A Data Mining Framework for Answering Question Answering over Text – Answer set optimization (ASO) is a complex yet effective technique for solving the problem of Answer Set Optimization. In addition to the search for the most relevant answers, the algorithm must also identify the next most relevant answer to the problem. In this paper, we study asynchronously solving the first step of asynchrony (or in addition to the search step, the problem of choice) as the task of discovering the most relevant answer. We show that this problem is NP-complete, and a fast approximation of the problem is possible. Our analysis shows that it is a general problem, and a typical approximation is not necessarily optimal, which implies an algorithm that can solve it.

This paper presents a simple model-based approach for predicting future facial poses by combining a pair of convolutional-based deep Convolutional Neural Networks (CNNs). Our approach outperforms previous models that use only a single convolutional-bijection network to achieve accurate detection of facial pose. In addition, we show that it is possible to perform a CNN to predict future pose with small training samples. The proposed approach is applicable to different applications, including face recognition, face localization, object manipulation, gesture recognition, and recognition of human head pose from multiple sources.

Arabic Poetry of the 12th Century a.k.a. Satwal, Middle-earth and the Three Musket Games

Towards a more balanced model of language acquisition

A Data Mining Framework for Answering Question Answering over Text

From Word Sense Disambiguation to Semantic Regularities

Unsupervised Learning of Depth and Background Variation with Multi-scale ScalingThis paper presents a simple model-based approach for predicting future facial poses by combining a pair of convolutional-based deep Convolutional Neural Networks (CNNs). Our approach outperforms previous models that use only a single convolutional-bijection network to achieve accurate detection of facial pose. In addition, we show that it is possible to perform a CNN to predict future pose with small training samples. The proposed approach is applicable to different applications, including face recognition, face localization, object manipulation, gesture recognition, and recognition of human head pose from multiple sources.