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Generating Feedback Reports for Adults Taking Basic Skills Tests
SkillSum is an Artificial Intelligence (AI) and Natural Language Generation (NLG) system that produces short feedback reports for people who are taking online tests which check their basic literacy and numeracy skills. In this paper, we describe the SkillSum system and application, focusing on three challenges which we believe are important ones for many systems which try to generate feedback reports from Web-based tests: choosing content based on very limited data, generating appropriate texts for people with varied levels of literacy and knowledge, and integrating the web-based system with existing assessment and support procedures
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-
Infering Air Quality from Traffic Data using Transferable Neural Network Models
This work presents a neural network based model for inferring air quality from traffic measurements.
It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial.
The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK.
Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks.
This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time.
By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Perceived Intelligence and Perceived Anthropomorphism of Personal Intelligent Agents: Scale Development and Validation
Personal intelligent agents are systems that are autonomous, aware of their environment, continuously learning and adapting to change, able to interact using natural language and capable of completing tasks within a favorable timeframe in a proactive manner. Examples include Siri and Alexa. Several unique characteristics distinguish these agents from other traditional information systems. Of particular interest in this work are characteristics of intelligence and anthropomorphism. This paper describes the process of developing two new measures with satisfactory psychometric properties that can be adapted by researchers to assess the users’ perceptions of intelligence and anthropomorphism of PIAs. The measures are validated using data collected from 232 experienced PIA users
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