Insight Research Collection
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At Insight we undertake high impact research in data analytics that has significant impact on industry and society by enabling better decision making.
Insight brings together leading Irish academics from 5 of Ireland's leading research centres (DERI, CLARITY, CLIQUE, 4C, TRIL), previously established by Science Foundation Ireland (SFI) and the Irish Industrial Development Authority (IDA), in key areas of priority research including:
- The Semantic Web
- Sensors and the Sensor Web
- Social network analysis
- Decision Support and Optimization
- Connected Health
For more information, please visit the official website.
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Recent Submissions
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- PublicationFeature-Based Evaluation of a Wearable Surface EMG Sensor against Laboratory Standard EMG during Force-Varying and Fatiguing Contractions(IEEE, 2020-03)Recent advances in wearable sensors enable recording of electromyography (EMG) outside the laboratory for extended periods of time. However, the properties of wearable EMG systems designed for long-term recording may differ from those of laboratory-standard systems, potentially impacting data. This study evaluated EMG features derived from signals recorded using a wearable system (BioStampRC, MC10 Inc.) against a reference laboratory system (Bagnoli, Delsys Inc.). Surface EMG data from the biceps brachii were recorded simultaneously using both systems during isometric elbow flexion, between 10% and 80% of maximum voluntary contraction (MVC), and during sustained submaximal fatiguing contraction, in twelve subjects. Linear and nonlinear EMG temporal and spectral features were then compared across both systems. No effect of recording system was detected on EMG onset/offset times, or on the relationship between force and EMG root mean squared amplitude. However, the relationships between force and median frequency, percentage determinism and multiscale entropy differed between systems. Baseline noise was also greater for the BioStampRC. Lower median frequencies were observed for the wearable system, likely due to the larger interelectrode distance, however, the relative change in EMG amplitude and median frequency during the fatiguing contraction was similar for both. Percentage determinism increased and multiscale entropy decreased during the fatiguing contraction for both systems, with higher and lower values respectively for the wearable system. Results indicate that the BiostampRC is appropriate for EMG onset/offset and amplitude estimation. However, caution is advised when comparing across systems as spectral and nonlinear features may differ due to electrode design differences.
Scopus© Citations 28 32 - PublicationPartially Observable Markov Decision Process Modelling for Assessing Hierarchies(ML Research Press, 2020-11-20)Hierarchical clustering has been shown to be valuable in many scenarios. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies produced from di erent techniques, particularly in the case where groundtruth labels are unavailable. This motivates us to propose a framework for assessing the quality of hierarchical clustering allocations which covers the case of no ground-truth information. This measurement is useful, e.g., to assess the hierarchical structures used by online retailer websites to display their product catalogues. Our framework is one of the few attempts for the hierarchy evaluation from a decision theoretic perspective. We model the process as a bot searching stochastically for items in the hierarchy and establish a measure representing the degree to which the hierarchy supports this search. We employ Partially Observable Markov Decision Processes (POMDP) to model the uncertainty, the decision making, and the cognitive return for searchers in such a scenario.
40 - PublicationLightweight Privacy-Preserving Data Classification(Elsevier, 2020-10)Internal attacks are of a huge concern, because they are usually delicately masqueraded under harmless-looking activities, which are very difficult to detect. Machine learning techniques have been successfully applied to identify insider threats. However, they may violate user privacy since they can legally access user’s sensitive information. To preserve user privacy, encryption algorithms have been lately exploited as a powerful tool, to hide private data in a multiple-party collaboration. A combination of encryption and data mining techniques raises high computational complexity. Hence, in order to improve the system’s performance while securing both user’s private data and the classifier, we propose a new secure data analysis protocol, namely SmartClass, by adopting the garbled circuit technique to speed-up the system performance. We developed an efficient encryption step that exploits the additive homomorphism and best properties of the binary Elliptic Curve Cryptography (ECC) algorithm, while keeping the protocol highly secure. We implemented the proposed system and study its effectiveness. Experimental results show the proposed approach is very promising.
61Scopus© Citations 10 - PublicationPredicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows(2021-01-07)Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance. The use of AI techniques to identify the early onset of this disease, thus has significant implications for the sustainability of this agricultural sector. Current approaches to treating mastitis involve antibiotics and this practice is coming under ever increasing scrutiny. Using machine learning models to identify cows at risk of developing mastitis and applying targeted treatment regimes to only those animals promotes a more sustainable approach. Incorrect predictions from such models, however, can lead to monetary losses, unnecessary use of antibiotics, and even the premature death of animals, so it is important to generate compelling explanations for predictions to build trust with users and to better support their decision making. In this paper we demonstrate a system developed to predict mastitis infections in cows and provide explanations of these predictions using counterfactuals. We demonstrate the system and describe the engagement with farmers undertaken to build it.
36 - PublicationInstance-Based Counterfactual Explanations for Time Series Classification(2020-09-27)In recent years there has been a cascade of research in attempting to make AI systems more interpretable by providing explanations; so-called Explainable AI (XAI). Most of this research has dealt with the challenges that arise in explaining black-box deep learning systems in classification and regression tasks, with a focus on tabular and image data; for example, there is a rich seam of work on post-hoc counterfactual explanations for a variety of black-box classifiers (e.g., when a user is refused a loan, the counterfactual explanation tells the user about the conditions under which they would get the loan). However, less attention has been paid to the parallel interpretability challenges arising in AI systems dealing with time series data. This paper advances a novel technique, called Native-Guide, for the generation of proximal and plausible counterfactual explanations for instance-based time series classification tasks (e.g., where users are provided with alternative time series to explain how a classification might change). The Native-Guide method retrieves and uses native in-sample counterfactuals that already exist in the training data as “guides” for perturbation in time series counterfactual generation. This method can be coupled with both Euclidean and Dynamic Time Warping (DTW) distance measures. After illustrating the technique on a case study involving a climate classification task, we reported on a comprehensive series of experiments on both real-world and synthetic data sets from the UCR archive. These experiments provide computational evidence of the quality of the counterfactual explanations generated.
46Scopus© Citations 44