Abstract: We propose an ensemble of kernel ridge regression based classifiers in this paper. Kernel ridge regression admits a closed form solution making it faster to compute and also making it suitable to use for ensemble methods for small and medium sized data sets. Our method uses random vector functional link network to generate training samples for kernel ridge regression classifiers. Several kernel ridge regression classifiers are constructed from different training subsets in each base classifier. The partitioning of the training samples into different subsets leads to a reduction in computational complexity when calculating matrix inverse compared with the standard approach of using all N samples for kernel matrix inversion. The proposed method is evaluated using well known multi-class UCI data sets. Experimental results show the proposed ensemble method outperforms the single kernel ridge regression classifier and its bagging version.

Abstract: In the context of elections, the Internet opens new and promising possibilities for parties and candidates looking for a better political strategy and visibility. In this way they can also organize their election campaign to gather funds, to mobilize support, and to enter into a direct dialogue with the electorate. This paper presents an ongoing research of recommender systems applied on e-government, particularly it is an extension of so-called voting advice applications (VAA's). VAA's are Web applications that support voters, providing relevant information on candidates and political parties by comparing their political interests with parties or candidates on different political issues. Traditional VAA's provide recommendations of political parties and candidates focusing on static profiles of users. The goal of this work is to develop a candidate profile based on different parameters, such as the perspective of voters, social network activities, and expert opinions, to construct a more accurate dynamic profile of candidates. Understanding the elements that compose a candidate profile will help citizens in the decision-making process when facing a lack of information related to the behavior and thinking of future public authorities. At the end of this work, a fuzzy-based visualization approach for a VAA design is given using as a case study the National Elections of Ecuador in 2013.

Abstract: The integration of semi-supervised learning and ensemble learning has been a promising research area. It is a typical procedure that one learner recommends the pseudo-labeled instances with high predictive confidence to another, so that the training dataset is expanded. However, the new learner’s demand on recommendation as well as the possibility of incorrect recommendation are neglected, which inevitably jeopardize the learning performance. To address these issues, this paper proposes the Discriminative Learning from Selective Recommendation (DLSR) method. On one hand, both reliability and informativeness of the pseudo-labeled instances are taken into account via selective recommendation. On the other hand, the potential in both correct and incorrect recommendation are formulated in discriminative learning. Based on DLSR, we further propose the selective semi-supervised AdaBoost. With both recommending and receiving learners engaged in ensemble model learning, the unlabeled instances are explored in a more effective way.

Abstract: Synchrotron radiation light source facilities are leading the way to ultrahigh resolution X-ray imaging. High resolution imaging is essential to understanding the fundamental structure and interaction of materials at the smallest length scale possible. Diffraction based methods achieve nanoscale imaging by replacing traditional objective lenses by pixelated area detectors and computational image reconstruction. Among these methods, ptychography is quickly becoming the standard for sub-30 nanometer imaging of extended samples, but at the expense of increasingly high data rates and volumes. This paper presents a new distributed algorithm for solving the ptychographic image reconstruction problem based on automatic differentiation. Input datasets are subdivided between multiple graphics processing units (GPUs); each subset of the problem is then solved either entirely independent of other subsets (asynchronously) or through sharing gradient information with other GPUs (synchronously). The algorithm was evaluated on simulated and real data acquired at the Advanced Photon Source, scaling up to 192 GPUs. The synchronous variant of our method outperformed an existing multi-GPU implementation in terms of accuracy while running at a comparable execution time.

Abstract: There exist thousands of Chinese characters, used across several countries and languages. Their huge number induces various processing difficulties by computers. One challenging topic is for example the automatic font generation for such characters. Also, as these characters are in many cases recursive compounds, pattern (i.e. sub-character) detection is an insightful topic. In this paper, aiming at addressing such issues, we describe a segmentation method for Chinese characters, producing wire-frame models, thus vector graphics, compared to conventional raster approaches. While raster output would enable only very limited reusing of these wire-frame models, vector output would for instance support the automatic generation of vector fonts (Adobe Type 1, Apple True Type, etc.) for such characters. Our approach also enables significant performance increase compared to the raster approach. The proposed method is then experimented with a list of several Chinese characters. Next, the method is empirically evaluated and its average time complexity is assessed.