17,397 research outputs found
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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Translating verbose information needs into crisp search queries is a
phenomenon that is ubiquitous but hardly understood. Insights into this process
could be valuable in several applications, including synthesizing large
privacy-friendly query logs from public Web sources which are readily available
to the academic research community. In this work, we take a step towards
understanding query formulation by tapping into the rich potential of community
question answering (CQA) forums. Specifically, we sample natural language (NL)
questions spanning diverse themes from the Stack Exchange platform, and conduct
a large-scale conversion experiment where crowdworkers submit search queries
they would use when looking for equivalent information. We provide a careful
analysis of this data, accounting for possible sources of bias during
conversion, along with insights into user-specific linguistic patterns and
search behaviors. We release a dataset of 7,000 question-query pairs from this
study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system
Engaging Qualities: factors affecting learner attention in online design studios
This study looks at the qualities of learner-generated online content, as rated by experts, and how these relate to learners’ engagement through comments and conversations around this content. The work uploaded to an Online Design Studio by students across a Design and Innovation Qualification was rated and analysed quantitatively using the Consensual Assessment Technique (CAT). Correlations of qualities to comments made on this content were considered and a qualitative analysis of the comments was carried out. It was observed that design students do not necessarily pay attention to the same qualities in learner-generated content that experts rate highly, except for a particular quality at the first level of study. The content that students do engage with also changes with increasing levels of study. These findings have implications for the learning design of online design courses and qualifications as well as for design institutions seeking to supplement proximate design studios with Online Social Network Services
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