A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.

Starting antidepressant use: a qualitative synthesis of UK and Australian data

Objective To explore people's experiences of starting antidepressant treatment. Design Qualitative interpretive approach combining thematic analysis with constant comparison. Relevant coding reports from the original studies (generated using NVivo) relating to initial experiences of antidepressants were explored in further detail, focusing on the ways in which participants discussed their experiences of taking or being prescribed an antidepressant for the first time. Participants 108 men and women aged 22–84 who had taken antidepressants for depression. Setting Respondents recruited throughout the UK during 2003–2004 and 2008 and 2012–2013 and in Australia during 2010–2011. Results People expressed a wide range of feelings about initiating antidepressant use. People's attitudes towards starting antidepressant use were shaped by stereotypes and stigmas related to perceived drug dependency and potentially extreme side effects. Anxieties were expressed about starting use, and about how long the antidepressant might begin to take effect, how much it might help or hinder them, and about what to expect in the initial weeks. People worried about the possibility of experiencing adverse effects and implications for their senses of self. Where people felt they had not been given sufficient time during their consultation information or support to take the medicines, the uncertainty could be particularly unsettling and impact on their ongoing views on and use of antidepressants as a viable treatment option. Conclusions Our paper is the first to explore in-depth patient existential concerns about start of antidepressant use using multicountry data. People need additional support when they make decisions about starting antidepressants. Health professionals can use our findings to better understand and explore with patients’ their concerns before their patients start antidepressants. These insights are key to supporting patients, many of whom feel intimidated by the prospect of taking antidepressants, especially during the uncertain first few weeks of treatment.

The Data of Things: Strategies, Patterns and Practice of Cloud-based Participatory Sensing

The broad capabilities of current mobile devices have paved the way for Mobile Crowd Sensing (MCS) applications. The success of this emerging paradigm strongly depends on the quality of received data which, in turn, is contingent to mass user participation; the broader the participation, the more useful these systems become. However, there is an ongoing trend that tries to integrate MCS applications with emerging computing paradigms such as cloud computing. The intuition is that such a transition can significantly improve the overall efficiency while at the same time it offers stronger security and privacy-preserving mechanisms for the end-user. In this position paper, we dwell on the underpinnings of incorporating cloud computing techniques to facilitate the vast amount of data collected in MCS applications. That is, we present a list of core system, security and privacy requirements that must be met if such a transition is to be successful. To this end, we first address several competing challenges not previously considered in the literature such as the scarce energy resources of battery-powered mobile devices as well as their limited computational resources that they often prevent the use of computationally heavy cryptographic operations and thus offering limited security services to the end-user. Finally, we present a use case scenario as a comprehensive example. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security and privacy do not hinder the migration of MCS systems to the cloud.