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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Cromwell, Megan Mesick, Sharon Malik, Mashkoor Gondle, Vidhyadhari Noaa National Ocean Service Silver SpringMD United States Noaa National Centers for Environmental Information Stennis Space Center MS United States Noaa Ocean Exploration Silver SpringMD United States Science and Technology Corp. Silver SpringMD United States

ISBN: (纸本)9798218142186

Underwater imagery (including video and still frame photos) are routinely collected with crewed and uncrewed submersible platforms and represent an unparalleled visual repository of knowledge about marine organisms and how they interact with their environment in a given spatiotemporal setting. Underwater images are of key importance for scientific analysis to inform policy and resource management decisions and are also in high demand for media and documentary releases. Underwater imagery requires accompanying metadata describing the contents of the imagery to achieve FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines. This essential metadata generation step is an intense - often manual - process requiring scientists to provide video annotations that are a prerequisite to achieving Open Science initiatives and ensuring that data are available for broad exploitation in standard or common formats. Unique data management challenges set underwater imagery apart from most other oceanographic and geophysical data collections. Consistently high data volumes coupled with rapidly evolving technical advances in sensors can exceed data processing, management, and user capabilities. These challenges often require unique storage and data access solutions. These concerns are compounded by imagery collected from previous sea-going expeditions. These historical data - often decades old - take on a new importance in the face of climate change, and represent an opportunity to evaluate - visually and quantitatively - change over time for a specific organism or location. It is necessary that these historically significant records are retained and equally available for comparative analysis. Through the past two decades, the National Oceanic and Atmospheric Administration has researched, implemented, and refined processes that support best data management practices at minimal cost. NOAA Ocean exploration has sponsored hundreds of ocean exploration missions since its incep

关键词： Information management

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational Modeling Approach;transient Milling Dynamics in Collective Motion with visual Occlusions;extended Swarming with Embodied Neural Computation for Human Control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive exploration Control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback Controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.

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Research on Forest Landscape based on Big data and CiteSpace visualization analysis 2

Research on Forest Landscape based on Big data and CiteSpace...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Lin, Xinya Xu, Han Dong, Jianwen Fujian Agriculture and Forestry University Fuzhou China

ISBN: (纸本)9798350323818

In order to fully grasp the development and research status of forest landscape ecology, this paper analyzed the literatures related to forest landscape from 1982 to 2020 on CNKI and Wanfang data Service platform. Based on Python language environment, this paper uses Scrapy framework to extract literature data, and uses CiteSpace visual analysis software to draw knowledge map. It analyzes and visualizes the amount of articles published, research groups, research hotspots and trends. The results showed that: (1) the development process of forest landscape ecology research was divided into three stages: initial exploration period from 1982 to 2002, rapid development period from 2003 to 2008, and stable development period from 2009 to present. (2) The number of scholars in this field is relatively large and scattered, and the influential core author group and institutional group has not yet been formed;(3) The hot research directions are forest landscape pattern, sustainable development of forest, and forest landscape planning and design. (4) The focus of forest landscape ecology research has gradually shifted from geographic information system to eco-Tourism, urban forest and planning and design, and the research has become specialized and meticulous. © 2023 IEEE.

关键词： Big data

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visual analysis of Spatio-temporal Phenomena with 1D Projections

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COMPUTER GRAPHICS FORUM 2021年第3期40卷 335-347页

作者： Franke, M. Martin, H. Koch, S. Kurzhals, K. Univ Stuttgart Stuttgart Germany Swiss Fed Inst Technol Zurich Switzerland Inst Adv Res Artificial Intelligence Vienna Austria

It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.

关键词： CCS Concepts • Human-centered computing → visual analytics Geographic visualization Information visualization visualization systems and tools

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Research on visual analysis Method of Software Function Testing Process

Research on Visual Analysis Method of Software Function Test...

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2021 International conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2021

作者： Zuo, Zihuan Wu, Yadong Chen, Yuansong Zhang, Xiaorong Qin, Renchao School of Computer Science and Technology Southwest University of Science and Technology Mianyang621000 China School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong643002 China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

The existing research on software testing visualization is mostly at the code and developer level, and the visualization content is mostly at the code line and document level, and the target user group is very limited. In addition, the thirdparty software evaluation agency involves a large number of paper documents in the testing and review, which is inconvenient to manage, difficult to consult and analyze, and work efficiency is low. In order to solve these problems, we propose a visual analysis framework for software functional testing process. Starting from the functional testing process and the key information of each process, a set of visual analysis process is constructed, the data extraction method of the test document and the visual analysis method of the test process are studied, and the visual analysis prototype system is constructed to support the software testing process. And the key information of each process for quick analysis and further exploration. Finally, we demonstrated the application of the system in actual test projects through case analysis. © 2022 SPIE.

关键词： Software testing

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German conference on Medical Image Computing, BVM 2024

German Conference on Medical Image Computing, BVM 2024

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German conference on Medical Image Computing, BVM 2024

ISBN: (纸本)9783658440367

The proceedings contain 93 papers. The special focus in this conference is on Medical Image Computing. The topics include: Displacement Representation for Conditional Point Cloud Registration;Joint Learning of Image Registration and Change Detection for Lung CT Images;Influence of Prompting Strategies on Segment Anything Model (SAM) for Short-axis Cardiac MRI Segmentation;Abstracting Volumetric Medical Images with Sparse Keypoints for Efficient Geometric Segmentation of Lung Fissures with a Graph CNN;Keynote: 4-D+ nanoSCOPE Project;advanced Deep Learning for Skin Histoglyphics at Cellular Level;combining Image- and Geometric-based Deep Learning for Shape Regression;Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging;effect of Training Epoch Number on Patient data Memorization in Unconditional Latent Diffusion Models;Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant;improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies;harmonized Import of Clinical Research data for the Open Source Image analysis Platform Kaapana;interactive exploration of Conditional Statistical Shape Models in the Web-browser;segment-wise Evaluation in X-ray Angiography Stenosis Detection;automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry;towards Unified Multi-modal dataset Creation for Deep Learning Utilizing Structured Reports;assessment of Scanner Domain Shifts in Deep Multiple Instance Learning;few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors;computational Ontology and visualization Framework for the visual Comparison of Brain Atrophy Profiles;comparative analysis of Radiomic Features and Gene Expression Profiles in Histopathology data using Graph Neural Networks;magnetisation Reconstruction for Quantum Metrology;attention-guided Erasing.

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Multi-Group Tensor Canonical Correlation analysis 23

Multi-Group Tensor Canonical Correlation Analysis

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14th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Zhou, Zhuoping Tong, Boning Tarzanagh, Davoud Ataee Hou, Bojian Saykin, Andrew J. Long, Qi Shen, Li Univ Penn Philadelphia PA 19104 USA Indiana Univ Bloomington IN USA

ISBN: (纸本)9798400701269

Tensor Canonical Correlation analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

关键词： Canonical Correlation analysis Tensor Decomposition Neuroimaging Alzheimer's Disease

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SamS-Vis: A Tool to visualize Summary View Using Sampled data 19th

SamS-Vis: A Tool to Visualize Summary View Using Sampled Dat...

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19th International-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) International conference on Human-Computer Interaction (INTERACT)

作者： Humayoun, Shah Rukh Zaidi, Salman AlTarawneh, Ragaad San Francisco State Univ Dept Comp Sci San Francisco CA 94132 USA Univ Kaiserslautern Kaiserslautern Germany Intel Corp Intel Labs Santa Clara CA USA

ISBN: (纸本)9783031422928;9783031422935

Many recent visual analytics tools use exploratory model analysis workflow to enable users exploring set of potential machine/deep learning models. As part of the workflow, these tools provide summary view of underlying dataset to enable the users to better understand trends in their data. Due to the iterative nature of such workflows, users may need to go back to data exploration phase multiple times. In order to save time and resources at data pre-processing and visualization time, we propose to use sampled data rather than complete dataset for showing trends in data summary views. As a proof-of-concept, we built a visualization tool, called SamS-Vis, that uses five sampling techniques to collect sampled data and then shows the summary views using histogram line-charts. It enables the users to see the whole data summary view of the selected field(s) using histogram bar-chart based on demand.

关键词： data summary visualization data sampling techniques

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Knowledge Map of Chinese Geriatric Medical Care Research Field: Research Status and Trend—data visualization analysis Based on Cooc Software 2

Knowledge Map of Chinese Geriatric Medical Care Research Fie...

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2022 2nd International conference on Health Big data and Smart Sports, HBDSS 2022

作者： Bao, Hong-Rui Cui, Yan-Yin Yue, Hui School of Health Management Changchun University of Chinese Medicine Changchun130117 China

ISBN: (纸本)9783800760428

In the long-term context of an ageing population, geriatric medical care is an important safeguard for the Chinese response to the problem of aging. The study aimed to explore the research hotspots and process of geriatric medical care. We conducted a visual analysis of the literatures from 1986 to 2021 in CNKI, Wanfang and VIP database by Louvain algorithm and bibliometric method. We found that the research of geriatric medical care in China lags behind that in developed countries, and the development mainly presents three stages: germination, development and adjustment. The number of publications presents a steady growth trend;Medical and Health journals are the main publication journals;The core research teams are mainly composed of universities and research institutes in Shanghai and Beijing;The clustering topics focus on exploring the elderly medical security system, the feasibility of the medical service model, the management of chronic diseases, the development of the silver industry, medical disputes and health education for the elderly. Finally, we come to the conclusion that the health management of chronic diseases has become a new research hotspot and the exploration of new medical service models is an important trend. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Human resource management

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Integration and visual analysis of Biomolecular Networks Through UNIPred-Web 2nd

Integration and Visual Analysis of Biomolecular Networks Thr...

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22nd International conference on Web Engineering (ICWE)

作者： Perlasca, Paolo Frasca, Marco Ba, Cheick Tidiane Gliozzo, Jessica Notaro, Marco Pennacchioni, Mario Valentini, Giorgio Mesiti, Marco Univ Milan Dept Comp Sci Anacleto Lab Milan Italy

ISBN: (纸本)9783031253799;9783031253805

The integration of heterogeneous biological data into a common network representation is of paramount importance in different areas of biology and medicine. The size of the generated network in many cases prevents the possibility of its graphical visualization, inspection, and identification of characteristics. In this paper, we present the main features of UNIPred-Web (UNIPred-Web is available at https://***/), a web application realized to support the user in the integration of several biomolecular networks and for their visualization and navigation at different resolution levels. In particular, the identification and effective visualization of hierarchical communities allow an easy exploration of the entire network, while protein function prediction functionalities support the user in the interpretation process.

关键词： visualization of biomolecular networks Web services for protein function Network integration Community hierarchy detection

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