publications

2026

1. 

   Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

   Xuxin Tang, Ibrahim Tahmid, Eric Krokos, and 3 more authors

   AVI, 2026

   HTML PDF

2025

1. 

   Enhancing Fairness and Accuracy in Diagnosing Type 2 Diabetes in Young Population

   Tanmoy Sarkar Pias, Yiqi Su, Xuxin Tang, and 3 more authors

   IEEE JBHI, 2025

   HTML

2. 

   Imprinto: Enhancing Infrared Inkjet Watermarking for Human and Machine Perception.

   Martin Feick, Xuxin Tang, Raul Garcia-Martin, and 5 more authors

   CHI 2025, 2025

   HTML PDF

2024

1. 

   Steering LLM Summarization with Visual Workspaces for Sensemaking

   Xuxin Tang, Eric Krokos, Can Liu, and 4 more authors

   IEEE VIS NLVIZ Workshop 2024, 2024

   PDF

2019

1. 

   Quantifying cloud elasticity with container-based autoscaling

   Fan Zhang, Xuxin Tang, Xiu Li, and 2 more authors

   Future Generation Computer Systems, 2019

   Abs PDF

   Containers have been a pervasive approach to help rapidly develop, test and update the Internet of Things applications (IoT). The autoscaling of containers can adaptively allocate computing resources for various data volumes over time. Therefore, elasticity, a critical feature of a cloud platform, is significant to measure the performance of lightweight containers. In this paper, we propose a framework with container auto- scaler. It monitors containers resource usage and accordingly scales in or scales out containers in need. Further, we define elasticity mathematically in order to quantify the cloud elasticity using the proposed framework. Extensive experiments are carried out with different workload modes, workload durations, and scaling cool-down period of times. Experiment results show that the framework captures the workload variation firmly with a very short delay. We also find out that the cloud platform shows the best elasticity in repeat workload mode due to its recurring and predictable feature. Finally, we discover the length of the cool-down period should be properly set up in order to balance system stability and good elasticity.

2017

1. 

   Quantifying Cloud Elasticity with Container-Based Autoscaling

   Xuxin Tang, Fan Zhang, Xiu Li, and 2 more authors

   15th Intl Conf on Pervasive Intelligence and Computing (PICom 2017), 2017

   Abs PDF

   Containers have been a pervasive approach to help rapidly develop, test and update the Internet of Things applications (IoT). The autoscaling of containers can adaptively allocate computing resources for various data volumes over time. Therefore, elasticity, a critical feature of a cloud platform, is significant to measure the performance of lightweight containers. In this paper, we propose a framework with container auto-scaler. It monitors containers resource usage and accordingly scales in or scales out containers in need. Further, we define elasticity mathematically in order to quantify the cloud elasticity using the proposed framework. Extensive experiments are carried out with different workload modes, workload durations, and scaling cool- down period of times. Experiment results show that the framework captures the workload variation firmly with a very short delay. We also find out that the cloud platform shows the best elasticity in repeat workload mode due to its recurring and predictable feature. Finally, we discover the length of the cool- down period should be properly set up in order to balance system stability and good elasticity.

2. 

   A night image enhancement algorithm based on guided filtering

   Xuxin Tang, Zhijiang Li, and Yuhang Chen

   Advanced Graphic Communications and Media Technologies, 2017

   Abs PDF

   Colored night images lead to various problems in real life, such as low dynamic range, detail blurring and so on, which are waiting to be solved. For this reason, it proposes an algorithm for night image enhancement based on guided filtering in the paper. This image process includes three main steps. Firstly, by transforming to HVS color space, the targeted image is separated coarsely into two images—base layer image and detail layer image in the result of guided filtering. In the second step, the base layer and detail layer are processed by different strategies selectively and respectively to lighten the whole, but remain details as much as possible. Eventually, the base layer, which has been previously increased contrast, is added detail layer to keep the details and margins. The result image can be retained after reverting to the original color space. Our result illustrates the com- parison among result images and images enhanced by other methods, which obviously proves that the algorithm in our paper can obtain relatively better results in the ways of detail enhancement and color fidelity.