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SMP Challenge: An Overview and analysis of Social Media Prediction Challenge 23

SMP Challenge: An Overview and Analysis of Social Media Pred...

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31st ACM International conference on Multimedia (MM)

作者： Wu, Bo Liu, Peiye Cheng, Wen-Huang Liu, Bei Zeng, Zhaoyang Wang, Jia Huang, Qiushi Luo, Jiebo MIT IBM Watson AI Lab Cambridge MA 02142 USA Beijing Univ Posts & Telecommun Beijing Peoples R China Natl Taiwan Univ Taipei Taiwan Microsoft Res Asia Beijing Peoples R China Int Digital Econ Acad Beijing Peoples R China Natl Yang Ming Chiao Tung Univ Taipei Taiwan Univ Surrey Guildford Surrey England Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9798400701085

Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (***) provides the latest information and news.

关键词： Social Multimedia visual Prediction Popularity Prediction

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Annotations in Different Steps of visual Analytics 16

Annotations in Different Steps of Visual Analytics

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 12th International conference on Information visualization Theory and Applications (ivAPP)

作者： Schmidt, Christoph Grundel, Bastian Schumann, Heidrun Rosenthal, Paul Univ Rostock Inst Visual & Analyt Comp Rostock Germany Ernst Moritz Arndt Univ Greifswald Eye Care Ctr Greifswald Germany

ISBN: (纸本)9789897584886

Annotations in visual Analytics (VA) have become a common means to support the analysis by integrating additional information into the VA system. Here, annotations often differ between the individual steps of VA. For example, during data preprocessing it may be necessary to add information on the data, such as redundancy or discrepancy information, while annotations, used during exploration, often refer to the externalization of findings and insights. Describing the particular needs for these step-dependent annotations is challenging. To tackle this issue, we examine the data preprocessing, data cleansing, and data exploration steps for the analysis of heterogeneous and error prone data in respect to the design of specific annotations. By that, we describe their peculiarities for each step in the analysis, and thus aim to improve the visual analytics approach on clinical data. We show the applicability of our annotation concept by integrating it into an existing visual analytics tool to analyze and annotate data from the ophthalmic domain. In interviews and application sessions with experts, we assess the usefulness of our annotation concept for the analysis of the visual acuity development for patients, undergoing a specific therapy.

关键词： Annotation visual Analytics Heterogeneous Clinical data data Preprocessing data Cleansing data exploration

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Applying visual analytics to firearms trafficking trails for the extraction of strategic intelligence

Applying visual analytics to firearms trafficking trails for...

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IEEE International conference on Big data

作者： E. Adamakis E. Sykianaki G. Margetis S. Ntoa C. Stephanidis Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece Department of Computer Science University of Crete Heraklion Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Big data analysis and insight extraction are critical in contemporary large-scale criminal investigations. Analyzing large sets of information related to criminal activities can assist in the identification of correlations between records which can be utilized as strategic intelligence information for the coordination of law enforcement efforts. A Law Enforcement Agency (LEA) officer tasked with this analysis requires tools that can enable Big data exploration while being intuitive and without requiring the need for prior knowledge. A common practice of data analysis software is the ability to create dashboards that can provide all the necessary visualizations and filters to support such an analysis. However, when it comes to dashboard-wide data exploration through drill-down or roll-up analysis; review and evaluation of correlations between data records; and creation of collections with information important to an investigation, current solutions fall short. In this paper, we present a system that addresses these limitations by facilitating the visualization and exploration of diverse data sources, allowing for individual or concurrent analysis via user-configured dashboards. Individual data records of interest to the analysis can be reviewed and their correlations evaluated by the user, allowing for the creation of collections of records and their relationships based on formalized types of correlations.

关键词： Correlation Filters data analysis Law enforcement Reviews visual analytics Soft sensors data visualization Big data data mining

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Efficientdet Based Visial Perception for Autonomous Driving 8

Efficientdet Based Visial Perception for Autonomous Driving

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8th International conference on Cloud Computing and Big data Analytics (ICCCBDA)

作者： Lyu, Chenxi Fan, Xinwen Qiu, Zhenyu Chen, Jun Lin, Jingsong Dong, Chen Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Fujian Jiangxia Univ Coll Elect & Informat Sci Fuzhou Peoples R China

ISBN: (纸本)9781665455336

In the realm of big data and cloud computing, the autonomous vehicle is an extremely promising and complex research topic, which is driven primarily by computer systems that employ AI technology, radar, GPS, visual computing, cloud computing, and other technologies to operate. autonomous vehicles use LIDAR to sense road information, and highly accurate maps are integrated with V2X (vehicle networking technology) to recognize relevant information such as traffic lights and speed limit signs. However, in some remote areas where map information needs to be completed, recognition of traffic lights and traffic signs cannot rely on positioning to achieve this. This paper uses the Efficientdet-d1 target detection algorithm built on Pytorch to simulate autonomous vehicles sensing pedestrian, vehicle, traffic light, and traffic sign information. This target detection algorithm uses EfficientNet-B1 as the backbone network and enhances the feature extraction process using four stacked BiFPN modules. The method involves using the open source big dataset LISA traffic light dataset and GTSRB German traffic sign dataset to train the model. Considering the uneven distribution of samples in the dataset, these classes are distributed into three Efficientdet-d1 target detection frameworks for pedestrians and various types of vehicles, traffic lights, and traffic signs, respectively. A multi-threaded approach allows the three detection processes to be executed simultaneously. The detection results are stored in a queue before aggregation for mapping to improve the speed of single-image execution. The experiments show the three prediction networks used achieved better results overall. The method proposed in this paper is a practical guide for autonomous vehicles to make road condition judgments during driving in remote areas.

关键词： computer vision efficientdet automatic driving big data cloud computing

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visual exploration of the inner representation learned by a convolutional neural network 25

Visual exploration of the inner representation learned by a ...

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25th International conference Information visualisation (iv) - AI and visual Analytics and data Science

作者： Serres, B. Bouali, F. Guinot, C. Venturini, G. Univ Tours LIFAT EA6300 Tours France Univ Lille IUT C Dept STID Lille France Univ Tours ILIAD3 Tours France

ISBN: (纸本)9781665438278

We present in this paper a visual method to explore the properties of an image dataset and its internal representation learned by a convolutional neural network. We consider the inner characteristics extracted by the network just before the classification layers. We build a neighborhood graph from this vector space by connecting data together according to specific topological properties. We define typical examples of topological anomalies to be detected (isolated points, erroneous points, class boundaries). Then we propose a visualization of this graph highlighting this information and offering an overview of the graph (groups of data) as well as local details (fine topological properties). This visualization includes a representation of the images in order to let the user understand what can cause an error (errors during image acquisition, pre-processing or labeling, or errors due to the choice of the network or the learning parameters, etc.). We perform several tests with the VGG16 network on samples of standard datasets.

关键词： visualization deep learning internal representation proximity graphs

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data Narrative Crafting via a Comprehensive and Well-Founded Process 26th

Data Narrative Crafting via a Comprehensive and Well-Founded...

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26th European conference on Advances in databases and Information Systems (ADBIS)

作者： El Outa, Faten Marcel, Patrick Peralta, Veronika da Silva, Raphael Chagnoux, Marie Vassiliadis, Panos Univ Tours Blois Loir & Cher France Rue89 Strastbourg Strastbourg France Univ Paris 08 Paris France Univ Ioannina Ioannina Greece

ISBN: (纸本)9783031157400;9783031157394

data narration is the activity of crafting narratives supported by facts extracted from data exploration and analysis, using interactive visualizations. While data narration has recently attracted much attention, the process of crafting data narratives is loosely documented and has not yet been formally described. In this article, we propose a comprehensive and well-founded process to fill this need. It aims at (i) supporting the complete cycle of data narration, from the exploration of data to the visual rendering of the narrative, (ii) being flexible enough to cover a wide range of crafting practices, and (iii) being well founded upon with a conceptual model of the domain.

关键词： data narrative crafting data journalism Process

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visualization analysis of research hotspots and development trends of technological innovation efficiency based on COOC 2

Visualization analysis of research hotspots and development ...

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2nd International conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2023

作者： Han, Changjuan Cao, Shikui School of Health Management Changchun University of Chinese Medicine China Graduate School Changchun University of Chinese Medicine China

ISBN: (纸本)9781510664968

Objective This study analyzes, summarizes and summarizes the hotspots and trends in the research literature on technological innovation efficiency, and provides a theoretical basis for future scientific research. Methods We searched the papers on "technological innovation efficiency" from 1999 to 2022 through the database of China Knowledge Network (CNKI), and used COOC13.4 software VOSviewer1.6.13 to analyze the key words, research institutions and authors in this field. The visual scientometric analysis was conducted using COOC13.4 software VOSviewer1.6.13 to visualize key words, research institutions and authors in this field. Results A total of 1,228 papers were included. The author found that the overall trend of the number of publications was on the rise through the knowledge mapping analysis, and the cooperation between the publishing institutions was not close. Research hotspots include technological innovation efficiency, technological innovation, data envelopment analysis (DEA), high-tech industry, enterprise scale, innovation environment, and influencing factors. Conclusion The research on technological innovation efficiency in recent years is divided into three main development stages respectively, the budding exploration stage, the rapid development stage, and the stable improvement stage. Future research needs to strengthen the cooperation between different institutions and authors. This paper predicts that this research field will be broadened in the future and the research on technological innovation efficiency will develop toward a diversified trend. © 2023 SPIE.

关键词： data envelopment analysis

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Dash: A Bimodal data exploration Tool for Interactive Text and visualizations

Dash: A Bimodal Data Exploration Tool for Interactive Text a...

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International conference on visualisation, VIS

作者： Dennis Bromley Vidya Setlur Tableau Research

ISBN: (数字)9798350354850

ISBN: (纸本)9798350354867

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.’s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model’s text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH’s text and chart integration capabilities when participants perform data exploration with the tool.

关键词： Annotations visual analytics Semantics data visualization Encoding Distance measurement data models

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INCREASING STRUCTURAL MODELLING EFFICIENCY USING COMPUTATIONAL visual WORKFLOWS 83

INCREASING STRUCTURAL MODELLING EFFICIENCY USING COMPUTATION...

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83rd EAGE conference and Exhibition 2022

作者： McLean, F. Reilly, C. Shearer, J. Petroluem Experts

ISBN: (纸本)9781713859314

The creation of geological models is an integral part of exploration and development. Advances in Artificial Intelligence and digitalization technology allow geoscientists to extract maximum value from seismic data, producing more comprehensive geological models than using traditional manual interpretation techniques alone. These technologies are typically of limited use when input data is of poor quality;often the case in structurally complex regions. Structural modelling techniques provide reliable, empirical approaches for supplementing incomplete or poor data to improve understanding and reduce uncertainty in geological models. Software-based structural modelling technologies are well-established and have traditionally been operated manually by domain specialists to perform structural analysis tasks. These processes, while allowing rigorous prediction of fault and horizon geometries where data are lacking, are time intensive and often inaccessible to generalists. Recent software developments have permitted automation of structural modelling techniques using computational visual workflows. The result is several orders of magnitude increase in modelling efficiency, reduction in subjectivity, reproducibility of modelling procedures and an increase in the accessibility of structural modelling to non-domain experts. Here, we present two case study examples using this new visual workflow modelling technology, which automates structural analysis techniques: fault shape prediction, and comprehensive analysis of fault propagation folding. © (2022) by the European Association of Geoscientists & Engineers (EAGE) All rights reserved

关键词： Structural analysis

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A Conceptual Framework for Immersive Behavioral data exploration Assisted by a Deep Learned Representation of data

A Conceptual Framework for Immersive Behavioral Data Explora...

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IEEE International conference on Artificial Intelligence and Virtual Reality (AivR)

作者： Victor Duvivier Matthieu Perreira Da Silva Yannick Prié École Centrale Nantes CNRS LS2N UMR 6004 Nantes Université Nantes France

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

By allowing to conduct experiments involving eco-logically valid tasks within controlled environments, Virtual Real-ity (VR) offers novel opportunities for studying human behavior. Several modalities can be leveraged, including event logs, motion trajectories, eye-tracking data, or physiological signals. However, analyzing such multimodal data presents considerable challenges due to their inherent complexity, the varied structures they imply, as well as the necessity to use exploratory approaches. There is therefore a need to design visual analytics tools for the exploration of immersive behavioral data without prior knowledge. Our idea is to integrate deep learned computational models -which leverage advanced techniques to extract valuable high-level features from unlabeled data- into exploratory visual Analytics tools. We introduce a conceptual framework for integrating deep learning models into the visual Analytics process of immersive behavioral data analysis, also focusing on the services that such systems should provide to the analysts.

关键词： Deep learning Solid modeling Analytical models visual analytics Biological system modeling Computational modeling Virtual reality Feature extraction Transformers data models

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