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39th IMAC, A Conference and Exposition on Structural Dynamics, 2021

作者： Eshkevari, Soheila Sadeghi Eshkevari, Soheil Sadeghi Pakzad, Shamim N. Muñoz-Avila, Héctor Kishore, Shalinee Department of Civil and Environmental Engineering Lehigh University BethlehemPA United States Senseable City Lab Massachusetts Institute of Technology CambridgeMA United States Department of Computer Science & Engineering Lehigh University BethlehemPA United States Department of Electrical & Computer Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9783030760038

Traditional public transportation mostly uses fixed schedules to meet society’s needs reliably. Alternatively, one can design a dynamic policy to update the schedule according to the state of the network. Using such policy, any dynamic changes in demand, travel times, traffic, geometry, or emergency modes will be reflected in the real-time scheduling. In this study, the application of reinforcement learning for public transportation scheduling and routing is proposed considering electric vehicles. Reinforcement learning is a right choice for online decision-making in a time-variant setting. Reflecting electric vehicles’ characteristics on public transportation scheduling supports the necessity of considering a dynamic scheduling policy, such as vehicle-to-grid transaction capability or dynamic charging strategies. The proposed scheduling methodology is evaluated for a customary six-stop network. The trained agent takes actions based on the number of waiting people, the drop-off requests, battery level, time, and the current location. The assumed models for the traffic load effect, the passenger flow per stops, and the electricity price add nonlinearity to the problem in which this algorithm will behave more conveniently compared to a fixed schedule setting. Using this method, the electric bus can choose the next destination, the optimal driving speed for each interval, and the amount of electricity to charge or to sell for optimal financial gain when connected to the charging infrastructure. The proposed environment is built numerically, and a learning-based agent is trained for this purpose. Our study supports that the optimal dynamic agent outperforms a fixed schedule policy in different modes of network performance, particularly, when the network undergoes abrupt demand changes. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Reinforcement learning

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Towards Secure and Private Smart Contracts in Ethereum: SafeSC ChatGPT-based Tool in Action

Towards Secure and Private Smart Contracts in Ethereum: Safe...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Osama Elghazaly Nidal Nasser Ahmed El Ouadrhiri Asmaa Ali IoT Research Lab Alfaisal University Riyadh Saudi Arabia Department of Mathematics Computer Science and Physics Rockford University Rockford Illinois USA Electrical and Computer Engineering Department Wastern University London Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Blockchain-based smart contracts, while transformative, pose privacy concerns due to Ethereum's transparency. To address this, we present Safe Smart Contracts (SafeSC), leveraging zk-SNARKs for privacy without compromising Ethereum's transparency. SafeSC's Python tool facilitates contract understanding and verification without accessing the source code. Our paper explores privacy preservation techniques, favoring Zero-Knowledge Proofs (ZKPs). SafeSC employs zk-SNARKs and Groth-16, achieving a delicate balance between transparency and privacy in smart contract development. The tool’s design, covering architecture, assumptions, data flow, and zero-knowledge proof workflow, marks a step toward secure smart contract solutions. We advocate for continued exploration and refinement to enhance blockchain technologies.

关键词： Privacy Source coding Smart contracts Chatbots Blockchains Global communication

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Skyrmionic spin textures in nonparaxial light

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Advanced Photonics 2025年第1期7卷 85-92页

作者： Xinrui Lei Aiping Yang Xusheng Chen Luping Du Peng Shi Qiwen Zhan Xiaocong Yuan Shenzhen University Institute of Microscale Optoelectronics and State Key Laboratory of Radio Frequency Heterogeneous IntegrationNanophotonics Research CenterShenzhen Key Laboratory of Micro-Scale Optical Information TechnologyShenzhenChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina Dongguan University of Technology Research Institute of Interdisciplinary Sciences and School of Materials Science and EngineeringDongguanChina Research Centre for Frontier Fundamental Studies Zhejiang LabHangzhouChina

Topological textures in optics such as skyrmions and merons are increasingly studied for their potential functions in light–matter interactions,deep-subwavelength imaging,and ***,they were previously generated either in strongly confined guided waves or in paraxial *** has posed a significant challenge in constructing skyrmions in nonparaxial propagating waves due to the lack of symmetry-breaking in the optical field and difficulty in characterizing the full three-dimensional spin textures at the *** theoretically propose and experimentally demonstrate the generation of skyrmionic spin textures in nonparaxial light,where skyrmionic textures with a Bloch-type scheme,including isolated skyrmioniums,skyrmion,and meron lattices are generated in free *** introduce the interplay between the Hertz potentials to break the dual symmetry of light and build well-defined domains of *** experimentally realized the topological textures by applying a hybrid polarized optical vortex and observed the complete three-dimensional spin distributions by a dual-mode waveguide *** bridging the gap in the skyrmionic group,we present a topologic diagram,showing how spin–orbit coupling of light governs the spin *** findings offer new insights into optical quasiparticles and electron–photon correspondence,potentially facilitating advanced applications in optical metrology,sensing,and storage.

关键词： spin topology photonic skyrmion spin–orbit coupling of light

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks 19

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Bohai Peng, Kai Leung, Victor C.M. Xia, Yunni Huaqiao University College of Engineering Quanzhou China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Shenzhen University School of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Chongqing University School of Computers Chongqing China

ISBN: (纸本)9798350346558

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches. © 2022 IEEE.

关键词： Multiobjective optimization

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Test-Time Personalization with Meta Prompt for Gaze Estimation

arXiv

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arXiv 2024年

作者： Liu, Huan Qi, Julia Li, Zhenhao Hassanpour, Mohammad Wang, Yang Plataniotis, Konstantinos Yu, Yuanhao Noah's Ark Lab Huawei Canada University of Waterloo Canada Department of Computer Science and Software Engineering Concordia University Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Canada

Despite the recent remarkable achievement in gaze estimation, efficient and accurate personalization of gaze estimation without labels is a practical problem but rarely touched on in the literature. To achieve efficient personalization, we take inspiration from the recent advances in Natural Language Processing (NLP) by updating a negligible number of parameters, "prompts", at the test time. Specifically, the prompt is additionally attached without perturbing original network and can contain less than 1% of a ResNet-18's parameters. Our experiments show high efficiency of the prompt tuning approach. The proposed one can be 10 times faster in terms of adaptation speed than the methods compared. However, it is non-trivial to update the prompt for personalized gaze estimation without labels. At the test time, it is essential to ensure that the minimizing of particular unsupervised loss leads to the goals of minimizing gaze estimation error. To address this difficulty, we propose to meta-learn the prompt to ensure that its updates align with the goal. Our experiments show that the meta-learned prompt can be effectively adapted even with a simple symmetry loss. In addition, we experiment on four cross-dataset validations to show the remarkable advantages of the proposed method. Code is available at https://***/hmarkamcan/TPGaze. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Optimized Vision Transformer Training using GPU and Multi-threading

Optimized Vision Transformer Training using GPU and Multi-th...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jonathan Ledet Ashok Kumar Dominick Rizk Rodrigue Rizk KC Santosh Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Department of Electrical Engineering and Computer Science Catholic University of America Washington DC USA Department of Computer Science Applied AI Research Lab University of South Dakota Vermillion SD USA

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Traditional Convolutional Neural Networks (CNNs) often struggle with capturing intricate spatial relationships and nuanced patterns in diverse datasets. To overcome these limitations, this work pioneers the application of Vision Transformer (ViT) models which have gained significant attention in the field of computer vision for their ability to capture long-range dependencies in images through self-attention mechanisms. However, training large-scale ViT models with a massive number of parameters poses computational challenges. In this paper, we present an optimized approach for training ViT models that leverages the parallel processing capabilities of Graphics Processing Units (GPUs) and optimizes the computational workload distribution using multi-threading. The proposed model is trained and tested on the CIFAR-10 dataset and achieved an outstanding accuracy of 99.92% after 100 epochs. The experimental results demonstrate the effectiveness of our approach in optimizing training efficiency compared to existing methods. This underscores the superior performance of ViT models and their potential to revolutionize image classification tasks.

关键词： Training computer vision Multithreading Computational modeling Neural networks Graphics processing units Convolutional neural networks

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Determination of 24-Hour EV Hosting Capacity of Qatar’s Utility Grid Considering Existing Load Patterns and Different Charging Speeds

Determination of 24-Hour EV Hosting Capacity of Qatar’s Uti...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Syed Rahman Irfan Khan Muhammad Ashar Ayaz Atif Iqbal Chun-Lien Su Electrical & Computer Engineering Texas A&M University College Station USA Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston USA Department of Electrical Engineering Qatar University Doha Qatar Department of Electrical Engineering National Kaohsiung University of Science & Technology Taiwan

ISBN: (数字)9798350345308

ISBN: (纸本)9798350345315

The widespread adoption of electric vehicles (EVs) has induced profound transformations in the resulting power demand and voltage profile of the low-voltage utility grids. The impact of this paradigm shift is contingent upon subtle factors, including the power rating of EV chargers, point of installation, prevailing power demand profiles, and driving patterns (dictating kWh energy of EV load). The introduction of EV loads without considering these factors and the subsequent comprehensive analysis will result in grid congestion, heightened peak demand, and augmented system losses. To address this, this paper presents a comprehensive case study of determining the EV hosting capacity of Qatar’s utility grid, considering real-world demand profile data, existing load types, and their corresponding variation pattern. Secondly, considering the energy and instantaneous power requirement of EV load, the EV hosting is estimated using a 24-hour profile considering all EV charger types (DC fast chargers, level-2 chargers, and domestic chargers). A 2-step optimization algorithm is proposed to maximize the hosting capacity by optimally distributing the charging load among the residential nodes, without any events of grid congestion. Detailed simulation results show that EV charging load up to 11.4% of the distribution system rating can be accommodated without grid congestions.

关键词： Low voltage Power demand Simulation Voltage Electric vehicle charging Optimization

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Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Localization of Image Splicing Under Segment Anything Model ...

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IEEE International Conference on Image Processing

作者： Ruhao Zhao Xian Zhong Liang Liao Wenxuan Liu Wenxin Huang Zheng Wang School of Computer Science and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology School of Electrical and Electronic Engineering Rapid-Rich Object Search Lab Nanyang Technological University College of Computing and Data Science Nanyang Technological University School of Computer Science and Information Engineering Hubei University School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated Compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM’s object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and Compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Location awareness Image segmentation Image coding Social networking (online) Image edge detection Splicing Feature extraction

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World Models: The Safety Perspective 35

World Models: The Safety Perspective

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35th IEEE International Symposium on Software Reliability engineering Workshops, ISSREW 2024

作者： Zeng, Zifan Zhang, Chongzhe Liu, Feng Sifakis, Joseph Zhang, Qunli Liu, Shiming Wang, Peng Huawei Technologies Duesseldorf GmbH Rams Lab Munich Germany Technical University of Munich School of Computation Information and Technology Garching Germany Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin Germany Univ. Grenoble Alpes Cnrs Grenoble Inp Verimag and SUSTECH/RITAS Grenoble France Huawei Technologies Co. Ltd. Rams Lab Shenzhen China

ISBN: (纸本)9798350367041

With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially in the context of AI agents. It is arguably evolving into an essential foundation for building AI agent systems. A WM is intended to help the agent predict the future evolution of environmental states or help the agent fill in missing information so that it can plan its actions and behave safely. The safety property of WM plays a key role in their effective use in critical applications. In this work, we review and analyze the impacts of the current state-of-the-art in WM technology from the point of view of trustworthiness and safety based on a comprehensive survey and the fields of application envisaged. We provide an in-depth analysis of state-of-the-art WMs and derive technical research challenges and their impact in order to call on the research community to collaborate on improving the safety and trustworthiness of WM. © 2024 IEEE.

关键词： Intelligent agents

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Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior

arXiv

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arXiv 2024年

作者： Yu, Yi Wang, Yufei Yang, Wenhan Guo, Lanqing Lu, Shijian Duan, Ling-Yu Tan, Yap-Peng Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Pengcheng Laboratory Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science Peking University Beijing China

Recent advancements in deep learning-based compression techniques have demonstrated remarkable performance surpassing traditional methods. Nevertheless, deep neural networks have been observed to be vulnerable to backdoor attacks, where an added pre-defined trigger pattern can induce the malicious behavior of the models. In this paper, we propose a novel approach to launch a backdoor attack with multiple triggers against learned image compression models. Drawing inspiration from the widely used discrete cosine transform (DCT) in existing compression codecs and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives that are adapted for a series of diverse scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality;2) attacking task-driven measures, such as face recognition and semantic segmentation in downstream applications. To facilitate more efficient training, we develop a dynamic loss function that dynamically balances the impact of different loss terms with fewer hyper-parameters, which also results in more effective optimization of the attack objectives with improved performance. Furthermore, we consider several advanced scenarios. We evaluate the resistance of the proposed backdoor attack to the defensive pre-processing methods and then propose a two-stage training schedule along with the design of robust frequency selection to further improve resistance. To strengthen both the cross-model and cross-domain transferability on attacking downstream CV tasks, we propose to shift the classification boundary in the attack loss during training. Extensive experiments also demonstrate that by employing our trained trigger injection models and making slight modifications to the encoder parameters of the compression model, our proposed attack can successfully inject multiple backdoors accompanied by their corresponding triggers int

关键词： Semantic Segmentation

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