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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shaohuai Shi Qiang Wang Xiaowen Chu Bo Li Yang Qin Ruihao Liu Xinxiao Zhao High-Performance Machine Learning Lab Hong Kong Baptist University Department of Computer Science and Engineering The Hong Kong University of Science and Technology Department of Computer Science and Technology Harbin Institute of Technology (Shenzhen) *** Shenzhen District Block Technology Co. Ltd.

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in large-scale deep learning applications, while it is known that the communication bottleneck limits the scalability of the distributed system. Gradient sparsification is a promising technique to significantly reduce the communication traffic, while pipelining can further overlap the communications with computations. However, gradient sparsification introduces extra computation time, and pipelining requires many layer-wise communications which introduce significant communication startup overheads. Merging gradients from neighbor layers could reduce the startup overheads, but on the other hand it would increase the computation time of sparsification and the waiting time for the gradient computation. In this paper, we formulate the trade-off between communications and computations (including backward computation and gradient sparsification) as an optimization problem, and derive an optimal solution to the problem. We further develop the optimal merged gradient sparsification algorithm with SGD (OMGS-SGD) for distributed training of deep learning. We conduct extensive experiments to verify the convergence properties and scaling performance of OMGS-SGD. Experimental results show that OMGS-SGD achieves up to 31% end-to-end time efficiency improvement over the state-of-the-art sparsified SGD while preserving nearly consistent convergence performance with original SGD without sparsification on a 16-GPU cluster connected with 1Gbps Ethernet.

关键词： Computational modeling Training Convergence Pipeline processing Machine learning Graphics processing units Clustering algorithms

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SmartBackup: An Efficient and Reliable Backup Strategy for Solid State Drives with Backup Capacitors

SmartBackup: An Efficient and Reliable Backup Strategy for S...

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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Min Huang Yi Wang Liyan Qiao Duo Liu Zili Shao School of Electrical Engineering and Automation Harbin Institute of Technology Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University College of Computer Science Chongqing University Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9781479989386

Unpredictable power outages in NAND flashbased Solid State Drives (SSDs) may cause system failure or reliability problems. Capacitors are widely adopted as the interim power supplier when power interruption happens. However, since the energy provided by backup capacitors is limited, and the capacitance of a capacitor will gradually degrade with time, it is imperative to improve the efficiency and reliability of the backup process for SSDs with capacitors. This paper presents a novel backup scheme called SmartBackupfor MLC NAND flash-based SSDs with backup capacitors. In SmartBackup, by exploiting the feature of the discharge process of capacitors, all available SSD channels are fully utilized and dynamically adjusted based on the available voltage, so data can be efficiently stored in a reliable manner. Moreover, in SmartBackup, to write data in a time/energyefficient manner with better reliability, we adopt a fast and reliable programming strategy by only writing LSB (Least Significant Bit) pages in MLC NAND flash. To the best of our acknowledge, this is the first work to jointly utilize the property of the multi-page architecture of MLC NAND flash and the discharge characteristic of capacitors to improvethe efficiency and reliability of the backup process in SSDswith backup capacitors. The experimental results show thatSmartBackup can effectively accelerate the backup processand improve the reliability.

关键词： Capacitors Ash Reliability Programming Arrays Acceleration Discharges (electric)

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Numerical simulations of attachment-line boundary layer in hypersonic flow, Part I: roughness-induced subcritical transitions

arXiv

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arXiv 2024年

作者： Xi, Youcheng Yan, Bowen Yang, Guangwen Sha, Xinguo Zhu, Dehua Fu, Song School of Aerospace Engineering Tsinghua University Beijing100084 China Institute of High Performance Computing Department of Computer Science and Technology Tsinghua University Beijing100084 China China Academy of Aerospace Aerodynamics Beijing100074 China

The attachment-line boundary layer is critical in hypersonic flows because of its significant impact on heat transfer and aerodynamic performance. In this study, high-fidelity numerical simulations are conducted to analyze the subcritical roughness-induced laminar-turbulent transition at the leading-edge attachment-line boundary layer of a blunt swept body under hypersonic conditions. This simulation represents a significant advancement by successfully reproducing the complete leading-edge contamination process induced by surface roughness elements in a realistic configuration, thereby providing previously unattainable insights. Two roughness elements of different heights are examined. For the lower-height roughness element, additional unsteady perturbations are required to trigger a transition in the wake, suggesting that the flow field around the roughness element acts as a disturbance amplifier for upstream perturbations. Conversely, a higher roughness element can independently induce the transition. A low-frequency absolute instability is detected behind the roughness, leading to the formation of streaks. The secondary instabilities of these streaks are identified as the direct cause of the final transition. © 2024, CC BY.

关键词： Surface roughness

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Ontology-based intelligent system for malware behavioral analysis

Ontology-based intelligent system for malware behavioral ana...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Hsien-Der Huang Tsung-Yen Chuang Yi-Lang Tsai Chang-Shing Lee National Applied Research Laboratories National Center for High Performance Computing Tainan Taiwan Department of Information and Learning Technology National University of Tainan Tainan Taiwan Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan

This paper proposes an ontology-based intelligent system for malware behavioral analysis. The design background and structure of the Taiwan Malware Analysis Net (TWMAN) are presented to analyze the malware behavior. The TWMAN is composed of the malware behavioral analysis agent and the ontology agent. All of the essential information of the TWMAN, including the malware behavioral ontology, which is store in an ontology repository. The malware behavioral analysis agent collects the malware behavioral information to build malware behavioral ontology and malware behavioral rules. The results from the system logs show that the TWMAN can work effectively based on the malware behavioral analysis to protect the computers from the attack of computer viruses and Trojans.

关键词： Ontologies Internet Analytical models Malware

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Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement

arXiv

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arXiv 2023年

作者： Li, Heng Lin, Ziqin Qiu, Zhongxi Li, Zinan Niu, Ke Guo, Na Fu, Huazhu Hu, Yan Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Computer School Beijing Information Science and Technology University Beijing China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement models using test data in the inference phase. A structure-preserving enhancement network is first constructed to learn a robust source model from synthesized training data. Then a teacher-student model is initialized with the source model and conducts source-free unsupervised domain adaptation (SFUDA) by knowledge distillation with the test data. Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks. Experiments were implemented on ten datasets from three medical image modalities to validate the advantage of the proposed algorithm, and setting analysis and ablation studies were also carried out to interpret the effectiveness of SAME. The remarkable enhancement performance and benefits for downstream tasks demonstrate the potential and generalizability of SAME. The code is available at https://***/liamheng/Annotation-free-Medical-Image-Enhancement. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Bridging Clinical Gaps: Multi-Dataset Integration for Reliable Multi-Class Lung Disease Classification with DeepCRINet and Occlusion Sensitivity

Bridging Clinical Gaps: Multi-Dataset Integration for Reliab...

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IEEE Symposium on computers and Communications (ISCC)

作者： Naeem Ullah Javed Ali Khan Ivanoe De Falco Giovanna Sannino Department of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy Department of Computer Science University of Hertfordshire Hatfield UK Institute for High-Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

This research presents DeepCRINet, a deep learning (DL) model designed for reliable performance across various Chest Radiography Images (CRIs) datasets, in response to the urgent need for quick and accurate lung disease identification utilizing CRIs. Our method builds on earlier research, which frequently used single-source datasets that might not adequately represent the heterogeneity present in clinical situations. Our model’s diagnostic adaptability and real-world dependability are improved by utilizing images from different datasets, which helps us overcome limitations such as dataset bias, robustness, generalizability, and underrepresentation of conditions. With validation on a broad dataset consisting of 14,096 images (from three different datasets), DeepCRINet provides a solution that demonstrates excellent flexibility in recognizing illnesses including TuBerculosis, Pneumonia, COVID-19, and Lung Opacity. Through data augmentation, we improve the dataset, supporting training and testing procedures and confirming the model’s ability to generalize. We used occlusion sensitivity as a kind of explainable AI to openly identify and visually emphasize regions important to proper classification. This ability not only shows that DeepCRINet is analytically better than other DL models and hybrid techniques, but it also improves patient outcomes and diagnosis, which makes it a vital tool for medical professionals like radiologists.

关键词： COVID-19 Training Analytical models Pneumonia Accuracy Tuberculosis Sensitivity analysis Robustness Diagnostic radiography Testing

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The SMART BEAR Project: An Overview of Its Infrastructure 7th

The SMART BEAR Project: An Overview of Its Infrastructure

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7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Su, Qiqi Peretokin, Vadim Basdekis, Ioannis Kouris, Ioannis Maggesi, Jonatan Sicuranza, Mario Acebes, Alberto Bucur, Anca Mukkala, Vinod Jaswanth Roy Pozdniakov, Konstantin Kloukinas, Christos Koutsouris, Dimitrios D. Iliadou, Elefteria Leontsinis, Ioannis Gallo, Luigi De Pietro, Giuseppe Spanoudakis, George Department of Computer Science City University of London London United Kingdom Philips Research Eindhoven Netherlands SPHYNX Technology Solutions AG Zug Switzerland Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Computer Science Department Università degli Studi di Milano Milan Italy Institute for High-Performance Computing and Networking National Research Council of Italy ICAR - CNR Naples Italy Atos Research and Innovation Madrid Spain 1st Otolaryngology University Department National and Kapodistrian University of Athens Athens Greece 1st Cardiology Clinic Medical School National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9783031374951

The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Health Care and Management based on Block chain and Machine Learning

Health Care and Management based on Block chain and Machine ...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Ruhi Bakh VeenaNair Sarkar Jerlin Priya Lovelin Auguskani Galiveeti Poornima Monali N. Shetty Meerjumla Govind Raj Dr. Ambedkar Institute of Management Studies and Research IIHMR UNIVERSITY. Department of Nursing College Aldrab Jazan University Kingdom of Saudi Arabia. High Performance Computing Lab School of Computer Science and Information Science Presidency University Bengaluru Computer Science Engineering Fr. CRCE St. Martin's Engineering College Secunderabad Telangana

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare.

关键词： Telemedicine Government Medical treatment Machine learning Medical services Knowledge discovery Blockchains

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Dual-Scale Enhanced and Cross-Generative Consistency Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： computer aided diagnosis

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A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

arXiv

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arXiv 2022年

作者： Lin, Zhi Lin, Junhao Zhu, Lei Fu, Huazhu Qin, Jing Wang, Liansheng Department of Computer Science School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Nursing The Hong Kong Polytechnic University Kowloon Hong Kong

Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at https://***/jhl-Det/CVA-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrasonic applications

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