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IET conference proceedings

IET Conference Proceedings

引用

8th international conference on computing in Engineering and Technology, ICCET 2023

ISBN: (纸本)9781839539237

the proceedings contain 85 papers. the topics discussed include: performance analysis of radial system with renewable sources;artificial intelligence framework for rice blast disease detection and classification using recurrent neural networks;road anomaly detection using self-supervised label generator with vertical disparity maps;sEMG signal analysis and classification for precision and pulp to pulp grip recognition;Internet of things and robotics: a review of concept, added value and applications;a deep learning based approach to enhance the Quality of Service (QoS) of next generation wireless systems by large intelligent surfaces;new applications of morphological methodology for the early detection of cancer;IoT based automatic fuel level monitoring system for automobiles;evolutionary technique for high utility sequential pattern mining;high utility sequence mining using cuckoo search algorithm;image restoration and object detection in unfavorable weather conditions for autonomous vehicles using deep learning approaches: a review;and a review of image super-resolution approaches based on deep learning methods.

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A Comprehensive Review of User Experience Research on Somatosensory Games for Older Adults 8

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

引用

8th IEEE international conference on computing, Communication, Control and Automation, ICCUBEA 2024

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

ISBN: (纸本)9798350391770

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. © 2024 IEEE.

关键词： Economic and social effects

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Automatic Embedding Interventions for the Classification of Hematopoietic Cells 26

Automatic Embedding Interventions for the Classification of ...

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Graebel, Philipp thull, Julian Crysandt, Martina Klinkhammer, Barbara M. Boor, Peter Bruemmendorf, Tim H. Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Dept Hematol & Oncol Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst Pathol Aachen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the classification of hematopoietic cells is the most essential step in automating the analysis of human bone marrow samples. However, the complex structure of cell classes as well as class imbalance make this a challenging task, even for neural networks. Based on projective latent interventions, we propose automatic interventions that iteratively update a learned embedding with suitable transformations that shift different cell types apart and contract samples of the same type together. We present different ways of applying these: either directly on a higher-dimensional embedding or in a parametric version in two dimensions. We analyze the hyper-parameters and evaluate the proposed approach on a challenging dataset of hematopoietic cells. the results show an improvement of up to 3 percentage points for the classification F-score.

关键词： Representation learning Neural networks Bones pattern recognition Task analysis Contracts

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

this study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. the paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. the research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. this study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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An identity-based message authentication scheme for Internet of vehicles 8

An identity-based message authentication scheme for Internet...

引用

8th IEEE international conference on Big Data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Yan, Chenyang Wang, Hongshuo Yan, Songsong QILU Institute of Technology School of Computing Science and Information Engineering Jinan China The University of Nottingham Nottingham United Kingdom School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

the Internet of Vehicles realizes information sharing through the interconnection between entities such as vehicles and vehicles, vehicles and roads. As the computing tasks of vehicles become more and more complex, the data load of traditional cloud computing centers also increases. If a large amount of data generated by the vehicle is all uploaded to the cloud server, and the data is processed by the cloud computing center, it will increase the burden on the cloud server. In order to reduce the request time in the vehicle and reduce the data transmission delay, edge computing is introduced, and an identity-based vehicle networking message authentication scheme in the edge computing scenario is designed to realize the rational use of computing and storage resources. the scheme satisfies unlinkability, unforgeability, identity privacy protection, and anti-replay attack. © 2022 IEEE.

关键词： Edge computing

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I-SPLIT: Deep Network Interpretability for Split computing 26

I-SPLIT: Deep Network Interpretability for Split Computing

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Cunico, Federico Capogrosso, Luigi Setti, Francesco Carra, Damiano Fummi, Franco Cristani, Marco Univ Verona Dept Comp Sci Verona Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

this work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server. So far, potential split locations have been identified exploiting uniquely architectural aspects, i.e., based on the layer sizes. Under this paradigm, the efficacy of the split in terms of accuracy can be evaluated only after having performed the split and retrained the entire pipeline, making an exhaustive evaluation of all the plausible splitting points prohibitive in terms of time. Here we show that not only the architecture of the layers does matter, but the importance of the neurons contained therein too. A neuron is important if its gradient with respect to the correct class decision is high. It follows that a split should be applied right after a layer with a high density of important neurons, in order to preserve the information flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a procedure that identifies the most suitable splitting points by providing a reliable prediction on how well this split will perform in terms of classification accuracy, beforehand of its effective implementation. As a further major contribution of I-SPLIT, we show that the best choice for the splitting point on a multiclass categorization problem depends also on which specific classes the network has to deal with. Exhaustive experiments have been carried out on two networks, VGG16 and ResNet-50, and three datasets, Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. the source code is available at https://***/vips4/I-Split.

关键词： Performance evaluation Source coding Pulmonary diseases Neurons Pipelines Computer architecture Servers

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Continual Learning for Document Image Binarization 26

Continual Learning for Document Image Binarization

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Garrido-Munoz, Carlos Sanchez-Hernandez, Adrian Castellanos, Francisco J. Calvo-Zaragoza, Jorge Univ Alicante Dept Software & Comp Syst San Vicente Spain

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the field of Document Image Analysis (DIA), it is common to find great heterogeneity in terms of the possible graphic domains. In this sense, it is interesting to build neural models that can be sequentially adapted to new domains without losing the knowledge from the domains already learned. this learning paradigm is known as Continual (or Lifelong) Learning (CL). Although the adaptation comes along with a training set of the new domain, neural networks suffer what is known as "catastrophic forgetting". therefore, assuming the constraint of not keeping data from the domains already addressed, this paradigm represents a challenge yet to be solved. this work presents an approach for CL in document image binarization, one of the most considered tasks within the DIA field. Our results report that it is indeed feasible to address CL in this field, given that the approach is successfully implemented and outperforms the baseline by a wide margin in most of the analyzed scenarios.

关键词： Training Graphics Adaptation models Text analysis Image analysis Neural networks pattern recognition

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Forecasting of depth and ego-motion with transformers and self-supervision 26

Forecasting of depth and ego-motion with transformers and se...

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Boulahbal, Houssem Eddine Voicila, Adrian Comport, Andrew I. Renault Software Factory Ile De France France Cote Azur Univ CNRS I3S Nice France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

this paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion. Given a sequence of raw images, the aim is to forecast both the geometry and ego-motion using a self supervised photometric loss. the architecture is designed using both convolution and transformer modules. this leverages the benefits of both modules: Inductive bias of CNN, and the multi-head attention of transformers, thus enabling a rich spatio-temporal representation that enables accurate depth forecasting. Prior work attempts to solve this problem using multi-modal input/output with supervised ground-truth data which is not practical since a large annotated dataset is required. Alternatively to prior methods, this paper forecasts depth and ego motion using only self-supervised raw images as input. the approach performs significantly well on the KITTI dataset benchmark with several performance criteria being even comparable to prior non-forecasting self-supervised monocular depth inference methods.

关键词： Geometry Convolution Benchmark testing Transformers Feature extraction pattern recognition Forecasting

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Face Forgery Detection Algorithm Based on Improved MobileViT Network 8

Face Forgery Detection Algorithm Based on Improved MobileViT...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Tiantian Lu, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou014010 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou014010 China Inner Mongolia University of Technology Institute of Information Engineering Hohhot010051 China

ISBN: (纸本)9798350302455

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization. © 2023 IEEE.

关键词： National security

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Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different Machine Learning Approaches 26

Hardware-Efficient Ultrasonic Entrance Counting: Comparing D...

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Langer, Tim Waschneck, Bernd Partzsch, Johannes Kelber, Florian Mayr, Christian Infineon Technol Dresden GmbH Co KG Dresden Germany Fraunhofer Inst Integrated Circuits Div Engn Adapt Syst Dresden Dresden Germany Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuro Microelec Dresden Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this work, the classification of walking direction based on ultrasonic signals has been examined for entrance counting. Feed-forward and recurrent neural network architectures as well as simpler machine learning techniques have been investigated and compared with classical signal processing techniques. Using only a single ultrasonic receiver, the focus was set on the development of a hardware-efficient system concept. Different ultrasonic measurement methods in time and frequency domain have been compared with the perspective of a holistic energy optimization. the analysis of the system's hardware efficiency was completed by an estimation of algorithmic latency, energy and storage consumption based on the arithmetic of the classification algorithms. All algorithms showed an estimated energy consumption of less than 10 mu J for a single inference on a state-of-the-art implementation of an ARM (R) Cortex (R) M4F micro-controller, which was found to be negligible compared to the energy of the measurement principle. Compared to other sensor types and multi-sensor systems, a state-of-the-art test accuracy of 99.72% could be achieved for differentiating between the two entrance directions of a present person and the absence of a person.

关键词： Entrance Counting Ultrasound Machine Learning Edge computing Low Power TinyML Smart Buildings

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