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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是61-70 订阅

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Incremental learning versus batch learning for classification of user's behaviour in medical imaging 8

Incremental learning versus batch learning for classificatio...

引用

8th international conference on Health Informatics, HEALthINF 2015

作者： Viana-Ferreira, Carlos Matos, Sérgio Costa, Carlos Instituto de Engenharia Electrónica e Telemática de Aveiro Universidade de Aveiro Campus Universitário de Santiago Aveiro Portugal

ISBN: (纸本)9789897580680

Communication latency still hinders the adoption of Cloud computing paradigms in medical imaging environments where it could serve as a reliable technology to support repository outsourcing solutions or inter-institutional workflows, for instance. One way to overcome this is by implementing cache repositories and prefetching mechanisms. Nevertheless, such solutions are usually based on static rules that may inefficiently manage the cache storage capacity. For that reason, this paper compares a pattern recognition system using incremental learning versus batch learning, in order to assess which one could be more appropriately used in a medical imaging cache mechanism.

关键词： pattern recognition

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Cogno-Detective System for Body Odor Sensing 8

Cogno-Detective System for Body Odor Sensing

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8th IEEE international conference on Computational Intelligence and computing Research (IEEE ICCIC)

作者： Kanakam, Prathyusha Hussain, S. Mahaboob Chakravarthy, A. S. N. MVGR Coll Engn Autonomous Dept Comp Sci & Engn Vizianagaram India Vishnu Inst Technol Dept Comp Sci & Engn Bhimavaram India JNTU Kakinada UCEV Dept Comp Sci & Engn Kakinada India

ISBN: (纸本)9781509066216

the Modern decade of future Internet stepping into all the technologies includes sensors, artificial intelligence, pattern recognition and machine learning. As sensor technology increases its liabilities by extending to novel recognition methods for identifying an individual. Smell print is a state-of-art technique to perform authentication, verification and validation of a person to apply in fields of human tracking, criminal investigation and canine training etc. this paper describes the compounds combination of human odor, the factors that influence the human odor and sources of smell print in a human body. It gives a prototype for Cogno-detective system for the human odor in order to imitate the human olfaction and procedure to implement the artificial olfaction mechanism.

关键词： sensors volatile organic compounds(VOCs) pattern recognition machine learning artificial intelligence cogno-detective system

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A Mahalanobis Taguchi Approach to Human Motion Control

A Mahalanobis Taguchi Approach to Human Motion Control

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8th international conference on Applied Human Factors and Ergonomics (AHFE) / international conference on Affective and Pleasurable Design

作者： Fukuda, Shuichi Keio Univ Syst Design & Management Kohoku Ku 4-1-1 Hiyoshi Yokohama Kanagawa 2238526 Japan

ISBN: (纸本)9783319604954;9783319604947

the problem of motion control is very difficult because the number of degrees of freedom is too large. there are researches using EMG, motion capture, etc. But these approaches are pursuing from the outside how we can control our motion successfully and they are analyzing the problem item by item. But we take the other way around and we attempted to pursue the problem from the inside and we introduced a holistic pattern based approach. the motion is represented as a pattern and successful motions are assembled and averaged to make up a standard pattern. And each time he moves, its pattern is compared with this standard pattern using Mahalanobis distance. If the distance is within an allowable distance, then his motion is successful, but if it exceeds the limit distance, it warns him that he has to adjust his motion and how much he has to.

关键词： Human motion control Somatosensory system Proprioception pattern approach Mahalanobis Taguchi System recognition Taguchi method Quantitative evaluation

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Velocity-driven particle swarm optimization 8

Velocity-driven particle swarm optimization

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8th international conference on computing and pattern recognition, iccpr 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. the performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. the historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. the experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems. © 2019 Association for computing Machinery.

关键词： Velocity

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2015 8th international conference on Ubi-Media computing, UMEDIA 2015 - conference Proceeedings

2015 8th International Conference on Ubi-Media Computing, UM...

引用

8th international conference on Ubi-Media computing, UMEDIA 2015

ISBN: (纸本)9781467382700

the proceedings contain 72 papers. the topics discussed include: ultrafast pattern recognition and tracking in FLIR imagery;embodied cognition and gesture-based learning;real-time stereo matching for depth estimation using GPU;collecting field data in an environment combining APP and fusion tables;a mobile application for an ecological campus navigation system using augmented reality;intelligent evacuation system integrated with image recognition technology;performance improvement on duplicate address detection in IP mobility protocols;non-commutative path planning strategy;locality-aware P2P networks based on cloud-assistance;an intelligent classroom management system based on wireless sensor networks;a fuzzy-based dynamic channel allocation scheme in cognitive radio networks;and offline tracking system for deep sea going vessels using GPS and GPRS.

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I-SPLIT: Deep Network Interpretability for Split computing 26

I-SPLIT: Deep Network Interpretability for Split Computing

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Cunico, Federico Capogrosso, Luigi Setti, Francesco Carra, Damiano Fummi, Franco Cristani, Marco Univ Verona Dept Comp Sci Verona Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

this work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server. So far, potential split locations have been identified exploiting uniquely architectural aspects, i.e., based on the layer sizes. Under this paradigm, the efficacy of the split in terms of accuracy can be evaluated only after having performed the split and retrained the entire pipeline, making an exhaustive evaluation of all the plausible splitting points prohibitive in terms of time. Here we show that not only the architecture of the layers does matter, but the importance of the neurons contained therein too. A neuron is important if its gradient with respect to the correct class decision is high. It follows that a split should be applied right after a layer with a high density of important neurons, in order to preserve the information flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a procedure that identifies the most suitable splitting points by providing a reliable prediction on how well this split will perform in terms of classification accuracy, beforehand of its effective implementation. As a further major contribution of I-SPLIT, we show that the best choice for the splitting point on a multiclass categorization problem depends also on which specific classes the network has to deal with. Exhaustive experiments have been carried out on two networks, VGG16 and ResNet-50, and three datasets, Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. the source code is available at https://***/vips4/I-Split.

关键词： Performance evaluation Source coding Pulmonary diseases Neurons Pipelines Computer architecture Servers

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Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology 8

Anomaly Detection Approaches for Secure Cloud Reference Arch...

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8th international conference on Cloud computing and Services Science (CLOSER)

作者： Oppermann, Alexander Toro, Federico Grasso thiel, Florian Seifert, Jean-Pierre Phys Tech Bundesanstalt PTB Dept Metrol IT 8 5 Berlin Germany Tech Univ Berlin Secur Telecommun Berlin Germany

ISBN: (纸本)9789897582950

Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. the presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using pattern recognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are presented.

关键词： Anomaly Detection Continous Monitoring Secure Cloud computing Anomaly Detection as a Service Distributed computing Legal Metrology

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Unintentional affective priming during labeling may bias labels 8

Unintentional affective priming during labeling may bias lab...

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8th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Shen, Judy Hanwen Lapedriza, Agata Picard, Rosalind W. MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728138886

Online platforms displaying long streams of examples are often employed to gather labels from both experts and crowd workers. While previous work in crowdsourcing focused on objective tasks and estimating error parameters of annotators, collecting labels in a subjective setting (e.g. emotion recognition) is more complicated due to different interpretations of examples. these interpretations could be influenced by many factors such as annotator mood and previously seen examples. In this work, we examine two hypotheses of order-dependent biases in sequential labeling tasks: negatively auto-correlated sequential decision making and positively auto-correlated affective priming. Using controlled generation of facial expressions, we find that i) annotators achieve higher agreement when presented examples in the same sequential order, ii) the valence label of the current image positively correlates with the previous labels given. While we also observe a positive correlation between labels and the number of preceding positive and negative images seen, this correlation is highly dependent on example ordering. Our findings demonstrate that randomized examples given to annotators may produce systematic bias in labels. Future data collection should present examples in orderings which mitigate such bias.

关键词： Affective computing emotion recognition computer vision crowdsourcing

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Automatic Embedding Interventions for the Classification of Hematopoietic Cells 26

Automatic Embedding Interventions for the Classification of ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Graebel, Philipp thull, Julian Crysandt, Martina Klinkhammer, Barbara M. Boor, Peter Bruemmendorf, Tim H. Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Dept Hematol & Oncol Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst Pathol Aachen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the classification of hematopoietic cells is the most essential step in automating the analysis of human bone marrow samples. However, the complex structure of cell classes as well as class imbalance make this a challenging task, even for neural networks. Based on projective latent interventions, we propose automatic interventions that iteratively update a learned embedding with suitable transformations that shift different cell types apart and contract samples of the same type together. We present different ways of applying these: either directly on a higher-dimensional embedding or in a parametric version in two dimensions. We analyze the hyper-parameters and evaluate the proposed approach on a challenging dataset of hematopoietic cells. the results show an improvement of up to 3 percentage points for the classification F-score.

关键词： Representation learning Neural networks Bones pattern recognition Task analysis Contracts

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Proceedings of the 8th international conference on Computer recognition Systems CORES 2013 1

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丛书名： Advances in Intelligent Systems and computing

2013年

作者： Juliusz L. Kulikowski (auth.), Robert Burduk, Konrad Jackowski, Marek Kurzynski, Michał Wozniak, Andrzej Zolnierek (eds.)

ISBN: (数字)9783319009698

ISBN: (纸本)9783319009681;9783319009698

the computer recognition systems are nowadays one of the most promising directions in artificial intelligence. this book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections:Biometrics Data Stream Classification and Big Data AnalyticsFeatures, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications pattern recognition and image processing in roboticsSpeech and word recognitionthis book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

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