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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 548 条记录，以下是41-50 订阅

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Examining the Relationship Between Playing a Chord with Expressions and Hand Movements Using MediaPipe

Examining the Relationship Between Playing a Chord with Expr...

引用

conference on human Interface and the Management of Information thematic Area (HIMI) Held as Part of the 26th international conference on human-computer interaction (HCII)

作者： Oshima, Chika Takatsu, Taiyo Nakayama, Koichi Saga Univ Saga Japan

ISBN: (纸本)9783031601248;9783031601255

this paper examined whether hand movements are responsible for expression in a piano performance. A player played a chord 100 times each with 12 different performance expressions, which consisted of three articulations (tenuto, heavy staccato, and light staccato) and fourlevel dynamics. the landmarks' coordinates of her right fingers, wrist, elbow, and shoulder estimated as she played the chord (12 x 100 times), judged by MediaPipe Pose and Hands, were used for machine learning training and testing. In the results for the learning model, the testing accuracy rate was 0.99. In each performance expression, F1-scores were 0.94-1.00. this suggested a relationship between performance expressions and hand movements. Moreover, when the player happened to play a different, unintended type of expression, her landmarks' coordinates were close to those when she had aimed exactly to play that type of performance expression.

关键词： Piano MIDI machine learning

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An Improved Adversarial Convolutional Transformer Model for Time Series Forecasting 5

An Improved Adversarial Convolutional Transformer Model for ...

引用

5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Zhang, Minglan Cheng, Weiqi Sun, Linfu Zou, Yisheng School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350368284

Time series forecasting, as an important research topic in time series analysis, plays a crucial role in many fields. Traditional Transformer model often suffers from low feature dimension extraction and poor generalization ability when performing time series forecasting tasks. To address these issues, this paper proposes an Improved Adversarial Convolutional Transformer (IACT) model for time series forecasting. First, multi-dimensional features are extracted using the multi-head attention mechanism, and then more features are captured by multi-dimensional convolutional subspace in different dimensions to improve the representation capabilities of features. Meanwhile, an adversarial training strategy is used to make the model adapt to a larger range of data variations, which makes the model have a stronger generalization ability. Finally, experiment results conducted on two real time series datasets show that the model can obtain excellent prediction effects and generalization performance. In addition, the proposed model outperforms benchmark models such as Transformer, Pyraformer, and Informer in terms of prediction accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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A Gamified Platform for Educating Children About their Legal Rights 5

A Gamified Platform for Educating Children About Their Legal...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Reddy, B. Ramasubba Neelima, P. Kumar, M.Sunil Sushama, C. Ganesh, D. Yethish, Y. Andhra Pradesh Tirupati India School of Engineering & Technology Department of Computer science and Engineering Spmvv Tirupati India Dept of Cse Andhra Pradesh Tirupati India

ISBN: (纸本)9798331504403

In order to raise children's understanding of their legal rights, this article suggests a gamified online platform. there is a lack of scale, involvement, and interaction in the present human rights education initiatives. the proposed platform's decision-making ability and support vector machine algorithms work together to provide children with age-appropriate, interactive content about their fundamental rights in the form of quizzes, tales, and situations. Various groups of youngsters demonstrate considerable advances in their rights knowledge, according to evaluations. the gamified approach's revolutionary use of artificial intelligence for adaptive learning and its widespread accessibility give it great promise for transforming child rights protection in India and providing children with legal information on a large scale. the platform offers an appealing solution that is focused on children and fills significant holes in current awareness campaigns. It has the potential to make a significant difference in protecting children's rights. © 2024 IEEE.

关键词： Adversarial machine learning

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Capturing the Details Around Me: Exploring the Innovation of Multimodal conference Headphones and User Satisfaction 5th

Capturing the Details Around Me: Exploring the Innovation of...

引用

5th international conference on human-Centered Design, Operation and Evaluation of Mobile Communications held as Part of the 26th international conference on human-computer interaction (HCII)

作者： Xu, Jing Huang, Guanghui Macau Univ Sci & Technol Fac Humanities & Arts Ave Wai Long Taipa Macau Peoples R China

ISBN: (纸本)9783031604867;9783031604874

this research delves into examining the correlation between channels for spreading information and the behaviour of users. the objective is to revamp the design of headphones that are suitable for use in office settings, to enhance the communication experience while on the move, and to enrich methods for presenting the information. the design process was informed by interviews with experts and observations of users. Before investing in the user experience, the system's usability was evaluated by experts in smart design and industrial design. the system facilitates information transmission through touch and visual methods, enabling real-time camera-based remote display. the results of the ANOVA single-factor analysis and Likert scale indicate that the combination of visual and tactile interaction modes enhances user satisfaction and the allure of expression. the article concludes that maintaining consistency between user expression and communication methods, as well as reducing the learning curve for new features, is crucial for enhancing user satisfaction. the research findings offer valuable insights for future innovative applications in new scenarios that involve multiple modes of interaction.

关键词： human-computer interaction Multimodal interaction channels Mobile office conference headset design User satisfaction

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Potential in Quantum machine learning for Real-World Problems 5

Potential in Quantum Machine Learning for Real-World Problem...

引用

5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Tiwari, Rajesh Kumar Hamid, Abu Bakar Bin Abdul Nyamasvisva, Tadiwa Elisha RVS College of Engineering and Tech. Research Scholar IUKL Malaysia IUKL Faculty of Business Information and Human Science Malaysia Head of Postgraduate Program IUKL Malaysia

ISBN: (纸本)9798350351378

Quantum computing offers exponential speedup over classical computing, particularly beneficial for machine learning tasks burdened by large datasets and complex models. Quantum machine learning (QML) exploits quantum principles like superposition and entanglement to enhance efficiency and scalability. QSVMs and quantum k-means algorithms exemplify QML's prowess in classification and clustering tasks, respectively. QML's potential extends to solving real-world problems in diverse domains, including chemistry, logistics, and healthcare. However, hardware limitations and the interdisciplinary nature of QML present challenges. Nonetheless, QML holds promise for revolutionizing computational solutions across industries, offering faster, more efficient, and scalable approaches. As research progresses in quantum computing and machine learning, the impact of QML on society and industry is expected to increase, heralding a new era of computational innovation. © 2024 IEEE.

关键词： K-means clustering

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Real Time Activity Monitoring Using Deep learning 5

Real Time Activity Monitoring Using Deep Learning

引用

5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sujatha, E. Janani, D. Saveetha Engn Coll Autonomous Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

computer vision, a cornerstone in computer science, equips machines with the ability to interpret digital images and videos, enhancing their intelligence. Activity recognition, a vital domain within computer vision, involves automatic categorization of agent actions, with a focus on identifying suspicious activities in diverse contexts. In real-time safety monitoring, it proves crucial in identifying anomalies and potential security breaches. Automated human activity recognition significantly impacts video surveillance, benefiting indoor and outdoor environments. Intelligent surveillance systems acting as vigilant digital eyes, identifying abnormal patterns or behaviors. Sensors like cameras and cell phones underpin human activity detection, extending beyond surveillance to facilitate seamless human-computer interaction. there is a pressing need for a proactive shift in surveillance systems. Instead of reactive responses post-incident, there should be a focus on proactive identification of anomalies, enabling authorities to intervene before situations escalate, ensuring the safety of individuals and communities. the integration of automated human activity recognition into intelligent video surveillance systems represents a technological leap, enhancing our understanding of human behavior and providing tools for preemptive action, fostering a safer environment for all. Recognizing objects in diverse environments using YOLO3 and CNN represents a intricate task within the domains of computer vision and artificial intelligence. the integration of advanced technologies such as AI, ML, and deep learning has greatly strengthened video surveillance systems. these systems employ a combination of these technologies to distinguish suspicious activities from live footage tracking. However, the unpredictable nature of human behavior adds complexity to this distinction between suspicious and normal activities. To address this challenge, deep learning methods are applied in settin

关键词： computer vision activity Yolo CNN object detection tracking alert

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A Healthcare Data Sharing Scheme Based on Homomorphic Encryption and Federated Metric learning 5

A Healthcare Data Sharing Scheme Based on Homomorphic Encryp...

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5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Zhang, Haoming Liu, Zhentao Wu, Lang Li, Han School of Applied Science Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798350368284

Medical data is highly sensitive, often leading to 'data silos' in sharing processes. To address this issue, this paper combines blockchain and federated metric learning technologies, and innovatively integrates them with homomorphic encryption technology to propose a privacy protection system suitable for application in medical data sharing scenarios. the system employs federated learning to train a global model across distributed client nodes without sharing raw data. Metric learning classifies clients as benign or malicious based on data similarity, only aggregating models from benign clients to improve efficiency and mitigate data distribution issues. Additionally, differential privacy and homomorphic encryption are used to further protect data privacy by preventing the original data from being reverse deduced and model parameter leakage during aggregation. Experimental results show that the system can achieve a prediction accuracy of 89.102564% under different data distributions, and its performance on medical datasets has been significantly improved compared to traditional federated learning. © 2024 IEEE.

关键词： Federated learning

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Research on Personalized Product Recommendation Algorithm Based on Deep learning 5

Research on Personalized Product Recommendation Algorithm Ba...

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5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Zhong, Ling Qu, Zhaoxin Li, Feng Liu, Zhenlong Shenyang University of Technology Shenyang China Shenyang University of Technology Department of Electronic Software Engineering Shenyang China Shenyang Mei Hang Technology Co. LTD Shenyang China

ISBN: (纸本)9798350368284

In view of the insufficiency of feature information utilization and overfitting in the recommendation task of Transformer network, this paper proposes a model combining random shared embeddings and multi-layer multi-head attention blocks to solve the problems still existing in the above deep learning-based recommendation algorithm. Aiming at the problem of multi-feature information utilization, this paper studies the use of multi-layer multi-head attention block to fuse multi-information. CARCA-SSE model is designed in this paper. Different from traditional location embedding, this paper uses the absolute date of user interaction as time embedding to make full use of existing information. To solve the overfitting problem, random shared Embedding is added to the recommended model based on CARCA, and the Embedding layer is randomly replaced by another Embedding with a certain probability. Using the replaced embedding can reduce the risk of overfitting and further improve the generalization ability, thus increasing the robustness of the model. © 2024 IEEE.

关键词： Stochastic models

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Dynamic Student Classification with Forgetting Mechanisms on Memory Networks for Knowledge Tracing 5

Dynamic Student Classification with Forgetting Mechanisms on...

引用

5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Huang, Guimin Qin, Xiangqian School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350368284

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods. © 2024 IEEE.

关键词： Contrastive learning

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Maneuvering Decision Modeling Method Based on Attitude Controller 5

Maneuvering Decision Modeling Method Based on Attitude Contr...

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5th international conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Yu, Yingfu Fu, Yupeng Zhu, Ziqiang Fang, Jun Naval Aviation University Yantai Shandong China

ISBN: (纸本)9798331528911

Reinforcement learning is developing rapidly in the field of CGF maneuver modeling, however, restricted by the uninterpretability of neural network and insufficient exploration of state space, the modeling of maneuver decision-making confronts poor stability under the boundary conditions, so it still needs the rule-based model as an effective supplement. A maneuver decision modeling method based on attitude controller is proposed, which can effectively avoid the singularity problem by using a quaternion attitude controller, and realize the shortest path transformation of any attitude by combining with the geometric spherical linear interpolation method. Integrated with a simple maneuver rule based on position advantage and threat assessment, the maneuver decision modeling is carried out. the platform is based on the JSBSim dynamics model, and the simulation results show that the controller is stable in 1 vs. 1 machine-machine and human-computer gaming with certain intelligence. © 2024 IEEE.

关键词： Reinforcement learning

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