Zobrazeno 1 - 10
of 2 103
pro vyhledávání: '"Liu Zhicheng"'
Autor:
Chen, Chen, Bako, Hannah K., Yu, Peihong, Hooker, John, Joyal, Jeffrey, Wang, Simon C., Kim, Samuel, Wu, Jessica, Ding, Aoxue, Sandeep, Lara, Chen, Alex, Sinha, Chayanika, Liu, Zhicheng
Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing chart corpora are high-level (e.g., chart types), hindering their utility for broader
Externí odkaz:
http://arxiv.org/abs/2410.12268
There is a growing interest in designing tools to support interactivity specification and authoring in data visualization. To develop expressive and flexible tools, we need theories and models that describe the task space of interaction authoring. Al
Externí odkaz:
http://arxiv.org/abs/2409.01399
Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed,
Externí odkaz:
http://arxiv.org/abs/2408.04798
Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and tran
Externí odkaz:
http://arxiv.org/abs/2408.04752
Automatically generating data visualizations in response to human utterances on datasets necessitates a deep semantic understanding of the data utterance, including implicit and explicit references to data attributes, visualization tasks, and necessa
Externí odkaz:
http://arxiv.org/abs/2407.06129
The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessib
Externí odkaz:
http://arxiv.org/abs/2406.11282
Large Language Models (LLMs) have demonstrated remarkable abilities in general scenarios. Instruction finetuning empowers them to align with humans in various tasks. Nevertheless, the Diversity and Quality of the instruction data remain two main chal
Externí odkaz:
http://arxiv.org/abs/2405.12915
Content creation today often takes place via collaborative writing. A longstanding interest of CSCW research lies in understanding and promoting the coordination between co-writers. However, little attention has been paid to individuals who write in
Externí odkaz:
http://arxiv.org/abs/2405.05474
In this paper, we establish the second-order randomized identification capacity (RID capacity) of the Additive White Gaussian Noise Channel (AWGNC). On the one hand, we obtain a refined version of Hayashi's theorem to prove the achievability part. On
Externí odkaz:
http://arxiv.org/abs/2404.13685
Autor:
Chen, Yuexi, Liu, Zhicheng
Non-native English speakers (NNES) face challenges in digital workspace communication (e.g., emails, Slack messages), often inadvertently translating expressions from their native languages, which can lead to awkward or incorrect usage. Current AI-as
Externí odkaz:
http://arxiv.org/abs/2404.07005