2016
Authors
Costa, T; Leal, JP;
Publication
WEB ENGINEERING (ICWE 2016)
Abstract
There are two main types of semantic measures (SM): similarity and relatedness. There are also two main types of datasets, those intended for similarity evaluations and those intended for relatedness. Although they are clearly distinct, they are similar enough to generate some misconceptions. Is there a confusion between similarity and relatedness among the semantic measure community, both the designers of SMs and the creators of benchmarks? This is the question that the research presented in this paper tries to answer. Authors performed a survey of both the SMs and datasets and executed a cross evaluation of those measures and datasets. The results show different consistency of measures with datasets of the same type. This research enabled us to conclude not only that there is indeed some confusion but also to pinpoint the SMs and benchmarks less consistent with their intended type.
2016
Authors
Moniz, Nuno; Torgo, Luis; Vinagre, Joao;
Publication
CoRR
Abstract
2016
Authors
Martin, OA; Correia, CM; Gendron, E; Rousset, G; Vidal, F; Morris, TJ; Basden, AG; Myers, RM; Ono, YH; Neichel, B; Fusco, T;
Publication
ADAPTIVE OPTICS SYSTEMS V
Abstract
CANARY is the Multi-Object Adaptive Optics (MOAO) pathfinder for the future MOAO-Assisted Integral-Field Units (IFU) proposed for Extremely Large Telescopes (ELT). The MOAO concept relies on tomographically reconstructing the turbulence using multiple measurements along different lines of sight. Tomography requires the knowledge of the statistical turbulence parameters, commonly recovered from the system telemetry using a dedicated profiling technique. For demonstration purposes with the MOAO pathfinder CANARY, this identification is performed thanks to the Learn & Apply (L&A) algorithm, that consists in model-fitting the covariance matrix of WFS measurements dependant on relevant parameters: Cn2(h) profile, outer scale profile and system mis-registration. We explore an upgrade of this algorithm, the Learn 3 Steps (L3S) approach, that allows one to dissociate the identification of the altitude layers from the ground in order to mitigate the lack of convergence of the required empirical covariance matrices therefore reducing the required length of data time-series for reaching a given accuracy. For nominal observation conditions, the L3S can reach the same level of tomographic error in using five times less data frames than the L&A approach. The L3S technique has been applied over a large amount of CANARY data to characterize the turbulence above the William Herschel Telescope (WHT). These data have been acquired the 13th, 15th, 16th, 17th and 18th September 2013 and we find 0.67"/8.9m/3.07m.s-1 of total seeing/outer scale/wind-speed, with 0.552"/9.2m/2.89m.s-1 below 1.5 km and 0.263"/10.3m/5.22m.s-1 between 1.5 and 20 km. We have also determined the high altitude layers above 20 km, missed by the tomographic reconstruction on CANARY, have a median seeing of 0.187" and have occurred 16% of observation time.
2016
Authors
de Queirós, RAP; Pinto, MT;
Publication
Gamification-Based E-Learning Strategies for Computer Programming Education
Abstract
Computer technologies are forever evolving and it is vital that computer science educators find new methods of teaching programming in order to maintain the rapid changes occurring in the field. One of the ways to increase student engagement and retention is by integrating games into the curriculum. Gamification-Based E-Learning Strategies for Computer Programming Education evaluates the different approaches and issues faced in integrating games into computer education settings. Featuring emergent trends on the application of gaming to pedagogical strategies and technological tactics, as well as new methodologies and approaches being utilized in computer programming courses, this book is an essential reference source for practitioners, researchers, computer science teachers, and students pursuing computer science.
2016
Authors
Vasconcelos, DM; Santos, SG; Lamghari, M; Barbosa, MA;
Publication
BIOMATERIALS
Abstract
The paradigm of metallic ions as exclusive toxic agents is changing. During the last 60 years, knowledge about toxicological and immunological reactions to metal particles and ions has advanced considerably. Hip prostheses, namely metal-on-metal bearings, have prompted studies about excessive and prolonged exposure to prosthetic debris. In that context, the interactions of metal particles and ions with cells and tissues are mostly harmful, inducing immune responses that lead to osteolysis and implant failure. However, in the last decade, new strategies to promote immunomodulation and healing have emerged based on the unique properties of metallic ions. The atom-size and charge enable ions to interact with key macromolecules (e.g. proteins, nucleic acids) that affect cellular function. Moreover, these agents are inexpensive, stable and can be integrated in biomaterials, which may open new avenues for a novel generation of medical devices. Herein, orthopedic devices are discussed as models for adverse responses to metal ions, and debated together with the potential to use metal ions-based therapies, thus bridging the gap between unmet clinical needs and cutting-edge research. In summary, this review addresses the two "faces" of metallic ions, from pathological responses to innovative research strategies that use metal ions for regenerative medicine.
2016
Authors
Torrao, G; Fontes, T; Coelho, M; Rouphail, N;
Publication
ACCIDENT ANALYSIS AND PREVENTION
Abstract
In general, car manufacturers face trade-offs between safety, efficiency and environmental performance when choosing between mass, length, engine power, and fuel efficiency. Moreover, the information available to the consumers makes difficult to assess all these components at once, especially when aiming to compare vehicles across different categories and/or to compare vehicles in the same category but across different model years. The main objective of this research was to develop an integrated tool able to assess vehicle's performance simultaneously for safety and environmental domains, leading to the research output of a Safety, Fuel Efficiency and Green Emissions (SEG) indicator able to evaluate and rank vehicle's performance across those three domains. For this purpose, crash data was gathered in Porto (Portugal) for the period 2006-2010 (N = 1374). The crash database was analyzed and crash severity prediction models were developed using advanced logistic regression models. Following, the methodology for the SEG indicator was established combining the vehicle's safety and the environmental evaluation into an integrated analysis. The obtained results for the SEG indicator do not show any trade-off between vehicle's safety, fuel consumption and emissions. The best performance was achieved for newer gasoline passenger vehicles (<5year) with a smaller engine size (<1400 cm(3)). According to the SEG indicator, a vehicle with these characteristics can be recommended for a safety-conscious profile user, as well as for a user more interested in fuel economy and/or in green performance. On the other hand, for larger engine size vehicles (>2000 cm(3)) the combined score for safety user profile was in average more satisfactory than for vehicles in the smaller engine size group (<1400 cm(3)), which suggests that in general, larger vehicles may offer extra protection. The achieved results demonstrate that the developed SEG integrated methodology can be a helpful tool for consumers to evaluate their vehicle selection through different domains (safety, fuel efficiency and green emissions). Furthermore, SEG indicator allows the comparison of vehicles across different categories and vehicle model years. Hence, this research is intended to support the decision-making process for transportation policy, safety and sustainable mobility, providing insights not only to policy makers, but also for general public guidance.
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