ABSTRACT
Agile development is known for efficient software development practices that enable teams to quickly develop software to cope with changing requirements. Although there is evidence that agile practices are helpful in such environments, the literature does not inform us as to whether agile practices can also be beneficial in hyper-agile environments. Such environments are characterized by an extremely fast pace of change with fluid requirements. COVID-19 vaccine distribution is one such problem that governments have had to deal with. To solve this problem, governments need to come up with robust responses by formulating teams that have the capability to provide software solutions enabling information visibility into the vaccine distribution process. Such emergent teams need to quickly understand the distribution process, oftentimes define the process itself because it might be non-existent, and build software systems to solve the problem in a matter of days. Not much is known about how systems can be developed at such a fast pace. We adopt a clinical research methodology and employ agile software development practices to develop such a mission-critical system. In the process of building the system, we learn important lessons that can be used to adapt and extend agile methodologies to be used in hyper-agile development environments. We offer these lessons as important first steps to understanding the best practices needed to develop software systems that have the capability to provide visibility into the unprecedented health challenge of distribution of life-saving COVID-19 vaccine.
ABSTRACT
OBJECTIVE: In this review, we investigate the relationship between agent transparency, Situation Awareness, mental workload, and operator performance for safety critical domains. BACKGROUND: The advancement of highly sophisticated automation across safety critical domains poses a challenge for effective human oversight. Automation transparency is a design principle that could support humans by making the automation's inner workings observable (i.e., "seeing-into"). However, experimental support for this has not been systematically documented to date. METHOD: Based on the PRISMA method, a broad and systematic search of the literature was performed focusing on identifying empirical research investigating the effect of transparency on central Human Factors variables. RESULTS: Our final sample consisted of 17 experimental studies that investigated transparency in a controlled setting. The studies typically employed three human-automation interaction types: responding to agent-generated proposals, supervisory control of agents, and monitoring only. There is an overall trend in the data pointing towards a beneficial effect of transparency. However, the data reveals variations in Situation Awareness, mental workload, and operator performance for specific tasks, agent-types, and level of integration of transparency information in primary task displays. CONCLUSION: Our data suggests a promising effect of automation transparency on Situation Awareness and operator performance, without the cost of added mental workload, for instances where humans respond to agent-generated proposals and where humans have a supervisory role. APPLICATION: Strategies to improve human performance when interacting with intelligent agents should focus on allowing humans to see into its information processing stages, considering the integration of information in existing Human Machine Interface solutions.
ABSTRACT
Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.
ABSTRACT
The role of the human element within complex socio-technical systems is continually being transformed and redefined by technological advancement. Autonomous operations across varying transport domains are in differing stages of realisation and practical implementation, and specifically within maritime operations, is still in its infancy. This study explores the potential effects of autonomous technologies on future work organisation and roles of humans within maritime operations. Ten Subject-Matter Experts working within industry and academia were interviewed to elicit their perspectives on the current state and future implications of autonomous technologies. Four main themes emerged: (i) Trust, (ii) Awareness and Understanding, (iii) Control, (iv) Training and Organisation of Work. A fuzzier fifth theme also appeared in the data analysis: (v) Practical Implementation Considerations, which encompassed various sub-topics related to real-world implementation of autonomous ships. The results provide a framework of human element issues relevant for the organisation and implementation of autonomous maritime operations. Practitioner summary: As autonomous shipping rapidly moves closer to real-world implementation, it is critical to develop an understanding of future roles of humans in autonomous maritime operations. By eliciting expert knowledge from academics and practitioners, we establish a framework of relevant issues facing humans in emerging autonomous systems and operations at sea.
