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

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Explainable Automatic Grading with Neural Additive Models 25th

Explainable Automatic Grading with Neural Additive Models

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

作者： Condor, Aubrey Pardos, Zachary Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9783031643019;9783031643026

The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-the-art ASAG models are large neural networks (NN) often described as "black box", providing no explanation for which characteristics of an input are important for the produced output. This inexplicable nature can be frustrating to teachers and students when trying to interpret, or learn from an automatically-generated grade. To create a powerful yet intelligible ASAG model, we experiment with a type of model called a Neural Additive Model that combines the performance of a NN with the explainability of an additive model. We use a Knowledge Integration (KI) framework from the learning sciences to guide feature engineering to create inputs that reflect whether a student includes certain ideas in their response. We hypothesize that indicating the inclusion (or exclusion) of predefined ideas as features will be sufficient for the NAM to have good predictive power and interpretability, as this may guide a human scorer using a KI rubric. We compare the performance of the NAM with another explainable model, logistic regression, using the same features, and to a non-explainable neural model, DeBERTa, that does not require feature engineering.

关键词： Explainable AI Automatic Grading ASAG Neural Additive Models

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Seeing with Touch: The Effect of Full-Body Positional Haptic Feedback During Low-Visibility Aviation Ground Operations 21st

Seeing with Touch: The Effect of Full-Body Positional Haptic...

引用

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

作者： Bodsworth, Christopher Blewitt, Will Blundell, James Coventry Univ Ctr Future Transport & Cities Coventry W Midlands England Coventry Univ Fac Engn Environm & Comp Coventry W Midlands England

ISBN: (纸本)9783031607301;9783031607318

Objective: Through interaction between the Teslasuit and XPlane 11, we evaluate the impact of full-body positional haptic feedback during low-visibility aviation ground operations. This paper presents a working pipeline for Teslasuit-based flight simulator experiments and provides a preliminary humanin-the-loop evaluation of the system based on objective and subjective pilot performance and workload results. Method: We interface between a Teslasuit and XPlane 11 via the Unity Game engine and an application specific dynamic linked library (DLL). Source files for the interface are provided as supplementary material within a GitHub repository. The preliminary study involved six non-pilot participants completing 8 simulated taxiing tasks in a light-aircraft between two locations at Munich airport (EDDM) in low-visibility conditions. Evaluation of the haptic human machine interface (HMI) on participant performance was based on stopping distance from a threat. Subjective workload data was collected via NASA TLX questionnaires that were administered after each trial. Results: Raw TLX results were significantly lower during trials with the haptic HMI (M: 7.36, SD: 2.33) compared to trials without (M: 9.53, SD: 3.87)-(t (5)= -2.01;p =.05). Conversely, participants stopping distance (metres) from threats was larger with the haptic HMI (M: 140.44, SD: 84.84) than without (M: 119.31, SD: 33.26). Conclusions: Full-body haptic feedback warnings reduced mental workload when performing ground-based taxi operations but did not improve stopping distance. A larger sample size, with a representative pilot population, would be helpful for ascertaining a fuller understanding of full-body haptic feedback benefits within airport ground operations.

关键词： Haptics tactile ground navigation reaction time mental workload

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Self-organising Maps for Comparing Flying Performance Using Different Inceptors 21st

Self-organising Maps for Comparing Flying Performance Using ...

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

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

ISBN: (纸本)9783031607301;9783031607318

This paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen's Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.

关键词： data analysis human-machine interactions

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Zero-Shot vs. Few-Shot Multi-speaker TTS Using Pre-trained Czech SpeechT5 Model 27th

Zero-Shot vs. Few-Shot Multi-speaker TTS Using Pre-trained C...

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

作者： Lehecka, Jan Hanzlicek, Zdenek Matousek, Jindrich Tihelka, Daniel NTIS New Technol Informat Soc Plzen Czech Republic Univ West Bohemia Pilsen Dept Cybernet Plzen Czech Republic

ISBN: (纸本)9783031705656;9783031705663

In this paper, we experimented with the SpeechT5 model pre-trained on large-scale datasets. We pre-trained the foundation model from scratch and fine-tuned it on a large-scale robust multi-speaker text-to-speech (TTS) task. We tested the model capabilities in a zero- and few-shot scenario. Based on two listening tests, we evaluated the synthetic audio quality and the similarity of how synthetic voices resemble real voices. Our results showed that the SpeechT5 model can generate a synthetic voice for any speaker using only one minute of the target speaker's data. We successfully demonstrated the high quality and similarity of our synthetic voices on publicly known Czech politicians and celebrities.

关键词： Multi-speaker TTS SpeechT5 Few-shot TTS Zero-shot TTS

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Leveraging Conceptual Similarities to Enhance Modeling of Factors Affecting Adolescents' Well-Being 27th

Leveraging Conceptual Similarities to Enhance Modeling of Fa...

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

作者： Sotolar, Ondrej Plhak, Jaromir Smahel, David Masaryk Univ Fac Informat Brno Czech Republic

ISBN: (纸本)9783031705656;9783031705663

While large language models consistently outperform their smaller transformer-based counterparts, there are constraints on their deployment. Model size becomes a critical limiting factor in cases involving sensitive data, particularly when the imperative is to execute inference on edge devices such as smartphones. We explore the possibility of detecting common positive and negative influence factors that impact adolescents' well-being in instant messenger communication using a newly annotated dataset. We show that by leveraging the similarities between the concepts, we can produce classifiers with a small ELECTRA-based model with 14M parameters that can run on resource-limited edge devices. Our findings can be used to advance intervention and parental control software, creating a safer digital environment for children and adolescents.

关键词： supportive interactions online risks instant messenger private dialogues

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A Non-contact Vital Signs Retrieving Method for Aviation Safety Personnel Using TVF-EMD 21st

A Non-contact Vital Signs Retrieving Method for Aviation Saf...

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

作者： Lu, Xiaoguang Ma, Xiao Suo, Chenhao Zhang, Zhe Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

ISBN: (纸本)9783031607301;9783031607318

Conducting real-time monitoring of aviation safety personnel's vital signs during their duty, like respiration and heart rate, is essential for ensuring the safety of civil aviation operations, especially in promptly detecting fatigue-related anomalies. Traditional contact or wearable measurement systems are inevitably inconvenient and even bring new operational hazards when applied in working scenarios. The Linear Frequency Modulated Continuous Wave Radar (FMCW) provides a non-contact vital signal monitoring means for pilots and air traffic controllers in their working environments. The Empirical Mode Decomposition (EMD) method is a typical time domain solution to retrieve time-varying vital sign signals. However, this method is subjected to the modal aliasing effect due to the respiration rate and heartbeat rate being close in frequency. The Time-Varying Filtered Empirical Mode Decomposition (TVF-EMD) method is then introduced to address this issue, which enhances signal separation performance by adaptively adjusting the local cutoff frequency of the signal. This method successfully resolves the issue of modal aliasing in retrieving respiration and heartbeat signals from mm-wave radar echoes. In addition, heartbeat waveforms are effectively reconstructed using the Instantaneous Mode Functions (IMFs) decomposed by TVF-EMD. This enables precise estimation of Inter-Beat Interval (IBI) and various Heart Rate Variability (HRV) metrics. Simulations and experiment results validate the effectiveness of the TVF-EMD method in accurately extracting vital sign information from millimeter-wave radar measurement signals.

关键词： Non-contact measurement Millimeter wave radar Vital sign monitoring Time-varying filter empirical mode decomposition Heart rate variability

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Knowledge Tracing as Language Processing: A Large-Scale Autoregressive Paradigm 25th

Knowledge Tracing as Language Processing: A Large-Scale Auto...

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

作者： Zhan, Bojun Guo, Teng Li, Xueyi Hou, Mingliang Liang, Qianru Gao, Boyu Luo, Weiqi Liu, Zitao Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China TAL Educ Grp Beijing Peoples R China

ISBN: (纸本)9783031643019;9783031643026

Knowledge tracing (KT) is the process of modelling students' cognitive states to forecast their future academic performance, using their historical learning interactions as a reference. Recent scholarly investigations have introduced a range of deep learning-based knowledge tracing (DLKT) methodologies, which have demonstrated considerable potential in their outcomes. Considering the excellent performance of large models in various domains, we have explored the possibility of migrating their architecture to the KT domain. We posit that the efficacy of the large language model (LLM) can be largely attributed to the utilization of an auto-regressive Transformer decoder, which facilitates the learning of comprehensive representations and the processing of extensive data. Hence, we propose a DLKT model, LLM-KT, which is based on the LLM architecture. This model addresses the long-term dependency between students' historical interactions and their subsequent performance through a stack of Transformer decoders. To fully utilize the potential of large models, we evaluated our model capabilities on EdNet, which is currently the world's largest real KT dataset. Through a series of quantitative and qualitative experimental analyses, we answer two key questions: (1) is it feasible to apply the LLM-like architectures in the KT domain? (2) can the continuous extension of models improve prediction performance in KT? To encourage reproducible research, we make our data and code publicly available at https://***/ai4ed/ AIED2024-LLM-KT.

关键词： Deep learning

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Content Knowledge Identification with Multi-agent Large Language Models (LLMs) 25th

Content Knowledge Identification with Multi-agent Large Lang...

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

作者： Yang, Kaiqi Chu, Yucheng Darwin, Taylor Han, Ahreum Li, Hang Wen, Hongzhi Copur-Gencturk, Yasemin Tang, Jiliang Liu, Hui Michigan State Univ E Lansing MI 48824 USA Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9783031642982;9783031642999

Teachers' mathematical content knowledge (CK) is of vital importance and need in teacher professional development (PD) programs. Computer-aided asynchronous PD systems are the most recent proposed PD techniques. However, current automatic CK identification methods face challenges such as diversity of user responses and scarcity of high-quality annotated data. To tackle these challenges, we propose a Multi-Agent LLMs-based framework, LLMAgent-CK, to assess the user responses' coverage of identified CK learning goals without human annotations. Leveraging multi-agent LLMs with strong generalization ability and human-like discussions, our proposed LLMAgent-CK presents promising CK identifying performance on a real-world mathematical CK dataset MaCKT.

关键词： Math Knowledge Development Large Language Models Multi-Agent Systems

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Corpus-Based Study on the Evolution of the Lexical Phonetic Forms from Old Chinese to Middle Chinese 24th

Corpus-Based Study on the Evolution of the Lexical Phonetic ...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Qiu, Bing Tsinghua Univ Dept Chinese Language & Literature Beijing 100083 Peoples R China

ISBN: (纸本)9789819705856;9789819705863

The evolution of lexical syllable forms from Old Chinese to Middle Chinese is an issue of great significance in the study of Chinese lexis. This paper, based on corpus linguistics methodology, selects credible and representative texts from both Old Chinese and Middle Chinese and conducts a quantitative study from multiple perspectives. In terms of word types, polysyllabic words remained the majority in both Old Chinese and Middle Chinese, and those with three or more syllables accounted for an increased proportion in Middle Chinese. In terms of word tokens, the average frequency and total occurrences of monosyllabic words were far higher than that of polysyllabic words, indicating that monosyllabic words occupied a primary position in language use. In terms of high-frequency words, monosyllabic words predominated from Old Chinese to Middle Chinese, but within a certain range of frequency, Middle Chinese translated Buddhist scriptures featured a significantly higher proportion of polysyllabic words than Old Chinese texts as well as Chinese native texts of the same period. In terms of the lexicon of given books, the lexical syllable forms in Chinese native texts displayed a slow and steady development from Old Chinese to Middle Chinese, while Middle Chinese translated Buddhist scriptures demonstrated a much higher degree of polysyllablization than Chinese native texts of the same period. On the whole, the syllable forms of Chinese native words presented a continuous and slow evolution from Old Chinese to Middle Chinese, and language contact accelerated the polysyllablization of Chinese lexis..

关键词： Corpus linguistics Middle Chinese Language contact Lexical syllable form

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Predicting Successful Programming Submissions Based on Critical Logic Blocks 25th

Predicting Successful Programming Submissions Based on Criti...

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

作者： Pan, Ka Weng Jeffries, Bryn Koprinska, Irena Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Grok Acad POB 144 Sydney NSW 2007 Australia

ISBN: (纸本)9783031642982;9783031642999

We propose an approach to infer the critical logic blocks contained within student submissions that can influence the passing or failing of individual exercises in introductory programming courses. Given a programming exercise, we extract critical logic blocks from the abstract syntax trees (ASTs) of its submissions and use a bag-of-words approach to train a decision tree classifier to predict the pass/failure of a submission given the logic blocks present in its AST. We apply this technique to two streams of an introductory Python programming course for high-school students, constructing decision trees for 66 programming exercises based on several thousand submissions for each exercise. We obtain classifiers with F1 scores of 92.4% for the beginners and 88.2% for the intermediate stream. These trained models are highly interpretable and can provide a visualisation of the key logic blocks that are critical in the pass/failure of a problem. We explain how the models can assist educators in understanding the common valid approaches or failed attempts carried out by students when tackling a specific problem, and they may also serve as a guide for suggesting hints to struggling students.

关键词： Students

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