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作者： Moreira, Catarina Simoes, Francisco P. M. Lee, Mark J. W. Zorzal, Ezequiel R. Lindeman, Robert W. Pereira, Joao Madeiras Johnsen, Kyle Jorge, Joaquim Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Universidade Federal Rural de Pernambuco Departamento de Computação Recife52171-900 Brazil La Trobe University Computer Science and Information Technology BundooraVIC3086 Australia University of Canterbury HIT Lab NZ Christchurch8041 New Zealand Instituto Superior Técnico Universidade de Lisboa INESC-ID Department of Computer Science and Engineering Lisboa1000-029 Portugal University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

The pandemic brought about an unprecedented number of virtual conferences, given the heavy restrictions on travel to in-person meetings. Despite all the advances in technology, people still complain about virtual events. There is Zoom fatigue, confinement malaise, and a longing for personal social interactions. This paper discusses our experience organizing the IEEE Virtual Reality Conference (IEEE VR) as a virtual event. IEEE VR was a success with 1200+ registered paying participants, dozens of workshops and tutorials, and hundreds of technical papers. We used (1) a virtual environment platform, together with (2) discussion tools and (3) videoconferencing/broadcast/online tools to further provide effective social interaction and increase engagement. In this paper, we explore the synergies between virtual environments and other online tools and assess user engagement by analyzing the messages exchanged between participants across different genders and geographical regions. To this end, we apply diverse engagement metrics for online conferences. Our analysis shows that these metrics have the potential to highlight engagement, diversity, and inclusion by combining textual messages, participant geographic and gender information, communities of participants, and visitation patterns in a virtual environment. Drawing on our results and experiences, we propose guidelines for organizing technical virtual events to increase diversity and social interaction. © 2013 IEEE.

关键词： Virtual reality

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Resilience of Data-Driven Cyberattack Detection systems in Smart Power Grids

Resilience of Data-Driven Cyberattack Detection Systems in S...

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European Signal Processing Conference (EUSIPCO)

作者： Abdulrahman Takiddin Rachad Atat Hayat Mbayed Muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Florida State University Tallahassee FL USA Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Center for Advanced Power Systems Florida State University Tallahassee FL USA Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data communications within the smart power grid components are susceptible to cyberattacks due to the inter-connected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic IEEE 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 – 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 – 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Deep learning Europe Feature extraction Graph neural networks Data models Smart grids Reliability computer crime Unsupervised learning Resilience

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Revealing Non-Hermitian Band Structure of Photonic Floquet Media

Revealing Non-Hermitian Band Structure of Photonic Floquet M...

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Frontiers in Optics, FiO 2022

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Center for Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally reveal the unique non-Hermitian band structures of a photonic Floquet medium emulated in the microwave regime. We observe the presence of the momentum gaps and the subharmonic parametric oscillations...

ISBN: (纸本)9781557528209

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DrivAerNet++: a large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks 24

DrivAerNet++: a large-scale multimodal car dataset with comp...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Mohamed Elrefaie Florin Morar Angela Dai Faez Ahmed Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA Morphing and Optimization Solutions BETA CAE SYSTEMS USA Inc Farmington Hills MI Department of Computer Science Technical University of Munich Garching Germany

ISBN: (纸本)9798331314385

We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations. Dataset and code available at: https://***/Mohamedelrefaie/DrivAerNet

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Why Go Full? Elevating Federated Learning Through Partial Network Updates

arXiv

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arXiv 2024年

作者： Wang, Haolin Liu, Xuefeng Niu, Jianwei Guo, Wenkai Tang, Shaojie State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo BuffaloNY United States Zhongguancun Laboratory Beijing China

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead. © 2024, CC BY.

关键词： Federated learning

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Kareem Ali Malalah Al-sharhanee Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the “‘terrestrial’ systems and the Unmanned Aerial Vehicles (UAVs)” collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing “the efficiency and efficacy of data collection… with the assistance of Unmanned Aerial Vehicles (UAVs)” which means this work reveals practical application solutions.

关键词： Wireless sensor networks Trajectory planning Autonomous aerial vehicles Turning Routing Rendering (computer graphics) Sensor systems Trajectory Sensors Vehicle dynamics

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Exploring the Feasibility of Hybrid PV-Wind Energy Plant for Costal-Tourism Sites

Exploring the Feasibility of Hybrid PV-Wind Energy Plant for...

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IEEE Power India Conference

作者： Md Ashraful Islam Md Shahadat Hossenk Fareesa Firdouse Md. Zahid Hasan M M Naushad Ali Golla Suribabu Hayitov Abdulla Nurmatovich Valisher Sapaev Odilbek Uglu Dept of Electrical and Electronic Engineering Green University of Bangladesh Dhaka Bangladesh Department of Electrical and Computer Engineering Gannon University USA Department of Computer Science Engineering Keshav Memorial Institute Of Technology (KMIT) Hyderabad India Civil Engineering Department SRKR Engineering College (A) Bhimavaram A.P. India Department of Transports Systems Urgench State University Uzbekistan Department of General Professional Sciences Mamun University Khiva Uzbekistan

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This study presents the design, optimization, and performance analysis of a PV-Wind hybrid energy system for Kuakata, a coastal region and popular tourist destination in Bangladesh. The hybrid system integrates Vertical Axis Wind Turbines (VAWT) and photovoltaic (PV) technology, addressing both noise reduction and safety concerns, making it particularly suitable for tourist-friendly environments. The system was optimized using HOMER Pro simulation software, which provided the optimal configuration based on technical, economic, and environmental parameters. The results show a 100% renewable energy fraction, a Levelized Cost of Energy (LCOE) of $0.041/kWh, and a Net Present Cost (NPC) of $4.58 million. Additionally, the system demonstrates significant environmental benefits by reducing 230,680 kg of CO 2 emissions annually. This study highlights the feasibility of deploying a PV-Wind hybrid system in coastal regions to meet growing energy demands while promoting sustainability and environmental stewardship. The proposed design also enhances the attraction of Kuakata as a tourist destination by offering a quiet and safe renewable energy solution.

关键词： Photovoltaic systems Renewable energy sources Wind energy Sea measurements Hybrid power systems Software Wind turbines Safety Performance analysis Sustainable development

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Coefficient Complexity in Low-Access Quantized Linear Computations

Coefficient Complexity in Low-Access Quantized Linear Comput...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Vinayak Ramkumar Netanel Raviv Itzhak Tamo Department of Electrical Engineering–Systems Tel Aviv University Tel Aviv Israel Department of Computer Science and Engineering Washington University in St. Louis St. Louis MO USA

Quantizing linear computations over the reals is a common practice in many distributed systems, with applications in efficient, private, or robust machine learning algorithms. Recent work studied the access parameter of such computations, i.e., the maximum number of data entries required to accommodate any given computation. It was shown that lower access can be achieved with higher redundancy and vice versa, and a corresponding scheme based on covering codes was given. Surprisingly, the resulting scheme is universal to all possible two-valued quantizations, meaning that the same storage protocol can be used to retrieve any linear combination with two distinct coefficients—regardless of what those coefficients are—with the same access parameter. In this paper we extend this universal protocol to all possible quantizations with any number of values; while the storage parameter remains identical, the access parameter increases according to a new additive-combinatorics property we call coefficient complexity. We then turn to study the coefficient complexity—we characterize the complexity of small sets of coefficients, provide bounds, and identify coefficient sets having the highest and lowest complexity. Interestingly, arithmetic progressions have the lowest possible complexity, and some geometric progressions have the highest possible complexity, the former being particularly attractive for its common use in uniform quantization.

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Defining Healthcare KPIs Using Process Mining and Patient Journey Maps 49

Defining Healthcare KPIs Using Process Mining and Patient Jo...

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49th Latin American Computing Conference, CLEI 2023

作者： Arias, Michael Rojas, Eric Aguirre, Santiago Cornejo, Felipe Munoz-Gama, Jorge Sepulveda, Marcos Capurro, Daniel Informática y Tecnología Universidad de Costa Rica Sede de Occidente Departamento de Ingeniería San Ramón Costa Rica Institute for Biological and Medical Engineering Schools of Engineering Medicine and Biological Sciences Pontificia Universidad Católica de Chile Santiago Chile Central Michigan University Business Information Systems Department MI United States School of Engineering Pontificia Universidad Católica de Chile Department of Computer Science Santiago Chile School of Computing and Information Systems University of Melbourne Victoria Australia

ISBN: (纸本)9798350318876

Over the years, patient satisfaction has become a key factor when evaluating the quality of healthcare. There is a constant desire to further analyze patients' needs and expectations, with the aim of improving their healthcare experience. With tools such as customer journey maps (CJM), two objectives are addressed: a) identify how patients interact through the phases of the care cycle and b) execute multiple analyses to deliver a wide variety of outcomes focused on improving patient experience, through the combination of these tools with emerging disciplines such as process mining. We propose a new method based on a predefined framework to optimize the creation of healthcare indicators through process mining. Our method focuses on identifying touchpoints, defining, calculating, validating, and visualizing Key Performance Indicators (KPIs) in a clinical process. The proposed method was applied to analyze an emergency room process as a case study. Results demonstrate the usefulness of the method to discover the main process interaction points, to create key indicators, and generate dashboards that support the goal of understanding and optimizing patient care. © 2023 IEEE.

关键词： Benchmarking

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Using Target Detection Probability to Evaluate Area Coverage by a UAV

Using Target Detection Probability to Evaluate Area Coverage...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Zachary Bortoff Alec Luterman Derek A. Paley Stephen M. Nogar Department of Aerospace Engineering University of Maryland College Park MD USA Department of Computer Science University of Maryland College Park MD USA Department of Aerospace Engineering Institute for Systems Research University of Maryland College Park MD USA DEVCOM Army Research Laboratory

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

A common task for unmanned aerial vehicles (UAVs) is wide area search using an onboard camera with an object detection model. However, constraints of flight time, camera optics, and onboard compute, particularly in time sensitive applications like search and rescue, requires tradeoffs in strategies that balance precision and speed. To address these needs, we propose a novel method for evaluating coverage path plans by estimating the probabilities of detection and false alarm for ground targets for a set of poses that the UAV can reach in the search domain. To demonstrate our method, we evaluate trajectories for various coverage path plans flown by a UAV in a high-fidelity simulation.

关键词： Target tracking Vehicle routing Robot vision systems Object detection Transforms Autonomous aerial vehicles Cameras Search problems Trajectory Time factors

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