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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Castner, Nora Geßler, Lea Geisler, David Hüttig, Fabian Kasneci, Enkelejda Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user's attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge. © 2020 The Authors. Published by Elsevier B.V.

关键词： Eye tracking

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

A Passivity-Based Variable Impedance Controller for Incremen...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition.

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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EmotionMap:Visual Analysis of Video Emotional Content on a Map

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Journal of computer science & Technology 2020年第3期35卷 576-591页

作者： Cui-Xia Ma Jian-Cheng Song Qian Zhu Kevin Maher Ze-Yuan Huang Hong-An Wang State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China Academy of Arts and Design Tsinghua UniversityBeijing 100084China

Emotion plays a crucial role in gratifying users’needs during their experience of movies and TV series,and may be underutilized as a framework for exploring video content and *** this paper,we present EmotionMap,a novel way of presenting emotion for daily users in 2D geography,fusing spatio-temporal information with emotional *** interface is composed of novel visualization elements interconnected to facilitate video content exploration,understanding,and *** allows understanding of the overall emotion at a glance while also giving a rapid understanding of the ***,we develop EmotionDisc which is an effective tool for collecting audiences’emotion based on emotion representation *** collect audience and character emotional data,and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical *** combines sketch interaction,providing a natural approach for users’active *** novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’feedback.

关键词： video visualization emotion analysis visual analysis sketch interaction

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iFBI: Lightweight Breed and Individual Recognition for Cats and Dogs

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Yuanze Zhang, Yimeng Li, Kexu Luo, Jinpeng Liu, Gang Pan, Rong Xidian University Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province School of Computer Science and Technology Xi’an 710126 China

As the pet industry develops, fine-grained breed recognition and individual recognition have emerged as essential components in biometric measurement systems for intelligent pet monitoring, aiming to identify the specific breed of a pet in an image and to recognize the same individual across multiple images. These capabilities lay the foundation for downstream tasks such as posture estimation and emotion analysis, supporting a wide range of real-world applications. Despite the substantial advancements achieved in existing research, two critical issues remain to be solved: the diversity of object poses affects representation in complex scenarios, and the conflict between model complexity and performance hinders application in resource-constrained conditions. To address the above issues, we propose an integrated Face-and-Body Information for Lightweight Breed and Individual Recognition (iFBI) scheme that integrates multiple pose information by a lightweight model. Specifically, a Face Alignment (FA) module and a Body Posture-Guided (BPG) module are proposed to separate face and body information from the input images, fully capturing the posture details while suppressing background areas. To further maximize the discrimination between individual samples, we design a Multi-level Representation Recognition (MRR) module that dynamically integrates complementary semantic features of face and body, consequently generating more discriminative features. In addition, a Dynamic Convolutional Model Compression (DCMC) method is implemented with an improved dual-branch backbone that significantly reduces computational costs while enhancing model performance. Extensive experiments on two self-built datasets, Pet with Fine-grained Breed Dataset (Pet-FB) and Pet with Diverse Posture Dataset (Pet-DP), and four public datasets indicate that iFBI yields superior performance in both fine-grained breed recognition and individual recognition tasks. The source code and self-built datasets—P

关键词： Face alignment Fine-grained breed recognition Individual recognition Lightweight

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Variable Selection for the Prediction of TSS, pH and TA of Intact Berries of Thompson Seedless Grapes from their NIS Reflection 30

Variable Selection for the Prediction of TSS, pH and TA of I...

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Chariskou, Chrysanthi Bazinas, Christos Daniels, Andries J. Opara, Umezuruike L. Nieuwoudt, Helene H. Kaburlasos, Vassilis G. Human-Machines Interaction Lab International Hellenic University Department of Computer Science Kavala Greece University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa University of Stellenbosch Faculty of AgriSciences Department of Horticultural Sciences Postharvest Technology Research Stellenbosch South Africa Institute for Wine Biotechnology University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

Wavenumbers of high absolute value of correlation coefficient to Total Soluble Solids (TSS), pH, or Titratable Acidity (TA) were selected from reflection Fourier transform near infrared (FT -NIR) spectra of intact grape berries of the white variety Thompson Seedless. Multiple linear regression (MLR) and partial least squares (PLS) regression were applied to the spectra to construct trained regression models able to predict TSS, pH, and TA. Square Pearson's correlation coefficient (R2) and the Mean Square Error (MSE) were used to evaluate the precision of prediction. TSS content was predicted with R2 score of 0.972 and MSE 0.094 using MLR and with R2 0.926 and MSE 0.223 using PLS regression. The pH prediction scores were R2 0.812 and MSE 0.002 with MLR. With PLS regression the values were R2 0.485 and MSE 0.004. TA can be predicted only from the second derivatives of the spectra. MLR produced R2 for prediction 0.745 and MSE 0.076, while the scores using PLS regression were R2 0.648 and MSE 0.114. It was concluded that variable selection could greatly improve the prediction accuracy. The appropriateness of the two regression methods depends on the structure of the spectra dataset and on the characteristics whose prediction is sought. © 2022 University of Split, FESB.

关键词： Multiple linear regression

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HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion and Intensity of Emotion Classes from Meme Images using Single and Multi Modalities 1

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion ...

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1st Workshop on Multimodal Fact-Checking and Hate Speech Detection, DE-FACTIFY 2022

作者： Nguyen, Thanh Tin Pham, Nhat Truong Nguyen, Ngoc Duy Nguyen, Hai Nguyen, Long H. Kim, Yong-Guk Human Computer Interaction Lab Department of Computer Engineering Sejong University Seoul Korea Republic of Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia Khoury College of Computer Sciences Northeastern University Boston United States Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

Nowadays, memes found on internet are overwhelming. Although they are innocuous and sometimes entertaining, there exist memes that contain sarcasm, offensive, or motivational feelings. In this study, several approaches are proposed to solve the multiple modality problem in analysing the given meme dataset. The imbalance issue has been addressed by using a new Auto Augmentation method and the uncorrelation issue has been mitigated by adopting deep Canonical Correlation Analysis to find the most correlated projections of visual and textual feature embedding. In addition, both stacked attention and multi-hop attention network are employed to efficiently generate aggregated features. As a result, our team, i.e. HCILab, achieved a weighted F1 score of 0.4995 for sentiment analysis, 0.7414 for emotion classification, and 0.5301 for scale/intensity of emotion classes on the leaderboard. This results are obtained by using concatenation between image and text model and our code can be found at https://***/JMRa8. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Sentiment analysis

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Exploring Uni-manual Around Ear Off-Device Gestures for Earables

arXiv

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arXiv 2024年

作者： Shimon, Shaikh Shawon Arefin Neshati, Ali Sun, Junwei Xu, Qiang Zhao, Jian School of Computer Science University of Waterloo 200 University Ave W WaterlooON Canada Ontario Tech University 2000 Simcoe St N OshawaONL1G 0C5 Canada Huawei Human-Machine Interaction Lab 19 Allstate Pkwy MarkhamON Canada School of Computer Science University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

Small form factor limits physical input space in earable (i.e., ear-mounted wearable) devices. Off-device earable inputs in alternate mid-air and on-skin around-ear interaction spaces using uni-manual gestures can address this input space limitation. Segmenting these alternate interaction spaces to create multiple gesture regions for reusing off-device gestures can expand earable input vocabulary by a large margin. Although prior earable interaction research has explored off-device gesture preferences and recognition techniques in such interaction spaces, supporting gesture reuse over multiple gesture regions needs further exploration. We collected and analyzed 7560 uni-manual gesture motion data from 18 participants to explore earable gesture reuse by segmentation of on-skin and mid-air spaces around the ear. Our results show that gesture performance degrades significantly beyond 3 mid-air and 5 on-skin around-ear gesture regions for different uni-manual gesture classes (e.g., swipe, pinch, tap). We also present qualitative findings on most and least preferred regions (and associated boundaries) by end-users for different uni-manual gesture shapes across both interaction spaces for earable devices. Our results complement earlier elicitation studies and interaction technologies for earables to help expand the gestural input vocabulary and potentially drive future commercialization of such devices. © 2024, CC BY.

关键词： Digital storage

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

arXiv

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arXiv 2024年

作者： Vedove, Matteo Dalle Lamon, Edoardo Fontanelli, Daniele Palopoli, Luigi Saveriano, Matteo Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Intelligent robots

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

arXiv

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arXiv 2024年

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China Zhidayuan AI Lab. Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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