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4th IEEE international conference on Parallel, Distributed and Grid computing (IEEE PDGC)

作者： Madhusudan Sharma, Aman Kumar Himachal Pradesh Univ Dept Comp Sci Shimla Himachal Prades India

ISBN: (纸本)9781509036691

the recent developments in the field of human-computer interaction have emphasized the focus of design more towards user-centered rather than computer-centered approach. this led to design of interfaces which are highly effective, intelligent and adaptive, and can adjust themselves with respect to the user's behavioral changes. In this study, interaction challenges for disabled people are listed and the possible alternatives via intelligent and affective devices are proposed for such users.

关键词： HDI human computing affective interfaces intelligent interfaces emotions

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BLH-Slice Modeling Used in human-computer interaction of Ellipsoidal Volume Clipping

BLH-Slice Modeling Used in Human-Computer Interaction of Ell...

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intelligent computing and human-computer interaction (ichci), international conference on

作者： Jiarun Wang Fan Yang Battlefield Environment Department North China Institute of Computing Technology (NCI) Beijing China

Volume clipping is a human-computer interaction in volume rendering, which is mainly used to display the interested parts in the volume data and hide other ones. However, in the ellipsoidal ray casting algorithm (ERCA) for digital earth, there is still a gap in constructing the selection object in volume clipping conforming to the geodetic or ellipsoidal coordinate system. this paper provides an ellipsoidal BLH-slice volume clipping algorithm (EBLHVC), which gives the simplest mathematical representation of three typical selection object surfaces in the geodetic or ellipsoidal coordinate system, and presents a direct ray sampling method based on the intersection calculation using algebraic equation to realize volume rendering of the selection object surface. the article provides a concise slice display for volume rendering on digital earth.

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Prediction Research of AIDS Monthly Incidence Cases and Mortality Cases based on Prophet Model 3

Prediction Research of AIDS Monthly Incidence Cases and Mort...

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3rd international conference on intelligent computing and human-computer interaction, ichci 2022

作者： Yang, Qiuying Gao, Xingyuan Wang, Ke Liang, Hongmei School of Biomedical Engineering Capital Medical University Beijing China Beihang University Beijing China China Railway 17th Bureau Group Central Hospital Shan Xi China

ISBN: (纸本)9781510661301

As a chronic disease endangering human life, AIDS seriously hurts human health and living. the study on the AIDS prediction model can provide a critical basis for the prevention and control decision-making and resource allocation. this paper selected the monthly AIDS incidence cases and mortality cases for ten years (July 2012-June 2022) as the research objects. We analysed the data, combined the national AIDS transmission characteristics, and established the prophet model. Realized the Prophet Model modelling, verification, and prediction of monthly AIDS incidence cases and mortality cases and forecast them for the next six months in China. the actual data showed a completely fluctuating trend. In addition, from the forecast results, this trend is still relatively explicit in the future. the MAE of incidence cases is 400.68, which accounts for 7.6% of the mean actual values. the MAE of mortality cases is 74.22, which accounts for 4.47% of the mean actual values. Effective modelling and prediction of HIV incidence cases and mortality cases enable AIDS patients to recognize the harm in a time and effectively prevent and control its occurrence. © 2023 SPIE.

关键词： Decision making

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TL-DREN: Transfer Learning Based Detection and Recognition of Electricity Nameplates 5

TL-DREN: Transfer Learning Based Detection and Recognition o...

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5th international conference on intelligent computing and human-computer interaction, ichci 2024

作者： Li, Jining Wen, Bao Cai, Kun Li, Yanming Ma, Ming Li, Xin Tan, Shufeng Wu, Danqi Wang, Juyuan Guangdong Electric Power Research Institute Guangzhou China Guangdong Power Grid Co. Ltd. Guangzhou China South China University of Technology Guangzhou China

ISBN: (纸本)9798350368284

this paper presents a novel approach that integrates traditional image processing techniques with deep learning to enhance the accuracy and robustness of fuzzy text detection and recognition in nameplate images of power equipment. Initially, traditional methods are employed to locate the nameplate and extract information, followed by perspective transformation to correct image distortions. Subsequently, deep learning models are utilized for text detection and recognition. Two specialized datasets were constructed for this study: one for text detection, comprising 841 high-resolution images captured under various environmental conditions, and another for text recognition, consisting of 42,556 cropped text region images derived from the corrected nameplate images. the PP-OCRv4 model was fine-Tuned on our curated dataset to improve its performance on text detection and recognition tasks specific to power equipment nameplates. Experimental results demonstrate that our proposed method achieved an F1-score of 0.524 for text detection and an accuracy of 0.82 for text recognition, showcasing the potential of this approach to enhance the efficiency of automated management of power equipment. © 2024 IEEE.

关键词： Deep Learning Image Recognition PP-OCRv4 Text Detection Text Recognition

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Constructing and sharing everyday memory for community knowledge co-evolution

International Conference on Knowledge-Based Intelligent Elec...

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international conference on Knowledge-Based intelligent Electronic Systems, Proceedings, KES 2000年 1卷 76-79页

作者： Hirata, Takashi Murakami, Harumi Nishida, Toyoaki Uemura, Shunsuke Nara Inst of Science and Technology Nara Japan

In this paper, we describe a method for supporting the process of accumulating personal everyday memory and sharing it in a community. Two ideas are used to implement. the first idea is intimate computing which enables people to externalize and accumulate personal memory using associative representation at any time and any place. the second idea is talking virtualized egos metaphor that visualizes community knowledge interaction. this helps community members to find out common interests and shared knowledge. Based on these ideas, we developed the system called CoMeMo which helps people to manage personal and community memory. In experiment, we observed the following: (a) Collaborative knowledge building. (b) Change of shared information, (c) Creation of new human relationships.

关键词： human computer interaction

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Modeling Operational Profile for AI Systems: A Case Study on UAV Systems

Modeling Operational Profile for AI Systems: A Case Study on...

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intelligent computing and human-computer interaction (ichci), international conference on

作者： Cong Pan Yan Gao Ai Gu China Electronics Reliability and Environmental Testing Institute Guangzhou China Key Laboratory of General Quality Technology and Application for Intelligent Manufacturing Equipment

Operational profile (OP) and reliability testing is vital to ensure the reliability of artificial intelligence (AI) systems. this paper presents the AIOP (AI-oriented Operational Profile) method, which enhances reliability testing for AI systems, by incorporating environmental and data profiles to comprehensively represent their internal functioning and interaction with the external environment. through the implementation of the AIOP framework and the introduction of feature-oriented profile modeling for environmental and data profiles, this study offers valuable insights for improving system reliability and reducing potential risks in the development and testing of AI systems across various domains. A case study on the UAV system, DroneResponse, demonstrates the effectiveness of the AIOP method.

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A Deep Learning Model for Classification of MDD Patients Studied with EEG Microstate Features 5

A Deep Learning Model for Classification of MDD Patients Stu...

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5th international conference on intelligent computing and human-computer interaction, ichci 2024

作者： Jiang, Yuchen Li, Yiyang Yang, Zhiwu Cao, Meng'An Li, Zhaobo Wu, Fengchun Wu, Kai Zhou, Jing School of Biomedical Sciences and Engineering South China University of Technology Guangzhou International Campus Guangzhou511442 China The Affiliated Brain Hospital Guangzhou Medical University Department of Psychiatry Guangzhou510370 China School of Material Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9798350368284

the quasi-steady electric fields in electroencephalogram (EEG) are usually called microstates. In the field of neuroscience, methodologies like fMRI, EEG, and MEG have uncovered essential insights into the dynamics of extensive brain networks. Additionally, these techniques have quantified network characteristics using computational neuroscience approaches, which are vital for comprehending both brain information processing and pathological alterations. this study explored the role of abnormal resting-state microstate features in the recognition of MDD patients by deep learning. through factor analysis of microstate parameters, a new feature set that could explain the original microstate features was created and applied to the construction of a convolutional neural network (CNN) classification model. At the same time, a backpropagation learning mechanism was introduced, and the model's performance in distinguishing patients with MDD from healthy individuals was comprehensively evaluated using the k-fold cross-validation approach, alongside metrics such as accuracy, precision, and AUC. this provides strong support for medical research and clinical decision-making. the findings indicate that the accuracy of CNN classification is 97.8%, the precision is 98.2%, and the AUC is 0.980. the performance of the model shows that features of resting-state EEG microstate segmentation can be obtained to effectively assist in the identification of MDD patients. © 2024 IEEE.

关键词： Electroencephalography

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Opening the Black Box: Interpretable Machine Learning Reveals the Relationship Between Lexical Diversity and Writing Quality

Opening the Black Box: Interpretable Machine Learning Reveal...

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intelligent computing and human-computer interaction (ichci), international conference on

作者： Minzi Li Zhiqing Lin Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies Guangzhou China School of English Studies Shanghai International Studies University Shanghai China

Machine learning serves as the core component of artificial intelligence (AI), however, there is scant interdisciplinary research between AI and applied linguistics. Very few studies touched upon the aspect of how to leverage interpretable machine learning to solve the problem in language learning, teaching, and assessment. this study aims to lift the veil and fill this gap by employing interpretable machine-learning approaches to explore the relationship between lexical diversity and writing quality. the findings contributed to providing a finer-grained understanding of the CET-4 writing construct and its rating scale. Furthermore, it may lend empirical evidence for the Dynamic Systematic theory (DST) in language acquisition, verify the validity of automatic essay scoring, and advance teachers' language assessment literacy in the long run.

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Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel computing

Optimization Study of KNN Classification Algorithm on Large-...

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intelligent computing and human-computer interaction (ichci), international conference on

作者： Kangjie Cao Jinyao Wu Qianhang Huang Yichao Gan Department of Computer Science and Technology Minzu University of China Beijing China

the K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression problems. However, the KNN algorithm faces serious challenges when dealing with high-dimensional large-scale datasets, mainly in terms of high computational complexity, low search efficiency, and difficulty in real time. In this study, an optimization strategy based on balanced KD tree and multithreaded parallel computing is proposed to address this problem. Experimental results show that the optimized KNN algorithm significantly improves the search speed while maintaining classification accuracy, realizes real-time classification of large-scale datasets, and provides a powerful tool for pattern recognition, data mining, and other fields in the era of big data.

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Design of human-computer interaction gesture operation based on STM32 and MPU6050 4

Design of human-computer interaction gesture operation based...

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4th IEEE international conference on Power, intelligent computing and Systems, ICPICS 2022

作者： Xie, Pengfei Wu, Wenhao Zhang, Yan Nanjing University Jinling College School of Information Science and Engineering Nanjing China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

In order to improve the convenience of computer use, a human-computer interaction mode of gesture operation is proposed. With the main functions of collecting and processing user environment information and obtaining their own state, and with the characteristics of fast, convenient, attached and anytime, anywhere, a wearable gesture operated human-computer interaction device is designed. the system is composed of STM32F103 single chip microcomputer, six axis sensor mpu6050, Bluetooth hid module XM-04-HID-K, etc. the six axis sensor fixed on the back of the hand obtains the hand inclination angle in real time. the CPU determines the instructions to be executed according to the received data and converts the operation into instructions. All instructions are integrated in a specific packet format and sent to the upper computer through the Bluetooth hid module, the test shows that the mouse pointer can be controlled by the movement of the hand. © 2022 IEEE.

关键词： human computer interaction

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