Limin Pan
Beijing Institute of Technology
46 Papers
46 Citations
Limin Pan is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Relationship extraction. The author has an hindex of 8, co-authored 27 publications.
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Papers
Cloud-based security and privacy-aware information dissemination over ubiquitous VANETs
TL;DR: This paper proposes a cloud-based security and privacy-aware information dissemination environment between vehicular nodes and cloud infrastructure, and takes on ciphertext policy attribute-based encryption (CP-ABE) to implement the access control systems and effective access policies for both cloud and VANETs.
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SVPS: Cloud-based smart vehicle parking system over ubiquitous VANETs
TL;DR: The proposed cloud-based smart vehicle parking system (SVPS) over ubiquitous VANETs offers a unique algorithm that provides an appropriate vacant parking space information along with booking and recommendation options to facilitate vehicles in an effective, real-time and precise manner.
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PIaaS: Cloud-oriented secure and privacy-conscious parking information as a service using VANETs
TL;DR: Cloud infrastructure process the Big Parking Data (BPD) and yields the most significant parking information in a dynamic, pertinent, privacy and confidentiality preserved manner and proposes a novel geo-location-based parking lock encryption mechanism for location privacy and non-frameability.
41
HAN-BSVD: A hierarchical attention network for binary software vulnerability detection
TL;DR: A hierarchical attention network for binary software vulnerability detection (HAN-BSVD), where the contextual information is first enriched by the preprocessor with unifying jump address and normalizing instruction, and then preserved by the instruction embedding network that composed of Bi-GRU and word-attention module.
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Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.
TL;DR: This work proposes a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative.