A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.Comment: 15 pages, 6 figure

Towards a Neuro-Cognitive Model of Human Sentence Processing

PACLIC 20 / Wuhan, China / 1-3 November, 200

Multiscale integration of contextual information during a naturalistic task

Everyday experience requires rapid and automatic integration of incoming stimuli with previously stored knowledge. Prior knowledge can help construct a general “situation model” of the event, as well as aid comprehension of an ongoing narrative. Using fMRI in healthy adult humans we investigated processing of videos whose locations and characters were always familiar but whose narratives were either a continuation or non-continuation of an earlier video (high context (HC) or low context (LC) respectively). Responses in parahippocampal gyrus and retrosplenial cortex were composed of an initial transient, locked to the video onsets, followed by a period of lower amplitude activation that was greater in the LC condition. This may reflect rapid processing of core components of situation models such as location and characters and more gradual incorporation of their narrative themes. By contrast, activity increases in left hemisphere middle temporal gyrus (MTG), angular gyrus and inferior frontal gyrus were maintained throughout the videos and were higher for HC versus LC videos. Further, activity in the left MTG peaked earlier in the HC condition. We suggest that these regions support representations of the specific inter-linked concepts necessary to comprehend an ongoing narrative, which are already established for the HC videos

The Unified Enterprise Modelling Language – Overview and further work

International audienceThe Unified Enterprise Modelling Language (UEML) aims at supporting integrated use of enterprise and IS models expressed using different languages. To achieve this aim, UEML offers a hub through which modelling languages can be connected, thereby paving the way for also connecting the models expressed in those languages. This paper motivates and presents the most central parts of the UEML approach: a structured path to describing enterprise and IS modelling constructs; a common ontology to interrelate construct descriptions at the semantic level; a correspondence analysis approach to estimate semantic construct similarity; a quality framework to aid selection of languages; a meta-meta model to integrate the different parts of the approach; and a set of tools to aid its use and evolution. The paper also discusses the benefits of UEML and points to paths for further work

Similarity-based fMRI-MEG fusion reveals hierarchical organisation within the brain's semantic system

Our ability to understand and interact with our environment relies upon conceptual knowledge of the meaning of objects. This process is supported by a distributed network of frontal, parietal, and temporal brain regions. Insight into the differential roles of various elements of this system can be inferred from the timing of activation, and here we use similarity-based fMRI-MEG fusion to understand when the representational spaces in different elements of the semantic system converge with representational spaces in the evolving MEG signal. Participants performed a semantic-typicality judgement of written words drawn from nine different semantic categories in separate fMRI and MEG sessions. Results indicate an initial period of congruence between MEG and fMRI informational spaces dominated by the posterior inferior temporal gyrus and the ventral temporal cortex between 350 and 450 msec. This is followed by a second period of convergence between 450 and 795 msec where MEG and fMRI representational spaces conform in left angular gyrus and precuneus in addition to ventral temporal cortex. Results are consistent with the multistage recruitment of the semantic system, initially involving automatic aspects of the representational system and later extending to broader elements of the semantic system more strongly associated with internalised cognition

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

The Implicit Learning of Mappings between Forms and Contextually-Derived Meanings

The traditional implicit learning literature has focused primarily on the abstraction of statistical regularities in form-form connections. More attention has been recently directed toward the implicit learning of form-meaning connections, which might be crucial in the acquisition of natural languages. The current article reports evidence for implicit learning of a mapping between a novel set of determiners and thematic roles, obtained using a newly developed reaction time methodology. The results conclude that contextually derived form-meaning connections might be implicitly learned.published_or_final_versio