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1st ACM internationalworkshop on Edge Systems, Analytics and Networking (EdgeSys)

作者： Xing, Tianwei Sandha, Sandeep Singh Balaji, Bharathan Chakraborty, Supriyo Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90024 USA IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781450358378

Edge devices rely extensively on machine learning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult to develop separate machine learning models for each scenario as manual labeling is not scalable. To reduce the amount of labeled data and to speed up the training process, we propose to transfer knowledge between edge devices by using unlabeled data. Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities. Using human activity recognition as a case study, over our collected CMActivity dataset, we observe that RecycleML reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch.

关键词： edge devices transfer learning cross modality shared latent representation activity recognition

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One More Reason to Reject Manuscript about machine learning for structural Health Monitoring 1

One More Reason to Reject Manuscript about Machine Learning ...

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1st international Conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Gunawan, Fergyanto E. Soewito, Benfano Surantha, Nico Mauritsius, Tuga Bina Nusantara Univ BINUS Grad Program Dept Ind Engn Ind Engn Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Comp Sci Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Management Informat Syst Informat Syst Management Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

Two major journals in mechanical engineering field put forward a special note for author(s) of articles discussing structural health monitoring. Only contributing new classifiers or new features without applying engineering knowledge or principle is considered insufficient. This article intends to provide empirical evidence to support the decision. This work shows the classification accuracy of a machine learning approach strongly depends on the number of training data. A high accuracy can simply be obtained by training the classifier with a large dataset, which is hardly realizable in practice. Meanwhile, the features and classifier that based on a sound engineering principle can provide a level of classification accuracy independent of the number of training data.

关键词： structural health monitoring machine learning power spectral density F statistic

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Evaluating the Max-Min Hill-Climbing Estimation of Distribution Algorithm on B-Functions 6th

Evaluating the Max-Min Hill-Climbing Estimation of Distribut...

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6th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Madera, Julio Ochoa, Alberto Univ Camaguey Dept Comp Sci Camaguey Cuba Inst Cibernet Matemat & Fis Havana Cuba

ISBN: (纸本)9783030011321;9783030011314

In this paper we evaluate a new Estimation of Distribution Algorithm (EDA) constructed on top of a very successful Bayesian network learning procedure, Max-Min Hill-Climbing (MMHC). The aim of this paper is to check whether the excellent properties reported for this algorithm in machine learning papers, have some impact on the efficiency and efficacy of EDA based optimization. Our experiments show that the proposed algorithm outperform wellknown state of the art EDA like BOA and EBNA in a test bed based on B-functions. On the basis of these results we conclude that the proposed scheme is a promising candidate for challenging real-world applications, specifically, problems related to the areas of data mining, Patter recognition and Artificial Intelligence.

关键词： Estimation of distribution algorithms B-functions Bayesian networks Dependency learning Evolutionary optimization

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Proceedings of the 1st ACM SIGSPATIAL international workshop on Advances in Resilient and Intelligent Cities, ARIC 2018

Proceedings of the 1st ACM SIGSPATIAL International Workshop...

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1st ACM SIGSPATIAL international workshop on Advances in Resilient and Intelligent Cities, ARIC 2018

ISBN: (纸本)9781450360395

The proceedings contain 8 papers. The topics discussed include: multiple evaluation in future population distribution for sustainable city;detecting street signs in cities based on object recognition with machine learning and GIS spatial analysis;D-record: disaster response and relief coordination pipeline;analysis, integration and visualization of urban data from multiple heterogeneous sources;SCOUTS: a smart community centric urban heat monitoring framework;real-time traffic light detection from videos with inertial sensor fusion;and an integrated visual analytics framework for spatiotemporal data.

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Image Processing and Image pattern recognition A Programming Tutorial 1

Image Processing and Image Pattern Recognition A Programming...

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1st IEEE international Conference on Artificial Intelligence for Industries (AI4I)

作者： Chakraborty, Amit Appl Data Tech Bangalore Karnataka India

ISBN: (纸本)9781538692097

Image recognition is a major area of application of machine learning - evolving at a rapid pace with a number of programming platforms available to developers. While each platform has its own uniqueness, the methodology of image recognition consists of a sequence of image processing tasks, development of a classifier algorithm, training and testing followed by deployment. This tutorial will delve into the programming aspects of image processing including thresholding, contouring and template matching. In order to provide practical hands on programming this tutorial will closely look at three real life applications of image pattern recognition namely ALPR using Tesseract OCR and will touch upon using CNN for character detection. The tutorial will explain the algorithm, implementation of pseudocode through Python using two major platforms: OpenCV and Tensorflow.

关键词： Image recognition

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Facing Digital Agriculture Challenges with Knowledge Engineering 1

Facing Digital Agriculture Challenges with Knowledge Enginee...

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1st IEEE international Conference on Artificial Intelligence for Industries (AI4I)

作者： Nery, Marcelo Santos, Rodrigo Santos, Wallas Lourenco, Vitor Moreno, Marcio IBM Res Rio De Janeiro Brazil

ISBN: (纸本)9781538692097

Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machine learning and new trends in AI are bringing a plethora of algorithms capable of performing advanced pattern recognition and data classification. The ability to link, to organize and to query the outputs of these algorithms as well as the ability to handle huge amounts of data and its multiple sources is crucial to maximize the potential of such advances, specially over large datasets. This paper presents challenges in the context of digital agriculture and our position in moving forward with these capabilities whilst using knowledge engineering techniques.

关键词： Knowledge Engineering machine learning Hybrid knowledge representation Digital agriculture

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sEMG signal classification with novel feature extraction using different machine learning approaches

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JOURNAL OF INTELLIGENT & FUZZY SYstEMS 2018年第5期35卷 5099-5109页

作者： Narayan, Yogendra Mathew, Lini Chatterji, S. Panjab Univ Natl Inst Tech Teachers Training & Res Dept Elect Engn Chandigarh 160019 India

Selection of suitable features plays a pivotal role in Electromyography pattern recognition (EMG-PR) based system designing. Time-domain features are widely used in EMG-PR based application and show improved proficiency in the development of rehabilitation robotics. Even though, the performance of existing features is not satisfactory. In this study, we proposed four novel time-domain features obtained by using first-order differentiation of original surface electromyogram (sEMG) signals feature. Here, sEMG signals were acquired from ten healthy volunteers with the help of myotrace400 device for six different arm movements. The data acquisition and pre-processing stage were carried out followed by the feature extraction process for better classification results. Four different classifiers namely, k-nearest neighbors (KNN), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Medium tree (MT) classifiers were utilized for the performance evaluation of proposed and conventional features. Experimental results demonstrate that proposed features extracted by using first-order differentiation of sEMG signals feature attained better classification accuracy with MT classifier as compared to the feature extracted from original sEMG signals with the conventional features. The accuracy of proposed feature based on first-order differentiation improved up to 6%. The results indicate that proposed features may be considered for developing the EMG-PR based system designing.

关键词： sEMG signal pattern recognition time domain features differentiation technique classification accuracy

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A Deep learning Approach for Face Detection using YOLO 1

A Deep Learning Approach for Face Detection using YOLO

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1st international Conference on data Science and Analytics, PuneCon 2018

作者： Garg, Dweepna Goel, Parth Pandya, Sharnil Ganatra, Amit Kotecha, Ketan Computer Engineering Department Devang Patel Institute of Advance Technology and Research CHARUSAT Changa Anand India Computer Science Engineering Department Navrachana University Vadodara India Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa Anand India Symbiosis Institute of Technology Symbiosis International University Pune India

ISBN: (纸本)9781538672785

Deep learning is nowadays a buzzword and is considered a new era of machine learning which trains the computers in finding the pattern from a massive amount of data. It mainly describes the learning at multiple levels of representation which helps to make sense on the data consisting of text, sound and images. Many organizations are using a type of deep learning known as a convolutional neural network to deal with the objects in a video sequence. Deep Convolution Neural Networks (CNNs) have proved to be impressive in terms of performance for detecting the objects, classification of images and semantic segmentation. Object detection is defined as a combination of classification and localization. Face detection is one of the most challenging problems of pattern recognition. Various face related applications like face verification, facial recognition, clustering of face etc. are a part of face detection. Effective training needs to be carried out for detection and recognition. The accuracy in face detection using the traditional approach did not yield a good result. This paper focuses on improving the accuracy of detecting the face using the model of deep learning. YOLO (You only look once), a popular deep learning library is used to implement the proposed work. The paper compares the accuracy of detecting the face in an efficient manner with respect to the traditional approach. The proposed model uses the convolutional neural network as an approach of deep learning for detecting faces from videos. The FDDB dataset is used for training and testing of our model. A model is fine-Tuned on various performance parameters and the best suitable values are taken into consideration. It is also compared the execution of training time and the performance of the model on two different GPUs. © 2018 IEEE.

关键词： Face recognition

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2018 ICAI workshops, ICAIW 2018 - Joint Proceedings of the workshop on data Engineering and Analytics, WDEA 2018, workshop on Smart Sustainable Cities, WSSC 2018, workshop on Intelligent Transportation Systems, WITS 2018 and workshop on Empirical Experiences on Software Reuse, WEESR 2018

2018 ICAI Workshops, ICAIW 2018 - Joint Proceedings of the W...

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1st international Conference on Applied Informatics workshops, ICAIW 2018 - Joint workshop on data Engineering and Analytics, WDEA 2018, workshop on Smart Sustainable Cities, WSSC 2018, workshop on Intelligent Transportation Systems, WITS 2018 and workshop on Empirical Experiences on Software Reuse, WEESR 2018

ISBN: (纸本)9781538694596

The proceedings contain 16 papers. The topics discussed include: software product lines to support language rehabilitation therapies: an experience report;formalistic modeling based on pattern recognition applied to the knowledge and human talent sector in Ecuador;relaxation state induction through binaural acoustic stimuli;machine learning techniques for PM10 levels forecast in Bogotáanalysis of the tertiary sector of Colombia and demand estimation using LEAP;and economic viability of hydrogen generators in transportation vehicles.

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Hand Gesture Controlled Drones: An Open Source Library 1

Hand Gesture Controlled Drones: An Open Source Library

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1st international Conference on data Intelligence and Security (ICDIS)

作者： Natarajan, Kathiravan Nguyen, Truong-Huy D. Mete, Mutlu Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75429 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Texas A&M Univ Commerce Grad Sch Commerce TX USA

ISBN: (纸本)9781538657621

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drones camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier [1] is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture datasets are open-sourced at project website.

关键词： Drone Image processing machine learning machine vision

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