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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

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computers, Materials & Continua 2022年第3期70卷 5583-5600页

作者： Anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Engineering Department of Electrical EngineeringCommunication EngineeringSana’a UniversityYemen

Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research *** main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital *** this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet *** this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing *** the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital *** is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital *** digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic *** extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.

关键词： Text analysis text-watermarking tampering detection text authentication

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Advancing Text-to-Image Generation: A Comparative Study of StyleGAN-T and Stable Diffusion 3 under Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 85卷 784-800页

作者： Sadek, Mohamed G. Hassan, A.Y. Diab, Tamer O. Abdelhafeez, Ahmed Benha Engineering Faculty Egypt Electrical Engineering Department Benha Engineering Faculty Benha University 13511 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This paper outlines an extensive comparison and analysis of some of the best models across both paradigms, namely StyleGAN-T, DF-GAN, AttnGAN, and BigGAN on the GAN side and Stable Diffusion 3 (SD3), DALL·E 3, Midjourney v6, and Imagen 2 on the diffusion side. We systematically inspect the architectural design, training protocols, text-conditioning processes, and domain adaptability of each model, highlighting how they address text-to-image generation challenges differently. Through qualitative and quantitative measurements—such as FID, CLIP Score, human preference surveys, and compositional accuracy, the work reveals performance tradeoffs concerning speed, control, creativity, semantic alignment, and photorealism. We use the Neutrosophic Set model to select the best model based on these evaluation matrices. We have different scores for each model based on evaluation matrices. So, the neutrosophic set is used to overcome the uncertainty information. We use the COPRAS method to rank the models and select the best one based on the evaluation matrix weights. © 2025, University of New Mexico. All rights reserved.

关键词： Photointerpretation

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An Efficient and Scalable Byzantine Fault Tolerant Consensus for Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Deshmukh, Ameya Baiju, Dheeraj Atish, Atish Alladi, Tejasvi Yu, F. Richard BITS-Pilani Department of Computer Science and Information Systems Pilani333031 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani333031 India Carleton University Department of Information Technology OttawaON Canada

Vehicular networks represent a new distributed system paradigm that requires robust fault tolerance to ensure reliable operation. As a burgeoning area of research, the scalability and optimization of consensus mechanisms for these networks are critical. Traditional Byzantine Fault Tolerant (BFT) algorithms like PBFT are not inherently optimized for the localized needs of vehicular networks, suffering from scalability issues due to their global nature and high messaging complexity. In response, we introduce a two-tiered consensus framework that refines PBFT for the specific context of vehicular networks. By organizing nodes into clusters based on geographic proximity, our approach reduces messaging complexity from O(n2) to O(n1.5), significantly improving scalability. The framework distinguishes between local and global state transitions, adding two phases to the PBFT protocol to manage these efficiently. This tailored consensus process aligns with the localized communication patterns of vehicular networks, enhancing both efficiency and scalability. The framework addresses the critical challenges of traditional BFT algorithms in vehicular networks, offering a solution that is both scalable and resilient. It is a step toward enabling vehicular networks to fulfil their potential as a reliable component of modern distributed systems. © 1967-2012 IEEE.

关键词： Blockchain Byzantine Fault Tolerance Distributed consensus Vehicular networks

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ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches

arXiv

引用

arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The Department of Electrical and Computer Engineering University of Waterloo Canada

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS2A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS2A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated. © 2024, CC BY.

关键词： Blockchain

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Semi-Supervised Federated Learning for Assessing Building Damage from Satellite Imagery

Semi-Supervised Federated Learning for Assessing Building Da...

引用

IEEE International Conference on Communications (ICC)

作者： Yu Zhang Yanmin Gong Yuanxiong Guo Department of Electrical and Computer Engineering University of Texas at San Antonio Department of Information Systems and Cyber Security University of Texas at San Antonio

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Accurate and timely building damage assessments are crucial for effective disaster response. However, traditional damage assessment methods heavily rely on manual evaluations by experts, which are labor-intensive and time-consuming. Recent research leverages machine learning (ML) and satellite remote sensing techniques to streamline the process. A major challenge of this method lies in the unlabeled nature of satellite imagery, which makes traditional ML frameworks impractical. Additionally, downloading the high-resolution satellite imagery for centralized ML is hindered by limited bandwidth and sporadic connectivity between the low Earth orbit (LEO) satellites and ground server. To address these challenges, we propose a novel semi-supervised federated learning framework named Semi-FedDA. It utilizes a small amount of labeled data on the ground server and a large amount of unlabeled data on the satellites to efficiently train a building assessment model without manual labeling. Moreover, this framework leverages intra-plane inter-satellite links (ISLs) to implement intra-orbit aggregations, which can significantly reduce the communication cost. We conduct extensive experiments on the real-world dataset. Numerical results show that our proposed framework can reduce training time by up to 94% compared with baselines, without sacrificing model accuracy.

关键词： Satellites Federated learning Disasters Buildings Low earth orbit satellites Manuals Satellite images

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Multi-step Sensor Attackability in Cyber-Physical systems

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IEEE Transactions on Automatic Control 2025年

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu The Institute of Systems Engineering Macau University of Science and Technology 999078 China The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

This work investigates cyber attacks targeting cyber-physical systems in the framework of discrete event systems. From intruders’ perspective, we propose a concept called k-step attackability to explore attack scenarios where intruders are constrained by a limited number of attack operations. In particular, we focus on a class of sensor attacks where sensor readings can be replaced, deleted, and inserted during transmission. We examine k-step attackability under such attacks and address its verification problem by proposing a structure termed an attack recognizer. It evaluates whether an arbitrary strategy generated by an attack structure can compromise a system within a certain number of attacks. Furthermore, we extend this work to general attack scenarios and provide a method to identify desired attack strategies. © 1963-2012 IEEE.

关键词： Cyber attacks

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Fault Isolation in Loops of Linear Dynamic Networks

Fault Isolation in Loops of Linear Dynamic Networks

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Lőrinc Márton Wijaya Kurniawan Department of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Faculty of Computer Science University of Brawijaya Malang Indonesia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper addresses the problem of fault isolation in networks containing loops where the dynamic behavior of the nodes can be modeled by finite gain linear systems. If the underlying graph of the network contains loops, isolating a faulty subsystem can be challenging. This is because the propagation of fault effects through the network interconnections is difficult to trace. In this study, it is shown that, if the gains of the subsystems satisfy certain assumptions, the fault can be isolated in the loops of the considered network. For each subsystem in the loop, a residual signal is generated. The isolation method examines the differences between the norms of these residual signals. Based on this information, the location of the faulty subsystem can be determined. Simulation measurements are provided to show the efficiency of the proposed fault isolation method.

关键词： Linear systems Computational modeling Simulation Directed graphs information technology Gain

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

arXiv

引用

arXiv 2024年

作者： Murshed, Rafid Umayer Ullah, Md Saheed Saquib, Mohammad Win, Moe Z. Department of Electrical and Computer Engineering The University of Texas DallasTX United States Department of Electrical and Computer Engineering University of Delaware Delaware United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts United States

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods. © 2024, CC BY.

关键词： Self-supervised learning

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