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Distributed stochastic source seeking and formation control based on delayed measurements

science China(Information sciences) 2025年第3期68卷 319-337页

作者： Pei-Pei ZHANG Lin-Yu YANG Shu-Jun LIU School of Mathematics Sichuan University School of Data and Computer Science Shandong Women's University

In this paper, we design a distributed stochastic source seeking algorithm based on time-delay measurements to implement source seeking and formation control, so that vehicles can achieve and maintain a specific formation during the source seeking process. First, we present continuous-time stochastic averaging theorems for nonlinear delay-differential systems with stochastic perturbations. Then, based on the stochastic extremum seeking method and the leaderless formation strategy,we design a distributed stochastic source seeking algorithm based on time-delay measurements to navigate multiple velocity-actuated vehicles to search for an unknown source while achieving and maintaining a predefined formation, and the effect of the delay is eliminated by adopting the one-stage sequential predictor approach. Moreover, based on our developed stochastic averaging theorems, we prove that the average position of vehicles exponentially converges to a small neighborhood of the source in the almost sure sense, and vehicles can achieve and maintain a predefined formation. Finally, we provide numerical examples to verify the effectiveness of our proposed algorithm.

关键词： distributed source seeking formation control stochastic extremum seeking stochastic averaging time delays

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SGformer:Boosting transformers for indoor lighting estimation from a single image

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Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Enhancing Entity Relationship Extraction in Dialogue Texts Using Hypergraph and Heterogeneous Graph

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Chinese Journal of Electronics 2025年第1期34卷 295-308页

作者： Shunmiao Zhang Siyuan Zheng Degen Huang Dan Li School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology School of Computer Science and Technology Dalian University of Technology Elsevier

Dialogue-based relation extraction(DialogRE) aims to predict relationships between two entities in dialogue. Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in dialogue data as well as complex entity relationships, such as a single entity with multiple types of connections. To address these issues, this paper presents a novel approach for dialogue relationship extraction termed the hypergraphs and heterogeneous graphs model(HG2G). This model introduces a two-tiered structure, comprising dialogue hypergraphs and dialogue heterogeneous graphs, to address the shortcomings of existing methods. The dialogue hypergraph establishes connections between similar nodes using hyper-edges and utilizes hypergraph convolution to capture multi-level features. Simultaneously, the dialogue heterogeneous graph connects nodes and edges of different types, employing heterogeneous graph convolution to aggregate cross-sentence information. Ultimately, the integrated nodes from both graphs capture the semantic nuances inherent in dialogue. Experimental results on the DialogRE dataset demonstrate that the HG2G model outperforms existing state-of-the-art methods.

关键词： Convolution Aggregates Semantics Feature extraction data mining

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Combining Innovative CVTNet and Regularization Loss for Robust Adversarial Defense

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Journal of computer science & Technology 2024年第5期39卷 1078-1093页

作者： Wei-Dong Wang Zhi Li Li Zhang Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China

Deep neural networks(DNNs)are vulnerable to elaborately crafted and imperceptible adversarial *** the continuous development of adversarial attack methods,existing defense algorithms can no longer defend against them ***,numerous studies have shown that vision transformer(ViT)has stronger robustness and generalization performance than the convolutional neural network(CNN)in various ***,because the standard denoiser is subject to the error amplification effect,the prediction network cannot correctly classify all reconstruction ***,this paper proposes a defense network(CVTNet)that combines CNNs and ViTs that is appended in front of the prediction *** can effectively eliminate adversarial perturbations and maintain high ***,this paper proposes a regularization loss(L_(CPL)),which optimizes the CVTNet by computing different losses for the correct prediction set(CPS)and the wrong prediction set(WPS)of the reconstruction examples,*** evaluation results on several standard benchmark datasets show that CVTNet performs better robustness than other advanced *** with state-of-the-art algorithms,the proposed CVTNet defense improves the average accuracy of pixel-constrained attack examples generated on the CIFAR-10 dataset by 24.25%and spatially-constrained attack examples by 14.06%.Moreover,CVTNet shows excellent generalizability in cross-model protection.

关键词： deep learning adversarial defense vision transformer image reconstruction

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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Automated Adversarial-Attack Removal with SafetyNet Using ADGIT 10

Automated Adversarial-Attack Removal with SafetyNet Using AD...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Halim, Steven Anandi, Arvio Devin, Vyos Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step. © 2024 IEEE.

关键词： Adversarial machine learning

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Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity 7

Cosmetics Recommendation System Based on Ingredients Using C...

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7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Alisha Firdaus, Sekar Dwi Nurmuthia, Laksmi Imtiyaz, Raina Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365351

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare. © 2024 IEEE.

关键词： Skin care products

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

引用

2025 International Conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Limicia, Jennifer Ardelia Chandra, Wilbert Gunawan, Alexander Agung Santoso Tedjasulaksana, Jeffrey Junior School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions. © 2025 IEEE.

关键词： Convolutional neural networks

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Simulation Study: Trajectory Tracking Control Of Parafoil System Based On Deep Reinforcement Learning

Journal of Applied Science and Engineering

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Journal of Applied science and Engineering 2025年第12期28卷 2439-2449页

作者： Zhang, Xuepu School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China

The parafoil system is nonlinear and complex with a large time delay. This makes it challenging for traditional control methods to control the parafoil system effectively. However, the Markov property of reinforcement learning offers a new possibility for controlling the parafoil system. Therefore, this paper employs the deep reinforcement learning (DRL) method to train a neural network controller for controlling the parafoil system, based on a modified deterministic version of the distributional soft actor-critic with three refinements (DSAC-T) algorithm and it is named MC-DSAC-T. The controller of the parafoil system is denoted as a multilayer perceptron (MLP) and the objective function of the policy introduces cumulative discounted rewards of a single episode to improve the stability of the iterative update of the policy, a Monte Carlo (MC) thinking. In addition, wind disturbances are introduced during training to enhance the robustness of the neural network controller. First, a nine-degree-of-freedom (nine-DOF) dynamic model of the parafoil system is developed. Secondly, the network structure of the MC-DSAC-T algorithm and the process of updating the network using sampling data were introduced. Finally, the control effects of the neural network controller trained by the proposed method were compared with those of the proportion integration differentiation (PID) controller in a wind environment. While tracking 100 randomly selected trajectory segments, the results show that the neural network controller is superior to the PID controller in distance control accuracy, which proves that the neural network controller can control the parafoil system to perform the tracking task and verify the effectiveness of the proposed method. © The Author(’s).

关键词： Reinforcement learning

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Minimal Context-Switching data Race Detection with dataflow Tracking

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Journal of computer science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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