Search results for "Application"
showing 10 items of 5559 documents
Detecting Emotions in Comments on Forums
2014
The paper presents one of the most important issues in Natural Language Processing (NLP), emotion identification and classification to implement a computational technology based on existing resources, open-source or freely available for research purposes. Furthermore, we are interested to use it for establishing Gold standards in sentiment analysis area, such as SentiWordNet. In this sense, we propose to recognize and classify the emotions (sentiments) of the public consumer from the written texts which appeared on the various Forums. We analyse the writing style which refers to how consumers construct sentences together when they write comments to indicate their passion about an entity (pe…
The Mediatization of E-Campaigning: Evidence From German Party Websites in State, National, and European Parliamentary Elections 2002-2009
2012
The rise of e-campaigning is often associated with its ability to circumvent journalistic principles of news selection and presentation. By this, parties and candidates are said to free themselves from the discretionary power of the mass media and to reach voters in an unfiltered way. This conventional wisdom is tested through a comparative content analysis of German party websites in state, national, and European parliamentary elections between 2002 and 2009. The results show that e-campaigns in all elections adhere in their messages to the media logic. Specifically, they replicate those patterns of offline coverage that have been held accountable for rising political alienation and civic …
Refinements on IEEE 802.11 Distributed Coordination Function Modeling Approaches
2010
With the popularity of the IEEE 802.11 standards, many analytical saturation throughput studies for the distributed coordination function (DCF) have been reported. In this paper, we outline a number of issues and criticalities raised by previously proposed models. In particular, a careful look at backoff counter decrement rules allows us to conclude that, under saturation conditions, the slot immediately following a successful transmission can be accessed only by the station (STA) that has successfully transmitted in the previous channel access. Moreover, due to the specific acknowledgment (ACK) timeout setting adopted in the standard, the slot immediately following a collision cannot be ac…
A new Media Access Control layer Quality of Service multicast scheme for IEEE 802.11s based wireless mesh networks
2014
Inderscience Publishers; International audience; We propose a new Media Access Control (MAC) layer enabling Quality of Service (QoS) multicast scheme for IEEE 802.11s networks, where a unicast routing protocol called HWMP (Hybrid Wireless Mesh Protocol) is defined. The HWMP protocol is more adapted for best effort traffic, that's why its usage is not suitable for real time multimedia applications. The goal of our proposed mechanism is to take into account multicast communication under QoS constraints for the IEEE 802.11s mesh networks where no QoS multicasting has been defined. Our multicasting scheme handles QoS guarantee for real time applications. Indeed, our scheme is based on finding t…
Ultra-Low Power Wake-up Radio for 5G IoT
2019
5G Internet of Things (5G IoT), which is currently under development by 3GPP, paves the way for connecting diverse categories of devices to the IoT via cellular networks. For battery-powered low-cost IoT devices, wake-up radio (WuR) appears as an eminent technique for prolonging the lifetime of such devices, thanks to its outstanding energy consumption performance. However, only some small-size battery-powered IoT devices are able to transmit to a cellular IoT base station (BS) directly. In this article, we present W2B-IoT, a prototype implementation of a WuR-based two-tier system, which bridges cellular IoT BS and WuR via a Bluetooth low energy (BLE)-enabled Android smartphone. Such a WuR-…
Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches
2019
Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive modeling, in particular, remain mainly unexplored. In predictive modeling models are validated with previously unseen examples, giving a more accurate estimate of their performance and generalization ability in real-life scenarios. Objective: To find and review existing overweight or obesity research from the perspective of employing childhood data and predictive modeling metho…
Crowd-Averse Cyber-Physical Systems: The Paradigm of Robust Mean-Field Games
2016
For a networked controlled system, we illustrate the paradigm of robust mean-field games. This is a modeling framework at the interface of differential game theory, mathematical physics, and $H_{\infty}$ - optimal control that tries to capture the mutual influence between a crowd and its individuals. First, we establish a mean-field system for such games including the effects of adversarial disturbances. Second, we identify the optimal response of the individuals for a given population behavior. Third, we provide an analysis of equilibria and their stability.
Multiprocessor SoC Implementation of Neural Network Training on FPGA
2008
Software implementations of artificial neural networks (ANNs) and their training on a sequential processor are inefficient because they do not take advantage of parallelism. ASIC and FPGA implementations employ specific hardware structures to exploit parallelism in order to improve processing speed; however, optimizing resource usage requires the use of fixed-point arithmetic, thereby losing precision, and the final system is restricted to a particular network topology. This paper presents a mixed approach based on a multiprocessor system-on-chip (SoC) on a FPGA. The use of software-driven embedded microprocessors with custom floating-point extensions for ANN related functions allows for gr…
Learning by the Process of Elimination
2002
AbstractElimination of potential hypotheses is a fundamental component of many learning processes. In order to understand the nature of elimination, herein we study the following model of learning recursive functions from examples. On any target function, the learning machine has to eliminate all, save one, possible hypotheses such that the missing one correctly describes the target function. It turns out that this type of learning by the process of elimination (elm-learning, for short) can be stronger, weaker or of the same power as usual Gold style learning.While for usual learning any r.e. class of recursive functions can be learned in all of its numberings, this is no longer true for el…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…