Collaborative Computer-Assisted Cognitive Rehabilitation System

Recently have been proposed different physical and cognitive rehabilitation system that allow people with some disabilities to improve and recover some lost capabilities. All these systems allow to carry out these therapies at home proving patients the possibility to accomplish a better rehabilitation, due to the fact that they can practice at home and in a more controlled environment. But, it is not so common that these systems include some social features that reduce the feeling of social isolation of the patients. Thus, in this paper we present an adaptation of a previous proposal including some multiuser therapies that try include some social features and other aspect related to videogames that increases the motivation and makes the treatment funny

Using trust degree for agents in order to assign spots in a Smart Parking

The process of searching for a parking spot could be a problem. There are computing solutions being developed to optimize this problem. One of these solutions is using multiagent systems (MAS). In this paper a MAS is developed in order to allocate spots in a smart parking using the framework JaCaMo. This MAS comprises of two types of agents: manager and drivers. The manager is responsible to administrate the parking spots which will be assigned for drivers according to a corresponding degree of trust. The trust degree is a value which shows the commitment of the driver before the manager. In order to verify the effectiveness of the MAS, several simulations were conducted in empirical scenarios. Experiments shows that the trust degree impacts in the parking spot allocation process

Development of a Graphical Tool to integrate the Prometheus AEOlus methodology and Jason Platform

Software Engineering (SE) is an area that intends to build high-quality software in a systematic way. However, traditional software engineering techniques and methods do not support the demand for developing Multiagent Systems (MAS). Therefore a new subarea has been studied, called Agent Oriented Software Engineering (AOSE). The AOSE area proposes solutions to specific issues related to the development of agent oriented systems. There are several methodologies to model MAS, however, until now, there is not a standard modelling language because they are very complex systems, and involve several different concepts. Another issue of this subarea is that there are very few tools that are able to automatically generate code, reducing its acceptance in the software development market. In this work, we propose a tool to support the Prometheus AEOlus Methodology, because it provides modelling artifacts to all MAS dimensions proposed by ~Demazeau: agents, environment, interactions and organization. The tool supports all Prometheus AEOlus artifacts and it can automatically generated code to the agent and interaction dimensions in the AgentSpeak(L) language, which is the language used in the Jason platform. We have done some validations with the proposed tool and a case study is presented. Our results indicate that our tool has full compatibility with the Jason platform, and it is able to automatic generate code in AgentSpeak(L). As future work, we intend to develop the integration of the artifacts with the JaCaMo framework, enabling a full integration between our tool and the Prometheus AEOlus methodology

Design of a Speed Assistant to Minimize the Driver Stress

Stress is one of the most important factors in traffic accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to estimate the optimal speed to minimize stress levels on upcoming road segments when driving. The prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section to be assessed, the road state (weather and traffic), and the previous drives made by the driver. We use this algorithm to build a speed assistant. The solution provides an optimum average speed for each road segment that minimizes the stress. A validation experiment has been conducted in a real setting using two different types of vehicles. The proposal is able to predict the stress levels given the average speed by 84.20% on average. On the other hand, the speed assistant reduces the stress levels (estimated from the driver’s heart rate signal) and the aggressiveness of driving regardless of the vehicle type. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap

A Comparison of the YCBCR Color Space with Gray Scale for Face Recognition for Surveillance Applications

Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In this paper, the performance of the individual channels from the YCBCR color space on face recognition for surveillance applications is investigated and compared with the performance of the gray scale. In addition, the performance of fusing two or more color channels is also compared with that of the gray scale. Three cases with different number of training images per persons were used as a test bed. It was found out that, the gray scale always outperforms the individual channel. However, the fusion of CBxCR with any other channel outperforms the gray scale when three images of the same class from the same database are used for training. Regardless of the cases used, the CBxCR channel always gave the best performance for the individual color channels. It was found that, in general, increasing the number of fused channels increases the performance of the system. It was also found that the gray scale channel is the better choice for face recognition since it contain better quality edges and visual features which are essential for face recognition

Malware propagation in Wireless Sensor Networks: global models vs Individual-based models

The main goal of this work is to propose a new framework to design a novel family of mathematical models to simulate malware spreading in wireless sensor networks (WSNs). An analysis of the proposed models in the scientific literature reveals that the great majority are global models based on systems of ordinary differential equations such that they do not consider the individual characteristics of the sensors and their local interactions. This is a major drawback when WSNs are considered. Taking into account the main characteristics of WSNs (elements and topologies of network, life cycle of the nodes, etc.) it is shown that individual-based models are more suitable for this purpose than global ones. The main features of this new type of malware propagation models for WSNs are stated

Discussing a new Divisive Hierarchical Clustering algorithm

We present DHClus, a new Divisive Hierarchical Clustering algorithm developed to detect clusters with arbitrary shapes. Our algorithm is able to solve clustering problems defined by different scales, i.e. clusters with arbitrarily dissimilar densities, connectivity or between cluster distances. The algorithm not only works under this difficult connditions but it is also able to find the number of clusters automatically. This paper describes this new algorithm and then present results on real gene expression data. We compare the results of DHClus with other algorithms to provide a reference frame.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Agent behavior monitoring using optimal action selection and twin gaussian processes

The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Development of a Graphical Tool to integrate the Prometheus AEOlus methodology and Jason Platform

Software Engineering (SE) is an area that intends to build high-quality software in a systematic way. However, traditional software engineering techniques and methods do not support the demand for developing Multiagent Systems (MAS). Therefore a new subarea has been studied, called Agent Oriented Software Engineering (AOSE). The AOSE area proposes solutions to issues related to the development of agent oriented systems. There is still no standardization in this subarea, resulting in several methodologies. Another issue of this subarea is that there are very few tools that are able to automatically generate code. In this work we propose a tool to support the Prometheus AEOlus Methodology because it provides modelling artifacts to all MAS dimensions: agents, environment, interaction, and organization. The tool supports all Prometheus AEOlus artifacts and can automatically generated code to the agent and interaction dimensions in the AgentSpeak Language, which is the language used in the Jason Platform. We have done some validations with the proposed tool and a case study is presented

Integrating Smart Resources in ROS-based systems to distribute services

[EN] Mobile robots execute complexes tasks that involve the management of several embedded sensors and actuators. Therefore, in many cases, a robot is characterized as an intelligent distributed system formed with a central unit, which manages the on-board embedded devices and distributes the tasks execution. Embedded devices are also evolving to more complex systems. These systems are developed not only for executing simple tasks but also for offering some advanced mechanisms. Thus, complex data processing, adaptive execution, or fault-tolerance routines are some common system features. The Smart Resource topology has been developed in order to manage these embedded systems. This topology offers high-level routines that rely on a certain physical hardware execution. Therefore, Smart Resources are defined as distributed services providers, which operates within some context and quality requirements. Provided services can adapt its execution in order accomplish the set requirements and maximize the system performance. How to improve the versatility of the Smart Resources by making their services compatibles with the Robot Operating System (ROS) is addressed along this work. This solution integrates all the execution mechanisms provided by ROS with the service distribution, adaptive execution, and fault-tolerance routines offered by the Smart Resources. This integration is tested through a set of experiments using the Turtlebot robot platform and a simulated version of it. In both approaches ROS mechanisms are used to access the Smart Resource Services. Finally, obtained results are used to characterize the performance of this proposal.Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): UPV-PAID-FPI-2013.Munera-Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2017). Integrating Smart Resources in ROS-based systems to distribute services. Advances in Distributed Computing and Artificial Intelligence Journal. 6(1):13-19. https://doi.org/10.14201/ADCAIJ2017611319S13196