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Basic Study on a Peristaltic Motion-Type In-Pipe Inspection Robot Using a Hyper-Extension Unit for Improving Locomotion Speed 27th

Basic Study on a Peristaltic Motion-Type In-Pipe Inspection ...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Okuma, Ryusei Matsuo, Sogo Ito, Fumio Nakamura, Taro Chuo Univ Precis Mech Kasuga 1-13-27 Tokyo 1128551 Japan

ISBN: (纸本)9783031707216;9783031707223

This paper proposes a peristaltic motion-type in-pipe inspection robot with a hyper-extension unit in the front part, to inspect small-diameter and longdistance pipes. This study aims to inspect long, narrow, and complex pipes. In conventional in-pipe inspection robots with a simple structure, it has been difficult to increase both the pull-in force of the traction wiring and the push-in force of the robot's head when passing through the bending pipe. Therefore, this paper proposed a peristaltic motion-type in-pipe inspection robot with a hyper-extension unit in the front of the robot, which can extend 294% even at low expansion rates. This robot operates in two distinct patterns: an inchworm-earthworm pattern that can exert large traction and an inchworm pattern that can operate at high speed under low loads. To verify the effect of changing the above two patterns on the inspection speed, horizontal straight pipe movement experiments were conducted by varying the applied traction. The speed of the inchworm pattern is 12.01 mm/s, and that of the inchworm-earthworm pattern is 3.43 mm/s without load. By contrast, the speed of the inchworm pattern is 1.28 mm/s, and that of the inchworm-earthworm pattern is 1.94 mm/s with the load. These results show that the proposed robot can effectively inspect pipes with various movement patterns, adapting to the traction exerted on the wiring.

关键词： In-pipe inspection robot Hyper-extension unit Peristaltic motion

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Heuristics for Designing Pervasive Game Experiences in the Older Adult Population 12th

Heuristics for Designing Pervasive Game Experiences in the O...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Salazar Cardona, Johnny Gutierrez Vela, Francisco Luis Lopez, Jeferson Arango Moreira, Fernando Univ Granada Dept Lenguajes & Sistemas Informat ETSI Informat Granada 18071 Spain Univ Caldas Dept Sistemas & Informat Fac Ingn Calle 65 26-10Edificio Parque Manizales Caldas Colombia Univ Portucalense REMIT IJP Rua Dr Antonio Bernardino Almeida 541-619 P-4200072 Porto Portugal Univ Aveiro IEETA Aveiro Portugal

ISBN: (纸本)9783031602238;9783031602245

Older adults face significant challenges when trying to incorporate contemporary technology into their lives, which negatively impacts their ability to take full advantage of technological advances in their daily routines. This problem is particularly evident in the entertainment domain, where technological video games can be beneficial to those who venture to use them. Although a growing number of older people are adopting digital games in their daily activities, increasing technological sophistication also creates obstacles in their adaptation process. An example of this is the proliferation of pervasive technological games, which make use of devices such as virtual assistants, sensors embedded in mobile devices, and virtual reality to provide advantages to the older adult population. This article will address the fundamental aspects aimed at improving the game experience in these pervasive environments, taking into consideration the specific needs of this demographic group. This results in the specification of a set of heuristics that can be used to identify potential design problems in game-based systems with some degree of pervasiveness oriented to the elderly population.

关键词： Older Adults Pervasive Game Based System Player Experience Playability

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Deep Learning for Object Detection in Images Development and Evaluation of the YOLOv8 Model Using Ultralytics and Roboflow Libraries 13th

Deep Learning for Object Detection in Images Development and...

引用

13th Computer Science Online Conference

作者： Kukartsev, V. V. Ageev, R. A. Borodulin, A. S. Gantimurov, A. P. Kleshko, I. I. Reshetnev Siberian State Univ Sci & Technol Dept Informat Econ Syst Krasnoyarsk 660037 Russia Bauman Moscow State Tech Univ Artificial Intelligence Technol Sci & Educ Ctr Moscow 105005 Russia HSE Univ Fac Comp Sci Moscow Russia

ISBN: (纸本)9783031702846;9783031702853

This paper presents a study of the application of the YOLOv8 model for object detection in images using Ultralytics and Roboflow libraries. Deep learning in computer vision is one of the most actively developing areas in modern technologies, allowing to automate the processes of analysis and recognition of objects in images. The YOLO (You Only Look Once) model is one of the most effective and widely used methods for real-time object detection. The study covers the main steps of working with the YOLOv8 model, from installing and configuring the necessary libraries and development environment, to training the model on the provided data and evaluating its performance. We present a detailed analysis of the training process, including selection of optimal hyperparameters, tuning of training parameters and data augmentation.

关键词： Contrastive Learning

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Driver Activity Recognition by Fusing Multi-object and Key Points Detection 6th

Driver Activity Recognition by Fusing Multi-object and Key P...

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6th Iberian Robotics Conference (Robot)

作者： Pardo-Decimavilla, Pablo Bergasa, Luis M. Lopez-Guillen, Elena Llamazares, Angel Abdeselam, Navil Ocana, Manuel Univ Alcala Dept Elect RobeSafe Res Grp Alcala De Henares Spain

ISBN: (纸本)9783031586750;9783031586767

Driver distraction recognition plays a fundamental role in road safety. In this paper, we present a modular architecture based on the fusion of key points and object detection for predicting driver's actions. From multi-camera infrared recordings, we will temporarily detect among a variety of actions that lead to distractions. Our system detects objects of interest and extracts key points from the driver. They are merged by generating features that relate them and processed with a ML-based classification algorithm. Finally, filters are applied to reduce bounces and add temporal context to the detections. Our proposal has been validated on two state-of-the-art datasets for driving distractions. Through several experiments we show that fusion substantially improves related action inference and improves domain adaptation. In addition, our framework is lightweight, explainable and has a low latency as it performs frame-by-frame inference. The modularity of the network allows us to upgrade parts independently or eliminate a camera without having to modify the entire network.

关键词： advanced driver distraction detection object detection pose estimation

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Novel Deep Neural Network Classifier Characterization Metrics with Applications to Dataless Evaluation

Novel Deep Neural Network Classifier Characterization Metric...

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Intelligent systems Conference

作者： Dean, Nathaniel Sarkar, Dilip Univ Miami Comp Sci Coral Gables FL 33124 USA

ISBN: (纸本)9783031663284;9783031663291

The mainstream AI community has seen a rise in large-scale open-source classifiers, often pre-trained on vast datasets and tested on standard benchmarks;however, users facing diverse needs and limited, expensive test data may be overwhelmed by available choices. Deep Neural Network (DNN) classifiers undergo training, validation, and testing phases using example datasets, with the testing phase focused on determining the classification accuracy of test examples without delving into the inner workings of the classifier. In this work we evaluate a DNN classifier's training quality without any example dataset. It is assumed that a DNN is a composition of a feature extractor and a classifier which is the penultimate completely connected layer. The quality of the classifier is estimated using its weight vectors. The feature extractor is characterized using two metrics that measure feature vectors it produces when synthetic data is fed as input. These synthetic input vectors are produced by backpropagating desired outputs of the classifier. Our empirical study of the proposed method for ResNet18, trained with CIFAR10 and CIFAR100 datasets, confirms that dataless evaluation of DNN classifiers is indeed possible.

关键词： Dataless DNN evaluation Feature-extractor evaluation metrics Within-class similarity and between-class dissimilarity metrics

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Enhancing Video Colorization with Deep Learning: A Comprehensive Analysis of Training Loss Functions

Enhancing Video Colorization with Deep Learning: A Comprehen...

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Intelligent systems Conference

作者： Stival, Leandro Torres, Ricardo da Silva Pedrini, Helio Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 Campinas SP Brazil Wageningen Univ & Res Wageningen Data Competence Ctr Wageningen Netherlands Norwegian Univ Sci & Technol Larsgardsvegen 2 N-6009 Alesund Norway

ISBN: (纸本)9783031663284;9783031663291

Traditional colorization approaches rely on the expertise of artists or researchers that meticulously paint or digitally add colors to an image (or video frames), which is often a time-consuming, laborious, and error-prone task. Automatic methods, based on deep learning techniques, have replaced such approaches to colorization. Despite the advances toward improving their accuracy, there is no consensus regarding the best training procedures for existing artificial neural network techniques proposed for video colorization. In this paper, in order to fill this gap, we focus on the impact of selecting an appropriate loss function. We investigate seven loss functions to find the combination that gives the best results with Deep Learning Video Colorization (DLVC) using a U-Net topology and an attention mechanism trained on the DAVIS dataset. An investigation of the current validation metrics for colorization results was also conducted to analyze their ability to accurately judge colors between frames.

关键词： Video colorization Training loss function Deep learning Evaluation metrics

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Beyond Topic Modeling: Comparative Evaluation of Topic Interpretation by Large Language Models

Beyond Topic Modeling: Comparative Evaluation of Topic Inter...

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Intelligent systems Conference

作者： de Melo, Tiago Merialdo, Paolo State Univ Amazonas Manaus Amazonas Brazil Roma Tre Univ Rome Italy

ISBN: (纸本)9783031663352;9783031663369

This study investigates the application of Large Language Models (LLMs) for interpreting topics derived from topic modeling within the domain of restaurant reviews in Brazilian Portuguese. Traditional topic modeling techniques often produce outputs that require further interpretation to fully capture the nuanced meanings within the data. This research leverages the advanced natural language processing capabilities of LLMs to provide deeper insights into these topics, aiming to bridge the gap between computational topic identification and human-like understanding. A comparative analysis of several LLMs, including ChatGPT versions 3.5 and 4.0 and Google's BARD, was conducted to assess their efficacy in interpreting the generated topics from a large dataset of restaurant reviews. The topics identified were subjected to interpretation by both human evaluators and LLMs, enabling a direct comparison between human and machine-generated interpretations. Preliminary results indicate that LLMs, especially with well-crafted prompts, can produce interpretations that are closely aligned with human understanding, underscoring their potential utility in qualitative data analysis within NLP applications. This research not only sheds light on the interpretative capabilities of LLMs but also opens new pathways for automating complex interpretive tasks across diverse linguistic and cultural landscapes.

关键词： Large language model Natural language processing Topic models

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Intelligent Vehicle Functions in Reducing the Uncertainty of the Driver's Environment and Reducing Risk 10th

Intelligent Vehicle Functions in Reducing the Uncertainty of...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Ezgeta, Drago Mehanovic, Mustafa Causevic, Samir Univ Sarajevo Fac Traff & Commun Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

Driving a road vehicle is a complex process that requires specific knowledge and skills of the driver in order to interact with the vehicle, road infrastructure and environment. In order for the driver to make correct and timely decisions while driving, he must have a sufficient amount of information that corresponds to the current situation and the context of the driver's environment in which decisions are made. The road transport system is a dynamically open system whose state changes under the influence of random events that generate a certain degree of uncertainty in the system. Due to the lack of sufficient amount of information or due to the short time to make decisions while driving, drivers need intelligent vehicle support systems in the decision-making process. Intelligent vehicles collect data from the environment of the vehicle and the driver and provide the driver with the necessary information in order to reduce the uncertainty of his environment and support him in the execution of control functions. This paper describes the possibilities of reducing the uncertainty of the driver's environment with intelligent vehicle functions that should be adapted to the individual requirements of the driver and the context of the traffic situation, taking into account the limitations of the driver and advanced driver support systems.

关键词： uncertainty driver's environment information intelligent vehicle

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Enhancing Telehealth Patient Experience with Emotion-Sensitive Large Language Models 9th

Enhancing Telehealth Patient Experience with Emotion-Sensiti...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Del Rosario, Indira Ghosh, Akash Huang, Bo Yan, Yan Zhang, Wenjun Lin, Wenjun Univ Saskatchewan Mech Engn Saskatoon SK Canada Thompson Rivers Univ Comp Sci Kamloops BC Canada Univ Guelph Sch Comp Sci Geulph ON Canada Univ Saskatchewan Biomed Engn Saskatoon SK Canada Algoma Univ Sch Comp Sci & Technol Digital Healthcare Innovat Lab Brampton ON Canada

ISBN: (纸本)9789819732883;9789819732890

This study explores the integration of Large Language Models (LLMs), specifically ChatGPT-4, to improve patient experience in telehealth. Addressing the challenge of patient anxiety during waiting periods, we implemented ChatGPT-4 for real-time emotion detection and dynamic background generation. Experiments using the FACES database and qualitative feedback on generated backgrounds show that ChatGPT-4 can accurately identify emotions and create calming visual environments. These findings suggest that LLMs can significantly enhance patient engagement and satisfaction. Future developments should focus on personalized AI training and real-time adaptive systems for a more nuanced approach to patient care in telehealth.

关键词： Telehealth Patient experience Large Language Models Emotion detection ChatGPT Dynamic background generation Personalization AI in healthcare

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MeciFace: Mechanomyography and Inertial Fusion-Based Glasses for Edge Real-Time Recognition of Facial and Eating Activities 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Bello, Hymalai Suh, Sungho Zhou, Bo Lukowicz, Paul German Res Ctr Artificial Intelligence DFKI D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci D-67663 Kaiserslautern Germany

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

The prevalence of stress-related eating behaviors and their profound impact on overall health underscore the urgent need for effective and widespread monitoring systems. To handle these issues, we introduce MeciFace, an innovative wearable technology meticulously crafted to monitor facial expressions and eating activities in real-time on-the-edge (RTE). MeciFace represents a significant advancement in health monitoring by offering a low-power, privacy-conscious, and remarkably accurate solution to promote healthy eating behaviors and facilitate stress management. At its core, MeciFace leverages lightweight convolutional neural networks as backbone models for both facial expression recognition and eating activity monitoring scenarios. One of the stand-out features of the MeciFace system is its commitment to efficiency, achieved through meticulous optimization resulting in a tiny memory footprint ranging from 11 to 19 KB. This ensures seamless operation without undue strain on resources, making it ideal for continuous usage in real-world scenarios. MeciFace demonstrates outstanding performance in rigorous RTE evaluations, achieving an F1-score of >= 86% for facial expression recognition and an impressive 94% for eating/drinking monitoring, for the RTE of unseen users. These results are obtained in a user-independent case, underscoring the robustness and generalizability of the system across diverse user profiles.

关键词： Real-Time On-the-edge TinyML Facial Expression Eating Monitoring

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