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16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022

作者： Zhao, Yunfei Huang, Linan Zhu, Quanyan Smidts, Carol Nuclear Engineering Program Department of Mechanical and Aerospace Engineering The Ohio State University 201 W 19th Ave ColumbusOH43210 United States Department of Electrical and Computer Engineering New York University 370 Jay Street BrooklynNY11201 United States

The trend of digitization in various industrial systems has exposed them to an increasing number of cyberattacks. Therefore, it is of vital importance to reduce the cybersecurity risk of industrial systems through cost-effective decisions on cybersecurity investment. In making such decisions, the defender is usually faced with challenges that arise from incomplete information about the attacker. In this paper, we propose a Bayesian game approach to model the optimal cybersecurity investment strategy under such situations. In this approach, the defender categorizes the attacker into a finite number of types, e.g., various levels of capability, and assigns a probability distribution over the different types of attackers. Then, the defender optimizes his/her cybersecurity investment based on risk assessment considering the possible attack efforts of these various types of attackers, with the objective of minimizing the expected cyberattack loss and the cybersecurity investment cost. The proposed method is demonstrated using a numerical example. We perform a sensitivity analysis for model parameters that can be difficult to obtain in practical applications, e.g., the defender's loss caused by a successful attack. Key observations of the example include the threshold principle (i.e., the defender should not make any investment if the loss of a successful attack is below a certain threshold) and the conservation of loss (i.e., losses for one type of attacker may correspond to gains for another type of attacker). The proposed method can be used to support cybersecurity investment decisions by industrial system owners. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

关键词： Investments

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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Energy Minimization using Custom-Designed Magnetic-Spring Actuators

Energy Minimization using Custom-Designed Magnetic-Spring Ac...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics

arXiv

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arXiv 2023年

作者： Murali, Nihal Puli, Aahlad Yu, Ke Ranganath, Rajesh Batmanghelich, Kayhan Intelligent Systems Program University of Pittsburgh United States Department of Computer Science New York University United States Department of Electrical and Computer Engineering Boston University United States

Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when deploying them in safety-critical applications. This paper aims to better understand the effects of spurious features through the lens of the learning dynamics of the internal neurons during the training process. We make the following observations: (1) While previous works highlight the harmful effects of spurious features on the generalization ability of DNNs, we emphasize that not all spurious features are harmful. Spurious features can be "benign" or "harmful" depending on whether they are "harder" or "easier" to learn than the core features for a given model. This definition is model and dataset dependent. (2) We build upon this premise and use instance difficulty methods (like Prediction Depth (Baldock et al., 2021)) to quantify "easiness" for a given model and to identify this behavior during the training phase. (3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN’s early layers. In other words, easy features learned by the initial layers of a DNN early during the training can (potentially) hurt model generalization. We verify our claims on medical and vision datasets, both simulated and real, and justify the empirical success of our hypothesis by showing the theoretical connections between Prediction Depth and information-theoretic concepts like V-usable information (Ethayarajh et al., 2021). Lastly, our experiments show that monitoring only accuracy during training (as is common in machine learning pipelines) is insufficient to detect spurious features. We, therefore, highlight the need for monitoring early training dynamics using suitable instance difficulty metrics. © 2023, CC BY.

关键词： Deep neural networks

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to Bioengineering and Related Disciplines

Application of 3D Printing and Design in Healthcare: A Focus...

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Frontiers in Education (FIE) Conference

作者： Cindy M. Zelada-Quiroz Brian L. Reyes-Santiago Eduardo I. Ortiz-Rivera Bio-Engineering Graduate Program University of Puerto Rico at Mayagüez Mayagüez PR Department of Computer Science and Engineering University of Puerto Rico at Mayagüez Mayagüez PR Department of Electrical and Computer Engineering University of Puerto Rico at Mayagüez Mayagüez PR

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Technological innovation Materials science and technology Three-dimensional displays Tissue engineering Medical services Three-dimensional printing Bones Mechanical factors Manufacturing Sparks

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Automatic LPI Radar Waveform Recognition Using Vision Transformer

Automatic LPI Radar Waveform Recognition Using Vision Transf...

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IEEE International Conference on Radar

作者： Junseob Kim Sunghwan Cho Sunil Hwang Yeongyoon Choi Department of Electrical Engineering Korea Military Academy Republic of Korea Maneuver System Coordination and Contract Team Defense Acquisition Program Administration Republic of Korea Department of Electrical Engineering and Computer Science GIST Republic of Korea

This paper investigates the classification of low probability of intercept (LPI) radar signals by exploiting the intrinsic advantages of the Vision Transformer (ViT). Due to the characteristics of LPI radar signals, such as intrapulse modulation, wide frequency bands, and low transmission power, these signals are challenging to be detected and classified using traditional analytic methods. This has led to the adoption of various deep learning techniques to overcome these limitations. On the one hand, the ViT, originally developed for natural language processing, has demonstrated outstanding performance in computer vision by replacing the structure of the convolutional neural network (CNN) with the transformer, specifically leveraging self-attention. Therefore, this paper explores a method based on the ViT technique for classifying LPI signal images. The simulation results show that the proposed ViT method outperforms the traditional CNN method by 12.8% at −10dB SNR.

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High Rectification, Low Leakage p-Si/n-AlN Heterojunction PN Diode

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年

作者： Lu, Yi Zhou, Jie Gong, Jiarui Liu, Yang Cao, Haicheng Nong, Mingtao Liu, Zhiyuan Alam, Md.Tahmidul Gupta, Chirag Li, Xiaohang Ma, Zhenqiang University of Wisconsin-Madison Department of Electrical and Computer Engineering Madison 53706 WI United States King Abdullah University of Science and Technology (KAUST) CEMSE Division Advanced Semiconductor Laboratory Electrical and Computer Engineering Program Thuwal 23955-6900 Saudi Arabia

Ultrawide bandgap AlN is a highly attractive material for power and radio frequency electronics. Unipolar n-AlN Schottky barrier diodes have demonstrated their strengths, whereas further development of AlN-based bipolar devices is desired but lacked. In this letter, we report single-crystalline p-Si/n-AlN p-n diodes (PNDs) with superior performance, accomplished by grafting a p-type Si nanomembrane onto an n-AlN film. Improved Ohmic contact directly on n-AlN was obtained through high temperature annealing at 1100 °C with contact resistivity of 4.9×10^-3 Ω. cm². The PNDs exhibited remarkable uniformity across the wafer, excellent rectifying characteristics with a high ratio of ~3×10⁷ at ±10 V, low leakage current density of ~6.25×10^-9 A/cm² at -10 V, and hysteresis-free operation from -10 V to 30 V. Furthermore, the devices demonstrated superior thermal stability, with the on-resistance decreasing from 15.8 to 0.33 Ω. cm² as temperature increased from room temperature to 100 °C, revealing an activation energy of ~425 meV for deep donor state in AlN. No destructive breakdown of the devices was observed up to –894 V. These results highlight the Si/AlN PNDs’ potential for development of AlN-based electronic devices. © 1980-2012 IEEE.

关键词： Aluminum nitride p-n diode semiconductor grafting semiconductor heterojunction silicon

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Membership Privacy-preserving GAN 33

Membership Privacy-preserving GAN

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Ha, Heonseok Hwang, Uiwon Jang, Jaehee Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Cyber Security Ewha Womans University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

A membership inference attack (MIA) identifies if an instance was included in the victim model's train dataset. Without an appropriate defense mechanism, MIA can result in serious privacy breaches. Although several methods have been proposed to protect membership privacy in discriminative models, research into generative adversarial networks (GANs), remains insufficient despite their vulnerability to MIAs. In this study, we propose a membership privacy-preserving GAN (MP-GAN), which plays an additional adversarial game for membership privacy between an auxiliary membership inference network M and a GAN. M seeks to find out whether an instance belongs to the reference or train dataset, whereas the generator and discriminator of the GAN attempt to deceive M. Our theoretical analysis results demonstrate that the MP-GAN improves membership privacy by not learning sample-specific features. We perform extensive empirical evaluations to show that the MP-GAN can successfully defend against MIAs under advantageous scenarios to the attacker (for example, white-box access to networks and small training dataset size). Furthermore, we demonstrate that the MP-GAN has several advantages over other privacy-preserving GAN training techniques. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Generative adversarial networks

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