Browsing by Subject "Task analysis"

Now showing 1 - 3 of 3

Open Access
AoII-optimum sampling of CTMC information sources under sampling rate constraints
(IEEE, 2024) Cosanda, Ismail; Akar, Nail; Ulukus, Sennur
We consider a sensor that samples an $N-\mathbf{state}$ continuous-time Markov chain (CTMC)-based information source process, and transmits the observed state of the source, to a remote monitor tasked with timely tracking of the source process. The mismatch between the source and monitor processes is quantified by age of incorrect information (AoII), which penalizes the mismatch as it stays longer, and our objective is to minimize the average AoII under an average sampling rate constraint. We assume a perfect reverse channel and hence the sensor has information of the estimate while initiating a transmission or preempting an ongoing transmission. First, by modeling the problem as an average cost constrained semi-Markov decision process (CSMDP), we show that the structure of the problem gives rise to an optimum threshold policy for which the sensor initiates a transmission once the AoII exceeds a threshold depending on the instantaneous values of both the source and monitor processes. However, due to the high complexity of obtaining the optimum policy in this general setting, we consider a relaxed problem where the thresholds are allowed to be dependent only on the estimate. We show that this relaxed problem can be solved with a novel CSMDP formulation based on the theory of absorbing MCs, with a computational complexity of $\mathcal{O}(N^{4})$, allowing one to obtain optimum policies for general CTMCs with over a hundred states.
Open Access
Fair task allocation in crowdsourced delivery
(Institute of Electrical and Electronics Engineers, 2018) Basik, F.; Gedik, B.; Ferhatosmanoglu, H.; Wu, K.
Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries. In this work, we introduce a new crowdsourced delivery platform that takes fairness towards workers into consideration, while maximizing the task completion ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase assignment model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that maximizes fairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task completion ratio, as well as fairness and assignment ratio. Experiments show that F-Aware runs around $10^7\times$ faster than the TAR-optimal solution and assigns 96.9% of the tasks that can be assigned by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm. IEEE
Open Access
Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images
(Institute of Electrical and Electronics Engineers, 2019) Sarı, Can Taylan; Gündüz-Demir, Çiğdem
Histopathological examination is today’s gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning based technique constructs a deep belief network of restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example of successfully using restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts. IEEE