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

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Future Flight Safety Monitoring: Comparison of Different Computational Methods for Predicting Pilot Performance Under Time Series During Descent by Flight Data and Eye-Tracking Data 21st

Future Flight Safety Monitoring: Comparison of Different Com...

引用

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

作者： Wang, Yifan Li, Wen-Chin Nichanian, Arthur Korek, Wojciech Tomasz Chan, Wesley Tsz-Kin Cranfield Univ SATM Cranfield Beds England

ISBN: (纸本)9783031607301;9783031607318

Introduction. Effective and real-time analysis of pilot performance is important for improving flight safety and enabling remote flight safety control. The use of flight data and pilot physiological data to analyse and predict pilot performance is an effective means of achieving this monitoring. Research question. This research aims to compare two forecasting methods (XGBoost and Transformer) in evaluating and predicting pilot performance using flight data and eye tracking data. Method. Twenty participants were invited to fly an approach using Instrument Landing System (ILS) guidance in the Future Systems Simulator (FSS) while wearing Pupil-Lab eye tracker. The deviation to the desired route, the pupil diameter and the gaze positions were selected for forecasting the flight performance indicator: the difference between the aircraft altitude and the reference altitude corresponding to the ideal 3-degree glide path. Utilize XGBoost and the Transformer forecasting technique to develop a forecasting model using the data from this research, and conduct a comparative analysis of the accuracy and convenience of both models. Results & Discussion. The result demonstrates that using XGBoost regression model had a higher prediction accuracy, (RMSEXG-Boots = 42.29, RMSETransformer = 102.10) and its easier to achieve a high prediction accuracy than Transformer as Transformer forecasting method placed a high demand on debugging model and computing equipment. The deviation to desired route and the pupil diameter were more important in the XGBoost model. Conclusion. The use of machine learning and deep learning methods enables the monitoring and prediction of flight performance using flight data and pilot physiological data. The comparison of the two methods shows that it is not necessarily the newer and more complex technology that can build more accurate and faster prediction models, but building the right model based on the data is important for real-time flight data monitoring

关键词： data analysis human-machine interactions eye-tracking data pilot performance monitoring

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Bilingual Lexicon Induction From Comparable and Parallel Data: A Comparative Analysis 27th

Bilingual Lexicon Induction From Comparable and Parallel Dat...

引用

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

作者： Denisova, Michaela Rychly, Pavel Masaryk Univ Nat Language Proc Ctr Brno Czech Republic Lexical Comp Brno Czech Republic

ISBN: (纸本)9783031705625;9783031705632

Bilingual lexicon induction (BLI) from comparable data has become a common way of evaluating cross-lingual word embeddings (CWEs). These models have drawn much attention, mainly due to their availability for rare and low-resource language pairs. An alternative offers systems exploiting parallel data, such as popular neural machine translation systems (NMTSs), which are effective and yield state-of-the-art results. Despite the significant advancements in NMTSs, their effectiveness in the BLI task compared to the models using comparable data remains underexplored. In this paper, we provide a comparative study of the NMTS and CWE models evaluated on the BLI task and demonstrate the results across three diverse language pairs: distant (Estonian-English) and close (Estonian-Finnish) language pair and language pair with different scripts (Estonian-Russian). Our study reveals the differences, strengths, and limitations of both approaches. We show that while NMTSs achieve impressive results for languages with a great amount of training data available, CWEs emerge as a better option when faced less resources.

关键词： Bilingual lexicon induction Cross-lingual word embeddings Neural machine translation systems

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Using Flight Quick Access Recorder (QAR) Data to Examine the Effect of Sun Glare on Landing Performance: An Initial Attempt 21st

Using Flight Quick Access Recorder (QAR) Data to Examine the...

引用

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

作者： Qiu, Weiwei Cao, Ziyue Chen, Xing Zhang, Jingyu Sun, Xianghong Zheng, Yinger Wang, Haofeng Univ Chinese Acad Sci Dept Psychol Beijing 100101 Peoples R China Chinese Acad Sci Inst Psychol Beijing 100101 Peoples R China Civil Aviat Flight Univ China Flight Technol Coll Guanghan 618307 Peoples R China China Acad Civil Aviat Sci & Technol Big Data Applicat Ctr Beijing 100028 Peoples R China

ISBN: (纸本)9783031607301;9783031607318

Flight safety during the landing phase is crucial, and many environmental factors may undermine this process. Sun glare has been identified as an important environmental factor affecting operation performance, but the empirical evidence concerning its effects requires further exploration. This study investigates the effect of sun glare on flight landing performance based on real flight QAR data. Five landing performance indicators were extracted from the QAR data encompassing 1235 flights at an east-west runway airport during clear weather conditions. We examined whether landing performance can be undermined when sun glare is more likely to happen, specifically during westward landing in the late afternoon and eastward landing in the early morning. The general linear model analysis findings reveal a marginally significant interaction effect between runway direction and time period on touchdown distance. Notably, in the morning, eastward landings exhibited longer touchdown distances compared to westward landings, while during the late afternoon, the opposite trend was observed with westward touchdown distances being longer. This study offers a novel perspective on how environmental factors can influence flight performance. The findings hold promise in devising strategies to mitigate the adverse effects of sun glare on flight operation performance, thereby enhancing overall flight safety standards.

关键词： Quick Access Recorder (QAR) Data Flight Landing Performance Sun Glare Aircraft Safety

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Automatic Classification of Parkinson's Disease Using Wav2vec Embeddings at Phoneme, Syllable, and Word Levels 27th

Automatic Classification of Parkinson's Disease Using Wav2ve...

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

作者： David Gallo-Aristizabal, Jeferson Escobar-Grisales, Daniel David Rios-Urrego, Cristian Noth, Elmar Rafael Orozco-Arroyave, Juan Univ Antioquia UdeA GITA Lab Fac Engn Medellin Colombia Friedrich Alexander Univ Pattern Recognit Lab Erlangen Germany

ISBN: (纸本)9783031705656;9783031705663

Parkinson's disease (PD) is a neurological condition that produces several speech deficits, typically known as hypokinetic dysarthria. PD involves motor impairments and muscle dysfunction in the phonatory apparatus, producing anomalies in oral communication. Speech signals have been used as a biomarker for diagnosis and monitoring of PD. In this work, we discriminate between PD patients and healthy controls based on patterns extracted from speech signals collected from Colombian spanish speakers, considering three different granularity levels: phoneme, syllable, and word. The Wav2vec 2.0 model is used to obtain frame-level representations of each utterance. These representations are grouped according to each granularity level using different statistical functionals. Each granularity level was evaluated independently, obtaining accuracies of 86%, 80%, and 83% for phonemes, syllables, and words, respectively. In addition, we identified the phonological classes with better discrimination capability. Nasals, approximant, and plosive classes were the three most accurate. We believe that this work constitutes a step forward in the development of automatic systems that support speech and language therapy of PD patients. For future work, we plan to model co-articulation information in words and syllables.

关键词： Parkinson's disease Speech impairments Phoneme Syllable Word Wav2vec

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Predicting Runway Configurations at Airports Through Soft Voting Ensemble Learning 21st

Predicting Runway Configurations at Airports Through Soft Vo...

引用

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

作者： Wang, Xinglong Liu, Linning Civil Aviat Univ China Key Lab Internet Aircrafts Tianjin 300300 Peoples R China

ISBN: (纸本)9783031607271;9783031607288

The runway is a crucial area for airport operations, and air traffic controllers determine the current runway configuration by selecting a subset of available runways and their directions. As the number of runways at an airport increases, the complexity of choosing an appropriate runway configuration for the current weather conditions and traffic volume also increases. Establishing a runway configuration assistance decision-making system suitable for multi-runway airports is of significant practical importance. This paper utilizes actual historical data from Hartsfield-Jackson Atlanta International Airport, which has five runways. By processing meteorological data, runway configurations, and relevant operational data, the paper predicts runway configurations for twelve-time points in the next six hours, with predictions made every hour. A comparative analysis is conducted on the prediction results using three basic models: XGBoost, Random Forest, and CatBoost. Based on the results, the better model is selected and the optimal combination of weights is found for it to build the soft voter model. The research focuses on providing timely and accurate assistance decision-making recommendations for runway configurations based on meteorological and historical operational data. The aim is to enhance runway operational capacity, improve airport operational efficiency, and provide theoretical references for the development of operations at airports with multiple runways.

关键词： Runway Configuration Soft Voting Machine Learning

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Student At-Risk Identification and Classification Through Multitask Learning: A Case Study on the Moroccan Education System 25th

Student At-Risk Identification and Classification Through Mu...

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

作者： Elbouknify, Ismail Berrada, Ismail Mekouar, Loubna Iraqi, Youssef Bergou, E. L. Houcine Belhabib, Hind Nail, Younes Wardi, Souhail Mohammed VI Polytech Univ Coll Comp Ben Guerir Morocco Minist Natl Educ Presch & Sports Rabat Morocco

ISBN: (纸本)9783031642982;9783031642999

Early identification of students at-risk of dropping out or failing is a critical challenge in education. In this paper, we address this imperative by deploying multitasking models with a dual purpose: early identification and subsequent classification into success, failure, or dropout. The multitask models used include the deep multitask model, as well as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) based multitask models. We provided a comparative analysis of the performance of traditional machine learning, deep learning, and multitasking models. Our proposed approach has been applied to the Moroccan education system using a proprietary dataset provided by the Moroccan Ministry of National Education, Pre-school, and Sports. The results show that the multitasking model demonstrates superior accuracy and effectiveness in both tasks, while also reducing training and prediction time.

关键词： Students At-Risk Multitask Learning Deep multitask Learning artificial intelligence in education

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A Study on the Combination of the "artificial/Natural" Semantics in the Chinese NN Attributive-Head Compounds 24th

A Study on the Combination of the "Artificial/Natural" Seman...

引用

24th Chinese Lexical Semantics Workshop (CLSW)

作者： Du, Tengteng Li, Jiapan Beijing Language & Culture Univ Teachers Coll Beijing Peoples R China

ISBN: (纸本)9789819705825;9789819705832

The semantic combination within a compound word affects the characteristics of the whole word. This paper focuses on Chinese noun-noun attributive-head compounds, using "natural/artificial" as classification criteria, and utilizing the theory of qualia structure to explore the qualia relationship between components of compound words and their implicit predicates under consistent and inconsistent modes of semantic classification. The research indicates that there is a selection tendency between the qualia role played by attributive components in compound words and their implicit predicates. Additionally, the semantic type of constituents in a compound word can influence its overall semantic type;this in turn restricts the qualia relationship between constituents within a word and selection for implicit predicates.

关键词： Noun-noun compound words Consistent semantics classification Inconsistent semantics classification Qualia structure theory Implicit predicates

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Speech Analysis 21st

Speech Analysis

引用

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

作者： Brenner, Malcolm Chevy Chase United States

ISBN: (纸本)9783031607271;9783031607288

With recent advances in computer memory, speech analysis is becoming practical for real-time workload monitoring. Speech analysis can potentially provide a remote measure of physiological stress, without the need to attach sensors to the speaker, providing workload evidence that is not readily recognized by the human ear. It is especially appropriate for remote CRM applications, potentially providing a continued monitoring of individual physiological responses that can be accessed in real-time. This paper reviews recent evidence for three basic speech measures of stress/workload: fundamental frequency, speaking rate, and amplitude. It summarizes results from a laboratory experiment and from three real-life accident situations: a helicopter accident, a 1994 accident involving the Boeing 737, and the 2014 disappearance of Malaysia flight 370.

关键词： Speech analysis Remote CRM Workload/stress assessment Air crash investigation

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Leveraging Large Language Models for Automated Chinese Essay Scoring 25th

Leveraging Large Language Models for Automated Chinese Essay...

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

作者： Feng, Haiyue Du, Sixuan Zhu, Gaoxia Zou, Yan Poh Boon Phua Feng, Yuhong Zhong, Haoming Shen, Zhiqi Liu, Siyuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Hohai Univ Business Sch Nanjing Jiangsu Peoples R China Webank Shenzhen Peoples R China Woodlands Secondary Sch Singapore Singapore

ISBN: (纸本)9783031643019;9783031643026

Automated Essay Scoring (AES) plays a crucial role in offering immediate feedback, reducing the workload of educators in grading essays, and improving students' learning experiences. With strong generalization capabilities, large language models (LLMs) offer a new perspective in AES. While previous research has primarily focused on employing deep learning architectures and models like BERT for feature extraction and scoring, the potential of LLMs in Chinese AES remains largely unexplored. In this paper, we explored the capabilities of LLMs in the realm of Chinese AES. We investigated the effectiveness of the application of well-established LLMs in Chinese AES, e.g., the GPT-series by OpenAI and Qwen-1.8B by Alibaba Cloud. We constructed a Chinese essay dataset with carefully developed rubrics, based on which we acquired grades from human raters. Then we fed in prompts to LLMs, specifically GPT-4, fine-tuned GPT-3.5 and Qwen to get grades, where different strategies were adopted for prompt generations and model fine-tuning. The comparisons between the grades provided by LLMs and human raters suggest that the strategies to generate prompts have a remarkable impact on the grade agreement between LLMs and human raters. When model fine-tuning was adopted, the consistency between LLMs' scores and human scores was further improved. Comparative experimental results demonstrate that fine-tuned GPT-3.5 and Qwen outperform BERT in QWK score. These results highlight the substantial potential of LLMs in Chinese AES and pave the way for further research in the integration of LLMs within Chinese AES, employing varied strategies for prompt generation and model fine-tuning.

关键词： Automated Chinese Essay Scoring Large Language Models GPT

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How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging 25th

How to Teach Programming in the AI Era? Using LLMs as a Teac...

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

作者： Ma, Qianou Shen, Hua Koedinger, Kenneth Wu, Sherry Tongshuang Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9783031643019;9783031643026

Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer Science education context, as these models are widely recognized as "AI pair programmers," it becomes increasingly important to train students on evaluating and debugging the LLM-generated code. In this work, we introduce HYPOCOMPASS, a novel system to facilitate deliberate practice on debugging, where human novices play the role of Teaching Assistants and help LLM-powered teachable agents debug code. We enable effective task delegation between students and LLMs in this learning-by-teaching environment: students focus on hypothesizing the cause of code errors, while adjacent skills like code completion are offloaded to LLM-agents. Our evaluations demonstrate that HYPOCOMPASS generates high-quality training materials (e.g., bugs and fixes), outperforming human counterparts fourfold in efficiency, and significantly improves student performance on debugging by 12% in the pre-to-post test.

关键词： LLM teachable agent debugging CS1

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