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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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A Comparative analysis of Traffic analysis Frameworks in Software-Defined Networking

A Comparative Analysis of Traffic Analysis Frameworks in Sof...

引用

visual Analytics and data visualization (ICvADv), International conference on

作者： Gorli Uday Kumar Prabu. U Yadlapalli Leelakrishna Pagadam Siddu Geetha. v Department of CSE Velagapudi Ramakrishna Siddhartha School of Engineering Siddhartha Academy of Higher Education Vijayawada India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Software-defined networking (SDN) has emerged as a transformative paradigm in modern networking by decoupling the control plane from the data plane, enabling centralized control and programmability. This architectural shift addresses the increasing complexity and dynamic requirements of networks by offering enhanced flexibility, scalability, and efficiency. A crucial aspect of SDN is the analysis of network traffic, which plays a pivotal role in performance optimization, network management, and troubleshooting. Recent frameworks have leveraged metrics such as round-trip time, throughput, bandwidth, and packet count to provide insights into network behavior and efficiency. Among these, the RYU controller stands out for its extensibility and flexibility in supporting traffic analysis tasks. A comprehensive review of the various techniques used in SDN traffic analysis highlights their strengths and limitations in optimizing SDN network performance and management.

关键词： Reviews Scalability visual analytics Telecommunication traffic Throughput Routing Robustness Real-time systems Resource management Software defined networking

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Integration of Large Language Models and Computer vision Algorithms in LMS: A Methodology for Automated verification of Software Tasks and Multimodal analysis of Educational data

Integration of Large Language Models and Computer Vision Alg...

引用

2025 International Russian Smart Industry conference, SmartIndustryCon 2025

作者： Markin, E.I. Zuparova, v.v. Martyshkin, A.I. Penza Russia

ISBN: (纸本)9798331511241

This paper presents a study on the design and development of a learning management system (LMS) integrated with large language models (LLMs) and image recognition/generation algorithms to automate software task validation and multimodal educational data analysis. The purpose of the development is to improve the quality of feedback for learners, deepen the understanding of errors made and expand the capabilities of the system by synthesizing formal LLM models and visual processing algorithms. The system is based on the use of domestic LLMs such as GigaChat and YandexGPT, Retrieval-Augmented Generation (RAG) technology, and modern computer vision algorithms. The system architecture is described, including Gitverse for code storage, Gitverse Actions for automated testing of solutions in Docker containers, and image processing modules for analyzing and generating visual data in the educational process. The conducted experiment demonstrates the effectiveness of the integrative approach, which allows to improve the quality of feedback and increase the performance of students. Prospects for further research include optimization of computational resources and development of techniques for integrated analysis of multimodal educational data. © 2025 IEEE.

关键词： Integration testing

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Pheno-Mapper: An Interactive Toolbox for the visual exploration of Phenomics data 21

Pheno-Mapper: An Interactive Toolbox for the Visual Explorat...

引用

12th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Zhou, Youjia Kamruzzaman, Methun Schnable, Patrick Krishnamoorthy, Bala Kalyanaraman, Ananth Wang, Bei Univ Utah Salt Lake City UT 84112 USA Univ Virginia Charlottesville VA USA Iowa State Univ Ames IA USA Washington State Univ Vancouver WA USA

ISBN: (纸本)9781450384506

High-throughput technologies to collect field data have made observations possible at scale in several branches of life sciences. The data collected can range from the molecular level (genotypes) to physiological (phenotypic traits) and environmental observations (e.g., weather, soil conditions). These vast swathes of data, collectively referred to as phenomics data, represent a treasure trove of key scientific knowledge on the dynamics of the underlying biological system. However, extracting information and insights from these complex datasets remains a significant challenge owing to their multidimensionality and lack of prior knowledge about their complex structure. In this paper, we present Pheno-Mapper, an interactive toolbox for the exploratory analysis and visualization of large-scale phenomics data. Our approach uses the mapper framework to perform a topological analysis of the data, and subsequently render visual representations with built-in data analysis and machine learning capabilities. We demonstrate the utility of this new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to existing approaches, the main advantage of Pheno-Mapper is that it provides rich, interactive capabilities in the exploratory analysis of phenomics data, and it integrates visual analytics with data analysis and machine learning in an easily extensible way. In particular, Pheno-Mapper allows the interactive selection of subpopulations guided by a topological summary of the data and applies data mining and machine learning to these selected subpopulations for in-depth exploration.

关键词： phenomics topological data analysis interactive visualization multidimensional biological data

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visual Analytics for Industrial Sensor data analysis 23

Visual Analytics for Industrial Sensor Data Analysis

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23rd International conference on Enterprise Information Systems (ICEIS)

作者： Langer, Tristan Meisen, Tobias Univ Wuppertal Chair Technol & Management Digital Transformat Rainer Gruenter Str 21 D-42119 Wuppertal Germany

ISBN: (纸本)9789897585098

Due to the increasing digitalization of production processes, more and more sensor data is recorded for subsequent analysis in various use cases (e.g. predictive maintenance). The analysis and utilization of this data by process experts raises optimization potential throughout the production process. However, new analysis methods are usually first published as non-standardized Python or R libraries and are therefore not available to process experts with limited programming and data management knowledge. It often takes years before those methods are used in ERP, MES and other production environments and the optimization potential remains idle until then. In this paper, we present a visual analytics approach to facilitate the inclusion of process experts into analysis and utilization of industrial sensor data. Based on two real world exemplary use cases, we define a catalog of requirements and develop a tool that provides dedicated interactive visualizations along methods for exploration, clustering and labeling as well as classification of sensor data. We then evaluate the usefulness of the presented tool in a qualitative user study. The feedback given by the participants indicates that such an approach eases access to data analysis methods but needs to be integrated into a comprehensive data management and analysis process.

关键词： Time Series analysis Industrial Sensor data visual Analytics Analytic Provenance

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Space exploration Using Sensors Based on Island Nanostructures 7

Space Exploration Using Sensors Based on Island Nanostructur...

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7th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Sidorova, Svetlana v. Kiryanov, Sergey v. Aksenova, Sofia A. Bauman Moscow State Technical University Electronic Technologies in Mechanical Engineering Department Moscow Russian Federation Bauman Moscow State Technical University Space-rocket Launch Complex Department Moscow Russian Federation

ISBN: (纸本)9798331531836

In this paper the relevance of using weak magnetic field sensors for space research is shown. The analysis of the potential integration of spin-tunnel magnetoresistive (STMR) sensors with island nanostructures into various spacecraft systems has been conducted. A possible application of the sensor in inertial navigation systems of spacecraft for long-duration missions was considered, specifically for calibrating data in regions with low Earth's magnetic field intensity. The feasibility of using the sensor in space missions to investigate remanent magnetization and measure the magnetic field of Mars at various distances from the planetary surface was explored. Through multiphysics modeling, the configuration and geometric parameters of the functional layer consisting of island nanostructures were determined for their use in designing components for spacecraft and navigation systems. © 2025 IEEE.

关键词： island nanostructures magnetic field magnetic sensors magnetoresistance multiphysics modeling spacecraft

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Advancing Optical Tomography Image analysis: Exploring Convolutional Neural Network Model variants for Retinal Damage Detection 10

Advancing Optical Tomography Image Analysis: Exploring Convo...

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10th IEEE Uttar Pradesh Section International conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Sathyan, S. Chanchal, M. Raghul, S. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382471

Retina is a vital component of the eye;it plays a crucial role in visual perception. Any damage to the retina can lead to vision impairment and severe ocular diseases. Early detection and accurate diagnosis of retinal conditions is crucial. In the domain of medical image analysis, especially within the scope of retinal imaging, convolutional neural network (CNN)-based deep learning models have exhibited promising results. This paper involves the detection of retinal conditions such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNv) and Drusen. The paper presents a comparative study of CNN models for the detection of these retinal damages using Optical Coherence Tomography (OCT) images. This study focuses on evaluating the performance of popular CNN architectures, like Residual Networks (ResNet), Dense Convolutional Networks (DenseNet), Inception Networks, visual Geometry Group (vGG). Also, the paper provides a comparative analysis of different depth variants of deep learning models such as ResNet (18, 50 and 101), DenseNet (121, 169, and 201), Inception-v3, Inception-ResNet-v2, and vGG (16 and 19). These models are assessed on a dataset containing OCT images of Normal, DME, CNv and Drusen retinal conditions. The effectiveness of the models are evaluated using evaluation metrics such as accuracy, precision, recall and F1 score. Additionally, visualizations such as confusion matrix, training accuracy versus validation accuracy plots, training loss versus validation loss plots are utilized to provide a better understanding of the model performance. The idea is to explore the impact of depth on the model performance and understand how deeper models demonstrate an enhanced ability to learn complex features and capture intricate details within the retinal data. Exploring these depth variants aims to provide insights into the advantages and potential trade-offs associated with increasing depth of deep learning models for retinal damage detection tasks. © 2

关键词： choroidal neovascularization deep learning DenseNet diabetic macular edema drusen Inception networks medical image analysis ocular diseases optical coherence tomography (OCT) ResNet retina retinal imaging vGG

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A Novel Survey on Panoptic Segmentation using Deep Learning Approaches 2

A Novel Survey on Panoptic Segmentation using Deep Learning ...

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2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Nagaraju, Pulipati Sudha, S.v. School of Computer Science and Engineering VIT-AP University Andhra Pradesh India

ISBN: (纸本)9798350347579

Image segmentation is a significant issue in computer vision and image processing, and many segmentation techniques are found in the literature. It has several applications, including robotic perception, augmented reality, medical image analysis, scene interpretation, photo compression and video surveillance. With Deep Learning (DL) becoming increasingly popular, new techniques for segmenting images with DL models have been created. The research discusses a variety of novel approaches in instance and semantic segmentation in this recently published literature review, which includes pyramid and multi-scale-based methods, convolutional pixel-labeling networks, generative and visual attention models and encoder-decoder architectures in competitive settings. This work examines some of the most well-known data sets, compares several DL-based segmentation models' advantages, disadvantages, and connections, assesses the outcomes, and discusses potential future research directions. © 2023 IEEE.

关键词： Computer vision

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Eliciting In-Context Learning in vision-Language Models for videos Through Curated data Distributional Properties

Eliciting In-Context Learning in Vision-Language Models for ...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yu, Keunwoo Peter Zhang, Zheyuan Hu, Fengyuan Storks, Shane Chai, Joyce Computer Science and Engineering Division University of Michigan Ann ArborMI United States

ISBN: (纸本)9798891761643

A major reason behind the recent success of large language models (LLMs) is their in-context learning capability, which makes it possible to rapidly adapt them to downstream text-based tasks by prompting them with a small number of relevant *** large vision-language models (vLMs) have recently been developed for tasks requiring both text and images, they largely lack in-context learning over visual information, especially in understanding and generating text about *** this work, we implement Emergent In-context Learning on videos (EILev), a novel training paradigm that induces in-context learning over video and text by capturing key properties of pre-training data found by prior work to be essential for in-context learning in *** our experiments, we show that EILev-trained models outperform other off-the-shelf vLMs in few-shot video narration for novel, rare ***, we demonstrate that these key properties of bursty distributions, skewed marginal distributions, and dynamic meaning each contribute to varying degrees to vLMs' in-context learning capability in narrating procedural *** results, analysis, and EILev-trained models yield numerous insights about the emergence of in-context learning over video and text, creating a foundation for future work to optimize and scale vLMs for open-domain video understanding and reasoning. © 2024 Association for Computational Linguistics.

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Comparative analysis of ISO GPS Knowledge and Usage in the Italian Market 18

Comparative Analysis of ISO GPS Knowledge and Usage in the I...

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18th CIRP conference on Computer Aided Tolerancing, CAT 2024

作者： Maltauro, Mattia Meneghello, Roberto Concheri, Gianmaria Department of Management and Engineering University of Padova Stradella San Nicola 3 Vicenza36100 Italy Department of Civil Environmental and Architectural Engineering Laboratory of Design Methods and Tools in Industrial Engineering University of Padova Via Venezia 1 Padova35131 Italy

This research consists in a detailed analysis of the ISO Geometrical Product Specifications (GPS) system's knowledge and usage in Italy. data was collected by means of a questionnaire, from a wide range of industrial sectors and academic institutions. The dataset encompasses 143 responses, forming the basis for an in-depth examination of ISO GPS system implementation in Italy. This analysis involves three levels of comparison: (1) an examination of knowledge level and typical usage between Beginners and Experienced individuals, (2) an investigation into ISO GPS adoption patterns in Industry and Academia, with and without specific ISO GPS training, and (3) an exploration of differences among professionals who are responsible for conveying geometric specifications and those in charge of interpreting and applying them. The findings reveal significant insights into ISO GPS implementation in both academic and industrial domains. They highlight the need for the improvement of ISO GPS education offer and the development of more effective utilization practices, with potential implications for future ISO GPS standards development strategies. © 2024 The Authors. Published by Elsevier B.v.

关键词： Geometric Specifications Management Geometrical Product Specifications ISO GPS Training

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The Construction of a visual Quantum Interactive System for Fuzzy Evaluation of Financial Budget Performance based on Big data 6

The Construction of a Visual Quantum Interactive System for ...

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6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Wei, Yong Yunnan Grain Industry Group Co. LTD Kunming650000 China

ISBN: (纸本)9781665410281

This paper proposes a new fuzzy evaluation system of financial budget performance based on big data. Based on the characteristics of budget security and efficiency, this paper constructs a four-layer indicator system: target layer, category layer, indicator layer and sub-indicator layer. In the weight analysis and index calculation, a new comprehensive fuzzy evaluation operator including a master-slave multiplication operator and a 0-1 multiplication operator is proposed. Combined with the fuzzy analytic hierarchy process, a multi-operator analytic hierarchy fuzzy evaluation model and corresponding solution method are established. Finally, combined with the visualization of quantum interaction, an exploratory conversation search model based on quantum interference is proposed. In the non-classical correlation judgment theory exploration part, it is pointed out that users have a certain quantum interaction system in the process of correlation judgment. © 2022 IEEE.

关键词： Big data

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