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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3588 条记录，以下是221-230 订阅

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An Extensive Analysis on Deep Neural Architecture for Classification of Subject-Independent Cognitive States 2020

An Extensive Analysis on Deep Neural Architecture for Classi...

引用

Joint international conference on 7th ACM IKDD international conference on data Science (CoDS) and 25th international conference on Management of data (COMAD)

作者： Dutta, Sumanto Nandy, Anup Natl Inst Technol Rourkela Dept Comp Sci & Engn Intelligent Data Anal Lab Rourkela Odisha India Natl Inst Technol Rourkela Dept Comp Sci & Engn Machine Intelligence & Biomot Lab Rourkela Odisha India

ISBN: (纸本)9781450377386

Human mental state can be measured by analyzing and understanding EEG (Electroencephalogram) signal in various applications such as neuro-science, brain-computer interfaces, etc. It is an important area of research where machine learning algorithms are being used to develop tools for mental state classification. the modern deep learning algorithms can be used on large EEG data set after applying the data augmentation process on them. In this paper, we apply the Deep Belief Network (DBN) model based on the Restricted Boltzmann Machine (RBM) for unsupervised feature learning of EEG signals to extract salient features for classification. this DBN model provides an unsupervised taxonomy-based system without human intervention. the efficiency of this model is evaluated on the ambulatory EEG signal with other deep learning algorithms. Experimental results demonstrate that DBN with Recurrent Neural Network-Long Short Term Memory (DBN-RNN-LSTM) provides an accuracy of 98.3% which is better than RNN-LSTM and other classical machine learning algorithm.

关键词： data Augmentation EEG (Electroencephalogram) Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) Restricted Boltzmann Machine (RBM) Deep Belief Network (DBN)

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Machine learning at the Network Edge for automated Home Intrusion Monitoring 25

Machine Learning at the Network Edge for Automated Home Intr...

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25th IEEE international conference on Network Protocols (ICNP)

作者： Dhakal, Aditya Ramakrishnan, K. K. Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

ISBN: (纸本)9781509065011

Monitoring of residences and businesses can be effectively performed using machine learning algorithms. As sensors and devices used for monitoring become more complex, having humans process the information to detect intrusions would be expensive and difficult to scale. We propose an automated home/business monitoring system which resides on edge servers performing online learning on streaming data coming from homes and businesses in the neighborhood. the edge servers run Open-NetVM, a Network Function Virtualization (NFV) platform, and host multiple machine learning applications instantiated on demand. this enables us to serve a set of customers in the neighborhood on a timely basis, permitting customization and learning of the behavior of each home. We combine the results of the multiple classifiers, with each classifier examining a distinct feature related to a distinct sensor, to finally infer whether the entry is a normal one or an intrusion. Our results show that our system is able to classify intrusions better than basing the decision on a single classifier, thus reducing false alarms. We have also shown that our system can effectively scale and monitor thousands of homes.

关键词： Sensors Monitoring Cameras Servers Business Noise measurement Image edge detection

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An intelligent Interface for Automatic Grading of Sketched Free Body Diagrams 20

An Intelligent Interface for Automatic Grading of Sketched F...

引用

25th ACM international conference on intelligent User Interfaces (IUI)

作者： Runyon, Matthew Williford, Blake Linsey, Julie Hammond, Tracy Texas A&M Univ College Stn TX 77843 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450375139

Sketching free body diagrams is an important skill that students learn in introductory physics and engineering classes;however, university class sizes are growing and often have hundreds of students in a single class. this creates a grading challenge for instructors as there is simply not enough time nor resources to provide adequate feedback on every problem. We have developed an intelligent user interface called Mechanix to provide automated, real-time feedback on hand-drawn free body diagrams for students. the system is driven by novel sketch recognition algorithms developed for recognizing and comparing trusses, general shapes, and arrows in diagrams. We have also discovered trends in how the students utilize extra submissions for learning through deployment to five universities with 350 students completing homework on the system over the 2018 and 2019 school year. A study with 57 students showed the system allowed for homework scores similar to other homework mediums while requiring and automatically grading the free body diagrams in addition to answers.

关键词： sketch recognition engineering education intelligent tutoring system

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Investigating the Effects of Cognitive and Metacognitive Scaffolding on Learners using a learning by Teaching Environment 25

Investigating the Effects of Cognitive and Metacognitive Sca...

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25th international conference on Computers in Education (ICCE) - Technology and Innovation - Computer-Based Educational Systems for the 21st Century

作者： Dumdumaya, Cristina Banawan, Michelle Rodrigo, Ma. Mercedes Ogan, Amy Yarzebinski, Evelyn Matsuda, Noburo Ateneo Manila Univ Dept Informat Syst & Comp Sci Quezon City Philippines Univ Southeastern Philippines Inst Comp Davao Philippines Ateneo Davao Univ Comp Studies Dept Quezon City Philippines Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA 15213 USA Texas A&M Univ Coll Educ & Human Dev College Stn TX 77843 USA

ISBN: (纸本)9789869401265

We compared the effects of cognitive and metacognitive scaffolding on students' performance within a learning-by-teaching intelligent tutor for algebra. Results revealed that metacognitive scaffolding facilitated learning for low prior ability learners while high prior ability students' performance was obstructed by their refusal to follow the hints. Moreover, the study found that metacognitive actions related to self-regulatory learning's reflection subfunction correlates negatively to the learning outcomes of low ability students.

关键词： intelligent tutoring systems learning-by-teaching scaffolding self-regulated learning SimStudent

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Supervoxel-based and Cost-Effective Active learning for Point Cloud Semantic Segmentation 25

Supervoxel-based and Cost-Effective Active Learning for Poin...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Ye, Shanding Fu, Yongjian Lin, Hu Yin, Zhe Pan, Zhijie Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Recent successes in point cloud semantic segmentation heavily rely on a large amount of annotated data to train a deep neural network. Furthermore, three dimensional (3D) point cloud data generally has no order and sparsity, and a point cloud often includes more than ten thousand points, thus increasing difficulties of point cloud annotation. To reduce the huge annotation efforts, we propose a supervoxel-based and cost-effective active learning pipeline which aims to select only uncertain and diverse segmented regions for annotation. To better exploit annotating budget, we first change the annotating units from a point cloud scan to segmented regions through two unsupervised methods. We further propose to leverage point cloud intensity when calculating the segmented region information for encouraging region diversity. Extensive experiments show that our approach greatly outperforms previous active learning methods, and we achieve up to 90% performance of a fully supervised trained deep neural network by only using 3% labeled data compared to 100% on SemanticKITTI dataset.

关键词： Point cloud compression Deep learning learning systems three-dimensional displays Costs Annotations Pipelines

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intelligent agent based low level control of complex robotic systems 25

Intelligent agent based low level control of complex robotic...

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25th IEEE international conference on intelligent engineering Systems (INES)

作者： Brassai, Sandor Tihamer Kovacs, Attila Sapientia Hungarian Univ Transylvania Dept Elect Engn Coruna 1c Sfantu Gheorghe Romania Sapientia Hungarian Univ Transylvania Msc Intelligent Control Syst Coruna 1c Sfantu Gheorghe Romania

ISBN: (纸本)9781665444996

the use of intelligent agents, trained with reinforcement learning methods for control of complex mechanical systems, like humanoid robots has the potential to revolutionize the way we think about control problems. this way of learning is very similar to how we humans learn most of the things in our early age, thus proving really promising if we want to make robots able to learn tasks that require some form of intelligence. throughout the research presented in this paper, a deep neural network based intelligent agent, with Actor-Critic architecture was trained with the Deep Deterministic Policy Gradient algorithm for the purpose of controlling a custom designed humanoid robot. For the training of the agent a simulation model of the physical robot is developed and integrated into a customizable simulated environment. the idea of low, actuator level control of complex systems by neural networks formulates the problem into a more abstract form while keeping the full control of the system without having to deal with the actual level of complexity. this can be further enhanced by expanding the abstraction from the software level to include some part of the hardware as well.

关键词： humanoid robotics intelligent agent reinforcement learning actor-critic architecture deep deterministic policy gradient

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Learner and Course Dashboards for intelligent learning management systems 25

Learner and Course Dashboards for intelligent learning manag...

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25th international conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Volaric, Tomislav Ljubic, Hrvoje Univ Mostar Fac Sci & Educ Mostar Bosnia & Herceg

ISBN: (纸本)9781538632123

Visualization of data in learning management systems became essential for easier analysis of learner's behavior and interpretation of results on such system. In this paper, we presented design and development process of dashboard for the prototype of program support-CM Tutor. After analysis of various approaches of designing a learning analytics dashboard, we selected most important items for display on the dashboard, and we created our own. We have designed and built a dashboard for intelligent learning management systems. the dashboard is intended for both students and teachers. the teacher has access to all the functionalities of dashboard, while student approach is limited.

关键词： Dashboard learning analytics learning Analytics Dashboards Visualization intelligent learning management systems

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A Large-Scale and data-Based Road Traffic Flow Estimation Method leveraging Topography Information and Population Statistics 25

A Large-Scale and Data-Based Road Traffic Flow Estimation Me...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Laraki, Mohamed De Nunzio, Giovanni Othman, Bassel IFP Energies Nouvelles Rond Point Echangeur Solaize BP 3 F-69360 Solaize France

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

this paper proposes an estimation method of the traffic flow in a road network, based on topographic characteristics, temporal variables, and population statistics. It provides an alternative to conventional traffic modeling approaches by defining a data-based low-computational and low-input model that can estimate and predict day-long variations in traffic flow throughout a road network. the approach is based on a succession of statistical, machine learning, and optimization modules. the optimization is introduced in order to ensure spatial coherence and traffic flow continuity over neighboring road-links. Overall, the proposed model was calibrated with loop-detectors measurements by learning correlations between traffic flow (as output) and open-source data (as input). the approach can therefore be easily applicable to any road network, without the need for local traffic flow measurements. An application to a different road network and a different time period is proposed in order to assess the model ability to extrapolate in time and space, and promising results are obtained.

关键词： Traffic flow estimation urban traffic network machine learning traffic patterns optimization

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Interaction between Standardisation and Research in Drafting an international Specification on learning Analytics 25

Interaction between Standardisation and Research in Drafting...

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25th international conference on Computers in Education (ICCE) - Technology and Innovation - Computer-Based Educational Systems for the 21st Century

作者： Hoel, Tore Chen, Weiqin Oslo & Akershus Univ Appl Sci Oslo Norway

ISBN: (纸本)9789869401265

Interoperability standards are key enablers for widespread adoption of learning technologies, e.g., new data-driven analytics of learning, an application domain explored in a case study presented in this paper. Standards-making is a design practice that relies on input from research and end-users, involving experts that represent diverse stakeholders spread all over the globe. However, the standards-setting culture and formal rules are sometimes at odds with the culture and practice of research. Based on previous research identifying lack of openness and transparency, and a suboptimal interaction with academic research as issues that could explain lack of success in a European setting, this paper studies how an ongoing international standards project on privacy and data protection policies for learning analytics has interacted with an international academic research community. the results of this study show that establishing feedback loops between standardisation, research, and development is essential in order to produce results.

关键词： Standardisation Design Practice learning Analytics Interoperability learning Analytics Systems Design Privacy data Protection

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An Interactive Approach to Bias Identification in a Machine Teaching Task 20

An Interactive Approach to Bias Identification in a Machine ...

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25th ACM international conference on intelligent User Interfaces (IUI)

作者： Tressel, Tara Rheault, Claudel Krol, Masha Tyler, Chris Element AI Montreal PQ Canada

ISBN: (纸本)9781450375139

Supervised machine learning requires labelled data examples to train models, and those examples often come from humans who may not be experts in artificial intelligence (i.e., "AI"). Currently, many resources are devoted to these labelling tasks;a majority of which are outsourced by companies to reduce costs, and oversight on such tasks can be cumbersome. Concurrently, biases in machine learning models and human cognition are a growing concern in applications of AI. In this paper, we present a machine teaching platform for non-AI experts that leverages interactive data exploration approaches to identify algorithmic and human (e.g., cognitive) biases. Our main objective is to understand how data exploration and explainability might impact the machine teacher (i.e., data labeller) and their understanding of AI, subsequently improving model performance, all while reducing potential bias concerns.

关键词： Machine teaching data labeling Cognitive biases Machine learning bias

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