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Use of personalized feedback reports in a blended conceptual modelling course 22

Use of personalized feedback reports in a blended conceptual...

引用

ACM/IEEE 22nd international conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Bogdanova, Daria Snoeck, Monique Katholieke Univ Leuven Res Ctr Management Informat Leuven Belgium

ISBN: (纸本)9781728151250

Despite the substantial number of existing publications on conceptual modelling education and feedback, in particular, the perfect balance between the effectiveness of feedback and the costs of the feedback design tailored to the field-specific needs of conceptual modelling remains an unanswered scientific and pedagogical question The existing educational literature and online courses on conceptual modelling tend to overlook the essential aspects of metacognition and self-regulation in the learning process. The problem of providing feedback is exacerbated by the time-consuming nature of manual feedback provision and the difficulties of automating the provision of personalized and elaborated feedback. This paper presents an experience report on designing and implementing a learning ontology-based personalized feedback report aimed at raising student self-awareness and self-regulated learning in a university level conceptual modelling course, while the design aims at automation of the feedback provisioning in the near future. It describes the stages of learning report development and provides directions for adapting this type of feedback for various learning settings in conceptual modelling education, in view of potential future automation of report provision.

关键词： Conceptual modelling education data modelling blended learning formative assessment formative feedback learning report

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Comparison of Deep learning Methods for Spoken Language Identification 22nd

Comparison of Deep Learning Methods for Spoken Language Iden...

引用

22nd international conference on Speech and Computer, SPECOM 2020

作者： Korkut, Can Haznedaroglu, Ali Arslan, Levent Sestek Istanbul Turkey Electrical and Electronics Engineering Department Bogazici University Istanbul Turkey

ISBN: (纸本)9783030602758

In this paper, we implement and compare deep learning based spoken language identification models. We also deploy two very recent and popular speech recognition methods, namely Wav2Vec and SpecAugment, in our classifiers and test if they are also applicable to the field of language identification. Out of the models we implement, X-vector based deep feed forward network classifier obtains the highest F1-score of 0.91, where the target set consists of five languages. SpecAugment data augmentation method turns out to increase the classification accuracy when applied to the input mel-spectrograms of the CRNN architecture. Although they obtain lower classification accuracies than some of the other methods, Wav2Vec speech representations also achieve promising results. © 2020, Springer Nature Switzerland AG.

关键词： Speech recognition

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Modeling adaptive learning agents for domain knowledge transfer 19

Modeling adaptive learning agents for domain knowledge trans...

引用

Proceedings of the 22nd international conference on Model Driven engineering Languages and Systems

作者： Moritz Höser Bauhaus Luftfahrt e.V. Taufkirchen Germany

ISBN: (纸本)9781728151250

The implementation of intelligent agents in industrial applications is often prevented by the high cost of adopting such a system to a particular problem domain. This paper states the thesis that when learning agents are applied to work environments that require domain-specific experience, the agent benefits if it can be further adapted by a supervising domain expert. Closely interacting with the agent, a domain expert should be able to understand its decisions and update the underlying knowledge base as *** result would be an agent with individualized knowledge that comes in part from the domain experts. The model of such an adaptive learning agent must take into account the problem domain, the design of the learning agent and the perception of the domain user. Therefore, already in the modeling phase, more attention must be paid to make the learning element of the agent adaptable by an operator. Domain modeling and meta-modeling methods could help to make inner processes of the agent more accessible. In addition, the knowledge gained should be made reusable for future agents in similar *** begin with, the existing methods for modeling agent systems and the underlying concepts will be evaluated, based on the requirements for different industrial scenarios. The methods are then compiled into a framework that allows for the description and modeling of such systems in terms of adaptability to a problem domain. Where necessary, new methods or tools will be introduced to close the gap between inconsistent modeling *** framework shall then be used to build learning agents for real-life scenarios and observe their application in a case study. The results will be used to assess the quality of the adapted knowledge base and compare it to a manual knowledge modeling process.

关键词： adaptive learning agents domain modeling multi-modeling knowledge engineering

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Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features 22

Semi-Generative Modelling: Covariate-Shift Adaptation with C...

引用

22nd international conference on Artificial Intelligence and Statistics (AISTATS)

作者： von Kuegelgen, Julius Mey, Alexander Loog, Marco Max Planck Inst Intelligent Syst Berlin Germany Univ Cambridge Cambridge England Delft Univ Technol Delft Netherlands Univ Copenhagen Copenhagen Denmark

Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation with semi-supervised learning. Using ideas from causality, we argue that this requires learning with both causes, X-C, and effects, X-E, of a target variable, Y, and show how this setting leads to what we call a semi-generative model, P(Y, X-E vertical bar X-C, theta). Our approach is robust to domain shifts in the distribution of causal features and leverages unlabelled data by learning a direct map from causes to effects. Experiments on synthetic data demonstrate significant improvements in classification over purely-supervised and importance-weighting baselines.

关键词： Supervised learning

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Use of personalized feedback reports in a blended conceptual modelling course 19

Use of personalized feedback reports in a blended conceptual...

引用

Proceedings of the 22nd international conference on Model Driven engineering Languages and Systems

作者： Daria Bogdanova Monique Snoeck KU Leuven Leuven Belgium

ISBN: (纸本)9781728151250

Despite the substantial number of existing publications on conceptual modelling education and feedback, in particular, the perfect balance between the effectiveness of feedback and the costs of the feedback design tailored to the field-specific needs of conceptual modelling remains an unanswered scientific and pedagogical question. The existing educational literature and online courses on conceptual modelling tend to overlook the essential aspects of metacognition and self-regulation in the learning process. The problem of providing feedback is exacerbated by the time-consuming nature of manual feedback provision and the difficulties of automating the provision of personalized and elaborated feedback. This paper presents an experience report on designing and implementing a learning ontology-based personalized feedback report aimed at raising student self-awareness and self-regulated learning in a university level conceptual modelling course, while the design aims at automation of the feedback provisioning in the near future. It describes the stages of learning report development and provides directions for adapting this type of feedback for various learning settings in conceptual modelling education, in view of potential future automation of report provision.

关键词： conceptual modelling education blended learning data modelling formative assessment learning report formative feedback

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When DevOps meets Meta-learning: A portfolio to rule them all 22

When DevOps meets Meta-Learning: A portfolio to rule them al...

引用

ACM/IEEE 22nd international conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Benni, Benjamin Blay-Fornarino, Mireille Mosser, Sebastien Precioso, Frederic Jungbluth, Guenther Univ Cote Azur CNRS I3S Nice France Univ Quebec Montreal PQ Canada

ISBN: (纸本)9781728151250

The Machine learning (ML) world is in constant evolution, as the amount of different algorithms in this context is evolving quickly. Until now, it is the responsibility of data scientists to create ad-hoc ML pipelines for each situation they encounter, gaining knowledge about the adequacy between their context and the chosen pipeline. Considering that it is not possible at a human scale to analyze the exponential number of potential pipelines, picking the right pipeline that combines the proper preprocessing and algorithms is a hard task that requires knowledge and experience. In front of the complexity of building a right ML pipeline, algorithm portfolios aim to drive algorithm selection, learning from the past in a continuous process. However, building a portfolio requires that (i) data scientists develop and test pipelines and (ii) portfolio maintainers ensure the quality of the portfolio and enrich it. The firsts are the developers, while the seconds are the operators. In this paper, we present a set of criteria to be respected, and propose a pipeline-based meta-model, to support a DevOps approach in the context of Machine learning Pipelines. The exploitation of this meta-model, both as a graph and as a logical expression, serves to ensure continuity between Dev and Ops. We depict our proposition through the simplified study of two primary use cases, one with developer's point-of-view, the other with ops'.

关键词： Machine learning Pipeline portfolio generation composition meta-learning

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Customer Segment Application of Machine learning in Business Operation of China Mobile

Customer Segment Application of Machine Learning in Business...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Qiaoyu Li Wei Yang Ying Liu Xiaofang Liu Yaohong Zhao Jun Chu Haitao Zeng Xuefeng Zhao Chao Deng Junlan Feng China Mobile Research Institute Beijing China

ISBN: (纸本)9781728176505

The purpose of this paper is to use machine learning technology to build more user profiles in the process of customer segmentation to assist marketing decision-making. Specifically, by introducing cross features into the construction of Feature engineering, the accuracy of target group prediction can be further improved and the user subgroup after feature crossing can be mined. In addition, this paper also proposes an improved decision tree model, which can build more comprehensive user profiles, and then assist in intelligent marketing. Firstly, both methods need to preprocess the data. In addition, for the subsequent feature crossing, it is necessary to discretize the continuous features and implement one-hot encoding for all data. In the process of discretization, the paper realizes the automatic selection of better discrete granularity value. A specific example is given and a comparative experiment is carried out to verify the effectiveness of the method.

关键词： Adaptation models Machine learning algorithms High performance computing conferences Decision making Machine learning Mobile communication

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Using behavioural Programming with Solver, Context, and Deep Reinforcement learning for Playing a Simplified RoboCup-type Game 22

Using behavioural Programming with Solver, Context, and Deep...

引用

ACM/IEEE 22nd international conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Elyasaf, Achiya Sadon, Aviran Weiss, Gera Yaacov, Tom Ben Gurion Univ Negev IL-8410501 Beer Sheva Israel

ISBN: (纸本)9781728151250

We describe four scenario-based implementations of controllers for a player in a simplified R. thoCup-type game. All four implementations are based on the behavioural programming (BP) approach. We first describe a simple controller for the player using the state-of-the-art BPjs tool and then show how it can be extended in various ways. The first extension is based on a version of BP where the Z3 SMT solver is used to provide mechanisms for richer composition of modules within the BP model. This allows for modules with higher cohesion and lower coupling. It also allows incrementality: we could use the scenarios we developed for the challenge of MDETOOLS'18 and extend the model to handle the new system. The second extension of BP demonstrated in this paper is a set of idioms for subjecting model components to context. One of the differences between this year's challenge and the challenge we dealt with last year is that following the ball is not the only task that a player needs to handle, there is much more to care for. We demonstrate how we used the idioms for handling context to parametrize scenarios like "go to a target" in a dynamic and natural fashion such that modelers can efficiently specify reusable components similar to the way modern user manuals for advanced products are written. Lastly, in an attempt to make the instructions to the robot even more natural, we demonstrate a third extension based on deep reinforcement learning. Towards substantiating the observation that it is easier to explain things to an intelligent agent than to dumb compiler, we demonstrate how the combination of BP and deep reinforcement learning (DRL) allows for giving abstract instructions to the robot and for teaching it to follow them after a short training session.

关键词： learning (artificial intelligence) learning Players instruction intelligent agents Solvers Controllers Hunting Game theory Game animals Robot Programming BOEING PETROLEUM SERVICES compilers Behavioural

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Towards data Driven Failure Analysis Using Infrared Thermography

Towards Data Driven Failure Analysis Using Infrared Thermogr...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Kaushal Arun Pareek Daniel May Mohamad Abo Ras Bernhard Wunderle Faculty for electrical engineering and information technologies Chair materials and reliability of microsystems Chemnitz University of Technology Chemnitz Germany Berliner Nanotest und Design GmbH Volmerstrasse 9B Berlin Germany

Electronic components of which reliability cannot be quantified are unacceptable and potentially hazardous, especially in safety-relevant areas such as driver assistance system and medical technology where the zero-error principle applies. Reliability as a quality criterion has its origin in production, i.e. process variations have a negative influence on the structural integrity of the contact elements on the packaging and interconnect technology and, thus on the device performance in the field under thermo-mechanical load (temperature changes, vibration, humidity). At present, to ensure reproducibility of the reliability of each component, regular quality tests are often carried out in practice. However, a better and reliable approach will carry out 100% inline checks for traceability and immediate readjustment. This work is the first step towards developing an intelligent non-destructive inline-capable failure analysis technique using infrared thermography. Good data forms the base on which robust and accurate AI algorithms can be trained and developed. However, the obtained thermographic images need to be processed so that subsurface defects can be detected. In this work, prominent algorithms, namely Pulse Phase Thermography (PPT), Thermographic Signal Reconstruction (TSR), Principal Component Analysis (PCT), Slope and Correlation Coefficient, have been thoroughly discussed and examined on the thermographic sequence from a plexiglass sample. A hybrid algorithm of TSR and PCT has also been suggested with promising results. In the end, potential post-processing algorithms from which the obtained results can be used for training an ML/AI model have been discussed.

关键词： Vibrations Training Performance evaluation Thermomechanical processes Failure analysis Production Signal reconstruction

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Machine learning and text analysis in the tasks of knowledge graphs refinement and enrichment 22

Machine learning and text analysis in the tasks of knowledge...

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22nd international conference on data Analytics and Management in data Intensive Domains, DAMDID/RCDL 2020

作者： Telnov, Victor Korovin, Yuri National Research Nuclear University MEPhI Obninsk249040 Russia

Working prototypes of the scalable semantic web portals, which are deployed on cloud platforms and intended for use in universities educational activity, are discussed. The first project is related to teaching in the field of nuclear physics and nuclear power engineering. The second project is related to training in computer science and programming. The possibility of using the DLLearner software in conjunction with the Apache Jena Reasoners in order to refine the ontologies that are designed on the basis of the SROIQ(D) description logic is shown. A software agent for the context-sensitive searching for new knowledge in the WWW has been developed as a toolkit for ontologies enrichment. The binary Pareto relation and Levenshtein metrics are used in order to evaluate the measure of compliance of the found content concerning a specific domain. It allows the knowledge engineer to calculate the measure of the proximity of an arbitrary network resource about classes and objects of specific knowledge graphs. The suggested software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services. Examples of applying the software and technologies under discuss are given. © 2020 CEUR-WS. All rights reserved.

关键词： Ontology

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