955 research outputs found

    Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

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    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

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    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

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    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

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    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

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    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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