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From Local to Remote: Scaling VisIVO Visual Analytics for large-Scale Astrophysical data visualization 33

From Local to Remote: Scaling VisIVO Visual Analytics for La...

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Tudisco, Giuseppe Vitello, Fabio Sciacca, Eva Becciani, Ugo Osservatorio Astrofisico di Catania Catania Italy

ISBN: (纸本)9798331524937

As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations. This massive influx of data presents significant challenges in terms of storage, processing, analysis and visualization. Scientific visualization becomes essential in this context, enabling researchers to interpret complex, high-dimensional datasets and extract valuable insights. This paper presents the latest developments of VisIVO Visual Analytics, an interactive visualization tool that is evolving to meet the needs of the SKA and similar large-scale astrophysical projects such as the James Webb Space Telescope and the Atacama large Millimeter/submillimeter Array (ALMA). Originally designed as a desktop application, VisIVO is transitioning to a client-server architecture, allowing it to efficiently run remote visualization pipelines on remote servers. This shift significantly enhances the tool's ability to handle large scale datasets, facilitating real-time, interactive analysis of complex data. This work also explores how VisIVO Visual Analytics can distribute computational workloads across multiple nodes in high-performance computing (HPC) clusters for parallel visualization pipelines, enabling advanced and interactive techniques to derive meaningful insights from the vast volumes of data generated by next-generation observatories. © 2025 ieee.

关键词： Cluster computing

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Improving Human Activity Recognition Models by Learnable Sparse Wavelet Layer

Improving Human Activity Recognition Models by Learnable Spa...

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ACM International symposium on Wearable Computers (ISWC)

作者： Zhao, Haibin Zhou, Yexu Riedel, Till Hefenbrock, Michael Beigl, Michael Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781450394246

Modern machine learning algorithms for human activity recognition based on artificial neural networks often require a large amount of labelled training data to generalize between human subjects and training contexts. large degrees of freedom make them susceptible to overfitting and often computationally intensive to implement on portable hardware. In this work, we introduce wavelet-based learnable filters as a feature extraction layer that greatly improves the generalization capability of the detector model. Our evaluations on six benchmark datasets show significant improvements in macro F-1 score when our wavelet-based learnable filter layer is prepended to three state-of-the-art human activity recognition models. As a side effect, in many cases we could drastically reduce the required model size to achieve competitive performance on the benchmark dataset, which is an important requirement for use in wearable computing.

关键词： human activity recognition learnable filters wavelet analysis

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Combining Degree of Interest Functions and Progressive visualization

Combining Degree of Interest Functions and Progressive Visua...

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International Conference on Visualisation, VIS

作者： Marius Hogräfer Dominik Moritz Adam Perer Hans-Jörg Schulz Schulz are with Aarhus University Carnegie Mellon University

When visualizing large datasets, an important goal is to emphasize data that is relevant to the task at hand. A common way of achieving this is to compute the relevance of the data using degree of interest (DOI) functions, which apply a scenario-specific metric to quantify the data items according to their relevance to the users and their tasks. These DOI values can then be used to adjust the visual encoding through mechanisms like focus+context or information hiding. For datasets too large to be visualized at once, an alternative approach is to visualize it progressively in chunks, allowing analysts to reason about partial results and concluding their analysis much earlier than had they waited for all data. Combining the advantages of both approaches to tailor the visualization seems synergistic, yet, in practice turns out to be challenging, as DOI functions require the context of all data to produce useful values, requiring lengthy computations that break analysts’ flow in progressive visualization. In this paper, we propose an approach for uniting DOI functions with progressive visualization. We first introduce a new model for quantifying the user interest in analysis scenarios where the data is only partially available, by computing the interest for available data and predicting it for the rest. We then propose regression trees for implementing this approach in practice and evaluate it in benchmarks. With DOI values now available for progressive visualization as well, our approach opens the door for tailoring the visualization of large datasets to the analysis task at interactive update rates.

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The visualization JUDGE: Can Multimodal Foundation Models Guide visualization Design Through Visual Perception?

The Visualization JUDGE: Can Multimodal Foundation Models Gu...

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Evaluation and Beyond - Methodological Approaches for visualization (BELIV)

作者： Matthew Berger Shusen Liu Vanderbilt University Lawrence Livermore National Laboratory

ISBN: (数字)9798331528461

ISBN: (纸本)9798331528478

Foundation models for vision and language are the basis of AI applications across numerous sectors of society. The success of these models stems from their ability to mimic human capabilities, namely visual perception in vision models, and analytical reasoning in large language models. As visual perception and analysis are fundamental to data visualization, in this position paper we ask: how can we harness foundation models to advance progress in visualization design? Specifically, how can multimodal foundation models (MFMs) guide visualization design through visual perception? We approach these questions by investigating the effectiveness of MFMs for perceiving visualization, and formalizing the overall visualization design and optimization space. Specifically, we think that MFMs can best be viewed as judges, equipped with the ability to criticize visualizations, and provide us with actions on how to improve a visualization. We provide a deeper characterization for text-to-image generative models, and multi-modal large language models, organized by what these models provide as out-put, and how to utilize the output for guiding design decisions. We hope that our perspective can inspire researchers in visualization on how to approach MFMs for visualization design.

关键词： Analytical models large language models data visualization Text to image Cognition Optimization Visual perception

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NAV: A Comparative analysis Tool for Nsight Systems GPU Traces 33

NAV: A Comparative Analysis Tool for Nsight Systems GPU Trac...

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Shama, Ethan Grant, Ryan E. Queen's University Electrical and Computer Engineering Department KingstonON Canada

ISBN: (纸本)9798331524937

High-performance computing (HPC) and data centers increasingly rely on Graphics Processing Units (GPUs) in large supercomputers. Yet, this reliance poses challenges in quickly understanding the performance impacts of code changes. This paper introduces NAV, a versatile tool designed to rapidly analyze and compare GPU performance traces. Built upon NVIDIA's NsightTM Systems (NSYS), NAV enhances NSYS's capabilities by accelerating visualization for large traces, increasing the variety of data representation formats, and adding comparative analysis capabilities. Our tool, NAV, provides complimentary functions on top of NSYS to quickly access performance data and perform comparative analysis. NAV offers detailed visual and written representations of traces at various granularity levels and efficiently handles large trace files through parallelization. It extracts trace data 1.15 to 3.5 times faster than comparable NSYS recipes for typical developer trace sizes, automating the generation of valuable data representations and streamlining workload analysis. This paper outlines NAV's key features and functionalities, demonstrating its effectiveness through use cases that highlight its benefits for rapid application analysis and assessing the impact of code changes. © 2025 ieee.

关键词： data visualization

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analysis of Conductance Variability in RRAM for Accurate Neuromorphic Computing 25

Analysis of Conductance Variability in RRAM for Accurate Neu...

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25th ieee Latin American Test symposium, LATS 2024

作者： Aziza, H. Postel-Pellerin, J. Fieback, M. Hamdioui, S. Xun, H. Taouil, M. Coulie, K. Rahajandraibe, W. Aix-Marseille University Cnrs IM2NP France Delft University Of Technology Computer Engineering Laboratory Delft2628CD Netherlands

ISBN: (纸本)9798350365559

While Resistive RRAM (RRAM) offers attractive features for artificial neural networks (NN) such as low power operation and high-density, its conductance variation can pose significant challenges when the storage of synaptic weights is concerned. This paper reports an experimental evaluation of the conductance variations of manufactured RRAMs at the memory array level. Working at the memory array level allows to catch cycle-to-cycle (C2C) as well as device-to-device (D2D) variability and, hence, to propose a realistic evaluation of the conductance variation. Variability is evaluated with respect to the RRAM low resistance state (LRS) and high resistance state (HRS) conductance ratio. This ratio is selected as the parameter of interest as it guarantees the proper operation of the RRAM: the larger the ratio, the more reliable and robust the RRAM cell is in storing and retrieving data. The measurement results show that the conductance ratio is heavily affected by variability. large spatial and temporal variations are reported, making challenging RRAM-based analog weight storage. © 2024 ieee.

关键词： RRAM

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Detection of Potential Specific Learning Disabilities in Children through Handwriting analysis Using Machine Learning

Detection of Potential Specific Learning Disabilities in Chi...

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2024 ieee Region 10 symposium, TENSYMP 2024

作者： Dinusha, P. Sreekumar, Subha Lijiya, A. National Institute of Technology Calicut Dept of Cse Kerala India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364866

Specific learning disability refers to a variety of learning difficulties that impact an individual's capacity to acquire and apply specific academic skills, including reading, writing, mathematics, and reasoning. Learning difficulties, if not identified, can adversely affect children's overall learning experience. Early identification of learning difficulties in children is crucial for providing the necessary support and interventions to help them overcome the challenges. Conventional methods used by psychologists to diagnose learning disabilities include subjective assessments that also examine the behavioral aspects of children. However, these tests are time-consuming and im-practical for large groups of children. Therefore, an automated initial screening method can provide a cost-effective solution for identifying children at risk of learning difficulties who need further testing by specialists. In this study, we present an efficient, low-cost system for identifying children at risk by analyzing the static features of their handwritten text. We extracted six hand-writing features from data collected from typically developing children and those with clinically diagnosed learning disability. These features were used as inputs to a machine learning model to predict the risk of specific learning disability. We compared the performance of various classifiers, and the random forest classifier achieved the best results, with an accuracy of 87.3 %. These findings indicate that the model effectively detects risk in children suggesting its potential as a valuable tool for early identification of specific learning disabilities. © 2024 ieee.

关键词： Adversarial machine learning

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Automated dataset Generation with Blender for Deep Learning-based Object Segmentation 20

Automated Dataset Generation with Blender for Deep Learning-...

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20th ieee Jubilee World symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Karoly, Artur I. Galambos, Peter Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary Óbuda Universty Doctoral School of Applied Informatics and Applied Mathematics Hungary

ISBN: (纸本)9781665497046

"Give a man a synthetic dataset and you help him for a project;teach a man to generate his own dataset and you help him for a lifetime."Developments in digital technology led to an unprecedented increase in the amount of data in industry and in science as well, including the field of computer vision. This enabled the evolution of convolutional neural networks (CNNs) and other deep learning (DL) architectures that require a large amount of data for their training. Even though the large-scale collection of visual data is relatively cheap and easy to implement, the annotation of the collected data is a time-consuming process. Although, with the use of pre-trained models only a small-scale fine-tuning dataset is required, the labeling of only a few hundred or few thousand images for object segmentation or instance segmentation is still a laborious task, if done manually. Recent trends suggest that synthetic data will play a significant role in the future of DL and there are numerous research papers promoting various synthetic datasets and/or models trained on them. Unfortunately, there is much less information on how to create such a dataset and what are the important points one has to keep in mind when creating their own. In this paper, we summarize some of the available tools for creating a synthetic dataset for object segmentation and provide a detailed example using the Blender 3D creation suite. During our example, we attract attention to the vital points and considerations for automatic dataset generation for object segmentation. © 2022 ieee.

关键词： Training Industries visualization Three-dimensional displays Object segmentation Market research Labeling

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Predicting Song Success: Understanding Track Features and Predicting Popularity Using Spotify data 22

Predicting Song Success: Understanding Track Features and Pr...

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22nd International symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Vardo, Lejla Jerkić, Jana Žunić, Emir Polytechnic Faculty Software Engineering Zenica Bosnia and Herzegovina

ISBN: (纸本)9781665475464

This paper presents the use of different prediction algorithms in order to recognise the popularity of a song. That recognition gives features that are directly affecting popularity of a song. For this research, data from several hundreds of the most popular songs were used in combination with songs that often appear on different playlists from different musicians. The reason for this mixing of songs is done to ensure that the model works as efficiently as possible by comparing popular songs features with those of that are no longer trending. The processing of the collected data gave an excellent insight into the importance of certain factors on the popularity of a certain song. As a result of research, month of release, acoustics and tempo were represented as features that are mostly correlated with popularity. Through the processing and analysis of a large amount of data, four models were created using different algorithms. Algorithms that were used are Decision Tree, Nearest Neighbour Classifier, Random Forest and Support Vector Classifier algorithms. The best results were achieved by training the model with the Decision Tree algorithm and accuracy of 100%. © 2023 ieee.

关键词： Forecasting

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Using Deep Learning Networks to Process Corona and Heating Characteristics of large Fittings in UHVDC Valve Halls and Structural Optimization 4

Using Deep Learning Networks to Process Corona and Heating C...

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4th International symposium on AI, ISAI 2024

作者： Shiling, Zhang Baojia, Deng Liangjun, Dai Yongsheng, He Electric Power Research Institute of State Grid Chongqing Electric Power Company Chongqing China State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing China

ISBN: (纸本)9798350342482

Considering the dual effects of the load current and voltage on the metal fittings, it is necessary to comprehensively analyze their corona and heating characteristics, and optimize the design and analysis of structure. The pressure equalization device is divided into three typical types of hardware: shielding ball, shielding ring, and the shielding cover, and the flexible connecting components are formed between the tubular busbars through flexible wires. Corona tests are conducted independently on the positive and the negative electrodes of the diameters, and corresponding test phenomena and data were recorded. During the experiment, no corona discharge was observed on balls with diameters of the 900 mm and 1000 mm under positive polarity, while corona discharge was observed on balls with the different diameters under negative polarity. Then, using deep learning networks to process corona and heating characteristics of large fittings in UHVDC valve halls and structural optimization. It is proposed that the corona field strength value of valve fittings is: for valve tower shielding plates, OCT, and the lightning arrester equalizing rings with an equivalent diameter of less than 200 mm, it is considered that their corona field strength is not less than 2500 Vomm-1. For the equal pressure shielding balls and casing shielding covers with equivalent diameter of 200 mm or more, it is considered that corona field strength is not less than 1500 Vomm-1. © 2024 ieee.

关键词： Lightning arresters

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