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Efficient Training and Inference in Highly Temporal Activity Recognition 2

Efficient Training and Inference in Highly Temporal Activity...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Charkhabi, Masoud Rahurkar, Nivedita Stanford Univ 450 Serra Mall Stanford CA 94305 USA

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

High-performance Activity Recognition models from video data are difficult to train and deploy efficiently. We measure efficiency in performance, model size, and run-time;during training and inference. Researchers have demonstrated that 3D convolutions capture the space-time dynamics well [13]. The challenge is that 3D convolutions are computationally intensive. [8] Propose the Temporal Shift Module (TSM) for train-efficiency, and [5] proposes DeepCompression for inference-efficiency. TSM is a simple yet effective way to gain near 3D convolution performance at 2D convolution computation cost. We apply these efficiency techniques to a newly labeled activity recognition data set through transfer learning. Our labeling strategy is meant to create highly temporal activity. We benchmark against a 2D ResNet50 backbone trained on individual frames, and a multilayer 3DCNN on multi-frame short videos. Our contributions are: 1. A new highly temporal activity recognition dataset based on egoHands [1]. 2. results that show a 3D backbone on videos outperforms a 2D one on frames. 3. With TSM we achieve 5x train efficiency in run-time with negligible performance loss. 4. With Quantization alone we achieve 10x inference efficiency in model size with negligible performance loss.

关键词： Activity Recognition Computer Vision machine learning

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learning from Software defect datasets 5

Learning from Software defect datasets

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Singh, Pradeep NIT Raipur Dept Comp Sci & Engn Chhattisgarh India

ISBN: (纸本)9781728139883

Development of high-quality software is very much essential now-a-days. Prediction of good quality software in the early phase during the development reduces the cost of the testing resources. Various data mining and machine learning algorithms are developed to predict the quality of the software. In the real-life scenarios while dealing with software modules many of the time the underline datasets are imbalanced. In order to increase the efficiency of the prediction or classification algorithms balancing algorithms are implemented as a preprocessing stage before the prediction phase. In this paper we conduct extensive experiments to explore the effect of imbalance learning including undersampling, oversampling and hybrid methods and its interaction with different types of classifiers on various projects. We evaluate six imbalance learning methods with six classifiers on 12 data sets. The imbalance learning methods used are Random Oversampling (ROS), Random under-sampling, SMOTE, TOMEK, SMOTE+TOMEK, and SMOTE+ENN. This study reveals that the appropriate combination of imbalanced method and classifier can improve the accuracy of software fault prediction models.

关键词： Software Fault Prediction imbalancing classification

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Design of human-computer interactive fire extinguishing training system based on virtual reality technology 2nd

Design of human-computer interactive fire extinguishing trai...

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2nd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2020

作者： Cui, Xue-yong Diao, Jun-qin Anhui Xinhua University Academy of Arts Hefei230601 China

ISBN: (纸本)9783030511029

In order to improve the ability of human-computer interactive fire extinguishing training, a visual simulation model of man-machine interactive fire extinguishing training based on virtual reality technology is proposed. Taking the large-scale emergency scene fire drill as the research object, the virtual scene image reconstruction model of human-computer interaction fire fighting training is constructed, the boundary feature detection and particle tracking filter processing are carried out for the scene image of human-computer interaction fire fighting training, the dynamic structure of the scene image of human-computer interaction fire fighting training is reorganized with fuzzy edge feature extraction method, and the virtual reality simulation model of human-computer interactive fire fighting training scene image is established. The multi-person cooperative control method is used to simulate the virtual reality in the process of human-computer interactive fire fighting training, and the 3D simulation image of virtual scene fire drill in large-scale emergency scene is followed and rendered, and the optimization design of virtual reality VR simulation model of human-computer interactive fire fighting training is realized. The test results show that the virtual scene simulation of human-computer interaction fire fighting training using this method has good cooperation, high image fusion performance and strong reconstruction ability of fire fighting training scene simulation. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： E-learning

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2ndWorkshop on Knowledge-aware and Conversational Recommender Systems - KaRS 19

2ndWorkshop on Knowledge-aware and Conversational Recommende...

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28th ACM international conference on Information and Knowledge Management (CIKM)

作者： Anelli, Vito Walter Di Noia, Tommaso Polytech Univ Bari Bari Italy

ISBN: (纸本)9781450369763

uOver the last years, we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation engine. Although very effective in in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move into the direction of providing novel and diverse results as well as generating an explanation for the recommended items or support interactive and conversational recommendation processes.

关键词： knowledge-aware linked data knowledge graph knowledge base natural language processing conversational agents

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Investigating Deep Neural Networks for Gravitational Wave Detection in Advanced LIGO Data 19

Investigating Deep Neural Networks for Gravitational Wave De...

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2nd international conference on Computer Science and Software Engineering (CSSE)

作者： Schmitt, Alexander Fu, Kaiyu Fan, Siyu Luo, Yuan Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781450371728

Since the first detection of a Gravitational Wave (GW) in September 2015 at the Laser Interferometer Gravitational-Wave Observatory (LIGO), it was unclear if the Einstein's Theory (E=MC2) was true and if GWs existed. A huge investment in highly advanced optical and electronic equipment was taken to build huge detectors for the possibility to record something which was, until then, only a theory. The LIGO detectors are so special because they can detect a change in length by one ten-thousandth the width of a proton. These constructions are huge and need big financial and technological investments to achieve the precisions. We evaluated Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Temporal Convolutional Networks (TCNs), Gated Recurrent Units ( GRUs) and Long Short-Term Memory (LSTM) on the performance of detecting GWs in a very noisy signal from the LIGO detectors. Higher precision in GW detection will help the LIGO team to more quickly classify GW events in recorded data streams and help to reduce the time span between measurement and detection of a GW. The reduced time is crucial for learning more about GWs because the telescopes and satellites ( for example the Hubble Space Telescope) need time to change direction and locate exactly the root of the GW. Every second saved in detection of the GW can lead to more seconds of recorded data from the observatories. During our research we found out that we could achieve good results with the Temporal Convolutional Networks (TCNs), which had in our implementation improved precision compared to the original matched-filtering and the CNN of D. George and E. A. Huerta [1]. The time for detection is thereby a mixture between the time for the detector from the trained TCN or CNN and the time for the scientist of LIGO to double check the wave data and evaluate the situation with advanced signal processing. The accuracy of detection between the TCN and CNN is drastically different because a TCN is 14% more ac

关键词： Deep learning Gravitational Wave Laser Interferometer Gravitational-Wave Observatory Convolutional Neural Network Long Short-Term Memory Gated Recurrent Unit Temporal Convolutional Network

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2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

2nd EAI International Conference on Advanced Hybrid Informat...

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2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

ISBN: (纸本)9783030190859

The proceedings contain 64 papers. The special focus in this conference is on Advanced Hybrid Information processing. The topics include: Design and Implementation of Pedestrian Detection System;1090ES ADS-B Overlapped signal Separation Research Based on Infomax Extension;real-Time Monitoring Technology of Potato Pests and Diseases in Northern Shaanxi Based on Hyperspectral Data;multi-mode Retrieval Method for Big Data of Economic Time Series Based on machine learning Theory;research on Parallel Forecasting Model of Short-Term Power Load Big Data;approximate Data Fusion Algorithm for Internet of Things Based on Probability Distribution;An Adaptive Threshold VIRE Algorithm for Indoor Positioning;two-Dimensional Super-Resolution Direction Finding Algorithm for Wideband Chirp signals;research on Data Synchronism Method in Heterogeneous Database Based on Web Service;automatic Calibration System of English Lesson Plan Information Under Big Data Analysis;research on the New Mode of Undergraduates’ Innovation and Entrepreneurship Education Under the Background of ‘Internet Plus’;design of Intelligent Driving System for Variable Speed Vehicle Based on Big Data Analysis;research on Trend Analysis Model of Movement Features Based on Big Data;research and Analysis on Comparison Scheme of IoT Encrypted Data in Cloud Computing Environment;DDoS Attack Detection Based on RBFNN in SDN;a Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection;a Method of Estimating Number of signal with Small Snapshots;a Large-Scale Image Retrieval Method Based on Image Elimination Technology and Supervised Kernel Hash;an Improved Eigenvalue-Based Channelized Sub-band Spectrum Detection Method;research on Secure Storage of Multimedia Data Based on Block Chaining Technology.

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2019 international conference on Image and Video processing, and Artificial Intelligence

2019 International Conference on Image and Video Processing,...

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2019 2nd international conference on Image and Video processing, and Artificial Intelligence, IVPAI 2019

ISBN: (纸本)9781510634091

The proceedings contain 109 papers. The topics discussed include: the application of transfer learning in film and television works;using electromagnetic parameters of targets and R language to enhance the accurate and rapid judgment;high-performance vehicle diagnostic information collection device for vehicle big data;deep learning-based visual inspection for the delayed brittle fracture of high-strength bolts in long-span steel bridges;the visual synaesthesia analysis of Chinese traditional music aesthetics;and human face detection based on skin color with principal component analysis (PCA) algorithm.

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GDPR and Business Processes: an effective solution 19

GDPR and Business Processes: an effective solution

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2nd international conference on Applications of Intelligent Systems (APPIS)

作者： Bartolini, Cesare Calabro, Antonello Marchetti, Eda Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg CNR Ist Sci & Tecnol Informaz A Faedo Pisa Italy

ISBN: (纸本)9781450360852

In the European Union, the recent update to data protection laws by virtue of the General Data Protection Regulation (GDPR) significantly changed the landscape of the processing of personal data. Consequently, adequate solutions to ensure that the controller and processor properly understand and meet the data protection requirements are needed. In enterprise reality it is quite common to use Business Process (BP) models to manage the different business activities. Hence the idea of integrating privacy concepts into BP models so as to leverage them to the role of GDPR recommenders. To this end, suggestions and recommendations about data management pursuant to GDPR provisions have been added to specific tasks of the BP, to improve both the process management and personnel learning and training. Feasibility of the proposed idea, implemented into an Eclipse plugin, has been provided through a realistic example.

关键词： learning Business Processes BPMN General Data Protection Regulation (GDPR) Privacy by Design

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Sentiment Analysis Using out of Core learning 2

Sentiment Analysis Using out of Core Learning

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2nd international conference on Electrical, Computer and Communication Engineering, ECCE 2019

作者： Hasan, Mahmudul Islam, Ishrak Hasan, K. M. Azharul Department of Computer Science and Engineering Khulna University of Engineering and Technology Bangladesh

ISBN: (纸本)9781538691113

Text sentiment detection for a particular language other than English is one of the challenging tasks presently. The reasons are;it needs a large dataset, language has no specific structure, one word has a different meaning, and it is hard for even human to understand the connotation of particular words. There exists several proposed architecture for detecting emotions in the Bengali language using machine learning and deep learning approaches, but they are not accurate enough to predict the perfect emotion of the sentence. And there is still no standalone architecture is available that can extract the sentiments hidden inside of a sentence in different languages. In this paper, we are proposing an abstract model that can enable sentiment analysis without any restriction of using a fixed language somewhat applicable to any language. With the use of natural language processing, we have extracted the features, and these features are then fed to different machine learning models for classification. As our main concern was to build up a general model, this model is confined to binary classification, i.e., positive and negative. Apart from this, In our system architecture, we have implemented stochastic gradient descent for optimization. So our model can be called out of core learning model where the model can be updated when new user data is inserted without training the whole model. For the evaluation of the performance of our model, we have trained the estimators against Bangla translated IMDB review dataset and calculated different evaluation metrics for our estimators. The dataset is translated into Bangla using google translator. © 2019 IEEE.

关键词： Stochastic systems

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Classification of human heart signals by novel feature extraction techniques for rescue application 5

Classification of human heart signals by novel feature extra...

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5th international conference on Image Information processing (ICIIP)

作者： Saha, Rajesh Singh, Ratnesh Kumar Kumar, Rajeev Singh, Ghanapriya Goel, Tushar Pal, Pankaj Kumar NIT Uttarakhand Dept Elect Engn Srinagar Garhwal India

ISBN: (纸本)9781728108995

In the paper, the methodology of detecting the heart rate signal wirelessly with regards to use it as an applications as an emerging tool for health care in emergency and surveillance application is presented. A novel feature extraction technique is presented to detect human heart signal or electrocardiogram (ECG) signals wirelessly and classify the signal as normal or abnormal. By the analysis of the heart rate signal the presence of an alive human person below the debris can be detected. After detection of heart rate signals via sensors, the challenge lies in classifying the human heart rate signals as normal or abnormal. Heart signals or Electrocardiogram (ECG) is the primary signal used to diagnose and detection of heart disease abnormality. In this work, ECG signals are de-noised and then analyzed to extract feature from it by a novel technique which is combining Discrete Wavelet Transform (DWT) and nonlinear, linear statistical feature. To verify the efficiency of extracted feature, here used various machine learning (ML) methods namely as Decision Trees (DT), Discriminant Analysis (DA), Logistic Regression (LR), Nearest Neighbor (KNN), Support Vector machine (SVM) and Ensemble have been used as a classifiers and their results are compared. KNN reports the lowest accuracy of 81.60% on MIT- BIH Arrythmia Database. SVM and Ensemble reports the highest accuracy of 97.40%.

关键词： ECG machine learning Discrete Wavelength Transform Features

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