Time-sensitive Tasks Research Articles

Multi-criteria--based Dynamic User Behaviour--aware Resource Allocation in Fog Computing

Fog computing is a promising computing paradigm in which IoT data can be processed near the edge to support time-sensitive applications. However, the availability of resources in computation devices is not stable, since they may not be exclusively dedicated to the Fog application processing in the Fog environment. This, combined with dynamic user behaviour, can affect the execution of applications. To address dynamic changes in user behaviour in resource-limited Fog devices, this article proposes a multi-criteria–based resource allocation policy with resource reservation to minimise overall delay, processing time, and SLA violations. This process considers Fog computing–related characteristics, such as device heterogeneity, resource constraints, and mobility, as well as dynamic changes in user requirements. We employ multiple objective functions to find appropriate resources for executing time-sensitive tasks in the Fog environment. Experimental results show that our proposed policy performs better than the existing one, reducing the total delay by 51%. The proposed algorithm also reduces processing time and SLA violations, which is beneficial for running time-sensitive applications in the Fog environment.

DDNN based data allocation method for IIoT

Abstract With the complete application of artificial intelligence in the field of industrial production and manufacturing and the rapid development of edge computing, industrial processing sites often need to deploy machine learning tasks at edges and terminals. We propose a data allocation method based on Distributed Deep Neural Networks (DDNN) framework, which allocates data to edge servers or stays locally for processing. DDNN divides deep learning tasks and deploys pre-trained shallow neural networks and deep neural networks at local or edges, respectively. However, all data is processed locally, and the failure is sent to the edge server or the cloud. It will lead to excessive pressure on local terminal equipment and long-term idle edge servers, which cannot meet industrial production’s real-time requirements on user privacy and time-sensitive tasks. In this paper, the complexity and inference error rate of machine learning model, the data processing speed of local equipment and edge server, and the transmission time are comprehensively considered to establish the system model. A joint optimization problem is proposed to minimize the total data processing delay. The optimal solution is derived analytically, and the optimal data allocation methhod is given. Simulation experiments are designed to verify the method’s effectiveness and study the influence of key parameters on the allocation method.

Online Policies for Efficient Volunteer Crowdsourcing

Nonprofit crowdsourcing platforms such as food recovery organizations rely on volunteers to perform time sensitive tasks. Thus, their success crucially depends on efficient volunteer utilization and engagement. To encourage volunteers to complete a task, platforms use nudging mechanisms to notify a subset of volunteers with the hope that at least one of them responds positively. However, since excessive notifications may reduce volunteer engagement, the platform faces a trade-off between notifying more volunteers for the current task and saving them for future ones. Motivated by these applications, we introduce the online volunteer notification problem, a generalization of online stochastic bipartite matching where tasks arrive following a known time-varying distribution over task types. Upon arrival of a task, the platform notifies a subset of volunteers with the objective of minimizing the number of missed tasks. To capture each volunteer’s adverse reaction to excessive notifications, we assume that a notification triggers a random period of inactivity, during which she will ignore all notifications. However, if a volunteer is active and notified, she will perform the task with a given pair-specific match probability that captures her preference for the task. We develop an online randomized policy that achieves a constant-factor guarantee close to the upper bound we establish for the performance of any online policy. Our policy as well as hardness results are parameterized by the minimum discrete hazard rate of the inter-activity time distribution. The design of our policy relies on sparsifying an ex-ante feasible solution by solving a sequence of dynamic programs. Further, in collaboration with Food Rescue U.S., a volunteer-based food recovery platform, we demonstrate the effectiveness of our policy by testing it on the platform's data from various locations across the U.S.

TIME TO DEATH-BASED SCHEDULING FOR INTERNET OF THINGS IN FOG COMPUTING

Internet of things (IoT) is a new modern communication model used to connect a large number of things (devices, humans, and animals) through the Internet. Some of these things have the ability to monitor and sense the environment and collect information that helps in enhancing daily life. Unfortunately, they have limited resources and cannot process, analyze, and store all of the collected data. This issue is solved by the support of cloud computing. However, due to challenges faced in cloud computing, which include transmission time and energy consumption, fog computing has been invented to be an efficient computing system for IoT applications. The IoT sends various types of tasks with diverse importance levels to the fog landscape. Some of these tasks are very important and need to be processed within a given time period (i.e., deadline). This issue has been addressed in fog computing using priority-based (deadline-based) scheduling techniques. However, these techniques are not accurate because they do not reschedule tasks based on current deadlines when new tasks arrive. They do not consider the waiting time of jobs in the queue when updating the deadline's value. This article presents a novel scheduling strategy called time to death-based scheduling for IoT tasks in fog computing. Time to death represents the remaining time to finish the deadline. The simulation results illustrated that the suggested algorithm could help in avoiding the disaster of exceeding the deadlines of time-sensitive tasks. Moreover, it produces good results compared to the delay/priority-aware offloading scheme according to meeting the deadline under various simulation scenarios.

Toward Computing Resource Reservation Scheduling in Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time sensitive (TS), while others are not, and more importantly, some tasks are nonpreemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process TS tasks is a critical issue. Although some existing scheduling schemes can deal with the latency requirements of TS tasks, they lack consideration for nonpreemptive tasks. To address this issue, in this article we consider a typical smart warehouse system as an example and propose a generic task scheduling scheme that reserves computing resources to wait for upcoming TS tasks in such an IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for TS tasks. To evaluate the proposed scheme, we have implemented a simulation platform for a smart warehouse and conducted extensive experiments. Our experimental results demonstrate the efficacy of our scheme, which can allocate computing resources so that the processing time for the TS tasks can be reduced. Additionally, we discuss some potential research directions toward improving performance in IIoT environments with respect to resource management, machine learning, and security and privacy.

Multimodal Representation Learning and Set Attention for LWIR In-Scene Atmospheric Compensation

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this article to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors (T, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O, O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss, and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared against fast line-of-sight atmospheric analysis of hypercubes-infrared (FLAASHIR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate eight times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real world, time sensitive tasks.

Robust Edge Detection Method for Segmentation of Diabetic Foot Ulcer Images

Segmentation is an open-ended research problem in various computer vision and image processing tasks. This pre-processing operation requires a robust edge detector to generate appealing results. However, the available approaches for edge detection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problem becomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitations of the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.

Requirements-preserving design automation for multiprocessor embedded system applications

The number of processors is increasing, but the usefulness of parallel computation is not better leveraged due to the inflexibility of design and implementation for multiprocessor embedded system applications. A higher level abstraction (i.e., a parallel programming framework) can ease the programmers to define parallelism for tasks in an application but designers still face the complexity of mapping high-level requirements to the syntax and semantics of a parallel programming interface. Nevertheless, embedded system applications execute various periodic tasks that are carried out repeatedly within a certain time interval and these tasks may be able to run in parallel to utilize the system efficiently. Therefore, in this paper, we propose a parallel loop-based task construct approach to automate the design process of embedded system applications from AADL models for a parallel programming framework. To illustrate the applicability of our mechanism, we use a well-known parallel programming interface called OpenMP to demonstrate the automation process in mapping tasks over multiple processor cores. To ensure meeting high-level requirements of embedded system applications, we analyze the existing OpenMP scheduling mechanisms and propose a layer of adaptation. We show that our proposed adaptation layer facilitates a tighter execution time bound for time-sensitive tasks or a better throughput for tasks that require higher quality of service. Thus, the proposed design automation framework is applicable for a variety of applications with different quality of service (QoS) requirements preserved at the lower level.

Mapping the Expert Mind: Integration Method for Revising the ACES Medical Simulation Curriculum.

Purpose:This article shares our experience developing an integrated curriculum for the ACES (Acute Critical Event Simulation) program. The purpose of the ACES program is to ensure that health care providers develop proficiency in the early management of critically ill patients. The program includes multiple different types of educational interventions (mostly simulation-based) and targets both specialty and family physicians practicing in tertiary and community hospitals.Methods:To facilitate integration between different educational interventions, we developed a knowledge repository consisting of cognitive sequence maps that make explicit the flow of cognitive activities carried out by experts facing different situations - the sequence maps then serving as the foundation upon which multimodal simulation scenarios would be built. To encourage participation of experts, we produced this repository as a peer-reviewed ebook. Five national organizations collaborated with the Royal College of Physicians and Surgeons of Canada to identify and recruit expert authors and reviewers. Foundational chapters, centered on goals/interventions, were first developed to comprehensively address most tasks conducted in the early management of a critically ill patient. Tasks from the foundational chapters were then used to complete the curriculum with situations. The curriculum development consisted of two-phases each followed by a peer-review process. In the first phase, focus groups using web-conferencing were conducted to map clinical practice approaches and in the second, authors completed the body of the chapter (e.g., introduction, definition, concepts, etc.) then provided a more detailed description of each task linked to supporting evidence.Results:Sixty-seven authors and thirty-five peer reviewers from various backgrounds (physicians, pharmacists, nurses, respiratory therapists) were recruited. On average, there were 32 tasks and 15 situations per chapter. The average number of focus group meetings needed to develop a map (one map per chapter) was 6.7 (SD ± 3.6). We found that the method greatly facilitated integration between different chapters especially for situations which are not limited to a single goal or intervention. For example, almost half of the tasks of the Hypercapnic Ventilatory Failure chapter map were borrowed from other maps with some modifications, which significantly reduced the authors’ workload and enhanced content integration. This chapter was also linked to 6 other chapters.Conclusions:To facilitate curriculum integration, we have developed a knowledge repository consisting of cognitive maps which organize time-sensitive tasks in the proper sequence; the repository serving as the foundation upon which other educational interventions are then built. While this methodology is demanding, authors welcomed the challenge given the scholarly value of their work, thus creating an interprofessional network of educators across Canada.

Offload Computation in Mobile Edge Computing with Wide-Band Support of Channel Bonding

Extensive enhancement in technology enables the use of mobile and Internet of Things (IoT) devices in the high demanding applications and services. The users' demands for quality of service (QoS) and computation intensive applications such as augmented reality, online gaming, e-health, smart home and environmental monitoring etc. are also increasing exponentially [1]. Although, mobile and IoT devices have become much more powerful in the last decades and can perform a variety of tasks in an efficient manner, these are still incapable for computation intensive and time critical applications [2]. With the low processing capacity, IoT devices cannot produce results within a given time constraint. Even with high CPU power, executing a computational expensive task depletes their battery energy and shortens the operational lifetime. Mobile cloud computing (MCC) is one of the most promising solutions in the last decade [3]. It empowers the resource constrained mobile and IoT devices with elastic computing power and large storage capacity. MCC enables the realization of centralized computing and allows offload computation for CPU hungry and time sensitive tasks at the cloud server. However, intrinsically the MCC has certain serious concerns such as long transmission latency, privacy/security of user data, immense increase in Internet traffic due to billions of IoT devices in the near future [4]. These issues motivate the emerging a new paradigm known as mobile edge computing (MEC). MEC decouples the cloud resources into edge servers which are located near the end user, usually alongside access point (AP).

Time-sensitivity-aware shared cache architecture for multi-core embedded systems

In embedded systems such as automotive systems, multi-core processors are expected to improve performance and reduce manufacturing cost by integrating multiple functions on a single chip. However, inter-core interference in shared last-level cache (LLC) results in increased and unpredictable execution times for time-sensitive tasks (TSTs), which have (soft) timing constraints, thereby increasing the deadline miss rates of such systems. In this paper, we propose a time-sensitivity-aware dead block-based shared LLC architecture to mitigate these problems. First, a time-sensitivity indication bit is added to each cache block, which allows the proposed LLC architecture to be aware of instructions/data belonging to TSTs. Second, portions of the LLC space are allocated to general tasks without interfering with TSTs by developing a time-sensitivity-aware dead block-based cache partitioning technique. Third, to reduce the deadline miss rate of TSTs further, we propose a task matching in shared caches and a cache partitioning scheme that considers the memory access characteristics and the time-sensitivity of tasks (TATS). The TATS is combined with our proposed dead block-based scheme. Our evaluation shows that the proposed schemes reduce deadline miss rates of TSTs compared to conventional shared caches. On a dual-core system, compared to a baseline, equal partitioning, and state-of-the-art quality-of-service-aware cache partitioning, our proposed dead block-based cache partitioning provides 9.3%, 30.5%, and 2.6% lower average deadline miss rates, respectively. On a quad-core system, compared to the baseline, equal partitioning, and state-of-the-art quality-of-service-aware cache partitioning, the combination of our proposed schemes provides 21.2%, 17.7%, and 4.1% lower average deadline miss rates, respectively.

Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing

The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.

Identifying and Addressing Language Needs in Primary Care: a Pilot Implementation Study.

Medical interpreters improve care for patients with Limited English Proficiency but are underused. Protocols to improve interpreter use in primary care are needed. Medical Assistants (MAs) screened patients for language needs and arranged for telephone interpreters during rooming in two pilot clinics (PCs). We interviewed MAs and providers and analyzed interviews using modified grounded theory, linking themes to the Promoting Action on Research Implementation in Health Services (PARiHS) framework categories of Context, Evidence, and Facilitation. Providers in PCs and four comparison clinics were surveyed. Context themes included issues with the telephone interpreter vendor; having established teams, roles and workflows; and difficulty incorporating time-sensitive tasks. Evidence themes included engagement in language screening; preferring in-person interpreters; improving the patient experience; and having mixed responses to the protocol. Facilitation themes included MAs needing more support. PC providers were more satisfied with care (OR = 12.7) and communication (OR = 7.6) than comparison clinic providers. The protocol may improve patient care and communication, but implementation was inconsistent. Language screening is a complex process and further research is needed to improve screening questions and procedures. Future interventions should capitalize on team members' drives to improve patient care and control costs but also need to consider the impacts of health system changes, and to consider the culture, training needs, roles, and relationships of team members.

Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers

BackgroundMeasuring adherence to processes is one of the established ways to quantify the quality of healthcare. Providing timely feedback to healthcare workers on the level of adherence can improve process measures. However, it is challenging to present data on adherence to repetitive time-sensitive tasks in a clear manner. ObjectivesWe used inpatient glucose monitoring as a test case to explore the feasibility of using visualizations to communicate adherence to repetitive scheduled tasks to healthcare workers. MethodsWe selected four candidate plots that represented distribution across time: histogram, probability density function plot (pdf plot), violin plot and cumulative density function plot (cdf plot). Doctors and nurses involved in inpatient diabetes care in a tertiary hospital were invited to complete a self-administered questionnaire that measured self-reported baseline knowledge, performance, and perception towards the visualizations. Performance was assessed by determining if a participant was able to correctly identify visualizations representing protocol adherence. We also assessed the perception of usability of these visualizations for monitoring protocol adherence. Binomial regression models were used to identify factors associated with overall performance and perception. Logistic regression models with generalized estimating equation were used to compare performance and perception between visualizations, and identify effect modifiers. ResultsA total of 57 doctors and nurses completed the questionnaire. Participants were most familiar with histogram (87.7%), followed by cdf plot (61.4%), pdf plot (40.4%), and violin plot (7%). However, the percentages of participants who identified non-adherence using these plots were generally lower, ranging from 29.8% to 40.4%. Participants’ perception of usability ranged from 14% to 17.5% across these visualizations. More favorable perceptions were found among participants with baseline knowledge for two or more visualizations (adjusted odds ratio: 3.21; 95%CI: 1.29, 7.96; p-value: 0.012) and having identified two or more non-adherent visualizations (adjusted odds ratio: 4.23; 95%CI: 1.95, 9.16; p-value: < 0.001). ConclusionsAdherence to repetitive time-sensitive tasks can be presented in the form of visualizations. However, nurses’ and doctors’ knowledge and understanding of these visualizations are generally poor. This may influence their perception of usability of these plots. Therefore, these visualizations need to be implemented in tandem with training on their interpretation, to enhance the usefulness of these plots in motivating quality improvement.

A Multi-Institutional Simulation Boot Camp for Pediatric Cardiac Critical Care Nurse Practitioners*

Assess the effect of a simulation "boot camp" on the ability of pediatric nurse practitioners to identify and treat a low cardiac output state in postoperative patients with congenital heart disease. Additionally, assess the pediatric nurse practitioners' confidence and satisfaction with simulation training. Prospective pre/post interventional pilot study. University simulation center. Thirty acute care pediatric nurse practitioners from 13 academic medical centers in North America. We conducted an expert opinion survey to guide curriculum development. The curriculum included didactic sessions, case studies, and high-fidelity simulation, based on high-complexity cases, congenital heart disease benchmark procedures, and a mix of lesion-specific postoperative complications. To cover multiple, high-complexity cases, we implemented Rapid Cycle Deliberate Practice method of teaching for selected simulation scenarios using an expert driven checklist. Knowledge was assessed with a pre-/posttest format (maximum score, 100%). A paired-sample t test showed a statistically significant increase in the posttest scores (mean [SD], pre test, 36.8% [14.3%] vs post test, 56.0% [15.8%]; p < 0.001). Time to recognize and treat an acute deterioration was evaluated through the use of selected high-fidelity simulation. Median time improved overall "time to task" across these scenarios. There was a significant increase in the proportion of clinically time-sensitive tasks completed within 5 minutes (pre, 60% [30/50] vs post, 86% [43/50]; p = 0.003] Confidence and satisfaction were evaluated with a validated tool ("Student Satisfaction and Self-Confidence in Learning"). Using a five-point Likert scale, the participants reported a high level of satisfaction (4.7 ± 0.30) and performance confidence (4.8 ± 0.31) with the simulation experience. Although simulation boot camps have been used effectively for training physicians and educating critical care providers, this was a novel approach to educating pediatric nurse practitioners from multiple academic centers. The course improved overall knowledge, and the pediatric nurse practitioners reported satisfaction and confidence in the simulation experience.

A Finite-Time Consensus Framework Over Time-Varying Graph Topologies With Temporal Constraints

Finite-time consensus has attracted significant research interest due to its wide applications in multiagent systems. Various results have been developed to enable multiagent systems to complete desired tasks in finite-time. However, most existing results in the literature can only ensure finite-time consensus without considering temporal constraints, where the time used to achieve consensus cannot be preset arbitrarily and is generally determined by the system initial conditions, prohibiting its application in time-sensitive tasks. Motivated to achieve consensus within a desired time frame, user-specified finite-time consensus is developed in the present work for a multiagent system to ensure consensus at a prespecified time instant. The interaction among agents (e.g., communication and information exchange) is modeled as a time-varying graph, where each edge is associated with a time-varying weight representing the time-varying interaction between neighboring agents. Consensus over such time-varying graph is then proven based on a time transformation and is guaranteed to be completed within a prespecified time frame. To demonstrate the developed framework, finite-time rendezvous of a multiagent system is considered as an example application, where agents with limited communication capabilities are desired to meet at a common location at a preset time instant with constraints on preserving global network connectivity. A numerical simulation is provided to demonstrate the efficiency of the developed result.

Home is Where the Lab is: A Comparison of Online and Lab Data From a Time-sensitive Study of Interruption

While experiments have been run online for some time with positive results, there are still outstanding questions about the kinds of tasks that can be successfully deployed to remotely situated online participants. Some tasks, such as menu selection, have worked well but these do not represent the gamut of tasks that interest HCI researchers. In particular, we wondered whether long-lasting, time-sensitive tasks that require continuous concentration could work successfully online, given the confounding effects that might accompany the online deployment of such a task. We ran an archetypal interruption experiment both online and in the lab to investigate whether studies demonstrating such characteristics might be more vulnerable to a loss of control than the short, time-insensitive studies that are representative of the majority of previous online studies. Statistical comparisons showed no significant differences in performance on a number of dimensions. However, there were issues with data quality that stemmed from participants misunderstanding the task. Our findings suggest that long-lasting experiments using time-sensitive performance measures can be run online but that care must be taken when introducing participants to experimental procedures.

Worker Replacement Costs and Unionization

The embattled state of U.S. labor has generated a voluminous body of research on the processes of deunionization contributing to its decline. Revisiting the less researched topic of unionization, this study explores the social conditions facilitating union growth during the labor movement’s formative years. Focusing on the first decade of the twentieth century—in many respects for labor, a period not unlike the present—I seek to explain the pattern of organizing success and failure across industries and occupations. I find that the most organized settings occurred where workers had greater disruptive capacities due to the high cost of being replaced during work stoppages. The highest replacement costs were associated with three conditions: scarcity of skilled labor, geographically isolated worksites that raised the cost of importing strikebreakers, and time-sensitive tasks that rendered replacement workers economically impractical. Workplaces that had at least one of these conditions formed the backbone of the early U.S. labor movement. The conclusion considers the impact of declining replacement costs on current challenges facing U.S. labor.

User Workflow Centered Design

Software systems are often built in a tool-centric fashion, providing hierarchical functional groups simultaneously to a user, and the user needs to find and select the right tools to use based on his/her current task needs. Even though this design approach provides some flexibility for the user, it fails to address the needs of the workflow support. Indeed, when such software is used in complex, dynamic, and stressful working domains, users are often overwhelmed and struggling to find the right functionality and data for their time sensitive tasks. Here, we report our experience of applying the user workflow centered design (UWCD) method for redesigning a tool-centric software system in medical intervention. We elaborate the UWCD process from capturing, documenting, and to modeling user workflow information. The user workflow model is then used to derive a user interface (UI) solution with the goal to “provide the right data and functionality to the right user at the right time”. We show that the new design can more effectively support users’ work in term of function selection and task switching as measured by time.

On the Optimal Scheduling of Independent, Symmetric and Time-Sensitive Tasks

Consider a discrete-time system in which a centralized controller (CC) is tasked with assigning at each time interval (or slot) K resources (or servers) to K out of M ≥ K nodes. A node can execute a task when assigned to a server . The tasks are independently generated at each node by stochastically symmetric and memoryless random processes and stored in a finite-capacity task queue. The tasks are time-sensitive since there is a non-zero probability, within each slot, that a task expires before being scheduled. The scheduling problem is tackled with the aim of maximizing the number of tasks completed over time (or the task-throughput) under the assumption that the CC has no direct access to the state of the task queues. The scheduling decisions at the CC are based on the outcomes of previous scheduling commands, and on the known statistical properties of the task generation and expiration processes.