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检索条件"任意字段=18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017"

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AutoLearn - automated Feature Generation and Selection 17

AutoLearn - Automated Feature Generation and Selection

引用

17th IEEE international conference on data Mining (ICDMW)

作者： Kaul, Ambika Maheshwary, Saket Pudi, Vikram Int Inst Informat Technol Kohli Ctr Intelligent Syst Data Sci & Analyt Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781538638354

In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications. For others, feature engineering requires substantial manual effort in designing and selecting features and is often tedious and non-scalable. We present AutoLearn, a regression-based feature learning algorithm. Being data-driven, it requires no domain knowledge and is hence generic. Such a representation is learnt by mining pairwise feature associations, identifying the linear or non-linear relationship between each pair, applying regression and selecting those relationships that are stable and improve the prediction performance. Our experimental evaluation on 18 UC Irvine and 7 Gene expression datasets, across different domains, provides evidence that the features learnt through our model can improve the overall prediction accuracy by 13.28%, compared to original feature space and 5.87% over other top performing models, across 8 different classifiers without using any domain knowledge.

关键词： Prediction algorithms Correlation Algorithm design and analysis Transforms Predictive models data models

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Human and automated Scoring of Fluency, Pronunciation and Intonation During Human-Machine Spoken Dialog Interactions 18

Human and Automated Scoring of Fluency, Pronunciation and In...

引用

18th Annual conference of the international-Speech-Communication-Association (INTERSPEECH 2017)

作者： Ramanarayanan, Vikram Lange, Patrick Evanini, Keelan Molloy, Hillary Suendermann-Oeft, David Educ Testing Serv R&D 90 New Montgomery StSuite 1500 San Francisco CA 94105 USA Educ Testing Serv R&D 660 Rosedale Rd Princeton NJ USA

ISBN: (纸本)9781510848764

We present a spoken dialog-based framework for the computer-assisted language learning (CALL) of conversational English. In particular, we leveraged the open-source HALEF dialog framework to develop a job interview conversational application. We then used crowdsourcing to collect multiple interactions with the system from non-native English speakers. We analyzed human-rated scores of the recorded dialog data on three different scoring dimensions critical to the delivery of conversational English - fluency, pronunciation and intonation/stress - and further examined the efficacy of automatically-extracted, hand-curated speech features in predicting each of these sub-scores. Machine learning experiments showed that trained scoring models generally perform at par with the human inter-rater agreement baseline in predicting human-rated scores of conversational proficiency.

关键词： dialog systems computer assisted language learning conversational assessment intelligent tutoring systems crowdsourcing

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DBLearn: Adaptive E-learning for Practical database Course - An Integrated Architecture Approach 18

DBLearn: Adaptive E-Learning for Practical Database Course -...

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18th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Nalintippayawong, Srinual Atchariyachanvanich, Kanokwan Julavanich, thanakrit King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Bangkok Thailand

ISBN: (纸本)9781509055043

In this paper, an integrated architecture approach in designing and developing a DBLearn web-based application is presented. the DBLearn system is a personalized and adaptive e-learning system designed especially for learning practices in database courses. this approach focused on topics that are important but difficult for new learners, such as database design and structured query language (SQL) command query. the concept of adaptive e-learning and autonomous agents were applied in this system to eliminate the traditional constraints of effective e-learning, such as the problem of different learning sensory and knowledge levels. Four approaches were used to solve this problem. First, learning style theory was used to classify the way of learning for each student. Second, the student activity (historical data) is kept in the system to analyze the next knowledge the student should learn or review. Next, the SQL query automated grader was used to judge the correctness of the student's query. this grader supports all the necessary commands in both DML and DDL. Finally, the SQL query question generator module that can generate SQL query questions automatically is presented. this will reduce the instructor's work load in creating enough questions and allow the students to practice at their own pace as much as they want. By using these four techniques, the students will have a better learning experience and becoming more successful in learning outcomes.

关键词： e-learning personalized learning adaptive learning database design structured query language learning outcome

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18th international System Design Languages Forum on Model-Driven Dependability engineering, SDL 2017

18th International System Design Languages Forum on Model-Dr...

引用

18th international System Design Languages Forum on Model-Driven Dependability engineering, SDL 2017

ISBN: (纸本)9783319680149

the proceedings contain 10 papers. the special focus in this conference is on Model-Driven Dependability engineering. the topics include: Interactive visualization of software;static syntax validation for code generation with string templates;on the impact of the SDL forum society conferences on academic research;intelligent resilience in the IoT;an ontology-based approach for iot data processing using semantic rules;a model-driven process enactment approach for network service design;model-based regression testing of autonomous robots;from metamodels to UML profiles and an automated change impact analysis approach to GRL models.

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Intrinsic Plagiarism Detection with Feature-Rich Imbalanced dataset learning 18th

引用

18th international conference on engineering Applications of Neural Networks (EANN)

作者： Polydouri, Andrianna Siolas, Georgios Stafylopatis, Andreas Natl Tech Univ Athens Intelligent Syst Content & Interact Lab Athens Greece

ISBN: (纸本)9783319651729;9783319651712

In the context of intrinsic plagiarism detection, we are trying to discover plagiarised passages in a text, based on the stylistic changes and inconsistencies within the document itself. the main idea consists in profiling the style of the original author and marking as outliers the passages that seem to differ significantly. Besides some novel stylistic and semantic features, the present work proposes a new approach to the problem, where machine learning plays a significant role. Notably, we also consider, for the first time, the reality of unbalanced training dataset in intrinsic plagiarism detection as a major parameter of the problem. Our detection system is tested on the data corpora of PAN Webis intrinsic plagiarism detection's shared tasks of 2009 and 2011 and is compared to the results of the highest score participations.

关键词： Intrinsic plagiarism detection Stylometry Supervised learning Unbalanced training data SMOTE PAN Webis

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intelligent integrated coking flue gas indices prediction 18

Intelligent integrated coking flue gas indices prediction

引用

18th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017

作者： Li, Yaning Wang, Xuelei Tan, Jie Liu, Chengbao Bai, Xiwei Institute of Automation Chinese Academy of Science Beijing100190 China University of the Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781509055043

Focus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration, O2 content and NOx concentration was proposed: the mechanism models of SO2, NOx concentration and O2 content were established according to the principle of material balance and reaction kinetics, respectively. For the prediction error, raw data was pretreated and the auxiliary variables were determined by principal component analysis, in order to improve the training speed and generalization ability of neural network, an improved RBFNN combining optimal stopping principle and dual momentum adaptive learning rate was proposed and used to compensate the error. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method were verified by simulation via comparison of various models. © 2017 IEEE.

关键词： Flue gases

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18th international conference on Artificial Intelligence in Education, AIED 2017

18th International Conference on Artificial Intelligence in ...

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18th international conference on Artificial Intelligence in Education, AIED 2017

ISBN: (纸本)9783319614243

the proceedings contain 82 papers. the special focus in this conference is on Artificial Intelligence in Education. the topics include: An adaptive coach for invention activities;collaboration improves student interest in online tutoring;improving sensor-free affect detection using deep learning;building a comprehensive automated essay scoring system for dutch language;technologies for monitoring group learning in real-time;an extensible domain-specific language for describing problem-solving procedures;effects of error-based simulation as a counterexample for correcting MIF misconception;algorithm for uniform test assembly using a maximum clique problem and integer programming;personalized tag-based knowledge diagnosis to predict the quality of answers in a community of learners;confronting adult low literacy with intelligent tutoring for reading comprehension;adapting step granularity in tutorial dialogue based on pretest scores;the impact of student individual differences and visual attention to pedagogical agents during learning with metatutor;automatic extraction of AST patterns for debugging student programs;dusting off the messy middle;but metacognitive supports did not enhance comprehension gains;inducing stealth assessors from game interaction data;supporting constructive video-based learning: requirements elicitation from exploratory studies;from engaged concentration to boredom to confusion;behavioral engagement detection of students in the wild;improving reading comprehension with automatically generated cloze item practice;identifying productive inquiry in virtual labs using sequence mining;gender effects and engagement with virtual learning companions and hint generation under uncertainty.

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An Adaptive Coach for Invention Activities

An Adaptive Coach for Invention Activities

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18th international conference on Artificial Intelligence in Education (AIED)

作者： Aleven, Vincent Connolly, Helena Popescu, Octav Marks, Jenna Lamnina, Marianna Chase, Catherine Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA 15213 USA Columbia Univ Teachers Coll New York NY 10027 USA

ISBN: (纸本)9783319614250;9783319614243

A focus in recent AIED research is to create adaptive support for learners in inquiry learning environments. However, only few examples of such support have been demonstrated. Our work focuses on Invention activities, inquiry activities in which students generate representations that explain data presented as contrasting cases. To help teachers implement these activities in their classrooms, we have created and pilot-tested a dedicated adaptive computer coach (the Invention Coach) and are currently evaluating it in a classroom study. the Coach's pedagogical strategy balances structuring and problematizing, unlike many ITSs, which favor structuring. the Coach is implemented in CTAT as a model-tracing tutor, with a rule-based model that captures its pedagogical coaching strategy, designed in part based on data from human tutors. We describe the Invention Coach and its pedagogical model. We present evidence from our pilot tests that illustrate the tutor's versatility and provide preliminary evidence of its effectiveness. the contributions of the work are: identifying an adaptive coaching strategy for Invention tasks that balances structuring and problematizing, and an automated coach for a successful instructional method (Invention) for which few tutors have been built.

关键词： Invention Adaptive coach intelligent tutoring system STEM education Productive failure Inquiry learning

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Long short-term memory networks for automatic generation of conversations

Long short-term memory networks for automatic generation of ...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Tomohiro Fujita Wenjun Bai Changqin Quan Department of Computational Science Kobe University Kobe Japan

ISBN: (纸本)9781509055050

Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent `chat bot', which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic `Turning Test' to evaluate the proposed system.

关键词： Earth Observing System Pragmatics Neural networks Machine learning Context modeling Computer architecture Computational modeling

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intelligent integrated coking flue gas indices prediction

Intelligent integrated coking flue gas indices prediction

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Yaning Li Xuelei Wang Jie Tan Chengbao Liu Xiwei Bai Chinese Academy of Science Institute of automation Beijing China University of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509055050

关键词： Combustion Ovens Predictive models Adaptation models Temperature measurement data models Process control

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