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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Julian Schiele Fabian Rabe Maximilian Schmitt Manuel Glaser Franziska Häring Jens O. Brunner Bernhard Bauer Björn Schuller Claudia Traidl-Hoffmann Athanasios Damialis Chair of Health Care Operations / Health Information Management Faculty of Business and Economics University Center of Health Sciences at Klinikum Augsburg (UNIKA-T) Chair of Software Methodologies for Distributed Systems Faculty of Computer Science University of Augsburg ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing Faculty of Computer Science University of Augsburg Chair and Institute of Environmental Medicine UNIKA-T Technical University of Munich and Helmholtz Zentrum München

Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allergenic pollen exposure have been provided only via forecasting models and conventional monitoring methods that are laborious. The aim of this study is to develop and evaluate our own pollen classification model based on deep neural networks. Airborne allergenic pollen have been monitored in Augsburg, Bavaria, Germany, since 2015, using a novel automatic Bio-Aerosol Analyzer (BAA 500, Hund GmbH). The automatic classification system is compared and evaluated against our own, newly developed algorithm. Our model achieves an unweighted average precision of 83.0 % and an unweighted average recall of 77.1 % across 15 classes of pollen taxa. Automatic, real-time information on concentrations of airborne allergenic pollen will significantly contribute to the implementation of timely, personalized management of allergies in the future. It is already clear that new methods and sophisticated models have to be developed so as to successfully switch to novel operational pollen monitoring techniques serving the above need.

关键词： Monitoring Training Neural networks Atmospheric modeling Biological system modeling Respiratory system Neurons

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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Automated design of error-resilient and hardware-efficient deep neural networks

arXiv

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arXiv 2019年

作者： Schorn, Christoph Elsken, Thomas Vogel, Sebastian Runge, Armin Guntoro, Andre Ascheid, Gerd Department of Dependable Connected Systems Bosch Corporate Research Renningen Germany Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen Germany Bosch Center for Artificial Intelligence Renningen Germany Department of Computer Science University of Freiburg Freiburg im Breisgau Germany

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this. However, the design of efficient and reliable hardware has become increasingly difficult, due to the increased complexity of modern integrated circuit technology and its sensitivity against hardware faults, such as random bit-flips. It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e.g. by optimizing the architecture of the DNN. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of easily computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience solely based on the network topology. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find significant differences regarding their effect on error resilience. Copyright © 2019, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Towards Self-Explainable Cyber-Physical systems

Towards Self-Explainable Cyber-Physical Systems

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ACM/IEEE International Conference on Model Driven Engineering Languages and systems Companion

作者： Mathias Blumreiter Joel Greenyer Francisco Javier Chiyah Garcia Verena Klos Maike Schwammberger Christoph Sommer Andreas Vogelsang Andreas Wortmann Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universitat Hannover Heriot-Watt University Software and Embedded Systems Engineering Technische Universitat Berlin Department of Computing Science University of Oldenburg Heinz Nixdorf Institute and Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universitat Berlin Software Engineering RWTH Aachen University

ISBN: (纸本)9781728151267

With the increasing complexity of Cyber-Physical systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.

关键词： Explainability Self-adaptive systems Cyber-physical systems Adaptive systems Cyber-physical systems MONITOR Runtime Buildings

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An energy-aware resource provisioning scheme for real-time applications in a cloud data center

An energy-aware resource provisioning scheme for real-time a...

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作者： Faragardi, Hamid Reza Dehnavi, Saeid Nolte, Thomas Kargahi, Mehdi Fahringer, Thomas School of Innovation Design and Engineering Mälardalen University Västerås Sweden Distributed and Parallel Systems Group Institute of Computer Science Innsbruck University Innsbruck Austria School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Iran

Based on a pay-as-you-go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy-aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real-time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real-time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy-aware resource provisioning mechanism for cloud data centers, which are capable of serving real-time periodic tasks following the Software as a Service model. The proposed method is compared against an energy-aware version of the RT-OpenStack. RT-OpenStack is a recently proposed approach to provide a time-predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy-aware version of the RT-OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large-scale data centers is more considerable. Copyright © 2018 John Wiley & Sons, Ltd.

关键词： Ant colony optimization

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Towards self-explainable cyber-physical systems

arXiv

引用

arXiv 2019年

作者： Blumreiter, Mathias Greenyer, Joel Garcia, Francisco Javier Chiyah Klös, Verena Schwammberger, Maike Sommer, Christoph Vogelsang, Andreas Wortmannz, Andreas Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universität Hannover Germany Heriot-Watt University Software and Embedded Systems Engineering Technische Universität Berlin Germany Department of Computing Science University of Oldenburg Heinz Nixdorf Institute Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universität Berlin Germany Software Engineering RWTH Aachen University

With the increasing complexity of Cyber-Physical systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- A nd explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet unexplainable behavior be detected by the system. Copyright © 2019, The Authors. All rights reserved.

关键词： embedded systems

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Context-dependent multimodal communication in human-robot collaboration

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Procedia CIRP 2018年 72卷 15-20页

作者： Csaba Kardos Zsolt Kemény András Kovács Balázs E. Pataki József Váncza EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control Hungarian Academy of Sciences Department of Manufacturing Science and Technology Budapest University of Technology and Economics Department of Distributed Systems Institute for Computer Science and Control Hungarian Academy of Sciences

In the dynamic environment of human-robot collaboration, a key for boosting the efficiency of human workers is supporting them with context-dependent work instructions, delivered via communication modalities that suit the actual context. Workers, in turn, should be supported in controlling the robot or other components of the production system by using the most convenient modality, thus lifting the limitations of traditional interfaces as push buttons installed at fixed locations. We introduce a workflow for context-dependent multimodal communication in a collaborative work environment and present its implementation in a Human-Machine Interface Controller system.

关键词： human-robot collaboration worker information systems multi-modal communication

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A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 186-194页

作者： Yufei Tang Chao Luo Jun Yang Haibo He IEEE Department of Computer & Electrical Engineering and Computer Science and the Institute for Sensing and Embedded Network Systems EngineeringFlorida Atlantic University School of Electrical Engineering Wuhan University Department of Electrical Computer and Biomedical EngineeringUniversity of Rhode Island

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

关键词： Constrained optimization Economics Electric load flow Electric power generation Intelligent systems Monte Carlo methods Nonlinear programming Optimization Particle swarm optimization (PSO) Problem solving Quadratic programming Scheduling Stochastic systems Wind power

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Impact of intra-grain spin wave reflections on nano-contact spin torque oscillators

arXiv

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arXiv 2020年

作者： Eklund, Anders J. Dvornik, Mykola Qejvanaj, Fatjon Jiang, Sheng Chung, Sunjae Åkerman, Johan Gunnar Malm, B. Department of Physics University of Oslo Box 1048 Blindern Oslo0316 Norway Department of Physics University of Gothenburg Gothenburg412 96 Sweden NanOsc AB Electrum 205 Kista164 40 Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology Electrum 229 Kista164 40 Sweden Department of Physics Education Korea National University of Education Cheongju28173 Korea Republic of Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Electrum 229 Kista164 40 Sweden

We investigate the origin of the experimentally observed varying current-frequency nonlinearity of the propagating spin wave mode in nano-contact spin torque oscillators. Nominally identical devices with 100 nm diameter are characterized by electrical microwave measurements and show large variation in the generated frequency as a function of drive current. This quantitative and qualitative device-to-device variation is described in terms of continuous and discontinuous nonlinear transitions between linear current intervals. The thin film grain microstructure in our samples is determined using atomic force and scanning electron microscopy to be on the scale of 30 nm. Micromagnetic simulations show that the reflection of spin waves against the grain boundaries results in standing wave resonance configurations. For a simulated device with a single artificial grain, the frequency increases linearly with the drive current until the decreased wavelength eventually forces another spin wave anti-node to be formed. This transition results in a discontinuous step in the frequency versus current relation. Simulations of complete, randomly generated grain microstructures additionally shows continuous nonlinearity and a resulting device-to-device variation in frequency that is similar to the experimental levels. The impact of temperature from 4 K to 300 K on the resonance mode-transition nonlinearity and frequency noise is investigated using simulations and it is found that the peak levels of the spectral linewidth as a function of drive current agrees quantitatively with typical levels found in experiments at room temperature. Copyright © 2020, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Driving path stability in VANETs

arXiv

引用

arXiv 2019年

作者： Laroui, Mohammed Sellami, Akrem Nour, Boubakr Moungla, Hassine Afifi, Hossam Hacene, Sofiane B. Evolutionary Engineering & Distributed Information Systems Laboratory Djillali Liabes University Sidi Bel Abbes Algeria LIPADE Paris Descartes University Sorbonne Paris Cite Paris France School of Computer Science Beijing Institute of Technology Beijing China UMR 5157 CNRS Institute Mines Telecom Telecom SudParis Nano-Innov CEA Saclay France

—Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Industrial research

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