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Predictive approaches for the UNIX command line: curating and exploiting domain knowledge in semantics deficit data

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Abstract

The command line has always been the most efficient method to interact with UNIX flavor based systems while offering a great deal of flexibility and efficiency as preferred by professionals. Such a system is based on manually inputting commands to instruct the computing machine to carry out tasks as desired. This human-computer interface is quite tedious especially for a beginner. And hence, the command line has not been able to garner an overwhelming reception from new users. Therefore, to improve user-friendliness and to mark a step towards a more intuitive command line system, we propose two predictive approaches that can benefit all kinds of users specially the novice ones by integrating into the command line interface. These methods are based on deep learning based predictions. The first approach is based on the sequence to sequence (Seq2seq) model with joint learning by leveraging continuous representations of a self-curated exhaustive knowledge base (KB) comprising an all-inclusive command description to enhance the embedding employed in the model. The other is based on the attention-based transformer architecture where a pretrained model is employed. This allows the model to dynamically evolve over time making it adaptable to different circumstances by learning as the system is being used. To reinforce our idea, we have experimented with our models on three major publicly available Unix command line datasets and have achieved benchmark results using GLoVe and Word2Vec embeddings. Our finding is that the transformer based framework performs better on two different datasets of the three in our experiment in a semantic deficit scenario like UNIX command line prediction. However, Seq2seq based model outperforms bidirectional encoder representations from transformers (BERT) based model on a larger dataset.

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Notes

  1. http://saul.cpsc.ucalgary.ca/pmwiki.php/HCIResources/HCIWWWUnixDataSets

  2. https://linux.die.net/man/

  3. http://www.schonlau.net/intrusion.html

  4. https://archive.ics.uci.edu/ml/datasets/UNIX+User+Data

  5. worst case

  6. Target word is the word for which we want to learn.

  7. Context word is the one that co-occurs with it in some contextual window

  8. ci, d denotes the word embedding (vector) of the word \(\tilde c_{j}\), and the dimensionality (a user-specified hyperparameter) respectively.

  9. The semantic relations that exists between the two entities within the KB used in the context.

  10. For instance, a context command co-occurring 4 tokens from a target command would impart to a count of \(\frac {1}{4}\) to co-occurrence.

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Authors and Affiliations

  1. Centre for Natural Language Processing (CNLP) and Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India

    Thoudam Doren Singh, Abdullah Faiz Ur Rahman Khilji,  Divyansha & Sivaji Bandyopadhyay

  2. Department of Electrical Engineering, National Institute of Technology Silchar, Assam, India

    Apoorva Vikram Singh

  3. Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Meghalaya, India

    Surmila Thokchom

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  1. Thoudam Doren Singh
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  2. Abdullah Faiz Ur Rahman Khilji
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Correspondence to Thoudam Doren Singh.

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Singh, T.D., Khilji, A.F.U.R., Divyansha et al. Predictive approaches for the UNIX command line: curating and exploiting domain knowledge in semantics deficit data. Multimed Tools Appl 80, 9209–9229 (2021). https://doi.org/10.1007/s11042-020-10109-y

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