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- Keyword
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- Activity discovery (2)
- Activity recognition (2)
- Argumentation (1)
- Artificial intelligence (1)
- Conflict resolution (1)
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- Data analysis (1)
- Deep learning (1)
- Defeasible reasoning (1)
- Interactive machine learning (1)
- Knowledge-representation (1)
- Machine learning (1)
- Medicine (1)
- Religion (1)
- Science fiction (1)
- Sequence prediction (1)
- Singularity (1)
- Social media (1)
- Technology (1)
- Text mining (1)
- Topic modelling (1)
- Transhumanism (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Argumentation For Knowledge Representation, Conflict Resolution, Defeasible Inference And Its Integration With Machine Learning, Luca Longo
Conference papers
Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible …
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Conference papers
Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we …
Techno-Apocalypse: Technology, Religion, And Ideology In Bryan Singer’S H+, Edward Brennan
Techno-Apocalypse: Technology, Religion, And Ideology In Bryan Singer’S H+, Edward Brennan
Books/Book chapters
This essay critically analyses the digital series H+. In the near future, adults who can afford them, have replaced tablets and cell phones with nanotechnology implants. The H+ implant acts as a medical diagnostic and can overlay the user's senses with a computer interface. The apocalypse comes in the form of a computer virus which infects the H+ network and instantly kills one third of humanity. The series represents the anxiety and religiosity that surrounds the possible social consequences of digital technology. It also explores the tensions and intersections between technology and faith. This essay makes the case, however, that …
Using Topic Modelling Algorithms For Hierarchical Activity Discovery, Eoin Rogers, John D. Kelleher, Robert J. Ross
Using Topic Modelling Algorithms For Hierarchical Activity Discovery, Eoin Rogers, John D. Kelleher, Robert J. Ross
Conference papers
Activity discovery is the unsupervised process of discovering patterns in data produced from sensor networks that are monitoring the behaviour of human subjects. Improvements in activity discovery may simplify the training of activity recognition models by enabling the automated annotation of datasets and also the construction of systems that can detect and highlight deviations from normal behaviour. With this in mind, we propose an approach to activity discovery based on topic modelling techniques, and evaluate it on a dataset that mimics complex, interleaved sensor data in the real world. We also propose a means for discovering hierarchies of aggregated activities …
Harnessing The Power Of Text Mining For The Detection Of Abusive Content In Social Media, Hao Chen, Susan Mckeever, Sarah Jane Delany
Harnessing The Power Of Text Mining For The Detection Of Abusive Content In Social Media, Hao Chen, Susan Mckeever, Sarah Jane Delany
Conference papers
Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and …