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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Relative positioning of autonomous systems using signals of opportunity

arXiv

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arXiv 2020年

作者： Souli, Nicolas Kolios, Panayiotis Ellinas, Georgios Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus

—For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values. Copyright © 2020, The Authors. All rights reserved.

关键词： Global positioning system

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A Microscopic Human-Inspired Adaptive Cruise Control for Eco-Driving

A Microscopic Human-Inspired Adaptive Cruise Control for Eco...

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European Control Conference (ECC)

作者： Marco Mirabilio Alessio Iovine Elena De Santis Maria Domenica Di Benedetto Giordano Pola Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of LAquila

The following topics are dealt with: control system synthesis; nonlinear control systems; predictive control; linear systems; feedback; stability; robust control; optimisation; closed loop systems; and optimal control.

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

关键词： Biological system modeling Automata Safety Acceleration Microscopy Fuels Cruise control

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HorizonBlock: Implementation of an Autonomous Counter-Drone System

HorizonBlock: Implementation of an Autonomous Counter-Drone ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： N. Souli R. Makrigiorgis A. Anastasiou A. Zacharia P. Petrides A. Lazanas P. Valianti P. Kolios G. Ellinas Department of Electrical and Computer Engineering and the KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

Unmanned Aircraft Systems (UASs) are techno-logically advancing at such a rapid pace that domain experts are now highly concerned of the potential misuse of the technology that can be used for unlawful actions with detrimental effects. The most effective measure to counteract the operation of rogue drones are electronic anti-drone systems that in one way or another intercept the normal operation of a rogue agent. In this work we develop an intelligent pursuer drone that implements novel lightweight functions to meet all necessary interception steps (i.e., detection, tracking and interception) in addition to self-localizing using signals of opportunity in order to maintain perception when performing wireless jamming against a rogue drone.

关键词： Drones Jamming Interference Global navigation satellite system Wireless communication Communication system security Tracking

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Relative Positioning of Autonomous Systems using Signals of Opportunity

Relative Positioning of Autonomous Systems using Signals of ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nicolas Souli Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering and the KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle's trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values.

关键词： Global navigation satellite system Receivers Position measurement Global Positioning System Transmitters Trajectory

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Opinion maximization in social trust networks 29

Opinion maximization in social trust networks

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Xu, Pinghua Hu, Wenbin Wu, Jia Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China Shenzhen Research Institute Wuhan University China Department of Computing Macquarie University Australia

ISBN: (纸本)9780999241165

Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states (e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks (STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. Moreover, we developed two matrix-based methods for these two problems and experiments on real-world datasets to demonstrate the practical utility of our methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Budget control

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Visual servo guided cyber-physical robotic assembly cell

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IFAC-PapersOnLine 2021年第1期54卷 595-600页

作者： Gábor Erdős Dániel Horváth Gergely Horváth Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Hungary Department of Software Technology and Methodology Eötvös Loránd University Hungary

Advanced robotic assembly systems are promising great benefits to the manufacturing sector, once the shortcomings of the current technologies are overcome. One of the biggest obstacles to introducing flexible robotic systems is the ability of robots to sense and perceive the semi-structured or unstructured environment. This paper presents a cyber-physical system based framework of a robotic assembly cell that is capable of recognizing resources of assembly processes, based on information from various visual sensors and updating and adapting the assembly plan in the digital twin of the robotic cell. Thus, calculates and executes feasible motion plans of the robots based on accurate digital information. The application of the framework is demonstrated in a physical assembly process.

关键词： Process Planning Intelligent Manufacturing Systems Intelligent Robot Services in Manufacturing

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LocalDrop: A hybrid regularization for deep neural networks

arXiv

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arXiv 2021年

作者： Lu, Ziqing Xu, Chang Du, Bo Ishida, Takashi Zhang, Lefei Sugiyama, Masashi The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia RIKEN Center for Advanced Intelligence Project Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Japan

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs), including drop rates and weight matrices, has been developed based on the proposed upper bound of the local Rademacher complexity by the strict mathematical deduction. The analyses of dropout in FCNs and DropBlock in CNNs with keep rate matrices in different layers are also included in the complexity analyses. With the new regularization function, we establish a two-stage procedure to obtain the optimal keep rate matrix and weight matrix to realize the whole training model. Extensive experiments have been conducted to demonstrate the effectiveness of LocalDrop in different models by comparing it with several algorithms and the effects of different hyperparameters on the final performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models

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Ain Shams engineering Journal 2025年第9期16卷

作者： Mustafa Tahsin Yilmaz Salman Badurayq Kemal Polat Ahmad H. Milyani Abdulaziz S. Alkabaa Osman Gul Furkan Turker Saricaoglu Department of Industrial Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science (AIADS) King Abdulaziz University Jeddah Saudi Arabia Production Chief at Pladis Global Phase 3 Industrial Area Jeddah Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Intelligent Engineering Systems (CEIES) King Abdulaziz University Jeddah Saudi Arabia Department of Food Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Turkey Department of Food Engineering Faculty of Engineering and Natural Science Bursa Technical University Bursa Turkey Faculty of Engineering Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu TR 14280 Turkey

In this study, we conducted a comparative analysis of the explainability of Decision Tree Regressor (DTR) and Gaussian Process Regressor (GPR) models in predicting the shear stress and viscosity of sesame protein isolate (SPI) systems, employing explainable machine learning (EML) techniques to elucidate complex, nonlinear relationships among processing parameters. SPI samples were processed across pressure levels ranging from 0 to 100 MPa and ion concentration (IC) values from 0 to 200 mM. DTR model accurately predicted shear stress ( R 2 = 0.999), while a GPR model achieved high performance for viscosity prediction ( R 2 = 0.9925). Formally, the modeling task is framed as learning a predicting mapping function f : R p → R , where x ∈ R p denotes the vector of predictors (pressure, IC, shear rate) and y ∈ R is the target variable (shear stress or viscosity), by minimizing a loss function such as mean squared error. Interpretation of model predictions using SHapley Additive exPlanations (SHAP), permutation importance, and partial dependence analysis revealed that pressure and IC are the most influential factors affecting shear stress and viscosity, with pressure inducing protein conformational changes that impact rheological properties. The shear rate exhibited a lesser direct impact within the systems examined. Partial Dependence Plots (PDPs) from the DTR model revealed strong, nearly linear positive relationships between pressure and shear stress, while the GPR model depicted more nuanced responses, highlighting the models’ differing sensitivities. Variance-Based Sensitivity Indices (VBSIs) further quantified these influences, with pressure and IC showing higher sensitivity scores in the DTR model compared to the GPR model. Permutation importance and SHAP interaction analyses corroborated these results, emphasizing the dominant role of pressure and IC, both independently and interactively, in determining shear stress. In contrast, viscosity predictions were influ

关键词： Explainable artificial intelligence Sesame protein isolates Steady shear rheology Tree-based machine learning models Gaussian Process regressor

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An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

arXiv

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arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

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