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检索条件"机构=Department of Computer Science and Computer Graphics"

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Movements Classification Based on Surface Electromyography Using Time-frequency Domain Features 27

Movements Classification Based on Surface Electromyography U...

引用

27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Kuznetsov, Ivan V. Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia

ISBN: (纸本)9798350363708

Feature selection makes significant role in movement classification based on electromyography data. It is assumed that the efficiency of movement classification is improved when time-domain (TD) and frequency-time-domain (TFD) features are used together. To validate the hypothesis, classifiers based on Support Vector Machines (SVM), K Nearest Neighbors (KNN), Random Forest (RF) were trained using NinaPro DB5 dataset. Classification efficiencies of 91.3% and 92.8% were achieved for Recall and Precision metrics, respectively, using KNN. The proposed methods can be used to create human-machine interfaces using muscle activity data. © 2024 IEEE.

关键词： Classification (of information)

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Automatic Lung Nodule Segmentation in CT Imaging using an Improved 3D-Res2Unet

Automatic Lung Nodule Segmentation in CT Imaging using an Im...

引用

Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Tummala, Pavan Hille, Georg Saalfeld, Sylvia Department of Simulation and Graphics Faculty of Computer Science University of Magdeburg Germany

ISBN: (纸本)9783658416560

Lung cancer represents one of the most common and lethal types of cancerous pathologies and lung nodules are an early indicator for pulmonary cancer. Hence, a precise and reliable segmentation of lung nodules could enhance early diagnosis and therapy and thus, increase patients’ survival rates. This work proposes a modified 3D-Res2Unet, combining an Unet-style neural network architecture with residual blocks and attention mechanisms. This network was tested on the publicly available LUNA16 CT dataset and achieved on average 91.27 ± 6.49 %. Therefore, the proposed method indicates state-of-the-art performance and could represent an important tool for early diagnosis of lung cancer. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： computerized tomography

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Visual Design with Representation of Patterns Using Composition Graphs 1

引用

20th International Conference on Cooperative Design, Visualization and Engineering, CDVE 2023

作者： Grabska, Ewa Mars, Agnieszka Department of Design and Computer Graphics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (数字)9783031438158

ISBN: (纸本)9783031438141

This paper concerns the computer-aided process of designing architectural objects in the form of 3D primitive configurations following the general-to-detail principle. Design objects are represented by labeled specific graphs called composition graphs (CP-graphs) with nodes that have bonds to which edges are attached. The problem of grouping 3D design primitives in patterns with the possibility of their multiple uses is considered. This paper aims to show how to create equivalents of patterns at the level of CP-graphs and what possibilities this type of graph structure gives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Secured Digital and QR Code Payments: A Comparative Analysis of Payment Models 4

Secured Digital and QR Code Payments: A Comparative Analysis...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Galhotra, Bhavna Bhaskar Bajaj, Shalini Jatain, Aman Jaglan, Vivek Amity University Jims Computer science dept Sec-5 Rohini India Amity University Computer science dept India Manav Rachna University Department of Computer Science & Technology India Graphics Era University Dehradun India

ISBN: (纸本)9798331540364

The internet's explosive expansion has paved the way for a wide range of smartphone applications that use QR codes and digital wallets to facilitate online payments. Security is a major concern for web-based applications since they are being delivered over the Internet at a rapid pace. Due to unlawful access to many accounts, which causes problems with end-to-end security, the security of these applications has grown to be a top concern for both customers and business owners. This work has addressed the several models that are accessible for security applications, also provided a comparative analysis of all the models, taking into account their security privacy, adaptability, accuracy reliability, vulnerabilities and suitability for digital wallets and online payment systems through QR codes. © 2024 IEEE.

关键词： Electronic money

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Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection 31

Are Explainability Tools Gender Biased? A Case Study on Face...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Huber, Marco Fang, Meiling Boutros, Fadi Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (纸本)9789464593600

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Face recognition

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Unsupervised Face Recognition using Unlabeled Synthetic Data 17

Unsupervised Face Recognition using Unlabeled Synthetic Data

引用

17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Boutros, Fadi Klemt, Marcel Fang, Meiling Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Tu Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350345445

Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data. The training code and pretrained model are publicly available under https://***/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data. © 2023 IEEE.

关键词： Face recognition

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany TU Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350302493

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available https://***/meilfang/***. © 2023 IEEE.

关键词： Face recognition

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Secure Image Retrieval in an Untrusted Cloud Environment 4th

Secure Image Retrieval in an Untrusted Cloud Environment

引用

4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Prakash, Vijay Khandelwal, Kavya Gupta, Disha Garg, Lalit University of Malta Msida Malta School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science & Engineering Graphics Era Hill University Dehradun India

ISBN: (纸本)9789819994410

Image processing in the scrambled area has received increasing attention for its many anticipated uses, such as providing efficient and safe solutions for protection-saving applications in untrustworthy environments. The high degree of ciphertext development caused by the existing homomorphic encryptions is one barrier to the widespread use of these algorithms. This paper considers the problem of managing images in scrambled space. We develop an image encryption scheme that limits ciphertext expansion while preserving the homomorphic characteristic using image design elements. The proposed encryption scheme first uses a probabilistic homomorphic cryptosystem to jumble the image’s pixels, then packs the complete scrambled image to conserve space. Our approach offers a significantly lower ciphertext development factor while maintaining the homomorphic characteristic than the component-wise encryption scheme. It is unnecessary to demand additional intuitive norms when using secure sign-handling tools on the compressed encoded image. We provide a rapid calculation for the encryption and pressure of the proposed image encryption plot, which expedites the computation and significantly increases the effectiveness of our strategy. The security, ciphertext extension percentage, and computing complexity are also being examined. Our validation and testing approach shows that the proposed computations are valid. The proposed algorithms for image encryption make sense as an image encryption scheme for safe picture management applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image retrieval

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Secure Image Retrieval in an Untrusted Cloud Environment Using Homomorphic and Attribute-Based Encryption 3rd

Secure Image Retrieval in an Untrusted Cloud Environment Usi...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Prakash, Vijay Nanda, Nandini Tiwari, Akriti Dhawan, Budh Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Graphics Era Hill University Dehradun India Faculty of ICT University of Malta Msida Malta

ISBN: (纸本)9789819748594

The increasing popularity of cloud computing has led to a growing demand for efficient and secure data storage and retrieval. This is particularly challenging when dealing with image data, which often contains sensitive information that must be protected to ensure privacy and confidentiality. This paper proposes a framework for secure image retrieval in an untrusted cloud environment. A combination of cryptographic techniques and access control policies has been used to address the challenges of secure image retrieval. Specifically, homomorphic encryption enables the secure computation of image features without compromising the privacy of the underlying data. Additionally, we incorporate a novel access control mechanism based on attribute-based encryption (ABE), which allows for fine-grained access control based on user attributes such as job title or department. The proposed framework has a wide range of applications in the fields such as health care, finance, and government, where the need for secure storage and retrieval of image data is critical. By leveraging homomorphic encryption and ABE, the proposed framework provides strong protection against unauthorized access while maintaining high levels of flexibility and scalability. The potential consequences of unauthorized access to sensitive image data can be severe, ranging from personal privacy violations to financial fraud and identity theft. To evaluate the performance of our system, we conduct extensive simulations and achieve high levels of security and efficiency in retrieving image data from an untrusted cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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