Data Science Commons^™

Multimodal Fusion Strategies For Outcome Prediction In Stroke, Esra Zihni, John D. Kelleher, Vince I. Madai, Ahmed Khalil, Ivana Galinovic, Jochen Fiebach, Michelle Livne, Dietmar Frey

Conference papers

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Eavesdropping Hackers: Detecting Software Vulnerability Communication On Social Media Using Text Mining, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how …

On The Inability Of Markov Models To Capture Criticality In Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John Kelleher

Conference papers

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which …

Explorobot: Rapid Exploration With Chart Automation, Tamara Matthews, Rohan Goel, John Mcauley

Conference papers

General-purpose visualization tools are used by people with varying degrees of data literacy. Often the user is not a professional analyst or data scientist and uses the tool infrequently, to support an aspect of their job. This can present difficulties as the user’s unfamiliarity with visualization practice and infrequent use of the tool can result in long processing time, inaccurate data representations or inappropriate visual encodings. To address this problem, we developed a visual analytics application called exploroBOT. The exploroBOT automatically generates visualizations and the exploration guidance path (an associated network of decision points, mapping nodes where visualizations change). These …

On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher

Articles

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …

Non-Manual Articulators In Irish Sign Language Verbs: An Analysis With Data Mining Association Rules, Robert G. Smith, Markus Hofmann

Conference Papers

The Signs of Ireland (SOI) corpus (Leeson et al., 2006) deploys a complex multi-tiered temporal data structure. The process of manually analyzing such data is laborious, cannot eliminate bias and often, important patterns can go completely unnoticed. In addition to this, as a result of the complex nature of grammatical structures contained in the corpus, identifying complex linguistic associations or patterns across tiers is simply too intricate a task for a human to carry out in an acceptable timeframe. This work explores the application of data mining techniques on a set of multi-tiered temporal data from the SOI corpus. Building …

Non-Linear Machine Learning With Active Sampling For Mox Drift Compensation, Tamara Matthews, Muhammad Iqbal, Horacio Gonzalez-Velez

Conference papers

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, …

Investigating The Efficacy Of Algorithmic Student Modelling In Predicting Students At Risk Of Failing In The Early Stages Of Tertiary Education: Case Study Of Experience Based On First Year Students At An Institute Of Technology In Ireland., Geraldine Gray

Doctoral

The application of data analytics to educational settings is an emerging and growing research area. Much of the published works to-date are based on ever-increasing volumes of log data that are systematically gathered in virtual learning environments as part of module delivery. This thesis took a unique approach to modelling academic performance; it is a first study to model indicators of students at risk of failing in first year of tertiary education, based on data gathered prior to commencement of first year, facilitating early engagement with at-risk students.

The study was conducted over three years, in 2010 through 2012, and …