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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ke Li, Junjie Wang, Shuai Wei, Yangjie Wang, Yi Wang, Yannan Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University China SDS SRIBD The Chinese University of Hong Kong Shenzhen China The Hong Kong Polytechnic University Hong Kong Tencent Ethereal Audio Lab Tencent Shenzhen China University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9798350368741

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model's generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline. © 2025 IEEE.

关键词： Cocktail party problem selective auditory attention speaker confusion speaker feature target speaker extraction

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

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IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems

Modeling Multi-Threaded Aggregated I/O for Asynchronous Chec...

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International Symposium on Parallel and Distributed computing

作者： Mikaila J. Gossman Bogdan Nicolae Jon C. Calhoun Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often result in more frequent failures, increasing the need and frequency to incorporate fault-tolerant mechanisms. One example is checkpoint-restart (C/R), where applications or systems save their data to non-volatile storage devices, such as a PFS. On failure, the system or application is restored to a saved state and computation continues. Today, asynchronous C/R is gaining traction for its ability to checkpoint data to permanent storage concurrently with the application. However, asynchronous C/R brings about many new challenges. For starters, asynchronous C/R introduces complex resource contention between the application and the C/R implementation. Additionally, some implementations adopt file-per-process writing strategies, which overwhelm PFS’ at high core counts. In this work, we explore how multi-threaded POSIX I/O impacts aggregated throughput. To this extent we characterize the influence of different I/O parameters, such as the number of writer threads and how they access storage devices, has on aggregated I/O. We use the information gathered in this study to identify best practices when performing aggregated I/O as a first step in designing an efficient I/O aggregation scheme for asynchronous C/R.

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer Systems science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Segmentation Uncertainty with Statistical Guarantees in Prostate MRI

Segmentation Uncertainty with Statistical Guarantees in Pros...

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European Signal Processing Conference (EUSIPCO)

作者： Kevin Mekhaphan Nguyen Alvaro Fernandez-Quilez Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Department of Radiology Stavanger Medical Imaging Laboratory (SMIL) Stavanger University Hospital Stavanger Norway

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Prostate volume (PV) is an important factor in prostate cancer (PC) patient management and diagnostic path-way. Over the past years, efforts have been made to develop artificial intelligence (AI) systems able to standardize and reduce inter-reader variability in prostate segmentation and subsequent PV estimation. In spite of the remarkable results of AI segmentation architectures such as nnU-Net, most benchmarks and prostate segmentation results neglect the uncertainty of the AI segmentation system as part of their evaluation protocol. In this study, we use conformal prediction (CP), a model-agnostic uncertainty quantification method that provides strong statistical guarantees to keep the error of a nnU-Net for prostate whole gland (WG) segmentation bounded by a pre-specified level. Our results show that nnU-Net coupled with CP and a confidence level of 95.00% is able to significantly improve segmentation results in terms of Volume Difference (VD) when compared to nnU-Net without CP for two independent data cohorts (VD 1 = 1.67±0.99 p<.00l and VD 2 = 2.86±0.28 p<.00l)

关键词： Solid modeling Uncertainty Protocols Magnetic resonance imaging Glands Europe Signal processing Predictive models Artificial intelligence Prostate cancer

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Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

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Infectious Disease Modelling 2024年第1期9卷 234-244页

作者： Ashley N.Micuda Mark R.Anderson Irina Babayan Erin Bolger Logan Cantin Gillian Groth Ry Pressman-Cyna Charlotte Z.Reed Noah J.Rowe Mehdi Shafiee Benjamin Tam Marie C.Vidal Tianai Ye Ryan D.Martin Department of Physics Engineering Physics&AstronomyQueen's UniversityKingstonONCanada Department of Medical Biophysics Western UniversityLondonONCanada Department of Mathematics and Statistics Queen's UniversityKingstonONCanada Department of Biology Queen's UniversityKingstonONCanada Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan Energetic Cosmos Laboratory Nazarbayev UniversityNur-SultanKazakhstan School of Computing Queen's UniversityKingstonONCanada Department of Biomedical and Molecular Sciences Queen's UniversityKingstonONCanada Department of Physics University of OxfordOxfordUnited Kingdom Department of Physics Stanford UniversityStanfordCAUnited States

This work introduces the Queen's University Agent-Based Outbreak Outcome Model(QUABOOM).This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health *** illustrate the use of the model by examining capacity restrictions during a *** find that public health measures should focus on the few locations where many people interact,such as grocery stores,rather than the many locations where few people interact,such as small *** also discuss a case where the results of the simulation can be scaled to larger population sizes,thereby improving computational efficiency.

关键词： Monte-carlo Agent-based epidemic modelling COVID-19 Small business capacity restrictions Public health Basic reproductive number

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Cooperative and Competitive Multi-Agent Systems:From Optimization to Games

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 763-783页

作者： Jianrui Wang Yitian Hong Jiali Wang Jiapeng Xu Yang Tang Qing-Long Han Jürgen Kurths Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Potsdam Institute for Climate Impact Research 14473 Potsdam Institute of Physics Humboldt University of Berlin12489 BerlinGermany

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation *** a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive *** survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative *** from cooperative optimization,the studies on distributed optimization and federated optimization are *** survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection ***,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,***-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other ***,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

关键词： Cooperative games counterfactual regret minimization distributed optimization federated optimization fictitious

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