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检索条件"丛书名=Communications in Computer and Information Science"

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Knowledge Graph Question Answering for Materials science (KGQA4MAT) 17th

Knowledge Graph Question Answering for Materials Science (KG...

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17th Research Conference on Metadata and Semantic Research (MTSR)

作者： An, Yuan Greenberg, Jane Uribe-Romo, Fernando J. Gomez-Gualdron, Diego A. Langlois, Kyle Furst, Jacob Kalinowski, Alex Zhao, Xintong Hu, Xiaohua Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA Univ Cent Florida Dept Chem Orlando FL 32816 USA Colorado Sch Mines Chem & Biol Engn Golden CO 80401 USA

ISBN: (纸本)9783031659898;9783031659904

We present a study on Knowledge Graph Question Answering in Materials science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured data, meta-data, and knowledge extracted from the literature. We aim to develop a natural language (NL) interface for domain expert to query the MOFKG. A first step is our benchmark, which consists of 161 complex questions involving comparison, aggregation, and intricate graph structures. Each question has been rephrased into three additional variations, totaling 644 questions and 161 KG queries. We then developed a systematic approach for utilizing ChatGPT to translate natural language questions into formal KG queries. We experimented with different prompt strategies. The research indicated that using an ontology, providing a few-shot examples, and offering a chain-of-thought explanation resulted in the top F1-score of 0.89. We also applied this method to the well-known QALD-9 dataset, achieving performance on par with the state-of-the-art techniques. The results indicate applicability of this model for MOF research and potentially other scientific foci.

关键词： knowledge graph question answering metal-organic frameworks pre-trained large language models natural language interface

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Heuristic Technique to Find Optimal Learning Rate of LSTM for Predicting Student Dropout Rate 25th

Heuristic Technique to Find Optimal Learning Rate of LSTM fo...

引用

25th International Conference on Artificial Intelligence in Education (AIED)

作者： Singh, Anuradha Kumari Karthikeyan, S. Banaras Hindu Univ Dept Comp Sci Varanasi 200105 Uttar Pradesh India

ISBN: (纸本)9783031643118;9783031643125

Predictive analytics is being increasingly recognized as being important for evaluating university students' academic achievement. Utilizing big data analytics particularly students' demographic information, offers valuable insights to bolster academic success and enhance completion rates. For instance, learning analytics is a vital element of big data within university settings, offering strategic decision-makers the chance to conduct time series analyses on learning activities. We have used semesters first and second records of Polytechnic Institute of Portalegre students. Advanced deep learning methods, such as the Long short-term memory (LSTM) model are employed to analyze students at risk of retention issues. Typically, the best design for a deep neural network model was found by trial and error, which is a laborious and exponential combinatorial challenge. Hence we proposed the heuristic technique to configure the parameters of the neural network. The parameter taken into account in this work is the learning rate of the Adam optimizer. In this study, we have presented the ant colony optimization (ACO) technique to determine the ideal learning rate for model training. Experimental results obtained with the predictive model indicated that prediction of student retention is possible with a high level of accuracy using ACO-LSTM approach.

关键词： Ant colony optimization Deep learning Educational Data Mining Long short-term memory Dropout prediction

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Research on the Improvement of Children's Attention Through Binaural Beats Music Therapy in the Context of AI Music Generation 2nd

Research on the Improvement of Children's Attention Through ...

引用

2nd Summit on Music Intelligence (SOMI)

作者： Yang, Weijia Huang, Chih-Fang Huang, Hsun-Yi Zhang, Zixue Li, Wenjun Wang, Chunmei Shandong Xiehe Univ Jinan 250109 Peoples R China Kyonggi Univ Seoul 03746 South Korea Kainan Univ Taoyuan 33857 Taiwan Univ Putra Malaysia Serdang 43400 Malaysia Chonnam Natl Univ Gwangju 61186 South Korea

ISBN: (纸本)9789819705757;9789819705764

In this study, we explored the potential of Binaural Beats Music Therapy (BBMT) augmented with AI-generated music to enhance attention in children diagnosed with Attention Deficit Disorder (ADD). Utilizing a 2x2 mixed experimental design, we differentiated between traditional and AI-generated music (between-subjects) and incorporated vs. excluded BBMT (within-subjects). We engaged 60 children, averaging 8 years of age, and diagnosed via the SNAP-IV scale. These children were randomly assigned to either the Original Music or AI Music groups, each consisting of 30 participants. Over a 4-week period, with 30-min sessions conducted five days a week, we gauged Heart Rate Variability (HRV) data before and after subjecting each group to their allocated music sessions, integrating BBMT where necessary. Subsequent analysis revealed a marked increase in attention post-intervention. Both music methodologies, when amalgamated with BBMT, significantly bolstered attention levels (P< 0.05). Intriguingly, AI music's efficacy paralleled that of its traditional counterpart, underscoring the maturity of AI in music generation and its viability for routine therapeutic application.

关键词： Binaural Beats Music Therapy AI Music Attention Deficit Disorder Heart Rate Variability

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Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering 25th

Virtual Reality (VR) in Safety Education: A Case Study of Mi...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Chang, Haoqian Pan, Ziqi Cristea, Alexandra, I Univ Durham Dept Comp Sci Durham DH1 3LE England Anhui Univ Sci & Technol Sch Econ & Management Huainan 232001 Peoples R China

ISBN: (纸本)9783031643118;9783031643125

Safety education and training are vital in the mining industry. However, traditional training relies on passive modalities, such as lectures, videos and brochures. These suffer from sever limitations - poor reproducibility, inefficient resource utilisation, and a lack of interactive feedback in one-size-fits-all training scenarios. Addressing these challenges, we introduce a novel hybrid approach combining virtual reality (VR) and electroencephalography (EEG) for training in the use of underground self-contained self-rescuers (SCSR). The VR component provides an interactive and immersive training experience, to facilitate a higher level of engagement compared to traditional methods. Initial EEG testing showed that VR training could elevate trainees' brain activity, which may result in higher ratings and satisfaction. Beyond EEG we use also after-scenario questionnaire (ASQ) and system usability scale (SUS).

关键词： VR Electroencephalography Safety training Mining

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Training on Social Media Cybersecurity Skills in the Healthcare Context 1st

Training on Social Media Cybersecurity Skills in the Healthc...

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1st International Workshop on Secure and Resilient Digital Transformation of Healthcare (SUNRISE)

作者： Fernandez-Tarraga, Mario Cayuela-Tudela, Alejandro-David Nespoli, Pantaleone Garcia-Alfaro, Joaquin Marmol, Felix Gomez Univ Murcia Dept Ingn Informac Comunicac Murcia 30100 Spain Inst Polytech Paris SAMOVAR Telecom SudParis 19 Pl Marguerite Perey F-91120 Palaiseau France

ISBN: (纸本)9783031558283;9783031558290

In the last decades, social media has experienced exponential growth due to its popularity and advantages in communication, connection, and broadcasting, etc. Nevertheless, social media also presents disadvantages and threats that can be exploited for evil ends. Indeed, it has become a vector of attack for cybercriminals and scammers. Broadcasting and public access have allowed the public to post fake news and disinformation. The wrong use of social media by users due to negligence or unawareness allows these threats to succeed. In this scenario, the healthcare sector is also affected by social media threats, as can be seen over the years in the frequent daily phishing attacks, vulnerable devices, data leaks of personal information, etc. This article proposes an automated tool for training social media cybersecurity competencies in the social and professional sectors of the healthcare environment, built on the Cyber Range context. It allows automating the generation and configuration of simulated social media exercises with several levels of content, difficulty, and realism, creating multiple hyperrealistic situations across a wide range of possibilities. Training phishing attacks, crisis management, social media attacks, and pandemic or disease crisis disinformation are some of the possibilities, both theoretical and practical.

关键词： Cyber Range Cybersecurity Cyberdefense Social media simulation Social media cybersecurity skills Digital education E-health Digital health Healthcare cybersecurity

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An Augmented Intelligence Framework as an Enabler of Digital Transformation and Innovation in the Pedagogical Evaluation of the Brazilian National Textbook Program (PNLD) 25th

An Augmented Intelligence Framework as an Enabler of Digital...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Silva, Luciane Araujo, Andre Araujo, Rafael Univ Fed Uberlandia Uberlandia Brazil Univ Fed Alagoas Ctr Excellence Social Technol NEES Maceio Brazil

ISBN: (纸本)9783031643118;9783031643125

This project investigates the integration of Artificial Intelligence (AI) into the Brazilian National Textbook Program (PNLD), aiming to expand human capacities and improve pedagogical assessment. The focus is to specify, develop, and evaluate a technological framework that uses Augmented Intelligence and Natural Language Processing (NLP) to automate repetitive tasks, reduce errors, and optimize decisionmaking through efficiently used data. The research proposes not only to enhance the interaction between humans and data but also to contribute to the improvement of public educational policies in Brazil, through prototypes that demonstrate the potential of AI to transform pedagogical assessment, promoting continuous development in the educational field.

关键词： Augmented Intelligence Digital Transformation in Government NLP PNLD

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Writing Analytics and AI for Special Education: Preliminary Results on Students with Autism Spectrum Disorder 25th

Writing Analytics and AI for Special Education: Preliminary ...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Colen Franca, Akira Borba Reategui, Eliseo Mintz, Joseph Meira, Ricardo Radaelli Motz, Regina Univ Fed Rio Grande do Sul PGIE Porto Alegre RS Brazil UCL Inst Educ London England Univ Republica Fac Ingn Montevideo Uruguay

ISBN: (纸本)9783031643118;9783031643125

This article discusses the utilization of writing analytics in Special Education, with a particular focus on students with Autism Spectrum Disorder (ASD). Research increasingly supports the use of data mining and Artificial Intelligence (AI) to analyze and support students' writing processes, showcasing the potential of these systems to enhance student engagement and the accuracy of automated feedback. However, concerns persist regarding potential biases and ethical implications. The literature highlights limitations in applying Writing Analytics and AI to atypical students since most research and tools are designed with typical students in mind, reflecting societal biases. Autistic students often encounter challenges in writing performance due to factors such as rigid style, limited vocabulary, and difficulties expressing thoughts. This paper presents a study involving the analysis of 2643 essays from secondary education students, including a subset with ASD, using text-to-network tools and NLP analysis to compare texts and examine computational linguistics metrics and text mining patterns. Preliminary findings suggest the necessity for tailored evaluation and interventions for ASD students. While AI offers opportunities for personalized interventions, further research is essential to effectively adapt current tools for atypical students.

关键词： Writing Analytics Autism Spectrum Disorder Special Education Text Mining Natural Language Processing

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ChemXP AR Edition, A Serious Game 1

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13th International Conference on Videogame sciences and Arts (VJ)

作者： Bandeira, Mario Vairinhos, Mario Dias, Paulo Soengas, Raquel Silva, Vera Univ Aveiro Dept Commun & Art Aveiro Portugal Univ Aveiro Dept Elect Telecommun & Informat Aveiro Portugal Univ Oviedo Fac Chem Oviedo Spain Univ Aveiro Dept Chem Aveiro Portugal

ISBN: (数字)9783031514524

ISBN: (纸本)9783031514517;9783031514524

This project presents the design and development of a Serious Game, that utilizes Augmented Reality (AR) technologies, aimed to involve and engage students in Organic Chemistry. The game's core features encompass several missions, and a tutorial, specifically targeting the challenging concept of determining the R/S Configuration of enantiomers. It also includes a physics-based molecular creation tool, using the attraction and repulsive forces. Two rounds of usability tests were carried out, where most participants were students from Digital Game Development and Multimedia. These tests would help to prepare the game for the end users-students of organic chemistry. So, the first round of usability tests involved eight students, which provided invaluable insights, as they gave suggestions, identified bugs and issues, resulting in a neutral-to-positive user experience (UX). The second round, consisting of fifteen participants, resulted in positive feedback, along with even more suggestions for further improvement. The game mode that was most liked by the participants was the Sandbox due to the freedom of building molecules and visualizing the smooth animations of atoms moving into their places. Also, for future work, tests will be carried out with the end users, the organic chemistry students, and will cover real-world usage scenarios.

关键词： Organic Chemistry Serious Game Augmented Reality

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Visual Analytic of Traffic Simulation Data: A Review 1

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6th Iberoamerican Congress on Smart Cities (ICSC-Cities)

作者： Almachi, Christopher Armas, Rolando Cuenca, Erick Yachay Tech Univ Urcuqui 100119 Ecuador

ISBN: (数字)9783031525179

ISBN: (纸本)9783031525162;9783031525179

In the contemporary world, transportation is one of the foremost necessities for modern society, particularly within urban locales. Effective traffic management and planning represent indispensable tasks to maintain a seamless traffic flow and reduce congestion. To achieve this objective, traffic simulation data plays a pivotal role by furnishing intricate insights into traffic patterns, vehicle positions, events, and other pertinent aspects of a specific area. The visualization of traffic simulation data assumes paramount importance, serving as a vital tool for comprehending this information and making informed decisions. This paper provides a comprehensive review of the state of the art regarding web-based visualization techniques focused on traffic data provided by simulators, especially Multi-Agent Transport Simulation (MATSim). In addition, it will shed light on the most commonly employed features designed to facilitate the temporal analysis of traffic data, encompassing movement and congestion patterns. These resources hold significant potential for transport planners and traffic management professionals in creating web-based visualizations.

关键词： Data visualization Traffic data Simulation

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Promoting Open science in Artificial Intelligence: An Interactive Tutorial on Licensing, Data, and Containers 25th

Promoting Open Science in Artificial Intelligence: An Intera...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Haim, Aaron Hutt, Stephen Shaw, Stacy T. Heffernan, Neil T. Worcester Polytech Inst Worcester MA 01609 USA Univ Denver Denver CO 80210 USA

ISBN: (纸本)9783031643118;9783031643125

Across the past decade, the open science movement has increased its momentum, making research more openly available and reproducible across different environments. In parallel, artificial intelligence (AI), especially within education, has produced effective models to better predict student outcomes, generate content, and provide a greater number of observable features for teachers. However, there is a discernible gap between the understanding and application of open science practices in artificial intelligence. In this tutorial, we will expand the knowledge base towards open data and open analysis. First, we will introduce the complexities of intellectual property and licensing within open science. Next, we will provide insights into data sharing methods that preserve the privacy of participants. Finally, we will conclude with an interactive demonstration on sharing research materials reproducibly. We will tailor the content towards the needs and goals of the participants, enabling researchers with the necessary resources and knowledge to implement these concepts effectively and responsibly.

关键词： Open science Reproducibility Licensing

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