检索结果-内蒙古大学图书馆

2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Giovannelli, Alexander Murphy, Fionn Davis, Trey Lee, Chaerin Abulikemu, Rehema Gallagher, Matthew Sharma, Sahil Lisle, Lee Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9798331514846

This paper presents our solution to the 2025 3DUI Contest challenge. We aimed to develop a collaborative, immersive experience that raises awareness about trash pollution in natural landscapes while enhancing traditional interaction techniques in virtual environments. To achieve these objectives, we created an engaging multiplayer game where one user collects harmful pollutants while the other user provides medication to impacted wildlife using enhancements to traditional interaction techniques: HOMER and Fishing Reel. We enhanced HOMER to use a cone volume to reduce the precise aiming required by a selection raycast to provide a more efficient means to collect pollutants at large distances, coined as FLOW-MATCH. To improve the animal feed distribution to wildlife far away from the user with Fishing Reel, we created RAWR-XD, an asymmetric bi-manual technique to more conveniently adjust the reeling speed using the non-selecting wrist rotation of the user. © 2025 IEEE.

关键词： Ambient intelligence

来源：评论

学校读者我要写书评

暂无评论

Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

引用

Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

来源：评论

学校读者我要写书评

暂无评论

JoTyMo: Joint Type and Movement Detection from RGB Images of before and after Interacting with Articulated Objects 11

JoTyMo: Joint Type and Movement Detection from RGB Images of...

引用

11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Forootan, Ehsan Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Moshiri, Behzad University of Tehran Human and Robot Interaction Laboratory Electrical and Computer Engineering Department Iran

ISBN: (纸本)9798350308105

This paper presents a CNN-based network architecture aimed to classify and detect joint types within Articulated objects, specifically the Push-P joints, P-joints, R-joints and objects lacking joints. The movement modeling of the detected objects is explored by processing pre and post interaction RGB images within the SAPIEN PartNet-Mobility dataset. In order to achieve this, an architecture is proposed to leverage consecutive CNN encoders based on the VGG architecture, in order to classify joints based on pre and post-interaction images. Additionally, in order to detect the effect-point and movement vector, a convolutional encoder is applied for each joint type. Moreover, extensive evaluation is made to showcase the results, achieving 96% accuracy in joint classification and 94% accuracy in regression on the considered dataset. Elaborate details regarding network architectures, training procedures, and testing methodologies are available at source code repository. © 2023 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

The fNIRS-based Emotion Recognition by Spatial Transformer and WGAN Data Augmentation Towards Developing A Novel Affective BCI

引用

IEEE Transactions on Affective Computing 2024年第02期16卷 875-890页

作者： Si, Xiaopeng Huang, He Yu, Jiayue Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China

The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions involve a large-scale distributed brain network. Compared to electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) offers a higher spatial resolution. It holds greater potential in capturing emotional spatial information, which may foster the development of new affective Brain-computer Interfaces (aBCI). We proposed a novel self-attention-based deep-learning transformer language model for fNIRS cross-subject emotion recognition, which could automatically learn the emotion's spatial attention weight information with strong interpretability. Besides, we performed data augmentation by introducing the wasserstein generative adversarial networks (WGAN). Results showed: (1) We achieved 84% three-category cross-subject emotion decoding accuracy. The spatial transformer module and WGAN improved the accuracy by 12.8% and 4.3%, respectively. (2) Compared with cutting-edge fNIRS research, we led by 10% in three-category decoding accuracy. (3) Compared with cutting-edge EEG research, we lead by 28% in arousal decoding accuracy, 10% in valence decoding accuracy, and 2% in three-category decoding accuracy. (4) Besides, our approach holds the potential to uncover the brain's spatial encoding mechanism of human emotion processing, providing a new direction for building interpretable artificial intelligence models. © 2010-2012 IEEE.

关键词： Near infrared spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

引用

computers, Materials & Continua 2022年第6期71卷 5235-5252页

作者： Syeda Amna Rizwan Yazeed Yasin Ghadi Ahmad Jalal Kibum Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi122612UAE Department of Human-Computer Interaction Hanyang UniversityAnsan15588Korea

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor *** proposed system first takes an input image pre-process it and then detects faces in the entire *** that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age *** proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups *** system achieved remarkable results over these benchmark datasets about recognition accuracy and computational *** proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

关键词： Feature extraction face expression model local transform features and recurrent neural network(RNN)

来源：评论

学校读者我要写书评

暂无评论

2D Skeleton-Based Keypoint Generation Method for Grasping Objects with Roughly Uniform Height Variation 11

2D Skeleton-Based Keypoint Generation Method for Grasping Ob...

引用

11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Sabzejou, Ali Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Department Iran

ISBN: (纸本)9798350308105

Robotic grasping poses a fundamental challenge in robotics, particularly when dealing with unknown objects. This skill has always been a focal point in robotics research due to its fundamental importance and inherent complexity. Recent advances utilize state-of-The-Art learning techniques, including deep learning and deep reinforcement learning, presenting their unique set of challenges. Training these models requires extensive data and computational resources, with the generalization to unknown objects presenting a significant obstacle for robots. This paper presents a comprehensive approach to unknown object robotic grasping, with a specific focus on top-down grasping actions. The process encompasses image preprocessing, the application of the Straight Skeleton (StSkel) method, and the systematic generation of grasp keypoints when applied to a selection of objects from the Dex-Net dataset. During the evaluation, the analysis incorporated crucial metrics, including the number of detected grasp keypoints for each object, the count of successfully generated grasp pairs, the overall success rate, and the success rate when considering the top 5 ranked grasp pairs. One of the notable strengths of this approach lies in its adaptability to a wide array of objects, ranging from simple shapes to complex ones. The proposed approach in this paper paves the way for automatically labeling grasping datasets, providing a valuable asset for developing auto-generating Deep-RL models. The StSkel method effectively captures essential structural information, making it suitable for real-world applications involving diverse objects. The experimental outcomes affirm the robustness and adaptability of the approach, as it most often achieved a high success rate in generating viable pairs of grasping points for most tested objects. Even complex objects yield impressive results, demonstrating the method's potential for real-world applications in grasping unknown objects. © 2023 IEEE.

关键词： Robotics

来源：评论

学校读者我要写书评

暂无评论

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep Learning Approaches 11

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based o...

引用

11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Ghasemi, Hamed Balazadeh, Elnaz Malekpour, Shayan Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

This article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elaborate their respective performance. Both methods leverage neural networks to facilitate navigation. The first approach employs a deep convolutional neural network to identify suitable areas for forward movement, while the second method utilizes the drone's front camera to capture images, which are then processed by a generative network to generate corresponding depth images. Importantly, both approaches rely solely on an RGB camera, resulting in cost savings for the robot and reduced energy consumption. The training data used for classifying collision-free and appropriate paths were gathered from simulated environments closely resembling real indoor settings. The proposed model calculates the probability associated with each segment of the input image, enabling the selection of the most viable area for drone flight. Incorporating generative networks as a depth estimation module enhances flight accuracy, making navigation in uncharted environments more straightforward. The accuracy of depth estimation was evaluated using standard criteria, achieving an accuracy rate of 84%. This result underscores the effectiveness of the proposed method. © 2023 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Emotional parameter estimation from emo-soundscapes dataset using Deep Convolutional Autoencoders

引用

Multimedia Tools and Applications 2024年 1-15页

作者： Serradilla, Francisco San Juan, Álvaro Martínez-Iñigo, David Department of Computer Systems Politechnic University of Madrid Madrid Spain Laboratory of Ecological Momentary Assessment of Human Behavior and Social Interaction Rey Juan Carlos University Alcorcón Spain

Predicting the emotional responses of humans to acoustic features in the surrounding environment has a highly potential of applications in different fields, ranging from videogames, therapeutic use of virtual reality to the emotional design of spaces according to their expected use. In this paper we model the estimation process of the classical emotion characterization parameters (arousal and valence) from sounds. By means of convolutional neural networks and convolutional autoencoders, the model is adjusted for the prediction of these parameters from a standard dataset [1], improving the results obtained in previous literature. The relevance of the work, apart from improving the results due to the use of autoencoders, is that it eliminates the need to compute handcrafted features, thus demonstrating the ability of convolutional neural networks to treat raw information. Other main contributions of the paper is a new way to visualize the errors in the joint estimation of arousal and valence that facilitates the evaluation of the results obtained by the models. Finally, the use of bootstrap to estimate the confidence intervals of the MSE and r2 of Deep Learning models shows that in comparison to non-overlapping samples, overlapping samples introduces performance bias. © The Author(s) 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues 25

Enhancing Immersive Sensemaking with Gaze-Driven Recommendat...

引用

30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Tahmid, Ibrahim Asadullah North, Chris Davidson, Kylie Whitley, Kirsten Bowman, Doug Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States Department of Defense College ParkMD United States

ISBN: (纸本)9798400713064

Sensemaking is a complex task that places a heavy cognitive demand on individuals. With the recent surge in data availability, making sense of vast amounts of information has become a significant challenge for many professionals, such as intelligence analysts. Immersive technologies such as mixed reality offer a potential solution by providing virtually unlimited space to organize data. However, the difficulty of processing, filtering relevant information, and synthesizing insights remains. We proposed using eye-tracking data from mixed reality head-worn displays to derive the analyst's perceived interest in documents and words, and convey that part of the mental model to the analyst. The global interest of the documents is reflected in their color, and their order on the list, while the local interest of the documents is used to generate focused recommendations for a document. To evaluate these recommendation cues, we conducted a user study with two conditions: a gaze-aware system, EyeST, and a "Freestyle"system without gaze-based visual cues. Our findings reveal that the EyeST helped analysts stay on track by reading more essential information while avoiding distractions. However, this came at the cost of reduced focused attention and perceived system performance. The results of our study highlight the need for explainable AI in human-AI collaborative sensemaking to build user trust and encourage the integration of AI outputs into the immersive sensemaking process. Based on our findings, we offer a set of guidelines for designing gaze-driven recommendation cues in an immersive environment. © 2025 Copyright held by the owner/author(s).

关键词： Information filtering

来源：评论

学校读者我要写书评

暂无评论

Korean Language NLP Model Based Emotional Analysis of LGBTQ Social Media Communities 17

Korean Language NLP Model Based Emotional Analysis of LGBTQ ...

引用

17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Chi, Younghyun Kim, Jang Hyun Sun, Seungjong College of Computing and Informatics Sungkyunkwan University Department of Computer Science Suwon Korea Republic of Intelligence Interaction Sungkyunkwan University Department of Interaction Science Department of Human-Artificial Seoul Korea Republic of

ISBN: (纸本)9781665453486

Sexual minorities are increasingly gaining social visibility and legal rights guarantees at the constitutional level across much of the world, from South America, the United States, and Europe to Japan, Taiwan, and Thailand. At the same time, the COVID-19 pandemic has brought on significant mental health challenges for the public due to accompanying social and economic impact and measures, most of them adverse. Given pre-existing studies highlighting the minority demographic's vulnerability to depression and other mental health symptoms, and the increasing availability of accessible NLP tools, datasets, and models, this paper uses an emotional classification model to analyze emotional trends in queer communities on social media. Using KoBERT with a pre-labelled dataset containing some forty thousand scraped social media posts labelled with emotions, patterns of emotional expression on Twitter in the queer community is revealed. Resulting data provided a validation of the viability of this method of analyzing trends in negative and positive emotional expression as well as the impact COVID-19 had on online queer communities in early 2020 but revealed limitations. © 2023 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：