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1. BI-GRU Capsule Networks for Student Answers Assessment

   Ait-Khayi, Nisrine; Rus, Vasile (August 2019, The 2019 KDD Workshop on Deep Learning for Education (DL4Ed) in conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), August 4-8, 2019, Anchorage, Alaska, USA)

   Motivated by the good results of capsule networks in text classification and other Natural Language Processing tasks, we present in this paper a Bi-GRU Capsule Networks model to automatically assess freely-generated student answers assessment within the context of dialogue-based intelligent tutoring systems. Our proposed model is composed of several important components: an embedding layer, a Bi-GRU layer, a capsule layer and a SoftMax layer. We have conducted a number of experiments considering a binary classification task: correct or incorrect answers. Our model has reached a highest accuracy of 72.50 when using an Elmo word embedding as detailed in the body of the paper. 

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2. BI-GRU Capsule Networks for Student Answers Assessment

   Ait-Khayi, N.; Rus, V. (January 2019, In Proceedings of The 2019 KDD Workshop on Deep Learning for Education (DL4Ed) in conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), August 4-8, 2019, Anchorage, Alaska, USA)

   Motivated by the good results of capsule networks in text classification and other Natural Language Processing tasks, we present in this paper a Bi-GRU Capsule Networks model to automatically assess freely-generated student answers assessment within the context of dialogue-based intelligent tutoring systems. Our proposed model is composed of several important components: an embedding layer, a Bi-GRU layer, a capsule layer and a SoftMax layer. We have conducted a number of experiments considering a binary classification task: correct or incorrect answers. Our model has reached a highest accuracy of 72.50 when using an Elmo word embedding as detailed in the body of the paper. 

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3. Attention Based Transformer for Student Answers Assessment

   Ait Khayi, Nisrine; Rus, Vasile (April 2020, Thirty-Third International FLAIRS Conference (FLAIRS-32))

   tInspired by Vaswani's transformer, we propose in this paper anattentionYbased transformer neural networ< with a multiYheadattention mechanism for the tas< of student answer assessment.Results show the competitiveness of our proposed model. Ahighest accuracy of 71.5% was achieved when using ELMoembeddings, 10 heads of attention, and 2 layers. This is verycompetitive and rivals the highest accuracy achieved by apreviously proposed BIYGRUYCapsnet deep networ< (72.5%)on the same dataset. The main advantages of using transformersover BIYGRUYCapsnet is reducing the training time and givingmore space for parallelization 

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4. A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition

   https://doi.org/10.3390/ma17174363  

   Wu, Sung-Heng; Tariq, Usman; Joy, Ranjit; Mahmood, Muhammad Arif; Malik, Asad Waqar; Liou, Frank (September 2024, Materials)

   In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups. 

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5. NeuralPolish: a novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks

   https://doi.org/10.1093/bioinformatics/btab354  

   Huang, Neng; Nie, Fan; Ni, Peng; Luo, Feng; Gao, Xin; Wang, Jianxin (May 2021, Bioinformatics)

   Robinson, Peter (Ed.)

   Abstract Motivation Oxford Nanopore sequencing producing long reads at low cost has made many breakthroughs in genomics studies. However, the large number of errors in Nanopore genome assembly affect the accuracy of genome analysis. Polishing is a procedure to correct the errors in genome assembly and can improve the reliability of the downstream analysis. However, the performances of the existing polishing methods are still not satisfactory. Results We developed a novel polishing method, NeuralPolish, to correct the errors in assemblies based on alignment matrix construction and orthogonal Bi-GRU networks. In this method, we designed an alignment feature matrix for representing read-to-assembly alignment. Each row of the matrix represents a read, and each column represents the aligned bases at each position of the contig. In the network architecture, a bi-directional GRU network is used to extract the sequence information inside each read by processing the alignment matrix row by row. After that, the feature matrix is processed by another bi-directional GRU network column by column to calculate the probability distribution. Finally, a CTC decoder generates a polished sequence with a greedy algorithm. We used five real datasets and three assembly tools including Wtdbg2, Flye and Canu for testing, and compared the results of different polishing methods including NeuralPolish, Racon, MarginPolish, HELEN and Medaka. Comprehensive experiments demonstrate that NeuralPolish achieves more accurate assembly with fewer errors than other polishing methods and can improve the accuracy of assembly obtained by different assemblers. Availability and implementation https://github.com/huangnengCSU/NeuralPolish.git. Supplementary information Supplementary data are available at Bioinformatics online. 

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