Outputs (291)

Benchmarking multimodal sentiment analysis (2018)
Conference Proceeding
Cambria, E., Hazarika, D., Poria, S., Hussain, A., & Subramanyam, R. (2018). Benchmarking multimodal sentiment analysis. In Computational Linguistics and Intelligent Text Processing (166-179). https://doi.org/10.1007/978-3-319-77116-8_13

We propose a deep-learning-based framework for multimodal sentiment analysis and emotion recognition. In particular, we leverage on the power of convolutional neural networks to obtain a performance improvement of 10% over the state of the art by com... Read More about Benchmarking multimodal sentiment analysis.

Adaptation of sentiment analysis techniques to Persian language (2018)
Conference Proceeding
Dashtipour, K., Hussain, A., & Gelbukh, A. (2018). Adaptation of sentiment analysis techniques to Persian language. In Computational Linguistics and Intelligent Text Processing (129-140). https://doi.org/10.1007/978-3-319-77116-8_10

In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment... Read More about Adaptation of sentiment analysis techniques to Persian language.

Method for identification of digital content (2018)
Patent
Buchanan, B., Lo, O., Penrose, P., Ramsay, B., & Macfarlane, R. (2018). Method for identification of digital content. World Intellectual Property Organization

Many areas oi investigation require searching through data that may be oi interest. One example oi data that may be involved in an investigation is copyrighted material that may be suspected of having been obtained or reproduced illegally by a third... Read More about Method for identification of digital content.

How do we use information to help us learn to innovate in the workplace? A case study of a Scottish University. (2018)
Presentation / Conference
Middleton, L. (2018, October). How do we use information to help us learn to innovate in the workplace? A case study of a Scottish University. Poster presented at ISIC 2018

Introduction. The use of information in the workplace is an established topic in information science research (Weiner, 2011). The focus has often fallen on the development of skills in the workplace. Specifically, the relationship between information... Read More about How do we use information to help us learn to innovate in the workplace? A case study of a Scottish University..

The Influence of Computer Self-efficacy and Subjective Norms on the Students’ Use of Learning Management Systems at King Abdulaziz University (2018)
Journal Article
Binyamin, S. S., Rutter, M. J., & Smith, S. (2018). The Influence of Computer Self-efficacy and Subjective Norms on the Students’ Use of Learning Management Systems at King Abdulaziz University. International journal of information and education technology (IJIET), 8(10), 693-699. https://doi.org/10.18178/ijiet.2018.8.10.1124

Technology acceptance model (TAM) has been a standout amongst the most well-known models in understanding the users’ acceptance of technologies. This study develops a model to predict the factors that influence the use of learning management systems... Read More about The Influence of Computer Self-efficacy and Subjective Norms on the Students’ Use of Learning Management Systems at King Abdulaziz University.

Style Neutralization Generative Adversarial Classifier (2018)
Conference Proceeding
Jiang, H., Huang, K., Zhang, R., & Hussain, A. (2018). Style Neutralization Generative Adversarial Classifier. In BICS: International Conference on Brain Inspired Cognitive Systems (3-13). https://doi.org/10.1007/978-3-030-00563-4_1

Breathtaking improvement has been seen with the recently proposed deep Generative Adversarial Network (GAN). Purposes of most existing GAN-based models majorly concentrate on generating realistic and vivid patterns by a pattern generator with the aid... Read More about Style Neutralization Generative Adversarial Classifier.

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection (2018)
Conference Proceeding
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F., Larijani, H., …Hussain, A. (2018). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. . https://doi.org/10.1007/978-3-030-00563-4_74

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially... Read More about Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection.

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis (2018)
Conference Proceeding
Guellil, I., Adeel, A., Azouaou, F., & Hussain, A. (2018). SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis. . https://doi.org/10.1007/978-3-030-00563-4_54

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the dif... Read More about SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis.

Saliency Detection via Bidirectional Absorbing Markov Chain (2018)
Conference Proceeding
Jiang, F., Kong, B., Adeel, A., Xiao, Y., & Hussain, A. (2018). Saliency Detection via Bidirectional Absorbing Markov Chain. . https://doi.org/10.1007/978-3-030-00563-4_48

Traditional saliency detection via Markov chain only consider boundaries nodes. However, in addition to boundaries cues, background prior and foreground prior cues play a complementary role to enhance saliency detection. In this paper, we propose an... Read More about Saliency Detection via Bidirectional Absorbing Markov Chain.

Exploiting Deep Learning for Persian Sentiment Analysis (2018)
Conference Proceeding
Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., & Hussain, A. (2018). Exploiting Deep Learning for Persian Sentiment Analysis. In Advances in Brain Inspired Cognitive Systems (597-604). https://doi.org/10.1007/978-3-030-00563-4_58

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspe... Read More about Exploiting Deep Learning for Persian Sentiment Analysis.