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VCOACH: A Virtual Coaching System Based on Visual Streaming

Procedia computer science 2023年 222卷 207-216页

作者： Fatma Youssef Victor Parque Walid Gomaa Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria 21934 Egypt. Department of Modern Mechanical Engineering Waseda University Tokyo 169-8050 Japan. Cyber-Physical Systems Lab Egypt-Japan University of Science and Technology Alexandria 21934 Egypt. Faculty of Engineering Alexandria University Alexandria 21544 Egypt.

A virtual coaching system is key for monitoring the performance during exercise regimes and rendering timely feedback to avoid potential physical harm. Ideally, the core technologies behind such a system imply exercise recognition/assessment for performance evaluation and subject identification for retrieval/update of personal profiles. In this work, we develop techniques based on learning models for inferences regarding exercise recognition, prediction of the subject's biometrics, exercise evaluation, and simultaneous recognition and evaluation. We address four indoor exercises: squats, push-ups, shoulder-press, and lunges. Different sorts of exercise evaluation are provided: (1) the overall-binary assessment, with a detailed assessment rendering the exact mistakes performed, and (2) the score-based evaluation giving a metric in the range of 0-10 for each aspect of the exercise. Thermal and RGB imagery are examined with various descriptors, such as raw images, Gait Energy Image (GEI), and skeletal pose, with a comparison between their performance. The GEI descriptor extracted from RGB imagery is introduced for exercise recognition and evaluation tasks achieving remarkable results. The GEI descriptor is further utilized for subject recognition and verification from the performed exercise showing the ability of subject discrimination from the way of exercising. Consequently, we introduce new kinds of biometrics based on indoor exercising inspired by the notion that individuals are recognizable from the fingerprint motion dynamics in exercise regimes. To the best of our knowledge, our work is the first to use GEI in such directions and show its remarkable effectiveness.

关键词： Exercise Recognition Subject Recognition and Verification Exercise Evaluation Multi-Task Learning

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A NOTE ON t-DESIGNS IN ISODUAL CODES

arXiv

引用

arXiv 2023年

作者： Awada, Madoka Miezaki, Tsuyoshi Munemasa, Akihiro Nakasora, Hiroyuki School of Fundamental Science and Engineering Waseda University Tokyo169–8555 Japan Faculty of Science and Engineering Waseda University Tokyo169–8555 Japan Graduate School of Information Sciences Tohoku University Tohoku980-8579 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

关键词： Polynomials

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth Robertas Damaševičius David Taniar Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Faculty of Information Technolog Monash University Melbourne VIC Australia

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital.

关键词： Glaucoma Accuracy Hospitals Databases Retina Feature extraction Vectors Physiology Classification algorithms Optimization

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme.

关键词： Measurement Image segmentation Accuracy Image analysis Boosting Arteries Medical diagnostic imaging

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Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel CNNs and BiLSTM Neural Networks 23

Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel...

引用

23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Bahroun, Sahbi Ghayoula, Ridha Manai, Elyes Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Institut Supérieur d'Informatique Université de Tunis El-Manar Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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A Dual-Band MIMO Monopole Antenna with Enhanced Isolation for Sub 6 GHz 5G Applications

A Dual-Band MIMO Monopole Antenna with Enhanced Isolation fo...

引用

2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Hamrioui, Fatma Zohra Bouaza, Abdelmaoula Touhami, Rachida Yagoub, Mustapha C.E. Azrar, Arab Electronic Department 10 Rue des Frères OUDEK El Harrach Algiers16200 Algeria Usthb university Bp32 Instrumentation laboratory Electronics and Informatics faculty El Alia Bab Ezzouar Algiers Algeria University of Ottawa Element laboratory School of Electrical Engineering and Computer Science Ottawa Canada Institute of Electrical and Electronic Engineering University M'Hamed BOUGARA-Boumerdes Signals and Systems Laboratory Electronics Department Boumerdes Algeria

ISBN: (纸本)9798350353754

In this paper, a novel dual-band MIMO monopole antenna for 5G applications is presented. The proposed MIMO antenna is made up of two rings that are T-shaped, generating two distinguished bands: the N77 band and the 6 GHz 5G band. A Tshaped ground branch with a bottom slit is integrated into the ground plane to mitigate the mutual coupling at lower and higher operating bands by redistributing the current, thus improving the isolation enhancement. A reversed T-shaped grounded parasitic element with extra grounded parasitic elements on the partial ground is inserted on top of the substrate, between the MIMO elements. The grounded process is introduced by inserting two metallic vias in each branch of the parasitic element. These parasitic elements effectively reduce the coupling between the MIMO antennas by restricting the current to the respective antenna, thus improving the isolation at the higher band. To verify the design principle of the suggested MIMO antenna, this latter is manufactured and tested. It exhibits excellent impedance matching and high isolation characteristics, and a compact footprint of 29 mm × 42.5 mm. Thus, it is attractive for 5G MIMO systems. © 2024 IEEE.

关键词： Monopole antennas

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth David Taniar Robertas Damaševičius Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Faculty of Information Technology Monash University Melbourne VIC Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database.

关键词： Training Deep learning Accuracy Infectious diseases Databases Medical treatment Feature extraction Skin Distance measurement Public healthcare

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Enhanced Gesture Recognition Through Graph-Based Multimodal Fusion

Enhanced Gesture Recognition Through Graph-Based Multimodal ...

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Mobeen Ur Rehman Talha Ilyas Lakmal Seneviratne Irfan Hussain KU Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates Monash Medical AI Group Faculty of Engineering and Computer Science Clayton 3168 Monash University Victoria Australia

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

This study introduces an advanced framework for recognizing hand gestures from a first-person view, leveraging the integration of multimodal data including optical flow, pose, depth, and RGB video recordings. Adeptly navigating the challenges and opportunities presented by the integration of multimodal data. At its core, the framework employs two pivotal components: a cross-attention based adaptive graph convolutional network and relational graph interactions for modality fusion. The former is designed to extract features from skeleton-based gesture data, ensuring a nuanced capture of hand movements by emphasizing the interconnections within the hand's skeletal structure. The latter component innovatively models each output modality feature as a node in a fully connected relational graph, facilitating the fusion of heterogeneous data types through dynamic interactions between modalities. This approach allows for the leveraging of each data type's strengths and the mitigation of their weaknesses, significantly enhancing the system's classification accuracy and robustness. Tested on a public benchmark dataset, the framework achieved a remarkable accuracy of 98.48%, demonstrating its efficacy. Moreover, it proves resilient, maintaining strong performance (93.48% accuracy) even in scenarios where only one modality is available, highlighting its potential for real-world applications. This advancement sets a new benchmark in hand gesture recognition, promising future developments in multimodal data fusion.

关键词： Accuracy Computational modeling Prevention and mitigation Gesture recognition Benchmark testing Signal processing Feature extraction Robustness Video recording Optimization

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The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

引用

arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

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Gx2Mol: De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning

arXiv

引用

arXiv 2024年

作者： Li, Chen Matsukiyo, Yuki Yamanishi, Yoshihiro Graduate School of Informatics Nagoya University Chikusa Nagoya464-8602 Japan Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka820-8502 Japan

De novo generation of hit-like molecules is a challenging task in the drug discovery process. Most methods in previous studies learn the semantics and syntax of molecular structures by analyzing molecular graphs or simplified molecular input line entry system (SMILES) strings;however, they do not take into account the drug responses of the biological systems consisting of genes and proteins. In this study we propose a deep generative model, Gx2Mol, which utilizes gene expression profiles to generate molecular structures with desirable phenotypes for arbitrary target proteins. In the algorithm, a variational autoencoder is employed as a feature extractor to learn the latent feature distribution of the gene expression profiles. Then, a long short-term memory is leveraged as the chemical generator to produce syntactically valid SMILES strings that satisfy the feature conditions of the gene expression profile extracted by the feature extractor. Experimental results and case studies demonstrate that the proposed Gx2Mol model can produce new molecules with potential bioactivities and drug-like properties. © 2024, CC BY.

关键词： Gene expression

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