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C2-MAGIC: Chord-Controllable Multi-track Accompaniment Generation with Interpretability and Creativity 2nd

C2-MAGIC: Chord-Controllable Multi-track Accompaniment Gener...

引用

2nd Summit on Music Intelligence (SOMI)

作者： Wu, Jingcheng Ji, Zihao Li, Pengfei StarX Beijing Peoples R China

ISBN: (纸本)9789819705757;9789819705764

Musical notes are usually grouped into chords to generate aesthetically pleasing music, which is a special characteristic in symbolic music modeling. Good manipulation of chords not only brings harmony to music but also makes interactive music generation more manageable. However, prior attempts at chord-conditioned music generation have several limits: 1) Most previous work only considers chords as auxiliary tokens by adding them explicitly to the original conditional sequence, nevertheless, this simple design doesn't provide enough information and isn't capable of gaining good control over chords. 2) Some research reports good control of chords in terms of analogy but is limited under the context of single-track music of limited length. 3) Some achieve good matches of chords, yet at the cost of other evaluation metrics. To overcome these limits, we propose a novel architecture under the framework of multi-task learning, which additionally learns a latent chord representation used as extra contextual information for the multitrack accompaniment generation task. We evaluate our model both on the public LMD dataset and a private dataset of pop music. Experiments show that our model largely outperforms the state-of-the-art multi-track accompaniment generation model in terms of chord control ability and further improves the validation perplexity of the accompaniment generation task. Extensive objective and subjective studies also demonstrate the effectiveness of our approach. Furthermore, we show that our model brings extra interpretability and creativity through various inference experiments.

关键词： Controllable music generation Multi-task learning Symbolic music

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Sport Action Evaluation Based on Human Pose Estimation: A Case of Evaluating Golf Swing Action 12th

Sport Action Evaluation Based on Human Pose Estimation: A Ca...

引用

12th International Conference on Learning Technology for Education Challenges (LTEC)

作者： Li, Xiaoyan Tao, Ran Wang, Yong Ding, Yi Luo, Xin Donghua Univ Shanghai Peoples R China

ISBN: (纸本)9783031616778;9783031616785

Sport action evaluation is a crucial segment in sports teaching. With the advancement of human pose estimation, the extraction of human body coordinates from images has become increasingly feasible. This paper proposes a sport action evaluation approach based on human pose estimation for comprehensive quantitative action evaluation. Firstly, key postures and features are designated by professional coaches from the standard action video according to sport action characteristics. Then, key postures and features are extracted based on human pose estimation technology to obtain actual features values from the test action video. Finally, a comprehensive action evaluation approach is be constructed with the use of Fuzzy Comprehensive Evaluation (FCE), which outputs an overall score to achieve quantitative evaluation. To verify the effectiveness of the approach, this paper established a dataset comprising 100 golf swing videos labeled with the hitting distances and conducted quantitative experiments on the dataset. The results show a positive correlation between the scores of the golf swing action and the actual hitting distance, which has the practical significance of the evaluation outcomes. The proposed approach in this paper could be applied to sport action evaluation which emphasize action standardization, offering a novel perspective for sport teaching.

关键词： Sport Action Evaluation Sport Teaching Human Pose Estimation Comprehensive Quantitative Evaluation Fuzzy Comprehensive Evaluation

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Exploring the Usefulness of Open and Proprietary LLMs in Argumentative Writing Support 25th

Exploring the Usefulness of Open and Proprietary LLMs in Arg...

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

作者： Gubelmann, Reto Burkhard, Michael Ivanova, Rositsa, V Niklaus, Christina Bermeitinger, Bernhard Handschuh, Siegfried Univ St Gallen ICS HSG St Gallen Switzerland Univ St Gallen IBB HSG St Gallen Switzerland

ISBN: (纸本)9783031643118;9783031643125

In this article, we present the results of an exploratory study conducted with our self-developed tool Artist. The goal of the tool is to give formative feedback to develop students' argumentation skills. We compare the feedback that two different LLMs, an open-sourced one by META and one of OpenAI's fully proprietary ones, give to students' argumentative writing. We find that, overall, students find the feedback provided by both LLMs helpful (7.51 vs. 7.65 on a scale from 1 to 10), and they rate the quality of the feedback as good to very good. We take this as a very encouraging provisional result that invites larger and more extensive studies on the topic.

关键词： Writing Support Argument Quality ChatGPT Large Language Model computer-Supported Learning Feedback Provision

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A Conceptual Approach for an AI-Based Recommendation System for Handling Returns in Fashion E-Commerce 20th

A Conceptual Approach for an AI-Based Recommendation System ...

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20th International Conference on Smart Business Technologies (ICSBT)

作者： Gry, Soeren Niederlaender, Marie Lodi, Aena Nuzhat Mutz, Marcel Werth, Dirk August Wilhelm Scheer Inst Saarbrucken Germany

ISBN: (纸本)9783031679032;9783031679049

The benefits of fashion E-commerce for customers are undisputed. Ordering clothes is easy, fast and usually free of delivery and return charges. Fashion E-commerce is moving the changing room from the brick-and-mortar store to the consumer's living room. These efforts, and the associated ever-increasing E-commerce sales, are accompanied by a consumer return behaviour that has high environmental and economic costs. Most studies in this area focus on the prevention of returns. However, many returns cannot be avoided, e.g. those based on selection orders or quality problems. This is the starting point for this study, which investigates how AI can be used to improve reverse logistics from an environmental and economic perspective, based on return forecasts. To this end, a literature review of existing approaches is conducted, followed by a detailed concept of an AI-based recommendation system for the best possible further processing of returns, with the aim of routing the returned product to a suitable sales channel as quickly as possible.

关键词： Returns forecasting Returns prediction Recommendation system ERP system Sustainable return management Logistics Sustainable supply chain E-commerce Fashion Apparel Artificial intelligence Machine learning

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Closer Reading of RDF Generated by NLP on Wikipedia Biography: Comparative Analysis 17th

Closer Reading of RDF Generated by NLP on Wikipedia Biograph...

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

作者： Sugimoto, Go Daza, Angel de Boer, Victor Vrije Univ Amsterdam De Boelelaan 1081NU Bldg NL-1081 HV Amsterdam Netherlands

ISBN: (纸本)9783031659898;9783031659904

Although Wikidata and DBpedia are closely related to Wikipedia, they often hold a small subset of its semantic information, due to the specific scopes and methodologies chosen for their Linked Data (LD) construction. When we look at biographies, a large amount of RDF statements focus on the person's core facts, and many semantic narratives are not included. To fill this knowledge gap, this paper seeks a solution with Natural Language Processing (NLP). We aim to assess to what extent out-of-the-box NLP tools can generate new LD from the biographical articles in Wikipedia. Unlike other NLP research, we put more emphasis on the qualitative analysis of NLP outputs ("close reading") than the statistical performance of NLP algorithms ("distant reading"). We evaluate the overlaps and gaps between Wikipedia, Wikidata and DBpedia, as well as other biographical ontologies. We also analyze the triple patterns from the NLP results in comparison with the RDF entity (instance) and ontologies. Our research revealed that we are able to capture new information about the entity that Wikidata and DBpedia do not hold. At the same time, some noise could not be easily eliminated. Our method presented a bottom-up approach to biographical ontology designing. We also briefly propose a possible solution for future work.

关键词： Wikipedia Wikidata DBpedia Natural Language Processing Knowledge Graph Biographies Linked Data Henry VIII

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Predicting Academic Performance: A Comprehensive Electrodermal Activity Study 25th

Predicting Academic Performance: A Comprehensive Electroderm...

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

作者： Machado, Guilherme Medeiros Soni, Aakash ECE Res Ctr LyRIDS Paris France

ISBN: (纸本)9783031643118;9783031643125

Electrodermal Activity (EDA) provides insights into the Sympathetic Nervous System (SNS) activation, primarily responsible for "fight-or-flight" response, including stress. Stress, in turn, significantly affects academic performance. Despite EDA's widespread application in various fields, its use in educational research remains underexplored. Existing studies on EDA's relationship with academic outcomes often focus on isolated aspects of the signal, thus narrowing their scope and findings. This paper presents a comprehensive analysis of the entire EDA signal, employing it as input for regression models for predicting students' grades. By augmenting the dataset to compensate for sample size limitations, we achieved low prediction errors (MSE of 0.002 and RMSE of 0.045) and high explanatory power (up to 80% of R-2). Our findings indicate a preference for regression models capable of capturing non-linear relationships with minimal complexity. This research underscores the potential of using EDA as a tool for educators to identify students whose academic performance may be adversely affected by stress, paving the way for targeted interventions.

关键词： Grades Prediction Sympathetic Nervous System Electrodermal Activity Regression Models Dataset Augmentation

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Parsing Post-deployment Students' Feedback: Towards a Student-Centered Intelligent Monitoring System to Support Self-regulated Learning 25th

Parsing Post-deployment Students' Feedback: Towards a Studen...

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

作者： Javiya, Prachee Alpeshkumar Kleinsmith, Andrea Chen, Lujie Karen Fritz, John Univ Maryland Baltimore Cty 1000 Hilltop Cir Baltimore MD 21250 USA

ISBN: (纸本)9783031643149;9783031643156

A student-centered intelligent monitoring system collects data from students and provides insights and feedback to students about various aspects of the learning process. It often leverages data collected from educational technology systems, such as Learning Management Systems (LMS), to support students' self-regulated learning. A well-designed system needs to strike a delicate balance between the power of machine intelligence (e.g., automatic characterization and inference of students' behaviors) and the need to promote students' agency (e.g., the desire to be responsible and take control of their own behaviors). This paper presents a comprehensive qualitative analysis of anonymous student survey data collected from over 500 students with experience with a Learning Activity Monitoring System (LAMS), which has been in operation for over a decade in a public minority-serving higher education institute in the US. The study offers valuable insights into the effectiveness and user perceptions of LAMS they use in their learning context. This analysis reveals the sense-making process or lack thereof, and its implication in exploiting the potential utility of the LAMS. The findings also highlight students' varied expectations and requirements, providing critical insights for the ongoing development and refinement of the LAMS system toward an intelligent monitoring system that truly centers students' agency and promotes self-regulated learning. This study contributes to the growing body of work in hearing and understanding students' genuine voices and the sparse literature of large-scale qualitative analysis of students' feedback during the post-deployment phase on student-facing data-driven monitoring systems in an ecologically valid context in higher education.

关键词： Student-Facing Monitoring System Self-Regulated Learning Student Agency

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The Application of ChatGPT in Literary Translation: A Case Study from Thai to Chinese 12th

The Application of ChatGPT in Literary Translation: A Case S...

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12th International Conference on Learning Technology for Education Challenges (LTEC)

作者： Zuo, Yu Ching, Gregory S. Khotsing, Rapat Natl Inst Dev Adm Bangkok Thailand Natl ChengChi Univ Taipei Taiwan Rajamangala Univ Technol Isan Nakhon Ratchasima Thailand

ISBN: (纸本)9783031616778;9783031616785

Natural Language Processing (NLP) is rapidly evolving, yet it still faces challenges in comprehending deep meanings and emotions, handling diverse languages and subtle linguistic nuances. Literary text translation remains particularly tough for machine translation, with limited research focusing on Thai-Chinese translation. This study investigates the Thai biographical essay '(sic),' (Mother Nao Hua) comparing quantitative and qualitative aspects of translations by Google Translate, Baidu Translate, and ChatGPT. The goal is to explore the difficulties faced in Thai-Chinese machine translation and how to issue clearer commands for literary translations. Quantitative analysis shows ChatGPT's translation as the best, followed by Google and Baidu Translate. Modified instructions improved ChatGPT's translation quality. Qualitative analysis reveals reasons for translation quality variance and machine translation's literary text issues. It finds challenges in Thai to Chinese translation, suggesting ChatGPT instruction refinement can improve quality. The study proposes three future research directions.

关键词： Natural Language Processing ChatGPT translation Literary Translation Cultural Context

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Predictive Model of School Dropout Based on Undergraduate Course Self-assessment Data 25th

Predictive Model of School Dropout Based on Undergraduate Co...

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

作者： Oliveira, Ronei Medeiros, Francisco Fed Inst Paraiba Joao Pessoa Paraiba Brazil

ISBN: (纸本)9783031643149;9783031643156

School dropout is a daily challenge for educational institutions. In the specific case of higher education, high dropout rates lead to financial losses and a shortage of professionals in the market. This research aimed to develop and evaluate a predictive model to identify students prone to dropout, using data from a semester-based self-assessment model of undergraduate courses at Federal University of Paraiba (UFPB). The study analyzed the relationship between school dropout and institutional self-assessment using educational data mining and the CRISP-DM methodology, followed by exploratory analysis and data preparation for classification. Various modeling techniques, such as Decision Trees, Random Forest, and Support Vector Machines, were applied, with the models evaluated using performance metrics, revealing an accuracy of 87.97%, precisionof 91.72%, recall of 91.67%, and F-measure of 91.57% in identifying students with a high probability of dropout. Approximately 59% of active students at UFPB, admitted from 2017 onwards, showed a high probability of abandoning their courses in the proposed predictive model tests. This information can support institutional decisions and the implementation of effective policies and actions against dropouts, aiming to improve academic outcomes. The study contributes to advancements in predicting school dropout, providing valuable insights for decision-making and preventive strategies at UFPB and other higher education institutions.

关键词： School dropout Educational data mining Predictive model Self-evaluation

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Radiology Report Terminology to Characterise Reports in Southern Africa 17th

Radiology Report Terminology to Characterise Reports in Sout...

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

作者： Mahlaza, Zola Zulu, Ernest Obbie Phiri, Lighton Univ Cape Town Dept Comp Sci Cape Town South Africa Univ Teaching Hosp Adult Hosp Lusaka Zambia Univ Zambia Dept Lib & Informat Sci Lusaka Zambia

ISBN: (纸本)9783031659898;9783031659904

There is a shortage of trained radiologists in Southern African countries such as Zambia. The shortage calls for the use of artificial intelligence to bolster the efforts of the few practising radiologists to improve efficiency. Such AI-guided tools require knowledge on how to author good quality reports. Since there is no normative standard for Zambian reports, metadata is required to annotate existing reports to determine characteristics of good reports. As there are no Zambian guidelines for the information to be included in reports, we analyse papers, international guidelines, published structured reports, and existing structured reporting templates to create contemporary and international radiology report terminology, as a first step towards metadata. We identified 3199 terms from vetted templates published by the Radiological Society of North America's RadReport Template Library (https://radreport. org/). We also augmented them with 323 terms extracted from published papers (71% were manually annotated with SNOMED codes for quality assurance).

关键词： Metadata Artificial Intelligence Radiology reports

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