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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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Hybrid Line-Based and Region-Based Interactive Set data visualization

Hybrid Line-Based and Region-Based Interactive Set Data Visu...

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CHI conference on Human Factors in Computing Systems

作者： Wang, Xiaohan Zhang, Chuyu Zhu, Yu Chen, Xueyi Shen, Liming Liu, Richen Qian, Rongtao Nanjing Normal Univ Sch Artificial Intelligence Sch Comp & Elect Informat Nanjing Peoples R China

ISBN: (纸本)9781450380959

A generic step in data analysis is to group data items into multiple sets based on specific attribute values. In this paper, we propose an interactive set-data exploration tool named Balloonvis, to make nested balloons as a visual metaphor to group set data over a timeline and visualize the set overlapping information while preserving the original layout. We employ a hybrid region-based and line-based scheme, which allows placing a representative image of each set data item at its region position and helps reduce visual clutter by line connection design. Energy optimization is exploited to compute the layouts of region-based set data items (balloons) and connected lines. The case study and the user study suggest the Balloonvis can visualize set data with more information. Besides, the proposed hierarchical scheme is more scalable on set data item size.

关键词： set visualization region-based visualization line-based visualization

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Universal visual Decomposer: Long-Horizon Manipulation Made Easy

Universal Visual Decomposer: Long-Horizon Manipulation Made ...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Zichen Zhang Yunshuang Li Osbert Bastani Abhishek Gupta Dinesh Jayaraman Yecheng Jason Ma Luca Weihs Allen Institute for AI University of Pennsylvania University of Washington

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands decomposing the overarching task into several manageable subtasks to facilitate policy learning and generalization to unseen tasks. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. To address these shortcomings, we propose Universal visual Decomposer (UvD), an off-the-shelf task decomposition method for visual long-horizon manipulation using pre-trained visual representations designed for robotic control. At a high level, UvD discovers subgoals by detecting phase shifts in the embedding space of the pre-trained representation. Operating purely on visual demonstrations without auxiliary information, UvD can effectively extract visual subgoals embedded in the videos, while incurring zero additional training cost on top of standard visuomotor policy training. Goal-conditioned policies learned with UvD-discovered subgoals exhibit significantly improved compositional generalization at test time to unseen tasks. Furthermore, UvD-discovered subgoals can be used to construct goal-based reward shaping that jump-starts temporally extended exploration for reinforcement learning. We extensively evaluate UvD on both simulation and real-world tasks, and in all cases, UvD substantially outperforms baselines across imitation and reinforcement learning settings on in-domain and out-of-domain task sequences alike, validating the clear advantage of automated visual task decomposition within the simple, compact UvD framework.

关键词： Training visualization Costs Reinforcement learning Aerospace electronics data mining Task analysis

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A Systematic Approach on Attention Based Multimodal Sentiment analysis in Convolutional Neural Network using Deep Learning Techniques

A Systematic Approach on Attention Based Multimodal Sentimen...

引用

Applied Intelligence and Computing (AIC), IEEE World conference on

作者： vinitha v S.K. Manju Bargavi Department of Computer Science and IT Jain (Deemed-to-be) University Bengaluru India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Sentiment analysis represents an emerging field of study in both natural language processing and computational intelligence with consumer evaluations of products and services are advancing in autonomy. Multimodal sentiment analysis aims to determine a user’s sentiment using multimodal data. It seeks to describe a novel approach to sentiment categorization that employs deep learning and natural language processing. The proposed framework for attention-based multimodal sentiment classification combines convolutional neural networks for image data with bidirectional encoder representations from transformers for textual data. The synergy between these two modalities enhances the model’s capability of capturing abundant contextual information and visual signals. The framework employs a convolutional neural network for image data that recovers hierarchical features while preserving spatial relationships. Simultaneously, the BERT model encodes textual information, enabling the model to comprehend semantic sequence. Combining these modalities requires an attention mechanism to learn and dynamically evaluate the importance of visual and textual signals during classification. Attention-based fusion enhances sentiment categorization performance by focusing on the most informative elements from both modalities. Extensive evaluations utilizing the publicly available CMU Multimodal Opinion Sentiment and Emotion Intensity dataset demonstrate the utility of the proposed framework with an accuracy of 95.87%. The experimental findings indicate that the convolutional neural network with the BERT method captures complex sentiment patterns across various domains more effectively.

关键词： Deep learning Sentiment analysis visualization Attention mechanisms Accuracy Bidirectional control Transformers Encoding data models Convolutional neural networks

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Design and exploration of Real Time Monitoring System for Power Grid Operation Safety Based on YOLO Object Detection Algorithm 3

Design and Exploration of Real Time Monitoring System for Po...

引用

3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wu, Jianhui Shangqiu Power Supply Company State Grid Henan Electric Power Company Shangqiu China

As a fundamental industry of the national economy, the development level of the power industry directly affects the national economic construction and people's lives. With the continuous acceleration of modernization, higher requirements have been put forward for the safe operation of the PG. Therefore, it is necessary to achieve the integration of remote real-time monitoring, management, and maintenance under the current situation, and combine modern information technology methods for research and analysis. To meet the system requirements and ensure the quality of power supply, a suitable and efficient job safety management system platform is designed for the practical situation of power grid (PG) operation safety, and is based on the YOLO (You Only Look Once) algorithm operating environment. This platform can be effectively applied in daily electricity management work to improve the production efficiency and economic benefits of PG enterprises. Afterwards, this article also conducted functional testing on the security real-time monitoring system, and the test results showed that the accuracy of the system reached over 80%;the recall rate was over 85%;the average accuracy was between 0.4 and 0.7;the system stability was between 90% and 97%;the scalability of the system was over 93%. © 2023 Elsevier B.v.. All rights reserved.

关键词： Economic and social effects

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Exploratory and statistical data analysis of Indian economy

Exploratory and statistical data analysis of Indian economy

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Bhawna Singh vandana Sandhya Umrao Sanjay Katiyar vanshika Sharma Department of CSE G.L. Bajaj Institute of Technology and Management Greater Noida India Department of CSE Noida Institute of Engineering and Technology Greater Noida India Department of CSE Greater Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Exploratory data analysis provides visual exploration of data and also allows for the identification of trends and relationships between variables. The aim of this research is to study about different factors involved in the economy of India. The dataset has been collected from year 1960 to 2020 and is analyzed by looking at the factors such as GDP, import and export value of goods and services, total reserves(include gold, current US Dollar) and health status of nation’s citizens. Thus, it suggests that all factors are important for a stable economy and it also provides different ways to improve these factors.

关键词： Gold visualization data analysis Oils Linear regression Production Market research

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Evaluation of 5G Edge and Cloud Computing for data Processing in visual Referencing of Mobile Robot Manipulators 56

Evaluation of 5G Edge and Cloud Computing for Data Processin...

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56th CIRP International conference on Manufacturing Systems, CIRP CMS 2023

作者： Boshoff, Marius Schuster, David Christ, Lukas Hesenius, Marc Gruhn, volker Kuhlenkötter, Bernd Production Systems Ruhr University Bochum Bochum Germany Institute for Software Engineering University of Duisburg-Essen Essen Germany

To fit the requirements of growing flexibility in production processes, intralogistics is increasingly automated, for example, by Mobile Robot Manipulators, which can transport material and perform assembly tasks. However, these transport systems have limited battery capacity and recharging involves occasional downtime. New approaches, such as Edge Computing, offer transport systems to offload intensive computational processes, e.g., for path planning or AI-based image analysis, and thus saving precious energy resources. However, since offloading such processes onto a server is tied to high security and transmission requirements, using 5G for communication might be an attractive solution. Therefore, an exemplary referencing use case of a Mobile Robot Manipulator is presented and the 5G based communication with two cloud servers and one edge server is examined. Our experimental results show that with a mean transmission time of 540 milliseconds Edge Computing facilitates both faster and more stable data transmission compared to Cloud Computing with a mean transmission time of 1,973 milliseconds. This has a significant impact on industrial processes whose efficiency and productivity depend on the quality of data transfer. © 2023 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International conference on Manufacturing Systems 2023.

关键词： 5G Cloud Communication Edge Mobile Robotics

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Real-Time Twitter Sentiment Analytics and visualization Using vader 2

Real-Time Twitter Sentiment Analytics and Visualization Usin...

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2nd IEEE International conference on Intelligent Technologies, CONIT 2022

作者： Pai, Aiswarya R Prince, Maria Prasannakumar, C.v. Amrita School of Arts and Sciences Department of Computer Science and It Kochi India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Twitter is an excellent source of real-time user-generated data. This is a platform where anyone may use Tweets to share their own views, opinions, or feedback on various topics. These themes can be from any field, including politics, business, and entertainment. Sentiment analysis is a technique of obtaining key consumer data from text in the form of a sentiment score, and it identifies the emotional tone behind a body of text. If this information can be obtained and assessed accurately, and can be used as a vital element in making a decision. However, due to the changing and complex formats of tweets, analyzing tweets has been more challenging. With sentiment values in mind, there is a huge amount of use of emoticons and slang. In this paper, we intend to provide visual demonstration of real-time tweets by classifying and determining the polarity score of live tweets where it is positive, negative, or neutral using vader, that is more efficient than traditional algorithms, and providing the user with a graph of the subject matter, which is updated in accordance with the tweets posted every second, trying to make decision-making a very simple process by providing information in a word cloud © 2022 IEEE.

关键词： Sentiment analysis

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An exploration of Consistency Learning with data Augmentation 35th

An Exploration of Consistency Learning with Data Augmentatio...

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35th International Florida Artificial Intelligence Research Society conference, FLAIRS-35 2022

作者： Shorten, Connor Khoshgoftaar, Taghi M. Florida Atlantic University United States

Deep Learning has achieved remarkable success with Supervised Learning. Nearly all of these successes require very large manually annotated datasets. data augmentation has enabled Supervised Learning with less labeled data, while avoiding the pitfalls of overfitting. However, Supervised Learning still fails to be Robust, making different predictions for original and augmented data points. We study the addition of a Consistency Loss between representations of original and augmented data points. Although this offers additional structure for invariance to augmentation, it may fall into the trap of representation collapse. Representation collapse describes the solution of mapping every input to a constant output, thus cheating to solve the consistency task. Many techniques have been developed to avoid representation collapse such as stopping gradients, entropy penalties, and applying the Consistency Loss at intermediate layers. We provide an analysis of these techniques in interaction with Supervised Learning for the CIFAR-10 image classification dataset. Our consistency learning models achieve a 1.7% absolute improvement on the CIFAR-10 original test set over the supervised baseline. More interestingly, we are able to dramatically reduce our proposed Distributional Distance metric with the Consistency Loss. Distributional Distance provides a more fine-grained analysis of the invariance to corrupted images. Readers will understand the practice of adding a Consistency Loss to improve Robustness in Deep Learning. © 2022 by the authors. All rights reserved.

关键词： Deep learning

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Color Enhancement for v-PCC Compressed Point Cloud via 2D Attribute Map Optimization

Color Enhancement for V-PCC Compressed Point Cloud via 2D At...

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IEEE visual Communications and Image Processing (vCIP)

作者： Jingwei Bao Yu Liu Zeliang Li Shuyuan Zhu Siu-Kei Au Yeung School of Information and Communication Engineering University of Electronic Science and Technology of China School of Science and Technology Hong Kong Metropolitan University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

video-based point cloud compression (v-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the v-PCC compressed point clouds. We propose the lightweight de-compression Unet (LDC-Unet), a 2D neural network, to optimize the projection maps generated during v-PCC encoding. The optimized 2D maps will then be back-projected to the 3D space to enhance the corresponding point cloud attributes. Additionally, we introduce a transfer learning strategy and develop a customized natural image dataset for the initial training. The model was then fine-tuned using the projection maps of the compressed point clouds. The whole strategy effectively addresses the scarcity of point cloud training data. Our experiments, conducted on the public 8i voxelized full bodies long sequences (8ivSLF) dataset, demonstrate the effectiveness of our proposed method in improving the color quality.

关键词： Point cloud compression Geometry Image coding Three-dimensional displays Image color analysis visual communication Transfer learning video sequences Image reconstruction Optimization

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Improving Short-Term Weather Predictions Using GANs

Improving Short-Term Weather Predictions Using GANs

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Progressive Innovations in Intelligent Systems and data Science (ICPIDS), International conference on

作者： Sanjay Singla Neeraj Kumar Kulwinder Singh Kavya Rawat vedant Singh Abhinav Sachdeva Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Short term weather forecast is very important to different fields like agriculture, disaster management and urban planning. Inherent complexity and non-linearity of atmospheric data presents a challenge for traditional forecasting methods. In a paper written this week, we propose a new way to improve short-term weather predictions using Generative Adversarial Networks (GANs), which are effective at generating high dimensional data. To increase the prediction accuracy, we propose a GAN architecture tailored for time series weather data that utilizes historical weather patterns. In the model are trained on extensive datasets of temperature, humidity, and precipitation and able to approximate the nature of weather phenomena. We show that the GAN based scheme outperforms conventional forecasting techniques in RMSE and MAE metrics. Additionally, visual comparisons show that GAN generated forecasts are closer to observed weather conditions. This work demonstrates the potential for use of GANs in transforming weather forecasting methodologies, the foundation for future exploration of their use in meteorology.

关键词： visualization Accuracy Urban planning Time series analysis Weather forecasting Computer architecture Generative adversarial networks Reliability Forecasting Meteorology

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