A statistical approach to the evaluation of characters to estimate evolutionary relationships among the species of the aquatic subterranean genus, Iberobathynella (Crustacea, Syncarida)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74801/1/j.1095-8312.1997.tb01493.x.pd

Principales factores y problemáticas más influyentes para el éxito en el sostenimiento de las micro y pequeñas empresas MYPE

Figura 1. Calificación por tópico de Colombia vs el promedio general en 62 países del estudio.Se plantea la identificación de los factores determinantes para que las Micro y Pequeña Empresa (MYPE) puedan crecer y sostenerse en el tiempo. La revisión bibliográfica nos dará la información requerida para conseguir a través de ella todos los datos que den cuenta, de cuáles son las problemáticas a las que se enfrenta un emprendedor cuando se ve motivado a crear su Micro y pequeña empresa (MYPE) para discernir un modelo guía que sirva de hoja de ruta para los empresarios del sector.The identification of the determining factors is proposed so that Micro and Small Enterprises (MYPE) can grow and sustain themselves over time. The bibliographic review will give us the information required to obtain through it all the data that account for, the results are the problems that an entrepreneur faces when he is motivated to create his Micro and small business (MYPE) to discern a model guide that serves as a roadmap for entrepreneurs in the sector

Adaptive K-means algorithm for overlapped graph clustering

Electronic version of an article published as International Journal of Neural Systems 2, 5, 2012, DOI: 10.1142/S0129065712500189 © 2012 copyright World Scientific Publishing CompanyThe graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.This work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872 and the grant BES-2011-049875 from the same Ministry

Combining social-based data mining techniques to extract collective trends from twitter

Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Networks is Twitter. This Social Network was created to share comments and opinions. The information provided by users is especially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining techniques (such as classification or clustering) will be used for knowledge extraction to distinguish the meaning of the opinions. Complex Network techniques are also helpful to discover influential actors and study the information propagation inside the Social Network. This work is focused on how clustering and classification techniques can be combined to extract collective knowledge from Twitter. In an initial phase, clustering techniques are applied to extract the main topics from the user opinions. Later, the collective knowledge extracted is used to relabel the dataset according to the clusters obtained to improve the classification results. Finally, these results are compared against a dataset which has been manually labelled by human experts to analyse the accuracy of the proposed method.The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the following projects: TIN2010-19872 and ECO2011-30105 (National Plan for Research, Development and Innovation), as well as the Multidisciplinary Project of Universidad Autónoma de Madrid (CEMU2012-034). The authors thank Ana M. Díaz-Martín and Mercedes Rozano for the manual classification of the Tweets

Co-operative learning, psychometric adaptation, and invariability of the academic satisfaction scale in Spanish university students

It is necessary to understand the measurement of academic satisfaction (AS) in a variety of cross-cultural contexts. The first aim was to evaluate the psychometric properties of AS scale, to explore its structural validity, to assess its differential item function, including gender and age invariance in university students. Study 2 aimed to assess whether AS improved after the application of a teaching instructional approach based on cooperative learning (CL), while a cross-sectional study was performed in several stages. Descriptive, confirmatory, and scale reliability analyses were carried out with indices for goodness-of-fit, such that a new scale was obtained with a single-factor structure. A reduction to 6-items in this sample exhibited better psychometric properties. Configural invariance by gender and age indicated that men and women had a similar understanding of the new scale. Given significant differences between groups, the CL group scored higher in AS.University of Jaen PIMED55_20192

GAMPP: Genetic algorithm for UAV mission planning problems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25017-5_16Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (way-points) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.This work is supported by Comunidad Autónoma de Madrid under project CIBERDINE S2013/ICE-3095, Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P and Savier Project (Airbus Defence & Space, FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, César Castro and Gemma Blasco

Acquisition of business intelligence from human experience in route planning

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 2015, available online at:http://www.tandfonline.com/10.1080/17517575.2012.759279The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e., plan the routes that the shippers have to follow to deliver the goods. In this paper we present an AI-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimized routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimizes the delivery process. The solution uses Data Mining to extract knowledge from the company information systems and prepares it for analysis with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a Genetic Algorithm (GA) that, given the processed information, optimizes the routes following several objectives, such as minimize the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, in average, the routes made by the human experts.This work has been partially supported by the SpanishMinistry of Science and Innovation under the projects ABANT (TIN 2010-19872) and by Jobssy.com company under Project FUAM-076913

Clustering avatars behaviours from Virtual Worlds interactions

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 4th International Workshop on Web Intelligence & Communities, http://dx.doi.org/10.1145/2189736.2189743Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.This work has been partly supported by: Spanish Ministry of Science and Education under the project TIN2010-1987

Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms

Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.Comment: This is a preprint version of the article submitted and published in Soft Computin