All Outputs (14)

CAM: A Combined Attention Model for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018, December). CAM: A Combined Attention Model for Natural Language Inference. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl... Read More about CAM: A Combined Attention Model for Natural Language Inference.

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018, December). An Exploration of Dropout with RNNs for Natural Language Inference. Presented at ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.

Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition (2018)
Journal Article
Alabbas, M., Jaf, S., & Khudeyer, S. R. (2018). Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition. Journal of Chinese Language and Computing, 28(1), 1-12

There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are faced with im... Read More about Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition.

BotDet: A System for Real Time Botnet Command and Control Traffic Detection (2018)
Journal Article
Ghafir, I., Prenosil, V., Hammoudeh, M., Baker, T., Jabbar, S., Khalid, S., & Jaf, S. (2018). BotDet: A System for Real Time Botnet Command and Control Traffic Detection. IEEE Access, 6, 38947-38958. https://doi.org/10.1109/access.2018.2846740

Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of... Read More about BotDet: A System for Real Time Botnet Command and Control Traffic Detection.

Security Threats to Critical Infrastructure: The Human Factor (2018)
Journal Article
Ghafir, I., Saleem, J., Hammoudeh, M., Faour, H., Prenosil, V., Jaf, S., …Baker, T. (2018). Security Threats to Critical Infrastructure: The Human Factor. Journal of Supercomputing, 74(10), 4986-5002. https://doi.org/10.1007/s11227-018-2337-2

In the 21st century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks... Read More about Security Threats to Critical Infrastructure: The Human Factor.

Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers (2017)
Presentation / Conference Contribution
Alabbas, M., Khudeyer, R., Jaf, S., Dong, M., Tseng, Y.-H., Lu, Y., Yu, L.-C., Lee, L.-H., Wu, C.-H., & Li, H. (2016, November). Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers. Presented at The 20th International Conference on Asian Language Processing., Tainan, Taiwan

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that co... Read More about Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers.

A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters (2017)
Presentation / Conference Contribution
Jaf, S., Dong, M., Tseng, Y.-H., Lu, Y., Yu, L.-C., Lee, L.-H., Wu, C.-H., & Li, H. (2016, November). A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters. Presented at The 20th International Conference on Asian Language Processing., Tainan, Taiwan

In this study, we outline a potential problem in normalising texts that are based on a modified version of the Arabic alphabet. One of the main resources available for processing resource-scarce languages is raw text collected from the Internet. Many... Read More about A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters.

Optimize BpNN using new breeder genetic algorithm (2016)
Presentation / Conference Contribution
Alabass, M., Jaf, S., Abdullah, A.-H. M., Hassanien, A. E., Shaalan, K., Gaber, T., Azar, A. T., & Tolba, M. F. (2016, October). Optimize BpNN using new breeder genetic algorithm. Presented at 2nd International Conference on Advanced Intelligent Systems and Informatics (AISI2016), Cairo, Egypt

In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is investigated. The multi-layer network (MLN) is taken into account as the ANN structure to be optimized. The idea presented here is to use the genetic alg... Read More about Optimize BpNN using new breeder genetic algorithm.

A Simple Approach to Unify Ambiguously Encoded Kurdish Characters (2016)
Presentation / Conference Contribution
Jaf, S. (2016, September). A Simple Approach to Unify Ambiguously Encoded Kurdish Characters. Presented at Second International Conference of Computational Linguistics in Bulgaria, Sofia, Bulgaria

In this study we outline a potential problem in the normalisation stage of processing texts that are based on a modified version of the Arabic alphabet. The main source of resources available for processing resource-scarce languages is raw text. We h... Read More about A Simple Approach to Unify Ambiguously Encoded Kurdish Characters.

The Selection of Classifiers for a Data-driven Parser (2015)
Presentation / Conference Contribution
Jaf, S., Ramsay, A., Sharp, B., Lubaszewski, W., & Delmonte, R. (2015, September). The Selection of Classifiers for a Data-driven Parser. Presented at The 12th International Workshop on Natural Language Processing and Cognitive Science., Krakow, Poland

There is a large number of classifiers that can be used for generating a parse model; i.e., as an oracle for guiding data-driven parsers when parsing natural languages. In this paper we present a general and simple approach for generating a parse mod... Read More about The Selection of Classifiers for a Data-driven Parser.

Deterministic Choices in a Data-driven Parser (2015)
Presentation / Conference Contribution
Jaf, S., Ramsay, A., Sharp, B., Lubaszewski, W., & Delmonte, R. (2015, September). Deterministic Choices in a Data-driven Parser. Presented at The 12th International Workshop on Natural Language Processing and Cognitive Science., Krakow, Poland

Data-driven parsers rely on recommendations from parse models, which are generated from a set of training data using a machine learning classifier, to perform parse operations. However, in some cases a parse model cannot recommend a parse action to a... Read More about Deterministic Choices in a Data-driven Parser.

The Application of Constraint Rules to Data-driven Parsing (2015)
Presentation / Conference Contribution
Jaf, S., Ramsay, A., Angelova, G., Bontcheva, K., & Mitkov, R. (2015, September). The Application of Constraint Rules to Data-driven Parsing. Presented at The 2015 International Conference on Recent Advances in Natural Language Processing, Hissar, Bulgaria

In this paper, we show an approach to extracting different types of constraint rules from a dependency treebank. Also, we show an approach to integrating these constraint rules into a dependency data-driven parser, where these constraint rules inform... Read More about The Application of Constraint Rules to Data-driven Parsing.

Parser Hybridisation for Natural Languages (2013)
Presentation / Conference Contribution
Jaf, S., & Allan, R. (2013, December). Parser Hybridisation for Natural Languages. Presented at 6th Language and Technology Conference (LTC'2013): Human Language Technologies as a Challenge for Computer Science and Linguistics, Poznan, Poland

Identifying and establishing structural relations between words in natural language sentences is called Parsing. Ambiguities in natural languages make parsing a difficult task. Parsing is more difficult when dealing with a structurally complex natura... Read More about Parser Hybridisation for Natural Languages.

Towards the Development of a Hybrid Parser for Natural Languages (2013)
Presentation / Conference Contribution
Jaf, S., Allan, R., Jones, A. V., & Ng, N. (2013, September). Towards the Development of a Hybrid Parser for Natural Languages. Presented at 2013 Imperial College Computing Student Workshop., London, United Kingdom

In order to understand natural languages, we have to be able to determine the relations between words, in other words we have to be able to 'parse' the input text. This is a difficult task, especially for Arabic, which has a number of properties that... Read More about Towards the Development of a Hybrid Parser for Natural Languages.