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2nd European symposium on Artificial Intelligence in Manufacturing-ESAIM

作者： Blanco-Filgueira, Beatriz Delgado, Tamara Coto, Andrea Gregores Petit, Celine Bruneel, David Romero, Pablo Muinos-Landin, Santiago AIMEN Technol Ctr Porrino 36410 Pontevedra Spain LASEA Seraing 4102 Belgium

ISBN: (纸本)9783031864919;9783031864896;9783031864889

High-power ultrashort pulse lasers (USPLs) represent a sustainable alternative for industrial surface processing. Laser Surface Texturing (LST) allows modification of surface properties such as mechanical, chemical or optical properties. The adoption of USPLs in industry will enable the surface functionalization of large 3D parts through the LST process. However, a real exploitation of USPLs still faces limitations related to high processing resolution, quality, productivity, and the need for expert knowledge in laser micro-structuring strategies. It is crucial to increase maturity, reliability and throughput of LST on a large scale, as well as to provide the industry with the necessary tools for selecting the suitable laser process for each specific application. Achieving these goals requires significant effort in data collection, which is costly in terms of processing times, results analysis and human resources. Additionally, the availability of software simulators for laser micro-structuring is limited, posing challenges for describing USPLs processes through numerical models and methods. In this context, Artificial Intelligence (AI) models represent a unique tool to enhance process understanding and results analysis, providing rapid prediction and visualization capabilities. However, developing AI-models requires vast amounts of data, which are not easily obtainable experimentally, but simulators can play a vital role in this aspect. We present a combined solution based on machine learning methods fed by synthetic data generated by LS-Plume (R) from LASEA for predicting femtosecond laser single beam-based grooves on stainless steel 316L. The methodology for developing such applications is described, and several regression models are compared. The analysis of metrics and prediction error demonstrates that leveraging the synergy between simulation tools and AI-models can be an effective strategy for training AI-models, thereby avoiding the need for a complete, sys

关键词： laser micro-structuring surface texturing ultrashort pulse laser light-matter interaction synthetic data AI-models

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Augmenting Feature Importance analysis: How Color and Size Can Affect Context-Aware AR Explanation visualizations? 21

Augmenting Feature Importance Analysis: How Color and Size C...

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21st ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Zheng, Mengya Thomas, Rosemary J. Pan, Xingyu Xu, Zixiang Liang, Yuan Campbell, Abraham G. Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (数字)9781665453257

ISBN: (纸本)9781665453257

Augmented Reality (AR) has shown significant potential in supporting in-situ decision-making in various application areas. Many prior works have shown how AR can visualize the decision support data in various contexts. However, prior research about AR-based decision support systems rarely explored how the explanations were visualized. Providing context-aware explanations within AR-based recommendation systems may help users instantly understand the recommendations they have been given. Therefore, this paper presents the world-first user study exploring AR explanation visualization designs. Three feature importance analysis visualizations that apply different color-coding and size-scaling strategies were designed to explain the recommendations provided by a context-aware AR shopping assistant system. Twenty-four participants were recruited to evaluate these three explanations in a shopping scenario. The results revealed novel findings that could help guide the appropriate utilization of descriptive parameters when designing AR explanation artifacts. The results also show the potential of providing intuitive visualization to explain recommendations in AR.

关键词： Human-centered computing visualization Empirical studies in visualization Human-centered computing Human computer interaction Empirical studies in HCI

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An Organ-Aware Diagnosis Framework for Radiology Report Generation

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ieee TRANSACTIONS ON MEDICAL IMAGING 2024年第12期43卷 4253-4265页

作者： Li, Shiyu Qiao, Pengchong Wang, Lin Ning, Munan Yuan, Li Zheng, Yefeng Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Tencent Jarvis Lab Shenzhen 518057 Peoples R China

Radiology report generation (RRG) is crucial to save the valuable time of radiologists in drafting the report, therefore increasing their work efficiency. Compared to typical methods that directly transfer image captioning technologies to RRG, our approach incorporates organ-wise priors into the report generation. Specifically, in this paper, we propose Organ-aware Diagnosis (OaD) to generate diagnostic reports containing descriptions of each physiological organ. During training, we first develop a task distillation (TD) module to extract organ-level descriptions from reports. We then introduce an organ-aware report generation module that, for one thing, provides a specific description for each organ, and for another, simulates clinical situations to provide short descriptions for normal cases. Furthermore, we design an auto-balance mask loss to ensure balanced training for normal/abnormal descriptions and various organs simultaneously. Being intuitively reasonable and practically simple, our OaD outperforms SOTA alternatives by large margins on commonly used IU-Xray and MIMIC-CXR datasets, as evidenced by a 3.4% BLEU-1 improvement on MIMIC-CXR and 2.0% BLEU-2 improvement on IU-Xray.

关键词： Radiology Task analysis Medical diagnostic imaging Lung Decoding visualization Training Medical report generation chest X-ray data imbalance organ-aware learning

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BNET: Batch Normalization With Enhanced Linear Transformation

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第7期45卷 9225-9232页

作者： Xu, Yuhui Xie, Lingxi Xie, Cihang Dai, Wenrui Mei, Jieru Qiao, Siyuan Shen, Wei Xiong, Hongkai Yuille, Alan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Univ Calif Santa Cruz Dept Comp Sci & Engn Santa Cruz CA 95060 USA

Batch normalization (BN) is a fundamental unit in modern deep neural networks. However, BN and its variants focus on normalization statistics but neglect the recovery step that uses linear transformation to improve the capacity of fitting complex data distributions. In this paper, we demonstrate that the recovery step can be improved by aggregating the neighborhood of each neuron rather than just considering a single neuron. Specifically, we propose a simple yet effective method named batch normalization with enhanced linear transformation (BNET) to embed spatial contextual information and improve representation ability. BNET can be easily implemented using the depth-wise convolution and seamlessly transplanted into existing architectures with BN. To our best knowledge, BNET is the first attempt to enhance the recovery step for BN. Furthermore, BN is interpreted as a special case of BNET from both spatial and spectral views. Experimental results demonstrate that BNET achieves consistent performance gains based on various backbones in a wide range of visual tasks. Moreover, BNET can accelerate the convergence of network training and enhance spatial information by assigning important neurons with large weights accordingly.

关键词： Task analysis Convolution Training visualization Neurons Kernel Performance gain Batch normalization linear transformation deep learning

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Vision Transformer With Contrastive Learning for Remote Sensing Image Scene Classification

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 738-749页

作者： Bi, Meiqiao Wang, Minghua Li, Zhi Hong, Danfeng Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China

Remote sensing images (RSIs) are characterized by complex spatial layouts and ground object structures. ViT can be a good choice for scene classification owing to the ability to capture long-range interactive information between patches of input images. However, due to the lack of some inductive biases inherent to CNNs, such as locality and translation equivariance, ViT cannot generalize well when trained on insufficient amounts of data. Compared with training ViT from scratch, transferring a large-scale pretrained one is more cost-efficient with better performance even when the target data are small scale. In addition, the cross-entropy (CE) loss is frequently utilized in scene classification yet has low robustness to noise labels and poor generalization performances for different scenes. In this article, a ViT-based model in combination with supervised contrastive learning (CL) is proposed, named ViT-CL. For CL, supervised contrastive (SupCon) loss, which is developed by extending the self-supervised contrastive approach to the fully supervised setting, can explore the label information of RSIs in embedding space and improve the robustness to common image corruption. In ViT-CL, a joint loss function that combines CE loss and SupCon loss is developed to prompt the model to learn more discriminative features. Also, a two-stage optimization framework is introduced to enhance the controllability of the optimization process of the ViT-CL model. Extensive experiments on the AID, NWPU-RESISC45, and UCM datasets verified the superior performance of ViT-CL, with the highest accuracies of 97.42%, 94.54%, and 99.76% among all competing methods, respectively.

关键词： Feature extraction Transformers Image analysis visualization Task analysis Training Semantics Joint loss function remote sensing scene classification supervised contrastive (SupCon) loss vision transformer

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A Grammar-Based Approach for Applying visualization Taxonomies to Interaction Logs

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 489-500页

作者： Gathani, Sneha Monadjemi, Shayan Ottley, Alvitta Battle, Leilani Univ Maryland College Pk MD 20742 USA Washington Univ St Louis MO 63110 USA Univ Washington Seattle WA 98195 USA

Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars. We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non-terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the taxonomies at the low-level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high-level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under-expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data-driven development of user behavior taxonomies.

关键词： CCS Concepts • Theory of computation → Regular languages Algebraic language theory • Human-centered computing → visualization theory concepts and paradigms

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AI-Driven Symptom Checker: Leveraging Big data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 ieee.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

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A Visual Representation-Guided Framework With Global Affinity for Weakly Supervised Salient Object Detection

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第1期34卷 248-259页

作者： Xu, Binwei Liang, Haoran Gong, Weihua Liang, Ronghua Chen, Peng Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-theart weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models.

关键词： visualization Task analysis Image edge detection Annotations Semantics Training Object detection General visual representation global affinity salient object detection scribble self-supervised transformer

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Driver Gaze Fixation and Pattern analysis in Safety Critical Events 34

Driver Gaze Fixation and Pattern Analysis in Safety Critical...

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34th ieee Intelligent Vehicles symposium (IV)

作者： Bhagat, Hirva Jain, Sandesh Abbott, Lynn Sonth, Akash Sarkar, Abhijit Virginia Tech Dept Comp Sci Blacksburg VA USA Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA USA Virginia Tech Transportat Inst Blacksburg VA USA

ISBN: (纸本)9798350346916

Driver attention is one of the major factors for roadway safety. This paper presents a comprehensive analysis of context-aware driver gaze behavior using large-scale naturalistic data. This paper first demonstrates a Point of Gaze (PoG) estimation system that provides an understanding of the interactions between a driver and the driving environment outside the vehicle. The driver's PoG positions were mapped to roadway actors that were detected using a road-facing camera, and a driver-facing camera detected the driver's head and eye motions. This paper presents a new algorithm that can effectively predict gaze fixation to outside objects. The temporal fixation patterns of drivers are studied through a Yarbus-style analysis of PoG. Correlations between gaze direction, salient objects, and the driving scenario are described and analyzed. These correlations have the potential to reveal how drivers behave in various traffic situations and may have the predictive power to aid automated safety improvement systems. Our code is available at: https://***/VTTI/gaze-fixation-and-object-saliency and https://***/VTTI/Event-Correlation

关键词： Gaze Driving Scene correlation Eye tracking Object detection Yarubs plot Gaze estimation PoG estimation

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LLM-Based Code Generation for Querying Temporal Tabular Financial data

LLM-Based Code Generation for Querying Temporal Tabular Fina...

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2024 Conference on Computational Intelligence for Financial Engineering and Economics

作者： Lashuel, Mohamed Kurdistan, Gulrukh Green, Aaron Erickson, John S. Seneviratne, Oshani Bennett, Kristin P. Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA Rensselaer Polytech Inst Dept Management Troy NY USA Rensselaer Polytech Inst Future Comp Inst Rensselaer Troy NY USA Rensselaer Polytech Inst Dept Comp Sci Troy NY USA

ISBN: (纸本)9798350354843;9798350354836

We examine the question of "how well large language models (LLMs) can answer questions using temporal tabular financial data by generating code?". Leveraging advanced language models, specifically GPT-4 and Llama 3, we aim to scrutinize and compare their abilities to generate coherent and effective code for Python, R, and SQL based on natural language prompts. We design an experiment to assess the performance of LLMs on natural language prompts on a large temporal financial dataset. We created a set of queries with hand-crafted R code answers. To investigate the strengths and weaknesses of LLMs, each query was created with different factors that characterize the financial meaning of the queries and their complexity. We demonstrate how to create specific zero-shot prompts to generate code to answer natural language queries about temporal financial tabular data. We develop specific system prompts for each language to ensure they correctly answer time-oriented questions. We execute this experiment on two LLMs (GPT-4 and Llama 3), assess if the outputs produced are executable and correct, and assess the efficiency of the produced code for Python, SQL, and R. We find that while LLMs have promising performance, their performance varies greatly across the languages, models, and experimental factors. GPT-4 performs best on Python (95.2% correctness) but has significantly weaker performance on SQL (87.6% correctness) and R (79.0% correctness). Llama 3 is less successful at generating code overall, but it achieves its best results in R (71.4% correctness). A multi-factor statistical analysis of the results with respect to the defined experimental factors provides further insights into the specific areas of challenge in code generation for each LLM. Our preliminary results on this modest benchmark demonstrate a framework for developing larger, comprehensive, unique benchmarks for both temporal financial tabular data and R code generation. While Python and SQL already have benc

关键词： large language models code generation gpt-4 llama3 financial data tabular data benchmarking financial industry automation

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