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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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Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention

Saliency Region Detection in Complex Scenes Based on Multi-s...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yifeng Wang Zhenfeng Huo Aoli Liu Lin Zhao Di Wang The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shanxi Province Xidian University China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781665441506

This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance.

关键词： Training Cloud computing Conferences Computational modeling Interference Reliability Convolutional neural networks

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Pre-training Multi-party Dialogue Models with Latent Discourse Inference

arXiv

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arXiv 2023年

作者： Li, Yiyang Huang, Xinting Bi, Wei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China NLP Center Tencent AI-Lab Hong Kong

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://***/EricLee8/MPD_EMVI. © 2023, CC BY.

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Arbitrary Motion Style Transfer with Multi-Condition Motion Latent Diffusion Model

Arbitrary Motion Style Transfer with Multi-Condition Motion ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xingliang Jin Shuai Li Chenglizhao Chen Aimin Hao Xia Hou Ning Li Hong Qin Beijing Information Science and Technology University China Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University College of Computer Science and Technology China University of Petroleum (East China) Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences Department of Computer Science Stony Brook University (SUNY at Stony Brook) Stony Brook New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

computer animation's quest to bridge content and style has historically been a challenging venture, with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality, ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories, recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory, motion content, and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow, guiding the overall structure and movement, while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow, enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers, setting a new benchmark in the realm of computer animation. The source code and model are available at https://***/XingliangJin/***.

关键词： Bridges computer vision Fluids Computational modeling Source coding Animation Diffusion models

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Chang Liu Wenxiao Zhong Yang Di Liang Zhao Xingwei An Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin P. R. China Tianjin Key Laboratory of Brain Science and Neuroengineering academy of medical engineering and translation of medicine Tianjin University Tianjin Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration State Key Laboratory of Advanced Medical Materials and Devices Tianjin University Tianjin Tianjin P. R. China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 μV in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Deep learning Visualization Accuracy Computational modeling Neural networks Virtual environments Brain modeling

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

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IEEE International Conference on Big Data

作者： Jangjae Lee Sangkeun Matthew Lee Stephanie German Paal Supriya Chinthavali Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model.

关键词： Measurement Accuracy Storms Electricity Predictive models Data models Ice Power system reliability Indexes Optimization

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Event-Guided Attention Network for Low Light Image Enhancement

Event-Guided Attention Network for Low Light Image Enhanceme...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiaobin Wang Haiyan Jin Haonan Su Zhaolin Xiao Department of Computer Science and Engineering Engineering Research Center Shaanxi Key Laboratory for Network Computing and Security Technology Human-Machine Integration Intelligent Robot Shaanxi University Xi'an University of Technology

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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The effect of distance and angle of virtual keyboard on user experience of bare hand interaction in VR

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Displays 2025年 90卷

作者： Haiqing Tang Chi Zhang Fan Yang Human-Computer Interaction Laboratory Department of Product Design School of Art and Design Dalian Polytechnic University Dalian China

Along with the popularization of natural computer interaction, bare hand interaction has become an important tool for text input in virtual spatial. However, there is still a lack of sufficient research on how to choose the placement of the virtual keyboard to enhance text input efficiency and user experience in using bare hand interaction techniques. This study examines how the interaction between the placement distance and angle of the virtual keyboard affects the text input efficiency and user experience, which provides suitable parameters for the use of bare hand interaction techniques in virtual reality. Text entry efficiency was obtained by evaluating words per minute (WPM) and error rates, and user experience was gathered by measuring user preference level, fatigue, and workload. We asked 33 participants to perform text input tasks of different difficulties at 3 angles (tilted, medium tilted, vertical) in 3 distances (near, medium near, far). The results showed that while WPM was generally higher in the near distance situation, it also caused greater stress and fatigue for the participants. Medium tilted and vertical in the medium near distance generally performs better in terms of text input efficiency and user experience, and were considered to be a suitable parameter for text input in bare hand interaction. In addition, the far distance showed higher WPM only at vertical angles, but performed poorly at all other angles.

关键词： Virtual prototyping

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Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension

arXiv

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arXiv 2023年

作者： Chen, Jialin Zhang, Zhuosheng Zhao, Hai School of Mathematical Sciences Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. In this paper, we propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning, to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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