INTAMAP: the design and implementation of an interoperable automated interpolation web service

INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52°North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.

An investigation into the effects of processing instruction on the acquisition of English relative clauses by Syrian learners

The purpose of this research was to investigate the effects of Processing Instruction (VanPatten, 1996, 2007), as an input-based model for teaching second language grammar, on Syrian learners’ processing abilities. The present research investigated the effects of Processing Instruction on the acquisition of English relative clauses by Syrian learners in the form of a quasi-experimental design. Three separate groups were involved in the research (Processing Instruction, Traditional Instruction and a Control Group). For assessment, a pre-test, a direct post-test and a delayed post-test were used as main tools for eliciting data. A questionnaire was also distributed to participants in the Processing Instruction group to give them the opportunity to give feedback in relation to the treatment they received in comparison with the Traditional Instruction they are used to. Four hypotheses were formulated on the possible effectivity of Processing Instruction on Syrian learners’ linguistic system. It was hypothesised that Processing Instruction would improve learners’ processing abilities leading to an improvement in learners’ linguistic system. This was expected to lead to a better performance when it comes to the comprehension and production of English relative clauses. The main source of data was analysed statistically using the ANOVA test. Cohen’s d calculations were also used to support the ANOVA test. Cohen’s d showed the magnitude of effects of the three treatments. Results of the analysis showed that both Processing Instruction and Traditional Instruction groups had improved after treatment. However, the Processing Instruction Group significantly outperformed the other two groups in the comprehension of relative clauses. The analysis concluded that Processing Instruction is a useful tool for instructing relative clauses to Syrian learners. This was enhanced by participants’ responses to the questionnaire as they were in favour of Processing Instruction, rather than Traditional Instruction. This research has theoretical and pedagogical implications. Theoretically, the study showed support for the Input hypothesis. That is, it was shown that Processing Instruction had a positive effect on input processing as it affected learners’ linguistic system. This was reflected in learners’ performance where learners were able to produce a structure which they had not been asked to produce. Pedagogically, the present research showed that Processing Instruction is a useful tool for teaching English grammar in the context where the experiment was carried out, as it had a large effect on learners’ performance.

Progmosis:evaluating risky individual behavior during epidemics using mobile network data

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.