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Research has shown that access to building occupant behavior data can reduce energy consumption and improve occupants’ productivity, comfort, and well-being. However, behaviors can vary across cultural, geographic, building, environmental, and contextual settings. Therefore, to increase our understanding of the long-term naturalistic behavior of occupants, more living labs are emerging across different countries, offering an opportunity to address existing research gaps. With the growth of IoT and ubiquitous computing, it has become easier to replicate and validate short and long-term data across different contexts. However, selecting sensors' type, quantity, and position needs to be more cohesive with building information and activity simulation to avoid inaccurate, redundant, and privacy-intrusive sensing issues. In this dissertation, we tackle these critical challenges of living lab by demonstrating: 1) a methodology for integrating building simulation models to identify optimal light sensor placements with privacy-preserving sensing considerations, 2) a longitudinal in-hospital case study that integrates medical events data and environmental sensor streams to predict momentary patient sleep disruptions and 3) a novel methodology for integrating information extracted from building plans to support fault detection of long-term energy harvesting sensor deployments. Overall, the three chapters in this dissertation demonstrate contributions to three main pillars of living labs: instrumentation, utility, and maintenance.

The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model.

International audience; Existent process models can hardly cope with the emerging issue of modelling exponential variable data volumes in systems' workflow, from specifications to operation. Given the strong relation between data context and data variability, this paper considers the automated integration of contextual models for processes with data variability. The proposed approach extends methodologically a platform independent model process, using a contextual data model, to obtain automatically the corresponding platform specific model. Contextual data are thus integrated to a process as a model, within a process. Two particular cases of contextual data models are studied in detail: substitution, when the contextual data model defines generated code, and enhancement, when learned data descriptions constitute the contextual data model. The feasibility and value of integrating a contextual model into a process to handle data variability are shown in detail describing these two use cases. Contextual model integration by substitution to include automatically variable ready to use application services to generate code, and contextual model integration by enhancement applied to supervised image classification based on variable descriptors. Results show that relating data variability and its context by means of automated integration of a designed system component model, simplifies variable data processing of system process models.

Background The need for a better understanding of food consumption behaviour within its behavioural context has sparked the interest of nutrition researchers for user-documented food consumption data collected outside the research context using publicly available nutrition apps. The study aims to characterize the scientific, technical, legal and ethical features of this data in order to identify the opportunities and challenges associated with using this data for nutrition research. Method A search for apps collecting food consumption data was conducted in October 2016 against UK Google Play and iTunes storefronts. 176 apps were selected based on user ratings and English language support. Publicly available information from the app stores and app-related websites was investigated and relevant data extracted and summarized. Our focus was on characteristics related to scientific relevance, data management and legal and ethical governance of user-documented food consumption data. Results Food diaries are the most common form of data collection, allowing for multiple inputs including generic food items, packaged products, or images. Standards and procedures for compiling food databases used for estimating energy and nutrient intakes remain largely undisclosed. Food consumption data is interlinked with various types of contextual data related to behavioural motivation, physical activity, health, and fitness. While exchange of data between apps is common practise, the majority of apps lack technical documentation regarding data export. There is a similar lack of documentation regarding the implemented terms of use and privacy policies. While users are usually the owners of their data, vendors are granted irrevocable and royalty free licenses to commercially exploit the data. Conclusion Due to its magnitude, diversity, and interconnectedness, user-documented food consumption data offers promising opportunities for a better understanding of habitual food consumption behaviour and its determinants. Non-standardized or non-documented food data compilation procedures, data exchange protocols and formats, terms of use and privacy statements, however, limit possibilities to integrate, process and share user-documented food consumption data. An ongoing research effort is required, to keep pace with the technical advancements of food consumption apps, their evolving data networks and the legal and ethical regulations related to protecting app users and their personal data.

This study describes differences between 63 countries in treatment for depression and explores explanations for these differences. Treatment for depression is measured as the overall chance that an individual receives treatment, plus as the chance to receive treatment given the presence of depressive symptoms. Using the World Health Survey (2002-2004, N=249,116), we find strong cross-national variation in the chance to receive treatment for depression. Additionally, multilevel regression analyses reveal that urbanization, employment status, marital status, level of education, gender, age, and national wealth all partly explain cross-national differences in the chance to receive treatment for depression. Copyright ?? 2014 Elsevier Ltd. All rights reserved.

Background The need for a better understanding of food consumption behaviour within its behavioural context has sparked the interest of nutrition researchers for user-documented food consumption data collected outside the research context using publicly available nutrition apps. The study aims to characterize the scientific, technical, legal and ethical features of this data in order to identify the opportunities and challenges associated with using this data for nutrition research. Method A search for apps collecting food consumption data was conducted in October 2016 against UK Google Play and iTunes storefronts. 176 apps were selected based on user ratings and English language support. Publicly available information from the app stores and app-related websites was investigated and relevant data extracted and summarized. Our focus was on characteristics related to scientific relevance, data management and legal and ethical governance of user-documented food consumption data. Results Food diaries are the most common form of data collection, allowing for multiple inputs including generic food items, packaged products, or images. Standards and procedures for compiling food databases used for estimating energy and nutrient intakes remain largely undisclosed. Food consumption data is interlinked with various types of contextual data related to behavioural motivation, physical activity, health, and fitness. While exchange of data between apps is common practise, the majority of apps lack technical documentation regarding data export. There is a similar lack of documentation regarding the implemented terms of use and privacy policies. While users are usually the owners of their data, vendors are granted irrevocable and royalty free licenses to commercially exploit the data. Conclusion Due to its magnitude, diversity, and interconnectedness, user-documented food consumption data offers promising opportunities for a better understanding of habitual food consumption behaviour and its determinants. Non-standardized or non-documented food data compilation procedures, data exchange protocols and formats, terms of use and privacy statements, however, limit possibilities to integrate, process and share user-documented food consumption data. An ongoing research effort is required, to keep pace with the technical advancements of food consumption apps, their evolving data networks and the legal and ethical regulations related to protecting app users and their personal data.

In Scotland, as in the rest of the UK, there is growing recognition that prior qualifications may not provide an adequate indication of the ‘potential’ of applicants from educationally-disadvantaged backgrounds to succeed at university. Universities are being encouraged to use contextual data on neighbourhood characteristics and school performance to identify disadvantaged applicants in the admissions process. Contextualised admissions have been pioneered at the University of Edinburgh since 2004, and this article reports findings on the prior qualifications, retention and degree outcomes of a sample of students who entered the University in 2004–2006. The article describes the distribution of contextual data and discusses the limitations of indicators based on geographical area and school characteristics. Differences in average prior qualifications, retention and degree outcomes associated with indicators of widening participation are small. Statistical models suggest that after taking account of prior qualifications WP-indicated students were as likely to complete an HE qualification and achieve an Honours degree as non-WP students, but they had a lower probability of achieving a higher classification of degree. The findings raise questions for the University about possible causes for lower achievement by disadvantaged students.

The Compliance and Interoperability Working Group of the Genomic Standards Consortium facilitates the establishment of a community of experts and the development of recommendations to describe genomic data and associated information. Here we present our ongoing conation to harmonise the reporting of contextual plant specimen data associated with genomics and functional genomics. This commentary summarises the current state of our plant sample contextual data harmonisation efforts to engage a broad plant science community.

In any sequencing project, the possible depth of comparative analysis is determined largely by the amount and quality of the accompanying contextual data. The structure, content, and storage of this contextual data should be standardized to ensure consistent coverage of all sequenced entities and facilitate comparisons. The Genomic Standards Consortium (GSC) has developed the “Minimum Information about Genome/Metagenome Sequences (MIGS/MIMS)” checklist for the description of genomes and here we annotate all 30 publicly available marine bacteriophage sequences to the MIGS standard. These annotations build on existing International Nucleotide Sequence Database Collaboration (INSDC) records, and confirm, as expected that current submissions lack most MIGS fields. MIGS fields were manually curated from the literature and placed in XML format as specified by the Genomic Contextual Data Markup Language (GCDML). These “machine-readable” reports were then analyzed to highlight patterns describing this collection of genomes. Completed reports are provided in GCDML. This work represents one step towards the annotation of our complete collection of genome sequences and shows the utility of capturing richer metadata along with raw sequences.

Ensuring high quality education is an important priority of state educational policy in the Russian Federation. Recently, the quality of education, which directly depends on many factors, has been widely discussed. The aim of the study is a statistical analysis of the results of regional qualimetric monitoring of the quality of knowledge based on contextual indicators that affect the educational results of students. As the main tools used correlation and regression analysis. The described technique was tested in the Pskov region. The results of the study provide relevant information to all interested parties: state and municipal levels of education management, heads and teachers of educational institutions, parents and students.