Task (computing)

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

Optimal Task Allocation in Near-Far Computing Enhanced C-RAN for Wireless Big Data Processing

Abstract: With the increasing popularity of user equipments, the corresponding UE generated big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggle to process those data and applications. This article proposes a near-far computing enhanced C-RAN (NFC-RAN) architecture that can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in the remote radio head,, which can quickly respond to delay-sensitive tasks from the UEs, while FEC sits next to a baseband unit pool, which can do other computation-intensive tasks. Task allocation between NEC and FEC is introduced in this article. Also, WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.

Process control device

Abstract: A method and apparatus provides a standard interface (14) to process control devices (20-22) which are adapted to differing field-bus protocols (18). The method and apparatus enable integration of process control devices (20-22) adapted to differing field-bus protocols (18) while avoiding the use of protocol gateways which would otherwise greatly complicate the task of developing process control application programs.

Multiple task transfer learning with small sample sizes

Abstract: Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Online specification of a system which compares determined devices and installed devices

Abstract: A system and method for online configuration of a measurement device for a measurement system. The user accesses a server with a client computer over a network and specifies a desired measurement task. If the user lacks the hardware required to perform the task, hardware specifications and configuration software and/or data specific to the user's application, i.e., to perform the task, are sent to a manufacturer, who pre-configures the hardware with the configuration software and/or data to perform the task and sends the pre-configured hardware to the user. The hardware may be re-configurable hardware, such as a programmable hardware element or processor/memory based device. Configuration software and/or data for configuring the user's measurement system hardware (and/or software) to perform the desired task may also be sent to the user. The configuration software sent to the user may comprise a graphical program usable by the measurement system to perform the task.

Assigning Tasks to Workers based on Historical Data: Online Task Assignment with Two-sided Arrivals

Abstract: Efficient allocation of tasks to workers is a central problem in crowdsourcing. In this paper, we consider a special setting inspired from spatial crowdsourcing platforms where both workers and tasks arrive dynamically. Additionally, we assume all tasks are heterogeneous and each worker-task assignment brings a distinct reward. The natural challenge lies in how to incorporate the uncertainty in the arrivals from both workers and tasks into our online allocation policy such that the total expected rewards are maximized. To attack this challenge, we assume the arrival patterns of worker "types'' and task "types'' are not erratic and can be predicted from historical data. To be more specific, we consider a finite time horizon T and assume in each time-step, a single worker and task are sampled (i.e., "arrive'') from two respective distributions independently, and this sampling process repeats identically and independently for the entire T online time-steps. Our model, called Online Task Assignment with Two-Sided Arrival (OTA-TSA), is a significant generalization of the classical online task assignment where the set of tasks is assumed to be available offline. For the general version of OTA-TSA, we present an optimal non-adaptive algorithm which achieves an online competitive ratio of 0.295. For the special case of OTA-TSA where the reward is a function of just the worker type, we present an improved algorithm (which is adaptive) and achieves a competitive ratio of at least 0.343. On the hardness side, along with showing that the ratio obtained by our non-adaptive algorithm is the best possible among all non-adaptive algorithms, we further show that no (adaptive) algorithm can achieve a ratio better than $0.581 (unconditionally), even for the special case of OTA-TSA with homogenous tasks (i.e., all rewards are same). At the heart of our analysis lies a new technical tool (which is a refined notion of the birth-death process), called the two-stage birth-death process, which may be of independent interest. Finally, we perform numerical experiments on two real-world datasets obtained from crowdsourcing platforms to complement our theoretical results.