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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15522 条记录，以下是151-160 订阅

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PCB Test-Pad Detection and Clustering using PySpark and Image Processing

PCB Test-Pad Detection and Clustering using PySpark and Imag...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee Conference on

作者： Shikha Gupta Bhavesh Panchal Uzair Afnan Shaikh Arya Sahane Kasturi Pednekar Akansha Wankhade Department of Computer Engineering Rajiv Gandhi Institute of Technology Mumbai India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The era of Big data demands effective and efficient processing and clustering of extensive image data. This paper presents a Spark-based approach to analyze, group, and visualize spatial and color information within image datasets. The method begins with initializing a Spark session, then loading and inspecting the dataset containing the required spatial coordinates (X, Y) and the corresponding RGB color values. We derive statistical insights into the color distributions and perform color-based clustering to identify different color clusters, focusing on isolating greyscale clusters. Then, spatial clustering algorithms are used to derive the patterns in spatial data distribution, facilitating comprehension of color correlations within the spatial settings. visualizations are created to aid with the understanding and interpretation of clustering results. This methodology presents an effective, scalable solution for analyzing large-scale spatial and colorbased data.

关键词： Technological innovation Image color analysis Robot kinematics data visualization Clustering algorithms Surface mount technology Big data Spatial databases Maintenance Sparks

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Revisiting Reliability in large-Scale Machine Learning Research Clusters

Revisiting Reliability in Large-Scale Machine Learning Resea...

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ieee symposium on High-Performance Computer Architecture

作者： Apostolos Kokolis Michael Kuchnik John Hoffman Adithya Kumar Parth Malani Faye Ma Zachary DeVito Shubho Sengupta Kalyan Saladi Carole-Jean Wu FAIR at Meta

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact of job failures across different scales remains unclear. This paper presents a view of managing two large, multi-tenant ML clusters, providing quantitative analysis, operational experience, and our own perspective in understanding and addressing reliability concerns at scale. Our analysis reveals that while large jobs are most vulnerable to failures, smaller jobs make up the majority of jobs in the clusters and should be incorporated into optimization objectives. We identify key workload properties, compare them across clusters, and demonstrate essential reliability requirements for pushing the boundaries of ML training at *** hereby introduce a taxonomy of failures and key reliability metrics, analyze 11 months of data from two state-of-the-art ML environments with 4 million jobs and over 150 million A100 GPU hours. Building on our data, we fit a failure model to project Mean Time to Failure for various GPU scales. We further propose a method to estimate a related metric, Effective Training Time Ratio, as a function of job parameters, and we use this model to gauge the efficacy of potential software mitigations at scale. Our work provides valuable insights and future research directions for improving the reliability of AI supercomputer clusters, emphasizing the need for flexible, workload-agnostic, and reliability-aware infrastructure, system software, and algorithms.

关键词： Training Statistical analysis Taxonomy Software algorithms Graphics processing units Machine learning Supercomputers Software reliability System software Resilience

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Discriminative analysis of Autistic Tendencies at 18 Months of Age Using Eye Gaze Characteristics in 4-, 10-, and 18-month-old Infants

Discriminative Analysis of Autistic Tendencies at 18 Months ...

引用

ieee/SICE International symposium on System Integration

作者： Rena Ueda Hirokazu Doi Akira Furui Koji Shimatani Hideaki Hayashi Toshio Tsuji Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Graduate School of Biomedical Sciences Nagasaki University Nagasaki Japan Faculty of Health and Welfare Prefectural University of Hiroshima Hiroshima Japan Institute for Datability Science Osaka University Osaka Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Anomalies in motor function and social communication skills constitute early signs of autism spectrum disorder (ASD). In recent years, researchers have paid attention to the possibility of the quantitatively evaluating infants’ social communication skills based on their eye movement. In this paper, we attempt to evaluate ASD risk using infants’ eye movement data by examining the relationship between eye movement patterns at 4, 10, and 18 months of age and ASD risk as assessed by caregivers using a checklist at 18 months of age. A total of 18 eye movement indices were calculated from eye movement data measured by an optical eye tracker. The results revealed an association between ASD risk and eye movement indices. The indices included those related to a preference for social information at 4 months of age, those related to visual exploration of images at 10 months of age, and those indices related to both preference and visual exploration at 18 months of age. These results indicate that eye movement analysis may potentially serve as a means for early screening of ASD risk.

关键词： visualization Accuracy Tracking Input variables System integration Variable speed drives Optical variables measurement Feature extraction Optical imaging Motors

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Behavior Monitoring System Leveraging Human Pose Estimation

Behavior Monitoring System Leveraging Human Pose Estimation

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ieee/SICE International symposium on System Integration

作者： Ryo Mitoma Takayuki Mukaeda Keisuke Shima Haruto Kai Masayuki Suzuki Keiji Kato Graduate School of Environment and Information Sciences Yokohama National University Kanagawa Japan Kanagawa Institute of Industrial Science and Technology (KISTEC) Kanagawa Japan College of Education Yokohama National University Kanagawa Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

In recent years, human-centric computer vision technologies, such as human pose estimation and action recognition, have garnered significant attention. This study, therefore, proposes the use of a classroom behavior monitoring system that integrates off-the-shelf human pose estimators to compute behavioral features from the video data. The extracted features include continuous values (body movements, head angles, etc.) and discrete features (timing of hand-raising), which were not leveraged in prior studies. The system can operate in real-time on consumer-grade GPUs. This makes it useful for real-time feedback during classes and post-class analysis of video recordings. The proposed system was experimentally validated using a motion capture dataset featuring scenes with occlusions. We demonstrated its accuracy and real-time performance, along with the challenges of capturing full-body poses in complex environments. Additionally, we applied this system to a novel learning analytics task: estimating cognitive and non-cognitive abilities from videos using real-world data. As part of our analysis, we processed a video from an elementary school classroom with our system and linked the computed metrics to self-reported skills. These experiments demonstrated promising results, highlighting the potential of evaluating student skills solely based on visual cues.

关键词： visualization Accuracy Pose estimation Streaming media Feature extraction Real-time systems Behavioral sciences Timing Monitoring Video recording

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Analyzing the Cognitive Impact of Trauma from a Metaphorical Perspective: A Case Study on the Attempted Assassination of Donald Trump

Analyzing the Cognitive Impact of Trauma from a Metaphorical...

引用

Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe), ieee symposium on

作者： Meihui Jia Rui Mao Yunhe Xie Shen Ren Erik Cambria Nanyang Technological University Singapore Harbin Institute of Technology China Continental Automotive Singapore

ISBN: (数字)9798331508418

ISBN: (纸本)9798331508425

Research indicates that trauma can significantly affect human cognition. While previous studies have extensively examined the psychological consequences of trauma, the majority have focused on the long-term effects, particularly in relation to post-traumatic stress disorder, due to challenges in data collection. This paper seeks to fill this gap by introducing an interdisciplinary approach that combines cognitive linguistics and trauma theory to investigate the immediate cognitive effects of trauma, specifically through the analysis of metaphorical language. We explore the cognitive impact of trauma by analyzing Donald Trump's presidential campaign speeches delivered before and after the 2024 attempted assassination. Using MetaPro, a computational tool designed for metaphor analysis, we identify cognitive patterns from a large corpus of speeches. Our findings suggest that the assassination had a certain effect on Mr. Trump's cognitive processing, as reflected in changes in his metaphorical concept mappings. This traumatic event may prompt Mr. Trump to psychologically retreat into the cognitive patterns in 2016.

关键词： Social networking (online) Psychology Human factors Linguistics data collection Stability analysis Natural language processing Cognition Computational intelligence

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An analysis of Unsafe Responses with Magic Expressions across large Language Models

An Analysis of Unsafe Responses with Magic Expressions acros...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Eojin Joo Chae-Gyun Lim Jeongyun Han Ho-Jin Choi School of Computing KAIST Daejeon South Korea Graduate School of Education University of Seoul Seoul South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

With advancements in AI models’ ability to understand input, the risk of generating harmful outputs in response to malicious prompts has grown. This has led to increased awareness among researchers regarding AI safety, resulting in diverse ongoing investigations. However, existing studies have primarily relied on coarse-grained datasets to define risk factors, with limited availability of high-quality datasets that address fine-grained risks. To address this gap, we developed a high-quality, Korean AI safety evaluation dataset with overlooked risk factors in current LLMs. This dataset was curated with the involvement of human annotators to ensure its quality and relevance. We found that adding simple modifications, such as magic expressions, increases the likelihood of bypassing model guardrails. The analysis further revealed that this effect varies across different categories of risk factors. Additionally, we evaluated the harmfulness of LLM-generated outputs by measuring the frequency of risk-related keywords in the responses. This approach used prompt-based evaluation methods to quantify the degree of risk, providing a structured framework for assessing the potential dangers of model outputs.

关键词： large language models Computational modeling Refining data visualization Big data data models Safety Frequency measurement data mining Immune system

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Trends and Challenges of Multimodal Solutions for Text and Image Context Extraction

Trends and Challenges of Multimodal Solutions for Text and I...

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Electrical, Electronic and Information Sciences (eStream), Open Conference of

作者： Tautvydas Kvietkauskas Pavel Stefanovič Faculty of Fundamental Science Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798331598730

ISBN: (纸本)9798331598747

The number of published images, texts, and other information rapidly increases in today’s digital space. The ability to simultaneously process textual and visual information helps to interpret content more accurately. It enables the application of artificial intelligence in complex situations, such as contextual analysis and real-time monitoring of social networks. Today’s large Language Models (LLMs) are based on text data, Object Recognition (OR) on visual data, and Vision-language (VL) models use text and visual data. The combinations of these different models can be used to create multimodal solutions to solve various context-extraction tasks. In such a way, this requires images and texts, which are input to the multimodal models. This paper systematically reviews the latest research on existing LLM, OR and VL models. The scientific articles were analysed on the Web of Science and Google Scholar databases. All the papers are free to access and date from 2019 to 2024. The main objective was to summarise the results of scientific research, tasks, and methods used in text, image, and image-text data analysis. The types of datasets and the language of the texts used in the research were also reviewed. Additionally, the results are useful for highlighting trends and challenges in the context extraction field that can be useful to other researchers.

关键词： visualization Analytical models Systematics Reviews large language models Market research data models Object recognition data mining Context modeling

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Texlivernet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation

Texlivernet: Leveraging Medical Knowledge and Spatial-Freque...

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ieee International symposium on Biomedical Imaging

作者： Xiaoyan Jiang Zhi Zhou Hailing Wang Guozhong Wang Zhijun Fang School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China School of Computer Science and Technology Donghua University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. How-ever, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To address these limitations, we developed datasets with lesion-specific text annotations for liver tumors and introduced the TexLiverNet model. TexLiverNet employs an agent-based cross-attention module that integrates text features efficiently with visual features, significantly reducing computational costs. Additionally, enhanced spatial and adaptive frequency domain perception is proposed to precisely delineate lesion boundaries, reduce background interference, and recover fine details in small lesions. Comprehensive evaluations on public and private datasets demonstrate that TexLiverNet achieves superior performance compared to current state-of-the-art methods. Code is available at https://***/sky-visionX/TexLiverNet.

关键词： Knowledge engineering Image segmentation visualization Annotations Frequency-domain analysis Merging Liver Feature extraction Lesions Medical diagnostic imaging

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Visual Prompting and Adaptation of Vision Language Models for Tumor Classification in Breast Ultrasound

Visual Prompting and Adaptation of Vision Language Models fo...

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ieee International symposium on Biomedical Imaging

作者： Ashirbani Saha Dibyendu Mukherjee Department of Oncology McMaster University Hamilton ON Canada CentRE for dAta science and digiTal hEalth (CREATE) Hamilton Health Sciences Hamilton ON Canada Independent Researcher Hamilton ON Canada

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Vision language models (VLMs) for medical image analysis constitute an active area of research. In this work, we systematically investigated the performance of four pre-trained VLMs, consisting of one non-medical (CLIP) and three recent medical VLMs (BiomedCLIP, MedCLIP, and PubMedCLIP) for tumor classification in six breast ultrasound datasets. The VLMs' performance was investigated under (a) zero-shot and few-shot test conditions through adaptation, (b) separate label sets including different text prompts, and (c) different prompting techniques involving text and visual markers. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used as the evaluation measures. All models exhibited susceptibility to the description of the text prompts in zero-shot mode but improved with visual markers and adaptation. Our results established the utility of visual prompting and adaptation in improving the performance and reducing text prompt sensitivity. PubMedCLIP emerged as the most stable VLM for tumor classification in breast ultrasound.

关键词： visualization Adaptation models Ultrasonic imaging Sensitivity Ultrasonic variables measurement Biological system modeling Breast Prompt engineering Tumors Biomedical imaging

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Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks

Correlation of Correlation Networks: High-Order Interactions...

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ieee International symposium on Biomedical Imaging

作者： Qiang Li Jingyu Liu Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University Atlanta GA United States

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend beyond pairwise interactions and play a crucial role in brain network dynamics. These interactions often reveal intricate relationships among multiple brain networks, significantly influencing cognitive processes. In this study, we explored the correlation of correlation networks and topological network analysis with resting-state fMRI to gain deeper insights into these higher-order interactions and their impact on the topology of brain networks, ultimately enhancing our under-standing of brain function. We observed that the correlation of correlation networks highlighted network connections while preserving the topological structure of correlation networks. Our findings suggested that the correlation of correlation networks surpassed traditional correlation networks, showcasing considerable potential for applications in various areas of network science. Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions. These high-order hubs played a crucial role in information integration within the human brain.

关键词： visualization Correlation Pairwise error probability Network topology Cognitive processes Network analyzers Functional magnetic resonance imaging Topology Behavioral sciences Biomedical imaging

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