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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

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TDT: Two-Stream Decoupled Transformer for Text-Driven Human Animation Generation 2

TDT: Two-Stream Decoupled Transformer for Text-Driven Human ...

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2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Zhou, JiaShuang Du, Xiaoqin Lv, Yifan Liu, Yongqi School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan China

ISBN: (纸本)9798350360363

The automatic generation of high-quality human animations in a controllable and natural manner has always been a goal pursued by experts in the field of animation. Text- driven human animation generation offers user-friendly and versatile application scenarios. In this paper, we propose a two-stream decoupled Transformer network, TDT, for extracting motion semantic information from the text modality and generating human animations based on it. Our contributions are: (1) The text is highly decoupled in its description of trajectories and motions, utilizing a two-stream Transformer network to extract trajectory features and motion features separately. (2) We employ vector orthogonalization to constrain the motion feature vector and the trajectory feature vector, promoting the decoupling of the motion manifold and the trajectory manifold. (3) We introduce a local loss function, the mean-variance reconstruction loss, as a complement to the global loss function, further facilitating feature fusion between the two modalities. Experimental results demonstrate that our proposed model outperforms state-of-the-art text-driven human animation generation models in terms of objective evaluation metrics. Moreover, according to visualization results, the generated motions exhibit greater consistency with the semantics described in the input text by the user and are closer to the ground truth data. © 2023 ieee.

关键词： Trajectories

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Ultrawide-band Millimeter-wave Photonic Radar Mounted on Drone 31

Ultrawide-band Millimeter-wave Photonic Radar Mounted on Dro...

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31st Asia-Pacific Microwave Conference, APMC 2023

作者： Koyabu, Yosuke Tokunaga, Haruka Wang, Yaheng Yi, Li Ohtsuka, Takahiro Tagami, Yasuyuki Nagatsuma, Tadao Osaka University Graduate School of Engineering Science Osaka Toyonaka Japan Osaka University Graduate School of Engineering Osaka Suita Japan Jfe Shoji Electronics Corporation Chiyoda-ku Tokyo Japan

ISBN: (纸本)9781665494182

To expand the use of millimeter-wave (MMW) radars in nondestructive testing applications, a drone-mounted radar system using photonics technologies for signal generation and transmission was developed. Stable measurement was realized not only by decreasing the data acquisition time but also by downsizing the drone with a light-weight radar frontend. visualization of defects was successfully demonstrated for test objects simulating defects in building walls. © 2023 ieee.

关键词： Drone Fiber-optic Millimeter-wave Nondestructive inspection (NDI)

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Visualizing Student Attention in Smart Classrooms: A Preliminary Study 15

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

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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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Deep Learning Model to Improve Teaching-Learning Process using Sentiment Analysis 14

Deep Learning Model to Improve Teaching-Learning Process usi...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chauhan, Ganpat Singh Garg, Neeraj Nahta, Ravi Meena, Yogesh Kumar Manipal University Department of Information Technology Jaipur India Skit Department of Computer Engineering Jaipur India Nirma University Department of Computer Engineering India Mnit Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350335095

The integration of teacher capabilities and teaching quality has become a crucial concern in today's Outcome-Based Education (OBE) in higher educational institutes. In addition, there is a requirement to utilize the vast amounts of textual qualitative data obtained through stakeholder's input, primarily the responses to open-ended inquiries, which pose a significant problem to academics and institutions alike. The present research assesses instructors' interpersonal abilities using sentiment analysis techniques. We evaluated certain skills using aspect-based sentiment severity of the over 4,000 inputs left by Stakeholders for higher learning. The latest deep learning models and visualization frameworks were used to carry out the sentiment assessment system. According to the research presented, certain abilities are brought to light using aspect-driven sentiment analysis by the course's structure, teacher's teaching, and student's learning. Additionally, the results show that stakeholders have an equally monolithic and converging perspective, supporting the legitimacy of their opinions and their significant involvement towards the tracking of the teaching environment generally concerning the ultimate aim of improving learning of the students. © 2023 ieee.

关键词： Sentiment analysis

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Correlation Network Analysis of ASEAN Foreign Direct Investment

Correlation Network Analysis of ASEAN Foreign Direct Investm...

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2023 ieee International Conference on Computing, ICOCO 2023

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Yeo, Ivan Abu Bakar, Mohd Aftar Kaur, Sukhminder Razak, Fatimah Abdul School of Mathematical Sciences College of Computing Informatics & Mathematics Universiti Teknologi Mara Shah Alam Malaysia Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Eng. & Science Dept. of Mathematical & Acturial Sciences Malaysia Universiti Kebangsaan Malaysia Faculty of Science & Technology Department of Mathematical Sciences Bangi Malaysia School of Computer Science Taylor's University Faculty of Innovation & Technology Subang Jaya Malaysia

ISBN: (纸本)9798350302684

Although ASEAN aims to promote regional economic and security cooperation, among others, the empirical evidence in this study evinces that the impact of ASEAN on the economic integration between ASEAN countries remains limited. The economic integration is investigated based on the association of foreign direct investments (FDI) between Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. The longest common period of the FDI series is 29 years beginning in 1993. We examine the yearly FDI performances using a time series plot and descriptive statistics including the coefficient of variance for performance consistency. The association, strength, and effect of ASEAN economic integration are measured and illustrated by using Pearson's correlation matrix-circle plot. There are 45 unique associations representing the economic integration between ASEAN countries that unfortunately exist only in moderate strength, at most. The highest association of 0.62 is between Cambodia and Laos, and there is negligible association between Philippines-Vietnam and Indonesia-Philippines. It is also noted that almost 60% of the associations are positively correlated to one another. For the second objective, the correlations are clustered and visualized using network plots. The correlation- based network plot reveals two subgroups linking Indonesia- Cambodia-Singapore-Laos and Malaysian-Vietnam-Brunei closer together, while Thailand, Myanmar, and the Philippines are isolated from the subgroups. The most long-lasting interconnections between economies are found in the subgroup centered on Cambodia. In addition, there are only 14 weak- medium integrated links when clustering the correlation values above magnitude 0.3. The results suggest that the vast economic cooperation opportunities have not been fully explored among the ASEAN countries. Therefore, strategies may be necessary, such as adding packages and quotas to attract FDI from individual i

关键词： data visualization

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Modeling of Metaheuristics with Deep Transfer Learning Based Visual data Mining for the Healthcare Sector 6

Modeling of Metaheuristics with Deep Transfer Learning Based...

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6th Iraqi International Conference on Engineering Technology and its Applications, IICETA 2023

作者： Abed, Z. Shnawa, Ammar H. Hadi, M.R. Ibrahim, Karrar Adnan, Myasar Mundher Hameed, Waleed Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Al-Zahraa University for Women Karbala Iraq The Islamic University Najaf Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

data mining is a process that interacts with a large dataset to determine complex interesting patterns from unknown structured data. Visual data mining (VDM) is considered a combination of two disciplines: data mining and visualization to explore useful and implicit knowledge from a huge dataset. Also, it is strongly associated with multimedia systems, high-performance computing, computer graphics, human-computer interaction, and pattern recognition. Recently, VDM approaches find useful in the healthcare sector to aid the decision-making process. In addition, the design of VDM approaches for healthcare applications needs special consideration to make sure security of data. Artificial intelligence (AI) methods play a vital role in achieving scalability and accurate analysis from real-time environments. Therefore, this study develops a blockchain-assisted quantum bacterial colonial optimization with deep learning (BAQBCO-DL) algorithm for VDM in the healthcare environment. The proposed BAQBCO-DL model exploits BC technology for secured data communication in the healthcare sector. In addition, the BAQBCO-DL model designs a U-Net-based segmentation with an optimal densely connected network (DenseNet) model for feature extraction, and the hyperparameter tuning process is performed using the QBCO algorithm. Finally, a probabilistic neural network (PNN) classifier is utilized to define appropriate class labels. An extensive experimental analysis is conducted to report the superior performance of the BAQBCO-DL technique and the outcomes are measured under several aspects. The simulation outcomes underlined the supremacy of the BAQBCO-DL algorithm over recent methodologies. © 2023 ieee.

关键词： data mining

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Hypothesis Classification of Weather on VGG19 CNN Model Fine-Tuned with the Adam Optimizer 3

Hypothesis Classification of Weather on VGG19 CNN Model Fine...

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

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Kapruwan, Akanksha Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9798350319125

Instances of unfortunate events resulting from adverse weather conditions have been seen on a recurring basis in many regions of the globe. Recent news has been marked by a series of unfortunate incidents, including airline catastrophes, ship collisions, train derails, and vehicle accidents. The problem of safety and security in hazardous settings has garnered significant attention within society. Numerous studies have been undertaken in the past to emphasise the susceptibility of transport services to interruptions resulting from adverse weather conditions. Researchers have the ability to use several methodologies, such as convolutional neural networks (CNNs) and decision trees, which are machine learning algorithms, to classify meteorological situations. Remote sensing data, such as satellite photos, may be used by researchers to discern diverse weather conditions and analyse patterns in weather phenomena. Weather categorization is a widely used approach in the field of weather research, serving as a beneficial tool for categorising various weather phenomena and comprehending their impacts on several aspects of society. This research focuses on the VGG 19 model, which exhibits superior accuracy in weather forecasting. Specifically, the model employs a batch size of 32 and an epoch of 15, employing the Adam optimizer. Remarkably, it achieves a remarkable accuracy of 96% in predicting weather conditions. © 2023 ieee.

关键词： Confusion Matrix Convolutional Neural Network Pre-processing VGG 19 Model visualization Weather Classification

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Open Tool-kit for AI-based Sleep Apnea Scoring 4

Open Tool-kit for AI-based Sleep Apnea Scoring

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4th International Conference for Emerging Technology, INCET 2023

作者： Ratheesh, Karthika Rajeev, Krishna Jyothika, S. Menon, Athira B Krishnan Pathinarupothi, Rahul Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amritapuri India

ISBN: (纸本)9798350335750

Sleep is the most essential and fundamental to an individual's well-being and vitality because it provides for the restoration and re-energizing of both the body and mind. Sleep apnea is a common sleep problem that affects millions of individuals worldwide. It is distinguished by breathing pauses or shallow breathing during sleeping, which can result in a variety of health problems such as heart disease, stroke, and diabetes [1]. Sleep apnea diagnosis and management can be difficult because the cost and time-consuming polysomnography (PSG) testing requires specialized equipment and trained personnel. To address these concerns, we created and validated an artificial intelligence (AI)-based sleep apnea scoring system that analyses electrocardiogram (ECG) signals to predict the severity of sleep apnea. The system analyses ECG signals using 1D-CNN to predict the Apnea-Hypopnea Index (AHI), a measure of the severity of sleep apnea. The tool is organized into three sections: data exploration, data visualization, and prediction, and it is aimed at accurately forecasting patients' risk of sleep apnea. Our research demonstrates the potential of AI-based approaches for the diagnosis and management of sleep apnea, and we believe that our system can help improve patient outcomes and quality of life. © 2023 ieee.

关键词： Electrocardiograms

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Towards Optimization of Math Pedagogical Techniques and Behaviors Using Decision and Ensemble Trees 4

Towards Optimization of Math Pedagogical Techniques and Beha...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Fawy, Mohamed Hamza, Meer Aziz, Rasha Abd El Yafi, Eiad Technology and Maritime Transport College of Management and Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport College of Computing and Information Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport Arab Academy for Science Businese Information Systems Department Alexandria Egypt University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9798350348248

Professional development programs for educators are thought to be generally ineffective and inefficient in raising student enjoyment and performance. As a result, new educators often express that the first years in their career are challenging and lacking in appropriate and effective support. New educators may frequently experience stress and anxiety from those factors as well as the inherent challenges of teaching young learners. The objectives of this paper and further research are to derive the most effective teaching techniques and behaviors using decision and ensemble tree methods. Six different models were trained from data collected on Egyptian preparatory language school mathematics teachers and their respective students. Two decision trees, two random forests and two gradient boosted trees are included, one set for classification tasks and the other for regression. The Student Evaluation Index (SEI) employed and generated by the student survey responses in this paper are used to derive the importance rankings of teaching techniques and behaviors. The tree structures can also be used to help teachers create improvement programs that will increase the performance and enjoyment of their students. The results of SEI prediction utilizing our tree methods are found to be critically dependent on techniques like visualization and the use of native Language. It stands to reason that preparatory language school teachers in Egypt should emphasize the use of visual aids while instructing students in their native language. Additional attributes of a popular or effective teachers include inspirational behavior and the usage of practice problem sets such as homework. © 2023 ieee.

关键词： Students

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Navigating the NFT Twittersphere: Insights from Geographic, Temporal, and Engagement Analysis 3

Navigating the NFT Twittersphere: Insights from Geographic, ...

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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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