Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs

Zhu, Haiyang; Zhu, Min; Feng, Yingchaojie; Hu, Yue; Wu, Shao-Ling; Wu, Xiangyang; Chen, Wei

doi:10.1016/j.visinf.2021.06.002

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…Experts noted that the tabular view may not facilitate effective comparisons when there were too many rows or columns, while excessive dots in feature projections may cause visual clutter. 100 Therefore, it is promising to design more effective visualizations to support the workflow at a larger scale. Additionally, our system currently employs SHAP values to evaluate feature contribution, which can be computationally expensive, especially given the vast volume of manuscript submissions encountered by leading conferences and journals.…”

Section: Discussionmentioning

confidence: 99%

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Weng,

Liu,

Zhu

et al. 2024

Information Visualization

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Large language models (LLMs) have gained popularity in various fields for their exceptional capability of generating human-like text. Their potential misuse has raised social concerns about plagiarism in academic contexts. However, effective artificial scientific text detection is a non-trivial task due to several challenges, including (1) the lack of a clear understanding of the differences between machine-generated and human-written scientific text, (2) the poor generalization performance of existing methods caused by out-of-distribution issues, and (3) the limited support for human-machine collaboration with sufficient interpretability during the detection process. In this paper, we first identify the critical distinctions between machine-generated and human-written scientific text through a quantitative experiment. Then, we propose a mixed-initiative workflow that combines human experts’ prior knowledge with machine intelligence, along with a visual analytics system to facilitate efficient and trustworthy scientific text detection. Finally, we demonstrate the effectiveness of our approach through two case studies and a controlled user study. We also provide design implications for interactive artificial text detection tools in high-stakes decision-making scenarios.

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Section: Discussionmentioning

confidence: 99%

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Weng,

Liu,

Zhu

et al. 2024

Information Visualization

Self Cite

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“…It is predicated on the notion that comparable data points typically have similar labels or values [16]. The KNN method employs the complete training dataset as a reference throughout the training phase [17], [18]. It uses a selected distance metric, such as Euclidean distance, to determine the distance between each training example and the input data point before making predictions [13], [19].…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding

Malik,

Riza,

Defit

2024

LKJITI

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This research aims to develop a fast and adaptive sentiment evaluation approach related to the use of white paracetamol using a combination of the K-Nearest Neighbors (KNN) algorithm, Synthetic Minority Over-Sampling Technique (SMOTE), and the Receptive Deciding concept. Imbalances in the dataset, where positive sentiment may predominate, are addressed through the use of SMOTE to synthesize minority class samples. The KNN algorithm is applied to build a sentiment classification model, while Receptive Deciding is used to provide adaptive intelligence to changes in sentiment. The SMOTE oversampling process is carried out to achieve class balance, while KNN is used to classify sentiment. Receptive Deciding is applied to increase the model's adaptability to changes in sentiment. The research results show that the integration of the SMOTE, KNN, and Receptive Deciding methods provides an effective approach in assessing sentiment accurately and adaptively. The developed model is able to recognize changes in sentiment over time and provide balanced evaluation results. These findings are expected to contribute to understanding public sentiment towards the use of white paracetamol, as well as being the basis for developing more effective health communication strategies.

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“…Representation learning embeds data into a highdimensional space by vectorization [161]. In this Traffic modelling [53,60,64,80] Data cleaning [13,20] Object tracking [114] Heuristic search [65,69,70,93,98] Simulation-based [16,32,87,113,120,124] Mathematical programming [105,116] Indexing [15,17,22 space, adversarial examples can also be generated for adversarial learning [75,76].…”

Section: Representation Learningmentioning

confidence: 99%

A survey of urban visual analytics: Advances and future directions

et al. 2022

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Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

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Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs

Cited by 17 publications

References 16 publications

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding

A survey of urban visual analytics: Advances and future directions

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