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Multi-Scale Generative Transformer-Based Primal-Dual PPO Framework for AAV-Aided Intelligent Transportation Networks

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Adam, Abuzar B. M. Kamal, Tahir Elhassan, Mohammed A. M. Alshahrani, Abdullah Alsamhi, Saeed Hamood Aziz, Ahmed Luxembourg City1855 Luxembourg University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Zhejiang Normal University School of Mathematics and Computer Science Jinhua321017 China University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21493 Saudi Arabia University of Galway Insight Centre for Data Analytics GalwayH91 TK33 Ireland Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Bahrain Polytechnic School of ICT Isa Town00973 Bahrain IBB University Faculty of Engineering Electrical Engineering Department Ibb Yemen Benha University Faculty of Computer and Artificial Intelligence Department of Computer Science Banha13511 Egypt Central Asian University Engineering School Tashkent111221 Uzbekistan

Intelligent transportation networks are increasingly integrating autonomous aerial vehicles (AAVs) to enable services essential for modernizing industries like transportation, logistics, and search and rescue. A significant beneficiary is the Internet of Connected Vehicles (IoCVs), where AAVs play a transformative role in supporting next-generation cellular networks. This paper addresses the challenge of minimizing the cost of delivering content to vehicles on road segments with congested traffic or insufficient infrastructure. Vehicles entering these segments request content from a AAV-hosted library that updates dynamically based on item popularity. Each vehicle submits a request, requiring the AAV to compute an optimal trajectory to maximize operational utility. Given the AAV’s limited energy, we aim to develop an energy-efficient solution. The problem is framed as a joint optimization of caching decisions, AAV trajectory planning, and radio resource allocation, formulated as a mixed integer non-linear programming (MINLP) problem. The environment’s complexity, with random vehicle arrivals and fluctuating content, makes traditional optimization techniques inadequate. To address this, we reformulate the problem as a constrained Markov decision process (CMDP) and employ a primal-dual proximal policy optimization (PPO) algorithm, enhanced by a multi-scale generative transformer (MGFormer) for improved speed and accuracy over traditional deep neural networks (DNNs). Simulation results demonstrate that the proposed framework reduces service costs by up to 30% and achieves robust performance even under high-density traffic and Doppler shifts, validating its superiority over traditional Primal-dual PPO. © 2000-2011 IEEE.

关键词： Markov processes

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with artificial intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Phase-change metasurfaces for dynamic image display and information encryption

arXiv

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arXiv 2022年

作者： Liu, Tingting Han, Zhou Duan, Junyi Xiao, Shuyuan Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Institute for Advanced Study Nanchang University Nanchang330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Nanchang University Nanchang330031 China

Optical metasurfaces enable to engineer the electromagnetic space and control light propagation at an unprecedented level, offering a powerful tool to achieve modulation of light over multiple physical dimensions. Here, we demonstrate a Sb2S3 phase-change metasurface platform that allows active manipulation of both amplitude and phase. In particular, we implement dynamic nanoprinting and holographic image display through tuning crystallization levels of this phase-change material. The Sb2S3 nanobricks tailored to function the amplitude, geometric and propagation phase modulation constitute the dynamic meta-atoms in the multiplexed metasurfaces. Using the incident polarizations as decoding keys, the encoded information can be reproduced into a naonprinting grayscale image in the near field and two holographic images in the far field. These images can be switched on and off by taking advantages of the reversible tunability of Sb2S3 nanostructure between amorphous and crystalline states. The proposed phase-change metasurfaces featuring manifold information and multifold encryption promise ultracompact data storage with high capacity and high security, which suggests an exciting direction for modern cryptography and security applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Cryptography

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MetAug: Contrastive Learning via Meta Feature Augmentation

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science Engineering The Hong Kong University of Science and Technology Hong Kong

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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Interventional Contrastive Learning with Meta Semantic Regularizer

arXiv

引用

arXiv 2022年

作者： Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in this paper, we uncover an ever-overlooked phenomenon: When the CL model is trained with full images, the performance tested in full images is better than that in foreground areas;when the CL model is trained with foreground areas, the performance tested in full images is worse than that in foreground areas. This observation reveals that backgrounds in images may interfere with the model learning semantic information and their influence has not been fully eliminated. To tackle this issue, we build a Structural Causal Model (SCM) to model the background as a confounder. We propose a backdoor adjustment-based regularization method, namely Interventional Contrastive Learning with Meta Semantic Regularizer (ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can be incorporated into any existing CL methods to alleviate background distractions from representation learning. Theoretically, we prove that ICL-MSR achieves a tighter error bound. Empirically, our experiments on multiple benchmark datasets demonstrate that ICL-MSR is able to improve the performances of different state-of-the-art CL methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Investigating Idiomaticity in Word Representations

arXiv

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arXiv 2024年

作者： He, Wei Vieira, Tiago Kramer Garcia, Marcos Scarton, Carolina Idiart, Marco Villavicencio, Aline University of Sheffield School of Computer Science United Kingdom Federal University of Rio Grande do Sul Institute of Informatics Brazil University of Santiago de Compostela CiTIUS Research Center Spain Federal University of Rio Grande do Sul Institute of Physics Brazil University of Exeter Institute for Data Science and Artificial Intelligence The Alan Turing Institute University of Sheffield School of Computer Science United Kingdom 211 Portobello SheffieldS1 4DP United Kingdom Av. Bento Gonçalves 9500 Porto Alegre90000 Brazil Rua de Jenaro de la Fuente Dominguez s/n Galiza Santiago de Compostela15782 Spain Innovation Centre Rennes Drive ExeterEX4 4RN United Kingdom

Idiomatic expressions are an integral part of human languages, often used to express complex ideas in compressed or conventional ways (e.g. eager beaver as a keen and enthusiastic person). However, their interpretations may not be straightforwardly linked to the meanings of their individual components in isolation and this may have an impact for compositional approaches. In this paper, we investigate to what extent word representation models are able to go beyond compositional word combinations and capture multiword expression idiomaticity and some of the expected properties related to idiomatic meanings. We focus on noun compounds of varying levels of idiomaticity in two languages (English and Portuguese), presenting a dataset of minimal pairs containing human idiomaticity judgments for each noun compound at both type and token levels, their paraphrases and their occurrences in naturalistic and sense-neutral contexts, totalling 32,200 sentences. We propose this set of minimal pairs for evaluating how well a model captures idiomatic meanings, and define a set of fine-grained metrics of Affinity and Scaled Similarity, to determine how sensitive the models are to perturbations that may lead to changes in idiomaticity. Affinity is a comparative measure of the similarity between an experimental item, a target and a potential distractor, and Scaled Similarity incorporates a rescaling factor to magnify the meaningful similarities within the spaces defined by each specific model. The results obtained with a variety of representative and widely used models indicate that, despite superficial indications to the contrary in the form of high similarities, idiomaticity is not yet accurately represented in current models. Moreover, the performance of models with different levels of contextualisation suggests that their ability to capture context is not yet able to go beyond more superficial lexical clues provided by the words and to actually incorporate the relevant semantic clue

关键词： Semantics

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The Design and Empirical Research of One Main Stem with Others Branches Monetary System Based on Blockchain Technology 20

The Design and Empirical Research of One Main Stem with Othe...

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Feng, Liu Shiying, Lu Jiayin, Qi Zhibing, Li East China Normal University School of Computer Science and Technology Shanghai China Changsha University of Science and Technology Changsha China Shanghai University of International Business and Economics Institute of Artificial Intelligence and Change Management Shanghai China Shanghai International Studies University School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781728181417

The European debt crisis impacted the international monetary status of the euro as well as triggered a large number of discussions and opinions on the design of the monetary mechanism of the eurozone in academic circles. This study adopts DEA method and takes the euro area as an example to evaluate the two different monetary systems from the perspective of technical efficiency, pure technical efficiency and scale efficiency. Through empirical analysis, it can be found that when the one main stem with many other branches monetary system is suffering from crisis, the system is more stable. In contrast, the current scale of the euro monetary system is inefficient which cause a lot of waste of resource. This article gives a new currency system based on blockchain, which can maintain the existing euro-structured currency system, and is compatible with the euro zone sovereign countries to issue their own sovereign electronic currency, and the technology is more efficient. This article has reference significance for trying to solve or improve the European debt crisis. © 2020 IEEE.

关键词： Blockchain

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Trust-based core social graph convolution: An innovative framework for location recommendation

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Expert Systems with Applications 2025年 285卷

作者： Xie, Tianyu Chen, Yunliang Wu, Yong Cui, Ningning Chen, Haofeng Lu, Xuanyu Huang, Xiaohui Wang, Yuewei Li, Jianxin School of Computer Science China University of Geosciences Wuhan Hubei430074 China Hardware and Artificial Intelligence Lab Institute of Computer Engineering Heidelberg University Heidelberg69120 Germany School of Data Science and Artificial Intelligence Chang'an University Xi'an710064 China System Design Institute of Hubei Aerospace Technology Academy Wuhan430040 China Discipline of Business Systems and Operations School of Business and Law Edith Cowan University Australia

With the increasing popularity of location-based social networks (LBSNs), recommendation based on LBSNs has attracted wide attention in academic and industrial domains. Traditional recommendation systems face critical challenges in handling data sparsity and underutilized social trust. Existing traditional models particularly struggle to incorporate both trust-level differences and implicit relationships effectively within large-scale personalized recommendations. This limitation directly leads to aggravated data sparsity, further exacerbating cold-start scenarios and significantly reducing recommendation accuracy for long-tail items. Existing widely-adopted deep learning models, such as graph convolutional networks (GCNs), apply equal propagation weights to all neighbor nodes, which not only causes feature convergence between high-trust and ordinary users (over-smoothing) but also incurs substantial computational overhead. We propose the Trust-Based Core Graph Collaborative Filtering (TCGCF) framework, integrating trust-weighted core graph analysis with trust-constrained graph convolution. TCGCF captures both explicit and implicit trust relationships, enhances personalization, and mitigates over-smoothing. Our trust-weighted core graph analysis identifies influential users in information propagation, while the trust-constrained convolution scheme enables precise, differentiated information flow. Experiments on real-world datasets demonstrate that TCGCF improves recommendation accuracy and computational efficiency, outperforming existing models in precision, recall, and suitability for large-scale applications. © 2025

关键词： Collaborative filtering

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Sample hardness based gradient loss for long-tailed cervical cell detection

arXiv

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arXiv 2022年

作者： Liu, Minmin Li, Xuechen Gao, Xiangbo Chen, Junliang Shen, Linlin Wu, Huisi Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China University of California Irvine United States

Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing state-of-the-art long-tailed learning methods in object detection focus on category distribution statistics to solve the problem in the long-tailed scenario, without considering the "hardness" of each sample. To address this problem, in this work we propose a Grad-Libra Loss that leverages the gradients to dynamically calibrate the degree of hardness of each sample for different categories, and re-balance the gradients of positive and negative samples. Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories. Extensive experiments on a long-tailed TCT WSI image dataset show that the mainstream detectors, e.g. RepPoints, FCOS, ATSS, YOLOF, etc. trained using our proposed Gradient-Libra Loss, achieved much higher (7.8%) mAP than that trained using cross-entropy classification loss. Copyright © 2022, The Authors. All rights reserved.

关键词： Cytology

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EXPLAINABLE artificial intelligence (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque artificial intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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