Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

A database solution for the quantitative characterisation and comparison of deep-marine siliciclastic depositional systems

In sedimentological investigations, the ability to conduct comparative analyses between deep-marine depositional systems is hindered by the wide variety in methods of data collection, scales of observation, resolution, classification approaches and terminology. A relational database, the Deep-Marine Architecture Knowledge Store (DMAKS), has been developed to facilitate such analyses, through the integration of deep-marine sedimentological data collated to a common standard. DMAKS hosts data on siliciclastic deep-marine system boundary conditions, and on architectural and facies properties, including spatial, temporal and hierarchical relationships between units at multiple scales. DMAKS has been devised to include original and literature-derived data from studies of the modern sea-floor, and from ancient successions studied in the sub-surface and in outcrop. The database can be used as a research tool in both pure and applied science, allowing the quantitative characterisation of deep-marine systems. The ability to synthesise data from several case studies and to filter outputs on multiple parameters that describe the depositional systems and their controlling factors enables evaluation of the degree to which certain controls affect sedimentary architectures, thereby testing the validity of existing models. In applied contexts, DMAKS aids the selection and application of geological analogues to hydrocarbon reservoirs, and permits the development of predictive models of reservoir characteristics that account for geological uncertainty. To demonstrate the breadth of research applications, example outputs are presented on: (i) the characterisation of channel geometries, (ii) the hierarchical organisation of channelised and terminal deposits, (iii) temporal trends in the deposition of terminal lobes, (iv) scaling relationships between adjacent channel and levee architectural elements, (v) quantification of the likely occurrence of elements of different types as a function of the lateral distance away from an element of known type, (vi) proportions and transition statistics of facies in elements and beds, (vii) variability in net-to-gross ratios among element types

Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game

Ocean Energy in Belgium - 2019

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ESTIMATION OF MEASUREMENT UNCERTAINTY OF SEAFLOOR ACOUSTIC BACKSCATTER

In the last three decades, Multibeam echo sounders (MBES) have become the tool of choice to study the seafloor. MBES collects two distinct types of data: bathymetry that provides topographic details of the seafloor and backscatter that has the potential to characterize the seafloor. While the uncertainty associated with MBE bathymetry has been well studied, the uncertainty in MBES backscatter measurement has received relatively little attention, hindering the improvements in quantitative analysis of backscatter data. Both acquisition and processing stages can introduce uncertainty in the final seafloor backscatter products. Application of well-established uncertainty quantification principles to seafloor backscatter data is challenging for several reasons: the uncertainty sources are not well known, they vary on a case-by-case basis, and standards do not exist for acquisition and processing. This dissertation focuses on assessing uncertainty in backscatter measurements and is comprised of four separate but related studies that identify and address the challenges of uncertainty quantification of backscatter measurements. The first study (Lucieer et al., 2018) which is presented as background, describes an end users’ survey identifying key uses and challenges of backscatter data acquisition and processing. The study identified that consistency and repeatability of backscatter measurements is a major constraint in the use and re-use of backscatter. The second study (Malik et al., 2018), identified the sources of uncertainty and categorized them as significant or insignificant based on various use cases. The most significant sources of uncertainty were found to be inherent statistical fluctuations in the backscatter measurement, calibration uncertainty, seafloor slope and water column absorption estimation. While calibration uncertainty remains the main issue in advancing the quantitative use of the backscatter, the other sources were also shown to cause large uncertainties. These include non-standardized methods used to account for seafloor slope and absorption, and data interpretation errors due to missing background information about the processing procedures. With a comprehensive list of uncertainty sources established, two uncertainty sources, seafloor slope and processing errors, were examined further in the third (Malik, 2019) and the fourth (Malik et al., submitted) study respectively. Seafloor slope corrections are important to correct for both the area insonified and the incidence angle. Both of these corrections are adversely affected if seafloor slope corrections are not applied. Even in cases where the seafloor slope is used, further uncertainty can occur if the highest resolution bathymetry is not used. The results from this study showed that for the purpose of accurate slope corrections, the spatial scale of backscatter data should be selected based on the best available bathymetry. The majority of end users depend on third-party software solutions to process the backscatter data. The fourth study evaluated the output of three commonly used software packages after inputting the same data set and found that there were significant differences in the outputs. This issue was addressed by working closely with software developers to explore options to make the processing chain more transparent. Two intermediate processing stages were proposed and implemented in three commonly used software tools. However, due to proprietary restrictions, it was not possible to know the full details of the software processing packages. Differing outputs likely result, in part, from the different approaches used by the various software packages to read the raw data. Quality assessment and uncertainty quantification of MBES backscatter measurements is still at an early stage and further work is required to develop data acquisition and processing standards to improve consistency in the backscatter acquisition and processing. Publications: Lucieer, V.; Roche, M.; Degrendele, K.; Malik, M.; Dolan, M.; Lamarche, G. User expectations for multibeam echo sounders backscatter strength data-looking back into the future. Mar. Geophys. Res. 2018, 39, 23–40. doi:10.1007/s11001-017-9316-5. Malik, M.; Lurton, X.; Mayer, L. A framework to quantify uncertainties of seafloor backscatter from swath mapping echosounders. Mar. Geophys. Res. 2018, 39, 151–168. doi.org/10.1007/s11001-018-9346-7. Malik, M. Sources and Impacts of Bottom Slope Uncertainty on Estimation of Seafloor Backscatter from Swath Sonars. Geosciences 2019, 9, 183. doi: 10.3390/geosciences9040183. Malik, M.; Schimel, A.; Masetti, G.; Roche, M.; Deunf, J.L.; Dolan, M.; Beaudoin, J.; Augustin, J.M.; Hamilton, T.; Parnum, I. Results from the first phase of the Backscatter Software Inter-comparison Project. Geosciences. Submitted