Browsing by Subject "Task assignment"
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Item Open Access Autonomous multiple teams establishment for mobile sensor networks by SVMs within a potential field(2012) Nazlibilek, S.In this work, a new method and algorithm for autonomous teams establishment with mobile sensor network units by SVMs based on task allocations within a potential field is proposed. The sensor network deployed into the environment using the algorithm is composed of robot units with sensing capability of magnetic anomaly of the earth. A new algorithm is developed for task assignment. It is based on the optimization of weights between robots and tasks. The weights are composed of skill ratings of the robots and priorities of the tasks. Multiple teams of mobile units are established in a local area based on these mission vectors. A mission vector is the genetic and gained background information of the mobile units. The genetic background is the inherent structure of their knowledge base in a vector form but it can be dynamically updated with the information gained later on by experience. The mission is performed in a magnetic anomaly environment. The initial values of the mission vectors are loaded by the task assignment algorithm. The mission vectors are updated at the beginning of each sampling period of the motion. Then the teams of robots are created by the support vector machines. A linear optimal hyperplane is calculated by the use of SVM algorithm during training period. Then the robots are classified as teams by use of SVM mechanism embedded in the robots. The support vector machines are implemented in the robots by ordinary op-amps and basic logical gates. Team establishment is tested by simulations and a practical test-bed. Both simulations and the actual operation of the system prove that the system functions satisfactorily. © 2012 Elsevier Ltd. All rights reserved.Item Open Access Cleaning ground truth data in software task assignment(Elsevier BV, 2022-05-25) Tecimer, K. A.; Tüzün, Eray; Moran, Cansu; Erdogmus, H.Context: In the context of collaborative software development, there are many application areas of task assignment such as assigning a developer to fix a bug, or assigning a code reviewer to a pull request. Most task assignment techniques in the literature build and evaluate their models based on datasets collected from real projects. The techniques invariably presume that these datasets reliably represent the “ground truth”. In a project dataset used to build an automated task assignment system, the recommended assignee for the task is usually assumed to be the best assignee for that task. However, in practice, the task assignee may not be the best possible task assignee, or even a sufficiently qualified one. Objective: We aim to clean up the ground truth by removing the samples that are potentially problematic or suspect with the assumption that removing such samples would reduce any systematic labeling bias in the dataset and lead to performance improvements. Method: We devised a debiasing method to detect potentially problematic samples in task assignment datasets. We then evaluated the method’s impact on the performance of seven task assignment techniques by comparing the Mean Reciprocal Rank (MRR) scores before and after debiasing. We used two different task assignment applications for this purpose: Code Reviewer Recommendation (CRR) and Bug Assignment (BA). Results: In the CRR application, we achieved an average MRR improvement of 18.17% for the three learning-based techniques tested on two datasets. No significant improvements were observed for the two optimization-based techniques tested on the same datasets. In the BA application, we achieved a similar average MRR improvement of 18.40% for the two learning-based techniques tested on four different datasets. Conclusion: Debiasing the ground truth data by removing suspect samples can help improve the performance of learning-based techniques in software task assignment applications.Item Open Access Context-aware hierarchical online learning for performance maximization in mobile crowdsourcing(Institute of Electrical and Electronics Engineers, 2018) Muller, S. K.; Tekin, Cem; Schaar, M.; Klein, A.In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance 1) may fluctuate, depending on both the worker's current personal context and the task context and 2) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. In addition, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.Item Open Access Exact algorithms for a task assignment problem(World Scientific Publishing Company, 2009) Kaya, Kamer; Uçar, B.We consider the following task assignment problem. Communicating tasks are to be assigned to heterogeneous processors interconnected with a heterogeneous network. The objective is to minimize the total sum of the execution and communication costs. The problem is NP-hard. We present an exact algorithm based on the well-known A* search. We report simulation results over a wide range of parameters where the largest solved instance contains about three hundred tasks to be assigned to eight processors. © World Scientific Publishing Company.Item Open Access Multilevel heuristics for task assignment in distributed systems(Bilkent University, 1998) İkinci, MuratTask assignment problem deals with assigning tasks to processors in order to minimize the sum of execution and communication costs in a distributed system. In this work, we propose a novel task clustering scheme which considei s the differences between the execution times of tasks to be clustered as well as the communication costs between them. We use this clustering approach witli proper assignment schemes to implement two-phase assignment algorithms which can be used to find suboptimal solutions to any task assignment problem. In addition, we adapt the multilevel scheme used in graph/hypergrapli partitioning to the task assignment. Multilevel assignment algorithms reduce the size of the original problem by collapsing tasks, find an initial assignment on the smellier problem, and then projects it towards the original problem l)y successively refining the assignment at each level. We propose several clustering schemes for multilevel assignment algorithms. The performance of all proposed algorithms are evaluated through an experimental study where the assignment qualities are compared with two up-to-date heuristics. Experimerita.l results show that our algorithms substantially outperform both of the existing heuristics.Item Open Access Online context-aware task assignment in mobile crowdsourcing via adaptive discretization(IEEE, 2022-09-22) Elahi, Sepehr; Nika, Andi; Tekin, CemMobile crowdsourcing is rapidly boosting the Internet of Things revolution. Its natural development leads to an adaptation to various real-world scenarios, thus imposing a need for wide generality on data-processing and task-assigning methods. We consider the task assignment problem in mobile crowdsourcing while taking into consideration the following: (i) we assume that additional information is available for both tasks and workers, such as location, device parameters, or task parameters, and make use of such information; (ii) as an important consequence of the worker-location factor, we assume that some workers may not be available for selection at given times; (iii) the workers' characteristics may change over time. To solve the task assignment problem in this setting, we propose Adaptive Optimistic Matching for Mobile Crowdsourcing (AOM-MC), an online learning algorithm that incurs O~(T(D¯+1)/(D¯+2)+ϵ) regret in T rounds, for any ϵ>0 , under mild continuity assumptions. Here, D¯ is a notion of dimensionality which captures the structure of the problem. We also present extensive simulations that illustrate the advantage of adaptive discretization when compared with uniform discretization, and a time- and location-dependent crowdsourcing simulation using a real-world dataset, clearly demonstrating our algorithm's superiority to the current state-of-the-art and baseline algorithms.Item Open Access Task assignment in heterogeneous computing systems(Academic Press, 2006-01) Ucar, B.; Aykanat, Cevdet; Kaya, K.; Ikinci, M.The problem of task assignment in heterogeneous computing systems has been studied for many years with many variations. We consider the version in which communicating tasks are to be assigned to heterogeneous processors with identical communication links to minimize the sum of the total execution and communication costs. Our contributions are three fold: a task clustering method which takes the execution times of the tasks into account; two metrics to determine the order in which tasks are assigned to the processors; a refinement heuristic which improves a given assignment. We use these three methods to obtain a family of task assignment algorithms including multilevel ones that apply clustering and refinement heuristics repeatedly. We have implemented eight existing algorithms to test the proposed methods. Our refinement algorithm improves the solutions of the existing algorithms by up to 15% and the proposed algorithms obtain better solutions than these refined solutions. © 2005 Elsevier Inc. All righs reserved.