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2023 International Conference on Artificial Intelligence and Automation Control, AIAC 2023

作者： Li, Yi Gao, Hongbo Qian, Chao Tan, Ruijin Wu, Lei Zhang, Rui Dali Branch of Yunnan Leaf Tobacco Redrying Co. Ltd. Yunnan Dali China Yunnan Agricultural University Yunnan Kunming China

ISBN: (纸本)9798350383805

In order to efficiently analyze large-scale power system electricity consumption data, this paper investigates the design of a data visualization and analysis system based on Hadoop. The system adopts a client-server architecture, with the server side deploying a Hadoop distributed cluster to collect, store, and process massive electricity consumption data. Spark is used for data cleaning and transformation, while various visualization techniques are employed to present electricity consumption data in multiple dimensions. The research is validated using actual 50GB-scale electricity consumption data, and the results show significant improvements in efficiency and quality compared to traditional methods. This study demonstrates that a Hadoop-based distributed computing framework can efficiently support the processing and analysis of large-scale electricity consumption data, providing crucial technical support for electricity system monitoring and management. © 2023 ieee.

关键词： data visualization

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Octave: an End-User Programming Environment for analysis of Spatiotemporal data for Construction Students

Octave: an End-User Programming Environment for Analysis of ...

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ieee symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Manesh, Daniel Luu, Andy Khalid, Mohammad Li, Jiangyue Okonkwo, Chinedu Akanmu, Abiola Awolusi, Ibukun Murzi, Homero Lee, Sang Won Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Myers Lawson Sch Construct Blacksburg VA USA UTSA Sch Civil & Environm Engn & Construct Management San Antonio TX USA Virginia Tech Dept Engn Educ Blacksburg VA USA

ISBN: (纸本)9798350329469

The construction industry is a new avenue for big data and data science with sensors and cyber-physical systems deployed in the field. Construction students need to develop computational thinking skills to help make sense of this data, but existing data science environments designed with textual programming languages create a significant barrier to entry. To bridge this gap, we introduce Octave, an end-user programming environment designed to help non-expert programmers analyze spatiotemporal data (e.g., as gathered by a GPS sensor) in an interactive graphical user interface. To aid exploration and understanding, Octave's design incorporates a high degree of liveness, highlighting the interconnection between data, computation, and visualization. We share the underlying design principles behind Octave and details about the system design and implementation. To evaluate Octave, we conducted a usability study with students studying construction. The results show that non-programmer construction students were able to learn Octave easily and were able to effectively use it to solve domain-specific problems from construction education. The participants appreciated Octave's liveness and felt they could easily connect it to real-life problems in their field. Our work informs the design of future accessible end-user programming environments for data analysis targeting non-experts.

关键词： Construction data analytics End-user Programming Spreadsheets Spatiotemporal data

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An fMRI-Based Centrality analysis of Brain Connectivity in Autism Spectrum Disorder

An fMRI-Based Centrality Analysis of Brain Connectivity in A...

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2024 ieee Region 10 symposium, TENSYMP 2024

作者： Uday, Nayana George, Judy K Sherly, Elizabeth Digital University Kerala SoCSE Kerala Thiruvananthapuram India

ISBN: (纸本)9798350364866

Recent studies have probed Autism Spectral Disorder's (ASD's) neural substrates, analyzing brain connectivity patterns through techniques like centrality using the resting-state functional Magnetic Resonance imaging (rs-fMRI) data, revealing deficits in social communication, repetitive behaviors, and sensory processing. This research aims to observe alterations in information flow and communication networks by assessing the centrality of key brain regions, distinguishing individuals with ASD from neurotypical controls. Evaluating centrality measures' accuracy using a Graph Attention Network (GAT) model classifier and model interpretability with the attention score visualization unveils significant contributors to the model's performance, providing insights into ASD's neural mechanisms and enhancing our understanding of brain connectivity in this disorder. © 2024 ieee.

关键词： Diseases

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Generative Shadow Synthesis and Removal for Remote Sensing Images Through Embedding Illumination Models

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Shao, Chenglin Li, Huifang Shen, Huanfeng Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Resource & Environm Sci Key Lab Geog Informat Syst Minist Educ Wuhan 430079 Peoples R China Wuhan Univ Key Lab Digital Cartog & Land Informat Applicat Minist Nat Resources Wuhan 430079 Peoples R China

Shadows significantly reduce the available information in remote sensing images, obstructing downstream tasks such as object detection, scene classification, and localization. Shadow removal from remote sensing images is, however, still an open issue for the following reasons. First, deep neural networks are difficult to train since the corresponding ground truth of shadows is almost always unavailable in practice. Second, the existing shadow removal methods still suffer from blurry details and boundary artifacts. In this article, we describe how a generative shadow synthesis and removal framework that couples data-driven methods with illumination models was developed to address the above challenges effectively. Various shadows were synthesized in shadow-free regions of remote sensing images by GSS-Net, which is a generative shadow synthesis network that considers the physical process of shadow illumination attenuation (SIA). In this way, a large-scale, diverse, and realistic shadow dataset (RS-SynShadow) was built. A generative shadow removal network-GSR-Net-embedding a histogram-enhanced illumination (HEI) model was then developed for high-fidelity shadow removal without artifacts. Extensive experiments conducted on synthetic and real data demonstrate that the proposed shadow synthesis and removal framework significantly outperforms the state-of-the-art methods, both visually and quantitatively.

关键词： Remote sensing Lighting Deep learning Training Image color analysis Attenuation visualization Generative adversarial networks data models data mining Generative learning illumination models remote sensing images shadow removal shadow synthesis

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Instrumenting Multiphysics Blood Flow Simulation Codes for In Situ visualization and analysis 11

Instrumenting Multiphysics Blood Flow Simulation Codes for I...

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ieee 11th symposium on large data analysis and visualization (LDAV)

作者： Bucaro, Anthony Murphy, Connor Ferrier, Nicola Insley, Joseph Mateevitsi, Victor Papka, Michael E. Rizzi, Silvio Tan, Jifu Northern Illinois Univ De Kalb IL 60115 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781665432832

Blood flow simulations have important applications in engineering and medicine, requiring visualization and analysis for both fluid (blood plasma) and solid (cells). Recent advances in blood flow simulations highlight the need of a more efficient analysis of large data sets. Traditionally, analysis is performed after a simulation is completed, and any changes of simulation settings require running the simulation again. With bi-directional in situ analysis we aim to solve this problem by allowing manipulation of simulation parameters in run time. In this project, we describe our early steps toward this goal and present the in situ instrumentation of two coupled codes for blood flow simulation using the SENSEI in situ framework.

关键词： Training Analytical models Solid modeling Fluids Codes Instruments data visualization

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Real-Time Visual analysis of High-Volume Social Media Posts

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第1期28卷 879-889页

作者： Knittel, Johannes Koch, Steffen Tang, Tan Chen, Wei Wu, Yingcai Liu, Shixia Ertl, Thomas Univ Stuttgart Stuttgart Germany Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

关键词： Social networking (online) Heuristic algorithms data visualization Clustering algorithms Real-time systems Text analysis Visual analytics Social media analysis dynamic clustering streaming data

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Time-Travel Debugging with visualization of data-Structures Based on Instrumentation

Time-Travel Debugging with Visualization of Data-Structures ...

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2022 ieee symposium on Visual Languages and Human-Centric Computing

作者： Moench, Kim Univ Bundeswehr Munchen Inst Software Technol CS Dept Neubiberg Germany

ISBN: (纸本)9781665442152;9781665442145

Although a large number of different debugging tools are available, very few CS students use them in programming courses. Instead, they try to understand and correct their code by adding countless print statements. We identified several reasons for these observations: (1) novice programmers find the standard debugging tools too complicated, especially the representation of the current program state, (2) they find it difficult to trace a problem back to its origin, and (3) it is not yet possible to provide them with a visualization of data structures that corresponds to the representation used in the classroom. We address these problems with BugVis, an alternative debugging approach. We describe the concept and basic ideas below, since BugVis has not yet been fully realized.

关键词： visualization Java Computer languages Codes Instruments data visualization Debugging

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TOPOLOGICAL analysis OF MOUSE BRAIN VASCULATURE VIA 3D LIGHT-SHEET MICROSCOPY IMAGES 21

TOPOLOGICAL ANALYSIS OF MOUSE BRAIN VASCULATURE VIA 3D LIGHT...

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21st ieee International symposium on Biomedical Imaging (ISBI)

作者： Yao, Jiachen Hagemann, Nina Xiong, Qiaojie Chen, Jianxu Hermann, Dirk M. Chen, Chao SUNY Stony Brook Stony Brook NY 11794 USA Univ Hosp Essen Essen Germany Leibniz Inst Analyt Wissensch ISAS eV Dortmund Germany

ISBN: (纸本)9798350313345;9798350313338

Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features(1) based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.

关键词： Vasculature Topological data analysis Persistent Homology

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Demystifying the Communication Characteristics for Distributed Transformer Models 31

Demystifying the Communication Characteristics for Distribut...

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31st ieee symposium on High-Performance Interconnects (HOTI)

作者： Anthony, Quentin Michalowicz, Benjamin Hatef, Jacob Xu, Lang Abduljabbai, Mustafa Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. (DK) Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9798350356021;9798350356014

Deep learning (DL) models based on the transformer architecture have revolutionized many DL applications such as large language models (LLMs), vision transformers, audio generation, and time series prediction. Much of this progress has been fueled by distributed training, yet distributed communication remains a substantial bottleneck to training progress. This paper examines the communication behavior of transformer models - that is, how different parallelism schemes used in multi-node/multi-GPU DL Training communicate data in the context of transformers. We use GPT-based language models as a case study of the transformer architecture due to their ubiquity. We validate the empirical results obtained from our communication logs using analytical models. At a high level, our analysis reveals a need to optimize small message point-to-point communication further, correlations between sequence length, per-GPU throughput, model size, and optimizations used, and where to potentially guide further optimizations in framework and HPC middleware design and optimization.

关键词： Neural Networks DNN MPI GPU large Language Models Interconnects Communication Characterization

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UnstrPrompt: large Language Model Prompt for Driving in Unstructured Scenarios

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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ieee JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2024年 8卷 367-375页

作者： Li, Yuchen Li, Luxi Wu, Zizhang Bing, Zhenshan Zhe, Xuanyuan Knoll, Alois Christian Chen, Long BNU HKBU United Int Coll Fac Sci & Technol Zhuhai 519087 Peoples R China Hong Kong Baptist Univ Fac Sci Hong Kong Peoples R China WAYTOUS Inc Beijing 100083 Peoples R China Tech Univ Munich Dept Informat Comp Sci D-85748 Munich Germany Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Inst Machine Learning & Intelligent Syst Shenzhen 518107 Peoples R China

The integration of language descriptions or prompts with large Language Models (LLMs) into visual tasks is currently a focal point in the advancement of autonomous driving. This study has showcased notable advancements across various standard datasets. Nevertheless, the progress in integrating language prompts faces challenges in unstructured scenarios, primarily due to the limited availability of paired data. To address this challenge, we introduce a groundbreaking language prompt set called "UnstrPrompt." This prompt set is derived from three prominent unstructured autonomous driving datasets: IDD, ORFD, and AutoMine, collectively comprising a total of 6K language descriptions. In response to the distinctive features of unstructured scenarios, we have developed a structured approach for prompt generation, encompassing three key components: scene, road, and instance. Additionally, we provide a detailed overview of the language generation process and the validation procedures. We conduct tests on segmentation tasks, and our experiments have demonstrated that text-image fusion can improve accuracy by more than 3% on unstructured data. Additionally, our description architecture outperforms the generic urban architecture by more than 0.1%. This work holds the potential to advance various aspects such as interaction and foundational models in this scenario.

关键词： Task analysis visualization Roads Autonomous vehicles Radiofrequency identification Object detection Computational modeling large language model unstructured scenarios segmentation UnstrPrompt

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