TASK DEFINITION: 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.
PROBLEM: We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Experiments on passively labeled data show that this approach reduces the label complexity required to achieve good predictive performance on many learning problems.

SOLUTION: Importance Weighted Active Learning

PROBLEM: 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)

SOLUTION: IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions

PROBLEM: We consider a broad class of first-order optimization algorithms which are oblivious, in the sense that their step sizes are scheduled regardless of the function under consideration, except for limited sideinformation such as smoothness or strong convexity parameters. With the knowledge of these two parameters, we show that any such algorithm attains an iteration complexity lower bound of Ω( √ L/ǫ) for L-smooth convex functions, and Ω̃( √ L/μ ln(1/ǫ)) for L-smooth μ-strongly convex functions. These lower bounds are stronger than those in the traditional oracle model, as they hold independently of the dimension. To attain these, we abandon the oracle model in favor of a structure-based approach which builds upon a framework recently proposed in [1]. We further show that without knowing the strong convexity parameter, it is impossible to attain an iteration complexity better than Ω̃ ((L/μ) ln(1/ǫ)). This result is then used to formalize an observation regarding L-smooth convex functions, namely, that the iteration complexity of algorithms employing time-invariant step sizes must be at least Ω(L/ǫ).

SOLUTION:
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms