Two papers accepted at CHI

We are pleased to announce that the following two papers were accepted for publication in the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2024). Congratulations to the authors!

1. 

   Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour

   Guanhua Zhang, Zhiming Hu, Mihai Bâce, Andreas Bulling

   Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–17, 2024.

   Abstract Links BibTeX Project

   The mouse is a pervasive input device used for a wide range of interactive applications. However, computational modelling of mouse behaviour typically requires time-consuming design and extraction of handcrafted features, or approaches that are application-specific. We instead propose Mouse2Vec – a novel self-supervised method designed to learn semantic representations of mouse behaviour that are reusable across users and applications. Mouse2Vec uses a Transformer-based encoder-decoder architecture, which is specifically geared for mouse data: During pretraining, the encoder learns an embedding of input mouse trajectories while the decoder reconstructs the input and simultaneously detects mouse click events. We show that the representations learned by our method can identify interpretable mouse behaviour clusters and retrieve similar mouse trajectories. We also demonstrate on three sample downstream tasks that the representations can be practically used to augment mouse data for training supervised methods and serve as an effective feature extractor.

   doi: 10.1145/3613904.3642141

   Paper: zhang24_chi.pdf

   @inproceedings{zhang24_chi, title = {Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour}, author = {Zhang, Guanhua and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas}, year = {2024}, pages = {1--17}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, doi = {10.1145/3613904.3642141} }
2. 

   SalChartQA: Question-driven Saliency on Information Visualisations

   Yao Wang, Weitian Wang, Abdullah Abdelhafez, Mayar Elfares, Zhiming Hu, Mihai Bâce, Andreas Bulling

   Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–14, 2024.

   Abstract Links BibTeX Project

   Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA – a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.

   doi: 10.1145/3613904.3642942

   Paper: wang24_chi.pdf

   Supplementary Material: wang24_chi_sup.pdf

   Dataset: https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-3884

   @inproceedings{wang24_chi, title = {SalChartQA: Question-driven Saliency on Information Visualisations}, author = {Wang, Yao and Wang, Weitian and Abdelhafez, Abdullah and Elfares, Mayar and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas}, year = {2024}, pages = {1--14}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, doi = {10.1145/3613904.3642942} }

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