Stress has a significant impact on the physical and mental health of an individual and is a growing concern for society. Facial video-based stress evaluation from non-invasive cameras has proven to be a more efficient method to evaluate stress in comparison to approaches that use questionnaires or wearable sensors. Plenty of classification models have been built for stress detection. However, most do not consider individual differences. Also, the results for such models are limited by a uni-dimensional definition of stress levels lacking a comprehensive quantitative definition of stress. We propose a framework that utilizes the multilevel video frame representations from deep learning that utilizes a baseline video and a target video of the same subject. The framework could output the quantitative stress score in multiple dimensions. We compared the assessment performance of different deep learning architectures based on the UBFC- Phys dataset. The results demonstrate the feasibility and effectiveness of using deep learning to capture the personalized stress features.

Time-Sync Comments (TSCs) are a sequence of comments associated with video contents at each timestamp. By applying textural analysis, researchers can transform the TSCs into labels that represent the semantic meaning of the original video content. Multiple studies have used the TSCs in video segmentation and tagging. TSCs can be either created by a single user or generated by various users. Thanks to the exploding of multimedia platforms, online comments have proved to be an efficient TSCs data source in multiple research since 2014. However, previous TSCs studies mainly focused on data sources targeting young non-English speaking audiences, potentially introducing data bias due to limited geographic regions and groups. This paper aims to solve this problem by proposing a universal data collection framework of TSCs generated by audiences worldwide from popular social media platforms. We first introduced an efficient data mining strategy for gathering such TSCs data in general. Then, we demonstrated how to build the autonomous pipeline and collected two large-scale TSCs datasets with different sets of keywords, namely LST-YF20 and LST-YT1000, directly from YouTube Lives. We also conducted an extensive experiment on the efficiency of our data pipeline with a group of fixed keywords. The result of our investigation suggests that our data pipeline could efficiently produce high-quality TSCs datasets while keeping a constrained budget. We believe our framework could further contribute to future research in the multimedia field.

Generative Adversarial Networks (GANs) are among the most actively researched neural networks in today’s artificial intelligence research. Scientists across different domains, particularly in image processing, constantly utilize variants of GANs to conduct research. The topic has increasingly drawn attention and interest in recent years. Our survey paper reviews the current literature and applications of GANs from both qualitative and quantitative perspectives. This survey also summarizes the challenges and improvement techniques of training GANs. We hope this paper may help researchers interested in GANs and serve as an informative source for ongoing and future work in this field.

This paper describes a cloud-based heart rate (HR) and heart rate variability (HRV) monitor, which can monitor an individual's heart rate and heart rate variability using minimal resources at the user's end. This HR and HRV monitor does not involve any contact sensors or costly medical equipment, and it does not require a high-definition camera to video record the user's face. This HR and HRV monitoring system simply requires the webcam of a personal computer or the camera of a mobile device such as a cell phone or tablet in order to video record the face of the user. The system utilizes the resources of the user's device while recording and encoding the video segments before transferring them to a server. All the processes to calculate the HR and HRV occur at the server.