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Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), ieee symposium on

作者： Larissa De Andrade Silva Juan G. Colonna Bernardo B. Gatto João Marcelo Protázio ICOMP - Institute of Computing Federal University of Amazonas Brazil MTI - Ltd Laboratory of Mathematical-Statistical Modeling (LAMEM) ICEN - UFPA Brazil

ISBN: (数字)9798331520151

ISBN: (纸本)9798331520168

This paper presents an automated explainable AI (XAI) method for characterizing variations in the calls of the House Wren (Troglodytes aedon musculus) under the influence of anthropogenic noise. We propose an End-to-End (E2E) Deep Learning model utilizing the pre-trained Biophony CNN architecture alongside Kapre, a Keras Audio Preprocessor. The outputs are interpreted through SHAP (SHapley Additive exPlanations) values, enabling the identification of fine-grained bioacoustic changes in vocalizations that differ between urban and rural environments. Our contributions include (i) the application of SHAP to bioacoustics data to interpret the impact of environmental noise on Wren’s vocalizations and (ii) novel visualizations using SHAP values, including the SHAP Spectrogram, SHAP Spectrum, and SHAP Time. These methods provide a transparent and detailed understanding of how urban noise affects bird vocalizations, bridging gaps in traditional statistical and signal processing approaches and offering a foundation for future studies in computational bioacoustics.

关键词： Deep learning Time-frequency analysis Explainable AI Biological system modeling Computational modeling Noise data visualization Predictive models Bioinformatics Spectrogram

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Identifying Representation Bias in large Language Models Used in Financial Sentiment analysis

Identifying Representation Bias in Large Language Models Use...

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Computational Intelligence for Financial Engineering and Economics (CiFer), ieee symposium on

作者： Alpay Sabuncuoglu Carsten Maple The Alan Turing Insitute University of Warwick The Alan Turing Institute

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

Financial sentiment analysis is the task of evaluating and quantifying the emotions and opinions expressed in financial news, reports, or social media to help investors and institutions make informed decisions. Financial institutions have been actively exploring the use of large language models (LLMs) to analyse market sentiment signals for a more nuanced understanding of a broader context. However, issues such as the scale of training data, model complexity, and the potential for human oversight can introduce or even amplify bias in these systems. Representation bias is a common challenge for LLMs as training data fail to properly represent the target groups, hence causes harmful bias in general-purpose use. Therefore, replacing current solutions with LLMs in financial organisations requires a robust evaluation methodology to ensure fairness. This paper investigates a three-level bias evaluation approach that specifically focuses on representation bias and presents a baseline evaluation of the FinBERT model. Step 1 uses a synthetic dataset that explicitly reveals sources of bias, structured as probability- and embedding-based evaluation recipes. Step 2 evaluates the model against data released by another country (e.g. Indian News dataset) to assess its performance in relation to more implicit biases. Step 3 examines individual problematic samples using token-based interpretability methods (e.g. integrated gradients). This paper presents the application of this structured bias evaluation process and its results on the FinBERT model. The evaluation code and dataset are available on GitHub (https://***/asabuncuoglu13/faid-test-financial-sentiment-analysis).

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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...

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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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Using Thermal Reflectance analysis with the Microsanj for Open Defect Localization

Using Thermal Reflectance Analysis with the Microsanj for Op...

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Annual International symposium on Reliability Physics

作者： Curtis Jacob Ritter Dustin Kendig Sean Duffy Daniel J. D. Sullivan EAG Laboratories Sunnyvale CA USA Microsanj LLC Fremont CA USA

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The Microsanj Thermal Reflectance (TR) Imaging method [3] for locating open circuits enables new Failure analysis (FA) paths for direct examination of defects. This method increases the probability of finding the root cause for open lines in flexible package traces. The method uses X-ray, C-SAM (Scanning Acoustic Microscopy) and Thermal imaging as non-destructive methods to locate open faults. This process was used to conduct electrical tests and destructive analysis to examine a failing flexible ribbon cable. The result thermal imaging data showed the location of an open fault in the cable signal trace without causing any disruptive damage to the sample under evaluation. Later laser exposure was used to remove the insulation material to confirm the location of the open fault in the sample under evaluation by optical imaging and electrical check. This paper demonstrates that Microsanj TR Imaging can provide clear visual results of open faults in metal traces found in flexible ribbon cables and flex circuits down to a resolution of several microns for failure analysis.

关键词： Reflectivity visualization Flexible printed circuits Failure analysis Thermoreflectance Electronic packaging thermal management Optical imaging Thermal analysis Circuit faults Time-domain analysis

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Proceedings - ieee 2001 symposium on Parallel and large-data visualization and Graphics

Proceedings - IEEE 2001 Symposium on Parallel and Large-Data...

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ieee symposium on Parallel and large-data visualization and Graphics

ISBN: (纸本)0780372239

The proceedings contain 17 papers. The topics discussed include: visualization challenges for a new cyberpharmaceutical computing paradigm;Delaunay based shape reconstruction from large data;parallel point reprojection;visualizing ocean currents with color and dithering;parallel lagrangian visualization applied to natural convective flows;real-time out-of-core visualization of particle traces;a unified infrastructure for parallel out-of-core isosurface and volume rendering of unstructured grids;parallel view-dependent isosurface extraction using multi-pass occlusion culling;parallel rendering with k-way replication;sort-last parallel rendering for viewing extremely large data sets on tile displays;multiresolution view-dependent splat based volume rendering of large irregular data;parallelizing a high accuracy hardware-assisted volume renderer for meshes with arbitrary polyhedra;and scalable interactive volume rendering using off-the-shelf components.

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Unsupervised Normative Learning for Quality Assessment on Diffusion MRI

Unsupervised Normative Learning for Quality Assessment on Di...

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ieee International symposium on Biomedical Imaging

作者： Jiahao Yu Tenglong Wang Yifei He Yiang Pan Jianzhong He Ye Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quality assessment of diffusion MRI (dMRI) aims to ensure that the presence of image artifacts does not affect the results of subsequent image analysis. Recently, a variety of deep learning methods have been proposed to enhance the quality and utility of these acquisitions. Still, they require a large amount of training data with artifact labeling. In this work, we developed an Unsupervised Quality Assessment tool (UNL-QA) for dMRI data that can handle a variety of artifacts such as ghosting, spike, noise and swap. Our method uses a vector quantized variational autoencoder to train typical patterns on a standard dMRI dataset, achieving 99.732% accuracy in detecting artifacts and standard data and 95% sensitivity in detecting artifacts on the training dataset. Performance across datasets is also excellent. It has also been demonstrated to be replicable on other datasets with different acquisition parameters. Without labeled artifact images necessary, UNL-QA allows fast and paves the way for efficient and effective artifact detection in large datasets. The code is available at https://***/yjh200319/***.

关键词： Training Sensitivity Magnetic resonance imaging Autoencoders Noise Training data Vectors Quality assessment Standards Unsupervised learning

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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 ...

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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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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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VIGMA: An Open-Access Framework for Visual Gait and Motion Analytics

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ieee Transactions on visualization and Computer Graphics 2025年 PP卷 PP页

作者： Omar, Kazi Shahrukh Wang, Shuaijie Kungumaraju, Ridhuparan Bhatt, Tanvi Miranda, Fabio University of Illinois Chicago Department of Computer Science IL60607 United States University of Illinois Chicago Department of Applied Health Sciences IL60607 United States

Gait disorders are commonly observed in older adults, who frequently experience various issues related to walking. Additionally, researchers and clinicians extensively investigate mobility related to gait in typically and atypically developing children, athletes, and individuals with orthopedic and neurological disorders. Effective gait analysis enables the understanding of the causal mechanisms of mobility and balance control of patients, the development of tailored treatment plans to improve mobility, the reduction of fall risk, and the tracking of rehabilitation progress. However, analyzing gait data is a complex task due to the multivariate nature of the data, the large volume of information to be interpreted, and the technical skills required. Existing tools for gait analysis are often limited to specific patient groups (e.g., cerebral palsy), only handle a specific subset of tasks in the entire workflow, and are not openly accessible. To address these shortcomings, we conducted a requirements assessment with gait practitioners (e.g., researchers, clinicians) via surveys and identified key components of the workflow, including (1) data processing and (2) data analysis and visualization. Based on the findings, we designed VIGMA, an open-access visual analytics framework integrated with computational notebooks and a Python library, to meet the identified requirements. Notably, the framework supports analytical capabilities for assessing disease progression and for comparing multiple patient groups. We validated the framework through usage scenarios with experts specializing in gait and mobility rehabilitation. © 1995-2012 ieee.

关键词： data reduction

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