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18th International Semantic Web conference (ISWC)

作者： Ibanez, Luis-Daniel Millard, Ian Glaser, Hugh Simperl, Elena Univ Southampton Southampton Hants England Seme4 Ltd Southampton Hants England

ISBN: (纸本)9783030307967;9783030307950

The European Commission has adopted Linked data principles and practices with the purpose of increasing the accessibility, inter-operability and value of the data that is made available openly by European public sector organisations. This includes investment in metadata development for describing open datasets, catalogs of resources with persistent URIs, and the European data Portal (EDP), which provides a single point of access, search and exploration of European open data. As the Public Sector Initiative (PSI) Directive is being revised, a critical question for the Commission is the extent to which open government data publishers have adopted Linked data, and how they are applying the underlying technologies. In this paper, we undertake a quantitative analysis to support this. We explore if and how open data portals indexed by the EDP are using Linked data and assess the quality of the datasets according to multiple dimensions.

关键词： Linked data Open government data data quality

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Packed-Memory Quadtree: A cache-oblivious data structure for visual exploration of streaming spatiotemporal big data

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COMPUTERS & GRAPHICS-UK 2018年 76卷 117-128页

作者： Toss, Julio Pahins, Cicero A. L. Raffin, Bruno Comba, Joao L. D. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Grenoble Alpes Inria CNRS Grenoble INPLIG F-38000 Grenoble France

The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure, which now has to support dynamic updates (insertions/deletions) and rebalancing operations to perform self reorganizations. In this work, we present the Packed-Memory Quadtree (PMQ), a novel data structure designed to support visual exploration of streaming spatiotemporal datasets. PMQ is cache-oblivious to perform well under different cache configurations. We store streaming data in an internal index that keeps a spatiotemporal ordering over the data following a quadtree representation, with support for real-time insertions and deletions. We validate our data structure under different dynamic scenarios and compare to competing strategies. We demonstrate how PMQ could be used to answer different types of visual spatiotemporal range queries of streaming datasets. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal datasets Streaming Real-time exploration Range queries

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visual understanding and personalization for an optimal recollection experience

Visual understanding and personalization for an optimal reco...

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作者： Ana, Garcia del Molino Nanyang Technological University

学位级别：博士

The affordability of wearable cameras such as the Narrative Clip and GoPro allows mass-market consumers to continuously record their lives, producing large amounts of unstructured visual data. Moreover, users tend to record with their smartphones more multimedia content than they can possibly share or review. We use each of these devices for different purposes: action cameras for travels and adventures; our smartphones to capture on the spur of the moment; a lifelogging device to record unobtrusively all our daily life activities. As a result, the few important shots end up buried among many repetitive images or uninteresting long segments, requiring hours of manual analysis in order to, say, select highlights in a day or find the most aesthetic pictures. Tackling challenges in end-to-end consumer video summarization, this thesis contributes to the state of the art in three major aspects: (i) Contextual Event Segmentation, an episodic event segmentation method that is able to detect boundaries between heterogeneous events and ignore local occlusions and brief diversions. CES improves the performance of the baselines by over 16% in F-measure, and is competitive with manual annotations. (ii) Personalized Highlight Detection, a highlight detector that is personalized via its inputs. The experimental results show that using the user history substantially improves the prediction accuracy. PHD outperforms the user-agnostic baselines even with only one single person-specific example. (iii) Active Video Summarization, an interactive approach to video exploration that gathers the user's preferences while creating a video summary. AVS achieves an excellent compromise between usability and quality. The diverse and uniform nature of AVS summaries makes it alsoa valuable tool for browsing someone else's visual collection. Additionally, this thesis contributes two large-scale datasets for First Person View video analysis, CSumm and R3, and a large-scale dataset for personalized v

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visual analysis of retinal changes with optical coherence tomography

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visual COMPUTER 2018年第9期34卷 1209-1224页

作者： Roehlig, Martin Schmidt, Christoph Prakasam, Ruby Kala Rosenthal, Paul Schumann, Heidrun Stachs, Oliver Univ Rostock Inst Comp Sci Rostock Germany Univ Rostock Dept Ophthalmol Rostock Germany

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in detecting a wide range of ocular diseases. Despite the diagnostic value of OCT, managing and analyzing resulting data is challenging. We apply two visual analysis strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, emphasizing, selecting, and comparing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of retinal OCT data for assisting medical diagnoses. Domain experts applied our solution successfully to study early retinal changes in patients suffering from type 1 diabetes mellitus.

关键词： visual analysis Optical coherence tomography OCT data Ophthalmology Retina

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Sketch-aided retrieval of incomplete 3D cultural heritage objects 12

Sketch-aided retrieval of incomplete 3D cultural heritage ob...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual conference of the European Association for Computer Graphics, EG 2019

作者： Lengauer, S. Komar, A. Labrada, A. Karl, S. Trinkl, E. Preiner, R. Bustos, B. Schreck, T. Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria Institute of Archaeology University of Graz Austria Millenium Institute for Foundational Research on Data Department of Computer Science University of Chile Chile

ISBN: (纸本)9783038680772

Due to advances in digitization technology, documentation efforts and digital library systems, increasingly large collections of visual Cultural Heritage (CH) object data becomes available, offering rich opportunities for domain analysis, e.g., for comparing, tracing and studying objects created over time. In principle, existing shape- and image-based similarity search methods can aid such domain analysis tasks. However, in practice, visual object data are given in different modalities, including 2D, 3D, sketches or conventional drawings like profile sections or unwrappings. In addition, collections may be distributed across different publications and repositories, posing a challenge for implementing encompassing search and analysis systems. We introduce a methodology and system for cross-modal visual search in CH object data. Specifically, we propose a new query modality based on 3D views enhanced by user sketches (3D+sketch). This allows for adding new context to the search, which is useful e.g., for searching based on incomplete query objects, or for testing hypotheses on existence of certain shapes in a collection. We present an appropriately designed workflow for constructing query views from incomplete 3D objects enhanced by a user sketch based on shape completion and texture inpainting. visual cues additionally help users compare retrieved objects with the query. We apply our method on a set of relevant 3D and view-based CH object data, demonstrating the feasibility of our approach and its potential to support analysis of domain experts in Archaeology and the field of CH in general. © 2019 The Author(s)

关键词： Textures

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Automatic Color Control Method of Low Contrast Image Based on Big data analysis 1

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3rd European-Alliance-for-Innovation (EAI) International conference on Advanced Hybrid Information Processing (ADHIP)

作者： Wang, Jia Yin, Zhiqin Xu, Xiyan Yang, Jianfei Yunnan Open Univ Mech Engn Coll Kunming 650500 Yunnan Peoples R China

ISBN: (数字)9783030364052

ISBN: (纸本)9783030364052;9783030364045

In order to improve the imaging quality of 3D image with visual feature reconstruction, it is necessary to control the color of low contrast image automatically. A color automatic control technology of low contrast image based on 3D color space packet template feature detection is proposed, the automatic color control model of image based on big data analysis is constructed. RGB decomposition technology is used to extract the color components of low contrast images, and color space gray feature fusion algorithm is used to segment fusion of low contrast images to improve the feature pairing performance of color peak points of low contrast images. Combined with the color space block fusion information of low contrast image, the edge features of high oscillatory region are detected, and the color automatic control of low contrast image is realized. The simulation results show that the color automatic control of low contrast image can improve the peak signal-to-noise ratio (PSNR) of image output, improve the automatic color control ability and imaging quality of low contrast image.

关键词： Big data analysis Low contrast image Fusion Color automatic control

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Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving

Imitative Reinforcement Learning Fusing Vision and Pure Purs...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Mingxing Peng Zhihao Gong Chen Sun Long Chen Dongpu Cao School of Data and Computer Science Sun Yat-sen University China Waterloo CogDrive Lab University of Waterloo Canada

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments and terrible weather conditions. In this paper, we propose a two-stage framework, called IPP-RL, to handle these problems. IPP means an Imitation learning method fusing visual information with the additional steering angle calculated by Pure-Pursuit (PP) method, and RL means using Reinforcement Learning for further training. In our IPP model, the visual information captured by camera can be compensated by the calculated steering angle, thus it could perform well under bad weather conditions. However, imitation learning performance is limited by the driving data severely. Thus we use a reinforcement learning method-Deep Deterministic Policy Gradient (DDPG)-in the second stage training, which shares the learned weights from pretrained IPP model. In this way, our IPP-RL can lower the dependency of imitation learning on demonstration data and solve the problem of low exploration efficiency caused by randomly initialized weights in reinforcement learning. Moreover, we design a more reasonable reward function and use the n-step return to update the critic-network in DDPG. Our experiments on CARLA driving benchmark demonstrate that our IPP-RL is robust to lousy weather conditions and shows remarkable generalization capability in unknown environments on navigation task.

关键词： Learning (artificial intelligence) Meteorology Robustness Task analysis Navigation Training Autonomous vehicles

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visual Relationship Prediction via Label Clustering and Incorporation of Depth Information 1

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15th European conference on Computer Vision (ECCV)

作者： Yang, Hsuan-Kung Cheng, An-Chieh Ho, Kuan-Wei Fu, Tsu-Jui Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (数字)9783030110123

ISBN: (纸本)9783030110123;9783030110116

In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group object labels with similar patterns of relationship distribution in the dataset into fewer categories. Label clustering not only mitigates both the large classification space and class imbalance issues, but also potentially increases data samples for each clustered category. We further propose to incorporate depth information as an additional feature into the instance segmentation model. The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver. We have rigorously evaluated the proposed techniques and performed various ablation analysis to validate the benefits of them.

关键词： Relationship prediction Instance segmentation Semantic segmentation Unsupervised clustering Depth information

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Sentiment analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa

Sentiment Analysis as an Indicator to Evaluate Gender dispar...

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Robotics and Mechatronics conference (RobMech)

作者： Jude Oyasor Mpho Raborife Pravesh Ranchod School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa School of Consumer Intelligence and Information Systems University of Johannesburg Johannesburg South Africa

ISBN: (数字)9781728141626

ISBN: (纸本)9781728141633

South Africa has one of the highest rates of sexual violence in the world, and with this technology era being the information age, it is not suprising that people are turning to social media to voice their views on this matter. As tweets on sexual violence grow continually, extracting insights from such data demands a robust, real-time, and scalable tools with flexibility in the database schema. Elasticsearch (ES) is an example of a free-license search engine written in Java and developed on Apache Lucene that meets the stated requirements. On the other hand, Kibana facilitates intuitive dashboard development, visual exploration, and real-time analysis of an index in ES through an intuitive graphical user interface. This study demonstrates how gender was inferred and evaluated through the integration of deep neural networks and Google's TensorFlow. We used the AFINN model to infer sentiment analysis as our measure of gender disparity in this instance. The Indexer, built of Node. js, and defined as the hub of the system connects with the Twitter streaming API to ingest tweets found within the boundaries of sexual violence. This system runs persistently with tweets through the ES search engine and visualised in Kibana.

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A blueprint for semantically lifting field trial data: Enabling exploration using knowledge graphs 12

A blueprint for semantically lifting field trial data: Enabl...

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12th International conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2019

作者： Burkow, D. Hollunder, J. Heinrich, J. Abdallah, F. Rojas-Macias, M.A. Wiljes, C. Cimiano, P. Senger, P. Bayer CropScience CLS Translational R&D Germany Semalytix GmbH Bielefeld Germany Semantic Computing Group Bielefeld University Germany

In many organizations, relevant data is distributed across multiple primary data sources using different data models, schemas, and vocabularies which prevents effective knowledge management at an organizational level. As a result, it is very cumbersome to retrieve and integrate relevant data necessary to answer specific business questions. We propose a semantic lifting architecture that lifts primary data resources into a single corporate knowledge graph, implemented following FAIR and Linked data principles, which assigns unique URIs to entities and maps the data using a uniform shared vocabulary. This structure supports integration of heterogeneous and distributed data sources to enable advanced search and analysis. In this paper we consider the case of field trial data collected and managed by Bayer, present an example of our architecture, and describe an application using this architecture that provides semantic search and visual exploration of field trial data to relevant stakeholders. As a main benefit, our architecture allows domain experts and analysts to retrieve timely and consistently labeled data originating from multiple primary sources while maintaining data context to enable cross-domain analytics. This provides the means to answer more complex questions with minimal data preparation, improving speed and precision by driving decisions with semantically lifted data. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

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