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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Visualizing Student Attention in Smart Classrooms: A Preliminary Study 15

Visualizing Student Attention in Smart Classrooms: A Prelimi...

引用

15th International conference on Innovations in information Technology, IIT 2023

作者： Parambil, Medha Mohan Ambali Alhammadi, Amna Mohammed Abdulla Alnajjar, Fady Trabelsi, Zouheir Ali, Luqman Swavaf, Muhammed United Arab Emirate University Department of Information Systems and Security College of IT Al Ain United Arab Emirates United Arab Emirate University Department of Computer Science and Software Engineering College of IT Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students' academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students' presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-Time visual depiction of students' classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated. © 2023 ieee.

关键词： Students

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An Educational Approach for Mixed Reality visualization of Agro-meteorological Parameters 2

An Educational Approach for Mixed Reality Visualization of A...

引用

2nd Edition ieee International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Capece, Nicola Manfredi, Gilda Gilio, Gabriele Erra, Ugo Toscano, Francesco Fiorentino, Costanza D'Antonio, Paola Università Degli Studi della Basilicata Italy

ISBN: (纸本)9798350300802

This paper proposes an innovative system to visualize real-time agro-meteorological parameters on real trees in a mixed reality (MR) environment. It uses Vuforia area targets identified and tracked using the Microsoft Hololens 2 MR headset and geographic coordinates to track and place virtual content on the trees precisely. The proposed system provides an immersive and interactive way to visualize weather station data in a natural environment, using real trees as markers to display real-time data. This system allows users to quickly identify changes in weather conditions, making informed decisions in real-time. We evaluated the system by developing a proof-of-concept MR application that showcases the potential of using trees as Vuforia Area Targets (VATs) with geographic coordinates for displaying weather station parameters. The application allows users to explore an urban park and learn about real trees and their environmental conditions interactively and engagingly. The proposed system is a serious game supporting living labs and educational purposes on real trees in a mixed reality (MR) environment. © 2023 ieee.

关键词： Mixed Reality Plants Precision Agriculture Sensors Serious Game

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Mixed Differential Privacy in Computer Vision

Mixed Differential Privacy in Computer Vision

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Golatkar, Aditya Achille, Alessandro Wang, Yu-Xiang Roth, Aaron Kearns, Michael Soatto, Stefano AWS AI Labs Seattle WA USA Univ Calif Los Angeles Los Angeles CA 90095 USA UCSB Santa Barbara CA USA Penn Philadelphia PA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167-311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis.

关键词： Training Privacy Computer vision Differential privacy visualization Upper bound Computational modeling

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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gominski, Dimitri Gouet-Brunet, Valerie Chen, Liming Univ Copenhagen Geog Copenhagen Denmark IGN LaSTIG St Mande France Ecole Cent Lyon LIRIS Ecully France

ISBN: (纸本)9781665487399

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or unseen visual distributions, but are hardly applicable to realworld situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach(1) exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training visualization Computer vision Image analysis conferences Image retrieval Feature extraction

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ObjectFormer for Image Manipulation Detection and Localization

ObjectFormer for Image Manipulation Detection and Localizati...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Junke Wu, Zuxuan Chen, Jingjing Han, Xintong Shrivastava, Abhinav Lim, Ser-Nam Jiang, Yu-Gang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Huya Inc Guangzhou Peoples R China Univ Maryland College Pk MD 20742 USA Meta AI New York NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.

关键词： Location awareness visualization Computer vision Frequency-domain analysis Computational modeling Prototypes Feature extraction

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Visual Domain Bridge: A source-free domain adaptation for cross-domain few-shot learning

Visual Domain Bridge: A source-free domain adaptation for cr...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yazdanpanah, Moslem Moradi, Parham Univ Kurdistan Erbil Iraq

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the covariate shift, deep neural networks performance always degrades when applied to novel domains. In order to mitigate this problem, domain adaptation techniques require samples from target data during the feature extraction training, which is not always applicable in real-world scenarios. Batch Normalization is a known component of computer vision models, aiming at reducing the training-time covariate shift. However, facing distribution shift results in an internal state mismatch inside the Batch-Norm layers during the inference time. In favor of alleviating the induced mismatch, this paper proposes a source-free, lightweight and straightforward approach by introducing the "Visual Domain Bridge" concept reducing the BatchNorm's internal mismatch in the cross-domain settings. Compared to the other BatchNorm-based source-free domain adaptation techniques such as AdaBN and Prediction-BN, our method formed a new state-of-the-art cross-domain few-shot fine-tuning method neglecting extra augmentations;while improving the performance in near-domain settings too. The proposed method can integrate with other domain adaptation methods and enhance their performance requiring just a few lines of modification in the BatchNorm's implementation. Implementations are available in https://***/MosyMosy/VDB

关键词： Bridges Training Deep learning visualization Computer vision conferences Neural networks

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DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image Synthesis

DPGEN: Differentially Private Generative Energy-Guided Netwo...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Jia-Wei Yu, Chia-Mu Kao, Ching-Chia Pang, Tzai-Wei Lu, Chun-Shien Acad Sinica IIS New Taipei Taiwan Acad Sinica CITI New Taipei Taiwan Natl Yang Ming Chiao Tung Univ New Taipei Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite an increased demand for valuable data, the privacy concerns associated with sensitive datasets present a barrier to data sharing. One may use differentially private generative models to generate synthetic data. Unfortunately, generators are typically restricted to generating images of low-resolutions due to the limitation of noisy gradients. Here, we propose DPGEN, a network model designed to synthesize high-resolution natural images while satisfying differential privacy. In particular, we propose an energy-guided network trained on sanitized data to indicate the direction of the true data distribution via Langevin Markov chain Monte Carlo (MCMC) sampling method. In contrast to the state-of-the-art methods that can process only low-resolution images (e.g., MNIST and Fashion-MNIST), DPGEN can generate differentially private synthetic images with resolutions up to 128 x 128 with superior visual quality and data utility.

关键词： Training visualization Monte Carlo methods Image resolution Image synthesis Markov processes Sampling methods

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Analysis, visualization and Interpretation of Determinants of Degree Program Selection of the Senior High School Graduates Utilizing Classification and Clustering Algorithms 4

Analysis, Visualization and Interpretation of Determinants o...

引用

4th International conference on Big Data Analytics and Practices, IBDAP 2023

作者： Echalar, Loyd Soriano Labayne, Jonalyn Joy B. Camba, Merardo

ISBN: (纸本)9798350300192

Preparing for the future life is one of the most important keys to success in life. The Republic of the Philippines issued the RA No. 10533 "An Act Enhancing the Philippine Basic Education System by Strengthening its Curriculum and Increasing the Number of Years for Basic Education"which results in the implementation of the K-12 Curriculum Program. The program aims to strengthen the basic education of the country by adding two more years in High School and integrating different specializations that prepare and guide the students to select a specialization that fits them in the future. Many cases occurred that senior high school graduates took a degree program that is not aligned with the specialization they took during senior high school. This research paper presents the analysis, visualization, and interpretation of determinants of degree program selection of senior high school graduates. Phyton and Jupyter Notebook was used to generate the results. Clustering and Classification Algorithms were used to analyze, visualize and interpret the data. Based on the results, it is found that the main reasons for the conflict are the passion of the students, the industry demand for specialization, and their family preferences. It is also found that the residence of the students affects choosing the degree program. As a recommendation, the education authority must review and revisit the rules and implement guidelines of the K-12 curriculum program to eliminate and avoid such confusion and conflicts in selecting degree programs. © 2023 ieee.

关键词： Python

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Comparison of MIMIC-III and MIMIC-IV for big data analytics of health informatics

Comparison of MIMIC-III and MIMIC-IV for big data analytics ...

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2023 ieee International conference on Big Data, BigData 2023

作者： Chrimes, Dillon Kim, Chanhee University of Victoria School of Health Information Science Victoria Canada

ISBN: (纸本)9798350324457

The use of a health big data via Medical information Mart for Intensive Care (MIMIC) data sets has significant advancements in health informatics and clinical research of electronic health records (EHRs) in hospital systems. MIMIC-III and MIMIC-IV data sets are the two latest publicly available iterations of data from electronic records that possess distinct features, variables, and structures in two separate large relational databases. This study aimed to provide big data analytics comparisons of MIMIC-III and MIMIC-IV for experiential learning of health informatics from *** data sets were quantitatively and qualitatively evaluated based on dataset properties (data, list of tables, timeline, patient encounters, software, system), data mining and visual categories (i.e., data size, data structure and schema, features/variables, data quality and completeness, clinical focus, access and use, dashboards, and visualization types), data usability heuristics and utilization (i.e., usability, complexity, granularity, applicability, and capacity). Results showed significant difference of 100 patients across 26 tables for MIMIC-III compared to 2,520 patients across 31 tables for MIMIC-IV data sets, respectively. There were 1716 diagnoses (ICD-9) and 503 procedures (ICD-9) for MIMIC-III. There were 262 diseases categories with >1000 disease instances and >2000 treatments for MIMIC-IV with no ICD codes. Moreover, these data sets contained large (big data) charting events in MIMIC-III of 758,356 rows, and MIMIC-IV eICU with charting events of 1,477,163 for nursing, and respiratory of 176,089 rows, respectively. The results suggest that MIMIC-III provided detailed information for retrospective clinical studies and operations in critical care with high data granularity in terms of re-admission (calculated fields from its admission table), length of stay, prescriptions, caregivers, and diagnosis and procedure (ICD-9). However, it lacked clinical capacity because of no diagno

关键词： dataset health big data health informatics intensive care unit

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A Display-driven Anti-Aliasing visualization Technology of Large-scale Vector Data in Real-Time 7

A Display-driven Anti-Aliasing Visualization Technology of L...

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7th ieee International conference on Big Data Intelligence and Computing, DataCom 2021

作者： Liu, Zebang Chen, Luo Ma, Mengyu Yang, Anran National University of Defense Technology College of Electronic Science Changsha China

ISBN: (纸本)9781665420716

efficient and rapid visualization of large-scale vector data is a research hotpot in geographic information science (GIS). In existing popular method, the computation increases dramatically as data volumes grows, even with parallel technologies. However, the display-driven computing model (DisDC) greatly improves the ability of visualization of large-scale vector data. In this paper, we proposed a rapid visualization method based on the characteristics of DisDC, efficient data organization strategies and anti-Aliasing pixel generation algorithms are designed to supporting rapid visualization and presenting better visualization effects respectively. visualization results are organized into a tile-pyramid structure to enable stepless zooming. Experiments show that our approach can significantly reduce computation time and provide real-Time visualization effects of million-scale vector data while remaining superior visualization effects. © 2021 ieee.

关键词： Data visualization

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