21,227 research outputs found
Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus
In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries.Comment: Accepted to Information Retrieval Journa
Case study:exploring children’s password knowledge and practices
Children use technology from a very young age, and often have to authenticate themselves. Yet very little attention has been paid to designing authentication specifically for this particular target group. The usual practice is to deploy the ubiquitous password, and this might well be a suboptimal choice. Designing authentication for children requires acknowledgement of child-specific developmental challenges related to literacy, cognitive abilities and differing developmental stages. Understanding the current state of play is essential, to deliver insights that can inform the development of child-centred authentication mechanisms and processes. We carried out a systematic literature review of all research related to children and authentication since 2000. A distinct research gap emerged from the analysis. Thus, we designed and administered a survey to school children in the United States (US), so as to gain insights into their current password usage and behaviors. This paper reports preliminary results from a case study of 189 children (part of a much larger research effort). The findings highlight age-related differences in children’s password understanding and practices. We also discovered that children confuse concepts of safety and security. We conclude by suggesting directions for future research. This paper reports on work in progress.<br/
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
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