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Randomized Transferable Machine

Randomized Transferable Machine

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international conference on pattern recognition

作者： Pengfei Wei Tze Yun Leong School of Computing National University of Singapore Singapore

Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is truly domain-invariant, and thus directly train a transfer model M on source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new learning strategy with a transfer model called Randomized Transferable Machine (RTM). More specifically, we work on source data with the new feature representation learned from existing feature-based transfer methods. the key idea is to enlarge source training data populations by randomly corrupting source data using some noises, and then train a transfer model ~M that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the target data can be covered by the constructed source populations. and thus better transfer performance can be achieved by ~M An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution with linear regression model and dropout noise. With a marginalization trick, we can train an RTM that is equivalently to training using infinite source noisy populations without truly conducting any corruption. More importantly, such an RTM has a closed-form solution, which enables very fast and efficient training. Extensive experiments on various real-world transfer tasks show that RTM is a promising transfer model.

关键词： Training Sociology Transfer learning Training data Data models pattern recognition Noise measurement

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Machine Learning-Based, Networking and computing Infrastructure Resource Management 17th

Machine Learning-Based, Networking and Computing Infrastruct...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Chochliouros, Ioannis P. Kostopoulos, Alexandros Payaro, Miquel Verikoukis, Christos di Vimercati, Sabrina De Capitani Vinogradov, Evgenii Ranjbar, Vida Vardakas, John Rahman, Md Arifur Soumplis, Polyzois Varvarigos, Emmanuel Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Univ Milan Milan Italy Katholieke Univ Leuven Leuven Belgium Iquadrat Informat SL Barcelona Spain IS Wireless Warsaw Poland Inst Commun & Comp Syst ICCS Athens Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, the MARSAL project [ 1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. At the network design domain, MARSAL targets the development of novel cell-free-based solutions. Namely, scalable and cost-efficient wireless access points deployment will be achieved by exploiting the distributed cell-free concept combined with wireless and wired serial fronthaul approaches. We will target the inclusion of these innovative functionalities in the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G cell site connectivity via different levels of fixed-mobile convergence. In the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul data centers, by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI and Blockchain technologies will be developed in order to guarantee a secured

关键词： Cell-free Distributed cloud Network automation Machine learning Secure multi-tenancy

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Geometric Numerical Integral Method in Compact Lie Group

Geometric Numerical Integral Method in Compact Lie Group

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Chao Liu Shengyi Yang Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Gui Yang China

ISBN: (纸本)9781665447300

three dimensions special orthogonal group SO (3) is widely used to describe the rotation kinematics of the rigid body without local coordinates, which can avoid rotation singularity and unwinding in traditional methods. Propagating the rotation kinematics in SO (3) with a specific geometric integration method is not only to obtained numerical results with improved qualitative behavior, but also provided more accurate long-time integration results. While many studies have focused on geometric integration algorithms to preserve the geometric structure, this work has the additional objective of studying result accuracy. Integral curves on SO (3) obtained using the third-order Crouch-Grossman Lie group method are compared with numerical results using the third-order RKMK algorithms, the exponential coordinates method, and the simple projection method. Results show that the use of the Crouch-Grossman Lie group method better preserves the geometric structure of SO (3) for the larger time steps considered. It is also found that the third-order Crouch-Grossman algorithm is more accurate than the RKMK except for the smallest time step used.

关键词： Heuristic algorithms Signal processing algorithms Kinematics Signal processing

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Over-the-Air Radar Emitter Signal Classification Based on SDR

Over-the-Air Radar Emitter Signal Classification Based on SD...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Yan Xia Zhiyuan Ma Zhi Huang School of Electronic Engineering Naval University of Engineering Wuhan China

ISBN: (纸本)9781665447300

At present, in the field of radar emitter classification, theoretical simulation is mostly used to carry out algorithm research. However, there are few schemes to study signal classification in real electromagnetic environment using actual hardware. therefore, this paper proposes a radar emitter classification scheme based on HackRF Software Defined Radio (SDR) and deep learning to solve the problem of weak engineering practice. Firstly, the GNU Radio development environment is used to realize the integration design of real space signal transceiver and time-frequency analysis algorithm application on HackRF hardware platform. then, a classification model with 11 layers network is constructed to automatically extract the deep features of intra-pulse signal time-frequency image. Finally, the classification performance of eight kinds of signals in real electromagnetic environment is tested. the total recognition accuracy of this scheme is more than 83% under 6dB low Signal-to-Noise Ratio (SNR), which proves the effectiveness of the scheme, and provides an important basis for practical engineering application in the future.

关键词： Time-frequency analysis Spaceborne radar Software algorithms pattern classification Feature extraction Hardware Radar signal processing

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MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

MCCNet: Multi-Color Cascade Network with Weight Transfer for...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Development and Evaluation of a Search Application for Elementary School Programming Materials

Development and Evaluation of a Search Application for Eleme...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Yuya Akinaga Tetsuro Kakeshita Computing Division Saga University Saga Japan

ISBN: (纸本)9781665424219

Programming education becomes compulsory in Japanese elementary schools from the fiscal year 2020. However, many elementary school teachers have no programming experience. therefore, in this study, we will develop a programming material search application to assist teachers of programming education in selecting teaching materials. In this paper, the application contains teaching materials presented by the Future Learning Consortium supported by the Ministry of Education, Culture, Sports, Science, and Technology, etc. It not only searches for teaching materials using various criteria but also displays examples of implementation and comments from teachers. the application was also reviewed by active elementary school teachers and received a good evaluation.

关键词： Seminars Training COVID-19 Education Planning Informatics Programming profession

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computing stable resultant-based minimal solvers by hiding a variable

Computing stable resultant-based minimal solvers by hiding a...

引用

international conference on pattern recognition

作者： Snehal Bhayani Zuzana Kukelova Janne Heikkilä Center for Machine Vision and Signal Analysis University of Oulu Finland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

Many computer vision applications require robust and efficient estimation of camera geometry. the robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e., solving minimal problems, in a RANSAC-style framework. Minimal problems often result in complex systems of polynomial equations. the existing state-of-the-art methods for solving such systems are either based on Gröbner bases and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a resultant computation using an extra variable. In this paper, we study an interesting alternative resultant-based method for solving sparse systems of polynomial equations by hiding one variable. this approach results in a larger eigenvalue problem than the action matrix and extra variable resultant-based methods; however, it does not need to compute an inverse or elimination of large matrices that may be numerically unstable. the proposed approach includes several improvements to the standard sparse resultant algorithms, which significantly improves the efficiency and stability of the hidden variable resultant-based solvers as we demonstrate on several interesting computer vision problems. We show that for the studied problems, our sparse resultant based approach leads to more stable solvers than the state-of-the-art Gröbner basis as well as existing resultant-based solvers, especially in close to critical configurations. Our new method can be fully automated and incorporated into existing tools for the automatic generation of efficient minimal solvers.

关键词： Geometry Computer vision Pose estimation Tools Cameras Eigenvalues and eigenfunctions pattern recognition

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Multi-Modal Deep Clustering: Unsupervised Partitioning of Images

Multi-Modal Deep Clustering: Unsupervised Partitioning of Im...

引用

international conference on pattern recognition

作者： Guy Shiran Daphna Weinshall School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

ISBN: (纸本)9781728188089;9781728188096

the clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering framework, which learns a deep neural network in an end-to-end fashion, providing direct cluster assignments of images without additional processing. Multi-Modal Deep Clustering (MMDC), trains a deep network to align its image embeddings with target points sampled from a Gaussian Mixture Model distribution. the cluster assignments are then determined by mixture component association of image embeddings. Simultaneously, the same deep network is trained to solve an additional self-supervised task of predicting image rotations. this pushes the network to learn more meaningful image representations that facilitate a better clustering. Experimental results show that MMDC achieves or exceeds state-of-the-art performance on six challenging benchmarks. On natural image datasets we improve on previous results with significant margins of up to 20% absolute accuracy points, yielding an accuracy of 82% on CIFAR-10, 45% on CIFAR-100 and 69% on STL-10.

关键词： Deep learning Neural networks Image representation Benchmark testing pattern recognition Task analysis Gaussian mixture model

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Case Study: Effect of Different Unstable Water Injection Modes with Horizontal Row Well pattern in Low Permeability Carbonate Reservoir of Middle East 10th

Case Study: Effect of Different Unstable Water Injection Mod...

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10th international Field Exploration and Development conference, IFEDC 2020

作者： Yang, Yang Li, Yong Zhang, Wen-qi Liu, Da-wang Chen, Yi-hang Wang, Yu-ning Gu, Fei Dong, Ruo-jing Department of Middle East E&D Research Institute of Petroleum Exploration and Development Beijing China Department of Oil Field Development Research Institute of Petroleum Exploration and Development Beijing China

ISBN: (纸本)9789811607622

K carbonate reservoir in Middle East is a low permeability reservoir with thief layer. It is developed by waterflooding with a horizontal row well pattern. However, due to the influence of the thief layer, the water breakthrough fast, and the water cut rises to more than 40% shortly. So different unstable water injection modes were applied to improve the supplemental recovery, including cyclic water injection (CWI), alternate water injection (AWI) and alternate water injection and production (AWIP). CWI means a period of water injection and a period of injection stop. the EOR mechanism of CWI is to improve the swept volume of low permeability layer in heterogeneous reservoir by elastic force and capillary force. AWI means two adjacent injectors, one for injection and one for stopping injection, injection and stopping injection alternates with each other periodically. the EOR mechanism of AWI is change the direction of streamline between injector and producer, so as to increase well spacing and improve the swept volume. Based on AWI, AWIP adjust producers, that means two adjacent producers, one for high fluid rate and one for low fluid rate, high fluid rate and low fluid rate alternates with each other periodically. And when the producer is producing with high fluid rate, the opposite injector has low water injection or stops injection, vice versa. the numerical simulation results show that all unstable water injection modes can effectively reduce the water cut rise speed and improve the recovery. Among them, alternate water injection and production (AWIP) has the best effect. the implementation results of four pilot areas also verify it. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Horizontal wells

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Prediction of pH of Soil Using Linear Regression, Decision Tree, GPR, and Gaussian SVM

Prediction of pH of Soil Using Linear Regression, Decision T...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Sandeep Kumar Sunori Pushpa Bhakuni Negi Amit Mittal Shweta Arora Somesh Sharma Sudhanshu Maurya Pradeep Juneja Graphic Era Hill University Bhimtal Campus Bhimtal India School of Management Graphic Era Hill University Bhimtal Campus School of Computing Graphic Era Hill University Bhimtal Campus India

ISBN: (纸本)9781665426879

the major part of Uttarakhand is economically dependent on agriculture as here, the soil is very rich in minerals and nutrients, and has a balanced pH value, due to which it has remarkable fertility. this is because of very perfect climatic conditions over here. In solving the classification and regression problems, machine learning techniques are well known to exhibit a remarkable accuracy. In this paper, an attempt is made to perform prediction of soil pH using various machine learning methods in Regression Learner App of MATLAB. Finally, the prediction error for all the developed models is compared.

关键词： Support vector machines Biological system modeling Linear regression Machine learning Soil Predictive models Minerals

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