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Full-Text Articles in Engineering
A New Approach To Keep The Privacy Information Of The Signer In A Digital Signature Scheme, Dung Hoang Duong, Willy Susilo, Viet Cuong Trinh
A New Approach To Keep The Privacy Information Of The Signer In A Digital Signature Scheme, Dung Hoang Duong, Willy Susilo, Viet Cuong Trinh
Faculty of Engineering and Information Sciences - Papers: Part B
In modern applications, such as Electronic Voting, e-Health, e-Cash, there is a need that the validity of a signature should be verified by only one responsible person. This is opposite to the traditional digital signature scheme where anybody can verify a signature. There have been several solutions for this problem, the first one is we combine a signature scheme with an encryption scheme; the second one is to use the group signature; and the last one is to use the strong designated verifier signature scheme with the undeniable property. In this paper, we extend the traditional digital signature scheme to …
Hime: Mining And Ensembling Heterogeneous Information For Protein Interaction Predictions, Huaming Chen, Yaochu Jin, Lei Wang, Chi-Hung Chi, Jun Shen
Hime: Mining And Ensembling Heterogeneous Information For Protein Interaction Predictions, Huaming Chen, Yaochu Jin, Lei Wang, Chi-Hung Chi, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
esearch on protein-protein interactions (PPIs) data paves the way towards understanding the mechanisms of infectious diseases, however improving the prediction performance of PPIs of inter-species remains a challenge. Since one single type of sequence data such as amino acid composition may be deficient for high-quality prediction of protein interactions, we have investigated a broader range of heterogeneous information of sequences data. This paper proposes a novel framework for PPIs prediction based on Heterogeneous Information Mining and Ensembling (HIME) process to effectively learn from the interaction data. In particular, the proposed approach introduces an ensemble process together with substantial features that …
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Faculty of Engineering and Information Sciences - Papers: Part B
Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early …
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Faculty of Engineering and Information Sciences - Papers: Part B
Knowledge tracing is a key area of research contributing to personalized education. In recent times, deep knowledge tracing has achieved great success. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-based Knowledge Tracing with Heterogeneous Information Network Embedding (AKTHE). First, we describe questions and their attributes with a heterogeneous information network and generate meaningful node embeddings. Second, we capture the relevance of historical data …