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OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs

Data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Age range estimation based on facial wrinkle analysis using hessian based filter 2nd

Age range estimation based on facial wrinkle analysis using ...

引用

2nd International Conference on Communication and computer Engineering, ICOCOE 2015

作者： Razalli, Husniza Rahmat, Rahmita Wirza O. K. Khalid, Fatimah Sulaiman, Puteri Suhaiza Signal Processing Lab TM Research and Development Cyberjaya Selangor Malaysia Computer Graphics Vision and Visualization Research Group Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (纸本)9783319245829

Aging is a normal process that has an effect on different parts of the human body under the influence of various biological and environmental aspects. The most prominent changes that occur on the face are the form of the skin wrinkles, which are the main objective of this research. Specific wrinkle detection is an important task in face textural analysis. Previously, some researchers have been proposed the age range estimation based on wrinkle analysis in literature, but poor localization limits the performance of the whole age estimation process. This is because, when less number of wrinkles are detected or extracted, it will consequently affect the process to estimate the correct age. Therefore, we address this issue to enhance age range estimation method using a new approach to extract correct facial wrinkles for further analysis. We propose a method to extract facial wrinkle in face image using Hessian based filter (HBF) for age estimation. In other word, this research focus on age range estimation method based on facial wrinkle analysis extracted from facial image obtained from FG-NET database using hessian based filter. The proposed filter is theoretically straightforward, however, it significantly increases the wrinkle analysis result compared to previous methods. The result shows that HBF successfully obtained higher accuracy with over 90 % estimation rate. © Springer International Publishing Switzerland 2016.

关键词： Bandpass filters

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Optimal gradient pursuit for face alignment

Optimal gradient pursuit for face alignment

引用

IEEE International Conference on Automatic Face and Gesture Recognition

作者： Liu, Xiaoming Visualization and Computer Vision Lab GE Global Research Niskayuna NY 12309 United States

ISBN: (纸本)9781424491407

Face alignment aims to fit a deformable landmark-based mesh to a facial image so that all facial features can be located accurately. In discriminative face alignment, an alignment score function, which is treated as the appearance model, is learned such that moving along its gradient direction can improve the alignment. This paper proposes a new face model named Optimal Gradient Pursuit Model, where the objective is to minimize the angle between the gradient direction and the vector pointing toward the ground-truth shape parameter. We formulate an iterative approach to solve this minimization problem. With extensive experiments in generic face alignment, we show that our model improves the alignment accuracy and speed compared to the state-of-the-art discriminative face alignment approach. © 2011 IEEE.

关键词： Alignment

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LPSM: Fitting shape model by linear programming

LPSM: Fitting shape model by linear programming

引用

IEEE International Conference on Automatic Face and Gesture Recognition

作者： Tu, Jilin Laflen, Brandon Liu, Xiaoming Bello, Musodiq Rittscher, Jens Tu, Peter Visualization and Computer Vision Lab General Electric 1 Research Circle Niskayuna NY United States

ISBN: (纸本)9781424491407

We propose a shape model fitting algorithm that uses linear programming optimization. Most shape model fitting approaches (such as ASM, AAM) are based on gradient-descent-like local search optimization and usually suffer from local minima. In contrast, linear programming (LP) techniques achieve globally optimal solution for linear problems. In [1], a linear programming scheme based on successive convexification was proposed for matching static object shape in images among cluttered background and achieved very good performance. In this paper, we rigorously derive the linear formulation of the shape model fitting problem in the LP scheme and propose an LP shape model fitting algorithm (LPSM). In the experiments, we compared the performance of our LPSM with the LP graph matching algorithm(LPGM), ASM, and a CONDENSATION based ASM algorithm on a test set of PUT database. The experiments show that LPSM can achieve higher shape fitting accuracy. We also evaluated its performance on the fitting of some real world face images collected from internet. The results show that LPSM can handle various appearance outliers and can avoid local minima problem very well, as the fitting is carried out by LP optimization with l1 norm robust cost function. © 2011 IEEE.

关键词： Linear programming

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Improving biometric identification through quality-based face and fingerprint biometric fusion

Improving biometric identification through quality-based fac...

引用

2010 IEEE computer Society Conference on computer vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Tong, Yan Wheeler, Frederick W. Liu, Xiaoming Visualization and Computer Vision Lab GE Global Research Niskayuna NY United States

ISBN: (纸本)9781424470297

Multi-modal biometric fusion is more accurate and reliable compared to recognition using a single biometric modality. However, most existing fusion approaches neglect the influence of the qualities of the biometric samples in information fusion. Our goal is to advance the state-of-the-art in biometric fusion technology by providing a more universal and more accurate solution for personal identification and verification with predictive quality metrics. In this work, we developed score-level multi-modal fusion algorithms based on predictive quality metrics and employed them for the task of face and fingerprint biometric fusion. The causal relationships in the context of the fusion scenario are modeled by Bayesian Networks. The recognition/verification decision is then made through probabilistic inference. Our experiments demonstrated that the proposed score-level fusion algorithms significantly improve the verification performance over the methods based on the raw match score of a single modality (face or fingerprint). Furthermore, the fusion framework with both face and fingerprint image qualities achieves the best verification performance and outperforms all other baseline fusion algorithms tested including other straightforward quality-based fusion methods. © 2010 IEEE.

关键词： Biometrics

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Region moments: Fast invariant descriptors for detecting small image structures

Region moments: Fast invariant descriptors for detecting sma...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Gianfranco Doretto Yi Yao Visualization and Computer Vision Lab GE Global Research Center Niskayuna NY USA

This paper presents region moments, a class of appearance descriptors based on image moments applied to a pool of image features. A careful design of the moments and the image features, makes the descriptors scale and rotation invariant, and therefore suitable for vehicle detection from aerial video, where targets appear at different scales and orientations. Region moments are linearly related to the image features. Thus, comparing descriptors by computing costly geodesic distances and non-linear classifiers can be avoided, because Euclidean geometry and linear classifiers are still effective. The descriptor computation is made efficient by designing a fast procedure based on the integral representation. An extensive comparison between region moments and the region covariance descriptors, reports theoretical, qualitative, and quantitative differences among them, with a clear advantage of the region moments, when used for detecting small image structures, such as vehicles in aerial video. The proposed descriptors hold the promise to become an effective building block in other applications.

关键词： Vehicle detection Geophysics computing Vehicles visualization computer vision Computational geometry computer applications Object detection Image recognition Noise robustness

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Improving biometric identification through quality-based face and fingerprint biometric fusion

Improving biometric identification through quality-based fac...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Yan Tong Frederick W. Wheeler Xiaoming Liu Visualization and Computer Vision Lab GE Global Research Center Niskayuna NY USA

关键词： Biometrics Fingerprint recognition Image matching Inference algorithms Face recognition Optical sensors Image quality Biosensors Sensor fusion Thermal sensors

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Boosting for transfer learning with multiple sources

Boosting for transfer learning with multiple sources

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Yi Yao Gianfranco Doretto Visualization and Computer Vision Lab GE Global Research Center Niskayuna NY USA

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. This work extends the boosting framework for transferring knowledge from multiple sources. Two new algorithms, MultiSource-TrAdaBoost, and TaskTrAdaBoost, are introduced, analyzed, and applied for object category recognition and specific object detection. The experiments demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases. TaskTrAdaBoost is a fast algorithm enabling rapid retraining over new targets.

关键词： Boosting Machine learning algorithms computer vision Machine learning Data visualization Algorithm design and analysis Object detection Probability distribution Testing Training data

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MULTI-CAMERA POSITIONING FOR AUTOMATED TRACKING SYSTEMS IN DYNAMIC ENVIRONMENTS

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International Journal of Information Acquisition 2010年第3期7卷 225-242页

作者： YI YAO CHUNG-HAO CHEN BESMA ABIDI DAVID PAGE ANDREAS KOSCHAN MONGI ABIDI Visualization and Computer Vision Lab GE Global Research USA Department of Mathematics and Computer Science North Carolina Central University USA Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville USA

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, handoff safety margin is introduced to sensor planning so that sufficient overlapped FOVs among adjacent cameras are reserved for successful and smooth target transition. In this paper, we investigate the sensor planning problem when considering the dynamic interactions between moving targets and observing cameras. The probability of camera overload is explored to model the aforementioned interactions. The introduction of the probability of camera overload also considers the limitation that a given camera can simultaneously monitor or track a fixed number of targets and incorporates the target's dynamics into sensor planning. The resulting camera placement not only achieves the optimal balance between coverage and handoff success rate but also maintains the optimal balance in environments with various target densities. The proposed camera placement method is compared with a reference algorithm by Erdem and Sclaroff. Consistently improved handoff success rate is illustrated via experiments using typical office floor plans with various target densities.

关键词： Sensor planning visual surveillance multiple camera systems camera handoff

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