Aniello De santo

Deep Learning for HDD Health Assessment: An Application Based on LSTM

IEEE Transactions on Computers · 2020 · 93 citations

Hard disk drive failures are one of the most common causes of service downtime in data centers. Predictive maintenance techniques have been adopted to extend the Remaining Useful Life (RUL) of these drives, and minimize service shortage and data loss. Several approaches based on machine and deep learning techniques have been proposed to address these issues, mostly exploiting models based on Self-Monitoring analysis and Reporting Technology (SMART) attributes. While these models have proven to b…

A deep learning approach for semi-supervised community detection in Online Social Networks

Knowledge-Based Systems · 2021 · 55 citations

Evaluating time series encoding techniques for Predictive Maintenance

Expert Systems with Applications · 2022 · 39 citations

MG Parsing as a Model of Gradient Acceptability in Syntactic Islands

ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2020-01-06 · 7 citations

It is well-known that the acceptability judgments at the core of current syntactic theories are continuous. However, an open debate is whether the source of such gradience is situated in the grammar itself, or can be derived from extra-grammatical factors. In this paper, we propose the use of a top-down parser for Minimalist grammars (Stabler, 2013; Kobele et al., 2013; Graf et al., 2017), as a formal model of how gradient acceptability can arise from categorical grammars. As a test case, we tar…

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

2021-01-01 · 5 citations

Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an uttera…