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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Guixia Xiao Normalia Binti Samian Winston K. G. Seah Mohd Izuan Hafez Bin Ninggal Masnida Binti Hussin Jyoti Sahni Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor Malaysia Modern Education Technology Center Changde Vocational Technical College Changde China School of Engineering and Computer Sciences Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Most existing transaction sharding technologies allocate transactions randomly or based on transaction addresses, leading to imbalanced workloads and high ratio of cross-shard transactions (CST). The uneven workload makes it impossible for blockchain to fully utilize resources for transaction processing, which degrades system throughput. CST incur additional overheads in communication, verification, and processing, increasing block confirmation latency. This paper proposes a blockchain sharding transaction allocation algorithm known as correlation and workload-based transaction allocation (CWTA) to address the above issues. CWTA regularly pumps a certain number of pending transactions from the transactions pool to establish a multiple-directed transaction graph (MDTG) and achieves faster allocation efficiency by quickly gathering the out-edges of each account based on MDTG. This process has successfully changed the distribution mode of transactions from one-by-one to group-by-group which depends on the correlation between the groups for current and upcoming workloads of each shard. CWTA has demonstrated its effectiveness through abundant transaction allocation results with 2-16 shards. Compared to similar existing work, CWTA can achieve a much more balanced transaction workload and less allocation time without increasing the CST ratio which denotes better scalability, and achieve better throughput.

关键词： Sharding Continuous wavelet transforms Correlation System performance Scalability Throughput Size measurement Blockchains Resource management Optimization

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Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation

arXiv

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arXiv 2023年

作者： Zhu, Xingyu Wang, Xin Freer, Jonathan Chang, Hyung Jin Gao, Yixing School of Artificial Intelligence Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China School of Computer Science University of Birmingham United Kingdom

Clothes grasping and unfolding is a core step in robotic-assisted dressing. Most existing works leverage depth images of clothes to train a deep learning-based model to recognize suitable grasping points. These methods often utilize physics engines to synthesize depth images to reduce the cost of real labeled data collection. However, the natural domain gap between synthetic and real images often leads to poor performance of these methods on real data. Furthermore, these approaches often struggle in scenarios where grasping points are occluded by the clothing item itself. To address the above challenges, we propose a novel Bi-directional Fractal Cross Fusion Network (BiFCNet) for semantic segmentation, enabling recognition of graspable regions in order to provide more possibilities for grasping. Instead of using depth images only, we also utilize RGB images with rich color features as input to our network in which the Fractal Cross Fusion (FCF) module fuses RGB and depth data by considering global complex features based on fractal geometry. To reduce the cost of real data collection, we further propose a data augmentation method based on an adversarial strategy, in which the color and geometric transformations simultaneously process RGB and depth data while maintaining the label correspondence. Finally, we present a pipeline for clothes grasping and unfolding from the perspective of semantic segmentation, through the addition of a strategy for grasp point selection from segmentation regions based on clothing flatness measures, while taking into account the grasping direction. We evaluate our BiFCNet on the public dataset NYUDv2 and obtained comparable performance to current state-of-the-art models. We also deploy our model on a Baxter robot, running extensive grasping and unfolding experiments as part of our ablation studies, achieving an 84% success rate. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Human Blood Cell (Haematologicalimages) Recognition by Applying Deep Convolution Methods

Human Blood Cell (Haematologicalimages) Recognition by Apply...

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International Conference for Convergence of Technology (I2CT)

作者： N. Krishna Kumar Ch. Sarada S. Suguna Mallika P Subhashini Sai Kumar K Sajad Ali Zearah Department of CSE GNITC Hyderabad Department of CSE CVR College of Engineering Hyderabad Telangana India Department of Computer Science and Information Technology MLR Institute of Technology Hyderabad Telangana India Computer Science School of Engineering Maisammaguda Dulapally Malla Reddy University Hyderabad Telangana Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Utilizing Deep Convolution Strategies to Perceive Human Platelets (Hematological Pictures): Deep Learning is rapidly becoming seen as a preferable method for learning over conventional ML models. This is on the grounds that it has previously been demonstrated to have the option to manage continuous issues in numerous application regions. Convolutional neural networks (CNNs), a sort of deep learning calculation, have a few advantages in the field of well-being, where a ton of pictures should be handled and inspected. The objective of the review is to make a deep learning model to take care of the issue of placing platelets into gatherings. It is one of the hardest things to sort out while checking blood out. A CNN-based framework has been made that consequently sorts pictures of platelets into various kinds of cells, like MONOCYTE, LYMPHOCYTE, NEUTROPHIL, and EOSINOPHIL. To further develop CNN innovation, specialists have investigated thoughts like the utilization of numerous initiation and misfortune capabilities, regularization, boundary advancement, and plan development. Be that as it may, changes to CNN's design have made it feasible for it to show much more. A block of layers can likewise be utilized as a structure block. This concentrates first glances at the inward scientific classifications of recently declared deep CNN frameworks. This is done so that new CNN configuration patterns can be placed into classes. The seven regions are channel gain, utilization of room, utilization of element maps, multipath, profundity, width, and consideration. Overall information on CNN's parts, its ongoing issues, and how it tends to be utilized is likewise given.

关键词： Deep learning Technological innovation Convolution Reviews Blood platelets Magnetic resonance imaging Medical services

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Digital Twin-Assisted Adaptive Preloading for Short Video Streaming

arXiv

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arXiv 2023年

作者： Liu, Shengbo Wu, Wen Li, Shaofeng Luan, Tom H. Shen, Xuemin Frontier Research Center Peng Cheng Laboratory Shenzhen China School of Cyber Science and Engineering Xidian University Xi’an China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

We propose a digital twin-assisted adaptive preloading scheme to enhance bandwidth efficiency and user quality of experience (QoE) in short video streaming. We first analyze the relationship between the achievable throughput and video bitrate and critical factors that affect the preloading decision, including the buffer size and bitrate selection. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing user’s historical throughput and tracking behavior information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user behavior, respectively. Using the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while guaranteeing user QoE. Simulation results demonstrate the effectiveness of our proposed scheme comparing with the state-of-the-art preloading schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Video streaming

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A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

arXiv

引用

arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

arXiv

引用

arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model

arXiv

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arXiv 2024年

作者： Zhang, Molin Golland, Polina Grant, P. Ellen Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approaches on volumetric EPI fetal MRI opens up a promising avenue for fetal motion monitoring and prediction. Challenges arise in fetal pose estimation due to limited number of real scanned fetal MR training images, hindering model generalization when the acquired fetal MRI lacks adequate pose. In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose. Additionally, an auxiliary pose-level loss is adopted to enhance model performance. Our work demonstrates the success of this proposed model by producing high-quality synthetic fetal MRI images with accurate and recognizable fetal poses, comparing favorably with in-vivo real fetal MRI. Furthermore, we show that the integration of synthetic fetal MR images enhances the fetal pose estimation model’s performance, particularly when the number of available real scanned data is limited resulting in 15.4% increase in PCK and 50.2% reduced in mean error. All experiments are done on a single 32GB V100 GPU. Our method holds promise for improving real-time tracking models, thereby addressing fetal motion issues more effectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Novel Power Divider With Tunable Power Ratios and Reconfigurable Output Phase Differences

A Novel Power Divider With Tunable Power Ratios and Reconfig...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Jialin Huang Xulong Guo Shanshan Xue Chang Xu Yong Mao Huang Xiaolong Wang College of Electronic Science and Engineering International Center of Future Science Jilin University Changchun China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot China School of Electrical and Electronic Information Xihua University Chengdu China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this letter, a novel power divider with a wide tunable range of power ratios and reconfigurable output phase differences is proposed. The tunable power ratio is implemented by replacing the branch transmission line of the conventional Gysel power divider. The branch transmission line equivalent circuit consists of three short-circuited varactors cascaded through two sections of transmission lines. Moreover, by selecting appropriate capacitances of the varactors, the proposed novel power divider can operate either in an in-phase mode or out-of-phase mode. Finally, the electromagnetic (EM) simulation of six examples was conducted and one experimental circuit of the proposed novel power divider was fabricated. The simulation and measurement results verified the feasibility of the proposed novel power divider.

关键词： Power dividers Varactors Semiconductor device measurement Power transmission lines Power measurement Phase measurement Transmission line measurements

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Context-aware Multi-level Question Embedding Fusion for visual question answering

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Information Fusion 2024年 102卷

作者： Li, Shengdong Gong, Chen Zhu, Yuqing Luo, Chuanwen Hong, Yi Lv, Xueqiang School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los Angeles CA90032 United States School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration 100083 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University 100101 China

Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions;although some question models use deep bidirectional Transformer encoder in external knowledge transfer and BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex multi-level contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it on SANs and UpDn, reduces the language priors of SANs and UpDn, and has preferable interpretability and applicability. Our code is available at https://***/lsdruc/CMQEF-for-VQA. © 2023 The Author(s)

关键词： Embeddings

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