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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 560 条记录，以下是521-530 订阅

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Virtual Reality as a Tool for the Cascade Control learning 5th

Virtual Reality as a Tool for the Cascade Control Learning<b...

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5th international conference on Augmented Reality, Virtual Reality, and computer Graphics (SALENTO AVR)

作者： Pruna, Edwin Rosero, Mauricio Pogo, Rai Escobar, Ivon Acosta, Julio Univ Fuerzas Armadas ESPE Sangolqui Ecuador

ISBN: (纸本)9783319952703;9783319952697

this work presents the development of an interactive didactic system for the learning of the cascade control technique of industrial processes, which uses an immersive virtual environment for the emulation of a plant behavior which can be monitored and controlled through an HMI (human-machine Interface). the virtual environment has been created through computer Aided Design software and a graphic engine and, in addition, peripheral devices are used to achieve the immersion and interaction with the environment. Finally, the experimental results that validate the system when it is tested with a real or simulated process are presented.

关键词： Virtual reality Unity3D Cascade control

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Cultural Association based on machine learning for Team Formation

Cultural Association based on Machine Learning for Team Form...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Hrishikesh Kulkarni Bradly Alicea Computer Engineering Department PVG's COET SPPU Pune INDIA Open Worm Foundation Orthogonal Research and Education USA

ISBN: (数字)9781728140421

ISBN: (纸本)9781728140438

Culture is core to human civilization. It evolved over time. Culture is about human intellectual achievements in social context. It is about how humans work together, perform particular task and overall lifestyle and dealing with other groups of civilization. thus, it is concerned with establishing ideas. Does culture play key role in success? Does it impact on how two individuals can work together in achieving certain goals? Are some key questions. In this paper, we establish a means to derive cultural association and map it to culturally mediated success. human interactions with the environment are typically in the form of expressions. Association between culture and behavior produce similar beliefs which lead to common principles and actions, while cultural similarity as a set of common expressions and responses. To measure cultural association among different candidates, we propose the use of a Graphical Association Method (GAM). the behaviors of candidates are captured through series of expressions and represented in the graphical form. Association among corresponding node and core nodes is used for the same. the proposed approach gives promising and interesting results.

关键词： machine learning natural language processing cultural computing computational social sciences human computer interaction data sciences

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Identifying the Responsible Group for Extreme Acts of Violence through Pattern Recognition 1

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5th international conference on HCI in Business, Government and Organizations (HCIBGO) Held as Part of the 20th international conference on human-computer interaction (HCI international)

作者： Hashemi, Mahdi Hall, Margeret Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (数字)9783319917160

ISBN: (纸本)9783319917160;9783319917153

the expansion of Internet has eased the broadcasting of data, information, and propaganda. the availability of myriads of social and televised media have turned the spotlight on violent extremism, widened the rift between different sides of the spectrum, and expanded the scope and impact of ideology-oriented acts of violence on citizens and nations. the human casualties and psychological impacts on societies make any study on such acts worthwhile, let alone attempting to detect patterns among them. this study focuses on mining the information about each violent act, including human casualties and fatalities, level of coordination and expertise, importance of the targeted process, and the extent of its impact on the process, to identify the responsible group. Decision tree, a non-linear classifier, reached 20% cross-validation accuracy in identifying the correct group among 38 groups. this is the highest accuracy achieved in comparison with other linear classifiers, including Perceptron, SVM, and least squares. Our results also underscored the human casualties and fatalities as the most important predictors. the other four variables, including level of coordination, level of expertise, importance of the targeted process, and the extent of the impact on the process were all partly correlated and less helpful. However, the single feature, generated by linear combination of these four features using PCA, was as good of a predictor as the human casualties and fatalities.

关键词： Decision tree Perceptron SVM Least squares Feature selection machine learning

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Smart Vessel Detection using Deep Convolutional Neural Network 5

Smart Vessel Detection using Deep Convolutional Neural Netwo...

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5th international conference on the HCT Information Technology Trends, ITT 2018

作者： Iwin thanakumar Joseph, S. Benson Edwin Raj, S. Sasikala, J. Sujitha, Juliet D Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamilnadu India Department of Computer Information Science Higher Colleges of Technology Fujairah United Arab Emirates Department of Information Technology Annamalai University Chidambaram Tamilnadu India

ISBN: (纸本)9781538671467

Detecting ships automatically from the satellite images is one of the major challenging constraint due to lot of disturbances because of cluttered scenes, ship sizes etc. Small object detection in satellite images like ships, boat, vessels is a challenging task. In the state of art method, numbers of features are used to enhance the accuracy of detection but that mostly happens in homogeneous environment. Few research articles deals with heterogeneous environment such as sea shore areas, harbors, islands etc. Deep learning is one of the emerging technology in the recent days especially in the field of computer vision to provide an accurate result. It paves the new channel to the entire machine learning paradigm and provides the advancement of artificial intelligence utilization and also enhances the human computer interaction in a much feasible manner. In this article the deep learning algorithms such as convolutional neural network is used with the proper hybrid and the performance is compared with the state of art method. the deep learning algorithms result excellent accuracy in terms of classification of satellite images without any manual feature extraction. Since the deep learning algorithms does not have the manual feature extraction, it provides an excellent experimental results in the classification of ships in satellite images that has complex background. © 2018 IEEE.

关键词： Image classification

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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5th international Workshop on Sensorbased Activity Recognition and interaction, iWOAR 2018

ISBN: (纸本)9781450364874

the proceedings contain 20 papers. the topics discussed include: is artificial intelligence (AI) taking over the world? are humans losing out to AI in the work place?;we all want to remain in our own homes;natural pursuits for eye tracker calibration;respiration rate estimation with depth cameras: an evaluation of parameters;fewer samples for a longer life span: towards long-term wearable PPG analysis;mobile assisted living: smartwatch-based fall risk assessment for elderly people;analysis of a public repository for the study of automatic fall detection algorithms;exploring accelerometer-based step detection by using a wheeled walking frame;real-time joint axes estimation of the hip and knee joint during gait using inertial sensors;attribute representation for human activity recognition of manual order picking activities;theodor: a step towards smart home applications with electronic noses;a machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems;using wrist-worn activity recognition for basketball game analysis;activity recognition using head worn inertial sensors;combining off-the-shelf image classifiers with transfer learning for activity recognition;dense 3D optical flow co-occurrence matrices for human activity recognition;towards a task-driven framework for multimodal fatigue analysis during physical and cognitive tasks;and outlining a novel framework for monitoring user's vital signs and activity data in caregiving facilities.

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human-Sensing: Low Resolution thermal Array Sensor Data Classification of Location-Based Postures 5th

Human-Sensing: Low Resolution Thermal Array Sensor Data Clas...

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5th international conference on Distributed, Ambient and Pervasive interactions (DAPI) Held as Part of 19th international conference on human-computer interaction (HCI international)

作者： Pontes, Bruno Cunha, Marcio Pinho, Rafael Fuks, Hugo Pontifical Catholic Univ Rio de Janeiro Dept Informat Rio De Janeiro Brazil

ISBN: (纸本)9783319586977;9783319586960

Ambient Assisted Living (AAL) applications aim to allow elderly, sick and disabled people to stay safely at home while collaboratively assisted by their family, friends and medical staff. AAL, recently empowered by the Internet of things, introduces a new healthcare connectivity paradigm that interconnects mobile apps and sensors allowing constant monitoring of the patient. Preserving privacy in the course of recognition of postures and classification of activities is one of the challenges for human-sensing and a critical factor of user acceptance, so there is a demand for solutions that do not require real live imaging in AAL. this paper addresses this challenge through the usage of a low resolution thermal sensor and machine learning techniques, discussing the feasibility of low cost and privacy protecting solutions. We evaluated decision tree models in two tasks of human-sensing, presence and posture recognition, using data of different days and volunteers. We also contribute by providing public domain datasets, collected in a bedroom and a bathroom, which are described in this paper.

关键词： Household monitoring human-sensing machine learning Privacy thermal sensors Low resolution

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Detecting stuttering events in transcripts of children’s speech 1

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5th international conference on Statistical Language and Speech Processing, SLSP 2017

作者： Alharbi, Sadeen Hasan, Madina Simons, Anthony J. H. Brumfitt, Shelagh Green, Phil Department of Computer Science The University of Sheffield Sheffield United Kingdom Department of Human Communication Sciences The University of Sheffield Sheffield United Kingdom

ISBN: (数字)9783319684567

ISBN: (纸本)9783319684550

Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. the main challenges however lie in the lack of training data and the high dimensionality of this data. this study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. the performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field. © Springer international Publishing AG 2017.

关键词： human computer interaction

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Painting Image Classification Using Online learning Algorithm 5th

Painting Image Classification Using Online Learning Algorith...

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5th international conference on Distributed, Ambient and Pervasive interactions (DAPI) Held as Part of 19th international conference on human-computer interaction (HCI international)

作者： Yang, Bing Yao, Jinliang Yang, Xin Shi, Yan Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Zhejiang Peoples R China Dalian Univ Technol Comp Sci & Technol Coll Linggong Rd Dalian 116024 Peoples R China Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783319586977;9783319586960

Recent years have witnessed a growing interesting in studying of painting images. It is obvious that there exists a deep gap between painting images and natural images, due to special characteristics of painting images. therefore, general image classification methods are not suitable to be applied directly to the painting images. this paper demonstrates a simple, yet powerful on-line learning algorithm to classify the category of painting images. Specifically, we use the multi-features combining of local and global features as the image descriptor, and then K-means is applied to initialize the dictionary. We resort to the online learning method to optimize the dictionary which then served as a codebook for spare coding of multi-features. Finally, we facilitate the linear support vector machine to classify images. the experimental results on two painting image datasets show that, compared with the traditional image classification method, our method has improved the accuracy of image classification. What's more, from the practical viewpoint, our online learning mechanism can be also useful for many other pattern recognition tasks. Based on the research results of this paper, it can be applied in various fields such as art field and art analysis, the research of this paper provides a new way for art researchers to explore the potential of computer-aided analysis of painting works and promote the development of art research. Painting image classification could be used in social internet of things such as in museum, to make people understand the painting more in-depth.

关键词： Painting image classification Multi-features Online learning

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5th international Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017

5th International Work-Conference on Bioinformatics and Biom...

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5th international Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017

ISBN: (纸本)9783319561530

the proceedings contain 64 papers. the special focus in this conference is on Bioinformatics and Biomedical Engineering. the topics include: Investigation of DNA sequences utilizing frequency-selective nanopore structures;comprehensive study of instable regions in pseudomonas aeruginosa;mixed-integer programming model for profiling disease biomarkers from gene expression studies;mixed-integer programming model for profiling disease biomarkers from gene expression studies;pairwise and incremental multi-stage alignment of metagenomes;an expanded association approach for rare germline variants with copy-number alternation;prediction of calmodulin binding proteins using short-linear motifs;application of a membrane protein structure prediction web service gpcrm to a gastric inhibitory polypeptide receptor model;exploring symmetric substructures in protein interaction networks for pairwise alignment;analysis of soil data from eastern of morocco based on data mining process;a multi-objective optimization framework for multiple sequence alignment with metaheuristics;analysis of gene expression discretization techniques in microarray biclustering;analysis of informative features for negative selection in protein function prediction;differential network analysis of anti-sense regulation;nonlinear control and simulation of a dielectric elastomer actuator-based compression bandage on flexible human calf;management of data structures generated during simulations of the evolution of multicellular systems;computational prediction of host-pathogen interactions through omics data analysis and machine learning;machine learning approaches for predicting high utilizers in health care and a case study on the integration of heterogeneous data sources in public health.

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A review of EEG signal classifier based on deep learning 5

A review of EEG signal classifier based on deep learning

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5th international conference on Information Science and Cloud Computing, ISCC 2017

作者： Lu, Yao Jiang, Huiping Liu, Wenqiang Brain Cognitive Computing Lab School of Information Engineering Minzu University of China Beijing100081 China

Electroencephalogram (EEG) signal recognition is an active research topic in the field of artificial intelligence and has been gaining extensive attention and engineering communities. this technology is an important basis of human computer interaction and many other fields. the deep learning theory has made remarkable achievements on feature extraction and gradually extended to the time sequences of EEG research. this paper reviews the traditional feature extraction of EEG recognition and discuss the traditional classification methods of EEG recognition such as linear discriminant analysis (LDA), support vector machine (SVM) and long short time memory (LSTM). Finally, this paper summarizes advantages and disadvantages of these methods. through the feature extraction by the wavelet transform and LSTM classification, it has achieved 98% accuracy and verified that LSTM is suitable for EEG signal with time sequence feature. © Copyright owned by the author(s).

关键词： Electroencephalography

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