Designing a goal-oriented smart-home environment

Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.     

An Infrastructure for Argumentative Agents

Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.     

Cubature formulae for nearly singular and highly oscillating integrals

The paper deals with the approximation of integrals of the type

$$\begin{aligned} I(f;{\mathbf {t}})=\int _{{\mathrm {D}}} f({\mathbf {x}}) {\mathbf {K}}({\mathbf {x}},{\mathbf {t}}) {\mathbf {w}}({\mathbf {x}}) d{\mathbf {x}},\quad \quad {\mathbf {x}}=(x_1,x_2),\quad {\mathbf {t}}\in \mathrm {T}\subseteq \mathbb {R}^p, \ p\in \{1,2\} \end{aligned}$$

where \({\mathrm {D}}=[-\,1,1]^2\), f is a function defined on \({\mathrm {D}}\) with possible algebraic singularities on \(\partial {\mathrm {D}}\), \({\mathbf {w}}\) is the product of two Jacobi weight functions, and the kernel \({\mathbf {K}}\) can be of different kinds. We propose two cubature rules determining conditions under which the rules are stable and convergent. Along the paper we diffusely treat the numerical approximation for kernels which can be nearly singular and/or highly oscillating, by using a bivariate dilation technique. Some numerical examples which confirm the theoretical estimates are also proposed.

     

Assessing refactorings for usability in e-commerce applications

Refactoring has been reported as a helpful technique to systematically improve non-functional attributes of software. This paper evaluates the relevance of refactoring for improving usability on web applications. We conducted an experiment with two replications at different locations, with subjects of different profiles. Objects chosen for the experiment were two e-commerce applications that exhibit common business processes in today’s web usage. Through the experiment we found that half of the studied refactorings cause a significant improvement in usability. The rest of the refactorings required a post-hoc analysis in which we considered aspects like user expertise in the interaction with web applications or type of application. We conclude that, when improving quality in use, the success of the refactoring process depends on several factors, including the type of software system, context and users. We have analyzed all these aspects, which developers must consider for a better decision support at the time of prioritizing improvements and outweighing effort.     

GPU-accelerated level-set segmentation

The level-set method, a technique for the computation of evolving interfaces, is a solution commonly used to segment images and volumes in medical applications. GPUs have become a commodity hardware with hundreds of cores that can execute thousands of threads in parallel, and they are nowadays ideal platforms to execute computational intensive tasks, such as the 3D level-set-based segmentation, in real time. In this paper, we propose two GPU implementations of the level-set-based segmentation method called Fast Two-Cycle. Our proposals perform computations in independent domains called tiles and modify the structure of the original algorithm to better exploit the features of the GPU. The implementations were tested with real images of brain vessels and a synthetic MRI image of the brain. Results show that they execute faster than a CPU-sequential implementation of the same method, without any significant loss of the segmentation quality and without requiring distributed parallel computer infrastructures.     

Dearomatization of pyrolysis gasoline with an ionic liquid mixture: Experimental study and process simulation

The pyrolysis gasoline is the main source of benzene, toluene, and xylenes. The dearomatization of this stream is currently performed by liquid – liquid extraction using sulfolane. However, the sulfolane process has high operating costs that could be minimized by employing ionic liquids as solvents because of their non‐volatile character. In this work, we proposed a novel process to perform the dearomatization of pyrolysis gasoline using a binary mixture of 1‐ethyl‐3‐methylimidazolium tricyanomethanide ([emim][TCM]) and 1‐ethyl‐4‐methylpyridinium bis(trifluoromethylsulfonyl)imide ([4empy][Tf₂N]) ILs. The composition in the IL mixture was optimized considering their extractive and thermophysical properties. The Kremser method was applied using the experimental data to determine the number of equilibrium stages in the liquid – liquid extractor which provides the same extraction yields of aromatics using the IL mixture that those of the sulfolane process. The recovery section was designed and simulated from the experimental vapor – liquid equilibrium between the hydrocarbons and the IL mixture. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4054–4065, 2017     

Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

The presence of noise in data is a common problem that produces several negative consequences in classification problems. In multi-class problems, these consequences are aggravated in terms of accuracy, building time, and complexity of the classifiers. In these cases, an interesting approach to reduce the effect of noise is to decompose the problem into several binary subproblems, reducing the complexity and, consequently, dividing the effects caused by noise into each of these subproblems. This paper analyzes the usage of decomposition strategies, and more specifically the One-vs-One scheme, to deal with noisy multi-class datasets. In order to investigate whether the decomposition is able to reduce the effect of noise or not, a large number of datasets are created introducing different levels and types of noise, as suggested in the literature. Several well-known classification algorithms, with or without decomposition, are trained on them in order to check when decomposition is advantageous. The results obtained show that methods using the One-vs-One strategy lead to better performances and more robust classifiers when dealing with noisy data, especially with the most disruptive noise schemes.     

AN INTELLIGENT SELF-CONFIGURABLE MECHANISM FOR DISTRIBUTED ENERGY STORAGE SYSTEMS

The next generation of smart grid technologies demands intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multi-agent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this article we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism that allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.     

Rank Estimation in Missing Data Matrix Problems

A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.