This study aimed to identify key drivers behind workers’ satisfaction, perceived productivity, and health in open-plan offices while at the same time understanding design similarities shared by high-performance workspaces. Results from a dataset comprising a total of 8827 post-occupancy evaluation (POE) surveys conducted in 61 offices in Australia and a detailed analysis of a subset of 18 workspaces (n = 1949) are reported here. Combined, the database-level enquiry and the subset analysis helped identifying critical physical environment-related features with the highest correlation scores for perceived productivity, health, and overall comfort of the work area. Dataset-level analysis revealed large-size associations with spatial comfort, indoor air quality, building image and maintenance, noise distraction and privacy, visual comfort, personal control, and connection to the outdoor environment. All high-performance, open-plan offices presented a human-centered approach to interior design, purposely allocated spaces to support a variety of work-related tasks, and implemented biophilic design principles. These findings point to the importance of interior design in high-performance workspaces, especially in relation to open-plan offices.

Viewer interest, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, 'non-neutral' FE and 'relatively higher and faster' HR is able to capture 60%-80% of goal, foul, and shot-ongoal soccer video events. FE is found to be more indicative than HR of viewer interest, but the fusion of these two modalities outperforms each of them.

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Deep learning-based person re-identification faces a scalability challenge when the target domain requires continuous learning. Service environments, such as airports, need to recognize new visitors and add new cameras over time. Training-at-once is not enough to make the model robust to new tasks and domain variations. A well-known approach is fine-tuning, which suffers forgetting problem on old tasks when learning new tasks. Joint-training can alleviate the problem but requires old datasets, which is unobtainable in some cases. Recently, Learning without forgetting (LwF) shows its ability to mitigate the problem without old datasets. This paper extends the benefit of LwF from image classification to person re-identification with further challenges. Comprehensive experiments are based on Market1501 and DukeMTMC4ReID to evaluate and benchmark LwF to other approaches. The results confirm that LwF outperforms fine-tuning in preserving old knowledge and joint-training in faster training.

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