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arXiv 2024年

作者： Banos, Oresti Comas-González, Zhoe Medina, Javier Polo-Rodríguez, Aurora Gil, David Peral, Jesús Amador, Sandra Villalonga, Claudia Department of Computer Engineering Automation and Robotics University of Granada Granada Spain Department of Computer Science and Electronics Universidad de la Costa Barranquilla Colombia Department of Computer Science University of Jaén Jaén Spain Department of Computer Technology and Computation University of Alicante Alicante Spain Department of Sotware and Computing Systems University of Alicante Alicante Spain

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather

关键词： Emotion Recognition

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Identification of Behavioural Driving Risks from Physiological Stress

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Procedia computer science 2024年 246卷 5151-5159页

作者： Wilhelm Daniel Scherz Dennis Grewe Maksym Gaiduk Ralf Seepold Juan Antonio Ortega Department of Computer Science Konstanz University of Applied Sciences Alfred-Wachtel-Straße 8 78462 Konstanz Germany School of Computer Engineering University of Seville Avenida de la Reina Mercedes s/n 41012 Sevilla Spain

Driving behaviour is a critical factor in accidents today. Physiological factors have a significant impact on driving behaviour. A potential solution lies in vehicle services that benefit from sensing environmental conditions to improve road safety, such as collision avoidance routines in driver assistance systems. Stress, assessed subjectively or physiologically, influences decision making and behaviour, with implications for individuals and the economy. In this paper we present a novel approach to formulate a risk index by combining data from subjective self-reports and objective physiological measures (in particular heart rate). The model identifies stress tendencies in driving behaviour by monitoring behavioural and physiological markers. We present our evaluation results and explore potential ways to implement the model in vehicle systems and its implications for improving road safety. We discuss potential enhancements to improve driving safety and enable timely responses in situations with an increased risk of accidents due to stress or drowsiness.

关键词： Stress Risk Index HRV RR Interval

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MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

MGCT: Mutual-Guided Cross-Modality Transformer for Survival ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Mingxin Liu, Yunzan Cui, Hui Li, Chunquan Ma, Jiquan Heilongjiang University Department of Computer Science and Technology Harbin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia University of South China Institute of Cardiovascular Disease Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China Cardiovascular Lab of Big Data and Imaging Artificial Intelligence Hengyang Medical School The First Affiliated Hospital Hengyang China University of South China School of Basic Medical Sciences Hengyang Medical School Department of Biochemistry and Molecular Biology Hengyang China

ISBN: (纸本)9798350337488

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000×150,000 pixels;(2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data;and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods. © 2023 IEEE.

关键词： Computational Pathology Multimodal Deep Learning Survival Prediction Weakly-Supervised Learning

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Optimized Vision Transformer Training using GPU and Multi-threading

Optimized Vision Transformer Training using GPU and Multi-th...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jonathan Ledet Ashok Kumar Dominick Rizk Rodrigue Rizk KC Santosh Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Department of Electrical Engineering and Computer Science Catholic University of America Washington DC USA Department of Computer Science Applied AI Research Lab University of South Dakota Vermillion SD USA

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Traditional Convolutional Neural Networks (CNNs) often struggle with capturing intricate spatial relationships and nuanced patterns in diverse datasets. To overcome these limitations, this work pioneers the application of Vision Transformer (ViT) models which have gained significant attention in the field of computer vision for their ability to capture long-range dependencies in images through self-attention mechanisms. However, training large-scale ViT models with a massive number of parameters poses computational challenges. In this paper, we present an optimized approach for training ViT models that leverages the parallel processing capabilities of Graphics Processing Units (GPUs) and optimizes the computational workload distribution using multi-threading. The proposed model is trained and tested on the CIFAR-10 dataset and achieved an outstanding accuracy of 99.92% after 100 epochs. The experimental results demonstrate the effectiveness of our approach in optimizing training efficiency compared to existing methods. This underscores the superior performance of ViT models and their potential to revolutionize image classification tasks.

关键词： Training computer vision Multithreading Computational modeling Neural networks Graphics processing units Convolutional neural networks

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On the Pressure Sensing of Biological Fluids Using Microwaves

On the Pressure Sensing of Biological Fluids Using Microwave...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Gaddam, Sunil Samaddar, Poulami Gopalakrishnan, Keerthy Damani, Devanshi Shivaram, Suganti Dey, Shuvashis Mitra, Dipankar Roy, Sayan Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States Mayo Clinic Department of Cardiology RochesterMN United States Mayo Clinic Department of Pharmacology RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

In vivo biological fluid pressures have been linked to many diseases. Elevated blood pressure is a major risk factor for heart disease that affects millions of people across the world. Non-invasive blood pressure monitoring techniques have been limited to extremities of the body, like limbs, and are unable to measure dynamic internal pressures observed in deeper internal anatomical spaces like cardiac chambers, principal veins, cranial cavity, etc. Novel methods to investigate the internal biological pressures non-invasively can unlock new therapies to treat a wide range of diseases. Microwave-based sensing for biomedical applications is an active research area with the potential to expand in sensing vital physiological indicators like pressure. This study is an experimental investigation into the role of pressure on the dielectric properties of water to broaden the biomedical applications of microwave-based sensing. An experimental setup consisting of a pressure chamber with water and a co-axial probe was built to measure the dielectric properties at pressures ranging from 0 to 240 mm of Hg over a frequency range of 0.5-20 GHz. A comparison of the measurements shows a small and significant change in dielectric properties with varying pressures revealing the challenges and a direction for future scientific research. © 2023 IEEE.

关键词： Blood pressure

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Towards Real-Time Monitoring of Thermal Ablation Lesions in Liver Using Microwave Imaging

Towards Real-Time Monitoring of Thermal Ablation Lesions in ...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Gaddam, Sunil Samaddar, Poulami Gopalakrishnan, Keerthy Damani, Devanshi Shivaram, Suganti Dey, Shuvashis Mitra, Dipankar Roy, Sayan Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States Department of Cardiology RochesterMN United States Mayo Clinic Department of Pharmacology RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

Minimally invasive ablative therapies have been used extensively to treat hepatic tumors in patients where surgical resections are not needed. Besides electroporation, ablation therapies induce extreme temperatures, i.e., hypothermia or hyperthermia, to cause irreversible damage to the tissue cells. The lesion volume created as a result depends on the temperature gradient in the tissue. Accurate and sensitive temperature monitoring is critical to optimize the treatment and reduce the risk of complications and extrahepatic injury. This study presents an approach to assess the tissue temperatures non-invasively using microwave imaging (MWI). A cross-section of a liver phantom is used for microwave imaging electromagnetic simulation at 915 MHz. Dielectric properties of porcine liver tissue at 915 MHz and varying temperatures are gathered from the literature. Point sources placed around the phantom model act as transmitters and receivers. Distorted Born Iterative Method (DBIM) was used to reconstruct the relative permittivity that is mapped to the temperature field and compared. The mapped temperature field from the reconstructed dielectric parameters resembles the temperature field used to create the phantom model demonstrating the feasibility of using MWI for the non-invasive assessment of liver tissue temperatures for ablative therapies © 2023 IEEE.

关键词： Ablation

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PN-Quant: An Automated Pulmonary Nodule Quantification Method for Full-Size CT Scans

PN-Quant: An Automated Pulmonary Nodule Quantification Metho...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinkai Tang Zhiyu Zhu Shengjuan Guo Feng Liu Yi Ping Phoebe Chen Wencai Huang Jiani Zou School of Computer Science Wuhan University Wuhan China School of Primary Education Wuhan City Polytechnic Wuhan China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Radiology General Hospital of Central Theater Command of the PLA Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Pulmonary nodule quantification is essential in forecasting and diagnosing potential malignant nodules, providing critical information for early intervention and treatment planning. However, most existing assistant diagnosis techniques primarily focus on the localization of lung nodules without comprehensive quantitative analysis, limiting their utility in clinical practice. To address this significant limitation, we present a novel and robust pulmonary nodule quantification framework named PN-Quant. It integrates a detection module, a segmentation module, and a quantification module to enable automated identification and precise measurement of lung nodules in full-size computer Tomography (CT) scans, which facilitates the extraction of geometric characteristics, including volume, surface area, mass, sphericity, compactness, and elongation, offering valuable quantitative data for accurate nodule assessment. This study evaluates multiple PN-Quant pipelines with diverse configurations using datasets LIDC-IDRI, LNDb-19, and MSD-lung. Notably, the pipeline combining SANet and 3D UX-Net demonstrated superior performance, yielding low relative errors of 16.8%, 39.5%, and 24.4% on the respective datasets. These results underscore the effectiveness of the automated pipeline based on PN-Quant in efficiently and accurately quantifying pulmonary nodules across diverse datasets. The findings from this research highlight the potential of PN-Quant as a valuable tool for enhancing the precision and reliability of pulmonary nodule analysis in clinical settings, ultimately contributing to improved patient outcomes and clinical decision-making. Our source code is available at https://***/Xinkai-Tang/PN-Quant.

关键词： Three-dimensional displays Statistical analysis Lungs Computed tomography Volume measurement Source coding Pipelines Surface morphology Biomedical measurement Reliability

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MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural language Processing and Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Maniriho, Pascal Mahmood, Abdun Naser Chowdhury, Mohammad Jabed Morshed Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia

The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been significantly enhanced and as a result, traditional malware detection techniques have become less effective. This work presents MalBehavD-V1, a new behavioural dataset of Windows Application Programming Interface (API) calls extracted from benign and malware executable files using the dynamic analysis approach. In addition, we present MalDetConV, a new automated behaviour-based framework for detecting both existing and zero-day malware attacks. MalDetConv uses a text processing-based encoder to transform features of API calls into a suitable format supported by deep learning models. It then uses a hybrid of convolutional neural network (CNN) and bidirectional gated recurrent unit (CNN-BiGRU) automatic feature extractor to select high-level features of the API Calls which are then fed to a fully connected neural network module for malware classification. MalDetConv also uses an explainable component that reveals features that contributed to the final classification outcome, helping the decision-making process for security analysts. The performance of the proposed framework is evaluated using our MalBehavD-V1 dataset and other benchmark datasets. The detection results demonstrate the effectiveness of MalDetConv over the state-of-the-art techniques with detection accuracy of 96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware from MalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. The experimental results show that MalDetConv is highly accurate in detecting both known and zero-day malware attacks on Windows devices. © 2022, CC BY.

关键词： Malware

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Developers’ Privacy Education: A game framework to stimulate secure coding behaviour

Developers’ Privacy Education: A game framework to stimulat...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Abdulrahman Hassan Alhazmi Mumtaz Abdul Hameed Nalin Asanka Gamagedara Arachchilage The Department of Computer Science IT at La Trobe University Australia and Jazan University Saudi Arabia Technovation Consulting & Training PVT The School of Computer Science The University of Auckland New Zealand and La Trobe University Australia

Software privacy provides the ability to limit data access to unauthorized parties. Privacy is achieved through different means, such as implementing GDPR into software applications. However, previous research revealed that the lack of poor coding behaviour leads to privacy breaches such as personal data breaching. Therefore, this research proposes a novel game framework as a training intervention enabling developers to implement privacy-preserving software systems. The proposed framework was empirically investigated through a survey study (with some exit questions), which revealed the elements that should be addressed in the game design framework. The developed game framework enhances developers’ secure coding behaviour to improve the privacy of the software they develop.

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Towards Efficient Motion Planning for UAVs: lazy A* Search with Motion Primitives

arXiv

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arXiv 2024年

作者： Wang, Wentao Shen, Yi Chen, Kaiyang Lu, Kaifan Department of Computer Science University of Southern California LA United States Robotics Department University of Michigan MI United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign United States

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency. © 2024, CC BY.

关键词： Trajectories

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