396 research outputs found
Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways
Principal component analysis (PCA) is a powerful data reductionmethod for
Structural Health Monitoring. However, its computa-tional cost and data memory
footprint pose a significant challengewhen PCA has to run on limited capability
embedded platformsin low-cost IoT gateways. This paper presents a
memory-efficientparallel implementation of the streaming History PCA
algorithm.On our dataset, it achieves 10x compression factor and 59x
memoryreduction with less than 0.15 dB degradation in the
reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over,
the algorithm benefits from parallelization on multiple cores,achieving a
maximum speedup of 4.8x on Samsung ARTIK 710
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
Sentiment analysis has various application scenarios in software engineering
(SE), such as detecting developers' emotions in commit messages and identifying
their opinions on Q&A forums. However, commonly used out-of-the-box sentiment
analysis tools cannot obtain reliable results on SE tasks and the
misunderstanding of technical jargon is demonstrated to be the main reason.
Then, researchers have to utilize labeled SE-related texts to customize
sentiment analysis for SE tasks via a variety of algorithms. However, the
scarce labeled data can cover only very limited expressions and thus cannot
guarantee the analysis quality. To address such a problem, we turn to the
easily available emoji usage data for help. More specifically, we employ
emotional emojis as noisy labels of sentiments and propose a representation
learning approach that uses both Tweets and GitHub posts containing emojis to
learn sentiment-aware representations for SE-related texts. These emoji-labeled
posts can not only supply the technical jargon, but also incorporate more
general sentiment patterns shared across domains. They as well as labeled data
are used to learn the final sentiment classifier. Compared to the existing
sentiment analysis methods used in SE, the proposed approach can achieve
significant improvement on representative benchmark datasets. By further
contrast experiments, we find that the Tweets make a key contribution to the
power of our approach. This finding informs future research not to unilaterally
pursue the domain-specific resource, but try to transform knowledge from the
open domain through ubiquitous signals such as emojis.Comment: Accepted by the 2019 ACM Joint European Software Engineering
Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
2019). Please include ESEC/FSE in any citation
Open-Retrieval Conversational Question Answering
Conversational search is one of the ultimate goals of information retrieval.
Recent research approaches conversational search by simplified settings of
response ranking and conversational question answering, where an answer is
either selected from a given candidate set or extracted from a given passage.
These simplifications neglect the fundamental role of retrieval in
conversational search. To address this limitation, we introduce an
open-retrieval conversational question answering (ORConvQA) setting, where we
learn to retrieve evidence from a large collection before extracting answers,
as a further step towards building functional conversational search systems. We
create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an
end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader
that are all based on Transformers. Our extensive experiments on OR-QuAC
demonstrate that a learnable retriever is crucial for ORConvQA. We further show
that our system can make a substantial improvement when we enable history
modeling in all system components. Moreover, we show that the reranker
component contributes to the model performance by providing a regularization
effect. Finally, further in-depth analyses are performed to provide new
insights into ORConvQA.Comment: Accepted to SIGIR'2
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