and Beyond: A Survey

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arXiv 2023年

作者： Liao, Kang Nie, Lang Huang, Shujuan Lin, Chunyu Zhang, Jing Zhao, Yao Gabbouj, Moncef Tao, Dacheng Institute of Information Science Beijing Jiaotong University Beijing100044 China Visual Intelligence + X International Joint Laboratory of the Ministry of Education China Department of Computing Sciences Tampere University Tampere33101 Finland School of Computer Science Faculty of Engineering The University of Sydney Australia

Camera calibration involves estimating camera parameters to infer geometric features from captured sequences, which is crucial for computer vision and robotics. However, conventional calibration is laborious and requires dedicated collection. Recent efforts show that learning-based solutions have the potential to be used in place of the repeatability works of manual calibrations. Among these solutions, various learning strategies, networks, geometric priors, and datasets have been investigated. In this paper, we provide a comprehensive survey of learning-based camera calibration techniques, by analyzing their strengths and limitations. Our main calibration categories include the standard pinhole camera model, distortion camera model, cross-view model, and cross-sensor model, following the research trend and extended applications. As there is no unified benchmark in this community, we collect a holistic calibration dataset that can serve as a public platform to evaluate the generalization of existing methods. It comprises both synthetic and real-world data, with images and videos captured by different cameras in diverse scenes. Toward the end of this paper, we discuss the challenges and provide further research directions. To our knowledge, this is the first survey for the learning-based camera calibration (spanned 10 years). The summarized methods, datasets, and benchmarks are available and will be regularly updated at https://***/KangLiao929/Awesome-Deep-Camera-Calibration. Copyright © 2023, The Authors. All rights reserved.

关键词： Pinhole cameras

STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction

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arXiv 2022年

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Gao, Wen School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Although many video prediction methods have obtained good performance in low-resolution (64∼128) videos, predictive models for high-resolution (512∼4K) videos have not been fully explored yet, which are more meaningful due to the increasing demand for high-quality videos. Compared with low-resolution videos, high-resolution videos contain richer appearance (spatial) information and more complex motion (temporal) information. In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction. On the one hand, we propose a Spatiotemporal Encoding-Decoding Scheme to preserve more spatiotemporal information for high-resolution videos. In this way, the appearance details for each frame can be greatly preserved. On the other hand, we design a Residual Predictive Memory (RPM) which focuses on modeling the spatiotemporal residual features (STRF) between previous and future frames instead of the whole frame, which can greatly help capture the complex motion information in high-resolution videos. In addition, the proposed RPM can supervise the spatial encoder and temporal encoder to extract different features in the spatial domain and the temporal domain, respectively. Moreover, the proposed model is trained using generative adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve the perceptual quality of the predictions. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods. © 2022, CC BY.

关键词： Signal encoding

TOWARDS NONLINEARITY. THE p-REGULARITY THEORY. APPLICATIONS AND DEVELOPMENTS.

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arXiv 2025年

作者： Bednarczuk, E. Brezhneva, O. Leśniewski, K. Prusińska, A. Tret’Yakov, A. Warsaw University of Technology Poland Department of Mathematics Miami University OxfordOH United States System Research Institute Polish Academy of Sciences Poland Faculty of Science University of Siedlce Poland Dorodnicyn Computing Center Federal Research Center "Computer Science and Control " Russian Academy of Sciences Moscow119333 Russia

We present recent advances in the analysis of nonlinear equations with singular operators and nonlinear optimization problems with constraints given by singular mappings. The results are obtained within the framework of p-regularity theory, which has developed successfully over the last forty years. We illustrate the theory with its applications to degenerate problems in various areas of mathematics. In particular, we address the problem of describing the tangent cone to the solution set of nonlinear equations in a singular case. The structure of p-factor operators is used to propose optimality conditions and construct numerical methods for solving degenerate nonlinear equations and optimization problems. The methods presented in the paper can be considered as the first numerical approaches targeting solutions of degenerate problems, such as the Van der Pol differential equation, boundary-value problems with a small parameter, partial differential equations where Poincaré’s method of small parameter fails, nonlinear degenerate dynamical systems, and others. There are various practical applications for the theory of p-regularity, including structural engineering, composite materials, and material design. For instance, the theory can be applied to analyze the behavior of materials with irregular or complex properties. By considering higher-order derivatives, it becomes possible to model and predict the response of materials to external forces, such as stress or temperature variations. In geophysics, the p-regularity theory can be utilized to analyze and interpret complex data obtained from seismic surveys, gravity measurements, or electromagnetic surveys. The theory also finds applications in the analysis of nonlinear differential equations arising in control systems, geometric and topological analysis, biomechanics, and many other fields. The theory offers a valuable approach to analyzing and understanding nonlinear phenomena in situations where classical regularity

关键词： Boundary value problems

Unsupervised Cortical Surface Registration Network for Aligning Gyralnet

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Unsupervised Cortical Surface Registration Network for Align...

IEEE International Symposium on Biomedical Imaging

作者： Jiale Cheng Fenqiang Zhao Dan Hu Chao Cao Zhengwang Wu Xinrui Yuan Kangfu Han Lu Zhang Tianming Liu Dajiang Zhu Gang Li Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill NC USA Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill and North Carolina State University NC USA Department of Computer Science and Engineering University of Texas at Arlington TX USA Department of Computer Science Indiana University Indianapolis IN USA School of Computing University of Georgia GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The cortical 3-hinge gyrus (3HG) and its network (GyralNet) play key roles in understanding the regularity and variability of brain structure and function. However, existing cortical surface registration methods overlook these features, resulting in suboptimal alignment across subjects. Currently, no 3HG and GyralNet atlas exist for registration, and generation of the corresponding atlas requires extensive runtime using traditional methods. To enable better registration of these features, we introduce an unsupervised learning framework to jointly develop 3HGs and GyralNet atlas and register the individual cortical features onto the atlas. To incorporate the graph structure of 3HGs and GyralNet into the registration network, we convert them into surface distance maps, facilitating effective integration. To effectively learn large deformations, a multi-level spherical registration network based on spherical U-Net is introduced to perform registration in a coarse-to-fine manner. Experiments demonstrate our approach's ability to generate 3HGs and GyralNet atlas with detailed patterns and effectively improve registration accuracy.

关键词： Brain Runtime Accuracy Deformation Registers Unsupervised learning Biomedical imaging

Forecasting Air Quality Index in Thailand Using Ensemble Method

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Forecasting Air Quality Index in Thailand Using Ensemble Met...

International computer science and Engineering Conference (ICSEC)

作者： Saksiri Lertnilkarn Suphakant Phimoltares Department of Mathematics and Computer Science Faculty of Science Advanced Virtual and Intelligent Computing (AVIC) Research Center Chulalongkorn University Pathumwan Bangkok Thailand

ISBN: (纸本)9781665462730

Air pollution is one of the most serious problems in many regions of the world. Thailand also has had to face this trouble unavoidably, especially in the northern region of Thailand, the area that has been highly contaminated by air pollution for so many years. In this paper, an ensemble method was introduced to forecast the level of air quality index (AQI) in the northern part of Thailand. The ensemble method, in this study, is a technique gaining the results from majority vote of outputs of three classification models—k-nearest neighbors, random forest, and support vector machine. The proposed model compared the voted accuracy with the accuracies of existing classification models. It made use of the 2018 - 2021 data from seven stations in four provinces of Northern Thailand. In the end, the proposed model yielded 99.68% - 99.84% accuracy rate on average higher than most of the performance of the other comparative models.

关键词： Support vector machines Training Atmospheric modeling Computational modeling Predictive models Air pollution Prediction algorithms

Anonymous Distributed Localisation via Spatial Population Protocols

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arXiv 2024年

作者： Gąsieniec, Leszek Kuszner, Lukasz Latif, Ehsan Parasuraman, Ramviyas Spirakis, Paul Stachowiak, Grzegorz Department of Computer Science University of Liverpool United Kingdom Institute of Informatics Physics and Informatics University of Gdańsk Poland AI4STEM Education Center Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States School of Computing University of Georgia AthensGA United States Institute of Computer Science University of Wroclaw Poland

In the distributed localization problem (DLP), n anonymous robots (agents) A0, . . . ,An-1 begin at arbitrary positions p0, . . . , pn-1 ∈ S, where S is a Euclidean space. Initially, each agent Ai operates within its own coordinate system in S, which may be inconsistent with those of other agents. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p0, . . . , pn-1, in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents Ai and Aj interact, they can not only exchange their current knowledge but also either determine the distance dij between them in S (distance query model) or obtain the vector -→vij spanning points pi and pj (vector query model). We examine three DLP scenarios, proposing and analysing several types of distributed localisation protocols, including: 1. Self-stabilising localisation protocol with distance queriesWe propose and analyse self-stabilising localisation protocol based on pairwise distance adjustment. We also discuss several hard instances in this scenario, and suggest possible improvements for the considered protocol, 2. Leader-based localisation protocol with distance queries We propose and analyse several leaderbased protocols which stabilise in o(n) parallel time. These protocols rely on efficient solution to multi-contact epidemic, an extension of one-way epidemic in population protocols, and 3. Self-stabil

关键词： Multi agent systems

Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

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arXiv 2024年

作者： Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou Institute for Artificial Intelligence Peking University Beijing China Computer School Beijing Information Science & Technology University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology State Key Lab of General Artificial Intelligence Peking University Beijing China State Key Laboratory of General Artificial Intelligence BIGAI Beijing China

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI’s potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection;2) goal-oriented negotiation with social reasoning;and 3) augmenting memory through self-play games for self-evolution without human in the loop. Copyright © 2024, The Authors. All rights reserved.

关键词： Strategic planning

Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

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arXiv 2024年

作者： Lee, Ho Hin Saunders, Adam M. Kim, Michael E. Remedios, Samuel W. Remedios, Lucas W. Tang, Yucheng Yang, Qi Yu, Xin Bao, Shunxing Cho, Chloe Mawn, Louise A. Rex, Tonia S. Schey, Kevin L. Dewey, Blake E. Spraggins, Jeffrey M. Prince, Jerry L. Huo, Yuankai Landman, Bennett A. Department of Computer Science Vanderbilt University Nashville United States Department of Electrical and Computer Engineering Vanderbilt University Nashville United States Department of Computer Science Johns Hopkins University Baltimore United States Department of Biomedical Engineering Vanderbilt University Nashville United States Department of Ophthalmology and Visual Sciences Vanderbilt University Medical Center Nashville United States Department of Biochemistry Vanderbilt University Nashville United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Department of Cell and Developmental Biology Vanderbilt University Nashville United States

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference. Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases. First, to restore spatial details from scans with a low through-plane resolution compared to a high in-plane resolution, we apply a deep learning-based super-resolution algorithm. Then, we generate an initial unbiased reference with an iterative metric-based registration using a small portion of subject scans. We register the remaining scans to this template and refine the template using an unsupervised deep probabilistic approach that generates a more expansive deformation field to enhance the organ boundary alignment. We demonstrate this framework using magnetic resonance images across four different tissue contrasts, generating four atlases in separate spatial alignments. Results: When refining the template with sufficient subjects, we find a significant improvement using the Wilcoxon signed-rank test in the average Dice score across four labeled regions compared to a standard registration framework consisting of rigid, affine, and deformable transformations. These results highlight the effective alignment of eye organs and boundaries using our proposed process. Conclusions: By combining super-resolution preprocessing and deep probabilistic models, we address the challenge of generating an eye atlas to serve as a standardized reference across a largely variable population. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging