Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Glavin, Frank G"'
Serverless computing is the latest paradigm in cloud computing, offering a framework for the development of event driven, pay-as-you-go functions in a highly scalable environment. While these traits offer a powerful new development paradigm, they hav
Externí odkaz:
http://arxiv.org/abs/2104.08031
Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is achieved through
Externí odkaz:
http://arxiv.org/abs/2012.05600
Machine Learning (ML) techniques for image classification routinely require many labelled images for training the model and while testing, we ought to use images belonging to the same domain as those used for training. In this paper, we overcome the
Externí odkaz:
http://arxiv.org/abs/2011.09236
This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we consider is identifying whether samples contain chlorinate
Externí odkaz:
http://arxiv.org/abs/2003.11842
Publikováno v:
In Procedia Computer Science 2023 217:316-325
Autor:
Smyth, David L., Abinesh, Sai, Karimi, Nazli B., Drury, Brett, Ullah, Ihsan, Glavin, Frank G., Madden, Michael G.
Publikováno v:
IWAISe, 2nd International Workshop on A.I. in Security, European Conference on Machine Learning 2018
Autonomous robotics and artificial intelligence techniques can be used to support human personnel in the event of critical incidents. These incidents can pose great danger to human life. Some examples of such assistance include: multi-robot surveying
Externí odkaz:
http://arxiv.org/abs/1809.06244
Publikováno v:
David L. Smyth, Frank G. Glavin, Michael G. Madden. "Using a Game Engine to Simulate Critical Incidents and Data Collection by Autonomous Drones", IEEE Games and Entertainment Media, National University of Galway, Ireland, 2018
Using a game engine, we have developed a virtual environment which models important aspects of critical incident scenarios. We focused on modelling phenomena relating to the identification and gathering of key forensic evidence, in order to develop a
Externí odkaz:
http://arxiv.org/abs/1808.10784
Autor:
Glavin, Frank G., Madden, Michael G.
Publikováno v:
IEEE Conference on Computational Intelligence and Games (CIG18), Maastricht, The Netherlands, (2018)
In this paper, we introduce a skill-balancing mechanism for adversarial non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in rea
Externí odkaz:
http://arxiv.org/abs/1806.07637
Autor:
Glavin, Frank G., Madden, Michael G.
Publikováno v:
Glavin, Frank G., and Michael G. Madden. "Adaptive shooting for bots in first person shooter games using reinforcement learning." IEEE Transactions on Computational Intelligence and AI in Games 7, no. 2: 180-192. (2015)
In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as non player characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, "sixth s
Externí odkaz:
http://arxiv.org/abs/1806.05554
Autor:
Glavin, Frank G., Madden, Michael G.
Publikováno v:
Glavin, Frank G., and Michael G. Madden. "Analysis of the effect of unexpected outliers in the classification of spectroscopy data." Artificial Intelligence and Cognitive Science (AICS), pp. 124-133. Springer, Berlin, Heidelberg, 2009
Multi-class classification algorithms are very widely used, but we argue that they are not always ideal from a theoretical perspective, because they assume all classes are characterized by the data, whereas in many applications, training data for som
Externí odkaz:
http://arxiv.org/abs/1806.05455