Desastres 2.0. Aplicación de tecnologías Web2.0 en situaciones de emergencia

This article presents a social approach for disaster management, based on a public portal, so-called Disasters 2.0, which provides facilities for integrating and sharing usergenerated information about disasters. The architecture of Disasters 2.0 is designed following REST principles and integrates external mashups, such as Google Maps. This architecture has been integrated with different clients, including a mobile client, a multiagent system for assisting in the decentralised management of disasters, and an expert system for automatic asignation of resources to disasters. As a result, the platform allows seamless collaboration of humans and intelligent agents, and provides a novel web2.0 approach for multiagent and disaster management research and artificial intelligence teaching

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.Comment: Accepted at AIME 201

A New Artificial Intelligence based Internet Online English Teaching Model with Curriculum of Ideological and Political Concern

With the development of artificial intelligence and the rapid spread of the Internet, online teaching has become an increasingly popular method of education. However, in the context of the post-epidemic era of COVID-19, online teaching has become even more important, as many educational institutions have been forced to transition to this model to ensure continuity of learning. In this context, there is a growing need to develop innovative approaches to online teaching that can effectively address the challenges posed by the pandemic. Online teaching has become increasingly important for higher education institutions around the world, and it has been particularly crucial during the COVID-19 pandemic. The teaching of English at universities and colleges exhibited significant performance for online teaching. The ideology concept performs online teaching in English for politics and comprises of different strategies. English teaching, several strategies can be implemented. This research paper proposes a novel approach to integrate artificial intelligence (AI) and cloud computing technologies in the online English teaching model with a curriculum of ideological and political concern for colleges and universities. The proposed model, referred to as AIIOE, aims to enhance the quality and effectiveness of online English teaching while also providing a comprehensive education on ideological and political issues. The AIIOE model utilizes natural language processing (NLP), machine learning, and cloud computing technologies to provide a personalized and interactive learning experience to students. The proposed curriculum includes topics related to political ideology, history, and culture to enhance students' awareness and understanding of their social and political environment. The study adopts a mixed-methods approach, including a survey of English teachers, focus group interviews with students, and an analysis of students' performance in English language proficiency and ideological and political awareness. The results indicate that the AIIOE model significantly improves students' English language proficiency, knowledge of ideological and political issues, and overall learning experience. The examination is evaluated based on the ideological and political curriculum with an Internet-based online teaching mode in English teaching. With the investigation of the Internet online teaching model, the significant contribution is evaluated. Through analysis, it is concluded that the concept of the Internet Online teaching model significantly contributed to ideological and political factors

Tactical AI in Real Time Strategy Games

The real time strategy (RTS) tactical decision making problem is a difficult problem. It is generally more complex due to its high degree of time sensitivity. This research effort presents a novel approach to this problem within an educational, teaching objective. Particular decision focus is target selection for a artificial intelligence (AI) RTS game model. The use of multi-objective evolutionary algorithms (MOEAs) in this tactical decision making problem allows an AI agent to make fast, effective solutions that do not require modification to the current environment. This approach allows for the creation of a generic solution building tool that is capable of performing well against scripted opponents without requiring expert training or deep tree searches. The experimental results validate that MOEAs can control an on-line agent capable of out performing a variety AI RTS opponent test scripts

Enabling Robots to Communicate their Objectives

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then it selects those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped. Our main contribution is to recognize that unlike robots, humans will not be exact in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain. We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.Comment: RSS 201

Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version