J
Jiuxin Cao
Researcher at Southeast University
Publications - 75
Citations - 813
Jiuxin Cao is an academic researcher from Southeast University. The author has contributed to research in topics: Computer science & Quality of service. The author has an hindex of 12, co-authored 56 publications receiving 509 citations. Previous affiliations of Jiuxin Cao include Chinese Ministry of Education.
Papers
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Journal ArticleDOI
IoT Forensics: Amazon Echo as a Use Case
TL;DR: An IoT-based forensic model is presented that supports the identification, acquisition, analysis, and presentation of potential artifacts of forensic interest from IoT devices and the underpinning infrastructure and uses the popular Amazon Echo as a use case to demonstrate how the proposed model can be used to guide forensics analysis of IoT devices.
Proceedings ArticleDOI
Who Am I? Personality Detection Based on Deep Learning for Texts
TL;DR: This paper proposes a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts to show that the structure of texts can be also an important feature in the study of personality detection from texts.
Proceedings ArticleDOI
Location-Based Influence Maximization in Social Networks
TL;DR: This paper proposes an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment and proposes a location-based influence maximization algorithm named TPH, which proves its high effectiveness.
Journal ArticleDOI
A segmentation method for web page analysis using shrinking and dividing
Jiuxin Cao,Bo Mao,Junzhou Luo +2 more
TL;DR: Experiments show that the iterated shrinking and dividing algorithm is suitable for web page segmentation, and does well in expansibility and performance.
Journal ArticleDOI
A context-aware personalized resource recommendation for pervasive learning
TL;DR: A context-aware resource recommendation model and relevant recommendation algorithm for pervasive learning environments are proposed and it is shown that the newly proposed method outperforms other state of-the-art algorithms on traditional and newly presented metrics and it may also be more suitable for pervasivelearning environments.