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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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How do we counter dangerous speech in Italy? 10

How do we counter dangerous speech in Italy?

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10th Italian Conference on Computational Linguistics, CLiC-it 2024

作者： Tonini, Vittoria Frenda, Simona Stranisci, Marco Antonio Patti, Viviana Computer Science Department University of Turin Torino Italy Aequa-Tech Torino Italy Interaction Lab Heriot-Watt University Edinburgh United Kingdom

The phenomenon of online dangerous speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists' and users' comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a new schema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing dangerous speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across datasets. © 2024 CEUR-WS. All rights reserved.

关键词： Abusive language Counter narrative Italian language Linguistic analysis

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Toward Anxiety-Reducing Pocket Robots for Children

Toward Anxiety-Reducing Pocket Robots for Children

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International Conference on Ubiquitous Robots (UR)

作者： Morten Roed Frederiksen Kasper Stoy Maja Matarić Computer Science Department Interaction Lab University of Southern California Copenhagen S Computer Science Department Interaction Lab University of Southern California

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot “AffectaPocket”, designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child's focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years; neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot's sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial process enabled the children to use the robot with little practice. This work demonstrates that the presented pocket robot could represent a step toward developing low-cost accessible technologies to help children suffering from anxiety disorders.

关键词： Anxiety disorders Sociology Medical treatment Tactile sensors Tutorials Games Aging

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Tactile Comfort: Lowering Heart Rate Through interactions with a Pocket Robot

Tactile Comfort: Lowering Heart Rate Through Interactions wi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Morten Roed Frederiksen Kasper Stoy Maja Matarić Interaction Lab at the University of Southern California Computer Science Department The IT-University of Copenhagen

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Children diagnosed with anxiety disorders are taught a range of strategies to navigate situations of heightened anxiety. Techniques such as deep breathing and repetition of mantras are commonly employed, as they are known to be calming and reduce elevated heart rates. Although these strategies are often effective, their successful application relies on prior training of the children for successful use when faced with challenging situations. This paper investigates a pocket-sized companion robot designed to offer a relaxation technique requiring no prior training, with a focus on immediate impact on the user’s heart rate. The robot utilizes a tactile game to divert the user’s attention, thereby promoting relaxation. We conducted two studies with children who were not diagnosed with anxiety: a 14-day pilot study with two children (age 8) and a main study with 18 children (ages 7-8). Both studies employed a within-subjects design and focused on measuring heart rate during tactile interaction with the robot and during non-use. Interacting with the robot was found to significantly lower the study participants’ heart rate (p<0.01) compared to the nonuse condition, indicating a consistent calming effect across all participants. These results suggest that tactile companion robots have the potential to enhance the therapeutic value of relaxation techniques.

关键词： Heart rate Training Pediatrics Atmospheric measurements Navigation Anxiety disorders Games Particle measurements Rhythm Robots

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Han, Zebei Yu, Gufeng Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350337488

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets. © 2023 IEEE.

关键词： Cancer genes feature alignment feature fusion Graph Transformer Network specific cancer genes

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Assessment and Quantification of Virtual Reality Induced Sickness in Relation to Age and Gender: A Multi-Modal Approach

Assessment and Quantification of Virtual Reality Induced Sic...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Reddy, Abhiram Kim, Jin Ryong University of Texas Dallas Allen High School Multimodal Interactions Lab AllenTX United States University of Texas at Dallas Department of Computer Science The Multimodal Interaction Lab DallasTX United States

ISBN: (纸本)9798350308600

This research paper examines the prevalence and severity of Virtual Reality (VR) induced sickness across various genders and age groups. Utilizing a novel multimodal methodology, we integrated objective physiological data with subjective self-reporting to capture a comprehensive understanding of VR sickness in relation to age and gender. Our study recruited 54 participants from the general population, consisting of an equal number of men and women distributed among four age groups. We recorded heart rate variability (HRV) and Electrodermal Activity (EDA) before and after exposure to a standardized 3-minute VR clip. Additionally, participants completed a questionnaire to self-report symptoms of VR sickness. By analyzing both sets of data, our research reveals that both age and gender impact the likelihood of experiencing VR sickness, with older individuals and females generally experiencing higher levels of discomfort induced by VR. © 2023 IEEE.

关键词： Virtual reality

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Haghpanah, Mohammad A. Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Engineering Tehran Iran University of Nevada Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9781665412162

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. © 2022 IEEE.

关键词： Emotion Recognition

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Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural interaction 3

Roopkotha: A Companion Robot for Enhancing Interactive Story...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Mehzabin, Kazi Mayesha Islam, Md. Zahidul Nur, Md. Ashaduzzaman Shidujaman, Mohammad Samani, Hooman Mi, Haipeng Independent University Bangladesh RIoT Center and Department of Computer Science and Engineering Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Tsinghua University Media and Interaction Lab Academy of Arts and Design Beijing China

ISBN: (纸本)9798350374766

Roopkotha is a storytelling robot that seamlessly combines traditional storytelling methods and technology, creating a captivating robot storyteller. We are creating a special prototype in the world of robots that tells stories in a way that is easy to understand and enjoy. In this era of technological advancement, Roopkotha combines voice recognition with Bangla Language processing, emotion recognition, human behavior detection. Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition technology. The emotion recognition feature helps the robot to have a profound connection with the users. © 2024 IEEE.

关键词： Emotion Recognition

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Elevating AI Research at Cognitive Agents and interaction lab: A Technical Report on High-Performance Computing Infrastructure

TechRxiv

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TechRxiv 2023年

作者： Khan, Md Mosaddek Cognitive Agents and Interaction Lab Department of Computer Science and Engineering University of Dhaka Bangladesh

The Cognitive Agents and interaction lab (CAIL) at the University of Dhaka has strategically developed a focused High-Performance Computing (HPC) facility, underpinning its niche in artificial intelligence (AI) research, with a special emphasis — but not limited to — deep and reinforcement learning within the context of multi-agent systems. This technical report outlines the HPC resources at CAIL, highlighting a number of advanced GPUs that drive our research forward. The facility, supported by multiple grants, exemplifies a model for small-scale, high-impact computational research environments. We detail how these resources empower our team to achieve significant strides in AI, despite the challenges of scale, and set a precedent for innovation in the field. © 2023, CC BY.

关键词： Multi agent systems

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An Interactive Augmented Reality Interface for Personalized Proxemics Modeling

arXiv

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arXiv 2024年

作者： Nigro, Massimiliano O’Connell, Amy Groechel, Thomas Velentza, Anna-Maria Matarić, Maja The Interaction Lab. Department of Computer Science University of Southern California Los AngelesCA90089 United States

Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users’ proxemics preferences during human-robot interactions. Using an interactive augmented reality interface, we collected a set of user-preferred distances from the robot and employed an active transfer learning approach to fine-tune a specialized deep learning model. We evaluated this approach through two user studies: 1) a convenience population study (N = 24) to validate the efficacy of the active transfer learning approach;and 2) a user study involving older adults (N = 15) to assess the system’s usability. We compared the data collected with the augmented reality interface and with the physical robot to examine the relationship between proxemics preferences for a virtual robot versus a physically embodied robot. We found that fine-tuning significantly improved model performance: on average, the error in testing decreased by 26.97% after fine-tuning. The system was well-received by older adult participants, who provided valuable feedback and suggestions for future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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