This paper presents GeoWatch to conduct the first large-scale measurement study of geoblocking practices towards China. Although prior studies have examined geoblocking in specific contexts as well as China's censorship, GeoWatch focuses on identifying which websites proactively block users from China. It employs advanced domain mining techniques and globally distributed vantage points to identify geoblocking websites, analyzing a total of 97.78 million domains worldwide and identifying 4.54 million geoblocking domains.

Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection. However, existing MSU benchmarks and approaches usually focus on sentence-level MSU. In document-level news, sarcasm clues are sparse or small and are often concealed in long text. Moreover, compared to sentence-level comments like tweets, which mainly focus on only a few trends or hot topics (e.g., sports events), content in the news is considerably diverse. Models created for sentence-level MSU may fail to capture sarcasm clues in document-level news. To fill this gap, we present a comprehensive benchmark for Document-level Multimodal Sarcasm Understanding (DocMSU). Our dataset contains 102,588 pieces of news with text-image pairs, covering 9 diverse topics such as health, business, etc. The proposed large-scale and diverse DocMSU significantly facilitates the research of document-level MSU in real-world scenarios. To take on the new challenges posed by DocMSU, we introduce a fine-grained sarcasm comprehension method to properly align the pixel-level image features with word-level textual features in documents. Experiments demonstrate the effectiveness of our method, showing that it can serve as a baseline approach to the challenging DocMSU.

Video anomaly understanding (VAU) aims to automat-ically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: “what anomaly occurred?”,”why did it happen?”, and “how severe is this abnormal event?”. In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically, each instance of the proposed benchmark involves three sets of human annotations to indicate the”what”, “why” and “how” of an anomaly, including 1) anomaly type, start and end times, and event descriptions, 2) natural language explanations for the cause of an anomaly, and 3) free text reflecting the effect of the abnormality. In addition, we also introduce MMEval, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anoma-lies. Finally, we propose a novel prompt-based method that can serve as a baseline approach for the challenging CUVA. We conduct extensive experiments to show the superiority of our evaluation metric and the prompt-based approach. Our code and dataset are available at https://github.com/fesvhtr/CUVA.

The agencies responsible for transportation corridors tend to hold large volumes of data that can be relevant for both operations and planning. Meanwhile, it is a challenge for these agencies to prioritize their investments addressing risk, benefits, and costs. The agencies recognize an opportunity to improve project selection and programming with a centralized database of performance measures that will aid a consistent application of evaluation metrics. To support the identification, planning, and selection of highway transportation projects, this research has used a data structure known as dynamic segmentation for cross-referencing heterogeneous data sources including projects, traffic, safety, bridge and pavement conditions, etc. The result is a multiscale method for agencies to utilize big-data analytics and multicriteria decision analysis to assemble evidence for project selection and prioritization. The paper describes an approach to (1) visualize multiple attributes along linearized road corridors to identify future projects, minimize conflicts with current projects, and identify potential project synergies and (2) prioritize projects to the Top-20 with multiple performance factors. The methods are demonstrated at several geographic scales. The effort supports a fast, repeatable, and evidence-driven prioritization of projects and provides a complement to the use of electronic map data that has been overwhelming the available computing resources.