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EpistAid: An interactive intelligent system for evidence-based health care 22

EpistAid: An interactive intelligent system for evidence-bas...

引用

22nd international conference on intelligent User Interfaces, IUI 2017

作者： Donoso-Guzmán, Ivania Computer Science Department Faculty of Engineering Pontificia Universidad Católica de Chile Vicuña Mackenna Macul4860 Chile

ISBN: (纸本)9781450348935

Evidence-based health care (EBHC) is an important practice of medicine which provides systematic scientific evidence to answer clinical questions. Epistemonikos is one of the most important online systems in the field. Currently, many tasks within this system require a large amount of manual effort, which could be improved by leveraging humanin- the-loop machine learning techniques. In this article we propose a system called EpistAid, which combines machine learning, relevance feedback and an interactive user interface to support Epistemonikos users' on EBHC information filtering tasks. Copyright is held by the owner/author(s).

关键词： Health care

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Characterizing and Modeling Linguistic Style in Dialogue for intelligent Social Agents 22

Characterizing and Modeling Linguistic Style in Dialogue for...

引用

22nd international conference on intelligent User Interfaces, IUI 2017

作者： Oraby, Shereen University of California Natural Language and Dialogue Systems Lab Santa Cruz United States

ISBN: (纸本)9781450348935

With increasing interest in the development of intelligent agents capable of learning, proficiently automating tasks, and gaining world knowledge, the importance of integrating the ability to converse naturally with users is more crucial now than ever before. This thesis aims to understand and characterize different aspects of social language to facilitate the development of intelligent agents that are socially aware and able to engage users to a level that was not previously possible with language generation systems. Using various machine learning algorithms and data-driven approaches to model the nuances of social language in dialogue, such as factual and emotional expression, sarcasm and humor and the related subclasses of rhetorical questions and hyperbole, we can come closer to modeling the characteristics of the social language that allows us to express emotion and knowledge, and thereby exhibit these styles in the agents we develop. © 2017 Owner/Author.

关键词： learning algorithms

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Sparse Recovery of Time-Frequency Representations via Recurrent Neural Networks 22

Sparse Recovery of Time-Frequency Representations via Recurr...

引用

2017 22nd international conference on Digital Signal Processing (DSP)

作者： Khalifa, Yassin Zhang, Zhenwei Sejdie, Ervin Electrical and Computer Engineering University of Pittsburgh PA USA

ISBN: (纸本)9781538618950

The impressive evolution of neural networks and deep learning techniques during the last few years has offered new incomparable routes to solve many complex problems. Moreover, the fact that neural networks are structured and supervised has made it possible to perform automatic parameter tuning that guarantees convergence to the best expressive model for the problem assessed. In this work, we investigated the use of recurrent neural networks (RNNs) to solve the sequential sparse recovery problem through unfolding the iterative soft thresholding algorithm (ISTA) into a stacked RNN. Specifically, we examined the performance of the unsupervised iterative algorithm and the supervised network for a purely compressive sampling reconstruction problem of time-frequency representations. Our results demonstrated that the trained stacked neural network outperforms the iterative algorithm in the quality of the reconstructed data and points to several future directions to improve the performance.

关键词： Time-frequency analysis Signal processing algorithms Training Iterative algorithms Recurrent neural networks Biological neural networks

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IUI'17 workshop summary for SmartLearn - intelligent interfaces for ubiquitous and smart learning 22

IUI'17 workshop summary for SmartLearn - Intelligent interfa...

引用

22nd international conference on intelligent User Interfaces, IUI 2017

作者： Celik, Ilknur Torre, Ilaria Computer Education and Instructional Technology Middle East Technical University Northern Cyprus Campus Turkey Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa Italy

ISBN: (纸本)9781450348935

New technologies are changing the way we learn and teach. Emerging technologies such as social semantic web, cloud computing, and the growing popularity of mobile devices, embedded devices and adaptive context-aware technologies are leading to a paradigm shift in the way educational services are provided. Through technologies and approaches such as ubiquitous and adaptive learning, learning becomes personalized, flexible, and suitable to meet diverse and rapidly changing technologies, environments and learner needs, while opening unprecedented possibilities for education. The aim of the "intelligent Interfaces for Ubiquitous and Smart learning" workshop has been to bring together researchers from industry and academia to address the challenges of the intelligent user interfaces and smart learning fields, discuss new ideas and present their research to the scientific community in order to enhance the methodologies and techniques for intelligent learning environments for the 21st century. The workshop program, program committee and further details are available on the website (http://***/). Copyright is held by the owner/author(s).

关键词： Computer aided instruction

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Monitoring response quality during campimetry via eye-tracking 22

Monitoring response quality during campimetry via eye-tracki...

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22nd international conference on intelligent User Interfaces, IUI 2017

作者： Vergani Dambros, Gustavo Ungewiss, Judith Kübler, Thomas C. Kasneci, Enkelejda Spüler, Martin Institute of Informatics UFRGS Germany Vision Research Aalen University Germany Computer Engineering University of Tübingen Germany

ISBN: (纸本)9781450348935

In a variety of use-cases, deriving information on user's fatigue is an important step for content adaptation. In this work, we investigate which eye-tracking related measures can predict the error rate (as a proxy of subject's fatigue) during a visual experiment. data was collected during a 40 minutes campimetric task, where the user has to detect visual stimuli (i.e., dots) of different contrast. We found that eye-tracking measures can be used to train a machine learning model to predict the error rate of a user with an average correlation of 0.72±0.17. The results show that this method can be used to measure the user's response quality. Copyright is held by the owner/author(s).

关键词： Eye tracking

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Taxi passengers demand prediction using deep learning 22

Taxi passengers demand prediction using deep learning

引用

22nd international conference of Hong Kong Society for Transportation Studies: Transport and Society, HKSTS 2017

作者： Ku, Donggyun Kim, Jooyoung Lee, Seungjae Department of Transportation Engineering University of Seoul Seoul02504 Korea Republic of

ISBN: (纸本)9789881581464

Recently, Deep learning using Tensor Flow has been activated, and various studies have been carried out to combine big data with Deep learning. In the case of taxi-related research, research has been conducted mainly on generating profits by providing demand-responsive services starting with users' calls. The purpose of this study is to implement demand forecasting service by predicting passenger demand of Taxi by conducting Supervised learning based on Taxi tachometer data, building data, and smart card data. Supervised learning is implemented based on the getting on point of Taxi in order to implement the taxi demand predictive service. Accuracy is secured by learning building data and public transportation getting on / off data in Taxi data implemented through Supervised learning. We could confirm the utilization of taxi demand predictive service through taxi passenger demand forecasting using the supervised learning of Deep learning. © 2017 Hong Kong Society for Transportation Studies Limited. All rights reserved.

关键词： Supervised learning

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A Collaborative Intrusion Detection Model using a novel optimal weight strategy based on Genetic Algorithm for Ensemble Classifier

A Collaborative Intrusion Detection Model using a novel opti...

引用

international conference on Computer Supported Cooperative Work in Design

作者： Shaohua Tengl Zhenhua Zhang Luyao Teng Wei Zhang Haibin Zhu Xiaozhao Fang Lunke Fei School of Computer Science and Technology Guangdong University of Technology Guangzhou China College of Engineering and Science Victoria University Footscray VIC Australia Collaborative Systems Laboratory Nipissing University North Bay Canada

Cybersecurity, especially intrusion detection, is becoming increasingly critical in our daily life. The intrusion detection systems (IDS) have been widely used to prevent disclosure of personal information and detect potentially suspicious attacks. Although many machine learning algorithms have been broadly applied to enhance the performance of IDS, low detection rate and high false alarm rate are still two critical problems. A collaborative and robust intrusion detection model using a novel optimal weight strategy based on Genetic Algorithm (GA) for ensemble classifier is proposed in this paper. Since network data stream can be divided into three categories according to network protocols, detectors are applied in the network protocol separately. All of the detectors can work collaboratively and efficiently. In the proposed model, GA is used to optimize the weight of each base classifier of ensemble classifier. In order to improve features quality, Principal Component Analysis (PCA) is used for dimension reduction and attribute extraction. The NSL-KDD datasets is used to test the effectiveness of the collaborative intrusion detection model. Experimental results show that the proposed model has a higher accuracy and better generalized performance than others in this field.

关键词： Intrusion detection Support vector machines Detectors Principal component analysis Feature extraction Genetic algorithms Protocols

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Retrieval of Manufacturing Knowledge Using Machine learning - A Review 15

Retrieval of Manufacturing Knowledge Using Machine Learning ...

引用

15th international conference on Manufacturing Research / 32nd National conference on Manufacturing Research

作者： Ostermeyer, Emeric Danjou, Christophe Durupt, Alexandre Duigou, Julien L. E. Univ Technol Compiegne Dept Mech Engn UMR Roberval 7337 Compiegne France ETS Dept Automated Prod Montreal PQ Canada

ISBN: (纸本)9781614997924;9781614997917

data-driven models, and maturating data-treatment tools, now allow for automation in a wide range of domains. The data-rich domain that is NC machining presents a strong potential for automation, in this regard, and the smart factory calls for knowledge capitalisation in manufacturing. The field of machining saw numerous data formats, and is still led by heterogeneous sources. To exploit this massive amount of data, capitalisation of information from those sources is compulsory. Such system should allow aggregation of different sources and formats, and accommodate for information about diverse elements regarding manufacturing Parts, tools, machines... In this paper, automated process planning generation methods are reviewed. The localisation and formalisation of data in manufacturing documents is considered, and emphasis is made on linking trajectories and features, to define strategies. Attempts to use machine learning techniques in manufacturing are reviewed, and the lack thereof in manufacturing knowledge retrieval is underlined. Then, a framework is proposed to realise knowledge retrieval from legacy programs, and several locks and improvement possibilities are identified.

关键词： CAPP-CAM Manufacturing knowledge Machine learning intelligent Manufacturing Systems

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Towards intelligent surgical microscopes: Surgeons' gaze and instrument tracking 22

Towards intelligent surgical microscopes: Surgeons' gaze and...

引用

22nd international conference on intelligent User Interfaces, IUI 2017

作者： Eivazi, Shahram Fuhl, Wolfgang Kasneci, Enkelejda University of Tübingen Perception Engineering Sand 14 Tübingen72076 Germany

ISBN: (纸本)9781450348935

After many decades of research, the presence of intelligent user interfaces is unquestionable in any modern operating room (OR). For the first time, we aim to bring proactive intelligent systems into microsurgery OR. The first step towards an intelligent surgical microscope is to design an activity-aware microscope. In this paper, we present a novel system that we have built to record both eyes and instruments movements of surgeons while operating with a surgical microscope. We present a case study in micro-neurosurgery to show how the system monitors the surgeon's activities. We achieved about 1 mm accuracy for gaze and instrument tracking. Now real-time ecologically valid data can be used to design, for example, a self-adjustable microscope. Copyright held by the owner/author(s).

关键词： Microscopes

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Adaptive segmentation of multimodal polysomnography data for sleep stages detection 22

Adaptive segmentation of multimodal polysomnography data for...

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2017 22nd international conference on Digital Signal Processing, DSP 2017

作者： Procházka, Aleš Kuchyňka, Jiri Yadollahi, Mohammadreza Araujo, Carmen Paz Suarez Vyšata, Oldřich University of Chemistry and Technology Department of Computing and Control Engineering Czech Republic Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Republic Charles University Department of Neurology Czech Republic Universidad de Las Palmas de Gran Canaria Spain

ISBN: (纸本)9781538618950

The paper presents a new algorithm for adaptive classification of sleep stages using multimodal data recorded in the sleep laboratory during overnight polysomnography records. The proposed method includes the learning process applied for the set of individuals with their sleep stages classified by an experienced neurologist. Features evaluated for time windows 30 s long and selected multimodal signals are used for construction and optimization of the proposed two-layer neural network model. Resulting computational system based upon breathing EEG and EOG features is used for analysis of new individuals to detect their sleep stages. Results include classification accuracy higher than 80% and 90% for Wake and REM stages, respectively. The proposed method can adaptively modify model coefficients to detect sleep stages and sleeping disorders using man-machine interaction. © 2017 IEEE.

关键词： Network layers

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