246 research outputs found

    Mapping the Situation of Educational Technologies in the Spanish University System Using Social Network Analysis and Visualization

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    Educational Technologies (EdTech) are based on the use of Information and Communication Technologies (ICT) to improve the quality of teaching and learning. EdTech is experiencing great development at different educational levels worldwide, especially since the appearance of Covid-19. The recent publication of a study by the ICT Sectorial of CRUE Universidades Españolas, the Spanish University Association, is the first report on the implementation of such technologies within SpainŽs University System. This paper presents two different maps based on the data from that report. Together, they illustrate the penetration of different types of EdTech in our university system and shed light on the strategic interest behind their adoption. Our goal is to produce self-explanatory maps that can be easily and directly interpreted. The first map reflects wide granularity in terms of the global importance of technologies, while the second points to relevant conclusions given the spatial position of SpainŽs universities, and the size of the nodes that represent them (directly related with their strategic interests on EdTech), as well as with the local relationships existing among them (identifying similarities on those strategic interests).Working Group in Online Training and Educational Technologies (FOLTE), of the ICT Sectorial of the Commission of Rectors of Spanish Universities (the Spanish University AssociationCRUE Universidades Españolas). Moreover, this work was supported by the Spanish Ministry of Science, Innovation and UniversitiesERDF under grant EXASOCO (PGC2018-101216-B-I00

    Visualising the structure of document search results: A comparison of graph theoretic approaches

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    This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion

    VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS

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    Variability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters. Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Water

    Influence-based motion planning algorithms for games

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    In games, motion planning has to do with the motion of non-player characters (NPCs) from one place to another in the game world. In today’s video games there are two major approaches for motion planning, namely, path-finding and influence fields. Path-finding algorithms deal with the problem of finding a path in a weighted search graph, whose nodes represent locations of a game world, and in which the connections among nodes (edges) have an associated cost/weight. In video games, the most employed pathfinders are A* and its variants, namely, Dijkstra’s algorithm and best-first search. As further will be addressed in detail, the former pathfinders cannot simulate or mimic the natural movement of humans, which is usually without discontinuities, i.e., smooth, even when there are sudden changes in direction. Additionally, there is another problem with the former pathfinders, namely, their lack of adaptivity when changes to the environment occur. Therefore, such pathfinders are not adaptive, i.e., they cannot handle with search graph modifications during path search as a consequence of an event that happened in the game (e.g., when a bridge connecting two graph nodes is destroyed by a missile). On the other hand, influence fields are a motion planning technique that does not suffer from the two problems above, i.e., they can provide smooth human-like movement and are adaptive. As seen further ahead, we will resort to a differentiable real function to represent the influence field associated with a game map as a summation of functions equally differentiable, each associated to a repeller or an attractor. The differentiability ensures that there are no abrupt changes in the influence field, consequently, the movement of any NPC will be smooth, regardless if the NPC walks in the game world in the growing sense of the function or not. Thus, it is enough to have a spline curve that interpolates the path nodes to mimic the smooth human-like movement. Moreover, given the nature of the differentiable real functions that represent an influence field, the removal or addition of a repeller/attractor (as the result of the destruction or the construction of a bridge) does not alter the differentiability of the global function associated with the map of a game. That is to say that, an influence field is adaptive, in that it adapts to changes in the virtual world during the gameplay. In spite of being able to solve the two problems of pathfinders, an influence field may still have local extrema, which, if reached, will prevent an NPC from fleeing from that location. The local extremum problem never occurs in pathfinders because the goal node is the sole global minimum of the cost function. Therefore, by conjugating the cost function with the influence function, the NPC will never be detained at any local extremum of the influence function, because the minimization of the cost function ensures that it will always walk in the direction of the goal node. That is, the conjugation between pathfinders and influence fields results in movement planning algorithms which, simultaneously, solve the problems of pathfinders and influence fields. As will be demonstrated throughout this thesis, it is possible to combine influence fields and A*, Dijkstra’s, and best-first search algorithms, so that we get hybrid algorithms that are adaptive. Besides, these algorithms can generate smooth paths that resemble the ones traveled by human beings, though path smoothness is not the main focus of this thesis. Nevertheless, it is not always possible to perform this conjugation between influence fields and pathfinders; an example of such a pathfinder is the fringe search algorithm, as well as the new pathfinder which is proposed in this thesis, designated as best neighbor first search.Em jogos de vĂ­deo, o planeamento de movimento tem que ver com o movimento de NPCs (“Non-Player Characters”, do inglĂȘs) de um lugar para outro do mundo virtual de um jogo. Existem duas abordagens principais para o planeamento de movimento, nomeadamente descoberta de caminhos e campos de influĂȘncia. Os algoritmos de descoberta de caminhos lidam com o problema de encontrar um caminho num grafo de pesquisa pesado, cujos nĂłs representam localizaçÔes de um mapa de um jogo, e cujas ligaçÔes (arestas) entre nĂłs tĂȘm um custo/peso associado. Os algoritmos de descoberta de caminhos mais utilizados em jogos sĂŁo o A* e as suas variantes, nomeadamente, o algoritmo de Dijkstra e o algoritmo de pesquisa do melhor primeiro (“best-first search”, do inglĂȘs). Como se verĂĄ mais adiante, os algoritmos de descoberta de caminhos referidos nĂŁo permitem simular ou imitar o movimento natural dos seres humanos, que geralmente nĂŁo possui descontinuidades, i.e., o movimento Ă© suave mesmo quando hĂĄ mudanças repentinas de direcção. A juntar a este problema, existe um outro que afeta os algoritmos de descoberta de caminhos acima referidos, que tem que ver com a falta de adaptatividade destes algoritmos face a alteraçÔes ao mapa de um jogo. Ou seja, estes algoritmos nĂŁo sĂŁo adaptativos, pelo que nĂŁo permitem lidar com alteraçÔes ao grafo durante a pesquisa de um caminho em resultado de algum evento ocorrido no jogo (e.g., uma ponte que ligava dois nĂłs de um grafo foi destruĂ­da por um mĂ­ssil). Por outro lado, os campos de influĂȘncia sĂŁo uma tĂ©cnica de planeamento de movimento que nĂŁo padece dos dois problemas acima referidos, i.e., os campos possibilitam um movimento suave semelhante ao realizado pelo ser humano e sĂŁo adaptativos. Como se verĂĄ mais adiante, iremos recorrer a uma função real diferenciĂĄvel para representar o campo de influĂȘncia associado a um mapa de um jogo como um somatĂłrio de funçÔes igualmente diferenciĂĄveis, em que cada função estĂĄ associada a um repulsor ou a um atractor. A diferenciabilidade garante que nĂŁo existem alteraçÔes abruptas ao campo de influĂȘncia; consequentemente, o movimento de qualquer NPC serĂĄ suave, independentemente de o NPC caminhar no mapa de um jogo no sentido crescente ou no sentido decrescente da função. Assim, basta ter uma curva spline que interpola os nĂłs do caminho de forma a simular o movimento suave de um ser humano. AlĂ©m disso, dada a natureza das funçÔes reais diferenciĂĄveis que representam um campo de influĂȘncia, a remoção ou adição de um repulsor/atractor (como resultado da destruição ou construção de uma ponte) nĂŁo altera a diferenciabilidade da função global associada ao mapa de um jogo. Ou seja, um campo de influĂȘncia Ă© adaptativo, na medida em que se adapta a alteraçÔes que ocorram num mundo virtual durante uma sessĂŁo de jogo. Apesar de ser capaz de resolver os dois problemas dos algoritmos de descoberta de caminhos, um campo de influĂȘncia ainda pode ter extremos locais, que, se alcançados, impedirĂŁo um NPC de fugir desse local. O problema do extremo local nunca ocorre nos algoritmos de descoberta de caminhos porque o nĂł de destino Ă© o Ășnico mĂ­nimo global da função de custo. Portanto, ao conjugar a função de custo com a função de influĂȘncia, o NPC nunca serĂĄ retido num qualquer extremo local da função de influĂȘncia, porque a minimização da função de custo garante que ele caminhe sempre na direção do nĂł de destino. Ou seja, a conjugação entre algoritmos de descoberta de caminhos e campos de influĂȘncia tem como resultado algoritmos de planeamento de movimento que resolvem em simultĂąneo os problemas dos algoritmos de descoberta de caminhos e de campos de influĂȘncia. Como serĂĄ demonstrado ao longo desta tese, Ă© possĂ­vel combinar campos de influĂȘncia e o algoritmo A*, o algoritmo de Dijkstra, e o algoritmo da pesquisa pelo melhor primeiro, de modo a obter algoritmos hĂ­bridos que sĂŁo adaptativos. AlĂ©m disso, esses algoritmos podem gerar caminhos suaves que se assemelham aos que sĂŁo efetuados por seres humanos, embora a suavidade de caminhos nĂŁo seja o foco principal desta tese. No entanto, nem sempre Ă© possĂ­vel realizar essa conjugação entre os campos de influĂȘncia e os algoritmos de descoberta de caminhos; um exemplo Ă© o algoritmo de pesquisa na franja (“fringe search”, do inglĂȘs), bem como o novo algoritmo de pesquisa proposto nesta tese, que se designa por algoritmo de pesquisa pelo melhor vizinho primeiro (“best neighbor first search”, do inglĂȘs)

    Network Simplification with Minimal Loss of Connectivity

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    The Evryscope Fast Transient Engine: Real-time Discovery of Rapidly Evolving Transients with Evryscope and the Argus Optical Array

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    Modern synoptic sky surveys are typically designed to detect supernovae-like transients, using a tiling strategy to identify objects that evolve on day-to-month timescales. Astrophysical phenomena with sub-hour durations, ranging from galactic stellar flares to optical flashes accompanying gamma-ray bursts, have largely escaped scrutiny. Due to their low intrinsic rates and short durations, surveys for fast transients must simultaneously cover significant fractions of the sky at sub-hour cadences, often by combining multiple telescopes. The Evryscopes represent an extreme of this approach, combining 43 small telescopes to image 38% of the entire sky every two minutes. To investigate bright and fast transients with the Evryscopes, I developed the Evryscope Fast Transient Engine (EFTE), a real-time transient detection and photometric analysis pipeline. EFTE uses a unique direct image subtraction routine suited to continuously monitoring the transient sky at minute cadence. Candidates are produced within two minutes for 98.5% of images, and are internally filtered using VetNet, a machine learning algorithm trained to sort real astrophysical events from false positives, both instrumental and astronomical, including millisecond-timescale reflections, or “glints” from satellites and debris in Earth orbit. Glints are a dominating foreground for astronomical surveys in the extreme time domain. I present the first measurements of the glint rate, noting that it exceeds the combined rate of public alerts from all active all-sky, fast-timescale transient searches, including neutrino, gravitational-wave, gamma-ray, and radio observatories. I further report spectroscopic followup of two stellar flares identified in real-time from the EFTE alert stream using glint-mitigation and science-driven selection metrics. These are the closest spectra relative to peak ever observed for flare stars outside of dedicated starting campaigns on known active stars, and provide unique constraints on the evolution of the flare continuum and temperature. Finally, EFTE is the software test bed for the pipelines of the Argus Optical Array, an upcoming all-sky survey based on the Evryscope concept scaled to the depths of the deepest operating sky surveys and a terabit per second data rate. This work concludes with a description of the Argus prototype series and pipelines, and an overview of fast transient science with the Array.Doctor of Philosoph

    Simplification of Networks by Edge Pruning

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    Discovering latent topical structure by second-order similarity analysis

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    This is the post-print of the Article - Copyright @ 2011 ASIS&TDocument similarity models are typically derived from a term-document vector space representation by comparing all vector-pairs using some similarity measure. Computing similarity directly from a ‘bag of words’ model can be problematic because term independence causes the relationships between synonymous and related terms and the contextual influences that determine the ‘sense’ of polysemous terms to be ignored. This paper compares two methods that potentially address these problems by modelling the higher-order relationships that lie latent within the original vector space. The first is latent semantic analysis (LSA), a dimension reduction method which is a well known means of addressing the vocabulary mismatch problem in information retrieval systems. The second is the lesser known, yet conceptually simple approach of second-order similarity (SOS) analysis, where similarity is measured in terms of profiles of first-order similarities as computed directly from the term-document space. Nearest neighbour tests show that SOS analysis produces similarity models that are consistently better than both first-order and LSA derived models at resolving both coarse and fine level semantic clusters. SOS analysis has been criticised for its cubic complexity. A second contribution is the novel application of vector truncation to reduce the run-time by a constant factor. Speed-ups of four to ten times are found to be easily achievable without losing the structural benefits associated with SOS analysis
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