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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15646 条记录，以下是1821-1830 订阅

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Proceedings - 2022 ieee 13th International symposium on Parallel Architectures, Algorithms and Programming, PAAP 2022

Proceedings - 2022 IEEE 13th International Symposium on Para...

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13th ieee International symposium on Parallel Architectures, Algorithms and Programming, PAAP 2022

ISBN: (纸本)9781665452182

The proceedings contain 28 papers. The topics discussed include: passivity-based finite-time consensus for nonlinear fractional-order multi-agent systems;accelerating GNN inference by soft channel pruning;a multi-object detection sampling algorithm for large scenes;comparative study on data sovereignty guarantee technology;cluster-based federated learning framework for intrusion detection;graph-based multi-view partial multi-label learning;hypergraphs: concepts, applications and analysis;traffic speed prediction of road cluster with heterogeneous sampling frequency;multi-selection attention for multimodal aspect-level sentiment classification;deep just-in-time consistent comment update via source code changes;do not have enough data? an easy data augmentation for code summarization;leveraging graph to improve lexicon enhanced Chinese sequence labelling;and parallel accelerating ultra-long read alignment by vertical partitioning data.

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Unsupervised Root-Cause analysis with Transfer Learning for Integrated Systems 39

Unsupervised Root-Cause Analysis with Transfer Learning for ...

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ieee 39th VLSI Test symposium (VTS)

作者： Pan, Renjian Li, Xin Chakrabarty, Krishnendu Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781665419499

The increasing complexity of integrated systems has exacerbated the problems associated with root-cause analysis. Leveraging advances artificial intelligence, a large amount of intelligent root-cause-analysis methods have been proposed in recent years. However, most of these methods rely on root-cause labels from repair history for defective samples, which are often expensive to obtain. In this paper, we propose an unsupervised root-cause-analysis method that utilizes transfer learning. A two-stage clustering method is first developed by exploiting model selection based on the concept of Silhouette score. Next, a data-selection method based on ensemble learning is proposed to transfer valuable information from a source product to improve the root-cause-analysis accuracy on the target product with insufficient data. Two case studies based on industry designs demonstrate that the proposed approach significantly outperforms other state-of-the-art unsupervised root-cause-analysis methods.

关键词： Learning systems

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Big data analysis for data visualizationA Review 9

Big Data Analysis for Data VisualizationA Review

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9th ieee International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Ishika Mittal, Neetu Amity University Amity Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9781665417037

Complexity is one of the main characteristics of measuring data. Heterogeneous data contributes to data integrity and the process of big data problems. Both are essential and difficult to visualize and interpret large-scale databases because they require large capacity for data processing and storage. The data age, in which data is growing exponentially, is a huge struggle to extract data in a way that the human mind can assimilate. This paper reviews and presents data visualization and description of heterogeneous distributed storage and its challenges using different methods from some previous research. Besides, the results of the reviewed research work are compared, and the fundamental transformation in the world of big data visualization for virtual reality is discussed. © 2021 ieee.

关键词： visualization

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Comparative analysis of Detailed Features in 3D Models for SAR Simulation

Comparative Analysis of Detailed Features in 3D Models for S...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chia Yee Ho Erick Mas Bruno Adriano Shunichi Koshimura Graduate School of Engineering Tohoku University Japan International Research Institute of Disaster Science (IRIDeS) Tohoku University Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The lack of pre-disaster data often poses challenges for reliable building damage predictions in disaster response using remote sensing. To address this limitation, we propose a framework that leverages Synthetic Aperture Radar (SAR) simulators to generate high-resolution simulated SAR images. Assessing the realism of these simulated images is crucial for their reliability in representing authentic SAR data. However, obtaining pre-disaster SAR images for city-scale areas can be *** this study, we utilized Google 3D Tiles and Blender-GIS model to recreate scenes for simulating pre-disaster SAR images. We conducted a comprehensive analysis, evaluating the similarity between simulated and authentic SAR images through similarity assessments. Preliminary results suggest that the SAR simulator successfully produces distinct signatures for different 3D models in simulated SAR imagery. This approach holds promise for use in disaster response scenarios.

关键词： Solid modeling Analytical models visualization Three-dimensional displays Shape Disasters Radar polarimetry

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RDMP: A Reference Detection and Mask Propagation Pipeline for Numerous 3D Glomeruli Segmentation in large Volumes

RDMP: A Reference Detection and Mask Propagation Pipeline fo...

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ieee International symposium on Biomedical Imaging

作者： Ziling Lin Zehua Li Zifan Chen Yao Lu Fangxu Zhou Bin Dong Li Zhang Haifeng Li Center for Data Science Peking University Beijing China Peking University First Hospital Beijing China College of Future Technology Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quantitative analysis of numerous repetitive structures in large volumes is one of the most challenging tasks in biomedical imaging, primarily due to its intensive manual requirements. Kidney glomeruli exemplify this challenge due to their dispersion across the cortical region, high density and microscopic size. To address this, we present a Reference Detection and Mask Propagation (RDMP) pipeline that combines object detection with 2D and 3D mask propagation, gradually achieving efficient segmentation of the entire volume by using a 2D reference example. Our method demonstrates robust performance, achieving a Dice Similarity Coefficient (DSC) of 0.9096 on microCT data, and when generalized to Synchrotron Radiation X-ray (SRX) data, reaching a DSC of 0.8431 with only minimal bounding box fine-tuning. Overall, this pipeline shows strong potential for efficient, large-scale kidney analysis, significantly reducing labor demands and accelerating kidney disease research.

关键词： Image segmentation Three-dimensional displays Statistical analysis Microscopy Pipelines Object detection Manuals Kidney Synchrotron radiation Biomedical imaging

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Accelerating Fully Homomorphic Encryption Through Microarchitecture-Aware analysis and Optimization

Accelerating Fully Homomorphic Encryption Through Microarchi...

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ieee International symposium on Performance analysis of Systems and Software (ISPASS)

作者： Jung, Wonkyung Lee, Eojin Kim, Sangpyo Kim, Namhoon Lee, Keewoo Min, Chohong Cheon, Jung Hee Ahn, Jung Ho Seoul Natl Univ Seoul South Korea Samsung Elect Seoul South Korea Ewha Womans Univ Seoul South Korea

ISBN: (纸本)9781728186436

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]-[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUS and FPGAS, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform. © 2021 ieee.

关键词： Application analysis Homomorphic Encryption Multicore processing

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CLeVer: Continual Learning Visualizer for Detecting Task Transition Failure

CLeVer: Continual Learning Visualizer for Detecting Task Tra...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Minsuk Chang Donghun Kim Hyeon Jeon Seokweon Jung Jinwook Seo Seoul National University

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

We introduce CLeVer, a novel visualization system designed to analyze and enhance the performance of continual learning models by detecting task transition failures. Based on the literature review, we discovered and classified three primary causes of task transition failure in classification tasks: class/position inconsistency, biased/noisy samples, and diverse class scopes. These problems are critical in terms of model performance but were not tackled in prior research on continual learning. CLeVer is designed to address these challenges as an integrated system for model experiments and visual analysis. Firstly, users can easily configure the tasks for continual learning through a single JSON file. Then, our system automatically simulates the continual learning process in a relatively short time while generating data for visualization. Finally, the transition visualizer provides an effective visual representation of the task transition process where users can easily detect the task transition failures in continual learning. Our interview with machine learning experts and a case study with three participants demonstrate CLeVer’s utility in detecting and addressing such failures. We also discuss the system’s potential applicability and adaptability for various computer vision tasks while suggesting our future work.

关键词： Continuing education Analytical models Computer vision Adaptation models Computational modeling Visual analytics data visualization

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Turbine Electrified Energy Management for Single Aisle Aircraft

Turbine Electrified Energy Management for Single Aisle Aircr...

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ieee/AIAA Transportation Electrification Conference / Electric Aircraft Technologies symposium (ITEC+EATS)

作者： Kratz, Jonathan Connolly, Joseph Amthor, Aria Buescher, Halle Bianco, Santino Culley, Dennis NASA Glenn Res Ctr Cleveland OH 44135 USA HX5 LLC Cleveland OH 44142 USA

ISBN: (纸本)9781665405607

Electrified aircraft propulsion technology is being developed to reduce the environmental impacts of the aviation industry. This is prompting the exploration of potential uses and benefits of hybrid systems in which electric powertrains are integrated with more traditional gas turbine propulsion systems. Turbine Electrified Energy Management (TEEM) is an energy management approach for hybrid-electric architectures in which electric machines are connected to the turbofan shafts and used to suppress the off-design operation naturally associated with engine transients. This reduces the need to maintain a large amount of compressor operability margin, thus allowing further exploration of the engine design space. In this study, a 19,000 lbf engine within a parallel hybrid propulsion system is considered along with a 30,000 lbf standalone engine. data from prior TEEM applications are used to approximate the electric machine sizing required to achieve operability benefits. The TEEM controller is shown to improve operability during transients through the reduction of stall margin undershoots and the decrease of transient variations in component performance maps by over 29%.

关键词： Shafts Industries Electric potential Electric machines Mechanical power transmission Transient analysis Energy management

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analysis of Grinding Motion using Force/Tactile Sensation

Analysis of Grinding Motion using Force/Tactile Sensation

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ieee International symposium on Industrial Electronics (ISIE)

作者： Tomoya Kitamura Takumi Hachimine Takamitsu Matsubara Yuki Saito Hiroshi Asai Kouhei Ohnishi Haptics Research Center Keio University Kanagawa Japan Graduate School of Science and Technology NAIST Nara Japan Kanagawa Institute of Industrial Science and Technology Kanagawa Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Understanding human movements is essential for teaching skills to novices and enabling robots to learn. Traditional movement analysis often relies on visual data, but incorporating force/tactile information is vital for object interactions. The spring-mass-damper model, used to represent force/tactile data, assumes continuous contact and consistent material properties, which are limitations when analyzing grinding actions that frequently alternate between contact and non-contact. This study proposes analyzing grinding by quantifying force/tactile sensations as the ratio of the root mean square (RMS) values of force to speed, using absolute impedance to measure sensations irrespective of contact. Findings indicated that operators typically initiate a pressing motion several seconds after detecting contact, and their reactions to samples of varying hardness differ significantly. These observations suggest a detailed breakdown of human grinding skills is feasible, highlighting the potential of force/tactile information in enhancing skill transfer to beginners and robots.

关键词： visualization Force measurement Impedance measurement Force Pressing Robot sensing systems Impedance

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A-Map: Interactive Visual Exploration of Intercity Accessibility Dynamics Based on Railway Network data

A-Map: Interactive Visual Exploration of Intercity Accessibi...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Kaichen Nie Hanning Shao Yuchu Luo Min Tian Hao Wu Wei Zeng Xin Fu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) School of AI Peking University Hong Kong University of Science and Technology Guangzhou Chang’an University National Engineering Laboratory for Big Data Analysis and Application Peking University

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Railway transportation is closely linked to everyday lives while also aiding domain experts in analyzing national or regional development. However, discrepancies between travel time reduced by railways and actual distance pose challenges in visualizing the accessibility. Existing methods either struggle to simultaneously depict the accessibility relationships among all cities or disregard genuine geographical positions, leading to spatial cognitive confusion. In this work, we propose a novel approach to balance between the geographical positions and travel time between cities. We construct linear cartograms to represent accessibility while preserving better local railway network structures compared with existing methods. We further propose the A-Map system, enabling users to interactively explore the extensive development of China’s railway system over decades. We validate the effectiveness of our proposed layout algorithm from qualitative metric evaluation and illustrate the applicability of our system with results.

关键词： Measurement visualization Heuristic algorithms Urban areas Layout Transportation data visualization

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