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Papageorgiou A.,CLOUD SYSTEMS
2015 18th International Conference on Intelligence in Next Generation Networks, ICIN 2015 | Year: 2015

Standardization activities that are or can become crucial for the Internet of Things (IoT) have mainly focused on reference architectures, device interoperability, and Machine-to-Machine communication protocols, leading to a connected IoT. However, the related specifications have already started taking a step further, namely towards enabling the support of features that come on-top of simple interoperability, thus leading from "connected IoT" to "efficient IoT". This paper investigates this transition based on a relevant Case Study, namely the current extensions of the device management protocol CWMP (widely known as TR-069) in that direction, including examples of what these extensions enable in practice. © 2015 IEEE. Source


Aldogan D.,CLOUD SYSTEMS | Yaslan Y.,Technical University of Istanbul
Lecture Notes in Electrical Engineering | Year: 2016

This paper is devoted to the comparison of different common base and ensemble classifiers for sentiment classification of reviews. It is also aimed to generate different feature sets and to observe their contribution to the classification accuracy. In detail, these feature sets are formed in an hierarchical manner, which is accomplished by first forming part-of-speech (POS) based word groups and then utilizing feature frequencies, SentiWordNet scores and their combination to obtain feature sets. In addition, several common base classifiers, namely Multinominal Naive Bayes (MNB), Support Vector Machine (SVM), Voted Perceptron (VP), K-Nearest Neighbor (k-NN), as well as common ensemble strategies, Random Forests (RFs), Stacking and Random Subspace (RSS) are each tested on the generated feature sets. Also, the Behavior-Knowledge Space (BKS) method has been derived to be applied on the set of outcomes for different algorithm and feature set combinations. Furthermore, a probability based meta-classifier technique has been tested on this set of outcomes. Finally, Information Gain (IG) feature selection technique has been applied to reduce the feature spaces. The experiments are conducted on a widely used movie review dataset and an equally common multi-domain review dataset. The results indicate that the probabilistic ensemble method generally gives comparatively better results than the other algorithms tested on the chosen datasets and that IG method can be utilized to save computational time while maintaining allowable accuracy. © Springer International Publishing Switzerland 2016. Source


Tan J.,North Carolina State University | Carmon D.,CLOUD SYSTEMS | Baron D.,North Carolina State University
IEEE Transactions on Information Theory | Year: 2014

Compressed sensing typically deals with the estimation of a system input from its noise-corrupted linear measurements, where the number of measurements is smaller than the number of input components. The performance of the estimation process is usually quantified by some standard error metric such as squared error or support set error. In this correspondence, we consider a noisy compressed sensing problem with any additive error metric. Under the assumption that the relaxed belief propagation method matches Tanaka's fixed point equation, we propose a general algorithm that estimates the original signal by minimizing the additive error metric defined by the user. The algorithm is a pointwise estimation process, and thus simple and fast. We verify that our algorithm is asymptotically optimal, and we describe a general method to compute the fundamental information-theoretic performance limit for any additive error metric. We provide several example metrics, and give the theoretical performance limits for these cases. Experimental results show that our algorithm outperforms methods such as relaxed belief propagation (relaxed BP) and compressive sampling matching pursuit (CoSaMP), and reaches the suggested theoretical limits for our example metrics. © 1963-2012 IEEE. Source


Perez J.F.,Imperial College London | Pacheco-Sanchez S.,CLOUD SYSTEMS | Casale G.,Imperial College London
Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS | Year: 2013

Parameterizing performance models for multi-threaded enterprise applications requires finding the service rates offered by worker threads to the incoming requests. Statistical inference on monitoring data is here helpful to reduce the overheads of application profiling and to infer missing information. While linear regression of utilization data is often used to estimate service rates, it suffers erratic performance and also ignores a large part of application monitoring data, e.g., response times. Yet inference from other metrics, such as response times or queue-length samples, is complicated by the dependence on scheduling policies. To address these issues, we propose novel scheduling-aware estimation approaches for multi-threaded applications based on linear regression and maximum likelihood estimators. The proposed methods estimate demands from samples of the number of requests in execution in the worker threads at the admission instant of a new request. Validation results are presented on simulated and real application datasets for systems with multi-class requests, class switching, and admission control. © 2013 IEEE. Source


A system and method for managing, routing and controlling devices and inter-device connections located within an environment to manage and control the environment using a control client is presented. A user configures a presentation environment into one or more sub-environments, restricts access to one or more devices of a presentation sub-environment, or schedules one or more resources within a presentation sub-environment.

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