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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Zhe Zhang, Wenjie Ren, Ying Kang, Xiangui Yan, Diqun Ma, Bin Wang, Shiqi Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks pose a significant threat during the model’s training phase. Attackers craft pre-defined triggers to break deep neural networks, ensuring the model accurately classifies clean samples during inference yet erroneously classifies samples added with these triggers. Recent studies have shown that speaker recognition systems trained on large-scale data are susceptible to backdoor attacks. Existing attackers employ unnoticed ambient sounds as triggers. However, these sounds are not inherently part of the training samples themselves. In essence, triggers can be designed to maintain an intrinsic connection with the original speech to enhance stealthiness. Our paper presents a novel attack methodology named Speed Master, which undermines deep neural networks by manipulating the speed of speech samples. Specifically, we execute poison-only backdoor attacks using speed or tempo adjustment. Changes in speech rate have become a common occurrence, as seen on platforms that allow users to adjust playback speed. In real-world scenarios, people naturally adjust their speaking rate depending on the context. As a result, changes in a speaker’s speech rate are typically perceived as normal and are unlikely to raise suspicion. Furthermore, detecting such subtle adjustments becomes challenging for users without reference speech. Our comprehensive experiments demonstrate that Speed Master can achieve an ASR over 99% in the digital domain, with only a 0.6% poisoning rate. Additionally, we validate the feasibility of Speed Master in the real world and its resistance to typical defensive measures. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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PhysioZoo: The Open Digital Physiological Biomarkers Resource 50

PhysioZoo: The Open Digital Physiological Biomarkers Resourc...

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50th Computing in Cardiology, CinC 2023

作者： Behar, Joachim A Levy, Jeremy Zvuloni, Eran Gendelman, Sheina Rosenberg, Aviv Biton, Shany Derman, Raphael Sobel, Jonathan A Alexandrovich, Alexandra Charlton, Peter H Goda, Marton A Technion Technion-IIT Faculty of Biomedical Engineering Haifa Israel Technion-IIT The Faculty of Electrical & Computer Engineering Haifa Israel Technion Technion-IIT Faculty of Computer Science Haifa Israel Rambam Medical Center Department of Anesthesiology Haifa Israel University of Geneva Geneva Switzerland University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798350382525

PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series. The platform currently comprises four modules, each consisting of a library, a user interface, and a set of tutorials: (1) PhysioZoo HRV, dedicated to studying heart rate variability (HRV) in humans and other mammals;(2) PhysioZoo SPO2, which focuses on the analysis of digital oximetry biomarkers (OBM) using continuous oximetry (SPO2) measurements from humans;(3) PhysioZoo ECG, dedicated to the analysis of electrocardiogram (ECG) time series;(4) PhysioZoo PPG, designed to study photoplethys-mography (PPG) time series. In this proceeding, we introduce the PhysioZoo platform as an open resource for digital physiological biomarkers engineering, facilitating streamlined analysis and data visualization of physiological time series while ensuring the reproducibility of published experiments. We welcome researchers to contribute new libraries for the analysis of various physiological time series, such as electroencephalography, blood pressure, and phonocardiography. You can access the resource at ***. We encourage researchers to explore and utilize this platform to advance their studies in the field of continuous physiological time-series analysis. © 2023 CinC.

关键词： Blood pressure

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Orchestrated desaturation reprogramming from stearoyl-CoA desaturase to fatty acid desaturase 2 in cancer epithelial-mesenchymal transition and metastasis

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Cancer Communications 2025年第3期45卷 245-280页

作者： Zhicong Chen Yanqing Gong Fukai Chen Hyeon Jeong Lee Jinqin Qian Jing Zhao Wenpeng Zhang Yamin Li Yihui Zhou Qiaobing Xu Yu Xia Liqun Zhou Ji-Xin Cheng Department of Obstetrics and Gynecology Center for Reproductive MedicineGuangdong Provincial Key Laboratory of Major Obstetric DiseasesGuangdong Provincial Clinical Research Center for Obstetrics and GynecologyGuangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal MedicineThe Third Affiliated HospitalGuangzhou Medical UniversityGuangzhouGuangdongP.R.China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMassachusettsUSA Department of Urology Peking University First HospitalBeijingP.R.China College of Biomedical Engineering&Instrument Science Key Laboratory for Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouZhejiangP.R.China MOE Key Laboratory of Bioorganic Phosphorus Chemistry&Chemical Biology Department of ChemistryTsinghua UniversityBeijingP.R.China State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijingP.R.China Department of Biomedical Engineering Tufts UniversityMedfordMassachusettsUSA

Background:Adaptative desaturation in fatty acid(FA)is an emerging hallmark of cancer metabolic *** such as stearoyl-CoA desaturase(SCD)and fatty acid desaturase 2(FADS2)have been implicated in multiple cancers,and their dominant and compensatory effects have recently been ***,how tumors initiate and sustain their self-sufficient FA desaturation to maintain phenotypic transition remains *** study aimed to explore the molecular orchestration of SCD and FADS2 and their specific reprogramming mechanisms in response to cancer ***:The potential interactions between SCD and FADS2 were explored by bioinformatics analyses across multiple cancer cohorts,which guided subsequent functional and mechanistic *** expression levels of desaturases were investigated with online datasets and validated in both cancer tissues and cell *** desaturation activities were characterized through various isomer-resolved lipidomics methods and sensitivity assays using desaturase ***-situ lipid profilingwas conducted using multiplex stimulated Raman scattering *** assays were performed both in vitro and in vivo,with RNA-sequencing employed for the mechanism ***:After integration of the RNA-protein-metabolite levels,the data revealed that a reprogramming fromSCD-dependent to FADS2-dependent desaturation was linked to cancer epithelial-mesenchymal transition(EMT)and progression in both patients and cell ***2 overexpression and SCD suppression concurrently maintained EMT plasticity.A FADS2/β-catenin selfreinforcing feedback loop facilitated the degree of lipid unsaturation,membrane fluidity,metastatic potential and EMT ***,SCD inhibition triggered a lethal apoptosis but boosted survival plasticity by inducing EMT and enhancing FA uptake via adenosine monophosphate-activated protein kinase ***,this desaturation reprogramming increased tra

关键词： metabolic reprogramming stearoyl-CoA desaturase fatty acid desaturase 2 epithelialmesenchymal transition cancer metastasis

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biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari Jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer Mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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Vision transformer-convolution for breast cancer classification using mammography images: A comparative study

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 67-83页

作者： Abimouloud, Mouhamed Laid Bensid, Khaled Elleuch, Mohamed Aiadi, Oussama Kherallah, Monji National Engineering School of Sfax University of Sfax Laboratory of Electrical Engineering (LAGE) University of KASDI Merbah Ouargla National School of Computer Science (ENSI) University of Manouba Artifcial Intelligence and Information Technology Laboratory (LINATI) Kasdi Merbah University Faculty of Sciences University of Sfax Advanced Technologies for Environment and Smart Cities (ATES Unit) University of Sfax

Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women's health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time. © 2024 - IOS Press. All rights reserved.

关键词： Breast cancer (BC) computer-aided diagnosis (CAD) convolutional networks (CNN) DDSM mammography vision transformers (VIT)

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Cerberus: The Power of Choices in Datacenter Topology Design

Cerberus: The Power of Choices in Datacenter Topology Design

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作者： Griner, Chen Zerwas, Johannes Blenk, Andreas Ghobadi, Manya Schmid, Stefan Avin, Chen School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Technical University of Munich Faculty of Computer Science University of Vienna Munich Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Boston United States Faculty of Electrical Engineering and Computer Science TU Berlin University of Vienna Berlin Germany

The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency "tax". To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: E.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional. © 2022 Owner/Author.

关键词： Topology

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A novel finite element formulation for static bending analysis of functionally graded porous sandwich plates

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Frontiers of Structural and Civil engineering 2022年第12期16卷 1599-1620页

作者： Van Chinh NGUYEN Trung Thanh TRAN Trung NGUYEN-THOI Quoc-Hoa PHAM Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoiVietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Mechanical-Electrical and Computer Engineering Van Lang UniversityHo Chi Minh CityVietnam Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh CityVietnam

This article aims to propose a finite element formulation based on Quasi-3D theory for the static bending analysis of functionally graded porous(FGP)sandwich *** FGP sandwich plates consist of three layers including the bottom skin of homogeneous metal,the top skin of fully ceramic and the FGP core layer with uneven porosity distribution.A quadrilateral(Q4)element with nine degrees of freedom(DOFs)per node is derived and employed in analyzing the static bending response of the plate under uniform and/or sinusoidally distributed *** accuracy of the present finite element formulation is verified by comparing the obtained numerical results with the published results in the ***,some numerical examples are performed to examine the effects of the parameters including power-law index k and porosity coefficient on the static bending response of rectangular FGP sandwich *** addition,a problem with a complicated L-shape model is conducted to illustrate the superiority of the proposed finite element method.

关键词： sandwich plates functionally graded porous static bending Quasi-3D theory Q4 element

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