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International Conference on Computer Graphics Theory and Applications, GRAPP 2012 and International Conference on Information visualization Theory and Applications, IVAPP 2012

作者： Narayan, Akshay Sreevalsan-Nair, Jaya Gaither, Kelly Hamann, Bernd International Institute of Information Technology - Bangalore 26/C Electronics City Hosur Road Bangalore 560100 India Texas Advanced Computing Center University of Texas at Austin 10100 Burnet Road Austin TX 78758 United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis One Shields Avenue Davis CA 95616 United States

ISBN: (纸本)9789898565020

Grid-based computational simulations often use hybrid unstructured grids consisting of various types of elements. Most commonly used elements are tetrahedral, pentahedral (namely, square pyramids and right triangular prisms) and hexahedral elements. Extracting isosurfaces of scalar fields defined on such hybrid unstructured grids is often done using indirect methods, such as, (a) subdividing all non-tetrahedral cells into tetrahedra and computing the triangulated isosurfaces using the marching tetrahedra algorithm, or (b) triangulating intersection points of edges of cells and computing the isosurface using a standard triangulation algorithm. Using the basic ideas underlying the well-established marching cubes and marching tetrahedra algorithms, which are applied to hexahedral and tetrahedral elements, respectively, we generate look-up tables for extracting isosurfaces directly from pentahedral elements. By using appropriate look-up tables, it is possible to process nearly all types of hybrid unstructured grids used in practical applications without the need for indirect methods. We construct look-up tables for square pyramidal and triangular prismatic cells with accurate topological considerations.

关键词： Table lookup

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C-Net: A reliable convolutional neural network for biomedical image classification

arXiv

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arXiv 2020年

作者： Barzekar, Hosein Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer aided diagnosis

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Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

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Journal of Advanced Research 2024年 62卷 155-163页

作者： Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Kharel, Rupak Das, Ashok Kumar Shetty, Sachin Rodrigues, Joel J.P.C. School of Psychology and Computer Science University of Central Lancashire Preston United Kingdom Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Punjab Sahiwal 57000 Pakistan Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center and Center for Cybersecurity Education and Research Old Dominion University Suffolk 23435 VA United States COPELABS Lusófona University Campo Grande 376 Lisbon 1749-024 Portugal

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. © 2024

关键词： Digital twins Industrial Internet of Things (IIoT) Key agreement Mutual authentication Physically Unclonable Functions (PUFs)

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Investigating the Influence of Hyperparameters on the Optimal Time-Series Prediction Ability of Generative Large Language Models

Investigating the Influence of Hyperparameters on the Optima...

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Information Technology and data science (CITDS), IEEE Conference on

作者： Arpad Pandy Róbert Lakatos Péter Pollner Eszter Csernai András Hajdu Department of Data Science and Visualization Faculty of Informatics University of Debrecen Debrecen Hungary Health Services Management Training Centre Semmelweis University Budapest Hungary GE HealthCare Chicago Illinois United States

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

This study investigates the optimal hyperparameter settings for Large Language Models (LLMs) in predicting time-series data. We conducted extensive experiments using both synthetic datasets and real agricultural data to evaluate the performance of several LLMs. Our findings reveal that the predictive accuracy of these models is highly dependent on the configuration of the temperature and Top- p hyperparameters. Specifically, models demonstrated optimal performance when the ratio between these two parameters was maintained below a threshold, with the most effective ratios being under 2.0 for Mistral-small and around 4.0 for Gemma-7B. Furthermore, Mistral-small showed lower variability in performance across different settings, indicating higher determinism. By aggregating data from multiple experimental conditions, we identified that maintaining a temperature to Top-p ratio below 1.36 covers 50% of top-performing pairs for Mistral-small and below 2.75 for Gemma-7B. If we consider only hyperparameters, that has such ratios, we can decrease the MSE value by 32% on average. These insights provide a foundation for enhancing the accuracy of LLMs in time-series forecasting, offering practical guidelines for hyperparameter optimization. Our results underscore the potential of LLMs to outperform traditional methods, particularly in datasets with periodic patterns, thereby contributing to the growing literature on the application of generative models in time-series analysis.

关键词： Temperature distribution Temperature dependence Accuracy Large language models Time series analysis Predictive models data models Forecasting Tuning Synthetic data

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A sensor web observing system simulator

A sensor web observing system simulator

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InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration

作者： Talabac, Stephen J. Seablom, Michael S. Higgins, Glenn J. Womack, Brice T. NASA Goddard Space Flight Center Greenbelt MD 20771 United States Northrop Grumman IT TASC Chantilly VA 20151 United States Sensor Web Information Systems Science Data Systems Branch Code 586 United States Software Integration and Visualization Office Code 610.3 United States Atmospheric Sciences and Engineering Department Northrop Grumman IT TASC United States Science Systems Engineering Northrop Grumman IT TASC United States

ISBN: (纸本)1563477394

Sensor Web observing systems may have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable environmental features and events. This improvement will come about by integrating novel data collection techniques, new improved instruments, emerging communications technologies, and interoperable planning and scheduling systems. In contrast with today's observing systems, "event-driven" sensor webs will synthesize near-real time measurements and information from other platforms and then reconfigure themselves to invoke new measurement modes and adaptive observation strategies. Meteorological prediction models may also serve to initiate new measurement modes (e.g., higher spatial, temporal resolution) or to target observations to specific regions. These "model-driven" sensor webs will complement event-driven measurements. Platforms will be tasked to target measurements within specific areas where sensitivity to initial conditions may cause ensemble forecasts to diverge when predicting the future state of atmospheric features (e.g., hurricane track) or when discriminating subtle yet critical differences in atmospheric states (e.g., winter precipitation type and location). The targeted measurements would then be assimilated to establish new initial conditions. This operations concept could contribute to reducing forecast model error growth, and concomitantly, forecast uncertainty. The sensor web concept contrasts with today's data collection techniques and observing system operations concepts. Although the technologies and capabilities of our space-, atmospheric-, and surface-based platforms and instruments have evolved significantly during the past four decades, operations concepts for present day observing systems remain essentially unchanged: independent platforms and instruments characterize today's "distributed data collection" systems. Information sharing between platforms and ins

关键词： Sensors

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A Survey of Research on Few-Shot Relation Classification

SSRN

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SSRN 2024年

作者： Zhang, YaChuan Guo, Yi Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Engineering Research Center of Big Data Internet Audience Shanghai China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

Relation classification is a crucial sub-task of information extraction, playing a significant role in extracting structured information from vast texts and constructing knowledge graphs. However, in practical scenarios, the availability of labeled data is often limited, and some datasets exhibit a long-tail distribution. To address these challenges, few-shot learning relation classification (FSRC) has emerged as a promising approach. The purpose of FSRC is to learn high-quality features with insufficient data for distinguishing the relation between given entities in a sentence. This task presents greater challenges compared to traditional relation classification tasks. Recently, FSRC has gained a lot of attention, prompting numerous researchers to provide innovative methods, datasets, and ideas to tackle this challenge. This paper focuses on relation classification based on few-shot learning and provides a comprehensive review of state-of-the-art techniques. Based on distinct research characteristics, we classified FSRC methods into five categories: metric-based method, optimization-based method, pre-trained and LLMs based method, prompt-based method, and lower source situation. We analyze the advantages and disadvantages of each category, provide a summary of the datasets, metrics, and results obtained by representative models. Finally, the paper concludes the current limitations of the few-shot relation classification and proposes future research directions. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Evolutionary morphing

Evolutionary morphing

引用

IEEE Conference on visualization

作者： D.F. Wiley N. Amenta D.A. Alcantara D. Ghosh Y.J. Kil E. Delson W. Harcourt-Smith F.J. Rohlf K. St John B. Hamann Institute for Data Analysis and Visualization and Department of Computer Science University of California Davis USA American Museum of Natural University of New York UK Department of Ecology and Evolution State University of New York Stony Brook USA

We introduce a technique to visualize the gradual evolutionary change of the shapes of living things as a morph between known three-dimensional shapes. Given geometric computer models of anatomical shapes for some collection of specimens - here the skulls of the some of the extant members of a family of monkeys - an evolutionary tree for the group implies a hypothesis about the way in which the shape changed through time. We use a statistical model which expresses the value of some continuous variable at an internal point in the tree as a weighted average of the values at the leaves. The framework of geometric morphometrics can then be used to define a shape-space, based on the correspondences of landmark points on the surfaces, within which these weighted averages can be realized as actual surfaces. Our software provides tools for performing and visualizing such an analysis in three dimensions. Beginning with laser range scans of crania, we use our landmark editor to interactively place landmark points on the surface. We use these to compute a "tree-morph" that smoothly interpolates the shapes across the tree. Each intermediate shape in the morph is a linear combination of all of the input surfaces. We create a surface model for an intermediate shape by warping all the input meshes towards the correct shape and then blending them together. To do the blending, we compute a weighted average of their associated trivariate distance functions and then extract a surface from the resulting function. We implement this idea using the squared distance function, rather than the usual signed distance function, in a novel way.

关键词： Shape Surface morphology Evolution (biology) Statistical analysis data visualization Genomics Bioinformatics Skull Humans data analysis

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Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

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JMIR medical informatics 2025年 13卷 e63906页

作者： Haitham Abaza Aliaksandra Shutsko Sophie A I Klopfenstein Carina N Vorisek Carsten Oliver Schmidt Claudia Brünings-Kuppe Vera Clemens Johannes Darms Sabine Hanß Timm Intemann Franziska Jannasch Elisa Kasbohm Birte Lindstädt Matthias Löbe Katharina Nimptsch Ute Nöthlings Marisabel Gonzalez Ocanto Tracy Bonsu Osei Ines Perrar Manuela Peters Tobias Pischon Ulrich Sax Matthias B Schulze Florian Schwarz Carolina Schwedhelm Sylvia Thun Dagmar Waltemath Hannes Wünsche Atinkut A Zeleke Wolfgang Müller Martin Golebiewski Scientific Databases and Visualization Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany. Information Centre for Life Sciences German National Library of Medicine (ZB MED) Cologne Germany. Core Facility Digital Medicine and Interoperability Universitätsmedizin Berlin Berlin Institute of Health at Charité Berlin Germany. Institute for Community Medicine University Medicine Greifswald Greifswald Germany. Department of Biometry and Data Management Leibniz Institute for Prevention Research and Epidemiology - BIPS Bremen Germany. Department of Medical Informatics University Medical Center Göttingen Georg-August-University Göttingen Germany. Department of Molecular Epidemiology German Institute of Human Nutrition Potsdam-Rehbruecke Nuthetal Germany. Institute for Medical Informatics Statistics and Epidemiology University Leipzig Leipzig Germany. Molecular Epidemiology Research Group Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany. Department of Nutrition and Food Science University of Bonn Bonn Germany. Information and Research Data Management Robert Koch Institute Berlin Germany.

BACKGROUND:Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors. The National Research data Infrastructure for Personal Health data (NFDI4Health) seeks to optimize data sharing among the clinical, epidemiological, and public health research communities while preserving privacy and ethical regulations. OBJECTIVE:We aimed to develop a tailored metadata schema (MDS) to support the standardized publication of health studies' metadata in NFDI4Health services and beyond. This study describes the development, structure, and implementation of this MDS designed to improve the FAIRness of metadata from clinical, epidemiological, and public health research while maintaining compatibility with metadata models of other resources to ease interoperability. METHODS:Based on the models of dataCite, ***, and other data models and international standards, the first MDS version was developed by the NFDI4Health Task Force COVID-19. It was later extended in a modular fashion, combining generic and NFDI4Health use case-specific metadata items relevant to domains of nutritional epidemiology, chronic diseases, and record linkage. Mappings to schemas of clinical trial registries and international and local initiatives were performed to enable interfacing with external resources. The MDS is represented in Microsoft Excel spreadsheets. A transformation into an improved and interactive machine-readable format was completed using the ART-DECOR (Advanced

关键词： FAIR accessibility chronic diseases clinical trial data models data sharing epidemiological epidemiology findability health research implementation interoperability development metadata standards nutrition public health reusability structure tool

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Large data visualization on Distributed Memory Multi-GPU Clusters 10

Large Data Visualization on Distributed Memory Multi-GPU Clu...

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Conference on High-performance Graphics

作者： Thomas Fogal Hank Childs Siddharth Shankar Jens Kriiger R. Daniel Bergeron Philip Hatcher Scientific Computing & Imaging Institute University of UtahDepartment of Computer Science University of New Hampshire Lawrence Berkeley National Laboratory Scientific Computing & Imaging Institute University of Utah Interactive Visualization and Data Analysis Group DFKI Germany Department of Computer Science University of New Hampshire

ISBN: (纸本)9781617827242

data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.

关键词： distributed memory data visualisation visualization dataset supercomputer Representation of data performance characteristics

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Kbpt: Knowledge-Based Prompt Tuning for Zero-Shot Relation Triplet Extraction

SSRN

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SSRN 2023年

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai200072 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogon

关键词： Ontology

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