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Energy internet has received increasing attention as a promising solution for the widespread use of renewable energy resources in recent years. One of critical issues in energy internet communications is how to guarantee the desired quality of service(QoS) while numerous smart devices access the wireless network. In this paper, we study the problem of access scheduling for QoS guarantees under massive...
SPARQL query optimization relies on the design and execution of query plans that involve reordering triple patterns, in the hopes of minimizing cardinality of intermediate results. In practice, this is not always effective, as many existing systems succeed in certain types of query patterns and fail in others. This kind of trade-off is often a derivative of the algorithms behind query planning. In...
Commercial clouds have become a viable platform for performing a significant range of large scale scientific analyses – due to the offerings of elasticity, specialist hardware, software infrastructure and pay-as-you-go cost model. Such clouds represent a low upfront capital cost alternative to the use of dedicated eScience infrastructure. However, there are still significant technical hurdles associated...
This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Boolean Kalman Filter (BKF). However, thus far in the literature only the case of white observation noise...
This contribution focuses, within the ℓ1-Potts model, on the automated estimation of the regularization parameter balancing the ℓ1 data fidelity term and the TVℓ0 penalization. Variational approaches based on total variation gained considerable interest to solve piecewise constant denoising problems thanks to their deterministic setting and low computational cost. However, the quality of the achieved...
Fast and accurate performance estimation is a key challenge in modern system design. Recently, machine learning-based approaches have emerged that allow predicting the performance of an application on a target platform from executions on a different host. However, existing approaches rely on expensive instrumentation that requires source code to be available. We propose a novel sampling-based, binary-level...
This paper proposes an online identification of the MIMO ARX-Laguerre model by giving an analytical solution to optimize the Laguerre poles and using recursive identification of Fourier coefficients. Theses parameters characterizing MIMO ARX-Laguerre model are identified basing on the regularised square error. The Laguerre poles optimization and the Fourier coefficients identification procedures are...
Due to the rapid technological advancements, the Grid computing has emerged as a new field, distinguished from conventional distributed computing. The load balancing is considered to be very important in Grid systems. In this paper, we propose a new dynamic and distributed load balancing method called "Enhanced GridSim with Load Balancing based on Cost Estimation" (EGCE) for computational...
We have developed a wearable upper limb support system (ULSS) for support during heavy overhead tasks. The purpose of this study is to develop the voluntary motion support algorithm for the ULSS, and to confirm the effectiveness of the ULSS with the developed algorithm through dynamic evaluation experiments. The algorithm estimates the motor intention of the wearer based on a bioelectrical signal...
High utility itemset mining provides more useful and realistic results than frequent pattern mining because of its ability to consider statistical correlation and semantic significance among the items. The state of art algorithms designed for mining high utility itemsets always consider the database as static. If they are used for dynamic databases for the same purpose, database is rescanned from...
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nested Expectation-Maximization (EM) algorithms. Under suitable sparsity assumptions...
One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-driven methods fail to work, incorporating supplemental information, like expert judgments, can improve the learning of BN parameters. In practice, expert judgments are provided and transformed into qualitative parameter constraints. Moreover, prior distributions of BN parameters are also useful information...
This paper treats the problem of continuous-time model identification with unknown time delays from sampled data. The proposed method estimates the plant and the time delays in a separable way. More precisely, the plant is estimated by the standard recursive Least Square algorithm while the time delays are explicitly estimated by the Gauss-Newton algorithm. This means clear separation between the...
High dynamic range (HDR) imaging is highly demanded in computer vision algorithms. An HDR image is composed with several low dynamic range (LDR) images, which usually have some disparities. In many HDR imaging algorithms, the disparities are estimated based on the texture information of the LDR images. However, the texture information is often lost completely if scenes include extremely bright and...
Accurate localization of randomly deployed sensor nodes is critically important in wireless sensor networks (WSNs) deployed for monitoring and tracking applications. The localization challenge has been posed as a multidimensional global optimization problem in earlier literature. Many swarm intelligence algorithms have been proposed for accurate localization. The untapped vast potential of the artificial...
Estimation of worker reliability on microtask crowdsourcing platforms has gained attention from many researchers. On microtask platforms no worker is fully reliable for a task and it is likely that some workers are spammers, in the sense that they provide a random answer to collect the financial reward. Existence of spammers is harmful as they increase the cost of microtasking and will negatively...
We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem. We extend state-of-the-art frequentist and Bayesian algorithms for single-agent MAB problems to cooperative distributed algorithms for multi-agent MAB problems in which agents communicate according to a fixed network graph. We rely on a running consensus algorithm for each agent's...
The paper explains about tracking a dynamically changing arbitrary shaped structure, important for determining their boundary and position estimation for advanced warning. The proposed algorithm uses active contour detection algorithm to construct the effective contour in each frame. Then Hungarian based Kalman filter is used to achieve best accuracy in estimation of future positions using multi-point...
This paper studies the finite-time leader-following tracking problems for a group of autonomous agents modeled by second-order nonlinear dynamics under a dynamic reference leader. First, based on distributed binary measurements, a class of finite-time leader-following tracking algorithms are only requiring a single-bit quantization error relative to each neighbor. Then, by using a topology-dependent...
This work describes the Dynse framework, which uses dynamic selection of classifiers to deal with concept drift. Basically, classifiers trained on new supervised batches available over time are add to a pool, from which is elected a custom ensemble for each test instance during the classification time. The Dynse framework is highly customizable, and can be adapted to use any method for dynamic selection...
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