Domain transfer for deep natural language generation from abstract meaning representations

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%

Optical studies of soot formation and the addition of organic peroxides to flames

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Information Sharing and Interoperability in Law Enforcement: An Investigation of Federal Criminal Justice Information Systems Use by State/Local Law Enforcement Organizations

This thesis investigates the frequency of use and perceptions of usefulness of federal criminal justice information systems among state and local law enforcement personnel and certain IS environmental factors that affect usage. The study is predicated by a demonstrated need for increased information sharing, interoperability, and collaboration among the three tiers of law enforcement as public safety threats within U.S. borders increase in complexity; e.g., the Murrah Federal Building bombing, Columbine High School shooting, 9/11 terrorist attacks, and D.C. sniper case. The results of this research indicate high usage and perceived usefulness of the National Crime Information Center Network (NCIC Net), National Law Enforcement Telecommunications System (NLETS), Uniform Crime Reporting/National Incident Based Reporting System (UCR/NIBRS), National Instant Criminal Background Check System (NICS), and federal LE websites. The results also indicated that the IS environmental factors information quality and trust influenced the usage and perceived usefulness of federal criminal justice information systems

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine sub-components, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy

This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy

We explore the recently released ChatGPT model, one of the most powerful conversational AI models that has ever been developed. This opinion provides a perspective on its strengths and weaknesses and a call to action for the AI community (including academic researchers and industry) to work together on preventing potential misuse of such powerful AI models in our everyday lives

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex environments and unpredictable conditions wherein they are deployed. While various studies have earlier utilised machine learning techniques for fault prediction in turbines, their black-box nature hampers explainabil-ity and trust in decision making. We propose the application of causal reasoning in operations & maintenance of wind turbines using Supervisory Control & Acquisition (SCADA) data, and harness attention-based convolutional neural networks (CNNs) to identify hidden associations between different parameters contributing to failures in the form of temporal causal graphs. By interpreting these non-obvious relationships (many of which may have potentially been disregarded as noise), engineers can plan ahead for unforeseen failures, helping make wind power sources more reliable

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, including neural networks, operate as black boxes: despite performing suitably well as predictive models, they are not able to identify causal associations within the data. For data-driven system to approach human-level intelligence in generating effective maintenance strategies, it is integral to discover hidden knowledge in the operational data. In this paper, we apply deep learning to discover causal relationships between multiple features (confounders) in SCADA data for faults in various sub-components from an operational turbine using convolutional neural networks (CNNs) with attention. Our technique overcomes the black box nature of conventional deep learners and identifies hidden confounders in the data through the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind turbine's operational status, and show that our technique contributes to explainable AI for wind energy applications by providing transparent and interpretable decision support

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults. We present a data-to-text generation system utilising transformers for generating corrective maintenance strategies for faults using SCADA data capturing the operational status of turbines. We achieve this in two stages: a first stage identifies faults based on SCADA input features and their relevance. A second stage performs content selection for the language generation task and creates maintenance strategies based on phrase-based natural language templates. Experiments show that our dual transformer model achieves an accuracy of up to 96.75% for alarm prediction and up to 75.35% for its choice of maintenance strategies during content-selection. A qualitative analysis shows that our generated maintenance strategies are promising. We make our human- authored maintenance templates publicly available, and include a brief video explaining our approach