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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Sharma, Manish Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

The advent of Non-Fungible Tokens (NFTs) has brought about a paradigm shift with profound implications in the digital domain. The prevalence of Non-Fungible Tokens (NFTs) has led to a notable increase in lively and impactful discussions pertaining to them on the social media platform, Twitter. This study explores the realm of discourse surrounding non-fungible tokens (NFTs) on the social media platform Twitter, utilizing a comprehensive dataset obtained through the Twitter API. The scope of our research entails a thorough examination of the dissemination of tweets, levels of user interaction, geographical variation, and temporal patterns within this dynamic context. By employing rigorous techniques for data preparation, research, and visualization, we are able to reveal intricate patterns and trends. The examination of geographical patterns uncovers the extent of worldwide discussions surrounding non-fungible tokens (NFTs), whilst the analysis of time delves into the levels of interaction observed on an hourly, daily, and yearly basis. This study paper offers significant insights for stakeholders who aim to comprehend and leverage the influence of talks pertaining to non-fungible tokens (NFTs) on the Twitter platform. It presents practical implications for content providers, marketers, and researchers as they navigate the complex NFT ecosystem. This study sets the foundation for future inquiries into sentiment analysis, event-driven interaction, and the dynamic nature of debate surrounding digital assets in the evolving NFT arena. © 2023 ieee.

关键词： Blockchain technology NFT Twitter Analysis

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Immersive Analytics at the Electronic visualization Laboratory

Immersive Analytics at the Electronic Visualization Laborato...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Luc Renambot Jonas Talandis Fabio Miranda Lance Long Daniel J. Sandin G. Elisabeta Marai Michael E. Papka Nikita Soni Andrew Johnson Thomas A. DeFanti Daria Tsoupikova Dana Plepys Maxine Brown Jason Leigh Univ of Illinois Chicago Argonne National Lab UC San Diego Univ of Hawaii Manoa

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

September 2023 marked the 50th anniversary of the Electronic visualization Laboratory (EVL). This paper summarizes EVL’s efforts in Visual data science, with a focus on the many networked, immersive, collaborative visualization and virtual-reality (VR) systems and applications the Lab has developed and deployed, as well as lessons learned and future plans.

关键词： visualization Three-dimensional displays Conferences data visualization Collaboration Virtual reality data science

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Greenhouse intelligent warning system for precision agriculture 19

Greenhouse intelligent warning system for precision agricult...

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19th International Conference on Intelligent Environments, IE 2023

作者： Padilla-Quimbiulco, Diego Morales-Garcia, Juan Cantabella, Magdalena Ayuso, Belen Munoz, Andres Cecilia, Jose M. Departament of Computer Science Murcia Spain Universidad de Cádiz Departament of Computer Engineering Cádiz Spain Valencia Spain

ISBN: (纸本)9798350312225

Greenhouses are complex systems where many variables are involved in order to optimize crops in an intensive agriculture framework. Therefore, monitoring and visualization of all these variables in real-time is mandatory to meet the trade-off between natural resource consumption and production maximization. In this article, we introduce an intelligent warning system to efficiently control agricultural activity in an operational greenhouse to increase productivity by optimizing crop production and energy consumption. The system includes a web application that allows the graphical and statistical representation of data measured by several sensors located inside a greenhouse. These sensors are located in strategic points that allow the reading of real-time data in a more accurate manner, therefore allowing the generation of information with the minimum percentage of error. In addition, the web application offers different data representations to allow a more exhaustive analysis of the data obtained. As a result, this warning system may help greenhouse managers to anticipate abnormal situations affecting their crops. © 2023 ieee.

关键词： Greenhouses

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The Role of Trust Transfer in Facilitating Stickiness Behavior in Live Streaming: A Socio-technical Perspective 8

The Role of Trust Transfer in Facilitating Stickiness Behavi...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Chen, Wen-Kuo Yanbegi, Fatih Silalahi, Andri Dayarana K. Silaban, Pantas H. Eunike, Ixora Javanisa Demirci, Serhan National Kaohsiung University of Science and Technology Department of Money and Banking Kaoshiung Taiwan Chaoyang University of Technology Department of Business Administration Taichung Taiwan Graduate School Master of Management Program University of Hkbp Nommensen Medan Indonesia Chaoyang University of Technology Department of Marketing and Logistics Management Taichung Taiwan

ISBN: (纸本)9798350326093

In recent years, research on live streaming has developed rapidly among scholars. Especially scholars are increasingly interested in understanding consumer behaviors regarding purchasing behavior on live streaming platforms, which is classified as transactional behavior. A separate consideration is a non-transactional behavior, as there are currently many live-streaming platforms that are not only used for sales activities but also to broadcast games, cooking, and eating activities, which do not focus on consumer behavior in live-streaming activities. This study investigates consumers' non-transactional behavior (e.g., stickiness behavior) on live streaming platforms using socio-technical theory and trust transfer. This study tested the hypothesis on 285 Indonesian participants participating in various live-streaming activities across various platforms. data analysis was performed using a structural equation modeling approach using Smart-PLS 4.0 software. In the study, interactivity and visualization significantly impacted cognitive trust more than affective trust. Although cognitive trust significantly impacts affective trust, it will also similarly impact consumers when they already possess it. The study also confirms that both types of trust increase stickiness behaviors. This is primarily because building cognitive trust is a priority for trust transfer. Therefore, it can increase stickiness behavior and affective trust, which, in this study, evolve significant managerial implications. © 2023 ieee.

关键词： Consumer behavior

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data Analytics on Opportunities for Women in the Field of Technology 5

Data Analytics on Opportunities for Women in the Field of Te...

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5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Pallavi, Lanke Kosuru, Sai Sathwik Dulam, Abhinav Goud Datla, Kaushik Varma Debbata, Kousthubha Gangavarapu, Raghuveer Chaitanya B v Raju Institute of Technology Department of Computer Science and Engineering Telangana State Medak District Narsapur502313 India

ISBN: (纸本)9798350337525

Women's opportunities are in the uptrend in recent days with recent laws, obligations, rules, regulations, and government promulgations in favor of women's participation in the global space. With emerging technologies and rising market competition, these opportunities are critical to women's societal development. Adding to this, various technology and IT companies are encouraging their female employees to take part in women-led events and seminars actively. After a score of deliberate perusals by the authors into the matter, it is found that a large proportion of women are least knowledgeable about these opportunities resulting in the persistence of poor outcomes. This paper takes these issues into consideration, where the authors performed data analysis on the existing women's opportunities, tabled them, and designed an analytical solution for the same. With the existence of powerful technologies like Power BI, this paper explores using power bi for visualizing the trends of opportunities in numbers and their impact on female societies. The research involves the basic requirements and salient features of Women related events, scholarships, and contributions as a result of the same. In this paper, we discuss the relevance and impact of such opportunities for women and the development of appropriate technical solutions for the same. The result of this research work contributes to the systematic aversion to the situations mentioned above and provides a valid solution by data-driven applications throughout the procedure of development. The solutions enable the users to have a clear idea about the statistics of women's opportunities in recent years and provide a clear visualization of the current scenarios involving the impact and statistical influence of these opportunities on the female society scaled to the global level. © 2023 ieee.

关键词： data Analytics

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Covid-19 Detection on X-Ray Image Using Deep Learning 4

Covid-19 Detection on X-Ray Image Using Deep Learning

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4th International Conference on Intelligent Engineering and Management, ICIEM 2023

作者： Boina, Roja Chaturvedi, Abhay Sharma, Manish Shrivastava, Anurag Kumar, Indradeep Rao, Aln Independent Researcher MorrisvilleNC United States GLA University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201308 India

ISBN: (纸本)9798350341126

An unexpected pandemic known as COVID-19 struck the entire world in the year 2020. Research in numerous sectors has been prompted to address it as a result of the lack of treatment. Understanding the new variety and developing a vaccination become more challenging as a virus evolves. Numerous nations are impacted by the rise of novel variations. Therefore, it is crucial to assess COVID-19's performance in addition to death forecasting. The proposal develops numerous analyses, visualizations, and predictive models that can forecast COVID 19 performance. The goal is to first track data visualization, later Covid 19 data are analyzed and predicted globally to raise awareness by applying machine learning techniques including linear regression, support vector regression, and Holt forecasting method. The objective of this study is to understand how machine learning methods and applications are used for various COVID-19-related tasks and investigations. An analysis of research published in science Direct, Springer, Hindawi, and MDPI on this topic in 2020 using the search terms COVID-19, machine learning, supervised learning, and unsupervised learning. In total, 16,306 articles were retrieved, but 14 searches from these publications were used in this study. It has been demonstrated that machine learning can be useful for understanding, predicting, and differentiating COVID-19. © 2023 ieee.

关键词： COVID-19

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Design, Implementation and Evaluation of an Information System for the Management of Digital Farming Scientific data 50

Design, Implementation and Evaluation of an Information Syst...

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50th Latin American Computing Conference (CLEI)

作者： da Silva, Allan Bastos Lopes Siqueira, Thiago Luis Fed Inst Selo Paulo IFSP Comp Engn BSc Piracicaba SP Brazil Fed Inst Sao Paulo IFSP Comp Engn Fac Piracicaba SP Brazil

ISBN: (纸本)9798331540982;9798331540975

Immediate attention and efforts are essential to mitigate hunger throughout the world. Although Latin America has diverse conditions to produce and offer a variety of foods, the improvement of production systems is continuous to enhance food security. Digital farming comprises cutting-edge technologies while smart farms are equipped with solutions that encompass internet of things (IoT), big data and artificial intelligence, for instance. The e-science is supported by theory, experimentation, and computational methods to connect a multidisciplinary collaborative network to research, develop and innovate to create solutions. Both agro-industry and academia can benefit from high precision and quality data collected in smart farms and made available for different stakeholders to analyze them. In this paper, we present an information system that has been designed, implemented and evaluated to manage scientific data regarding digital farming, which: (i) can receive field data acquired and transferred by IoT devices placed in smart farms;(ii) stores data and enables data retrieval;(iii) allows the user to create, read, update and remove experiments and phases;(iv) allows the user to register IoT devices and attach files and URLs related to the experiments;(v) enables sharing experiment's data among users by means of requests and permissions;(vi) displays data in tables and charts and enables filtering. The evaluation of the system comprised a demo for stakeholders, execution while collecting field data, deployment and scalability simulations, and execution on smartphones. All of them succeeded. Our system can improve digital farming and promote e-science as it provides means of receiving, storing, retrieving, visualizing, and sharing scientific data sourced from smart farms. Furthermore, it can be used to motivate collaborative research, development, and innovations to benefit food security.

关键词： smart farms e-science IoT web development databases visualization

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Preface Vis X Vision 2023

Preface Vis X Vision 2023

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Vis X Vision, ieee

Welcome to the 3rd biennial installment of the VISXVISION workshop at ieee VIS 2023 (22-27 October) in person in Melbourne, Australia. Join us on Monday, October 23, for talks and discussions of past, current, and potential new research at the intersection of data visualization and vision science. This workshop aims to connect researchers studying visual perception with those studying visualization science to increase collaboration and generate new research ideas that cross boundaries. We will explore methods and findings that investigate perception and cognition as they may relate to visualization, highlighting research in color perception, ensemble encoding, cognitive accessibility, perceptual variability, and design effectiveness.

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VOLO: Vision Outlooker for Visual Recognition

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 6575-6586页

作者： Yuan, Li Hou, Qibin Jiang, Zihang Feng, Jiashi Yan, Shuicheng Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Peoples R China PengCheng Lab Shenzhen 518066 Peoples R China Nankai Univ Sch Comp Sci TMCC Tianjin 300071 Peoples R China Natl Univ Singapore ECE Singapore 119077 Singapore Sea AI Lab Singapore 609027 Singapore

Recently, Vision Transformers (ViTs) have been broadly explored in visual recognition. With low efficiency in encoding fine-level features, the performance of ViTs is still inferior to the state-of-the-art CNNs when trained from scratch on a midsize dataset like ImageNet. Through experimental analysis, we find it is because of two reasons: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines, leading to low training sample efficiency;2) the redundant attention backbone design of ViTs leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we present a new simple and generic architecture, termed Vision Outlooker (VOLO), which implements a novel outlook attention operation that dynamically conduct the local feature aggregation mechanism in a sliding window manner across the input image. Unlike self-attention that focuses on modeling global dependencies of local features at a coarse level, our outlook attention targets at encoding finer-level features, which is critical for recognition but ignored by self-attention. Outlook attention breaks the bottleneck of self-attention whose computation cost scales quadratically with the input spatial dimension, and thus is much more memory efficient. Compared to our Tokens-To-Token Vision Transformer (T2T-ViT), VOLO can more efficiently encode fine-level features that are essential for high-performance visual recognition. Experiments show that with only 26.6 M learnable parameters, VOLO achieves 84.2% top-1 accuracy on ImageNet-1 K without using extra training data, 2.7% better than T2T-ViT with a comparable number of parameters. When the model size is scaled up to 296 M parameters, its performance can be further improved to 87.1%, setting a new record for ImageNet-1 K classification. In addition, we also take the proposed VOLO as pretrained models and report superior performance on downstream tasks, such

关键词： Transformers Computer architecture Computational modeling Training data models Task analysis visualization Tokens-to-token representation learning vision transformer vision outlooker outlook attention image classification

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A Bayesian Filtering Framework for Continuous Affect Recognition From Facial Images

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 3709-3722页

作者： Pei, Ercheng Zhao, Yong Oveneke, Meshia Cedric Jiang, Dongmei Sahli, Hichem Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Sci Xian 710121 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Natl Engn Lab Integrated Aerospace Ground Ocean B Shaanxi Key Lab Speech & Image Informat Proc NPU VUB Joint AVSP Res LabSch Comp Sci Xian 710072 Peoples R China Vrije Univ Brussel VUB Dept ETRO B-1050 Brussels Belgium Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Vrije Univ Brussel VUB Dept Elect & Informat ETRO VUB NPU Joint AVSP Res Lab B-1050 Brussels Belgium

Continuous affective state estimation from facial information is a task which requires the prediction of time series of emotional state outputs from a facial image sequence. Modeling the spatial-temporal evolution of facial information plays an important role in affective state estimation. One of the most widely used methods is Recurrent Neural Networks (RNN). RNNs provide an attractive framework for propagating information over a sequence using a continuous-valued hidden layer representation. In this work, we propose to instead learn rich affective state dynamics. We model human affect as a dynamical system and define the affective state in terms of valence, arousal and their higher-order derivatives. We then pose the affective state estimation problem as a jointly trained state estimator for high-dimensional input images, combining an RNN and a Bayesian Filter, i.e. Kalman filters (KF) and Extended Kalman filters (EKF), so that all weights in the resulting network can be trained using backpropagation. We use a recently proposed general framework for designing and learning discriminative state estimators framed as computational graphs. Such approach can handle high dimensional observations and efficiently optimize, in an end-to-end fashion, the state estimator. In addition, to deal with the asynchrony between emotion labels and input images, caused by the inherent reaction lag of the annotators, we introduce a convolutional layer that aligns features with emotion labels. Experimental results, on the RECOLA and SEMAINE datasets for continuous emotion prediction, illustrate the potential of the proposed framework compared to recent state-of-the-art models.

关键词： Bayes methods Computational modeling visualization data models Kalman filters Emotion recognition Adaptation models Recurrent neural network Bayesian filter adaptive alignment continuous affect recognition

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