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检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是261-270 订阅

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Anticipating Student Abandonment and Failure: Predictive Models in High School Settings 25th

Anticipating Student Abandonment and Failure: Predictive Mod...

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25th International Conference on artificial intelligence in Education (AIED)

作者： Queiroga, Emanuel Marques Santana, Daniel da Silva, Marcelo de Aguiar, Martim dos Santos, Vinicius Mello, Rafael Ferreira Bittencourt, Ig Ibert Cechinel, Cristian Fed Inst Educ Sci & Technol Sul Rio Grandense IFS Pelotas RS Brazil Inter Amer Dev Bank IDB Brasilia DF Brazil Fed Univ Alagoas UFAL Ctr Excellence Social Technol NEES Maceio Alagoas Brazil Inst Unibanco Sao Paulo Brazil Ctr Estudos & Sistemas Avancados Recife CESAR Recife PE Brazil Fed Univ Santa Catarina UFSC Florianopolis SC Brazil

ISBN: (纸本)9783031643019;9783031643026

Addressing the issue of high school non-completion poses a crucial challenge for contemporary education. This research introduces a machine learning-based methodology to identify students at risk of failure and abandonment in a specific Brazilian state, aiming to establish an early warning system utilizing academic, socioeconomic, and performance indicators for proactive interventions. The methodology followed here ensures the explainability of predictions and guards against bias in relation to certain features. The analysis of data from 79,165 students resulted in the creation of six accurate classification models, with accuracy rates ranging from 69.4% to 92.7%. This underscores the methodology's effectiveness in identifying at-risk students, highlighting its potential to alleviate failure and abandonment. The implementation of this methodology could positively influence proactive educational policies and enhance educational metrics within the state.

关键词： Learning Analytics Educational Data Mining Educational Technology

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Multi-agent Financial Systems with RL: A Pension Ecosystem Case 24th

Multi-agent Financial Systems with RL: A Pension Ecosystem C...

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24th International Workshop on Multi-Agent-Based Simulation (MABS)

作者： Ozhamaratli, Fatih Barucca, Paolo UCL 66-72 Gower St London WC1E 6EA England

ISBN: (纸本)9783031610332;9783031610349

This paper introduces a multi-agent reinforcement learning (MARL) model for the pension ecosystem, aiming to optimise the contributor's saving and investment strategies. The multi-agent approach enables the examination of endogenous and exogenous shocks, business cycle impacts, and policy decisions on contributor behaviour. The model generates synthetic income trajectories to develop inclusive savings strategies for a broad population. Additionally, this research innovates by developing a multi-agent model capable of adapting to various environmental changes, contrasting with traditional econometric models that assume stationary employment and market dynamics. The non-stationary nature of the model allows for a more realistic representation of economic systems, enabling a better understanding of the complex interplay between agents and their responses to evolving economic conditions (A variation of this article was included as a chapter in the PhD Thesis of Ozhamaratli, F. submitted on 22 Jan 2024).

关键词： Multi Agent Systems Financial Computing Multi Agent Reinforcement Learning Agent Based Modelling Hardware Accelerated Simulation Lifetime Consumption and Portfolio Optimisation

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Sensory Features in Affective Analysis: A Study Based on Neural Network Models 24th

Sensory Features in Affective Analysis: A Study Based on Neu...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Xia, Yuhan Zhao, Qingqing Long, Yunfei Xu, Ge Univ Essex Sch Comp Sci & Elect Engn Wivenhoe Pk Colchester CO4 3SQ England Chinese Acad Social Sci Inst Linguist Beijing Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Peoples R China

ISBN: (纸本)9789819705856;9789819705863

This study proposes an ensemble model to incorporate sensory features of lexical items in English from external resources into neural affective analysis frameworks. This allows the models to take the combined effects of bi-directional feeling between the sensory lexicon and the writer to infer human affective knowledge. We evaluate our model on two affective analysis tasks. The ensemble model exhibits the best accuracy and the results with 1% F1-score improvement over the baseline LSTM model in the sentiment analysis task. The performance shows that perceptual information can contribute to the performance of sentiment classification tasks significantly. This study also provides a support for the linguistic finding that correlations exist between sensory features and sentiments in the language.

关键词： Affective analysis Sensory feature Sentiment

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Evaluation Method and Capability Improvement Research Based on Controller Capability Characteristics 21st

Evaluation Method and Capability Improvement Research Based ...

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21st International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE)

作者： Liu, Jimin Lu, Ting-Ting Li, Ning East China Reg Air Traff Management Bur Civil Avi Strateg Dev Dept Shanghai 200335 Peoples R China Civil Aviat Univ China Coll Air Traff Management Tianjin 300300 Peoples R China Civil Aviat Adm China Air Traff Control Ind Management Off Beijing 100010 Peoples R China

ISBN: (纸本)9783031607301;9783031607318

First of all, this paper analyzed the status quo of domestic and foreign research on controller capability assessment and capability improvement. Then, based on 10 dimensions, such as professional character, language ability, reaction ability, mathematical application ability, spatial orientation ability, short-term memory ability, logical inference ability, situational awareness ability, attention switching ability, multitasking ability, the controller ability evaluation model was established. The rationality of the model was verified by selecting controllers at different stages for evaluation. Finally, based on 10 capability dimensions of controllers, a controller capability enhancement model was established. The research of this paper has important theoretical and practical significance, filling the theoretical gap in Chinese controller capability assessment and capability improvement research, and laying a good theoretical foundation for improving the overall capability of the controller team and ensuring flight safety.

关键词： Evaluation Method Capability Improvement Controller Capability Characteristics Evaluation Index System

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Grading Documentation with Machine Learning 25th

Grading Documentation with Machine Learning

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25th International Conference on artificial intelligence in Education (AIED)

作者： Messer, Marcus Shi, Miaojing Brown, Neil C. C. Kolling, Michael Kings Coll London Dept Informat London England Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031643019;9783031643026

Professional developers, and especially students learning to program, often write poor documentation. While automated assessment for programming is becoming more common in educational settings, often using unit tests for code functionality and static analysis for code quality, documentation assessment is typically limited to detecting the presence and the correct formatting of a docstring based on a specified style guide. We aim to investigate how machine learning can be utilised to aid in automating the assessment of documentation quality. We classify a large set of publicly available human-annotated relevance scores between a natural language string and a code string, using traditional approaches, such as Logistic Regression and Random Forest, fine-tuned large language models, such as BERT and GPT, and Low-Rank Adaptation of large language models. Our most accurate mode was a fine-tuned CodeBERT model, resulting in a test accuracy of 89%.

关键词： Automated Grading Assessment Computer Science Education Machine Learning Large Language Models Documentation Programming Education

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Explaining Metaphors in the French Language by Solving Analogies Using a Knowledge Graph 27th

Explaining Metaphors in the French Language by Solving Analo...

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27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Roux, Jeremie Guenoune, Hani Lafourcade, Mathieu Moot, Richard Univ Montpellier CNRS LIRMM Montpellier France

ISBN: (纸本)9783031705625;9783031705632

An analogy is a relation which operates between two pairs of terms representing two distant domains. It operates by transferring meaning from a concept that is known to another that one would like to clarify or define. In this report, we address analogy both from the aspect of modeling and by automatically explaining it. We will then propose a system of resolution of analogical equations in their notation in symbol chains. The model, based on the common sense knowledge base JeuxDeMots (a semantic network), operates by generating a list of potential candidates from which it chooses the most suitable solution. We conclude by evaluating our model on a collection of equations, and reflecting upon future work.

关键词： Analogy Metaphor Figurative language Natural language processing Knowledge base

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A Corpus-Based Study of Lexical Chunks in Chinese Academic Discourse: Extraction, Classification, and Application 24th

A Corpus-Based Study of Lexical Chunks in Chinese Academic D...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Zhou, Qihong Mou, Li Sichuan Int Studies Univ Coll Chinese Language & Culture Chongqing 400031 Peoples R China Univ Teknol Malaysia Fac Social Sci & Humanities Sch Educ Johor Baharu 81310 Utm Johor Bahru Malaysia Chongqing City Management Coll Sch Gerenal Educ Chongqing 401331 Peoples R China

ISBN: (纸本)9789819705856;9789819705863

This study applies AI technology to build academic Chinese corpora. Python was employed to extract lexical chunks of various lengths, including 3-gram, 4-gram, 5-gram, and 6-gram. The identification of these lexical chunks was performed using the New-MI algorithm and filtered based on semantic relevance completeness. Subsequently, manual intervention was applied to eliminate duplicate entries and identify 1431 continuous word chunks. These lexical chunks were classified into three categories according to their functions: research-oriented, text-oriented, and participation-oriented. It was found that there were some differences in the use of chunks between Korean Chinese learners and native Chinese writers, with research-oriented chunks being used more frequently in both groups than in other categories. Korean Chinese learners used research-oriented, text-oriented, and participant-oriented chunks less frequently than native speakers. This study might provide a reference for academic Chinese writing and academic Chinese textbook development for Chinese language learners.

关键词： Academic Chinese Lexical Chunks N-gram Lexical Chunks Function Chinese Learners

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Mars, Minecraft, and AI: A Deep Learning Approach to Improve Learning by Building 25th

Mars, Minecraft, and AI: A Deep Learning Approach to Improve...

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25th International Conference on artificial intelligence in Education (AIED)

作者： Hum, Samuel Shipley, Evan Gadbury, Matt Lane, H. Chad Ginger, Jeffrey Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9783031642982;9783031642999

Middle school students learned about astronomy and STEM concepts while exploring Minecraft simulations of hypothetical Earths and exoplanets. Small groups (n = 24) were tasked with building feasible habitats on Mars. In this paper, we present a scoring scheme for habitat assessment that was used to build novel multi/mixed-input AI models. Using Spearman's rank correlations, we found that our scoring scheme was reliable with regards to team size and face-to-face instruction time and validated with self-explanation scores. We took an exploratory approach to analyzing image and block data to compare seven different input conditions. Using one-way ANOVAs, we found that the means of the conditions were not equal for accuracy, precision, recall, and F1 metrics. A post hoc Tukey HSD test found that models built using images only were statistically significantly worse than conditions that used block data on the metrics. We also report the results of optimized models using block only data on additional Mars bases (n = 57).

关键词： Scoring Scheme artificial intelligence Habitat Building Informal Learning Minecraft

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A Comparative Study of the General Classifier Ge and Its Near-Synonyms in Modern Chinese-Taking Ge, Zhong, and Jian as Examples 24th

A Comparative Study of the General Classifier Ge and Its Nea...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Rui, Jingying Hong, Jia-Fei Minjiang Univ Sch Humanities Fuzhou Peoples R China Taiwan Normal Univ Dept Chinese Language 2 Taipei Taiwan

ISBN: (纸本)9789819705825;9789819705832

As the only general classifier in modern Chinese, the attribute of Ge (up arrow) inevitably leads to semantic overlap with other classifiers resulting in misuse, which is worthy of further research. This study conducts a comparative analysis of Ge and its near-synonyms, Zhong (sic) and Jian (sic), based on corpus research. It shows that Ge and Jian have co-occurrence contexts when measuring nouns about events, but the use of Ge is usually limited to colloquial language, while Jian is applicable to both colloquial and written language. The noun Shijian (sic) 'event' is usually modified by Ge not by Jian. There is no semantic overlap between Ge and *** and they cannot be used interchangeably. Some nouns can appear simultaneously in the Num/RNum+Ge/***+N structure, and choosing which classifier is only determined by quantitative or categorical concepts they expressed.

关键词： Classifier Semantics Ge Zhong Jian

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Stream-based Active Learning for Speech Emotion Recognition via Hybrid Data Selection and Continuous Learning 27th

Stream-based Active Learning for Speech Emotion Recognition ...

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27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Moreno-Acevedo, Santiago A. Vasquez-Correa, Juan Camilo Martin-Donas, Juan M. Alvarez, Aitor Basque Res & Technol Alliance BRTA Fdn Vicomtech Mikeletegi 57 Donostia San Sebastian 20009 Spain

ISBN: (纸本)9783031705656;9783031705663

This work proposes a novel stream-based Active Learning (AL) approach applied to Speech Emotion Recognition (SER) in reallife scenarios where new data are generated from different domains. The goal is to address major challenges in this field, including the lack of large-labeled data, the difficulty in the annotation, and the retrieval of representative emotional data. AL aims to address these problems by selecting/querying a small and valuable subset to be annotated with optimized labeling efforts and minimum resources. To this end, we consider a stream-based AL methodology leveraging MLOps principles and human-in-the-loop methods to continuously adapt previously trained deep learning models, ensuring both challenging and diverse audio samples and reducing the performance gap related to data diversity, cross-domain contexts, and continuous data ingestion. The considered pipeline was tested across several domains within three distinct scenarios, including both no stream- and stream-based approaches, as well as a pocket stream alternative to only update the previously trained models when significant improvements are obtained. The experimental outputs show that our proposed method achieved competitive results following an AL pocket stream-based strategy with just 20% of the original training data. This ensures good performance with a low allocated budget and continuous adaptation for practical, real-world environments.

关键词： Active Learning Stream-based Clustering Speech Emotion Recognition Cross-domain learning

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