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17th International conference on Human-computer Interaction, HCI International 2015

17th International Conference on Human-Computer Interaction,...

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17th International conference on Human computer Interaction, HCI International 2015

ISBN: (纸本)9783319210056

The proceedings contain 56 papers. The special focus in this conference is on Quality for the Web, Social Media, HCI in Business, Industry, Societal and Cultural Impact of technology. The topics include: Heuristic to support the sociability evaluation in virtual communities of practices;using a lexical approach to investigate user experience of social media applications;short scales of satisfaction assessment;the effect of banner location on banner recognition in a Turkish government website;the importance of accessible rich data sources to enable innovation;a discussion focusing on public versus private sector organizations;the evolution of the argon web framework through its use creating cultural heritage and community-based augmented reality applications;historical registry of our families through textiles;early prototype assessment of a new virtual system for training procedural skills of automotive service operators;helping students create innovative products and experiences via technical and business mentorship;multiparty conversation based driving agents in futuristic vehicle;simulation of an affordance-based human-machine cooperative control model using an agent-based simulation approach;post-implementation ERP success assessment;interactive evaluation of pragmatic features in spoken journalistic texts;socio-cultural aspects in the design of multilingual banking interfaces in the Arab region;prospecting HCI challenges for extreme poverty communities;bridging the gap between concepts and impact in a civic computing initiative;exploring the effects of multimodal feedback on virtual world immersion and factors influencing online shop layout preferences.

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Construction Project Management Using Natural Language Processing technology 6

Construction Project Management Using Natural Language Proce...

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6th International conference on Contemporary Computing and Informatics, IC3I 2023

作者： Balasubramani, M. Palav, Manesh R. Rao, Kamarajugadda Tulasi Vigneswara Maranan, Ramya Salve, Archana Ravindra Raja Ambethkar, M. VS.B. Engineering College Computer Science and Engineering Tamil Nadu Karur India Global Business School & Research Centre MBA Department Dr. D. Y. Patil Vidyapeeth Pimpri Maharashtra Pune India NICMAR Business School NICMAR University Maharashtra Pune India Saveetha School of Engineering SIMATS Department of Research & Innovation Tamil Nadu Chennai India Indira College of Engineering and Management MBA Faculty Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350304480

The effectiveness of the structure business is generally because of innovative headways. Utilizing an intelligent thought of natural language processing innovation, this study expects to determine correspondence cooperation issues to foster a stage for project management. This paper looks at the review and execution of Natural Language Processing (NLP) innovation in the management of construction projects, determined to resolve issues like the developing size and volume of construction projects and records, the intrinsic trouble and intricacy of their management, the tedious and blunder inclined nature of manual resemblance and acceptance, and the absence of mastery among experts to give complete, opportune, and precise conferences. The most predominant issues in design can be tended to by changing over unstructured reports into organized data. © 2023 IEEE.

关键词： Artificial intelligence Construction project management Natural Language Processing

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Recess-Patterned Ohmic Contact technology for AlGaN/GaN Heterostructures 15

Recess-Patterned Ohmic Contact Technology for AlGaN/GaN Hete...

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15th IEEE International conference on ASIC, ASICON 2023

作者： Tang, Xinyi Tao, Nick Jiang, Yang Wang, Qing Du, Fangzhou Yu, Hongyu School of Microelectronics Southern University of Science and Technology Shenzhen China Maxscend Microelectronics Company Limited Wuxi China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Eng. Res. Center of Integrated Circuits for Next-Generation Communications Ministry of Education Shenzhen China Southern University of Science and Technology GaN Device Engineering Technology Research Center of Guangdong Shenzhen China Southern University of Science and Technology The Key Laboratory of the Third Generation Semi-Conductor Shenzhen China

ISBN: (纸本)9798350312980

In this paper, a recess-patterned ohmic contact technology is presented, which shows a promising way to reduce the ohmic contact resistance for AlGaN/GaN heterostructures. The influences of annealing temperature, recessed-pattern size and shape are investigated, respectively. The mean value of ohmic contact resistance (RC) is 0.345 Ω·mm, which is 33.8% lower than conventional non-recessed metal-stacking scheme. The technology computer Aided Design (TCAD) simulation results indicate the two-dimensional electron gas (2-DEG) concentration significantly increasing near the edge of recessed-pattern. This effect shortens the distance between ohmic contact metal and 2-DEG, which may effectively contribute to the superior ohmic contact performance. © 2023 IEEE.

关键词： AlGaN/GaN heterostructures Ohmic contact resistance Recess-Patterned TCAD simulation two-dimensional electron gas

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Unsupervised Domain Adaptive Method Based on Time Domain Mask Consistency in Bearing Fault Diagnosis 6

Unsupervised Domain Adaptive Method Based on Time Domain Mas...

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6th IEEE International conference on Civil Aviation Safety and Information technology, ICCASIT 2024

作者： Ji, Pengxiang Li, Jie Xu, Fangzheng Li, Mingze Li, Yuexin China University of Mining and Technology School of Computer Science and Technology Xuzhou China The First People's Hospital of Xuzhou Department of Medical Imaging Xuzhou China

ISBN: (纸本)9798350389418

Unsupervised domain adaptation approaches have been introduced into the fault diagnosis domain due to the domain bias caused by the differences in real-life working conditions. However, the model still lacks effective supervised learning to guide, resulting in poor identification of fine-grained differences in the target data and difficulty in predicting data far from the sample centroid. This paper proposes a time domain mask consistency method based on unsupervised domain adaptation. Since there is a good contextual correlation ability in time series, we encourage the model to predict the masked vibration data as a way to enable the model to learn the contextual correlations of the time domain data. In this paper, the method is validated using a bearing fault dataset for different domain adaptation tasks, and the results show that the method can be flexibly applied to different unsupervised domain adaptation methods for bearing fault diagnosis and achieves a performance improvement of 12.8 % on the open-set domain adaptation task, which is the most effective. © 2024 IEEE.

关键词： Time domain analysis

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The effectiveness of combining the likelihood maps of different filters in improving detection of calcification objects

The effectiveness of combining the likelihood maps of differ...

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Pattern Recognition Association of South Africa and Robotics and Mechatronics International conference, PRASA-RobMech 2015

作者： Chikamai, Keith Viriri, Serestina Tapamo, Jules-Raymond School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville College Durban4000 South Africa School of Engineering University of KwaZulu-Natal Howard College Durban4041 South Africa

ISBN: (纸本)9781467374507

Breast cancer is the most prevalent form of cancer diagnosed in women. Mammograms offer the best option in detecting the disease early, which allows early treatment and by implication, a favorable prognosis. This study looks to combine the Wavelet, Median, Gaussian and a Finite Impulse Response filters for the task of detecting Malignant and Benign calcifications, which are among the primary indicators of breast cancer in digital Mammograms. These filters individually detect calcifications to varying degrees of success, but also create artifacts especially along the boundaries of curvilinear structures. They are combined in a way that improves overall detection, while diminishing their individual side effects. An Entropy-based thresholding technique is finally used to segment the calcifications from the background. Experimental results show that the proposed model achieves a 100% detection rate, which shows the effectiveness of combining the likelihood maps from various filters in detecting calcification objects. © 2015 IEEE.

关键词： Diseases

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Facial expression recognition based on SSVM algorithm and multi-source texture feature fusion using KECA

Facial expression recognition based on SSVM algorithm and mu...

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International conference on Intelligent Computing, Communication and Devices, ICCD 2017

作者： Liu, Limin Yang, Li Chen, Yu Zhang, Xingyan Hu, Luokai Deng, Fang College of Computer Hubei University of Education WuhanHubei430205 China Hubei Education Cloud Service Engineering Technology Research Center Hubei University of Education WuhanHubei430205 China

ISBN: (纸本)9789811089435

Automatic expression recognition of human faces has been an active research area for decades. In this work, to improve the facial expression recognition effect, a new method based on SSVM algorithm and multi-source texture feature fusion using KECA is proposed. Multi-source texture features are introduced to describe the facial expression, containing GMCL, Gabor feature, and HOG feature. The results indicate that multi-source texture features are conducive to improve the recognition effect and make up for the deficiency of single texture feature in facial expression description. In addition, KECA and SSVM algorithms show better performance than traditional methods in feature extraction and classification. To further verify the effectiveness of the proposed method, three sets of comparative experiments are carried out: PCA+SVM (based on Gabor feature), PCA+SVM (based on GMCL+Gabor+HOG feature), KECA+SVM (based on GMCL+Gabor+HOG feature). The results of JAFFE database indicate that the accuracy of proposed method, equal to 93.04%, is at least 2.61% higher than conventional method. The results of JAFFE database demonstrate the validity of proposed method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Face recognition

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Automatic Classification of Government Texts Based on Improved CNN and Skip-gram Models 2

Automatic Classification of Government Texts Based on Improv...

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2nd International conference on Information science and education, ICISE-IE 2021

作者： Wen, MingXi Chen, YongQi Wu, Hao Hou, SenHao Yang, Yun School of Computer Science Southwest Petroleum University Chengdu China

ISBN: (纸本)9781665438292

Aiming at the characteristics of government texts with rich semantic information, the impact of convolutional neural network models on the classification and recognition of government text features is studied, and an optimization method to assist in the classification of official government information resources is proposed so that government staff can better investigate public opinion. Based on machine learning and deep learning theories, this paper conducts an experimental study on automatic classification and recognition of texts using convolutional neural network models and conducts a comprehensive comparative analysis from the perspective of information details and core topic sentences. After data cleaning, Word2Vec is used to construct a word vector matrix, and this information is integrated into the convolutional neural network model for comparison and optimization. And based on the use of a convolutional neural network based on the improved convolutional layer technology, combined with a Skip-gram word vector model, the text is converted into low-latitude feature vectors as the input of the improved convolutional neural network model to achieve the automatic classification of government texts. The experimental results show that the model of this study achieves 92.7% Fl mean and 93.4% ACC in the evaluation of automatic classification of government texts, which not only solves the problem of excessive dimensionality but also significantly improves the execution performance and classification accuracy compared with the traditional methods, and effectively solves the automatic classification of information contents and labels. © 2021 IEEE

关键词： Convolutional neural networks

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Classification and Prediction of Hypoglycemia in Patients with Type 2 Diabetes Mellitus Using Data from the EHR and Patient Context 9

Classification and Prediction of Hypoglycemia in Patients wi...

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9th International conference on Internet of Things, Big Data and Security, IoTBDS 2024

作者： Gubert, Luis Claudio Zeiser, Felipe André André da Costa, Cristiano Kunst, Rafael Software Innovation Laboratory SOFTWARELAB Universidade do Vale do Rio dos Sinos Unisinos São Leopoldo Brazil Department of Computer Science IFRS RS Ibirubá Brazil

ISBN: (纸本)9789897586996

The increase in obesity, a sedentary lifestyle, and population aging are considered the main factors for the increase in Type 2 Diabetes Mellitus (T2DM) worldwide. Global estimates indicate that around 400 million people live with T2DM, reaching 600 million in 2035. This scenario generates a high social and financial cost for the patient and the healthcare system. In this context, this work evaluates machine learning models to classify and predict hypoglycemic crises in patients with T2DM. A dataset with data from a clinical center in southern Brazil is constructed. Patient data involves Electronic Health Records (EHR) and data collected in the patient context through Internet of Things (IoT). This dataset is used to run classification and prediction models. Results show that the proposed approach is promising, achieving an AUC of 0.8200 and a sensitivity of 90.00% for classifying hypoglycemia. In addition, the Clarke Error Grid plot demonstrates an assertiveness of prediction for high blood glucose in clinical terms. These results demonstrate that the proposed method achieves comparable or superior results to related works in the literature. The combined use of EHR, IoT, and Machine Learning can be a promising alternative to improve the monitoring of chronic and long-term diseases, such as T2DM, contributing to a more accurate and effective diagnosis. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Internet of things

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Tweet sarcasm detection using deep neural network 26

Tweet sarcasm detection using deep neural network

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26th International conference on Computational Linguistics, COLING 2016

作者： Zhang, Meishan Zhang, Yue Fu, Guohong School of Computer Science and Technology Heilongjiang University China Singapore University of Technology and Design Singapore

ISBN: (纸本)9784879747020

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features. © 1963-2018 ACL.

关键词： Recurrent neural networks

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