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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Rengifo, Santiago Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China Guangdong Artificial Intelligence and Digital Economy Laboratory China European Commission Joint Research Centre Italy Universidad del Atlantico Medio Spain Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition12, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB13 and SVC2021_EvalDB14, and standard experimental protocols. © 2021, CC BY-NC-ND.

关键词： Deep learning

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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

Importance of Wearable Health Monitoring Systems Using IoMT;...

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International Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

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Optimising knee injury detection with spatial attention and validating localisation ability

arXiv

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arXiv 2021年

作者： Belton, Niamh Welaratne, Ivan Dahlan, Adil Hearne, Ronan T. Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin Department of Radiology Mater Misericordiae University Hospital Dublin Ireland School of Electronic Engineering University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Impact of Distributed Training on Mask R-CNN Model Performance for Image Segmentation

Impact of Distributed Training on Mask R-CNN Model Performan...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), Conference on

作者： Mercy Prasanna Ranjit Gopinath Ganapathy Ranjit Frederick Manuel AI & Machine Learning Microsoft Corporation India Ltd Bangalore India School of Computer Science and Engineering Bharathidasan University Trichy India Data Analytics & AI ITC Infotech India Ltd Bangalore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

The paper compares and discusses the impact of distributed deep learning training using the Horovod framework on the performance metrics of image segmentation model trained using the Mask R-CNN (Region-based Convolutional Neural Network) architecture in comparison with the baseline metrics obtained from running the training on a single node. Image segmentation is a complex problem in comparison with image classification and the fluctuations introduced due to the distributed gradient computations while running the training distributed is less pronounced on simpler problems like image classification while it is more pronounced for image segmentation where we measure the performance at pixel level. It is important to know how the latest algorithms and distribution frameworks perform when training the model on distributed compute with parallel gradient calculations, adjusted learning rates and averaged neural network weight updates. The paper also aimed to understand if the effective batch size per training iteration while performing the training distributed did not negatively impact the ability of the model to generalize when increasing the batch size as indicated in the previous studies. The distributed training with Horovod was performed using the Azure machine learning service on scalable cloud compute cluster. The aim of the study was not comparison in-terms of the execution time which has been well established on previous studies but on understanding the impact on the quality of the predictions measured with standard validation metrics for image segmentation using the ISIC (International Skin Imaging Collaboration) 2018 Skin Lesion image dataset.

关键词： Image Segmentation Distributed training Horovod Deep learning Mask-RCNN image segmentation Training Functional training Image classification Distribute execution time Performance metrics Masks

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Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

arXiv

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arXiv 2020年

作者： Durall, Ricard Keuper, Margret Keuper, Janis Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany Data- and Webscience Group University Mannheim Germany IWR University of Heidelberg Germany Institute for Machine Learning and Analytics Offenburg University Germany

Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Problems and Prospectives of Big data Storage and Processing Standartization

Problems and Prospectives of Big Data Storage and Processing...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Evgeniy N. Pavlovskiy Stream Data Analytics and Machine Learning lab Novosibirsk State University Novosibirsk Russia

In the paper, we analyze the problem of standardization in the domain of storage and processing of big data in the application to the Internet of things. We highlight the underlying problems of big data; analyze the scientific, technological and economic barriers to the development of big data. We offer perspective research directions in the domain of development of standards of data representation.

关键词： Big data Internet of Things Standards Task analysis Neural networks data structures

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Development of water flood model for oil production enhancement

Development of water flood model for oil production enhancem...

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Science and Artificial Intelligence conference (S.A.I.ence)

作者： Jetina J. Tsvaki Dmitry O. Tailakov Evgeniy N. Pavlovskiy Novosibirsk State University Novosibirsk Russia Laboratory for Fundamentals of Energy Technologies Kutateladze Institute of Thermophysics SB RAS Novosibirsk Russia Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia

ISBN: (数字)9780738131115

ISBN: (纸本)9781665415439

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.

关键词： Production Oils Numerical models Computational modeling Recurrent neural networks Genetic algorithms data mining

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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Watch Your Up-Convolution: CNN Based Generative Deep Neural ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ricard Durall Margret Keuper Janis Keuper Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany IWR University of Heidelberg Germany Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics Offenburg University Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

关键词： Gallium nitride Convolution Distortion Neural networks Training Generative adversarial networks

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Deep reinforcement learning for motion planning of mobile robots

arXiv

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arXiv 2019年

作者： Butyrev, Leonid Edelhäußer, Thorsten Mutschler, Christopher Fraunhofer Institute for Integrated Circuits Iis Precise Positioning and Analytics Department Machine Learning and Information Fusion Group Nuremberg Germany Computer Science Department Machine Learning and Data Analytics Lab Erlangen Germany

This paper presents a novel motion and trajectory planning algorithm for nonholonomic mobile robots that uses recent advances in deep reinforcement learning. Starting from a random initial state, i.e., position, velocity and orientation, the robot reaches an arbitrary target state while taking both kinematic and dynamic constraints into account. Our deep reinforcement learning agent not only processes a continuous state space it also executes continuous actions, i.e., the acceleration of wheels and the adaptation of the steering angle. We evaluate our motion and trajectory planning on a mobile robot with a differential drive in a simulation environment. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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A Deep Generative Model for Molecule Optimization via One Fragment Modification

arXiv

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arXiv 2020年

作者： Chen, Ziqi Min, Martin Renqiang Parthasarathy, Srinivasan Ning, Xia The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs America PrincetonNJ08540 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size;and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe. © 2020, CC BY-NC-SA.

关键词： Molecules

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