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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 558 条记录，以下是281-290 订阅

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Application of Deep learning-Based User Intent Recognition in human-computer interaction

Application of Deep Learning-Based User Intent Recognition i...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Lingling Qiu Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

With the rapid development of human-computer interaction (HCI) technology, accurately recognizing user intent has become crucial for enhancing user experience. this paper focuses on deep learning-based user intent recognition technology and explores its innovative applications in HCI. We first review recent advances in deep learning in the field of natural language processing, particularly its superior performance in user intent recognition tasks. By constructing deep learning models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Transformers, this research aims to extract high-level features from user input data including text, speech, or behavioral data to achieve precise user intent recognition.

关键词： human computer interaction Deep learning Intent recognition Reviews Speech recognition Feature extraction Visual effects Transformers User experience Optimization

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Maximizing Sum Rate in Wireless Communication Systems with Dual Intelligent Reflecting Surfaces

Maximizing Sum Rate in Wireless Communication Systems with D...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Yuxian Zhu Xiangwei Dang Wenle Bai School of Information Science and Technology North China University of Technology Beijing China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Intelligent Reflecting Surface (IRS) has emerged as a promising technology to significantly enhance the performance of wireless communication systems. this paper presents a comprehensive study of a double-IRS assisted multi-user communication system, proposing a novel joint optimization method. A joint optimization approach for passive beamforming in dual IRSs and active beamforming at the base station is applied to maximize the system sum rate. the problem is formulated as a non-convex optimization challenge, addressed through a proposed low-complexity iterative algorithm based on alternating optimization with semi-closed-form solutions. Simulation results validate our theoretical analysis and demonstrate the significant performance advantages of the proposed scheme.

关键词： Wireless communication machine learning algorithms Array signal processing Simulation Optimization methods machine learning Reflection Numerical models Iterative methods Optimization

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An Implemented review for Intent Creation using different Clustering Techniques 5

An Implemented review for Intent Creation using different Cl...

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5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Magoo, Chandni Singh, Manjeet J.C. Bose University of Science and Technology Department of Computer Science India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

One of the major challenges for any interactive system is understanding the user needs or technically called intent detection. A human interacts with a machine with a same expectation as with another person. the key factor for a machine to make a human like interaction is to know the intent of the user before answering the query. thus intent detection is one of the crucial and important tasks of any interactive system. Moreover detection of intent prior requires labeled data for training under supervision which can be expensive. though very few papers have been published showing the methods for intent creation, the main focus of this research is to create the intents before its detection. this paper proposes and implements various techniques like K-means, Agglomerative and LDA for creating the labels. After the creation of the clusters as labels, transfer learning technique called zero shot learning is applied for detecting the intents. Experiment analysis shows that Agglomerative clustering generates better clusters as compared to K-means and LDA. © 2022 IEEE.

关键词： Zero-shot learning

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Comparison of Channel Attention Mechanisms and Graph Attention Mechanisms Applied in Multi-Robot Path Planning Based on Graph Neural Networks

Comparison of Channel Attention Mechanisms and Graph Attenti...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Yunzhao Xie Yunsheng Wang Shipu Xu Shanghai Institute of technology Fengxian Shanghai China Shanghai Academy of Agricultural Sciences Fengxian Shanghai China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Graph Neural Networks (GNNs) are applied to multi-robot path planning due to their advantages in learning communication strategies in decentralized multi-intelligence systems, and to prioritize the important information, we introduce the channel attention mechanism and the graph attention mechanism in GNNs. the channel attention mechanism automatically obtains the importance of each feature channel through network learning, assigns different weight coefficients to each channel, and calculates the importance of each channel of the input image so as to enhance the important features and suppress the unimportant features. Unlike the channel attention mechanism, which calculates the importance of each channel to improve the feature representation, the graph attention mechanism is a new type of neural network that operates on graph-structured data and uses a masked self-attention layer to address the shortcomings of graph convolution-based methods.

关键词： human computer interaction Attention mechanisms machine learning algorithms Noise System recovery Robot sensing systems Path planning Graph neural networks Stability analysis Robustness

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Objective Metrics for Assessing Visual Complexity of Vehicle Dashboards: A machine-learning Based Study 5th

Objective Metrics for Assessing Visual Complexity of Vehicle...

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5th international conference on HCI in Mobility, Transport, and Automotive Systems, MobiTAS 2023, held as part of the 25th international conference on human-computer interaction, HCII 2023

作者： Bai, Huizhi Liu, Zhizi Fu, Ziqi Liu, Zihao Zhang, Huihui Zhu, Honghai Zhang, Liang Institute of Psychology Chinese Academy of Sciences Beijing China Chongqing Changan Automobile Co. Chongqing China Department of Cognitive Science McGill University Montreal Canada Research Center of Adolescent Psychology and Behavior School of Education Guangzhou University Guangzhou China School of Psychology Nanjing Normal University Nanjing China

ISBN: (纸本)9783031359071

Dashboard is a central component of an in-vehicle information system (IVIS), and plays a crucial role in providing drivers with key information related but not limited to driving. With the expansion of the IVIS features, modern dashboards additionally integrate various new elements, which often leads to an increase in their visual complexity. Since high visual complexity of the dashboards threatens driving safety and performance, it is essential for researchers and designers to understand what objective features of the dashboards are related to their perceived visual complexity (PVC) so as to establish more cognitively efficient dashboards. In the present study, we refined the objective metrics of assessing visual complexity proposed in previous research and added two new dimensions, colors and animation, to better characterize recent development in the dashboard displays. We then utilized the indicators in the metrics to predict the dashboard PVC. machine learning was innovatively applied, and the models were found to have stable performance. the study contributes reliable metrics and novel methodology to evaluate the visual complexity of the dashboards for the reference of future studies. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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X-DeepID: An Explainable Hybrid Deep learning Method for Enhancing IoT Security with Intrusion Detection 1

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5th international conference on Soft Computing and its Engineering Applications (IcSoftComp)

作者： Bhagat, Gautam Mishra, Khushboo Dutta, Tanima Indian Inst Technol BHU Varanasi Varanasi Uttar Pradesh India

ISBN: (数字)9783031537318

ISBN: (纸本)9783031537301;9783031537318

the Internet of things (IoT) seamlessly integrates numerous devices with minimal human intervention, enabling effective communication between them, which has further enhanced the reliability across the diverse range of applications, including healthcare, intelligent agriculture, home security, industrial settings, and smart cities. However, the inherent nature of IoT infrastructure and its complex deployment components give rise to novel security challenges. Traditional security measures like encryption and access control prove insufficient in detecting attacks. therefore, it becomes imperative to enhance the current security mechanisms in order to establish a safeguarded IoT environment. the progression of deep learning techniques introduces embedded intelligence to the IoT domain, addressing various security concerns. Nonetheless, these machine learning models tends to yield elevated false-positive rates, due to which comprehending the reasons behind its forecasts can be complex, even for experienced experts. the capacity to grasp the rationale behind an Intrusion Detection System's (IDS) choice to block a specific packet is crucial for cyber security professionals. this comprehension empowers them to verify the system's efficacy and to engineer more robust and cyber-resilient systems. In this work, we put forth a framework for intrusion detection system which is based on explainable hybrid deep learning model. this framework aims to enhance the clarity and robustness of Deep learning-based IDS within IoT networks. the experimental findings have showcased the exceptional achievement of the proposed work, achieving an impressive accuracy of 99.25% and an outstanding F1 score of 98.91%. these results vividly demonstrate the framework's capacity to safeguard IoT networks effectively against sophisticated cyber attacks.

关键词： Hybrid approach Explainable Framework Internet of things

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Fault Alarm Communication Scheduling Architecture and Method for Power System 5

Fault Alarm Communication Scheduling Architecture and Method...

引用

5th IEEE international conference on computer and Communication Engineering Technology, CCET 2022

作者： Sun, Xiangjv Zhan, Wen Hao Li, Ying State Grid Gansu Electric Power Company Digital Business Department Lanzhou China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Today, operation and maintenance security has become a key factor for the stable development of enterprises. the flexible alarm information communication scheduling structure and intelligent fault alarm method are of great significance to improve the accuracy of alarm judgment, which can greatly realize the safe production of power enterprises and effectively ensure their steady and healthy development. therefore, this paper proposes a modular structure. the alarm information communication scheduling architecture of the system uses unsupervised machine learning algorithms to predict fault information, analyze the correlation between faults, and at the same time achieve a high degree of coordination between modules, thereby providing an efficient and stable alarm method. © 2022 IEEE.

关键词： learning algorithms

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Body Language Decoder Using Python

Body Language Decoder Using Python

引用

Emerging Technology (INCET), international conference for

作者： Husanpreet Kaur Jyoti Sanjana Devi Rahul Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In human connection, nonverbal cues, especially body language, are extremely important. Although it might be difficult to interpret these subtle indications, doing so can provide important insights into the motivations and behaviors of people. Body Language decoder using Scikit-learn, OpenCV, MediaPipe, and Python automatically decipher and analyze body language cues from recorded or live video streams. captures and processes video frames in realtime using OpenCV, a potent computer vision toolkit. the Body Language Decoder provides a flexible foundation for comprehending human communication patterns and enabling more user-friendly interfaces, ranging from emotion recognition to gesture interpretation. By detecting gestures, facial expressions, and body postures, this initiative seeks to improve human-computer interaction while offering important insights into the intents and behavior of the user. the technology provides an invaluable resource for improving communication, comprehending social dynamics, and developing more efficient human-machine interfaces by automating the interpretation of body language clues.

关键词： human computer interaction Visualization Webcams Virtual environments machine learning Streaming media Feature extraction

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Speech Emotion Recognition, Datasets, Features and Models: A Review 5

Speech Emotion Recognition, Datasets, Features and Models: A...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Geethu, V. Vrindha, M.K. Anurenjan, P.R. Deepak, S. Sreeni, K.G. College of Engineering Computer Vision Lab Kerala Trivandrum India Rajiv Gandhi Institute of Technology Kerala Kottayam India APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9798350334128

there are several ways in which we can express our emotions, such as through our voice, body language, facial expressions, and eye contact. Speech is a crucial means of conveying our feelings and viewpoints to others, and as a result, emotions play a vital role in human communication and connection. therefore, we can use speech to understand our own emotions as well as those of others. With the advancement of deep learning, speech emotion recognition (SER) has gained increasing importance in fields like human-machine interaction. SER technology can analyze the emotional content of speech and provide insights into the speaker's emotional state. this technology has the potential to improve communication and interaction between humans and machines, as well as offer valuable insights in various fields such as psychology, marketing, and customer *** review covers a range of techniques for identifying emotions in speech and speech-text pairs. A variety of deep learning and machine learning techniques have been developed in this field, including CNN-based, LSTM-based, and SVM-based systems for processing voice signals, and BERT, GRU, and other networks for processing text. the main focus of the study is on feature extraction, classification techniques, widely used datasets, and result comparison. In other words, the review discusses how different methods can be used to extract relevant features from speech and text data, how these features can be used to classify emotions, the datasets that are commonly used for evaluating these techniques, and how the results of different methods compare to each other. Overall, this review aims to provide a comprehensive overview of the current state of the field of speech emotion recognition and identify areas for further research. © 2023 IEEE.

关键词： Emotion Recognition

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Conceptual Design of Virtual Avatars Based on Intelligent human-machine Interface in Autonomous Driving

Conceptual Design of Virtual Avatars Based on Intelligent Hu...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Runting Tang Jianmin Wang College of Arts and Media Tongji University Shanghai China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

this study explores the conceptual design of virtual avatars in autonomous driving and their trigger mechanisms in the context of the Intelligent human-machine Interface (HMI). through the intelligent system's perception of the situation, virtual avatars can be dynamically triggered based on vehicle status, environmental information, and driver behavior, enabling precise information delivery and real-time feedback. the research indicates that users have high expectations for virtual avatars in terms of safety notifications, emotional expression, and personalized design. this study provides a reference for the design of virtual avatars in future autonomous driving systems and proposes prospects for further adaptive optimization.

关键词： human computer interaction Surveys Avatars human-machine systems Real-time systems Safety Intelligent systems Driver behavior Autonomous vehicles Vehicles

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