The use of natural language interfaces (NLIs) to create charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.

Efficient and accurate detection of anomalies in photovoltaic (PV) strings is essential for ensuring the normal operation of PV power stations. Most existing studies focus on developing automated anomaly detection models based on temporal abnormalities in PV strings. However, since analyzing anomalies often requires domain knowledge, existing automated methods have significant limitations in assisting experts to understand the causes and impact of these anomalies. In close collaboration with domain experts, this work has summarized the specific user requirements for PV string anomaly detection and designed PVeSight, an interactive visual analysis system to help experts discover and analyze anomalies in PV strings. We use dimensionality reduction techniques to generate string pattern map. These maps are used for anomaly detection, classifying anomalies, comparative analysis between strings, and hierarchical analysis under inverters and combiner boxes. This helps experts trace the causes of anomalies and acquire valuable insights into anomalous PV strings. Through case studies and expert evaluation, we verified the usability and effectiveness of PVeSight for PV string anomaly detection.

Plain tables excel at displaying data details and are widely used in data presentation, often polished to an elaborate appearance for readability in many scenarios. However, existing authoring tools fail to provide both flexible and efficient support for altering the table layout and styles, motivating us to develop an intuitive and swift tool for table prototyping. To this end, we contribute Table Illustrator, a table authoring system taking a novel visual metaphor, puzzle, as the primary interaction unit. Through combinations and configurations on puzzles, the system enables rapid table construction and supports a diverse range of table layouts and styles. The tool design is informed by practical challenges and requirements from interviews with 10 table practitioners and a structured design space based on an analysis of over 2,500 real-world tables. User studies showed that Table Illustrator achieved comparable performance to Microsoft Excel while reducing users’ completion time and perceived workload.

In this paper, we present EV-Eye, a first-of-its-kind large scale multimodal eye tracking dataset aimed at inspiring research on high-frequency eye/gaze tracking. EV-Eye utilizes an emerging bio-inspired event camera to capture independent pixel-level intensity changes induced by eye movements, achieving sub-microsecond latency. Our dataset was curated over a two-week period and collected from 48 participants encompassing diverse genders and age groups. It comprises over 1.5 million near-eye grayscale images and 2.7 billion event samples generated by two DAVIS346 event cameras. Additionally, the dataset contains 675 thousands scene images and 2.7 million gaze references captured by Tobii Pro Glasses 3 eye tracker for cross-modality validation. Compared with existing event-based high-frequency eye tracking datasets, our dataset is significantly larger in size, and the gaze references involve more natural eye movement patterns, i.e., fixation, saccade and smooth pursuit. Alongside the event data, we also present a hybrid eye tracking method as benchmark, which leverages both the near-eye grayscale images and event data for robust and high-frequency eye tracking. We show that our method achieves higher accuracy for both pupil and gaze estimation tasks compared to the existing solution.