Task (computing)

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

Tracking the World State with Recurrent Entity Networks

Abstract: We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respond as is the case for a Memory Network (Sukhbaatar et al., 2015). Like a Neural Turing Machine or Differentiable Neural Computer (Graves et al., 2014; 2016) it maintains a fixed size memory and can learn to perform location and content-based read and write operations. However, unlike those models it has a simple parallel architecture in which several memory locations can be updated simultaneously. The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting. We also demonstrate that it can solve a reasoning task which requires a large number of supporting facts, which other methods are not able to solve, and can generalize past its training horizon. It can also be practically used on large scale datasets such as Children's Book Test, where it obtains competitive performance, reading the story in a single pass.

On a human-robot collaboration in an assembly cell

Abstract: This is a study of a Human-Robot Collaboration HRC framework for the execution of collaborative tasks in hybrid assembly cells. Robots and humans coexist in the same cell and share tasks according to their capabilities. An intelligent decision-making method that allows human-robot task allocation is proposed and is integrated within a Robot Operating System ROS framework. The proposed method enables the allocation of sequential tasks assigned to a robot and a human in separate workspaces. The focus is rather given to the human–robot coexistence for the execution of sequential tasks, in order for the automation level in manual or even hybrid assembly lines to be increased. Body gestures are the means of a human’s interaction with a robot for commanding and guiding reasons. The proposed framework is implemented into a case coming from the manual assembly lines of an automotive industry. A preliminary design of a hybrid assembly cell is presented, focusing on the assembly of a hydraulic pump by robots and humans.

Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

Abstract: With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

Method, system, and product for assessing a server application performance

Abstract: Method and system for assessing the performance of a server application that acquires performance information from the perspective of a simulated user and has significantly reduced hardware requirements. Particularly, actual user behavior is modeled so that accurate determinations can be made as to the number of users a given server application can adequately support. User behavior is modeled in a client profile that contains user parameters corresponding to the nature, timing, and frequency of user activities in operating a client that in turn corresponds to client tasks. A plurality of processes and process threads are initiated to contact a server as a plurality of simulated clients from a single client computer, each simulated client making a separate logical connection to the server. A task scheduler will schedule the simulated client tasks that are determined for each simulated user by reference to the user parameters in the client profile throughout a work day. The scheduler also introduces a random element so that the tasks simulate natural variability in user behavior. User receivable response times for the task corresponding to simulated user activity are maintained in a log file and the 95th percentile time or score for each task type is calculated. The individual task type scores may be weighted and averaged together to arrive at a weighted average response time. The weighted average response time can then be used as a threshold value to determine the total number of users a server application can adequately support.

Method and computer program product for offloading processing tasks from software to hardware

Abstract: Offloading specific processing tasks that would otherwise be performed in a computer system's processor and memory, to a peripheral device. The computing task is then performed by the peripheral, thereby saving computer system resources for other computing tasks. In one preferred embodiment, the disclosed method is utilized in a layered network model, wherein computing tasks that are typically performed in network applications are instead offloaded to the network interface card (NIC) peripheral.