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检索条件"任意字段=10th ACM Symposium on Access Control Models and Technologies"

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Data Protection and Machine-Learning-Supported Decision-Making at the EU Border: ETIAS Profiling Under Scrutiny 10th

Data Protection and Machine-Learning-Supported Decision-Maki...

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10th Annual Privacy Forum (APF)

作者： Pesch, Paulina Jo Dimitrova, Diana Boehm, Franziska Karlsruhe Inst Technol D-76131 Karlsruhe Germany FIZ Karlsruhe Leibniz Inst Informat Infrastruct D-76344 Eggenstein Leopoldshafen Germany Vrije Univ Brussel LSTS Grp Brussels Belgium

ISBN: (纸本)9783031073144;9783031073151

ETIAS is an upcoming, largely automated IT system to identify risks posed by visa-exempt third Country Nationals (TCNs) traveling to the Schengen area. It is expected to be operational by the end of 2022. the largely automated ETIAS risk assessments include the check of traveller data against not yet defined abstract risk indicators which might discriminate against certain groups of travellers. Moreover, there is evidence for the planned use of machine learning (ML) for risk assessments under the ETIAS framework. the risk assessments that could result in personal data being entered into terrorist watchlists or in a refusal of a travel authorisation have strong impacts especially on the fundamental right to data protection. the use of ML-trained models for such risk assessments raises concerns, since existing models lack transparency and, in some cases, have been found to be significantly biased. the paper discusses selected requirements under EU data protection law for ML-trained models, namely human oversight, information and access rights, accuracy, and supervision. the analysis considers provisions of the AI Act Proposal of the European Commission as the proposed regulation can provide guidance for the application of existing data protection requirements to AI.

关键词： Machine learning Artificial intelligence Automated decisions Data protection EU border control

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Industry 4.0 technologies in Maternal Healthcare: A Systematic Review 10th

Industry 4.0 Technologies in Maternal Healthcare: A Systemat...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and control (MIM)

作者： Sibanda, Khulekani Ndayizigamiye, Patrick Twinomurinzi, Hossana Natl Univ Sci & Technol Bulawayo Zimbabwe Univ Johannesburg Dept Appl Informat Syst Johannesburg South Africa Univ Johannesburg Ctr Appl Data Sci Johannesburg South Africa

Industry 4.0 technologies have revolutionized how care is provided to patients and how operations in hospitals are conducted. the Sustainable Development Goal (SDG) 3.1 aims to ensure access to healthcare facilities and a reduction in the global maternal mortality ratio to less than 70 per 100 000 live births. this paper investigates the state of the art in the adoption of Industry 4.0 technologies in maternal healthcare: how they are transforming methods of treatment, diagnosis and monitoring of pregnancy, and how they are reorganizing the management and organization of healthcare systems. A systematic literature review was carried out with 43 papers that met specified inclusion and exclusion criteria. It was found that most of the research focus is on the provision of solutions for low- to medium-income countries that are still lagging behind in reducing maternal and child mortality rates even though there has been an advancement in terms of Industry 4.0 technology use. the research was largely quantitative in nature with models and frameworks developed as opposed to prototypes. It mainly combined the Internet of things (IoT) with Cloud computing and Big data analytics (BDA). Artificial intelligence (AI) in maternal healthcare was similarly trending. Only one paper focused on the use of blockchain in facilitating health information exchange of maternal records. Recommendations for policy and a future research agenda are made. Copyright (C) 2022 the Authors.

关键词： Industry 4.0 41R Fourth Industrial Revolution IoT Internet of things BDA Big data analytics Blockchain Cloud computing AI Artificial intelligence Maternal health Health 4.0

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Observer-based gust load alleviation via reduced-order models 10th

Observer-based gust load alleviation via reduced-order model...

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10th IFAC symposium on Robust control Design (ROCOND)

作者： de Souza, Alex dos Reis Vuillemin, Pierre Quero, David Poussot-Vassal, Charles Onera French Aerosp Lab 2 Ave Edouard Belin F-31400 Toulouse France Inst Aeroelast DLR German Aerosp Ctr Bunsenstr 10 D-37073 Gottingen Germany

In this paper, we investigate the design of linear active output feedback control for aircraft gust load alleviation, involving standard sensors and control surfaces. the control architecture is composed of (i) a robust observer, allowing for estimating some wing bending moments despite the wind perturbation, and (ii) a structured H-infinity-norm oriented control law exploiting this additional information. To cope with the complexity of the aeroelastic model and make the control and observer synthesis computationally tractable, a model reduction step, based on Petrov-Galerkin projections, is first applied. One specificity of this work is that the obtained projector is exploited in the observer design, linking the reduction and the estimation. the effectiveness of the methodology is illustrated through numerical simulation involving an open access high complexity aeroelastic model. Copyright (C) 2022 the Authors.

关键词： Estimation Robust control Projection-based Model reduction Aircraft control

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Declarative access control for Aggregations of Multiple Ownership Data 20

Declarative Access Control for Aggregations of Multiple Owne...

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25th acm symposium on access control models and technologies (SacmAT)

作者： Rosa, Marco Di Cerbo, Francesco Lozoya, Rocio Cabrera SAP Secur Res Sophia Antipolis France Univ Bergamo Sophia Antipolis France

ISBN: (纸本)9781450375689

Data aggregation operations are popular in domains like data analytics, machine learning and artificial intelligence. However, despite the availability of information, situations like fragmented ownership and legal frameworks hinder data processing, requiring companies to design complex human-driven processes in order to gather, aggregate, and process data in a compliant way. Our proposal addresses this lack of automation with an access control mechanism extending XacmL, an access control standard with language and implementation, to regulate operations with multiple data policies.

关键词： access control Data Aggregation Authorization Policies

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Data-Driven Design of Model Predictive control for Powertrain-Aware Eco-Driving Considering Nonlinearities Using Koopman Analysis 10th

Data-Driven Design of Model Predictive Control for Powertrai...

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10th IFAC symposium on Advances in Automotive control (AAC)

作者： Shen, Daliang Han, Jihun Karbowski, Dominik Rousseau, Aymeric Argonne Natl Lab Vehicle & Mobil Syst Grp Lemont IL 60439 USA

Eco-driving is a highly nonlinear control problem. the nonlinearities include the complex energy conversion/dissipation in the powertrain, environmental influences such as road grade and aerodynamic drag, constraints due to traffic signs, safety issues, and physical limits of the vehicle system. In recent years, researchers have increasingly revisited the Koopman operator to linearize nonlinear dynamics. this paper adopts such an approximation technique to construct the lifted state space in a data-driven procedure that allows us to incorporate nonlinearities and system perturbations in the cost function. In addition, the nonlinear constraints in states can also be handled linearly. the resultant formulation of a linearly constrained quadratic program can be readily applied to design a model predictive control that enjoys a low computation load as with a linear dynamic system. Simulation results demonstrate additional energy saving potential compared to a linear approach. Copyright (c) 2022 the Authors. this is an open access article under the CC BY-NC-ND license

关键词： Connected and automated vehicles Nonlinear model reduction Model predictive control Koopman operator Legendre polynomial approximation

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A Sequential Quadratic Programming Approach to Combined Energy and Emission Management of a Heavy-Duty Parallel-Hybrid Vehicle 10th

A Sequential Quadratic Programming Approach to Combined Ener...

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10th IFAC symposium on Advances in Automotive control (AAC)

作者： Mennen, S. C. M. Willems, F. P. T. Donkers, M. C. F. Eindhoven Univ Technol Eindhoven Netherlands

Combined Energy and Emission Management (CEEM) problems are a class of optimal control problems that aim to minimize operational costs of (hybrid electric) powertrains with after-treatment system subject to constraints on emissions imposed by legislation. In this paper, a parallel-hybrid heavy-duty vehicle with a Variable Turbine Geometry (VTG) and an Exhaust-Gas Recirculation (EGR) system is considered. the CEEM problem is solved using Sequential Quadratic Programming (SQP) for which the powertrain and after-treatment models are approximated as smooth functions. It will be shown that solving the CEEM problem using SQP is computationally much more efficient when compared to other techniques like dynamic programming. It will also be shown that most of the benefits from CEEM come from the hybrid powertrain and not from regulating the VTG and ERG mass flows. Furthermore, zero emission zones and local emission constraints can also be included without too much effort. Copyright (c) 2022 the Authors. this is an open access article under the CC BY-NC-ND license

关键词： Nonlinear and Optimal Automotive control Engine Modelling and control

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Autonomous Driving using Linear Model Predictive control with a Koopman Operator based Bilinear Vehicle Model 10th

Autonomous Driving using Linear Model Predictive Control wit...

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10th IFAC symposium on Advances in Automotive control (AAC)

作者： Yu, Siyuan Shen, Congkai Ersal, Tulga Univ Michigan Mech Engn Ann Arbor MI 48109 USA

this paper presents a real-time Model Predictive control (MPC) formulation for autonomous driving based on a lifted bilinear vehicle model developed using the Koopman operator. Koopman operator based models can closely mimic the original nonlinear behaviors with a higher dimensional linear structure, which is attractive for computationally efficient linear MPC formulations for controlling nonlinear systems. However, current linear models based on linear Koopman realizations cannot capture the control-affine dynamics in nonlinear systems. this may result in large discrepancies between the original nonlinear system and the data-driven linear model, hindering its use in MPC. To address this gap, first, a novel Koopman bilinear vehicle model that takes control-affine dynamics into consideration is constructed and tested in open-loop simulations. this bilinear Koopman model is then linearized to serve as a prediction model in MPC, and is shown to have higher accuracy compared to the state-of-the-art linear models. the model is then used to develop a linear MPC formulation for simultaneous planning and control of an autonomous vehicle. the formulation is tested on lane change scenarios with obstacles against the nonlinear MPC and standard linear MPC benchmarks. the results show that the new formulation can achieve a lane change performance closer to the nonlinear MPC with a computational performance similar to the standard linear MPC. the new formulation is observed to be successful in handling high speeds where the standard linear MPC fails. Copyright (c) 2022 the Authors. this is an open access article under the CC BY-NC-ND license

关键词： Koopman operator bilinear systems real-time model predictive control autonomous driving

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Deploying access control Enforcement for IoT in the Cloud-Edge Continuum with the help of the CAP theorem 20

Deploying Access Control Enforcement for IoT in the Cloud-Ed...

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25th acm symposium on access control models and technologies (SacmAT)

作者： Ahmad, Tahir Morelli, Umberto Ranise, Silvio FBK ICT Secur & Trust Unit Trento Italy Univ Genoa DIBRIS Genoa Italy

ISBN: (纸本)9781450375689

the CAP theorem is used by distributed system practitioners to investigate the necessary trade-offs in the design and development of distributed systems, mainly databases and web applications. In this paper, we use it to reason about access control systems designed for the Internet of things (IoT). We validate our approach by experimentally investigating alternative architectural designs to enforce access control in a smart lock system using the cloud-edge IoT platform offered by Amazon Web Services. We discuss the trade-off between security and performance that may help IoT designers choose the most suitable architecture supporting their requirements.

关键词： access control Internet of things CAP theorem

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Fault Diagnosis of Exhaust Gas Treatment System Combining Physical Insights and Neural Networks 10th

Fault Diagnosis of Exhaust Gas Treatment System Combining Ph...

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10th IFAC symposium on Advances in Automotive control (AAC)

作者： Jung, Daniel Kleman, Bjorn Lindgren, Henrik Warnquist, Hakan Linkoping Univ Dept Elect Engn Linkoping Sweden Scania CV AB Sodertalje Sweden

Fault diagnosis is important for automotive systems, e.g., to reduce emissions and improve system reliability. Developing diagnosis systems is complicated by model inaccuracies and limited training data from relevant operating conditions, especially for new products and models. One solution is the use of hybrid fault diagnosis techniques combining model-based and data-driven methods. In this work, data-driven residual generation for fault detection and isolation is investigated for a system injecting urea into the aftertreatment system of a heavy-duty truck. A set of recurrent neural network-based residual generators is designed using a structural model of the system. the performance of this approach is compared to a baseline model-based approach using data collected from a heavy-duty truck during different fault scenarions with promising results. Copyright (c) 2022 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Methods based on neural networks for FDI Structural analysis and residual evaluation methods AI methods for FDI Modeling supervision control and diagnosis of automotive systems Filtering and change detection

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Active Learning of Relationship-Based access control Policies 20

Active Learning of Relationship-Based Access Control Policie...

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25th acm symposium on access control models and technologies (SacmAT)

作者： Iyer, Padmavathi Masoumzadeh, Amirreza SUNY Albany Albany NY 12222 USA

ISBN: (纸本)9781450375689

Understanding access control policies is essential in understanding the security behavior of systems. However, often times, a complete and accurate specification of the enforced access control policy in a system is not available. In fact, scale and complexity of a system, or unavailability of its source code, may prevent users and even its developers from having access to such accurate specification. In this paper, we propose a novel, systematic approach for learning access control policies where target systems are treated as black boxes. In particular, we show how we can construct a deterministic finite automaton (DFA) characterizing the relationship-based access control (ReBAC) policy of a system by interacting with its access control engine using minimal number of access requests. Our experiments on realistic application scenarios and their promising results demonstrate the feasibility, scalability and efficiency of our learning approach.

关键词： relationship-based access control authorization black box model learning active learning

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