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IEEE International Symposium on Biomedical Imaging

作者： Gege Cai Zhenghua Guan Xiangwen Cai Nina Cheng Wenzheng Hu Tengda Zhang Yi Liu Baiying Lei College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Positron Emission Tomography (PET) and Structural Magnetic Resonance Imaging (sMRI) are widely used for early Alzheimer's disease (AD) diagnosis, providing anatomical and metabolic insights. However, current fusion methods, like simple addition or concatenation, fail to leverage their complementary information fully. To address it, we proposed a 3D multi-modal feature interaction fusion network for AD diagnosis. Specifically, we leverage a robust 3D ResNet backbone network as the architecture for feature extraction. To enable the model to dynamically focus on the areas of interest, we added a local attention module at each layer of the network. We further introduced a collaborative attention module to facilitate the interaction and fusion of features between PET and sMRI modalities, thereby enhancing the overall capability of the model to utilize complementary information from both modalities. Finally, an enhanced feature fusion module based on Transformer is integrated into the network to further strengthen the representation of the fused features. The experiment shows that our model performs better than other representative models and validates the effectiveness of the proposed method on the ADNI dataset.

关键词： Solid modeling Three-dimensional displays Magnetic resonance imaging Collaboration Feature extraction Transformers Medical diagnosis Alzheimer's disease Positron emission tomography Biomedical imaging

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Crack Detection Method for Asphalt Pavement Combining Multi-scale Features and Attention Mechanism

Crack Detection Method for Asphalt Pavement Combining Multi-...

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Advances in Electrical engineering and computer Applications( AEECA), 2020 IEEE International Conference on

作者： Zijian Xu Lili Wang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China

Crack, as one of the common diseases of asphalt pavements, seriously affects the health of asphalt pavements. To cope with the demand of crack detection in the context of complex pavements, an improved network model with an encoder-decoder structure is proposed. First, dense long and short connections are combined with full-scale jump connections, and the jump connection structure yields fullscale feature information to each node of the decoding layer. Second, spatial and channel attention modules are incorporated into the proposed network. The former is used at the low level of the network to improve the ability to capture crack detail information, and the latter is applied at the high level of the proposed network to obtain the semantic information. Finally, improve network performance by building deep supervision network. The proposed network is compared with on three datasets, DeepCrack, CFD, and Crack500, and the F-score reaches 86.79%. In this paper, the network is effective in crack detection and plays a certain role in maintaining road safety.

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Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT

arXiv

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arXiv 2023年

作者： Wang, Zecong Cheng, Jiaxi Cui, Chen Yu, Chenhao School of Software Technology Zhejiang University Hangzhou China School of Computer Science and Engineering University of New South Wales SydneyNSW Australia Computer and Information Engineering College Inner Mongolia Normal University Hohhot China

The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation. Copyright © 2023, The Authors. All rights reserved.

关键词： Automation

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DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

DNN Partitioning and Assignment for Distributed Inference in...

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International Conference on Distributed Computing Systems

作者： Yuepeng Li Deze Zeng Lin Gut Song Guo Albert Y. Zomaya School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Distributed Deep Neural Network (DNN) inference is a promising technology to explore the distributed resources in edge cloud to realize edge intelligence. Meanwhile the inherent resource sharing nature of edge cloud infrastructure also raises serious concerns on security and privacy. software Guard Ex-tensions (SGX) emerges as a potential hardware-level solution but its limited secure memory (i.e., enclave page cache) imposes new challenges, especially in contrast to memory-hungry DNN models. A task's performance will be severely affected when its memory footprint is beyond the enclave page cache size, due to expensive secure page swapping. In this case, how to appropriately partition a DNN model and assign the partitions to distributed edge servers to efficiently utilize edge resources for fast secure inference becomes a challenging problem. In this paper, we first show that this problem is NP-hard. We further propose a MEmory -aware Distributed Inference Acceleration (MEDIA) algorithm, whose guaranteed approximation ratio is also formally analyzed. We have implemented a prototype system and applied some well-known representative DNN models to evaluate MEDIA's performance. Through extensive experiments, we verify the efficiency of MEDIA by the fact that it reduces the inference time by 19.5%-38.1 % in comparison with state-of-the-art approaches.

关键词： Computational modeling Prototypes Artificial neural networks Media Approximation algorithms Inference algorithms software

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Privacy-preserving cloud-fog–based traceable road condition monitoring in VANET

Privacy-preserving cloud-fog–based traceable road condition...

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作者： Wang, Wei Wu, Lei Qu, Wenlei Liu, Zhaoman Wang, Hao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Provincial Key Laboratory for Software Engineering Jinan China

With the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on-demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy-preserving cloud-fog–based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency. © 2020 John Wiley & Sons, Ltd.

关键词： Fog

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lujain Alshomali Rania Khorma Abdullah Al-Refai Abedalrhman Alkhateeb Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Lakehead University Thunder Bay Canada

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ 2 ) was used to compare categorical data. The Chi-Square "Best first" method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p<0.05). A significant correlation was found between TMB and Race, Neoplasm Histologic Grade, and gender when applying the Best First/Chi-Square method to clinical variables and level of TMB. This enables further investigation and application of the prediction models of the level of TMB, responsiveness to immunotherapy, and prognosis based on the clinical features of the patients.

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A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds

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Frontiers of computer Science 2021年第5期15卷 25-36页

作者： Yao QIN Hua WANG Shanwen YI Xiaole LI Linbo ZHAI School of Computer Science and Technology Shandong UniversityJinan 250101China Shanghai Police College Shanghai 200137China School of Software Shandong UniversityJinan 250101China School of Information Science and Engineering Linyi UniversityLinyi 276005China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China

Recently,a growing number of scientific applications have been migrated into the *** deal with the problems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow ***,the previous works ignore some details,which are challenging but *** existing multi-objective work-flow scheduling algorithms overlook weight selection,which may result in the quality degradation of ***,we find that the famous partial critical path(PCP)strategy,which has been widely used to meet the deadline constraint,can not accurately reflect the situation of each time ***-flow scheduling is an NP-hard problem,so self-optimizing algorithms are more suitable to solve *** this paper,the aim is to solve a workflow scheduling problem with a deadline *** design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning(RL)called *** uses the Chebyshev scalarization function to scalarize its *** method is good at choosing weights for *** propose an improved version of the PCP strategy called *** sub-deadlines in MPCP regularly update during the scheduling phase,so they can accurately reflect the situation of each time *** optimization objectives in this paper include minimizing the execution cost and energy consumption within a given ***,we use four scientific workflows to compare DCMORL and several representa-tive scheduling *** results indicate that DCMORL outperforms the above *** far as we know,it is the first time to apply RL to a deadline constrained workflow scheduling problem.

关键词： workflow scheduling energy saving multiobjective reinforcement learning deadline constrained cloud computing

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A Survey of Medical Vision-and-Language Applications and Their Techniques

arXiv

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arXiv 2024年

作者： Chen, Qi Zhao, Ruoshan Wang, Sinuo Phan, Vu Minh Hieu van den Hengel, Anton Verjans, Johan Liao, Zhibin To, Minh-Son Xia, Yong Chen, Jian Xie, Yutong Wu, Qi Australian Institute for Machine Learning University of Adelaide Australia School of Software Engineering South China University of Technology China South Australia Medical Imaging Australia School of Computer Science & Engineering Northwestern Polytechnical University China

Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Their applications are versatile and have the potential to improve diagnostic accuracy and decision-making for individual patients while also contributing to enhanced public health monitoring, disease surveillance, and policy-making through more efficient analysis of large data sets. MVLMS integrate natural language processing with medical images to enable a more comprehensive and contextual understanding of medical images alongside their corresponding textual information. Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making. Popular clinical applications of MVLMs include automated medical report generation, medical visual question answering, medical multimodal segmentation, diagnosis and prognosis and medical image-text retrieval. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We conduct a detailed analysis of various vision-and-language model architectures, focusing on their distinct strategies for cross-modal integration/exploitation of medical visual and textual features. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics. Furthermore, we highlight potential challenges and summarize future research trends and directions. The full collection of papers and codes is available at: https://***/MVLMs. © 2024, CC BY.

关键词： Clinical research

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Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction 7

Ensemble Online Sequential Extreme Learning Machine for Air ...

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7th International Conference on Control Science and Systems engineering, ICCSSE 2021

作者： Liu, Ye Cao, Weipeng Liu, Yiwen Li, Dachuan Wang, Qiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of International Exchange Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9781665444057

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms. © 2021 IEEE.

关键词： Distribution functions

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Reinforced Learning for Label-Efficient 3D Face Reconstruction

Reinforced Learning for Label-Efficient 3D Face Reconstructi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hoda Mohaghegh Hossein Rahmani Hamid Laga Farid Boussaid Mohammed Bennamoun School of Computer Science and Software Engineering University of Western Australia Australia Department of Computing and Communications Lancaster University England Information Technology Discipline Murdoch University Australia School of Engineering Electrical Electronic and Computer Engineering University of Western Australia Australia

3D face reconstruction plays a major role in many human-robot interaction systems, from automatic face authentication to human-computer interface-based entertainment. To improve robustness against occlusions and noise, 3D face reconstruction networks are often trained on a set of in-the-wild face images preferably captured along different viewpoints of the subject. However, collecting the required large amounts of 3D annotated face data is expensive and time-consuming. To address the high annotation cost and due to the importance of training on a useful set, we propose an Active Learning (AL) framework that actively selects the most informative and representative samples to be labeled. To the best of our knowledge, this paper is the first work on tackling active learning for 3D face reconstruction to enable a label-efficient training strategy. In particular, we propose a Reinforcement Active Learning approach in conjunction with a clustering-based pooling strategy to select informative view-points of the subjects. Experimental results on 300W-LP and AFLW2000 datasets demonstrate that our proposed method is able to 1) efficiently select the most influencing view-points for labeling and outperforms several baseline AL techniques and 2) further improve the performance of a 3D Face Reconstruction network trained on the full dataset.

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