2,719 research outputs found
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
Algorithmic transparency of conversational agents
A lack of algorithmic transparency is a major barrier to the adoption of artificial intelligence technologies within contexts which require high risk and high consequence decision making. In this paper we present a framework for providing transparency of algorithmic processes. We include important considerations not identified in research to date for the high risk and high consequence context of defence intelligence analysis. To demonstrate the core concepts of our framework we explore an example application (a conversational agent for knowledge exploration) which demonstrates shared human-machine reasoning in a critical decision making scenario. We include new findings from interviews with a small number of analysts and recommendations for future
research
EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS
Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loopâwith unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio
XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat
XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics-âincluding link estimation, entity disambiguation, and event detection-âto build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario
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Bilingual/bicultural education for the Spanish-speaking students in Massachusetts : an analysis of perceived dimensions of an ideal bicultural teacher.
Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach
Les situations spatio-temporelles dynamiques sont des situations qui Ă©voluent dans lâespace et dans le temps. LâĂȘtre humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des dĂ©cisions. Ces configurations de situations peuvent aussi ĂȘtre appelĂ©es « situations dâintĂ©rĂȘt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systĂšmes dâacquisition de donnĂ©es souvent prĂ©sents dans diverses industries grĂące aux rĂ©cents dĂ©veloppements technologiques et qui gĂ©nĂšrent des bases de donnĂ©es de plus en plus volumineuses. On relĂšve un problĂšme important dans la littĂ©rature liĂ© au fait que les formalismes de reprĂ©sentation utilisĂ©s sont souvent incapables de reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques et complexes qui reflĂštent la rĂ©alitĂ©. De plus, ils ne prennent pas en considĂ©ration lâapprĂ©hension cognitive (modĂšle mental) que lâhumain peut avoir de son environnement. Ces facteurs rendent difficile la mise en Ćuvre de tels modĂšles par des agents logiciels. Dans cette thĂšse, nous proposons un nouveau modĂšle de reprĂ©sentation des situations dâintĂ©rĂȘt sâappuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modĂšle de reprĂ©sentation. Le modĂšle se base sur les notions dâĂ©vĂ©nement et dâĂ©tat pour reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques. Il intĂšgre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons dĂ©tectĂ©s. Nous proposons aussi un outil de gĂ©nĂ©ration automatisĂ©e des relations qualitatives de proximitĂ© spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter lâintĂ©gration de notre modĂšle dans des applications industrielles rĂ©elles. Ainsi, les contributions principales de notre travail sont : Un formalisme de reprĂ©sentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la dĂ©finition des patrons spatio-temporels basĂ©e sur lâintĂ©gration de lâinformation contextuelle. ; Un outil de gĂ©nĂ©ration automatique des relations spatiales qualitatives de proximitĂ© basĂ© sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de dĂ©tection des patrons spatiotemporels basĂ©e sur lâextension dâun moteur de traitement des Ă©vĂ©nements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humansâ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as âsituations of interestsâ or âspatiotemporal patternsâ. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the humanâs mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
ORĂCULO: Detection of Spatiotemporal Hot Spots of Conflict-Related Events Extracted from Online News Sources
Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAchieving situational awareness in peace operations requires understanding
where and when conflict-related activity is most intense. However, the irregular nature
of most factions hinders the use of remote sensing, while winning the trust of the host
populations to allow the collection of wide-ranging human intelligence is a slow process.
Thus, our proposed solution, ORĂCULO, is an information system which detects
spatiotemporal hot spots of conflict-related activity by analyzing the patterns of events
extracted from online news sources, allowing immediate situational awareness. To do so,
it combines a closed-domain supervised event extractor with emerging hot spots analysis
of event space-time cubes. The prototype of ORĂCULO was tested on tweets scraped
from the Twitter accounts of local and international news sources covering the Central
African Republic Civil War, and its test results show that it achieved near state-of-theart
event extraction performance, significant overlap with a reference event dataset, and
strong correlation with the hot spots space-time cube generated from the reference event
dataset, proving the viability of the proposed solution. Future work will focus on
improving the event extraction performance and on testing ORĂCULO in cooperation
with peacekeeping organizations.
Keywords: event extraction, natural language understanding, spatiotemporal analysis,
peace operations, open-source intelligence.Atingir e manter a consciĂȘncia situacional em operaçÔes de paz requer o
conhecimento de quando e onde Ă© que a atividade relacionada com o conflito Ă© mais
intensa. Porém, a natureza irregular da maioria das façÔes dificulta o uso de deteção
remota, e ganhar a confiança das populaçÔes para permitir a recolha de informaçÔes é
um processo moroso. Assim, a nossa solução proposta, ORĂCULO, consiste num sistema
de informaçÔes que deteta âhot spotsâ espĂĄcio-temporais de atividade relacionada com o
conflito atravĂ©s da anĂĄlise dos padrĂ”es de eventos extraĂdos de fontes noticiosas online,
(incluindo redes sociais), permitindo consciĂȘncia situacional imediata. Nesse sentido, a
nossa solução combina um extrator de eventos de domĂnio limitado baseado em
aprendizagem supervisionada com a anĂĄlise de âhot spotsâ emergentes de cubos espaçotempo
de eventos. O protĂłtipo de ORĂCULO foi testado em tweets recolhidos de fontes
noticiosas locais e internacionais que cobrem a Guerra Civil da RepĂșblica Centro-
Africana. Os resultados dos seus testes demonstram que foram conseguidos um
desempenho de extração de eventos próximo do estado da arte, uma sobreposição
significativa com um conjunto de eventos de referĂȘncia e uma correlação forte com o
cubo espaço-tempo de âhot spotsâ gerado a partir desse conjunto de referĂȘncia,
comprovando a viabilidade da solução proposta. Face aos resultados atingidos, o
trabalho futuro focar-se-å em melhorar o desempenho de extração de eventos e em testar
o sistema ORĂCULO em cooperação com organizaçÔes que conduzam operaçÔes paz
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