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Rethinking Uncertainly Missing and Ambiguous visual Modality in Multi-Modal Entity Alignment 22nd

Rethinking Uncertainly Missing and Ambiguous Visual Modality...

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22nd International Semantic Web conference (ISWC)

作者： Chen, Zhuo Guo, Lingbing Fang, Yin Zhang, Yichi Chen, Jiaoyan Pan, Jeff Z. Li, Yangning Chen, Huajun Zhang, Wen Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China Donghai Lab Zhoushan Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Univ Manchester Manchester Lancs England Univ Oxford Oxford England Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9783031472398;9783031472404

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information. However, existing MMEA approaches primarily concentrate on the fusion paradigm of multi-modal entity features, while neglecting the challenges presented by the pervasive phenomenon of missing and intrinsic ambiguity of visual images. In this paper, we present a further analysis of visual modality incompleteness, benchmarking latest MMEA models on our proposed dataset MMEA-UMvM, where the types of alignment KGs covering bilingual and monolingual, with standard (non-iterative) and iterative training paradigms to evaluate the model performance. Our research indicates that, in the face of modality incompleteness, models succumb to overfitting the modality noise, and exhibit performance oscillations or declines at high rates of missing modality. This proves that the inclusion of additional multi-modal data can sometimes adversely affect EA. To address these challenges, we introduce UMAEA, a robust multimodal entity alignment approach designed to tackle uncertainly missing and ambiguous visual modalities. It consistently achieves SOTA performance across all 97 benchmark splits, significantly surpassing existing baselines with limited parameters and time consumption, while effectively alleviating the identified limitations of other models. Our code and benchmark data are available at https://***/zjukg/UMAEA.

关键词： Entity Alignment Knowledge Graph Multi-modal Learning Uncertainly Missing Modality

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Autism Spectrum Disorder Identification from visual exploration of Images 1

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22nd International conference on Image analysis and Processing (ICIAP)

作者： Bolpagni, Marco Setti, Francesco Univ Verona Dept Comp Sci Verona Italy Univ Verona Dept Engn Innovat Med Verona Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Autism Spectrum Disorder (ASD) affects 1 in 77 children in Italy, but the diagnostic process is slow and costly. As autistic individuals exhibit different gaze patterns from healthy controls in visual exploration of images and semantic interpretation, these are promising biomarkers to exploit in diagnosis. This study aims at developing a model to assist in the diagnosis of ASD using gaze data when static images are presented to the subjects. We first propose a set of features, each one motivated by psychological studies and findings. Then we apply a feature selection mechanism based on Boruta algorithm with SHAP values. Finally we use CatBoost to perform binary classification, and a strategy to optimize model hyperparameters using a multivariate Tree Parzen Estimator. We validated our model on the popular Saliency4ASD dataset, outperforming state of the art models tested with the same protocol by more than 3% in accuracy. We also provide an in-depth analysis of the feature importance and we show how these results are in line with the psychological literature.

关键词： Autism Spectrum Disorder Eye tracking CatBoost

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A Survey Convolutional Neural Networks Using SPSS Statistics 1

引用

7th International conference on Intelligent Computing and Optimization, ICO 2023

作者： Sasikala, T.S. Roy, E. Rushit Gnana Akshera, R.v. Sivakami, S. Ramachandran, M. Jaya Engineering College Tamil Nadu Thiruninravur India REST Labs Tamil Nadu Krishnagiri India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Convolutional Neural Networks (CNNs) are a deep learning architecture that has revolutionized computer vision tasks. Their biological inspiration traces back to the seminal work by Hubel and Wiesel in 1959, who unveiled the specialized cells in the visual cortex responsible for light differentiation. This led to Fukushima’s creation of the Neocognitron in 1980, a precursor to modern CNNs. Current CNNs excel in various domains, notably radiology, by utilizing layers such as convolutional, pooling, and fully connected ones. These layers facilitate the adaptive learning of spatial feature hierarchies through backpropagation. In this study, we employ IBM’s SPSS Statistics for robust data analysis, multivariate testing, and advanced predictive modeling to assess CNN architectures. SPSS excels in data management and outcome production, adapting to various data formats. The evaluation metrics include the Loss function, Regularization, Optimization, Fast processing, and practical Applications. The reliability of the CNN model was gauged using Cronbach’s Alpha, yielding a value of .442, indicating a 44% reliability rate. Compared to the literature threshold of 39%, our model is deemed suitable for further analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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StyleGAN in Paper Fiber analysis: Evaluating Image Generation with Consumer-Grade Digital Camera 13

StyleGAN in Paper Fiber Analysis: Evaluating Image Generatio...

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13th IEEE Global conference on Consumer Electronic, GCCE 2024

作者： Ashino, Kosuke Zhou, Yexin Ohyanagi, Yoichi Shibazaki, Koji Kamiya, Naoki Aichi Prefectural University Graduate School of Information Science and Technology Department of System Science Aichi Nagakute Japan Seirei Women's Junior College Life and Culture Department Akita Akita Japan Aichi University of the Arts Faculty of Fine Arts Aichi Nagakute Japan Aichi Prefectural University Graduate School of Information Science and Technology Department of Information Systems Aichi Nagakute Japan

ISBN: (纸本)9798350355079

This study investigates StyleGAN's potential to generate digital camera images of paper fibers (Kozo, Mitsumata, and Gampi) for augmenting deep convolutional neural network (DCNN) training data. The generated images achieved Fréchet Inception Distance (FID) scores of 24.39, 22.85, and 23.07 for Kozo, Mitsumata, and Gampi, respectively, with visual inspection confirming fiber-specific structural features. To evaluate data augmentation effectiveness, the training dataset was doubled with generated images. Classification accuracy was compared between models trained on the augmented dataset and those trained on the original dataset using EfficientNet-B0 and InceptionResNet-v2. Results showed a slight decrease in accuracy (0.30% and 0.26%, respectively) when including generated images in the training data. However, generated images of each fiber were classified with higher accuracy than the test set of real images by a classifier trained on original images, suggesting that StyleGAN3R can generate high-quality paper fiber images. While the current augmentation approach did not improve classification performance, the generated images have fiber characteristics recognizable by DCNNs. In conclusion, it was demonstrated that StyleGAN3R can generate consumer-grade digital camera images that capture the distinct characteristics of each fiber. This approach may have the potential to overcome the limitations of data-driven paper fiber analysis in the future. © 2024 IEEE.

关键词： Convolutional neural networks

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data visualization in Transforming Raw data into Compelling visual Narratives

Data Visualization in Transforming Raw Data into Compelling ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International conference on

作者： Prithu Sarkar Yadavalli Devi Priya Pravina B. Patel Prasanta Chatterjee Biswas Sreenivasulu Arigela Sravanthi Sallaram Amity School of Communication Amity University Kolkata Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Vishakapatanam Andhra Pradesh Department of Computer Science Ganpat University Mehsana Gujarat India CDOE Parul University Vadodara Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering St.Martin's Engineering College

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the virtualization of data, storytelling, and data visualization’s Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling.

关键词： visualization Quantum computing Instruments data visualization Market research virtualization Business

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ICEvis: Interactive Clustering exploration for tumor sub-region analysis in multiparametric cancer imaging 22

ICEVis: Interactive Clustering Exploration for tumor sub-reg...

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15th International Symposium on visual Information Communication and Interaction, vINCI2022

作者： Mörth, Eric Eichner, Tanja Haldorsen, Ingfrid Bruckner, Stefan Smit, Noeska N. Dept. of Informatics Mmiv UiB/HUS Norway Faculty of Informatics / Mmiv Norway Tu Vienna UiB Austria Mmiv Dept. of Clinical Medicine Hus / UiB Norway

ISBN: (纸本)9781450398077

Tumor tissue characteristics derived from imaging data are gaining importance in clinical research. Tumor sub-regions may play a critical role in defining tumor types and may hold essential information about tumor aggressiveness. Depending on the tumor's location within the body, such sub-regions can be easily identified and determined by physiology, but these sub-regions are not readily visible to others. Regions within a tumor are currently explored by comparing the image sequences and analyzing the tissue heterogeneity present. To improve the exploration of such tumor sub-regions, we propose a visual analytics tool called ICEvis. ICEvis supports the identification of tumor sub-regions and corresponding features combined with cluster visualizations highlighting cluster validity. It is often difficult to estimate the optimal number of clusters;we provide rich facilities to support this task, incorporating various statistical measures and interactive exploration of the results. We evaluated our tool with three clinical researchers to show the potential of our approach. © 2022 Owner/Author.

关键词： Tumors

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SmartMLvs: LLM-enabled Multiple Linked views Generation for Interactive visualization

SmartMLVs: LLM-enabled Multiple Linked Views Generation for ...

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Pacific (formerly Asia-Pacific APvIS) visualization Symposium

作者： Tian Qiu Fen Wang Shaohua Huang Meng Guo Yuheng Zhao Jincheng Li Siming Chen Fudan University Henan Institute of Advanced Technology Zhengzhou University Tongji University Beijing Normal University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Automating the generation of multiple linked view visualization is imperative for improving data analysis efficiency. Large Language Models (LLMs) offer substantial potential for enabling this automation, yet they encounter notable challenges in understanding complex queries and producing relevant interactive visualizations. To tackle these challenges, we introduce SmartMLvs, a system designed to harness LLMs for automatic interactive multiple linked views generation with human guidance. First, we analyze the challenges LLMs may encounter when designing visualizations in place of experts. To address these challenges, we gather the essential domain knowledge required for visual analysis process and propose a framework consisting of decomposition, visualization and linking. The decomposition process applies a human-AI interaction method to clarify user requirements. For each decomposed question, the generation process handles chart type selection, data processing and visualization generation. Finally, the linking process adds interactions for views and provides users with data insights. For better human-AI collaboration, we design a system for data exploration. Our system applies the entire framework, supporting users’ interactive exploration with multiple linked views, and can iteratively generate linked views based on user feedback. We examine the effectiveness of our method through usage scenarios and evaluations.

关键词： Knowledge engineering data analysis Automation Large language models visual analytics data visualization Collaboration Prompt engineering

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Preliminary study on multiphysics transient calculations for pebble bed reactors using high-fidelity depletion data

Preliminary study on multiphysics transient calculations for...

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2024 International conference on Physics of Reactors, PHYSOR 2024

作者： Siaraferas, Tatiana Jantzen, Ludovic v. Robert, Yves Alseaidan, Faisal A. Fratoni, Massimiliano University of California Berkeley Department of Nuclear Engineering BerkeleyCA United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9780894487972

This paper introduces an innovative methodology for multiphysics transient analysis in Pebble Bed Reactors (PBRs) using the results of the Hyper-Fidelity Depletion (HxF) as input ***, incorporating Monte Carlo neutron transport and realistic motion, provides detailed insights into pebble-wise flux, power, and temperature *** HxF depletion data, the study utilizes the MOOSE framework, employing Griffin for neutronic modeling and Pronghorn for thermal-hydraulic *** this work, an equilibrium state of a small molten salt PBR obtained from HxF serves as a test case, and a 3D model is *** between HxF and Griffin steady-state calculations reveal promising correlations in power distribution with a difference of 11.65%.More significant discrepancies are presented in the thermal and fast flux, highlighting areas for refinement and emphasizing the need for further *** research sets the stage for future advancements, including the integration of Super Homogenization (SPH)-corrected cross-sections, optimal Griffin solver search, and exploration of varying the number and size of the radial and axial *** study lays the groundwork for comprehensive transient studies, including the coupling of Griffin with Pronghorn, for a holistic understanding of PBR *** temperature and power profiles will be obtained to evaluate the most challenging conditions for the *** ultimate goal is to use this insightful data to perform accurate and detailed TRISO fuel performance analyses during normal and off-normal operating *** pushing the boundaries of traditional analyses, this work contributes to the development and licensing of PBRs, providing a higher level of information on the operating conditions. © 2024 AMERICAN NUCLEAR SOCIETY. All rights reserved.

关键词： Transient analysis

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Understanding the Effects of visualizing Missing values on visual data exploration

Understanding the Effects of Visualizing Missing Values on V...

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IEEE visualization conference (IEEE vIS)

作者： Song, Hayeong Fu, Yu Saket, Bahador Stasko, John Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781665433358

When performing data analysis, people often confront data sets containing missing values. We conducted an empirical study to understand the effect of visualizing those missing values on participants' decision-making processes while performing a visual data exploration task. More specifically, our study participants purchased a hypothetical portfolio of stocks based on a data set where some stocks had missing values for attributes such as PE ratio, beta, and EPS. The experiment used scatterplots to communicate the stock data. For one group of participants, stocks with missing values simply were not shown, while the second group saw such stocks depicted with estimated values as points with error bars. We measured participants' cognitive load involved in decision-making with data with missing values. Our results indicate that their decision-making workflow was different across two conditions.

关键词： Human-centered computing visualization Empiriacal evaluation Missing data

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Enhancing DNS and IoT Firewall Rule-Based Algorithms and Techniques 1st

Enhancing DNS and IoT Firewall Rule-Based Algorithms and Tec...

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1st International conference on data Engineering and Machine Intelligence, ICDEMI 2023

作者： Senthil Kumar, P. Asha, v. Kanaga Suba Raja, S. Samuel Peter James, I. Shadan Women’s College of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirappalli India Shadan Women’s College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819776153

In the realm of network security, next-generation firewalls serve as the primary line of defense against potential threats, ensuring the integrity and confidentiality of data transmission. The research initiative commences by establishing the fundamental concepts of firewalls, DNS, and IoT firewalls as indispensable components in the protective arsenal of network security. The exploration navigates the transition toward the next generation of DNS and IoT firewalls, illuminating their heightened capabilities in countering domain-specific attacks. Their indispensable role in shielding IoT devices and networks from threats takes center stage in this exploration. When evaluating effectiveness, the F1 score functions as a statistical measure for assessing classification models, and the F1 score’s significance within the firewall ecosystem is vividly contextualized. As the inquiry unfurls into the realm of firewall technologies, their burgeoning importance and far-reaching implications within the realms of cyber security and network defense come into sharper focus. This study assesses the efficacy of two firewall methods, namely DNS Firewall and IoT Firewall. In the case of the DNS Firewall, the analysis yielded 93 True Positives (TP), 7 False Positives (FP), 11 False Negatives (FN), and an F1 Score of 0.91. Conversely, the IoT Firewall Technique exhibited 88 True Positives (TP), 15 False Positives (FP), and 4 False Negatives (FN), along with an associated F1 Score of 0.88. These findings shed light on the performance and capabilities of these firewall techniques in bolstering network security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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