检索结果-内蒙古大学图书馆

NODKANT: Exploring Constructive Network Physicalization

Computer Graphics Forum 2025年

作者： Pahr, D. Di Bartolomeo, S. Ehlers, H. Filipov, V.A. Stoiber, C. Aigner, W. Wu, H.-Y. Raidou, R.G. Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Institute of CreativeMedia/Technologies St. Pölten University of Applied Sciences St. Pölten Austria

Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://***/tk3g5/. © 2025 The Author(s). Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

关键词： Flow visualization

来源：评论

学校读者我要写书评

暂无评论

Deep Learning: Classification and Automated Detection Earlier of Alzheimer's Disease Using Brain MRI Images

Deep Learning: Classification and Automated Detection Earlie...

引用

International Laser technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Kadhim, Karrar A. Mohamed, Farhan Khudhair, Zaid Nidhal Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam ja'Afar Al-sadiq University Baghdad Iraq UTM-IRDA MaGICX Institute of Human Centered Engineering Universiti Teknologi Malaysia

Alzheimer's disease (AD) progression can be avoided by conducting diagnosis beforehand. This diagnosis acquired quick preventive care which could be possibly done by specialists. Fast and accurate evaluation at the earliest and most challenging stage were required to detect in the diagnosis of AD. In this paper, previous studies were reviewed into a better approach that recognizes the presence of disease in sagittal magnetic resonance automatically (MRI) images that are unusually used. The MRI brain images were used to identify and distinguish characteristics using a range of characteristics recognition techniques. The review of research papers on Alzheimer's Disease published in reputable journals from 2017 to 2020 were presented and discussion of various strategies related to the latest tools used in early diagnosis is our main focus in this study, which could enable researchers to understand current algorithms and techniques in this area, and eventually develop new and more effective algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

AVTENet: A human-Cognition-Inspired Audio-visual Transformer-Based Ensemble Network for Video Deepfake Detection

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

来源：评论

学校读者我要写书评

暂无评论

arXiv

引用

arXiv 2020年

作者： Sebernegg, Anna Kan, Peter Kaufmann, Hannes Virtual and Augmented Reality Group Institute of Visual Computing Human-Centered Technology Vienna University of Technology Austria

The analysis of human motion opens up a wide range of possibilities, such as realistic training simulations or authentic motions in robotics or animation. One of the problems underlying motion analysis is the meaningful comparison of actions based on similarity measures. Since the motion analysis is application-dependent, it is essential to find the appropriate motion similarity method for the particular use case. This state of the art report provides an overview of human motion analysis and different similarity modeling methods, while mainly focusing on approaches that work with 3D motion data. The survey summarises various similarity aspects and features of motion and describes approaches to measuring the similarity between two actions. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion analysis

来源：评论

学校读者我要写书评

暂无评论

HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

arXiv

引用

arXiv 2024年

作者： Wisnu, Dyah A.M.G. Rini, Stefano Zezario, Ryandhimas E. Wang, Hsin-Min Tsao, Yu Taiwan International Graduate Program—Social Network and Human Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Audio Quality Index (HAAQI) that require intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. By utilizing a Bidirectional Long Short-Term Memory (BLSTM) architecture with attention mechanisms and features extracted from the pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. Experimental results demonstrate HAAQI-Net’s effectiveness, achieving a Linear Correlation Coefficient (LCC) of 0.9368, a Spearman’s Rank Correlation Coefficient (SRCC) of 0.9486, and a Mean Squared Error (MSE) of 0.0064 and inference time significantly reduces from 62.52 to 2.54 seconds. To address computational overhead, a knowledge distillation strategy was applied, reducing parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores like the Mean Opinion Score (MOS) through fine-tuning. This adaptation significantly improved prediction accuracy, validated through statistical analysis. Furthermore, the robustness of HAAQI-Net was evaluated under varying Sound Pressure Level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a scalable solution for music audio quality assessment in hearing aid applications, contributing to efficient and accurate models in audio signal processing and hearing aid technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

A Survey on Brain Tumor Diagnosis and Edema Detection Based on Machine Learning

A Survey on Brain Tumor Diagnosis and Edema Detection Based ...

引用

International Laser technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Neamah, K. Mohamed, F. Adnan, M.M. Thajeel, S.A. Faculty of Engineering School of Computing University Technology of Malaysia Johor Bahru Malaysia UTM-IRDA MaGICX Institute of Human Centered Engineering Universiti Teknologi Malaysia Johar Bahru Malaysia School of Computing Faculty of Engineering University Technology of Malaysia Johor Bahru Malaysia Islamic University Najaf Iraq Computer Science Department College of Education Mustansiriyah University Baghdad Iraq

Early brain tumor diagnosis has a significant role in reducing the risk of disease, as well as led to get better treatment results. Usually, magnetic resonance imaging (MRI) images are evaluated manually through visual inspection, which is difficult, time-consuming and often erroneous;this process is performed by radiologists or clinical experts, and its accuracy depends on their experience. Recently, computer-aided diagnosis (CAD) becomes very essential to overcome these limitations. This paper provides a comprehensive assessment of the existing techniques and methodologies for automated detection of brain tumor coupled with oedema detection methods utilisation, with an emphasis on machine learning models. Moreover, this paper provides an analysis of the integrated procedure that pertains to the retrieval of brain pictures by identifying particular data sets in the procedure to recognise the stipulated attributes. © Published under licence by IOP Publishing Ltd.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and human Perception

arXiv

引用

arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

来源：评论

学校读者我要写书评

暂无评论

Objective and subjective evaluation of a multi-stereo 3D reconstruction system 31

Objective and subjective evaluation of a multi-stereo 3D rec...

引用

31st Stereoscopic Displays and Applications Conference, SD and A 2020

作者： Kapeller, Christian Sespede, Braulio Nezveda, Matej Labschütz, Matthias Flöry, Simon Seitner, Florian Gelautz, Margrit AIT Austrian Institute of Technology GmbH High Performance Vision Vienna1210 Austria Vienna University of Technology Institute of Visual Computing and Human-Centered Technology Vienna1040 Austria emotion3D GmbH Vienna1040 Austria Rechenraum GmbH Vienna1050 Austria

In recent years, 3D reconstruction systems comprising multiple depth sensors have received increasing interest for dynamic scene reconstruction and related applications. Publicly available ground truth data are of limited usefulness when dealing with quality assessment of self-recorded data delivered by customized stereo configurations. In this paper, we propose a framework that incorporates versatile strategies for quantitative and qualitative evaluation of a multi-stereo reconstruction system and its intermediate products. Besides the design of suitable calibration objects for quantitative measurements, the framework exploits multi-view data redundancy and generated novel views for objective quality assessment and to obtain subjective ratings from users. We demonstrate the applicability of our evaluation system in experiments with several stereo matching algorithms and view fusion approaches along with a pair-comparison based user study. We believe that our proposed evaluation framework is beneficial for the assessment of 3D products derived from self-recorded dynamic data of comparable set-ups, for example, in the context of subsequent augmented reality applications. © 2020, Society for Imaging Science and technology

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Non-Verbal Virtual Agent Behavior on User Engagement in Argumentative Dialogues

arXiv

引用

arXiv 2024年

作者： Aicher, Annalena Bea Minker, Wolfgang Matsuda, Yuki André, Elisabeth Yasumoto, Keichii Ultes, Stefan Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Institute of Communications Engineering Ulm University Ulm Germany Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan Ubiquitous Computing Systems Laboratory NAIST Ikoma Nara Japan Natural Language Generation and Dialogue Systems University of Bamberg Bamberg Germany

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user’s motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user’s engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent’s co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents. © 2024, CC BY-NC-ND.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Single-Modal Video Analysis of Personality Traits using Low-Level visual Features

Single-Modal Video Analysis of Personality Traits using Low-...

引用

Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Daniel Helm Martin Kampel Computer Vision Lab TU Wien Institute of Visual Computing Human-Centered Technology Vienna Austria

ISBN: (数字)9781728187501

ISBN: (纸本)9781728187518

The ability to analyze the first impression of a person automatically enables novel applications in human-computer interaction and other areas. A person's first impression can decide about a positive or negative outcome in different daily-life situations. The human brain is able to get a picture of the counterpart's personality at short notice. The main aim of this paper is to show how a system based on a standard Convolutional Neural Network (CNN) as well as a 3D-CNN architecture, can be built to solve a multi-label regression task using only visual low-level features. This paper investigates how various pre-processing methods, such as face-extraction and data-augmentation, influence the predicted personality confidences. Furthermore, it explores different training strategies and optimization techniques e.g. regularization in order to improve the model performance. The results of this paper demonstrate an image-based as well as a time-sequence-based system to predict the Big-Five personality dimensions of humans in short video sequences by solving a multi-label regression task. The approaches reach an overall accuracy of 0.891 by using visual features only.

关键词： Training visualization Image recognition Video sequences Computer architecture Pattern recognition Task analysis

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：