Action inquiry and vocal ergonomics: A pilot study with sports coaches

BACKGROUND: Coaches critically rely on voice for occupational functioning, which has associated risks to vocal health. However, vocal occupational health and safety (OHS) and vocal ergonomics are not typically considered for, by, or with coaches. OBJECTIVE: This study piloted a participatory approach to vocal ergonomics, aiming to collaboratively (i) understand coaches’ vocally reliant occupational participation, and (ii) consider vocal ergonomic factors. METHODS: This research was undertaken at an international tournament for floorball (also known as ‘Innebandy’, ‘Salibandy’, or ‘Unihockey’). Three national coaches (n = 3) and the lead researcher undertook cooperative action inquiry. This piloted a participatory vocal ergonomics programme. Action inquiry methods included fieldnotes, interviews, observations, a workshop, ergonomics approaches, and a focus group. Multi-level analyses supported the findings, including categorical aggregation, direct interpretation, and reflexive thematic analysis. RESULTS: Participants identified vocal ergonomic factors present at the tournament; including personal, activity, physical environmental, and organisational factors. Participants developed four vocal ergonomic approaches responsive to factors. These were: (1) player consultation, (2) ongoing feedback discussions, (3) movement and postural change, and (4) specific task adaptation. Approaches 1–2 directly supported coaches’ voices. Coaches posited limitations to other strategies, but made recommendations for future use. Coaches also reflected that this collaboration provided actionable voice insights and opportunities to address vocal ergonomics. They advocated for extended engagement with coaches, increased focus on vocal health, and inclusion of early career coaches in future programmes. CONCLUSIONS: These findings support engagement of coaches, and other vocally reliant workers, in addressing voice use and vocal health at work.

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.

Dopamine-Related Genotype Predicts Trajectories of Gait Reserve in Older Adults

The capacity to increase one’s gait speed is critical for maintaining safe community ambulation. There is limited work on the longitudinal changes in this capacity and its predictors. Because lower dopamine is associated with lower task adaptation and motivation, we hypothesized that lower dopamine would predict more decline in rapid gait speed. Catechol-O-methyltransferase (COMT) polymorphism and at least 3 repeated rapid and usual pace gait speed assessments were obtained over 10 years in 1,261 older adults (mean age=75.2, 867 White, 659 women). Linear mixed models computed person-specific rapid and usual pace gait speed trajectories. Regression models adjusted for usual gait trajectory tested whether COMT predicted rapid gait trajectory; covariates included, demographic, psychological, cognitive, and physical factors. Val/Val carriers (lower dopamine) declined more in rapid gait compared to Met/Met carriers (higher dopamine; adjusted b=-.002, SE=.001, p=.042). Modifying dopamine may positively influence the ability to maintain rapid gait over time.

Internal Learning for Image Super-Resolution by Adaptive Feature Transform

Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely observed in visual entities, internal self-similarity is expected to help improve image super-resolution. In this paper, we focus on exploiting a complementary relation between external and internal example-based super-resolution methods. Specifically, we first develop a basic network learning external prior from large scale training data and then learn the internal prior from the given low-resolution image for task adaptation. By simply embedding a few additional layers into a pre-trained deep neural network, the image-adaptive super-resolution method exploits the internal prior for a specific image, and the external prior from a well-trained super-resolution model. We achieve 0.18 dB PSNR improvements over the basic network’s results on standard datasets. Extensive experiments under image super-resolution tasks demonstrate that the proposed method is flexible and can be integrated with lightweight networks. The proposed method boosts the performance for images with repetitive structures, and it improves the accuracy of the reconstructed image of the lightweight model.

Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations

Identifying causal relations of events is a crucial language understanding task. Despite many efforts for this task, existing methods lack the ability to adopt background knowledge, and they typically generalize poorly to new, previously unseen data. In this paper, we present a new method for event causality identification, aiming to address limitations of previous methods. On the one hand, our model can leverage external knowledge for reasoning, which can greatly enrich the representation of events; On the other hand, our model can mine event-agnostic, context-specific patterns, via a mechanism called event mention masking generalization, which can greatly enhance the ability of our model to handle new, previously unseen cases. In experiments, we evaluate our model on three benchmark datasets and show our model outperforms previous methods by a significant margin. Moreover, we perform 1) cross-topic adaptation, 2) exploiting unseen predicates, and 3) cross-task adaptation to evaluate the generalization ability of our model. Experimental results show that our model demonstrates a definite advantage over previous methods.