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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Aakash Suresh Lakshmi C Gopirajan PV K Dhanasekaran School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe.

关键词： Radio frequency Logistic regression Accuracy Uncertainty Computational modeling Heuristic algorithms Exoplanet data augmentation data models Synthetic data

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Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： Nisarg Doshi Sagar Bhavsar D Rajeswari R Srinivasan Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.

关键词： Deep learning Measurement Autonomous systems Atmospheric modeling Estimation Feature extraction Knowledge discovery

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FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint

arXiv

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arXiv 2025年

作者： Shao, Shuo Zhu, Haozhe Yao, Hongwei Li, Yiming Zhang, Tianwei Qin, Zhan Ren, Kui State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Department of Computer Science City University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models by preventing their unauthorized reuse. It is promising and convenient since it does not necessitate modifying the protected model. In this paper, we revisit existing fingerprinting methods and reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model. We demonstrate that this vulnerability mostly stems from their untargeted nature, where they generally compare the outputs of given samples on different models instead of the similarities to specific references. Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks. Specifically, FIT-Print transforms the fingerprint into a targeted signature via optimization. Building on the principles of FIT-Print, we develop bit-wise and list-wise black-box model fingerprinting methods, i.e., FIT-ModelDiff and FIT-LIME, which exploit the distance between model outputs and the feature attribution of specific samples as the fingerprint, respectively. Extensive experiments on benchmark models and datasets verify the effectiveness, conferrability, and resistance to false claim attacks of our FIT-Print. © 2025, CC BY.

关键词： Open systems

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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks

Forest-based Evolutionary Algorithm for Reconstructing Boole...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Nicolò Stranieri Francesca M. Buffa Andrea Tangherloni Department of Computing Sciences Bocconi University Milan Italy Department of Oncology University of Oxford Oxford UK Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Gene Regulatory Networks (GRNs) play a fundamental role in orchestrating the expression of our genes through complex interactions between DNA, RNA, proteins, and other molecules. Accurately reconstructing such networks from gene expression data is a critical yet challenging task in Systems Biology due to their intricate nature and limited data availability. In this work, we introduce a novel Forest-based Evolutionary Algorithm (FP) designed for reconstructing Boolean GRNs from time series data of gene expressions. Unlike traditional methods that struggle with scalability and accurate representation of regulatory interactions, FP utilizes a forest structure where each tree represents the logical relationships between genes, enhancing the model’s capacity to depict complex networks efficiently. Our comprehensive testing indicates that FP rapidly converges towards potential solutions within a limited number of generations, although a higher fitness score does not always equate to a more accurate GRN representation. Implementing mini-batching techniques, inspired by their effectiveness in gradient descent optimization, shows promise in improving computational efficiency without sacrificing performance. A comparative analysis against the main state-of-the-art approaches reveals FP’s tendency towards conservative predictions, emphasizing precision over recall, making it particularly suitable for contexts where the cost of false positives is high. These initial results suggest that FP is a robust and efficient tool for GRN inference.

关键词： Measurement Accuracy Scalability Time series analysis Forestry Evolutionary computation Computational efficiency Gene expression Optimization Testing

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MGSR: 2D/3D MUTUAL-BOOSTED GAUSSIAN SPLATTING FOR HIGH-FIDELITY SURFACE RECONSTRUCTION UNDER VARIOUS LIGHT CONDITIONS

arXiv

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arXiv 2025年

作者： Zhou, Qingyuan Gong, Yuehu Yang, Weidong Li, Jiaze Luo, Yeqi Xu, Baixin Li, Shuhao Fei, Ben He, Ying School of Computer Science Fudan University China School of Data Science Fudan University China College of Computing and Data Science Nanyang Technological University Singapore Department of Information Engineering The Chinese University of Hong Kong Hong Kong

Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS). Despite recent progress, these tasks are often addressed independently, with GS-based rendering methods struggling under diverse light conditions and failing to produce accurate surfaces, while GS-based reconstruction methods frequently compromise rendering quality. This raises a central question: must rendering and reconstruction always involve a trade-off? To address this, we propose MGSR, a 2D/3D Mutual-boosted Gaussian splatting for Surface Reconstruction that enhances both rendering quality and 3D reconstruction accuracy. MGSR introduces two branches—one based on 2D-GS and the other on 3D-GS. The 2D-GS branch excels in surface reconstruction, providing precise geometry information to the 3D-GS branch. Leveraging this geometry, the 3D-GS branch employs a geometry-guided illumination decomposition module that captures reflected and transmitted components, enabling realistic rendering under varied light conditions. Using the transmitted component as supervision, the 2D-GS branch also achieves high-fidelity surface reconstruction. Throughout the optimization process, the 2D-GS and 3D-GS branches undergo alternating optimization, providing mutual supervision. Prior to this, each branch completes an independent warm-up phase, with an early stopping strategy implemented to reduce computational costs. We evaluate MGSR on a diverse set of synthetic and real-world datasets, at both object and scene levels, demonstrating strong performance in rendering and surface reconstruction. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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A Comparative Study and Impact Analysis of Different Oversampling Techniques for CIP 1

A Comparative Study and Impact Analysis of Different Oversam...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Bora, Dibya Jyoti Navlani, Avinash Mohari, Ankit Department of Information Technology Kaziranga University Assam India School of Data Science & Forecasting Devi Ahilya Vishwavidyalaya Indore India Department of Information Technology School of Computing Science Kaziranga University Assam India

ISBN: (纸本)9781607685395

In the area of data science, machine learning-based classification techniques are the first choice for an accurate analysis of a huge amount of data. The first requirement while developing such classification techniques is the robustness that it will imply for accurate and efficient classification. However, it is seen that many times these algorithms suffer from class-imbalance problems, shortly CIP. Due to CIP, many difficulties arise during the learning process, which as a whole result in a poor classification process. Resampling the data set is one common technique for dealing with CIP were in general oversampling the size of the rare class is made. We have different oversampling techniques available in the literature like SMOTE, ADYSN, and Random Oversample are the noted ones. In this paper, an effort is made to compare these different techniques as well as their impact on classification performance. © The Electrochemical Society

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Digital Forensics Analysis of iMazing Phone Explorer Tool to Aid Mobile Forensics Investigation

Digital Forensics Analysis of iMazing Phone Explorer Tool to...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ashar Neyaz Avinash Kumar Bingyu Liu School of Computing & Data Science Wentworth Institute of Technology Boston MA USA Department of Computer Science Sam Houston State University Huntsville TX USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Apple devices continue to lead the smart device industry, with over 2.2 billion active devices worldwide. These devices, including iPhones and iPads, store extensive personally identifiable information (PII), making them vital in digital forensic investigations. This study specifically examines the retrieval and analysis of PII from an iPhone and an iPad using iMazing software as the sole forensic tool. data acquisition was conducted via a Lightning-to-VSB-A connection without jailbreaking, focusing on two distinct scenarios-one for each device-restricting access to user-space data. The analysis uncovered a range of forensic artifacts, including iCloud-synced SSID data, message logs, Wi-Fi security protocols, and other critical digital traces. Extracted data appeared in multiple formats, such as text files, CSV files, and plist files, providing forensic investigators with valuable insights. While this study is limited to iMazing, its findings highlight the potential and limitations of third-party tools in mobile forensics. By leveraging iMazing's capabilities, this research contributes to the evolving field of mobile device forensics, reinforcing the role of Apple devices in enhancing digital investigative methodologies.

关键词： data privacy Protocols Digital forensics Tablet computers Software Mobile handsets Security Smart devices Wireless fidelity Identification of persons

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One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing

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IEEE Transactions on Multimedia 2025年

作者： Ji, Yuzhu Zheng, Chuanxia Cham, Tat-Jen Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China University of Oxford Department of Engineering Science Oxford United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Singapore

Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the large variation in motion and articulation complexity. In addition, the accuracy and precision of DensePose degrade the image quality for neural-rendering-based methods. To address the limitations and by both considering the importance of appearance and geometry for motion transfer, in this work, we proposed a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry, partly by exploiting the information from DensePose, yet adapting to its inherent limited accuracy. Our model takes advantage of multiple modalities by jointly training and fusing them, which allows it to robust neural texture features that cope with geometric errors as well as multi-scale dense motion flow that better preserves appearance. Experimental results with full and half-view body video datasets demonstrate that our model can generalize well and achieve competitive results, and that it is particularly effective in handling challenging cases such as those with substantial self-occlusions. © 1999-2012 IEEE.

关键词： Mapping

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High-Quality dataset-Sharing and Trade Based on A Performance-Oriented Directed Graph Neural Network

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15576-15587页

作者： Zeng, Yingyan Zhou, Xiaona Chilukuri, Premith Kumar Lourentzou, Ismini Jin, Ran University of Cincinnati Department of Mechanical and Materials Engineering CincinnatiOH45242 United States University of Illinois Urbana-Champaign Siebel School of Computing and Data Science ChampaignIL61820 United States University of Illinois Urbana-Champaign School of Information Sciences ChampaignIL61820 United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Grado Department of Industrial and Systems Engineering BlacksburgVA24060 United States

The advancement of Artificial Intelligence (AI) models heavily relies on large high-quality datasets. However, in advanced manufacturing, collecting such data is time-consuming and labor-intensive for a single enterprise. Hence, it is important to establish a context-aware and privacy-preserving data sharing system to share small-but-high-quality datasets between trusted stakeholders. Existing data sharing approaches have explored privacy-preserving data distillation methods and focused on valuating individual samples tied to a specific AI model, limiting their flexibility across data modalities, AI tasks, and dataset ownership. In this work, we propose a performance-oriented representation learning (PORL) framework in a Directed Graph Neural Network (DiGNN). PORL distills raw datasets into privacy-preserving proxy datasets for sharing and learns compact meta data representations for each stakeholder locally. The meta data will then be used in DiGNN to forecast the AI model performance and guide the sharing via graph-level supervised learning. The effectiveness of the PORL-DiGNN is validated by two case studies: data sharing in the semiconducting manufacturing network between similar processes to create similar quality defect models;and data sharing in the design and manufacturing network of Microbial Fuel Cell anodes between upstream (design) and downstream (Additive Manufacturing) stages to create distinct but related AI models. © 2004-2012 IEEE.

关键词： data flow graphs

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Domain Generalization Study of Empirical Risk Minimization from Causal Perspectives

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IEEE Transactions on Multimedia 2025年

作者： Mo, Zhenling Zhang, Zijun Tsui, Kwok-Leung City University of Hong Kong Department of Data Science College of Computing Kowloon Hong Kong Virginia Tech Grado Department of Industrial and Systems Engineering BlacksburgVA United States

Empirical risk minimization (ERM) is a celebrated induction principle for developing data-driven models. However, ERM has received both pros and cons for its capability on domain generalization (DG). To this end, this paper attempts to study the success and failure of ERM at supervised DG classification tasks, both theoretically and empirically, with causal perspectives. In the theoretical aspect, we first explore different properties of a causal metric termed information flow, followed with discussing relationships between the information flow and the mutual information in the proposed causal graph. Next, we analyze the roles of the transformed causal feature and the transformed spurious feature on modeling performances. It reveals that the interaction between the spurious influencer and the transformed causal feature is the key determining the failure or success of ERM on DG. In the empirical study, we first simulate various DG settings based on the MNIST, Fashion MNIST, and CIFAR10 datasets. Next, we verify developed theories by testing three different neural network configurations in designed experiments. In addition, experiments based on real-world datasets are conducted to further consolidate key points of the proposed theories. To extend application benefits of the theoretical discoveries, a new risk minimization framework with a novel feature intervention for regulating ERM is proposed. It achieves DG improvements over ERM on real-world datasets of image segmentation, image classification, and text classification. © 1999-2012 IEEE.

关键词： Self-supervised learning

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