246 research outputs found
Mapping the Situation of Educational Technologies in the Spanish University System Using Social Network Analysis and Visualization
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 SpainÂŽs 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 SpainÂŽs 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
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or âspatialisationâ 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 âcluster growingâ 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
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
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 todayâs 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, Dijkstraâs 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*, Dijkstraâs, 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 (âNon-Player Charactersâ, 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 nós 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 (âbest-first searchâ, 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 hå 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 não são
adaptativos, pelo que não 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 verå mais adiante, iremos recorrer a uma função real diferenciåvel para representar
o campo de influĂȘncia associado a um mapa de um jogo como um somatĂłrio de
funçÔes igualmente diferenciåveis, em que cada função estå associada a um repulsor
ou a um atractor. A diferenciabilidade garante que não 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.
Além disso, dada a natureza das funçÔes reais diferenciåveis que representam um campo
de influĂȘncia, a remoção ou adição de um repulsor/atractor (como resultado da destruição
ou construção de uma ponte) não 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 sessão 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 nó 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 (âfringe searchâ, do inglĂȘs), bem como o novo algoritmo de pesquisa proposto
nesta tese, que se designa por algoritmo de pesquisa pelo melhor vizinho primeiro (âbest
neighbor first searchâ, do inglĂȘs)
The Evryscope Fast Transient Engine: Real-time Discovery of Rapidly Evolving Transients with Evryscope and the Argus Optical Array
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 âglintsâ 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
Discovering latent topical structure by second-order similarity analysis
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 âbag of wordsâ model can be problematic because term independence causes the relationships between synonymous and related terms and the contextual influences that determine the âsenseâ 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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