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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

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proceedings - 2021 international conference on Future Internet of things and Cloud, FiCloud 2021

Proceedings - 2021 International Conference on Future Intern...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

ISBN: (纸本)9781665425742

the proceedings contain 59 papers. the topics discussed include: secure and privacy-preserving lightweight blockchain for energy trading;feedback learner framework for enhancing user automations in iot smart home environment;improving IoT module testability with test-driven development and machine learning;new virtual machine placement approach based on the micro genetic algorithm in cloud computing;an energy-aware multi-criteria federated learning model for edge computing;energy cost of IoT design patterns;ransomware analysis using cyber kill chain;access pattern hiding in searchable encryption;rule-based adaptations to control cybersickness in social virtual reality learning environments;and business recommender system through matchmaking with supervised machine learning in distributed digital platforms: energy complexity analysis.

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Robust Zero Watermarking Algorithm for Medical Images Using Local Binary pattern and Discrete Cosine Transform 8th

Robust Zero Watermarking Algorithm for Medical Images Using ...

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Liu, Wenyi Li, Jingbing Shao, Chunyan Ma, Jixin Huang, Mengxing Bhatti, Uzair Aslam School of Information and Communication Engineering Hainan University Haikou570228 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China

ISBN: (纸本)9783031067631

thanks to the rapid development of the telemedicine, medical treatment has turned in great convenience. However, as the internet network can be addressed by any user, the medical images cannot avoid the defects of suffering from information leakage and require information encryption techniques. To protect the privacy of patients in medical images, a robust watermarking method, which combines medical images with watermark information, is proposed in this paper. First, the low-frequency feature vector of the medical image is extracted by combining local binary pattern (LBP) and transformation with discrete cosine transform (DCT). Second, hash sequence is implemented on to convert feature vector into binary sequences. At last, the medical image is embedded with hash sequence and watermark preprocessed by chaotic encryption technology using the zero-watermark technology. the experimental results show that the method can encrypt the medical images’ watermark effectively and has good robustness against geometric attacks. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Local binary pattern

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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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A Spark-Based Open Source Framework for Large-Scale Parallel Processing of Rich Text Documents 8

A Spark-Based Open Source Framework for Large-Scale Parallel...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Chen, Qiang Chen, Yinong Wu, Sheng Zhang, Zili College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781665425742

the large amount of unstructured educational data is an important data source of educational big data analysis. However, there is a lack of efficient and simple distributed solutions to process these unstructured documents. To address this problem, this study proposes a Spark-based parallel processing framework of large-scale rich text documents (mainly as MS Word documents), which can seamlessly to extend the serial open-source algorithm to distributed computing algorithm to accomplish parallel processing of large-scale rich text documents. We focus on how to achieve non-intrusive deployment of clusters and how Python codes access to HDFS files. the experimental results show that by using 8 physical nodes (with 128 cores all together), the speedup reaches 48 times compared to the standalone Python-docx in the experiments of extracting 232 GB docx files. the experiments also analyze the Spark parameters and explore how to optimize the parameter settings. © 2021 IEEE.

关键词： Big data

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 22

Multi-Scale Features Integration for Handwritten Mathematica...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Xianghao Liu Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method.

关键词： Multi-Scale Handwritten Mathematical Expression recognition, Dense Atrous Spatial Pyramid Pooling, Symbol Classifier

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Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object Tracking 22

Aggregating Correlation Filter with Multiple Kernels Learnin...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. the handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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BiSon-e: A Lightweight and High-Performance Accelerator for Narrow Integer Linear Algebra computing on the Edge 27

BiSon-e: A Lightweight and High-Performance Accelerator for ...

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27th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Reggiani, Enrico Ramirez Lazo, Cristobal Figueras Bague, Roger Cristal, Adrian Olivieri, Mauro Sabri Unsal, Osman Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain Barcelona Supercomp Ctr Barcelona Spain Sapienza Univ Rome Rome Italy

ISBN: (纸本)9781450392051

Linear algebra computational kernels based on byte and sub-byte integer data formats are at the base of many classes of applications, ranging from Deep Learning to pattern Matching. Porting the computation of these applications from cloud to edge and mobile devices would enable significant improvements in terms of security, safety, and energy efficiency. However, despite their low memory and energy demands, their intrinsically high computational intensity makes the execution of these workloads challenging on highly resource-constrained devices. In this paper, we present BiSon-e, a novel RISC-V based architecture that accelerates linear algebra kernels based on narrow integer computations on edge processors by performing Single Instruction Multiple Data (SIMD) operations on off-the-shelf scalar Functional Units (FUs). Our novel architecture is built upon the binary segmentation technique, which allows to significantly reduce the memory footprint and the arithmetic intensity of linear algebra kernels requiring narrow data sizes. We integrate BiSon-e into a complete System-on-Chip (SoC) based on RISC-V, synthesized and Place&Routed in 65nm and 22nm technologies, introducing a negligible 0.07% area overhead with respect to the baseline architecture. Our experimental evaluation shows that, when computing the Convolution and Fully-Connected layers of the AlexNet and VGG-16 Convolutional Neural Networks (CNNs) with 8-, 4-, and 2-bit, our solution gains up to 5.6x, 13.9x and 24x in execution time compared to the scalar implementation of a single RISC-V core, and improves the energy efficiency of string matching tasks by 5x when compared to a RISC-V-based Vector Processing Unit (VPU).

关键词： Binary Segmentation Edge computing Convolutional Neural Network String Matching RISC-V Hardware Accelerator Narrow Integer Arithmetic Low-power design Number Representation

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Pose Detection of Dead Body in Crime Scene Investigation 23

Pose Detection of Dead Body in Crime Scene Investigation

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Phornvipa Werukanjana Norapattra Permpool Prush Sa-Nga-Ngam Weerachai Muttitanon Royal Police Cadet Academy Thailand Mahidol University Thailand Rajamangala University of Technology Tawan-ok Thailand

ISBN: (纸本)9798400707988

this paper explores the potential of real-time object detection in Crime Scene Investigation to assist investigators in determining the cause of death and bringing justice to those who deserve it. the study discusses the critical steps in implementing dead body pose estimation using the custom dataset with YOLOv8, including data collection, model training, fine-tuning, and testing results. However, human expertise and integration are necessary in the subsequent stages of crime scene investigation to enhance the effectiveness of the proposed system. the experiment uses Automatic Mixed Precision (AMP) by bbox and poses estimation, with suitable performance matrices of the bbox. the result shows all class mPA50 at 0.98 of bbox prediction, while the mPA50 of the pose estimation is 0.969. When dealing with small custom datasets, the precision needed for predicting keypoints results in lower performance of mAP values compared to bbox detection.

关键词： Crime Scene Investigation Forensic science Object Detection POSE Estimation YOLOv8

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A Comprehensive Review of User Experience Research on Somatosensory Games for Older Adults

A Comprehensive Review of User Experience Research on Somato...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Hafizan Mat Som Hafizul Fahri Hanafi K. S. Savita Computer and Information Sciences Department Universiti Teknologi PETRONAS Perak Malaysia Faculty of Computing & META Technology Universiti Pendidikan Sulytan Idris Perak Malaysia

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In light of the global aging population, there is an increasing recognition of the urgent need to investigate the satisfaction levels of the elderly concerning their physical well-being and sociocultural entertainment needs. Sensor-based gaming has gained widespread acclaim among elderly players for its potential to enhance both physical health and psychological well-being. However, the factors influencing the accessibility and enthusiasm of elderly individuals to engage in sensor-based gaming, as well as the extent of scholarly research in this area, remain unclear. this study utilized the PRISMA methodology to systematically gather 36 English-language scholarly sources from three major databases—Web of Science, Scopus, and IEEE Xplore—covering the years 2019 to 2023. the goal was to critically synthesize and evaluate existing empirical research findings while conducting a comprehensive review of achievements, limitations, drawbacks, and future challenges and opportunities within the current literature on user experiences in elderly sports gaming. the findings indicate that factors such as the physical activity and cognitive capabilities of the elderly, game incentive mechanisms, cooperative-competitive modes, personalized feature design, group participation, and gaming frequency are pivotal determinants of the successful engagement of elderly individuals in sensor-based gaming. the results of this study provide crucial and well-supported insights for using sensor-based gaming to address the challenges of an aging population. these insights will help researchers and policymakers delve deeper into existing challenges to enhance the satisfaction of elderly individuals regarding both physical and psychological well-being.

关键词： Somatosensory Automation Reviews Psychology IEEE Xplore Games Aging User experience Older adults Sports

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An Energy-Aware Multi-Criteria Federated Learning Model for Edge computing 8

An Energy-Aware Multi-Criteria Federated Learning Model for ...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Al-Saedi, Ahmed A. Casalicchio, Emiliano Boeva, Veselka Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Sapienza University of Rome Departement of Computer Science Italy

ISBN: (纸本)9781665425742

the successful convergence of Internet of things (IoT) technology and distributed machine learning have leveraged to realise the concept of Federated Learning (FL) with the collaborative efforts of a large number of low-powered and small-sized edge nodes. In Wireless Networks (WN), an energy-efficient transmission is a fundamental challenge since the energy resource of edge nodes is restricted. In this paper, we propose an Energy-aware Multi-Criteria Federated Learning (EaMC-FL) model for edge computing. the proposed model enables to collaboratively train a shared global model by aggregating locally trained models in selected representative edge nodes (workers). the involved workers are initially partitioned into a number of clusters with respect to the similarity of their local model parameters. At each training round a small set of representative workers is selected on the based of multi-criteria evaluation that scores each node representativeness (importance) by taking into account the trade-off among the node local model performance, consumed energy and battery lifetime. We have demonstrated through experimental results the proposed EaM C-Flmodel is capable of reducing the energy consumed by the edge nodes by lowering the transmitted data. © 2021 IEEE.

关键词： Edge computing

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