215 research outputs found
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Outsourcing data storage to the remote cloud can be an economical solution to
enhance data management in the smart grid ecosystem. To protect the privacy of
data, the utility company may choose to encrypt the data before uploading them
to the cloud. However, while encryption provides confidentiality to data, it
also sacrifices the data owners' ability to query a special segment in their
data. Searchable symmetric encryption is a technology that enables users to
store documents in ciphertext form while keeping the functionality to search
keywords in the documents. However, most state-of-the-art SSE algorithms are
only focusing on general document storage, which may become unsuitable for
smart grid applications. In this paper, we propose a simple, practical SSE
scheme that aims to protect the privacy of data generated in the smart grid.
Our scheme achieves high space complexity with small information disclosure
that was acceptable for practical smart grid application. We also implement a
prototype over the statistical data of advanced meter infrastructure to show
the effectiveness of our approach
Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation
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Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge
Multimodal Named Entity Recognition (MNER) on social media aims to enhance
textual entity prediction by incorporating image-based clues. Existing studies
mainly focus on maximizing the utilization of pertinent image information or
incorporating external knowledge from explicit knowledge bases. However, these
methods either neglect the necessity of providing the model with external
knowledge, or encounter issues of high redundancy in the retrieved knowledge.
In this paper, we present PGIM -- a two-stage framework that aims to leverage
ChatGPT as an implicit knowledge base and enable it to heuristically generate
auxiliary knowledge for more efficient entity prediction. Specifically, PGIM
contains a Multimodal Similar Example Awareness module that selects suitable
examples from a small number of predefined artificial samples. These examples
are then integrated into a formatted prompt template tailored to the MNER and
guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired
knowledge is integrated with the original text and fed into a downstream model
for further processing. Extensive experiments show that PGIM outperforms
state-of-the-art methods on two classic MNER datasets and exhibits a stronger
robustness and generalization capability.Comment: Accepted to Findings of EMNLP 202
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