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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Sanhuan Yang Zhou Su Qichao Xu Rui Xing Dongfeng Fang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost.

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MIXING ON GENERALIZED ASSOCIAHEDRA

arXiv

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arXiv 2024年

作者： Chang, William Defant, Colin Frishberg, Daniel Department of Applied Mathematics University of California Los Angeles United States Department of Mathematics Harvard University CambridgeMA02138 United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

Eppstein and Frishberg recently proved that the mixing time for the simple random walk on the 1-skeleton of the associahedron is O(n3 log3 n). We obtain similar rapid mixing results for the simple random walks on the 1-skeleta of the type-B and type-D associahedra. We adapt Eppstein and Frishberg's technique to obtain the same bound of O(n3 log3 n) in type B and a bound of O(n13 log2 n) in type D;in the process, we establish an expansion bound that is tight up to logarithmic factors in type B. © 2024, CC BY.

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Exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies 20th

Exploring Equity: Integrating Knowledge Graphs in Fairness ...

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20th International Conference on Data science, ICDATA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Dabu, Jonathan Bernabe Rahim, Muhammad Abdul Basit Ur Abid, Muhammad Hussain, Shahid Computer Science and Computer Engineering California State University Long BeachCA United States Computer Science Florida Polytechnic University LakelandFL United States Computer Science Penn State Erie The Behrend College EriePA United States

ISBN: (纸本)9783031858550

In the Big Data era, machine learning and artificial intelligence have reshaped decision-making, especially in sectors like healthcare and finance. However, this evolution raises significant concerns about fairness and bias. The abundance of data increases the risk of inherent biases, exacerbating social inequality and discrimination. Ensuring ethical machine functioning, where every user receives equal treatment, is essential. Conventional methods for handling large datasets struggle to identify and rectify biases, as big data often equals skewed data. To address this, we propose a knowledge graph-based approach alongside grammar-based testing to enhance machine learning fairness. By integrating domain-specific knowledge graphs into the pipeline, biases can be identified and mitigated during preprocessing, ensuring comprehensive fairness. Index Terms—Machine Learning, Artificial Intelligence, Big Data, Fairness, Knowledge Graphs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models 24

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Clara J. Almeida David Guerrero-Pantoja Erik Pautsch Silvio Rizzi George K. Thiruvathukal Maria Pantoja Robotics and Electrical Engineering University of California Santa Cruz Santa Cruz USA Computer Science and Game Design University of California Santa Cruz Santa Cruz USA Computer Science Loyola University Chicago Chicago USA Argonne Leadership Computing Facility Argonne National Laboratory Lemont USA Computer Science and Software Engineering California Polytechnics State University San Luis Obispo USA

ISBN: (纸本)9798350355543

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know." In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset.

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A Novel Hybrid Multi-resolution Image Registration Algorithm with Curvelet Trasnform and Artificial Neural Networks

A Novel Hybrid Multi-resolution Image Registration Algorithm...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Choi, Hyunjong Yu, Xiao-Hua San Diego State University Department of Computer Science San DiegoCA United States California Polytechnic State University San Luis Obispo Department of Electrical Engineering San Luis ObispoCA United States

ISBN: (纸本)9798350320282

The purpose of image registration is to transform multiple images of the same subject taken from different points of view, times, depths, or sensors into one coordinate system. In this study, a novel hybrid image registration approach is developed based on curvet transform, discrete cosine transform, and artificial neural networks. Curvelet transform is an emerging multi-resolution analysis method that can effectively represent objects with highly anisotropic elements such as lines and curves. The proposed algorithm combines the orientation selective property of curvelet transform and the 'energy compaction' property of discrete cosine transform together to provide a more efficient way to extract features of curvilinear structures from images. Besides, the learning ability and nonlinear mapping ability of artificial neural network provide a flexible and intelligent tool for data fusion on feature matching and parameter estimation. The performances of the proposed approaches are studied and compared with other methods on medical magnetic resonance images (MRI) via computer simulations. © 2022 IEEE.

关键词： Neural networks

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Automation 5.0: The Key to Systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Cooperative Secure Transmission for Hybrid Aerial IRS-assisted Communication System

Cooperative Secure Transmission for Hybrid Aerial IRS-assist...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yihao Qi Zhou Su Qichao Xu Dongfeng Fang Yuntao Wang Yiliang Liu School of Mechatronic Engineering and Automation Shanghai University China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Department of Computer Science California Polytechnic State University San Luis Obispo USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Aerial intelligent reflecting surface (AIRS), integrating unmanned aerial vehicle (UAV) with IRS, has emerged as a promising paradigm to improve the transmission quality and security in emergency communication, space-air-ground-integrated network and mobile edge computing, etc. However, the size of a single AIRS is constrained by the limited energy and payload capacity of the UAV, as well as the path loss of the air-to-ground reflective link, which makes the gain from a single AIRS finite. To address these problems, we propose a hybrid aerial IRS-assisted cooperative secure transmission system, where an aerial active IRS and an aerial simultaneously transmitting and reflecting IRS (STAR-IRS) are employed to achieve reflection amplification and 360-degree ubiquitous coverage, respectively. Additionally, the cooperative beamforming gain generated by the secondary reflection between the hybrid AIRSs can further improve communication quality. Specifically, an optimization problem is proposed with the objective of maximizing the sum secrecy rate by jointly optimizing the transmitting beamforming and the reflection coefficients of each AIRS. We first reformulated the original non-convex problem by fractional programming method, and a three-layer alternating optimization algorithm is introduced to address the proposed problem with the successive convex approximation (SCA) as well as penalty convex-concave procedure (PCCP) techniques. Finally, extensive simulations are conducted to demonstrate that the proposed scheme substantially improves the sum secrecy rate compared to other baseline schemes.

关键词： Array signal processing Simulation Programming Autonomous aerial vehicles Propagation losses Hybrid power systems Reflection Reflection coefficient Security Optimization

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Energy-Efficient Task Offloading in UAV-RIS-Assisted Mobile Edge Computing with NOMA

Energy-Efficient Task Offloading in UAV-RIS-Assisted Mobile ...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Mingyang Zhang Zhou Su Qichao Xu Yihao Qi Dongfeng Fang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science California Polytechnic State University San Luis Obispo USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Mobile Edge Computing (MEC) may face coverage and channel quality limitations due to obstructe performance-degraded communication between ground users and MEC servers. In this paper, we propose a novel MEC system. In this system, a reconfigurable intelligent surface (RIS) is utilized on a deployed unmanned aerial vehicle (UAV) to facilitate communication between ground users (GUs) and the MEC server. In addition, to further improve spectrum utilization efficiency, non-orthogonal multiple access (NOMA) technology is utilized for MEC to increase the transmission rate. Besides, the optimization of both the UAV location and RIS passive beamforming is undertaken to minimize the overall energy consumption of the system. To tackle the non-convex nature of this problem, the joint optimization problem is decomposed into two separate sub-problems to facilitate more efficient solution approaches. The complex circle manifold optimization (CCMO) algorithm and genetic algorithm (GA) are utilized to address these two sub-problems alternately. Simulation results demonstrate that the proposed scheme effectively reduces the overall energy consumption of the MEC system.

关键词： NOMA Energy consumption Multi-access edge computing Simulation Reconfigurable intelligent surfaces Autonomous aerial vehicles Servers

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ANUBIS: Hybrid FPAA-FPGA Architecture for Entropy-Based True Random Number Generation in Secure UAV Communication

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IEEE Embedded Systems Letters 2024年

作者： El-Hadedy, Mohamed Abelian, Andrea Lee, Kenny Cheng, Benny Hwu, Wen-Mei California State Polytechnic University Department of Electrical and Computer Engineering Pomona United States University of Illinois at Urbana-Champaign Coordinated Science Laboratory United States Naval Surface Warfare Center United States

Field-Programmable Gate Arrays (FPGAs) and Field-Programmable Analog Arrays (FPAAs) are reconfigurable circuits that enable flexible digital and analog implementations post-manufacturing. FPGAs are widely used in telecommunications, mixed-signal, and embedded systems due to their parallel processing and reconfigurability. Meanwhile, FPAAs provide flexibility for analog systems, which is crucial for modern mixed-signal processing. This study introduces ANUBIS, a hybrid system combining FPGA and FPAA technologies to generate true random numbers (TRNGs) for secure UAV communication. Due to its reliability and cost efficiency, ANUBIS leverages a thermistor circuit as an entropy source. The FPAA amplifies the analog noise generated by the thermistor, while the FPGA digitizes and processes the signal using Von Neumann Whitening (VNW) to remove bias. The ASCON hash function is applied to the whitened bitstream to generate cryptographically secure keys. These keys are utilized in a DHKE to enable secure communication via Bluetooth Low Energy (BLE), an ideal protocol for energy-constrained UAV applications. ANUBIS demonstrates reconfigurability, power efficiency, and ease of implementation, showcasing its potential for secure communication applications. It achieves robust randomization, setting a new standard for UAV communication security and addressing applications requiring reliable TRNG solutions. The system consumes 1.615 W in total, with 1.54 W consumed by the FPGA and 75 mW by the FPAA. Resource utilization on the PYNQ-Z1 board includes 5,186 LUTs (9.75%), 549 units of memory (3.15%), and 5.5 units of BRAM (3.93%), indicating moderate resource usage with room for future enhancements. By integrating reliable analog noise harvesting with efficient digital post-processing, ANUBIS offers a novel approach to TRNG design, demonstrating the potential for broader cryptographic applications in resource-constrained environments. © 2009-2012 IEEE.

关键词： Random number generation

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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Clara J. Almeida David Guerrero-Pantoja Erik Pautsch Silvio Rizzi George K. Thiruvathukal Maria Pantoja Robotics and Electrical Engineering University of California Santa Cruz Santa Cruz USA Computer Science and Game Design University of California Santa Cruz Santa Cruz USA Computer Science Loyola University Chicago Chicago USA Argonne Leadership Computing Facility Argonne National Laboratory USA Computer Science and Software Engineering California Polytechnics State University San Luis Obispo USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

关键词： Uncertainty Statistical analysis Atmospheric modeling Computational modeling Predictive models Spatial databases Numerical models Reliability Automotive components Standards

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