Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

System and method for modeling, analyzing and executing work process plans

Abstract: A method of using a computer system to model work processes that allows access by multiple users and dynamic modification the work process model. The method defines a plurality of tasks that are completed in the course of completing the work process. For each of the tasks, a set of expected courses of action performed in completing the task is defined. For each of the expected courses of action, one or more obligations that must be fulfilled upon the performance of such expected courses of action are associated with the expected courses of action. The method also included associating obligations with the activation of one or more of other tasks. The method tracks the activation and completion of the tasks. After the activation and completion of the tasks begins, tasks, expectations and obligations may be modified or added.

The effect of work-rest schedules and type of task on the discomfort and performance of VDT users.

Abstract: Consideration of the literature survey indicates that video display terminal (VDT) operators tend to have a high incidence of musculoskeletal problems, visual fatigue, and job stress. Although a number of ergonomic improvements in workstation design and work environment can help to reduce these problems, a proper work-rest schedule deserves consideration since it is easily applicable and inexpensive. The objective of this study was to compare the work-rest schedules for VDT operators considering data entry and mental arithmetic tasks. An experiment was conducted with 10 male college students as participants. The methodology included a discomfort questionnaire and performance measures. The independent variables were the work-rest schedule (60-minute work/10-minute rest, 30-minute work/5-minute rest, and 15-minute work/micro breaks) and the type of task (data entry and a mental arithmetic task). The results were analysed using multiple analysis of variance followed by separate analyses. The 15/micro schedule resulted in significantly lower discomfort in the neck, lower back, and chest than the other schedules for data entry task. The 30/5 schedule followed by 15/micro schedule resulted in the lowest eyestrain and blurred vision. Discomfort in the elbow and arm was the lowest with the 15/micro schedule for the mental arithmetic task. The 15/micro schedule resulted in the highest speed, accuracy, and performance for both of the tasks, compared with the 60/10 and 30/5 schedules. The data entry task resulted in significantly increased speed, accuracy, and performance, and lower shoulder and chest discomfort than the mental arithmetic task.

Authority delegation with secure operating system queues

Abstract: Methods, systems, and devices are provided for delegating security rights to Java servlets and other executable tasks by using secure operating system queues In particular embodiments, the invention allows secure loading of Java servlets on a Novell NetWare server The invention allows users to run servlets from various locations with the same rights, namely, the user's rights The servlet submitted by a given user runs in the context of that user's rights A system according to the invention verifies that the user has the right to submit the task to a given task queue; the queue is managed by the system, and the user is authenticated to the system Queue servers which receive tasks from the queue and service them by executing the tasks are likewise authenticated by the system When a queue server attempts to service a task in a queue, the system verifies that the queue server has rights to service that queue and that job This two way verification—that a user has rights to submit the task, and that the queue server has rights to service the task—allows the user and the queue server to establish a trusted relationship using the operating system's trusted queues Moreover, existing user rights databases and access control systems can be used to determine and enforce rights and trust levels

Formal Approach to the Deployment of Distributed Robotic Teams

Abstract: We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment.

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Abstract: Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection.