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With the development of Multimodal Large Language Models (MLLMs), the evaluation of multimodal models in the context of mathematical problems has become a valuable research field. Multimodal visual-textual mathematical reasoning serves as a critical indicator for evaluating the comprehension and complex multi-step quantitative reasoning abilities of MLLMs. However, previous multimodal math benchmarks have not sufficiently integrated visual and textual information. To address this gap, we proposed MathScape, a new benchmark that emphasizes the understanding and application of combined visual and textual information. MathScape is designed to evaluate photo-based math problem scenarios, assessing the theoretical understanding and application ability of MLLMs through a categorical hierarchical approach. We conduct a multi-dimensional evaluation on 11 advanced MLLMs, revealing that our benchmark is challenging even for the most sophisticated models. By analyzing the evaluation results, we identify the limitations of MLLMs, offering valuable insights for enhancing model performance.

YouTube Shorts, a new section launched by YouTube in 2021, is a direct competitor to short video platforms like TikTok. It reflects the rising demand for short video content among online users. Social media platforms are often flooded with short videos that capture different perspectives and emotions on hot events. These videos can go viral and have a significant impact on the public's mood and views. However, short videos' affective computing was a neglected area of research in the past. Monitoring the public's emotions through these videos requires a lot of time and effort, which may not be enough to prevent undesirable outcomes. In this paper, we create the first multimodal dataset of short video news covering hot events. We also propose an automatic technique for audio segmenting and transcribing. In addition, we improve the accuracy of the multimodal affective computing model by about 4.17% by optimizing it. Moreover, a novel system MSEVA for emotion analysis of short videos is proposed. Achieving good results on the bili-news dataset, the MSEVA system applies the multimodal emotion analysis method in the real world. It is helpful to conduct timely public opinion guidance and stop the spread of negative emotions. Data and code from our investigations can be accessed at: http://xxx.github.com., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible