检索结果-内蒙古大学图书馆

Predicting Learning Performance with Large Language Models: A Study in Adult Literacy

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Liang Lin, Jionghao Borchers, Conrad Sabatini, John Hollander, John Cao, Meng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Memphis MemphisTN38152 United States Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning. Our study investigates the application of advanced AI models, including Large Language Models (LLMs) like GPT-4, for predicting learning performance in adult literacy programs in ITSs. This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities. By using reading comprehension datasets from the ITS, AutoTutor, we evaluate the predictive capabilities of GPT-4 versus traditional machine learning methods in predicting learning performance through five-fold cross-validation techniques. Our findings show that the GPT-4 presents the competitive predictive abilities with traditional machine learning methods such as Bayesian Knowledge Tracing, Performance Factor Analysis, Sparse Factor Analysis Lite (SPARFALite), tensor factorization and eXtreme Gradient Boosting (XGBoost). While XGBoost (trained on local machine) outperforms GPT-4 in predictive accuracy, GPT-4-selected XGBoost and its subsequent tuning on the GPT-4 platform demonstrates superior performance compared to local machine execution. Moreover, our investigation into hyper-parameter tuning by GPT-4 versus grid-search suggests comparable performance, albeit with less stability in the automated approach, using XGBoost as the case study. Our study contributes to the field by highlighting the potential of integrating LLMs with traditional machine learning models to enhance predictive accuracy and personalize adult literacy education, setting a foundation for future research in applying LLMs within ITSs. © 2024, CC BY.

关键词： Factor analysis

Enhancing Functional and Extra Motor Abilities: A Focus Group Study on the Re-Design of an Extra-Robotic Finger

学校读者我要写书评

暂无评论

Enhancing Functional and Extra Motor Abilities: A Focus Grou...

IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Sjoerd Hendriks Basma Hasanen Naqash Afzal Irfan Hussain Mohammad Obaid Department of Computer Science and Engineering Interaction Design Unit Chalmers University of Technology Gothenburg Sweden Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The utilization of extra-robotic limbs to regain and extend motor abilities poses challenges. Nonetheless, the evidence suggesting the extension of these abilities without functional interference holds promise for the advancement of extra-robotic fingers or limbs. To gain insights into design improvement from diverse perspectives, we conducted a focus group study utilizing a prototype supernumerary robotic finger (SRF). The main purpose of the study is to gain knowledge on the current prototype, and then use the outcomes in a future co-designing process of SRF having the potential to be used as an assistive and augmented tool. Fifteen healthy subjects with an engineering background participated in the current study. We analyzed the collected data through a thematic analysis process to generate key design recommendations. These recommendations contain suggestions for designing for personal and social acceptance, adaptability, and multi-modal interaction. The results serve as a subset of future subsequent studies to be carried out with other stakeholders of the prototype, including patients, clinicians, and designers. Future work focuses on using the recommendations in a co-design process to inform the prototyping of SRF technologies supporting and extending motor abilities in upper limbs.

关键词： Fingers Prototypes Interference Motors Stakeholders Robots

Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relation

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

ON THE RELATION BETWEEN INTERNAL LANGUAGE MODEL AND SEQUENCE DISCRIMINATIVE TRAINING FOR NEURAL TRANSDUCERS

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yang, Zijian Zhou, Wei Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Internal language model (ILM) subtraction has been widely applied to improve the performance of the RNN-Transducer with external language model (LM) fusion for speech recognition. In this work, we show that sequence discriminative training has a strong correlation with ILM subtraction from both theoretical and empirical points of view. Theoretically, we derive that the global optimum of maximum mutual information (MMI) training shares a similar formula as ILM subtraction. Empirically, we show that ILM subtraction and sequence discriminative training achieve similar effects across a wide range of experiments on Librispeech, including both MMI and minimum Bayes risk (MBR) criteria, as well as neural transducers and LMs of both full and limited context. The benefit of ILM subtraction also becomes much smaller after sequence discriminative training. We also provide an in-depth study to show that sequence discriminative training has a minimal effect on the commonly used zero-encoder ILM estimation, but a joint effect on both encoder and prediction + joint network for posterior probability reshaping including both ILM and blank suppression. Copyright © 2023, The Authors. All rights reserved.

关键词： Transducers

ANALYSIS OF SELF-SUPERVISED SPEECH MODELS ON CHILDREN’S SPEECH AND INFANT VOCALIZATIONS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Jialu Hasegawa-Johnson, Mark McElwain, Nancy L. Department of Electrical and Computer Engineering University of Illinois United States Beckman Institute for Advanced Science and Technology University of Illinois United States Department of Human Development and Family Studies University of Illinois United States

To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused on analyzing the encoded information of the SSL layer representations in adult speech. Limited work has investigated how pre-training and fine-tuning affect SSL models encoding children’s speech and vocalizations. In this study, we aim to bridge this gap by probing SSL models on two relevant downstream tasks: (1) phoneme recognition (PR) on the speech of adults, older children (8-10 years old), and younger children (1-4 years old), and (2) vocalization classification (VC) distinguishing cry, fuss, and babble for infants under 14 months old. For younger children’s PR, the superiority of fine-tuned SSL models is largely due to their ability to learn features that represent older children’s speech and then adapt those features to the speech of younger children. For infant VC, SSL models pre-trained on large-scale home recordings learn to leverage phonetic representations at middle layers, and thereby enhance the performance of this task. © 2024, CC BY.

关键词： Supervised learning

Learning Medical Subject Headings in PubMed Articles to Enhance Deep Predictions

学校读者我要写书评

暂无评论

Learning Medical Subject Headings in PubMed Articles to Enha...

International Conference on Computational Cybernetics (ICCC)

作者： Zolzaya Dashdorj Zoljargal Jargalsaikhan Stanislav Grigorev Andrey Trufanov Tae-Koo Kang Erdenebaatar Altangerel Computer Science Mongolian University of Science and Technology Ulaanbaatar Mongolia School of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk Russia Department of Human Intelligence and Robot Engineering Sangmyung University Seoul South Korea

ISBN: (数字)9798350350159

ISBN: (纸本)9798350350166

MeSH is a vocabulary of terms used in the life sciences and biomedical fields. A large amount of Medical Subject Headings (MeSH) occur in PubMed articles with different weights. In this research, we analyze a disease-symptom network exploiting MeSH metadata co-occurring in 7 million PubMed articles and configure a deep model in the context of biomedical and health care data. Optimized and efficient data networks are important to compile machine learning models. We optimize the deep model by reducing important features. Our result highlights that the degree of the severity of symptoms associated with a disease improves the accuracy of disease prediction models. The network was well optimized by reducing the number of symptoms. This finding may have important implications in healthcare: healthcare chatbots, clinical significance, practical implications.

关键词： Analytical models Vocabulary Accuracy Biological system modeling Computational modeling Medical services Machine learning

RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhou, Wei Beck, Eugen Berger, Simon Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only. For closed-vocabulary scenarios, public tools supporting lexical-constrained decoding are usually only for classical ASR, or do not support all S2S models. To eliminate this restriction on research possibilities such as modeling unit choice, we present RASR2 in this work, a research-oriented generic S2S decoder implemented in C++. It offers a strong flexibility/compatibility for various S2S models, language models, label units/topologies and neural network architectures. It provides efficient decoding for both open- and closed-vocabulary scenarios based on a generalized search framework with rich support for different search modes and settings. We evaluate RASR2 with a wide range of experiments on both switchboard and Librispeech corpora. Our source code is public online. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

Imagining and building wise machines: The centrality of AI metacognition

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Johnson, Samuel G.B. Karimi, Amir-Hossein Bengio, Yoshua Chater, Nick Gerstenberg, Tobias Larson, Kate Levine, Sydney Mitchell, Melanie Rahwan, Iyad Schölkopf, Bernhard Grossmann, Igor University of Waterloo Department of Psychology Canada University of Waterloo Department of Electrical and Computer Engineering Canada Université de Montréal Department of Computer Science and Operations Research Canada Warwick Business School Behavioural Science Group United Kingdom Stanford University Department of Psychology United States University of Waterloo Cheriton School of Computer Science Canada Allen Institute for Artificial Intelligence United States Santa Fe Institute United States Max Planck Institute for Human Development Germany Max Planck Institute for Intelligent Systems Germany

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack transparency in their reasoning (explainability), face challenges in communication and commitment (cooperation), and pose risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems—those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive—through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition—the ability to reflect on and regulate one’s thought processes—is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one’s knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values—a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations. © 2024, CC BY.

关键词： Artificial intelligence

END-TO-END TRAINING OF A NEURAL HMM WITH LABEL AND TRANSITION PROBABILITIES

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Mann, Daniel Raissi, Tina Michel, Wilfried Schlüter, Ralf Ney, Hermann AppTek GmbH Aachen52062 Germany Machine Learning and Human Language Technology Computer Science Department RWTH Aachen University Aachen52074 Germany

We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contemporary sequence-to-sequence models allow for from-scratch training by summing over all possible label segmentations in a given topology. In our approach there are explicit, learnable probabilities for transitions between segments as opposed to a blank label that implicitly encodes duration statistics. We implement a GPU-based forward-backward algorithm that enables the simultaneous training of label and transition probabilities. We investigate recognition results and additionally Viterbi alignments of our models. We find that while the transition model training does not improve recognition performance, it has a positive impact on the alignment quality. The generated alignments are shown to be viable targets in state-of-the-art Viterbi trainings. © 2023, CC BY.

关键词： Hidden Markov models