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13th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2020

作者： El Idrissi, Touria Idri, Ali Department of Computer Sciences EMI University Mohamed V Rabat Morocco Software Project Management Research Team RITC ENSIAS University Mohamed V Rabat Morocco Complex Systems Engineering University Mohammed VI Polytechnic Ben Guerir Morocco

ISBN: (纸本)9783030723781

To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30 min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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The Development of a Visual Novel Role-Playing Game [VN RPG] as an Open Educational Resource [OER] for Philippine Literature Educators Administering the 'Noli Me Tangere' Module 13

The Development of a Visual Novel Role-Playing Game [VN RPG]...

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2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Sales, Nina Ana Marie Jocelyn Villanueva, Nathaniel Evangelista, Matthew Robert Cestina, Joshua Luigi Emmanuel Camba, Jhona De La Salle University College of Computer Studies Software Technology Department Manila Philippines Gokongwei College of Engineering de la Salle University Industrial and Systems Engineering Department Manila Philippines De La Salle University International Center Office of the Vice President for External Relations and Internationalization Manila Philippines

ISBN: (纸本)9781665401678

Many scholars saw opportunities of using digital interactive video games as a learning tool during the pandemic. However, it was indeed complex for educators, especially those who teach history-driven novels to prepare lesson plans for classes not only to get the learning outcomes but also to produce high technology materials such as video games. Given the predicament, this study aims to measure the effectiveness of the developed video game named 'Touch Me Not' under the genre Visual Novel Role-Playing Game (VN RPG) as an Open Education Resource (OER) to educators who teach the novel, Noli Me Tangere or Touch Me Not. To accomplish the proposed objective, the Understanding by Design (UbD) approach was analyzed through the administered survey questionnaire, pre-test, and post-test. The findings show that using the 'Touch Me Not' VN RPG to complement the standard class modules had a more significant effect on the educators' performance. Thus, it proved that using VN RPG is an effective complementary pedagogical tool in teaching history-driven classes. © 2021 IEEE.

关键词： Serious games

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Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

Robust Generalization Against Photon-Limited Corruptions via...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University JD Explore Academy Department of Automation University of Science and Technology of China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Department of Computer Science Hong Kong Baptist University

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

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CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control

arXiv

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arXiv 2023年

作者： Zhang, Baozhe Chen, Xinwei Li, Zhehan Beltrame, Giovanni Xu, Chao Gao, Fei Cao, Yanjun Huzhou Institute of Zhejiang University Huzhou313000 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Chinese University of Hong Kong Shenzhen518172 China Department of Computer Engineering and Software Engineering Poly-technique Montreal Canada

This paper presents a novel solution for UAV control in cooperative multi-robot systems, which can be used in various scenarios such as leader-following, landing on a moving base, or specific relative motion with a target. Unlike classical methods that tackle UAV control in the world frame, we directly control the UAV in the target coordinate frame, without making motion assumptions about the target. In detail, we formulate a nonlinear model predictive controller of a UAV, referred to as the agent, within a non-inertial frame (i.e., the target frame). The system requires the relative states (pose and velocity), the angular velocity and the accelerations of the target, which can be obtained by relative localization methods and ubiquitous MEMS IMU sensors, respectively. This framework eliminates dependencies that are vital in classical solutions, such as accurate state estimation for both the agent and target, prior knowledge of the target motion model, and continuous trajectory re-planning for some complex tasks. We have performed extensive simulations to investigate the control performance with varying motion characteristics of the target. Furthermore, we conducted real robot experiments, employing either simulated relative pose estimation from motion capture systems indoors or directly from our previous relative pose estimation devices outdoors, to validate the applicability and feasibility of the proposed approach. © 2023, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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Reinforcement Learning Based Oscillation Dampening Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test

arXiv

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arXiv 2024年

作者： Jang, Kathy Lichtlé, Nathan Vinitsky, Eugene Shah, Adit Bunting, Matthew Nice, Matthew Piccoli, Benedetto Seibold, Benjamin Work, Daniel B. Delle Monache, Maria Laura Sprinkle, Jonathan Lee, Jonathan W. Bayen, Alexandre M. University of California Berkeley Department of Electrical Engineering and Computer Sciences United States University of California Berkeley Department of Mechanical Engineering United States University of California Berkeley Department of Civil and Environmental Engineering United States University of California Berkeley Institute for Transportation Studies United States École des Ponts Paristech Marne la Vallée France Vanderbilt University Institute for Software Integrated Systems United States Temple University Department of Mathematics United States Rutgers University-Camden Department of Mathematical Sciences United States

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Qemtrace: A multi-platform memory tracer based on system emulation

Qemtrace: A multi-platform memory tracer based on system emu...

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2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Aurora Dugo, Alexy Torres Lefoul, Jean-Baptiste de Magalhães, Felipe Göhring Nicolescu, Gabriela Assal, Dahman Department of computer engineering Polytechnique Montréal MontréalQC Canada MANNARINO Systems and Software MontréalQC Canada

ISBN: (纸本)9781713819462

Memory simulation is now widely used in system design processes. This process allows debugging, profiling and sometimes validating all memory-related tasks. The step before memory profiling is memory tracing, which consists in collecting memory events. State-of-the-art solutions rely on well-known environments provided by operating systems. Such environments might not be available when designing custom operating systems or bare-metal applications (e.g., applications directly running on hardware). In this context, aside from hardware-assisted approaches, no memory tracing mean is available. In this paper, we propose QEMTrace, a QEMU based open-source tool for memory tracing. QEMTrace provides a flexible way to trace memory accesses on operating systems, bare-metal applications and regular applications. After traces collection, we inject them in a cache simulator to show its usage. A shared memory interface to allow co-simulation of trace driven simulators is also presented. © 2020 Society for Modeling & Simulation International (SCS)

关键词： Virtualization

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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