955 research outputs found
Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events
Delivering customer services through video communications has brought new
opportunities to analyze customer satisfaction for quality management. However,
due to the lack of reliable self-reported responses, service providers are
troubled by the inadequate estimation of customer services and the tedious
investigation into multimodal video recordings. We introduce Anchorage, a
visual analytics system to evaluate customer satisfaction by summarizing
multimodal behavioral features in customer service videos and revealing
abnormal operations in the service process. We leverage the semantically
meaningful operations to introduce structured event understanding into videos
which help service providers quickly navigate to events of their interest.
Anchorage supports a comprehensive evaluation of customer satisfaction from the
service and operation levels and efficient analysis of customer behavioral
dynamics via multifaceted visualization views. We extensively evaluate
Anchorage through a case study and a carefully-designed user study. The results
demonstrate its effectiveness and usability in assessing customer satisfaction
using customer service videos. We found that introducing event contexts in
assessing customer satisfaction can enhance its performance without
compromising annotation precision. Our approach can be adapted in situations
where unlabelled and unstructured videos are collected along with sequential
records.Comment: 13 pages. A preprint version of a publication at IEEE Transactions on
Visualization and Computer Graphics (TVCG), 202
Detection and Classification of Anomalies in Railway Tracks
Em Portugal, existe uma grande afluência dos transportes ferroviários. Acontece que as
empresas que providenciam esses serviços por vezes necessitam de efetuar manutenção às
vias-férreas/infraestruturas, o que leva à indisponibilização e/ou atraso dos serviços e máquinas,
e consequentemente perdas monetárias. Assim sendo, torna-se necessário preparar um plano
de manutenção e prever quando será fundamental efetuar manutenções, de forma a minimizar
perdas.
Através de um sistema de manutenção preditivo, é possível efetuar a manutenção apenas
quando esta é necessária. Este tipo de sistema monitoriza continuamente máquinas e/ou
processos, permitindo determinar quando a manutenção deverá existir. Uma das formas de
fazer esta análise é treinar algoritmos de machine learning com uma grande quantidade de
dados provenientes das máquinas e/ou processos.
Nesta dissertação, o objetivo é contribuir para o desenvolvimento de um sistema de
manutenção preditivo nas vias-férreas. O contributo específico será detetar e classificar
anomalias. Para tal, recorrem-se a técnicas de Machine Learning e Deep Learning, mais
concretamente algoritmos não supervisionados e semi-supervisionados, pois o conjunto de
dados fornecido possui um número reduzido de anomalias.
A escolha dos algoritmos é feita com base naquilo que atualmente é mais utilizado e apresenta
melhores resultados. Assim sendo, o primeiro passo da dissertação consistiu em investigar
quais as implementações mais comuns para detetar e classificar anomalias em sistemas de
manutenção preditivos.
Após a investigação, foram treinados os algoritmos que à primeira vista seriam capazes de se
adaptar ao cenário apresentado, procurando encontrar os melhores hiperparâmetros para os
mesmos. Chegou-se à conclusão, através da comparação da performance, que o mais
enquadrado para abordar o problema da identificação das anomalias seria uma rede neuronal
artifical Autoencoder. Através dos resultados deste modelo, foi possível definir thresholds para
efetuar posteriormente a classificação da anomalia.In Portugal, the railway tracks commonly require maintenance, which leads to a stop/delay of
the services, and consequently monetary losses and the non-full use of the equipment. With
the use of a Predictive Maintenance System, these problems can be minimized, since these
systems continuously monitor the machines and/or processes and determine when
maintenance is required.
Predictive Maintenance systems can be put together with machine and/or deep learning
algorithms since they can be trained with high volumes of historical data and provide diagnosis,
detect and classify anomalies, and estimate the lifetime of a machine/process.
This dissertation contributes to developing a predictive maintenance system for railway
tracks/infrastructure. The main objectives are to detect and classify anomalies in the railway
track. To achieve this, unsupervised and semi-supervised algorithms are tested and tuned to
determine the one that best adapts to the presented scenario. The algorithms need to be
unsupervised and semi-supervised given the few anomalous labels in the dataset
Manufacturing Process Optimization Using Edge Analytics
Most manufacturing plants contain some amount of time series sensor data – streams of values and time stamps. This data, however, isn’t useful with most types of analytics or machine learning for the purpose of process optimization. This thesis presents a novel and innovative solution to the problem using a software stack leveraging the Predix Complex Event Processing Engine (Edge Analytics) to condition the data, combined with RFID for serialization. Each step in the formation of the solution is documented, from connecting equipment to analyzing and ingesting data produced by the edge analytic. This solution was developed and piloted at the GE Grid Solutions plant in Clearwater, FL
On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report
Information Technology has become a critical component in various industries,
leading to an increased focus on software maintenance and monitoring. With the
complexities of modern software systems, traditional maintenance approaches
have become insufficient. The concept of AIOps has emerged to enhance
predictive maintenance using Big Data and Machine Learning capabilities.
However, exploiting AIOps requires addressing several challenges related to the
complexity of data and incident management. Commercial solutions exist, but
they may not be suitable for certain companies due to high costs, data
governance issues, and limitations in covering private software. This paper
investigates the feasibility of implementing on-premise AIOps solutions by
leveraging open-source tools. We introduce a comprehensive AIOps infrastructure
that we have successfully deployed in our company, and we provide the rationale
behind different choices that we made to build its various components.
Particularly, we provide insights into our approach and criteria for selecting
a data management system and we explain its integration. Our experience can be
beneficial for companies seeking to internally manage their software
maintenance processes with a modern AIOps approach
Scenario-based requirements elicitation for user-centric explainable AI
Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder’s requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenario-based design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection
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