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EmoAda: A Multimodal Emotion Interaction and Psychological Adaptation System 30th

EmoAda: A Multimodal Emotion Interaction and Psychological A...

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Dong, Tengteng Liu, Fangyuan Wang, Xinke Jiang, Yishun Zhang, Xiwei Sun, Xiao School of Computer Science and Information Engineering Hefei University of Technology Hefei China AHU-IAI AI Joint Laboratory Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Hefei Zhongjuyuan Intelligent Technology Co. Ltd Hefei China

ISBN: (纸本)9783031533013

In recent years, anxiety and depression have placed a significant burden on society. However, the supply of psychological services is inadequate and costly. With advancements in multimedia computing and large language model technologies, there is hope for improving the current shortage of psychological resources. In this demo paper, we proposed a multimodal emotional interaction large language model (MEILLM) and develop EmoAda, A Multimodal Emotion Interaction and Psychological Adaptation System, providing users with cost-effective psychological support. EmoAda possesses multimodal emotional perception, personalized emotional support dialogue, and multimodal emotional interaction capabilities, helping users alleviate psychological stress and enhance psychological adaptation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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An Perception Enhanced Human-Robot Skill Transfer Method for Reactive Interaction 25th

An Perception Enhanced Human-Robot Skill Transfer Method for...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Si, Weiyong Dong, Jiale Wang, Ning Yang, Chenguang School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom South China University of Technology Guangzhou510640 China Bristol Robotics Lab University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031720611

In this paper, we propose a trust human-robot skill transfer framework, interpretive and reactive dynamic system (IRDS), by investigating the behaviour tree (BT) and dynamic movement primitives (DMPs) enhanced by the feedback from perceived information for dynamic and uncertain tasks and environments. Human sensorimotor control allows interactions with various environments and accomplishes complex manipulation tasks with uncertainty;however, it is still hard for robots to own this capability. In this work, we aim to transfer these reactive skills to robots, which enable robots to interact with humans and environments under varying uncertainty. The main challenges of robot skill learning are the generalization, safety and stability issues during the skills learning and execution autonomously. BT was investigated for task planning and reactive interaction during the robot execution. The dynamic system-based model generates action for the low-level compliant controller. The convergence of the proposed IRDS framework under dynamic interaction and disturbance was proved by control theory. We conducted simulations and physical experiments on the real robots to evaluate the generalization performance and the convergence capability under uncertainty. And the results show that the reactivity, convergence and interaction performance can be guaranteed, and the learned skills can be transferred among different physical robot platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human robot interaction

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Exercise Recognition and Repetition Counting for Automatic Workout Documentation Using computer Vision 15th

Exercise Recognition and Repetition Counting for Automatic W...

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15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Volschenk, Francois Vadapalli, Hima van der Haar, Dustin Univ Johannesburg Acad Comp Sci & Software Engn Cnr Univ Rd & Kingsway Ave ZA-2092 Johannesburg South Africa

ISBN: (纸本)9783031610653;9783031610660

This paper aims to study various approaches using deep learning methods to perform human action recognition (HAR). More specifically, a subset of HAR focused on recognising exercises and counting repetitions using deep learning. The paper discusses two approaches used in an attempt to produce a machine-learning model that is capable of identifying certain exercises from video input. This model is then incorporated into a system that can document a person's workout by identifying the exercises being done and counting the repetitions of each exercise. The study uses artificial training data in 3D animated videos of avatars performing the exercises. The dataset used is InfiniteRep from InfinityAI. Feature extraction and repetition counting are performed using the Mediapipe pose estimation model. An LSTM-based model and a 1D time-distributed CNN are used for exercise recognition. The models were compared on classification metrics: accuracy, precision, and recall. The LSTM-based model produced a 96% accuracy on the dataset, whereas the CNN-based model produced 97.3% accuracy on the same dataset. The CNN-based model is also capable of performing in near real-time.

关键词： computer Vision Action Recognition Machine Learning

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Closing the Information Gap in Unidentified Anomalous Phenomena (UAP) Studies 19th

Closing the Information Gap in Unidentified Anomalous Phenom...

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19th International Conference on Wisdom, Well-Being, Win-Win, iConference 2024

作者： Stahlman, Gretchen R. Florida State University TallahasseeFL32306 United States

ISBN: (纸本)9783031578496

Unidentified Anomalous Phenomena (UAP), also known as Unidentified Flying Objects (UFOs), has shifted from being a stigmatized topic on the fringes of scientific inquiry to a legitimate subject of scientific interest with a need for high quality, curated data, and rigorous scientific investigation. This paper presents a preliminary scoping review and analysis of scholarly literature related to UAP from 1967 until 2023, exploring a diverse range of research areas across disciplines to illustrate scholarly discourse about the topic. The paper focuses on characterizing papers published in recent years and notes that Library & Information science is unrepresented in the current UAP literature. The paper also discusses how researchers across the iFields can contribute to UAP studies through inherent expertise such as data curation and data science as well as information behavior and information literacy, among others. The paper concludes by emphasizing that UAP Studies offer a rich intellectual realm for information science research, with the iFields well positioned to play a crucial role in supporting and engaging in the study of UAP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： iFields Information science Literature Review UAP

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Rethinking Sampling for Music-Driven Long-Term Dance Generation 17th

Rethinking Sampling for Music-Driven Long-Term Dance Genera...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Truong-Thuy, Tuong-Vy Bui-Le, Gia-Cat Nguyen, Hai-Dang Le, Trung-Nghia University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819609161

Generating dance sequences that synchronize with music while maintaining naturalness and realism is a challenging task. Existing methods often suffer from "freezing" phenomena or abrupt transitions. In this work, we introduce DanceFusion, a conditional diffusion model designed to address the complexities of creating long-term dance sequences. Our method employs a past and future-conditioned diffusion model, leveraging the attention mechanism to learn the dependencies among music, past, and future motions. We also propose a novel sampling method that completes the transitional motions between two dance segments by treating previous and upcoming motions as conditions. Additionally, we address abruptness in dance sequences by incorporating inpainting strategies into a part of the sampling process, thereby improving the smoothness and naturalness of motion generation. Experimental results demonstrate that DanceFusion outperforms state-of-the-art methods in generating high-quality and diverse dance motions. User study results further validate the effectiveness of our approach in generating long dance sequences, with participants consistently rating DanceFusion higher across all key metrics. Code and model are available at https://***/trgvy23/DanceFusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Conditional Diffusion Model Music-to-Dance Sampling Strategy

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Core Stability and Nash Stability in k-Tiered Coalition Formation Games 8th

Core Stability and Nash Stability in k-Tiered Coalition Fo...

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8th International Conference on Algorithmic Decision Theory, ADT 2024

作者： Arnold, Nathan Goldsmith, Judy Department of Computer Science University of Kentucky 329 Rose St LexingtonKY40506-0633 United States Department of Computer Science and Information Technology Eastern Kentucky University 521 Lancaster Ave. RichmondKY40475 United States

ISBN: (纸本)9783031739026

The problem of k-tiered coalition formation games (k-TCFGs) has been considered for ranking members of a stochastic, intransitive round robin tournament, with the restriction that the ordering must have exactly k nonempty ranks for some integer k. As with other coalition formation games, an outcome of a k-TCFG may be evaluated for its stability, using the notions of Nash stability or core stability. An outcome is Nash stable if no one agent can move to a more preferable position, either by forming its own coalition or joining an existing one. An outcome is core stable if no set of agents can form a new coalition such that all agents in the set benefit. Previous research on k-TCFGs has focused on preferences derived from matchups, and has indicated that, under these matchup-oriented preferences, core stable outcomes may be significantly easier to find than Nash stable outcomes. However, the extent of this trend has not been explored. Here, we prove that for a key subset of k-TCFGs with matchup-oriented preferences, there is always at least one core stable partition. We include an illustration of the difference between Nash stabilizability and core stabilizability on an example game. We introduce a preference notation that can be used to represent any preference framework for k-TCFGs, and prove that under the subset of k-TCFGs which this notation can represent within polynomial space, the problem of determining if a game has a Nash stable list is NP-complete. © The Author(s), under exclusive license to Springer Nature Switzerland 2025.

关键词： Stochastic systems

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Adaptive Graph Convolutional Fusion Network for Skeleton-Based Abnormal Gait Recognition 27th

Adaptive Graph Convolutional Fusion Network for Skeleton-Bas...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Liang Wu, Jianning College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9783031783883

Dynamic interactions between human joints and bones convey significant information for skeleton-based abnormal gait recognition. Existing graph convolutional networks (GCNs)-based methods either only consider the locomotion information of the joints or treat the motion information of joints and bones independently, failing to explore the implicit dynamic interactions between joints and bones effectively. These interactions also contain rich and useful abnormal gait representation information. In this work, we propose a novel adaptive graph convolutional fusion network (AGCFN) for skeleton-based abnormal gait recognition. The joint motion information and bone motion information are modelled as a joint spatiotemporal gait graph and a bone spatiotemporal gait graph, respectively. Our AGCFN is designed to explore the interaction information between joints and bones through learning the inter-graph relationships between the above two gait graphs, so as to obtain more discriminative gait feature representations. Extensive experiments on our abnormal gait dataset demonstrate that the generalization performance of our model exceeds the state-of-the-art by a significant margin. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Joints (anatomy)

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Attention-Guided Self-supervised Framework for Facial Emotion Recognition 20th

Attention-Guided Self-supervised Framework for Facial Emoti...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Hassan, Saif Ullah, Mohib Imran, Ali Shariq Cheikh, Faouzi Alaya Norwegian University of Science and Technology Gjøvik2815 Norway Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9789819970247

Facial expression recognition is pivotal in computer vision and finds applications across various domains. In this paper, we proposed a self-supervised learning approach for precise facial expression recognition. Our approach leverages recent advancements in diffusion models, specifically the Classification and Regression Diffusion (CARD) model. To enhance the discriminative capability of our model, we integrate the Convolutional Block Attention Module (CBAM), an effective attention mechanism, to extract pertinent and discriminative feature maps. Furthermore, we capitalize on unlabelled data by using the simple contrastive learning framework of self-supervised learning (SSL) to extract meaningful features. To evaluate the performance, we conduct extensive experiments on the FER2013 dataset, comparing our results with existing benchmarks. The findings reveal significant performance improvements, achieving 66.6% accuracy on the FER2013 dataset. The quantitative results demonstrate the efficacy of our proposed SSL-based model in achieving accurate and robust facial expression recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Supervised learning

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Asynchronous Fully-Decentralized SGD in the Cluster-Based Model 1

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Attiya, Hagit Schiller, Noa Department of Computer Science Technion Haifa Israel

ISBN: (数字)9783031304484

ISBN: (纸本)9783031304477

This paper presents fault-tolerant asynchronous Stochastic Gradient Decent (SGD) algorithms. SGD is widely used for approximating the minimum of a cost function Q, a core part of optimization and learning algorithms. Our algorithms are designed for the cluster-based model, which combines message-passing and shared-memory communication layers. Processes may fail by crashing, and the algorithm inside each cluster is wait-free, using only reads and writes. For a strongly convex Q, our algorithm can withstand partitions of the system. It provides convergence rate that is the maximal distributed acceleration over the optimal convergence rate of sequential SGD. For arbitrary smooth functions, the convergence rate has an additional term that depends on the maximal difference between the parameters at the same iteration. (This holds under standard assumptions on Q). In this case, the algorithm obtains the same convergence rate as sequential SGD, up to a logarithmic factor. This is achieved by using, at each iteration, a multidimensional approximate agreement algorithm, tailored for the cluster-based model. The general algorithm communicates with nonfaulty processes belonging to clusters that include a majority of all processes. We prove that this condition is necessary when optimizing some non-convex functions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Message passing

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Image Super-Resolution with Multi-scale Hybrid Attention 27th

Image Super-Resolution with Multi-scale Hybrid Attention

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Ningzhi Shi, Hanyi Ruan, Wenna Zeng, Lingbin School of Artificial Intelligence Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Field Engineering College Army Engineering University of PLA Nanjing China National University of Defense Technology Changsha China

ISBN: (纸本)9783031784972

Super-resolution reconstruction stands as a critical task within the domain of computer vision. To enhance texture information extraction and improve visual perception, we introduce the Multi-Scale Hybrid Attention Network (MSHA), a novel single-image super-resolution model engineered to augment image global processing capabilities while accentuating detail reconstruction. The MSHA architecture seamlessly integrates a Multi-Scale Feature Block (MFB) for comprehensive feature extraction and a Parallel Hybrid Attention Module (PHA) for refining detail reconstruction capabilities. Through extensive experimentation and comparative analyses against state-of-the-art models, we substantiate the superior performance of the MSHA network in producing high-quality super-resolution images. Our methodology effectively addresses the shortcomings of existing approaches by emphasizing multi-scale feature extraction and detail reconstruction, thereby significantly advancing the field of image super-resolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-scale Feature Extraction Parallel Hybrid Attention Single Image Super-Resolution

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