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2020 Annual conference of the Prognostics and Health Management Society, PHM 2020

作者： Li, Guoyi Feng, Chao Woldesenbet, Addishiwot King, Bruce Hadavi, Hamid Moku, Vikasini Loken, Kurt Kunkel, Gary Seagate Technology BloomingtonMN United States

ISBN: (纸本)9781936263059

Increased capabilities in data storage and exploration provide significant insights for quality assurance in a high volume manufacturing environment. However, these opportunities are associated with great challenges in analytical model development, application deployment, system throughput and reliability assurance. While no commercial software system fully meets the needs of recording head factories in Seagate, a novel strategy named optical inspection with centralized analysis (OPICA) has been developed to detect defects of trailing edges of the recording heads of hard disk drives, and fail the parts when necessary. Leveraging the robust state-of-the-art artificial intelligence technologies, a deep learning based semantic segmentation engine is built using convolutional neural networks for optical inspection. It has shown an improved accuracy to that of visual inspection performed by human. Meanwhile, a high performance computation engine has been built as a Kubernetes cluster with multiple GPU and CPU units. It is able to achieve the target throughput of three million high-resolution images in each day (i.e., 12 TB image data and 35 images per second). With the high fidelity offered by a Kubernetes cluster, the developed applications (image inference engine, preprocessor, postprocessor, etc.) serve as containerized microservices independently. Such an architecture ensures the vertical and horizontal scalabilities according to the computation of each individual deployment, while all deployments communicate through an Advanced Message Queuing Protocol (AMQP) cluster without human interference. This analytic framework enables Industry 4.0 recording head manufacturing by integrating advanced AI technologies with a robust edge computation architecture. © 2020 Prognostics and Health Management Society. All rights reserved.

关键词： Quality assurance

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VarCopy: a visual Exploratory data analysis Platform for Copy Number Variation Studies

VarCopy: a Visual Exploratory Data Analysis Platform for Cop...

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International conference on Information visualisation (IV)

作者： Fabio Bove Federica Mandreoli Riccardo Martoglia Valentino Pisi Cristian Taccioli Chiara Vischioni FIM - University of Modena and Reggio Emilia Italy MAPS - University of Padova Italy

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

The study of such a complex phenomenon as cancer, which depends on several but unexplored and unclear factors, needs new ways to visualize, analyze and combine different data both on species characteristics and genes function. To this respect, we propose a novel platform, named VarCopy, supporting visual Exploratory data analysis (EDA) in the context of Copy Number Variation (CNV) data. The platform will be publicly available as a web application soon, and is, to our best knowledge, the first tool allowing visual, interactive exploration and analysis of the CNV landscape of multiple species, allowing the identification of new target genes that might be useful for biomedical research.

关键词： Knowledge engineering visualization data analysis data visualization Tools data models Cancer

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Reflectance spectroscopy to validate remote sensing data/algorithms for satellite-based lithium (Li) exploration (Central East Portugal) 11

Reflectance spectroscopy to validate remote sensing data/alg...

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conference on Earth Resources and Environmental Remote Sensing/GIS Applications XI held at SPIE Remote Sensing conference

作者： Cardoso-Fernandes, J. Silva, J. Lima, A. Teodoro, A. C. Perrone, M. Cauzid, J. Roda-Robles, E. Ribeiro, M. A. Univ Porto Fac Sci Dept Geosci Environm & Land Planning Rua Campo Alegre Porto Portugal Pole Univ Porto Inst Earth Sci ICT Porto Portugal Geol Survey Brazil CPRM Geol & Mineral Resources Board Directors Rua Costa Sao Paulo Brazil Univ Lorraine Lab GeoRessources CNRS GeoRessources F-54000 Nancy France Univ Pais Vasco UPV EHU Dept Mineral & Petrol Bilbao Spain

ISBN: (数字)9781510638822

ISBN: (纸本)9781510638822;9781510638815

The acquisition of field data plays an important role in the calibration of the remotely sensed data, especially when combined with reflectance spectroscopy studies. The study area of this work is the Fregeneda-Almendra pegmatite field (spreading from Portugal to Spain) where different lithium (Li)-pegmatites are known. Several image processing techniques were applied to satellite images for Li exploration in the region, including machine learning classifiers. However, these algorithms identified several zones as Li-pegmatites false positives. Taking this into account, the following objectives were delineated: (i) validate the training areas used in previous studies and collect field data for training area refinement;(ii) assess the reason for the false positives previously obtained through field surveys. For that, various outcropping lithologies (Li-pegmatite, metasediments, granite) were sampled for laboratory spectral analysis. The spectral signature of Li-pegmatite was compared with the remaining outcropping lithologies. Also, the spectral signature of the sampled false positive areas was confronted with the spectra of Li-minerals. It was possible to conclude that these two classes present similar water/hydroxide and Al-OH-related features. The sampled granitic and metasedimentary rocks also presented water and/or hydroxide absorption features that can lead to some spectral confusion. However, Li- pegmatites can be discriminated from the remaining lithologies either in the training areas and the false positive areas due to the absence of iron-related absorption features and to the distinct reflectance magnitudes.

关键词： Remote sensing spectral library geology field validation pegmatite lithium

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Semi-automated Quality Assurance for Domain-Expert-Driven data exploration - An Application to Principal Component analysis 1

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3rd IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain conference for Machine Learning and Knowledge Extraction (CD-MAKE)

作者： Wartner, Sandra Wiesinger-Widi, Manuela Girardi, Dominic Furthner, Dieter Schmitt, Klaus RISC Software GmbH Res Unit Med Informat Hagenberg Austria Salzkammergut Klinikum Dept Pediat & Adolescent Med Vocklabruck Austria Kepler Univ Hosp Dept Pediat & Adolescent Med Linz Austria

ISBN: (数字)9783030297268

ISBN: (纸本)9783030297268;9783030297251

Processing and exploring large quantities of electronic data is often a particularly interesting but yet challenging task. Both the lack of statistical and mathematical skills and the missing know-how of handling masses of (health) data constitute high barriers for profound data exploration - especially when performed by domain experts. This paper presents guided visual pattern discovery, by taking the well-established data mining method Principal Component analysis as an example. Without guidance, the user has to be conscious about the reliability of computed results at any point during the analysis (GIGO-principle). In the course of the integration of principal component analysis into an ontology-guided research infrastructure, we include a guidance system supporting the user through the separate analysis steps and we introduce a quality measure, which is essential for profound research results.

关键词： Principal Component analysis data Quality Guidance visual Analytics data mining Doctor-in-the-Loop

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analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circumpapillary OCT Images 21st

Analysis of Hand-Crafted and Automatic-Learned Features for ...

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21st International conference on Intelligent data Engineering and Automated Learning

作者： Garcia, Gabriel Colomer, Adrian Naranjo, Valery Univ Politecn Valencia I3B Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783030623647;9783030623654

Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to elucidate that traditional machine-learning techniques could outperform deep-learning algorithms, especially when the image data set is small. The experiments were performed on a private database composed of 194 glaucomatous and 198 normal B-scans diagnosed by expert ophthalmologists. As a novelty, we only considered raw circumpapillary OCT images to build the predictive models, without using other expensive tests such as visual field and intraocular pressure measures. The results ratify that the proposed hand-driven learning model, based on novel descriptors, outperforms the automatic learning. Additionally, the hybrid approach consisting of a combination of both strategies reports the best performance, with an area under the ROC curve of 0.85 and an accuracy of 0.82 during the prediction stage.

关键词： Glaucoma Circumpapillary OCT Hand-driven learning Deep learning Hybrid classification

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visual Relationship Attention for Image Captioning

Visual Relationship Attention for Image Captioning

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International Joint conference on Neural Networks (IJCNN)

作者： Zhang, Zongjian Wu, Qiang Wang, Yang Chen, Fang Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Univ Technol Sydney Sch Software Sydney NSW Australia

ISBN: (纸本)9781728119854

visual attention mechanisms have been broadly used by image captioning models to attend to related visual information dynamically, allowing fine-grained image understanding and reasoning. However, they are only designed to discover the region-level alignment between visual features and the language feature. The exploration of higher-level visual relationship information between image regions, which is rarely researched in recent works, is beyond their capabilities. To fill this gap, we propose a novel visual relationship attention model based on the parallel attention mechanism under the learnt spatial constraints. It can extract relationship information from visual regions and language and then achieve the relationship-level alignment between them. Using combined visual relationship attention and visual region attention to attend to related visual relationships and regions respectively, our image captioning model can achieve state-of-the-art performances on the MSCOCO dataset. Both quantitative analysis and qualitative analysis demonstrate that our novel visual relationship attention model can capture related visual relationship and further improve the caption quality.

关键词： visualization Decoding Adaptation models Semantics Feature extraction Australia Dogs

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V-Dream: Immersive exploration of Generative Design Solution Space 22nd

V-Dream: Immersive Exploration of Generative Design Solution...

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22nd International conference on Human Computer Interaction,HCii 2020

作者： Keshavarzi, Mohammad Bidgoli, Ardavan Kellner, Hans University of California Berkeley United States Carnegie Mellon University Pittsburgh United States Autodesk Research San Francisco United States

ISBN: (纸本)9783030601164

Generative Design workflows have introduced alternative paradigms in the domain of computational design, allowing designers to generate large pools of valid solutions by defining a set of goals and constraints. However, analyzing and narrowing down the generated solution space, which usually consists of various high-dimensional properties, has been a major challenge in current generative workflows. By taking advantage of the interactive unbounded spatial exploration, and the visual immersion offered in virtual reality platforms, we propose V-Dream, a virtual reality generative analysis framework for exploring large-scale solution spaces. V-Dream proposes a hybrid search workflow in which a spatial stochastic search approach is combined with a recommender system allowing users to pick desired candidates and eliminate the undesired ones iteratively. In each cycle, V-Dream reorganizes the remaining options in clusters based on the defined features. Moreover, our framework allows users to inspect design solutions and evaluate their performance metrics in various hierarchical levels, assisting them in narrowing down the solution space through iterative cycles of search/select/re-clustering of the solutions in an immersive fashion. Finally, we present a prototype of our proposed framework, illustrating how users can navigate and narrow down desired solutions from a pool of over 16000 monitor stands generated by Autodesk’s Dreamcatcher software. © 2020, Springer Nature Switzerland AG.

关键词： data visualization

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visual exploration of an ensemble of classifiers

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COMPUTERS & GRAPHICS-UK 2019年 85卷 23-41页

作者： Ribeiro, Paula Ceccon Schardong, Guilherme G. Barbosa, Simone D. J. de Souza, Clarisse Sieckenius Lopes, Helio Pontificia Univ Catolica Rio de Janeiro PUC Rio Dept Informat Rio De Janeiro Brazil

Inspecting the outputs of classification algorithms is becoming progressively difficult due to the increase in both scale and complexity of both the data and the algorithms. This has led to research efforts to develop new techniques to interpret the behavior of these algorithms and to facilitate the understanding of their results. A common classification approach is the "ensemble of classifiers", where a set of classifiers c is an element of C is trained on the input data set and the final classification is computed by "voting", i.e., ranking their results. One of the issues with this approach, however, is that instead of having only one classifier to analyze, now there are vertical bar C vertical bar each with its characteristics. Thus, there is a demand for methods that provide insights into the results of an ensemble of classifiers and at the same time allow a detailed analysis of each classifier in the ensemble. Our work proposes to draw on dimensionality reduction techniques to provide visual tools to interpret the results of an ensemble of classifiers, while also giving insights into how each classifier contributes to the final results. Our approach also presents a measure of classification uncertainty by highlighting regions where there is a divergence among the classifiers in the ensemble, allowing one to focus their analysis on these regions. We tested our approach using the Digits MNIST and Fashion MNIST data sets. Through the use of maps that provide an overview of a classifier behavior to instance-based visualizations, we show how our approach can assist in the interpretation of why a specific decision (classification) was made. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ensemble of classifiers Decision boundary visualization Dimensionality reduction Inverse projection visual inspection

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Personalised exploration Graphs on Semantic data Lakes

Personalised Exploration Graphs on Semantic Data Lakes

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27th International conference on Cooperative Information Systems, (CoopIS) / International conference on Ontologies, databases, and Applications of Semantics (ODBASE) / conference on Cloud and Trusted Computing (C and TC)

作者： Bagozi, Ada Bianchini, Devis De Antonellis, Valeria Garda, Massimiliano Melchiori, Michele Univ Brescia Dept Informat Engn Via Branze 38 I-25123 Brescia Italy

ISBN: (纸本)9783030332464;9783030332457

Recently, organisations operating in the context of Smart Cities are spending time and resources in turning large amounts of data, collected within heterogeneous sources, into actionable insights, using indicators as powerful tools for meaningful data aggregation and exploration. data lakes, which follow a schema-on-read approach, allow for storing both structured and unstructured data and have been proposed as flexible repositories for enabling data exploration and analysis over heterogeneous data sources, regardless their structure. However, indicators are usually computed based on the centralisation of the data storage, according to a less flexible schema on write approach. Furthermore, domain experts, who know data stored within the data lake, are usually distinct from data analysts, who define indicators, and users, who exploit indicators to explore data in a personalised way. In this paper, we propose a semantics-based approach for enabling personalised data lake exploration through the conceptualisation of proper indicators. In particular, the approach is structured as follows: (i) at the bottom, heterogeneous data sources within a data lake are enriched with Semantic Models, defined by domain experts using domain ontologies, to provide a semantic data lake representation;(ii) in the middle, aMulti-Dimensional Ontology is used by analysts to define indicators and analysis dimensions, in terms of concepts within Semantic Models and formulas to aggregate them;(iii) at the top, Personalised exploration Graphs are generated for different categories of users, whose profiles are defined in terms of a set of constraints that limit the indicators instances on which the users may rely to explore data. Benefits and limitations of the approach are discussed through an application in the Smart City domain.

关键词： Semantic data lake data exploration Smart City

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NEMESIS (NEtwork MEdicine analysis): Towards visual exploration of Network Medicine data 10

NEMESIS (NEtwork MEdicine analySIS): Towards Visual Explorat...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 10th International conference on Information visualization Theory and Applications (IVAPP)

作者： Angelini, Marco Blasilli, Graziano Farina, Lorenzo Lenti, Simone Santucci, Giuseppe Sapienza Univ Rome DIAG Dept Comp Control & Management Engn Antonio Ruber Rome Italy

ISBN: (纸本)9789897583544

The emerging Network Medicine domain is causing a shift between diagnosis based on the conventional reductionist approach, arguing that biological factors work in a simple linear way, and the analysis of perturbations within the comprehensive network map of molecular components and their interactions, i.e., the "Interactome". As a consequence, clinicians are investigating more than 140,000 interactions between more than 13,000 genes and their connections with drugs and diseases, along a sequence of "networks". Making sense of this complex structure is a challenging activity and the visual analytics application NEMESIS tries to attack such a problem allowing for interactively exploring this large body of knowledge, focusing on subsets of data and investigating their relationships with other relevant dimensions, pursuing the main goal of facilitating hypothesis formulation and validation.

关键词： Network Medicine visual Analytics Interactome

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