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Simulating learning from Language and Examples 24th

Simulating Learning from Language and Examples

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Weitekamp, Daniel Rachatasumrit, Napol Wei, Rachael Harpstead, Erik Koedinger, Kenneth Carnegie Mellon Univ Pittsburgh PA 15289 USA Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9783031363351;9783031363368

Simulations of human learning can be used as computational models for evaluating theories of learning. they can also be taught interactively to author intelligent tutoring systems. Prior simulated learner systems have learned inductively from worked examples and correctness feedback. this work introduces a mechanism where simulated learners can also learn from natural language. Using a neural grammar parser with additional symbolic processing steps, we simulate the production of loose interpretations of verbal instructions. these interpretations can be combined with worked examples to resolve the ambiguities of either form of instruction alone. We find that our system has practical benefits over an alternative method using github Copilot and slightly better accuracy.

关键词： learning Simulations Computational Models of learning Natural Language Processing Code Synthesis Authoring Tools

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Review on Research of automated Machine learning 23

Review on Research of Automated Machine Learning

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Proceedings of the 2023 7th international conference on Computer Science and Artificial Intelligence

作者： Yuyanzhen Zhong Chuan Yang Xinyi Su Biao Li Xiaoping Huang Yong Shuai Chongqing CEPREI Industrial Technology Research Institute Co. Ltd China

ISBN: (纸本)9798400708688

automated Machine learning (AutoML) can automatically discover high-performance models to build deep learning systems without human assistance, with the ultimate goal of reducing the complexity and entry barriers of building deep learning systems. Although AutoML has achieved a certain degree of automation through four important steps: data preparation, feature engineering, model generation, and model evaluation, there is still a significant gap compared to the ultimate ideal of achieving truly intelligent lifelong learning. therefore, a deep understanding of AutoML can help drive the development of artificial intelligence. Firstly, we comprehensively reviewed the latest technologies and achievements involved in these four steps, then we introduced their shortcomings and challenges. Secnodly, a detailed introduction was given to the existing AutoML libraries and the theoretical and practical applications of AutoML. Finally, we summarized AutoML models and Proposed an outlook.

关键词： automated Machine learning Automatic Processing Lifelong learning Neural Architecture Search

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Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced datasets 24th

Robust Educational Dialogue Act Classifiers with Low-Resourc...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Lin, Jionghao Tan, Wei Nguyen, Ngoc Dang Lang, David Du, Lan Buntine, Wray Beare, Richard Chen, Guanliang Gasevic, Dragan Monash Univ Clayton Vic Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA Stanford Univ Stanford CA 94305 USA VinUniv Hanoi Vietnam Murdoch Childrens Res Inst Melbourne Vic Australia

ISBN: (纸本)9783031362712;9783031362729

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. the DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios.

关键词： Educational Dialogue Act Classification Model Robustness Low-Resource data Imbalanced data Large Language Models

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Building Educational Technology Quickly and Robustly with an Interactively Teachable AI 24th

Building Educational Technology Quickly and Robustly with an...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Weitekamp, Daniel Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9783031363351;9783031363368

intelligent tutoring systems (ITSs) aim to support student learning through comprehensive adaptive features, making for costly development times-about 200-300 hours of development time per hour of instruction. this proposal outlines plans to overcome several technical challenges toward building authoring tools whereby a non-programmer can build ITSs by interactively teaching simulated students. I propose both interaction design considerations and machine-learning innovations. these include a multi-modal natural language processing mechanism that mimics student learning from narrated tutorial instruction, and an active-learning mechanism that identifies training examples likely to eliminate inaccuracies in the simulated student's induced production rules. I propose to evaluate these features over 3 user studies and evaluate the generality of this authoring method in a final open-ended authoring study. this work aims to democratize ITS authoring by opening new authoring opportunities to non-programmers by making authoring as time-efficient and natural as human-to-human tutoring.

关键词： Simulated Students ITS Authoring Interactive Task learning

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A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network

A Novel Smartphone-Based Human Activity Recognition Approach...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Dipanwita thakur Sandip Roy Suparna Biswas Edmond S. L. Ho Samiran Chattopadhyay Sachin Shetty Department of Computer Science Banasthali Vidyapith Jaipur Rajasthan India Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Kolkata India School of Computing Science University of Glasgow Scotland United Kingdom Department of Information Technology Jadavpur University Kolkata India

In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. there is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. the simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.

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Gamiflow: Towards a Flow theory-Based Gamification Framework for learning Scenarios 24th

Gamiflow: Towards a Flow Theory-Based Gamification Framework...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Challco, Geiser Chalco Bittencourt, Ig Ibert Reis, Marcelo Santos, Jario Isotani, Seiji Fed Rural Univ Semiarid Reg BR-59900 Pau Dos Ferros RN Brazil Univ Fed Alagoas NEES Ctr Excellence Social Technol BR-57072970 Maceio Alagoas Brazil Harvard Grad Sch Educ Cambridge MA 02138 USA

ISBN: (纸本)9783031363351;9783031363368

Participating in gamified learning scenarios increases students' motivation and engagement, leading them to improve their learning outcomes. Nevertheless, these benefits are only accomplished when gamification is grounded in theories of human motivation and behavior. In this paper, we present a gamification design framework based on Csikszentmihalyi's flow theory, as well as its empirical validation in eight different real-world contexts. these results indicate that our framework is usable, pertinent, and yields high-quality outputs, enabling gamification of learning scenarios that effectively promote the flow state and learning. his framework also facilitates the systematization of the application of flow theory, which is the first step in the development of intelligent gamification recommendation systems.

关键词： Gamification Framework Flow State learning Scenario

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Hongli Liu Shaoxian Zhu Liu Yang Qiang Li Jingran Lin School of Information and Communication Engineering University of Electronic Science and Technology of China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

the Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers’ fingerprints are inevitably introduced, and different receivers affect emitters’ fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers’ effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters’ features are disentangled from the receivers’, through multi-receiver’s training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination.

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Log Parsing using Semantic Filtering based Prompt learning

Log Parsing using Semantic Filtering based Prompt Learning

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Zhun Xu Baohua Huang Ningjiang Chen School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computiong (Guangxi University) Nanning China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

Log parsing is the initial stage of automated log analysis, which mainly involves converting semi-structured log messages into structured log templates. However, the initial log data size of software systems and services is relatively small, which limits the effectiveness of existing $\log$ parsers. Traditional data-driven log parsers use the structured nature of logs to construct domain rules, which may make it difficult to maintain stable performance when dealing with log data of different sizes and types due to mismatches. To address the limitations of existing methods, this paper proposes a log parsing method called LogSPL, which captures high-quality samples from small-scale log data based on semantics and extracts log templates using prompt learning. the research in this paper performs extensive experiments on 16 benchmark $\log$ datasets. the results show that LogSPL improves parsing accuracy and group accuracy by 3.2% and 2.6% over the optimal parser.

关键词： Accuracy Semantics Software quality Software systems data models Robustness Software reliability

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Ballot Tabulation Using Deep learning

Ballot Tabulation Using Deep Learning

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Fei Zhao Chengcui Zhang Nitesh Saxena Dan Wallach Akm Shahariar Azad Rabby Dept. of Computer Science The University of Alabama at Birmingham Dept. of Computer Science & Engineering Texas A&M University Dept. of Computer Science Rice University

Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. the current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a hand-marked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL model-based tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate.

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Prediction of Students' Self-confidence Using Multimodal Features in an Experiential Nurse Training Environment 24th

Prediction of Students' Self-confidence Using Multimodal Fea...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Vatral, Caleb Lee, Madison Cohn, Clayton Davalos, Eduardo Levin, Daniel Biswas, Gautam Vanderbilt Univ Inst Software Integrated Syst Nashville TN 37235 USA Vanderbilt Univ Peabody Coll Nashville TN 37203 USA

ISBN: (纸本)9783031363351;9783031363368

Simulation-based experiential learning environments used in nurse training programs offer numerous advantages, including the opportunity for students to increase their self-confidence through deliberate repeated practice in a safe and controlled environment. However, measuring and monitoring students' self-confidence is challenging due to its subjective nature. In this work, we show that students' self-confidence can be predicted using multimodal data collected from the training environment. By extracting features from student eye gaze and speech patterns and combining them as inputs into a single regression model, we show that students' self-rated confidence can be predicted with high accuracy. Such predictive models may be utilized as part of a larger assessment framework designed to give instructors additional tools to support and improve student learning and patient outcomes.

关键词： Experiential learning Simulation-based Training Multimodal learning Analytics (MMLA) Self Confidence Machine learning

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