Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency on the server side. Through an analysis of 15 million users, however, we find that for short-form video apps, user experience depends on both response latency and recommendation accuracy. This observation brings a dilemma to service providers since improving recommendation accuracy requires adopting complex strategies that demand heavy computation, which substantially increases response latency.

Deep learning has achieved excellent performance in computer vision tasks, like image recognition, natural language processing, etc. However, in real-world applications, special circumstances brought about by the external world may create domain bias caused by distribution discrepancy between training and testing data, leading to degrading model performance. For example, when auto-driving meets hazy weather, the model performance will drop significantly. In this paper, we explore to solve this problem by utilizing modern Domain Adaptation (DA) methods, which generalizes from the source domain to the target domain by minimizing the distribution difference caused by dataset bias. We firstly propose the cross-domain haze image datasets and benchmark the five classic DA methods. The experiments show that DA methods can mitigate the negative effect of haze and significantly improves the model performance for visual recognition.

Intelligent Cushion (iCushion) technology is recently booming with embedded pressure array sensors to enable individual-specific sitting experiences. iCushion has the build-in functionality to identify users throughout its use in a continuous and non-intrusive manner. Due to the variability in sitting posture and the angle of seated deflection, the accuracy of user identification remains unstable or unclear with existing solutions. Aiming at this problem, this study develops a two-stage pressure map algorithm based on robust spatial-temporal features. First, pressure maps are collected constantly without limiting the user's posture, based on which an accumulated identity library is established for sitting postures by extracting features from pressure maps. To be specifically, we create a decision tree to classify maps by distances between both ischia and then variances in both areas around ischia in maps are analyzed. Second, the similarity between both maps are measured by the Euclidean distance between feature vectors around ischia for matching maps data. A k-NN voting mechanism is developed to achieve reliability of identification. The resulted iCushion prototype has successfully identified 92.2% of maps with three randomly chosen individuals through four-hour non-stop testing. It holds potentials of non-intrusive and reliable activity recognition in other pervasive applications.