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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning 37

ALIM: Adjusting Label Importance Mechanism for Noisy Partial...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Xu, Mingyu Lian, Zheng Feng, Lei Liu, Bin Tao, Jianhua The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Automation Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781713899921

Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical interpretations, called "Adjusting Label Importance Mechanism (ALIM)". It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on multiple benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. Our code is available at: https://***/zeroQiaoba/ALIM. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Benchmarking

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Supercapacitor Digital Twin Management System Based on Cloud Environment 23

Supercapacitor Digital Twin Management System Based on Cloud...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Yingze Zhao, Qinxian Gao, Dianzhu Rong, Jieqi Fan, Yunsheng Li, Heng Peng, Jun School of Computer Science and Engineering Central South University Changsha410083 China School of Automation Central South University Changsha410083 China Hunan Engineering Laboratory of Rail Vehicles Braking Technology Changsha410083 China

ISBN: (纸本)9781665494571

With the continuous promotion of the green transportation concept, supercapacitors have gained popularity for their excellent charging and discharging characteristics. However, the unreasonable management of supercapacitor will lead to poor safety and reliability of the supercapacitor system. Aiming at this problem, a supercapacitor cloud management system based on the digital twin is proposed in this paper, which improves the computing power and data storage capacity through cloud computing for the whole life cycle state monitoring of supercapacitors. Firstly, the mathematical modeling of the supercapacitor system is carried out, and the state parameters during the charging and discharging of the supercapacitor are obtained in real-time at the physical layer and uploaded to the data interaction network. Secondly, in the network layer, all data related to the supercapacitor are measured and transmitted to the cloud through IoT, and a digital twin cloud model of the supercapacitor management system is established to evaluate the usage status of the supercapacitor by using the real-time data in the data interaction network. Finally, the supercapacitor charging and discharging control strategy is adjusted in real-time based on the state evaluation results at the control layer and given a visualization window visible to users. The application of the supercapacitor system in the digital twin is explored by developing a parameter estimation algorithm suitable for cloud computing. The experimental results verify the feasibility and practicality of the proposed supercapacitor cloud management system. © 2021 IEEE.

关键词： Cloud computing Smart cities Supercapacitors Real-time systems Data models Digital twins Topology

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Review of micro-expression spotting and recognition in video sequences

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Virtual Reality & Intelligent Hardware 2021年第1期3卷 1-17页

作者： Hang PAN Lun XIE Zhiliang WANG Bin LIU Minghao YANG Jianhua TAO School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China

Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress,hide,disguise,or *** expressions can reflect a person's real emotions and have a wide range of application in public safety and clinical *** analysis of facial micro-expressions in video sequences through computer vision is still relatively *** this research,a comprehensive review on the topic of spotting and recognition used in micro expression analysis databases and methods,is conducted,and advanced technologies in this area are *** addition,we discuss challenges that remain unresolved alongside future work to be completed in the field of micro-expression analysis.

关键词： Facial expression Micro-expression spotting Micro-expression recognition Database Review

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing 62

MIKE: A New Benchmark for Fine-grained Multimodal Entity Kno...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Li, Jiaqi Du, Miaozeng Zhang, Chuanyi Chen, Yongrui Hu, Nan Qi, Guilin Jiang, Haiyun Cheng, Siyuan Tian, Bozhong School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Tencent AI Lab China Zhejiang University China

ISBN: (纸本)9798891760998

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge, as FG entity recognition is pivotal for the practical deployment and effectiveness of MLLMs in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a comprehensive benchmark and dataset specifically designed for the FG multimodal entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition. In addition, a new form of knowledge editing, Multi-Step Editing, is introduced to evaluate the editing efficiency. Through our extensive evaluations, we demonstrate that the current state-of-the-art methods face significant challenges in tackling our proposed benchmark, underscoring the complexity of FG knowledge editing in MLLMs. Our findings spotlight the urgent need for novel approaches in this domain, setting a clear agenda for future research and development efforts within the community. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Reinforcement Learning-Based Optimal Control for Continuous-Time Quantized Systems via Adaptive Dynamic Programming

Reinforcement Learning-Based Optimal Control for Continuous-...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Omar Qasem Weinan Gao Omar R. Daoud Electrical and Computer Engineering Department School of Engineering and Computing American International University Al-Jahra Kuwait State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.

关键词： Quantization (signal) Adaptive systems Regulators Simulation Heuristic algorithms Optimal control Stability analysis Dynamic programming Trajectory Thermal stability

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ARF-NET:Method of spinal segmentation in deformed spine radiographs 11

ARF-NET:Method of spinal segmentation in deformed spine radi...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Lin, XiaTian Liu, Yingli Shen, Tao Gao, Ming Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China The First People's Hospital of Yunnan Province Kunming650500 China

ISBN: (纸本)9798350342406

Deformed spine radiograph spine segmentation can help doctors to analyze the disease site faster, which is a necessary part of the current development of competent healthcare. For scoliosis diagnosis, this paper designs an Attention-guided ResNet with an FPN-like Upsampling model(ARF-Net) for segmenting the cone part of the spine radiograph map. In this paper, ResNet50 is used as the backbone to propose the feature map. Then the Global-Coordinate Attention (GCA) attention mechanism is added to obtain the X-axis, Y-axis, and global features. Finally, through the SIMAMUP module, the feature map is upsampled to introduce more detailed information to prevent cone segmentation errors and to improve the cone segmentation capability. The final experimental findings demonstrate that the model described in this study has greater segmentation precision in the SpineWebl6 public dataset, with the Jaccard and Dice coefficients reaching 87.70% and 93.45%, respectively. © 2023 IEEE.

关键词： Image segmentation

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Improved Sliding Window Smoothing for Video Temporal Action Segmentation and Recognition

Improved Sliding Window Smoothing for Video Temporal Action ...

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2023 China automation Congress, CAC 2023

作者： Li, Ce Tian, Yihan Sheng, Longshuai Chen, Junzhi Wang, Tian Wei, Xianlong Computer Science and Technology China University of Mining & Technology Beijing Beijing China Peking University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Sklsde Beihang University Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350303759

Despite substantial research on human action segmentation in videos to determine the type and timing of activities, the topic is still unresolved because of the dearth of large-scale annotation data in video analysis applications. Supervised video action segmentation employs a number of temporal convolutional network (TCN) models to address this problem. The process is still difficult, because of the intricate temporal duration division of the movements in the videos. In order to create a soft and flexible video partition, we incorporate an improved sliding window smoothing (ISWS) technique into a TCN baseline model in this study. When screening the target video segmentation sequence, our research method carefully selects three discriminative frames and cleverly integrates them into the adaptive sliding window to more specifically optimize the smoothing effect of the entire prediction sequence. It is particularly worth noting that we implement a doubling penalty mechanism when the window slides to the wrong category position. In order to learn the resultants of effective and ineffective segmentation paths, we designed a new loss function to smooth the candidate frames of the segmentation points in the sliding window using the ISWS scheme. So that our method can increase the receptive field of video segmentation effectively to gain the optimal action segmentation. Experiments on the breakfast, 50salads, and GTEA datasets demonstrate that our method significantly improved the frame accuracy for action segmentation in videos when compared to the state-of-the-art techniques. © 2023 IEEE.

关键词： Image segmentation

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