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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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A Novel Lesion Segmentation Algorithm based on U-Net Network for Tuberculosis CT Image 10

A Novel Lesion Segmentation Algorithm based on U-Net Network...

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10th international conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Wen, Shaoyue Liu, Jing Xu, Wenge Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China The Fifth People's Hospital of Suzhou Department of Radiology Suzhou China Birmingham City University DMT Lab Birmingham United Kingdom

ISBN: (纸本)9781665440295

Lung CT images provide several essential information for lung disease diagnosis and lung surgery. However, the traditional detection method through manual segmentation is laborious and time-consuming. this paper presents automatic tuberculosis (TB) lesion segmentation method based on U-Net neural network for detecting TB. In addition, we combined an edge detection algorithm called canny edge detector with this network to get a more accurate TB lesion boundary. this method is trained on two split databases with 3576 lung CT images obtained by data enhancement on 447 discontinuous lung CT images. the results show that the proposed approach is validated for complex TB lesions with a high dice coefficient (91.2%). © 2021 IEEE.

关键词： Image enhancement

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Adaptive Replication for Real-Time Applications based on Mobile Edge computing 10

Adaptive Replication for Real-Time Applications based on Mob...

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10th IEEE international conference on Communication, Networks and Satellite (IEEE COMNETSAT)

作者： Hsu, Kuo-Shiang Chang, Wan-Chi Huang, Wei-Hsun Wang, Pi-Chung Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan

ISBN: (纸本)9781665434348

With the popularity of mobile devices, data transmission between mobile devices and cloud datacenters not only introduces tremendous data computation and network traffic but also causes high transmission delay. Mobile edge computing (MEC) is proposed to improve Quality of Service (QoS) for mobile applications in 5G networks. However, the data consistency for mobile applications still relies on replication schemes. Frequent updates may thus result in high communication overhead and long transmission latency. In this paper, we design an adaptive replication scheme for real-time mobile applications by considering the number of failed requests and the read/write request ratios. Our scheme adaptively allocates replicas to share loads among MEC servers. It can avoid overloading MEC servers and shorten latency. the simulation results show that our scheme can reduce the response time of replication requests to increase QoS performance.

关键词： mobile edge computing 5G quality of service mobile cloud game

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Machine Learning and the Prevention of Mass Shooting in the United States

Machine Learning and the Prevention of Mass Shooting in the ...

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international conference on ICT For Smart Society (ICISS)

作者： Arish Madataly Benedictus Filbert Federico Daniel Eric Phangandy Nunung Nurul Qomariyah Raymond Bahana Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

Mass shootings have been a rising issue for decades now and it started rising again in the USA lately. not only does it take the lives of people, but it also creates anxiety for others. Data shows that they even increase people's fear of going to public places. Research carried out by psychologist Jillian Peterson and sociologist James Densley called the Violence Project investigates the personal life of individuals that committed mass shootings. they recorded all the data into an open and updated database for use in research. At the time of this research, it had the data of 181 mass shooting events that took place from 1966 to 2022 in the United States of America. Our goal during this research is to find the relation between a person's life events and their likelihood to commit a mass shooting using machine learning. the research was a success as we were able to see an emerging pattern between the offenders' life factors. Certain trauma events and mental illnesses were present in the majority of the lives of the people we studied. the data in fact proved that mental illnesses or traumas on their own are not sufficient information for the prediction but their combination allows more meaningful results. Hence, our results suggest that a high presence of mental instabilities can increase the likelihood of someone becoming a mass shooter.

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Modem Myoelectric Control – Is it Time to Change the Algorithmic Focus?

Modem Myoelectric Control – Is it Time to Change the Algori...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rami N. Khushaba Oluwarotimi W. Samuel Ali H. Al-Timemy Guanglin Li Transport for NSW Alexandria NSW Australia School of Computing and Data Science Research Centre University of Derby Derby United Kigndom Department of Biomedical Eng. Al-Kkwarizmi College of Eng University of Baghdad Baghdad Iraq Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

this paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. this prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies— Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. the observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. these findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals.

关键词： Wrist Deep learning Rockets Convolution Time series analysis Transforms Feature extraction Electromyography pattern recognition Kernel

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10th EAI international conference on Cloud computing, CloudComp 2020

10th EAI International Conference on Cloud Computing, CloudC...

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10th EAI international conference on Cloud computing, CloudComp 2020

ISBN: (纸本)9783030699918

the proceedings contain 14 papers. the special focus in this conference is on Cloud computing. the topics include: Research on the Development of Natural Human-Computer Interaction for Mobile Terminals;encoding Dual Semantic Knowledge for Text-Enhanced Cloud Services;personalized Medical Diagnosis Recommendation Based on Neutrosophic Sets and Spectral Clustering;ECG Arrhythmia Heartbeat Classification Using Deep Learning Networks;A Dual-Index Based Representation for Processing XPath Queries on Very Large XML Documents;IAS-BERT: An Information Gain Association Vector Semi-supervised BERT Model for Sentiment Analysis;a Concept Lattice Method for Eliminating Redundant Features;exploring Self-attention Mechanism of Deep Learning in Cloud Intrusion Detection;a Fraud Detection Approach Based on Combined Feature Weighting;data Privacy Protection of Industrial Blockchain;Lightweight Anonymous Communication Model Based on Anonymous IBE;research on Distributed Trust Management in IoT.

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Passive BLE Sensing for Indoor pattern recognition and Tracking 18

Passive BLE Sensing for Indoor Pattern Recognition and Track...

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18th international conference on Mobile Systems and Pervasive computing, MobiSPC 2021, the 16th international conference on Future Networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： Afyouni, Imad Musleh, Mashaal Basalamah, Anas Tariq, Zaid Bin Department of Computer Science University of Sharjah United Arab Emirates Computer Science and Engineering Department University of Minnesota United Arab Emirates Rensselaer Polytechnic Institute TroyNY United States Computer Engineering Department Umm Al Qura University Saudi Arabia

Crowd-centric sensing using smart phones enables a diverse range of applications evolving from large outdoor environments (e.g., smart cities) to small-scale indoor environments (e.g., smart homes, smart buildings). Tracking users' patterns in indoor environments is a valuable and challenging aspect that is not yet fully addressed. Active indoor localization systems are generally energy-inefficient and cannot be applied to crowd monitoring applications. this paper focuses on the development of a passive and energy-efficient indoor tracking and pattern recognition technique on top of a managed Bluetooth Low-Energy (BLE) network. Particularly, our system model is based on a passive monitoring of a network of BLE tags, which continuously broadcast their unique identifiers, and the current timestamp. Multiple protocols were implemented to extract moving objects' locations in indoors. the trajectory building process consists of different phases: 1) data sampling, 2) outlier detection and removal, 3) location estimation with a weighted centroid approach, 4) spatio-temporal map matching, and finally 5) trajectory smoothing. A series of experiments was conducted to demonstrate the efficiency and accuracy of the proposed approach, with respect to active triggering approaches and BLE-based localization systems. © 2021 Elsevier B.V.. All rights reserved.

关键词： Electric power utilization

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Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows 1

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42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual computing for Biology and Medicine, VCBM 2020

作者： Draxler, Felix Schwarz, Jonathan Schnörr, Christoph Köthe, Ullrich Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Visual Learning Lab Heidelberg University Heidelberg Germany Image and Pattern Analysis Group Heidelberg University Heidelberg Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given the seemingly limited expressiveness of individual affine layers. In this work, we take a first step towards theoretical understanding by analyzing the behaviour of a single affine coupling layer under maximum likelihood loss. We show that such a layer estimates and normalizes conditional moments of the data distribution, and derive a tight lower bound on the loss depending on the orthogonal transformation of the data before the affine coupling. this bound can be used to identify the optimal orthogonal transform, yielding a layer-wise training algorithm for deep affine flows. Toy examples confirm our findings and stimulate further research by highlighting the remaining gap between layer-wise and end-to-end training of deep affine flows. © 2021, Springer Nature Switzerland AG.

关键词： Maximum likelihood estimation

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Automated Sinhala Speech Emotions Analysis Tool for Autism Children 10

Automated Sinhala Speech Emotions Analysis Tool for Autism C...

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10th international conference on Information and Automation for Sustainability, ICIAfS 2021

作者： Welarathna, K.T Kulasekara, Veerandi Pulasinghe, Koliya Piyawardana, Vijani Sri Lanka Institute of Information Technology Faculty of Computing Colombo Sri Lanka

ISBN: (纸本)9781665444293

Autism Spectrum Disorder (ASD) is a neurological disorder that impairs children's development and symptoms that can be noticed in early childhood. One of the main diagnosis characteristics of ASD is the child having unusual emotions and expressions during social interactions. the main problem is how to distinguish these symptoms. Only 14 out of 100 Autistic kids, before they reach the age of 24 months, get medical treatments since the unavailability of resources to identify them early. If they can be recognized early, a therapeutic engagement can be done to help them overcome those issues in social interactions, when they reach school-going age. the focus of this research is to develop a tool to screen atypical children from typical children. this research attempts to recognize the correct emotion of a child, while the child is talking. the input audio stream of children was normalized into a specific range, sub-framed into 2s length for language-independent, noise reduction, and age independence features, and extracting the most effective 40 audio features. the Convolutional Neural Network (CNN) based model classifies eight different emotions of sad, disgust, surprise, neutral, happy, calm, fear, and angry with an accuracy matrix of F1 score of 0.90, even in the uncontrol environment. If the classifying emotions have small frequency variances, the trained model has the ability to handle them. © 2021 IEEE.

关键词： Deep neural networks

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Using Transmit Antenna pattern for Accurate Propagation Loss Predictions 10

Using Transmit Antenna Pattern for Accurate Propagation Loss...

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10th IEEE-APS Topical conference on Antennas and Propagation in Wireless Communications (APWC) / 22nd international conference on Electromagnetics in Advanced Applications (ICEAA) / USNC-URSI Radio Science Meeting (USNC-URSI RSM)

作者： Petsalis, Evangelos Xu, Lan Yee, Brandon R. Chew, Hubert Aerosp Corp El Segundo CA 90245 USA

ISBN: (纸本)9781665413886

We investigate whether a Gaussian initial field approximation is adequate for use in a split-step parabolic equation-based propagation model. Of particular interest is the case when the model is used to predict the propagation loss to areas that are off-angle from the transmit antenna’s main lobe. We discuss the advantage of using a realistic initial field condition instead.

关键词： Wireless communication Transmitting antennas Predictive models Propagation losses Brain modeling Mathematical model

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Image-text integration using a multimodal fusion network module for movie genre classification 11

Image-text integration using a multimodal fusion network mod...

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11th international conference of pattern recognition Systems, ICPRS 2021

作者： Braz, Leodécio Teixeira, Vinícius Pedrini, Helio Dias, Zanoni Institute of Computing University of Campinas Campinas Brazil

ISBN: (纸本)9781839534300

Multimodal models have received increasing attention from researchers for using the complementarity of data to obtain a better inference on the dataset. these multimodal models have been applied to several deep learning tasks, such as emotion recognition, video classification and audio-visual speech enhancement. In this paper, we propose a multimodal method that has two branches, one for text classification and another for image classification. In the image classification branch, we use the Class Activation Mapping (CAM) method as an attention module for the identification of relevant regions of the images. To validate our method, we used the MM-IMDB dataset, which consists of 25959 movies with their respective plot outlines, poster and genres. Our results showed that our method averaged 0.6749 in F1-Weight, 0.6734 in F1-Samples, 0.6750 in F1-Micro and 0.6159 in F1-Macro, achieving better results than the state of the art in the F1-Weight and F1-Macro metrics, and being the second best result in the F1-Samples and F1-Micro metrics. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： Image classification

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