In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: The Internet of Things is arriving to our homes or cities through fields already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of environmental conditions of cities can help to adapt the indoor locations of the cities in order to be more comfortable for people who stay there. A way to improve the indoor conditions is an efficient temperature control, however, it depends on many factors like the different combinations of outdoor temperature and humidity. Therefore, adjusting the indoor temperature is not setting a value according to other value. There are many more factors to take into consideration, hence the traditional logic based in binary states cannot be used. Many problems cannot be solved with a set of binary solutions and we need a new way of development. Fuzzy logic is able to interpret many states, more than two states, giving to computers the capacity to react in a similar way to people. In this paper we will propose a new approach to control the temperature using the Internet of Things together its platforms and fuzzy logic regarding not only the indoor temperature but also the outdoor temperature and humidity in order to save energy and to set a more comfortable environment for their users. Finally, ∗Corresponding author Email addresses: danielmeanallorian@gmail.com (Daniel Meana-Llorián), gonzalezgarciacristian@hotmail.com (Cristian González Garćıa), crispelayo@uniovi.es (B. Cristina Pelayo G-Bustelo), cueva@uniovi.es (Juan Manuel Cueva Lovelle), nestor@uniovi.es (Nestor Garcia-Fernandez)
[A]: IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions


[Q]: The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly wellsuited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many realworld applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the SELFSPARRING algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings.
[A]: Multi-dueling Bandits with Dependent Arms


[Q]: Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Lowrank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data. But previous low-rank regression model only works on single view data. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model. Most importantly, we provide a closed-form solution to the multi-view low-rank regression model. Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multiview low-rank structure is very helpful. Introduction In many tasks, a single object can be described using information from different channels (or views). For example, a 3-D object can be described using pictures from different angles; a website can be described using the words it contains, and the hyperlinks it contains; an image can be described using different features, such as SIFT feature, and HOG feature; in daily life, a person can be characterized using age, height, weight and so on. These data all comes from different aspects and channels. Multi-view problems aim to improve existing single view model by learning a model utilizing data collected from multiple channels (Rüping and Scheffer 2005) (de Sa 2005) (Zhou and Burges 2007). Low-rank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data (Xiang et al. 2012) (Evgeniou and Pontil 2007) (Cai et al. 2013). Existing regression models only work on single view data. To be specific, linear regression finds a linear model with respect to the single view feature data to fit target class data (Seber and Lee 2012). Let matrix B ∈ <p×c be the parameter of the linear model. Linear regression solves a problem of minB ||Y − XB||F , where Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. X = [x1,x2, ...,xn] ∈ <p×n is the single view feature data matrix and Y ∈ <n×c is the target class indicator matrix. Ridge regression can achieve better results by adding a Frobenius norm based regularization on linear regression loss objective (Hoerl and Kennard 1970) (Marquaridt 1970). Ridge regression solves the problem minB ||Y −XB||F + λ||B||F , where λ is the regularization weight parameter. Cai (Cai et al. 2013) showed that whenB is low-rank, regression is equivalent to linear discriminant analysis based regressions. However, all these work only works for single-view problems. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on regression model. This model can be solved using closed-form solution directly. In linear regression, low rank parameter matrix B is dependent on view ν. Through theoretical analysis, we show that multi-view low-rank regression model is equivalent to do regression in the subspace of each view. In other words, let B = AνB, and it is equivalent to find the shared regression parameter matrix B under the subspace transformation Aν with respect to view ν. Extensive experiments performed on 4 multi-view datasets show that the proposed model outperforms single-view regression model and reveals that low-rank structure can improve the classification result of a full-rank model. Notations. In this paper, matrices are written in uppercase letters, such as X, Y . Vectors are written in bold lower case letters, such as x, y. Tr(X) means the trace operation for matrix X . Multi-view Low Rank Regression Assume that there are v views and c classes, pν is the dimension of view ν, nj is the sample size of the j-th class, and n is the total sample size. Let Xν = [x1 , ...,x ν n] ∈ <pν×n be the data matrix of view ν, ν = 1, 2, ..., v, and Y = [y1, ...,yc] ∈ <n×c is the normalized class indicator matrix, i.e. Yij = 1/ √ nj if the i-th data point belongs to the j-th class and Yij = 0 otherwise. We try to minimize the residual of low rank regression model in each class and in each view. Loss function of multiProceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence 1973 ar X iv :1 61 0. 04 66 8v 1 [ cs .L G ] 1 4 O ct 2 01 6 view low rank ridge regression can be proposed as in Eq.(1):
[A]:
A Closed Form Solution to Multi-View Low-Rank Regression