App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10

A system of monitoring and analyzing human indoor mobility and air quality

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies

An ambient-physical system to infer concentration in open-plan workplace

One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this article, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces

Deregulation of the miR-222-ABCG2 regulatory module in tongue squamous cell carcinoma contributes to chemoresistance and enhanced migratory/invasive potential.

Chemoresistance is often associated with other clinical characteristics such as enhanced migratory/invasive potential. However, the correlation and underlying molecular mechanisms remain unclear. The aim of this study was to elucidate the function of the miR-222-ABCG2 pathway in the correlation between cisplatin (DDP) resistance and enhanced cell migration/invasion in tongue squamous cell carcinoma (TSCC). Using TSCC cell lines and primary cultures from TSCC cases, we first confirmed the correlation among DDP resistance (measured by IC50 values and ABCG2/ERCC1 expression), migratory/invasive potential (assessed by migration/invasion assays) and miR-222 expression. In TSCC cells, siRNA-mediated ABCG2 knockdown led to enhanced DDP responsiveness and reduced migratory/invasive potential, whereas ABCG2 overexpression induced DDP resistance and enhanced cell migration/invasion. Luciferase assays revealed that ABCG2 is a direct target of miR-222. In addition to reducing cell migration/invasion, functional analyses in TSCC cells indicated that miR-222 can reduce expression of the ABCG2 gene and enhance DDP responsiveness. However, co-transfection with ABCG2 cDNA restored both DDP resistance and migration/invasion. Moreover, miR-222 mimics and ABCG2 siRNA inhibited tumor growth and lung metastasis in vivo. Thus, our results verified that DDP resistance is correlated with enhanced migratory/invasive potential in TSCC. ABCG2 is a direct target of miR-222,and deregulation of the miR-222-ABCG2 regulatory module in TSCC contributes to both DDP resistance and enhanced migratory/invasive potential

OccuSpace: Towards a robust occupancy prediction system for activity based workplace

Workplace occupancy detection is becoming increasingly important in large Activity Based Work (ABW) environments as it helps building and office management understand the utilisation and potential benefits of shared workplace. However, existing sensor-based technologies detect workstation occupancy in indoor spaces require extensive installation of hardware and maintenance incurring ongoing costs. Moreover, accuracy can depend on the specific seating styles of workers since the sensors are usually placed under the table or overhead. In this research, we provide a robust system called OccuSpace to predict occupancy of different atomic zones in large ABW environments. Unlike fixed sensors, OccuSpace uses statistical features engineered from Received Signal Strength Indicator (RSSI) of Bluetooth card beacons carried by workers while they are within the ABW environment. These features are used to train state-of-the-art machine learning algorithms for prediction task. We setup the experiment by deploying our system in a realworld open office environment. The experimental results show that OccuSpace is able to achieve a high accuracy for workplace occupancy prediction

Imagining future digital assistants at work: A study of task management needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice