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The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system. [ABSTRACT FROM AUTHOR]

Recently, document embeddings methods have been proposed aiming at capturing hidden properties of the texts. These methods allow to represent documents in terms of fixed-length, continuous and dense feature vectors. In this paper, we propose to learn document vectors based on n-grams and not only on words. We use the recently proposed Paragraph Vector method. These n-grams include character n-grams, word n-grams and n-grams of POS tags (in all cases with n varying from 1 to 5). We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus. Experimental results show that our method outperforms word-based embeddings and character n-gram based linear models, which are among the most effective approaches for identifying the writing style of an author. [ABSTRACT FROM AUTHOR]

Because of numerous parameters existing in the Cloud's environment, it is helpful to introduce a general solution for dynamic resource provisioning in Cloud that is able to handle uncertainty. In this paper, a novel adaptive control approach is proposed which is based on continuous reinforcement learning and provides dynamic resource provisioning while dealing with uncertainty in the Cloud's environment. The proposed dynamic resource provisioner is a goal directed controller which provides ability of handling uncertainty specifically in Cloud's spot markets where competition between Cloud providers requires optimal policies for attracting and maintaining clients. This controller is aimed at hardly preventing from job rejection (as the primary goal) and minimizing the energy consumption (as the secondary goal). Although these two goals almost conflict (because job rejection is a common event in the process of energy consumption optimization), the results demonstrate the perfect ability of the proposed method with reducing job rejection down to near 0 % and minimizing energy consumption down to 9.55 %. [ABSTRACT FROM AUTHOR]

The aim of this paper is to construct a modified greedy algorithm applicable for an ill-posed function approximation problem in presence of data noise. We provide a detailed convergence analysis of the algorithm in presence of noise, and discuss the choice of the iteration parameters. This yields a stopping rule for which the corresponding algorithm is a regularization method with convergence rates inL2 and under weak additional assumptions also in Sobolev-spaces of positive order.Finally, we discuss the application of the modified greedy algorithm to sigmoidal neural networks and radial basis functions, and supplement the theoretical results by numerical experiments. [ABSTRACT FROM AUTHOR]