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6th Australian Microwave Symposium, AMS 2025

作者： Bialkowski, K.S. Zhang, L. The University of Queensland School of Electrical Engineering and Computer Science Australia

ISBN: (纸本)9798350379969

In multi-receiver passive radar systems, coherent detection assumes accurate synchronisation between receivers. However, achieving this in practice is challenging. By creating a virtual radar environment we can investigate the effect of time, frequency offset and drift in different receiver configurations. Using the Generalised Canonical Correlation Analysis (GCCA) detector, a high performance coherent detector, the effect on localization performance is analysed under controlled conditions. The key output of this work demonstrates that synchronization errors significantly affect localization performance, particularly where the detection performance is relying on coherent detection. Understanding the impact of synchronization errors is crucial for improving and developing future multi-receiver passive radar and distributed beamforming systems. © 2025 IEEE.

关键词： Radar receivers

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Spectrogram-Based Imagification Applying Deep Learning on Omics Data 25

Spectrogram-Based Imagification Applying Deep Learning on Om...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Chong, Darren Singh, Sonit Sowmya, Arcot School of Computer Science and Engineering Unsw Sydney Australia

ISBN: (纸本)9798350379037

In the field of bioinformatics, 'omics' refers to the comprehensive study and analysis of various biological molecules such as genes, proteins and metabolites, to understand their roles, relationships and functions within an organism. In recent years, the proliferation of omics data and advancements in machine learning have transformed the field of bioinformatics. Although deep neural networks such as convolutional neural networks have been widely applied in fields such as computer vision and biomedical image computing, they have been less applied to biological data such as omics. One of the reasons for this is that omics data is generally one-dimensional (1D), whereas convolutional neural networks need input in the form of a two-dimensional (2D) matrix for enhanced feature extraction compared to 1D CNN networks. In this work, we propose a novel spectrogram-based method to convert high-dimensional omics data into 2D images, which facilitates the application of convolutional neural networks for downstream tasks such as cancer classification. We evaluate the proposed method against state-of-the-art techniques such as DeepInsight and Fotomics across five diverse datasets. Our proposed method not only shows its effectiveness by performing well across standard evaluation metrics but also is computationally less demanding compared to existing methods. We believe that the proposed methodology, when applied to omics data by transforming 1D data into 2D images, can help leverage the benefit of deep learning models, in turn benefitting various bioinformatics applications such as gene-gene prediction, drug discovery and identification of potential biomarkers for diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

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Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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Deep Learning-based Hybrid Recommendation System for Safe Approach to Curated Cosmetics 3

Deep Learning-based Hybrid Recommendation System for Safe Ap...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rohitha Chowdary, G. Tharun, J. Sivaiah, G. Raveena, S. Surendran, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Insititute of Medical and Technical Science Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

In the rapidly evolving cosmetics market, it is imperative to prioritize customer safety while offering personalized recommendations. This study introduces a hybrid recommendation system for cosmetics, using deep learning to combine the advantages of collaborative filtering with content-based filtering. The system assesses user preferences, skin types, and ingredient sensitivities, recommending solutions that are both effective and safe. The system proposes an integration of user-based and item-based collaborative filtering methodologies with deep neural networks to effectively elucidate intricate links among individuals, items, and their characteristics. This hybrid technique facilitates the system's ability to propose goods that provide both aesthetic appeal and skin safety, therefore fostering a healthier and more confident lifestyle. The system has an impressive accuracy of 92.5% in recommending suitable cosmetic products for individual clients. This enhanced accuracy reduces the likelihood of negative reactions and elevates customer satisfaction via personalized suggestions. © 2025 IEEE.

关键词： Contrastive Learning

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ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models

ATAP: Automatic Template-Augmented Commonsense Knowledge Gra...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Fu Ding, Yifan Cheng, Jingwei School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)9798891761643

The mission of commonsense knowledge graph completion (CKGC) is to infer missing facts from known commonsense knowledge. CKGC methods can be roughly divided into two categories: triple-based methods and text-based methods. Due to the imbalanced distribution of entities and limited structural information, triple-based methods struggle with long-tail entities. Text-based methods alleviate this issue, but require extensive training and fine-tuning of language models, which reduces efficiency. To alleviate these problems, we propose ATAP, the first CKGC framework that utilizes automatically generated continuous prompt templates combined with pre-trained language models (PLMs). Moreover, ATAP uses a carefully designed new prompt template training strategy, guiding PLMs to generate optimal prompt templates for CKGC tasks. Combining the rich knowledge of PLMs with the template automatic augmentation strategy, ATAP effectively mitigates the long-tail problem and enhances CKGC performance. Results on benchmark datasets show that ATAP achieves state-of-the-art performance overall. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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A Decoupling-Type Strategy for the Allen-Cahn Equation on Curved Surfaces

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Journal of Mathematical Research with Applications 2025年第1期45卷 73-83页

作者： Xiaoman XIE Qing PAN School of Computer and Communication Engineering Changsha University of Science & Technology

In this paper, we construct an efficient decoupling-type strategy for solving the Allen-Cahn equation on curved surfaces. It is based on an FEM-EIEQ(Finite Element Method and explicit-Invariant Energy Quadratization) fully discrete scheme with unconditional energy stability. Spatially the FEM is adopted, using a triangular mesh discretization strategy that can be adapted to complex regions. Temporally, the EIEQ approach is considered, which not only linearizes the nonlinear potential but also gives a new variable that we combine with the nonlocal splitting method to achieve the fully decoupled computation. The strategy can successfully transform the Allen-Cahn system into some completely independent algebraic equations and linear elliptic equations with constant coefficients, we only need to solve these simple equations at each time step. Moreover, we conducted some numerical experiments to demonstrate the effectiveness of the strategy.

关键词： FEM-EIEQ Allen-Cahn equation surface

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Triangle Grids Resampling Bidirectional Path Tracing 24

Triangle Grids Resampling Bidirectional Path Tracing

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2024 International Conference on Image Processing, Intelligent Control and computer engineering, IPICE 2024

作者： Liu, Fuyan Tan, Zheng School of Computer Engineering and Science Shanghai University China

ISBN: (纸本)9798400710285

Recent advancements in GPU computational power have enabled ray tracing algorithms, traditionally used for offline rendering, to enter the realm of real-time rendering. Path tracing, a widely adopted global illumination technique in ray tracing pipelines, suffers from image noise in scenes requiring multiple light bounces. Although filtering techniques alleviate noise, results often remain suboptimal. Bidirectional path tracing addresses sampling inefficiencies but introduces challenges such as computational demands and memory usage, limiting its real-time applicability. To enhance rendering quality in complex scenes, this paper introduces Triangle Grid Resampling Bidirectional Path Tracing (TGRBDPT). Leveraging triangle mesh resampling, TGRBDPT improves upon traditional methods under equivalent sampling conditions. Experimental comparisons across various scenes demonstrate TGRBDPT’s superiority over path tracing and bidirectional path tracing in terms of image quality and computational efficiency, Increasing application scenarios for bidirectional path tracing algorithms. © 2024 Copyright held by the owner/author(s).

关键词： Rendering (computer graphics)

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Fake News Detection using Machine Learning 1

Fake News Detection using Machine Learning

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Satheesha, T.Y. Somanath, Sowmya Supreeth, S. Yerriswamy, T. Chavan, Pundalik Malyadri, Neelam Reva University School of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331529246

The digital age has seen a rise in fake news, necessitating effective methods to distinguish between real and fake articles. This research addresses the growing challenge of fake news by developing a machine learning-based classification approach. Utilizing text preprocessing, CountVectorizer, TfidfVectorizer, and HashingVectorizer for feature extraction, models including Multinomial Naive Bayes, Passive Aggressive Classifier, and a Voting Classifier were trained and evaluated. Key metrics like accuracy, F1 score, and log loss were analyzed. Results indicate the approach effectively distinguishes real from fake news, with HashingVectorizer enhancing memory efficiency and processing speed without sacrificing accuracy. Notably, the Passive Aggressive Classifier (TF-IDF) achieved 92.7% accuracy, while the Voting Classifier, combining Multinomial Naive Bayes and Passive Aggressive models, attained a 93.5% F1 score, demonstrating robust performance. © 2025 IEEE.

关键词： Adversarial machine learning

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