Dendrogramix: A hybrid tree-matrix visualization technique to support interactive exploration of dendrograms

This article discusses visualization as a technique to enhance programming comprehension. It points out that current animation approach, being machine-based technique, is inadequate due to the fact that machine-based animation tools are difficult to integrate in the current teaching materials. In addition, machine-centered animators do not guarantee the learner with absolute engagement. In this paper, MTL, as a visualization technique which is absolutely learner-driven is demonstrated and discussed. It is shown that MTL can be integrated with current materials for teaching and learning programming and it can guarantee absolute authority to the learner.

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Explainable Visualization for Interactive Exploration of CNN on Wikipedia Vandal Detection

2019 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata47090.2019.9006128 ◽

2019 ◽

Author(s):

Zerong Liu ◽

Aidong Lu

Keyword(s):

Interactive Exploration

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Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽

10.3390/s21093046 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3046

Author(s):

Shervin Minaee ◽

Mehdi Minaei ◽

Amirali Abdolrashidi

Keyword(s):

Deep Learning ◽

Facial Expression ◽

Facial Expression Recognition ◽

Expression Recognition ◽

Visualization Technique ◽

Convolutional Network ◽

The Past ◽

Multiple Datasets ◽

The Face ◽

Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

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Interactive Exploration of Multi-Dimensional Information Spaces with Preference Support

ACM SIGIR Forum ◽

10.1145/2701583.2701602 ◽

2014 ◽

Vol 48 (2) ◽

pp. 118-118

Author(s):

Panagiotis Papadakos

Keyword(s):

Information Spaces ◽

Interactive Exploration

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Unsteady Flow Phenomena in Rotating Centrifugal Impeller Passages

Journal of Engineering for Power ◽

10.1115/1.3445302 ◽

1970 ◽

Vol 92 (1) ◽

pp. 65-71 ◽

Cited By ~ 25

Author(s):

E. Lennemann ◽

J. H. G. Howard

Keyword(s):

Unsteady Flow ◽

Secondary Flow ◽

Centrifugal Impeller ◽

Bubble Flow ◽

Hydrogen Bubble ◽

Visualization Technique ◽

Rotating Systems ◽

Flow Phenomena ◽

Relative Flow ◽

Zero Flow

The phenomena of unsteady relative flow observed in a centrifugal impeller passage running at part capacity and zero flow are discussed. The mechanisms of passage stall for a shrouded and unshrouded impeller are investigated and a qualitative correlation is developed for the influence of secondary flow and inducer flow on the passage stall. The hydrogen bubble flow visualization technique is extended to higher velocities and rotating systems and provides the method for obtaining the experimental results.

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