Illustrating Changes in Time-Series Data With Data Video

Lu, Junhua; Wang, Jie; Hui, Yang; Gu, Yuhui; Ding, Zhiyu; Xu, Mingliang; Chen, Wei

doi:10.1109/mcg.2020.2968249

Cited by 15 publications

(3 citation statements)

References 13 publications

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“…The most important thing in achieving deep learning is constantly questioning whether core literacies are being implemented. According to Lu, authentic teaching is conducted when students understand why and how knowledge exists, how it is developed, and when the learning is integrated into their individual experiences [8]. Vrysis believes that deep learning requires attention to each student's real needs and non-intellectual factors such as interests, aspirations, ideals, ideologies, emotions, attitudes, and values in the development process [9].…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Deep Learning for Sequential Data Processing in Music Education

Jing

2024

IJCAI

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In response to the shortcomings of insufficient music structure, this article proposes a structured model based on motivational phrases and phrases. Starting from the composition structure of motivational phrases, deep learning techniques are used to learn composition. In the music generation model, a Scratch music generation model that can generate Pianoroll format music is constructed by using a generative adversarial network based on emotions and time structures. And use convolutional neural networks in the generator and discriminator to improve training speed. The effectiveness and practicality of the two algorithm models were verified through multiple comparative experiments and algorithm effectiveness experiments. This method achieves structural feature extraction of music by designing feature extractors at different music granularities. By designing feature expression functions at multi-scale music granularity, the music structure embedded in the music itself is incorporated into the reward function. Use forward backward propagation method to update the parameters of the model, and use dropout technique to improve the model's ability to resist overfitting. The test results show that the model has specific generalization ability, with an accuracy rate of 90%, and high recall and accuracy of the model.The experimental results show that this method can achieve better music generation results than the reward function method based on manual rules and before and after relationships. Solved the problem of lacking knowledge of music theory to propose rules, and compensated for the pain of insufficient utilization of music structure information in network models based on context. Povzetek: Študija uvaja evolucijsko globoko učenje za glasbeno izobraževanje, ki uporablja GAN za generiranje glasbe in CNN za izboljšanje hitrosti učenja.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Deep Learning for Sequential Data Processing in Music Education

Jing

2024

IJCAI

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“…Another investigation [19] established a task-sensitive process to assist journalists in selecting the most suitable visual representation for their data. Moreover, multiple studies have focused on specific applications, such as health data [20], data videos [21][22][23], and sonification [24] for data journalism.…”

Section: Data Visualization In Journalismmentioning

confidence: 99%

From Shock to Shift: Data Visualization for Constructive Climate Journalism

Morini,

Eschenbacher,

Hartmann

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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We present a multi-dimensional, multi-level, and multi-channel approach to data visualization for the purpose of constructive climate journalism. Data visualization has assumed a central role in environmental journalism and is often used in data stories to convey the dramatic consequences of climate change and other ecological crises. However, the emphasis on the catastrophic impacts of climate change tends to induce feelings of fear, anxiety, and apathy in readers. Climate mitigation, adaptation, and protection-all highly urgent in the face of the climate crisis-are at risk of being overlooked. These topics are more difficult to communicate as they are hard to convey on varying levels of locality, involve multiple interconnected sectors, and need to be mediated across various channels from the printed newspaper to social media platforms. So far, there has been little research on data visualization to enhance affective engagement with data about climate protection as part of solution-oriented reporting of climate change. With this research we characterize the unique challenges of constructive climate journalism for data visualization and share findings from a research and design study in collaboration with a national newspaper in Germany. Using the affordances and aesthetics of travel postcards, we present Klimakarten, a data journalism project on the progress of climate protection at multiple spatial scales (from national to local), across five key sectors (agriculture, buildings, energy, mobility, and waste), and for print and online use. The findings from quantitative and qualitative analysis of reader feedback confirm our overall approach and suggest implications for future work.

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“…A large variety of animations being applied to different domains. Thus, the need for emphasizing crucial parts of animation has motivated researchers to study attention guidance for dynamic visualization [10,15,22,31]. For example, in the education domain, De Koning et al [13] attempted to transfer successful cueing approaches from static visualization to animation for instructional design.…”

Section: Related Workmentioning

confidence: 99%

Improving Engagement of Animated Visualization with Visual Foreshadowing

Wang

Zhang

et al. 2020

Preprint

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Animated visualization is becoming increasingly popular as a compelling way to illustrate changes in time series data. However, maintaining the viewers focus throughout the entire animation is difficult because of its time-consuming nature. Viewers are likely to become bored and distracted during the ever-changing animated visualization. Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. To demonstrate our approach, we built a proof-of-concept animated visualization authoring tool that incorporates visual foreshadowing techniques with various styles. Our user study indicates the effectiveness of our foreshadowing techniques on improving engagement for animated visualization.

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Illustrating Changes in Time-Series Data With Data Video

Cited by 15 publications

References 13 publications

Evolutionary Deep Learning for Sequential Data Processing in Music Education

Evolutionary Deep Learning for Sequential Data Processing in Music Education

From Shock to Shift: Data Visualization for Constructive Climate Journalism

Improving Engagement of Animated Visualization with Visual Foreshadowing

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