2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献

Fusion-based Music Recommender System Using Music Affective Space based on Serendipity 基于Serendipity的音乐情感空间融合音乐推荐系统

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00039

T. Saito, E. Sato-Shimokawara, Lieu-Hen Chen

{"title":"Fusion-based Music Recommender System Using Music Affective Space based on Serendipity","authors":"T. Saito, E. Sato-Shimokawara, Lieu-Hen Chen","doi":"10.1109/TAAI57707.2022.00039","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00039","url":null,"abstract":"In recent years, as the popularity of music distribution services has increased, research on music recommendation has become more active. In particular, factors not only accuracies but also contextual information such as user’ $s$ emotion and “serendipity” are also considered necessary to improve the quality of music recommendations and attract attention. Serendipity is defined as novelty, unexpectedness, and preference; these items are considered important factors as evaluation criteria for recommender systems. In this paper, we propose a system that recommends music, named FUSION MUSIC, based on the affective information of two favorite music the user selects. The FUSION MUSIC is based on two concepts; one is a music affective space that reflects the affective information of each music, and another is a fusion-based music recommendation method that creates a partial affective space within that space and recommends serendipitous music. We finally developed FUSION MUSIC using these concepts and the Spotify API. The results of the evaluation experiment showed that FUSION MUSIC has the potential to recommend more serendipitous music compared to Spotify. For the proposed system, we will investigate more detailed validation methods in the future.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126268189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introducing Simplicity in Document Summarization by Leveraging Coreference and Pseudo-Summary 利用共同参考和伪摘要引入文档摘要的简便性

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00027

Charkkri Limbud, Yen-Hao Huang, Alejandro Cortes, Yi-Shin Chen

{"title":"Introducing Simplicity in Document Summarization by Leveraging Coreference and Pseudo-Summary","authors":"Charkkri Limbud, Yen-Hao Huang, Alejandro Cortes, Yi-Shin Chen","doi":"10.1109/TAAI57707.2022.00027","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00027","url":null,"abstract":"Document summarization has rapidly gained importance due to the exponentially increasing data. Generally, studies in document summarization focused on generating summaries having high coverage and fluency. Such summaries can be challenging for readers with limited language proficiency. This paper introduces the simplification concept in document summarization tasks. Our method is divided into two phases to handle the challenges of the task. The first phase is to tackle the problem of long documents with unnecessary details that can affect key information or coverage of the summaries. Thus, we propose a method to condense key information by utilizing coreference resolution. The second phase uses the condensed documents as inputs. This phase handles the challenge of having no dataset with a simplification concept in summarization tasks. Therefore, this research proposes an unsupervised training framework without relying on golden summaries. The training first outputs summaries with high coverage called pseudo-summaries. Then, it is used as a reference to generate final summaries with words that are more familiar and commonly used, resulting in easier-to-understand summaries for readers.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rule Based Predictions for Loan Defaults of Used Cars Based on DRSA and FCA 基于DRSA和FCA的二手车贷款违约规则预测

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00042

Shu-Ping Chen, Y. Lue, Chi-Yo Huang

{"title":"Rule Based Predictions for Loan Defaults of Used Cars Based on DRSA and FCA","authors":"Shu-Ping Chen, Y. Lue, Chi-Yo Huang","doi":"10.1109/TAAI57707.2022.00042","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00042","url":null,"abstract":"Numerous algorithms and frameworks have been proposed by scholars to solve credit scoring problems in the past. Only a few studies have examined the factors affecting second car loan default. However, this issue is of great importance to the auto loan industry. Therefore, this study intends to define a hybrid multi-criteria decision making (MCDM) model to mine the database of defaulting customers of loans of second hand cars. First, this study introduces the Dominance Based Rough Set Approach (DRSA) to analyze the characteristics of the defaulting clients, derive the core attributes as well as the decision rules. Then, the Formal Concept Analysis (FCA) is adopted to derive the main concepts affecting the default of auto loans. The empirical results can be used as a reference for auto loan companies. Based on the database of one of major financial institutions in Taiwan, the feasibility of the analytic framework was verified. According to the mining results of the customer database, age, gender, marital status, education, income and loan amount are the core attributes, and 15 decision rules are derived. The results of this study can be used as a basis for future loan verification by financial institutions, as well as for the introduction of intelligent automatic loan verification mechanism and the development of intelligent vehicle loan platform.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123649544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Concept Drift Detection Using Dynamic Crucial Feature Distribution Test in Data Streams 基于数据流动态关键特征分布测试的无监督概念漂移检测

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00033

Yen-Ning Wan, Bijay Prasad Jaysawal, Jen-Wei Huang

引用次数: 0

Online Water Usage Monitoring under Anomalous Interference in Residential Households 异常干扰下居民家庭用水在线监测

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00021

R. Chao, Lo Pang-Yun Ting, Kun-Ta Chuang

引用次数: 0

Entropy-Based Two-Phase Optimization Algorithm for Solving Wordle-like Games 求解类词博弈的基于熵的两阶段优化算法

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00014

Yen-Chi Chen, Hao-En Kuan, Yen-Shun Lu, Tzu-Chun Chen, I-Chen Wu

{"title":"Entropy-Based Two-Phase Optimization Algorithm for Solving Wordle-like Games","authors":"Yen-Chi Chen, Hao-En Kuan, Yen-Shun Lu, Tzu-Chun Chen, I-Chen Wu","doi":"10.1109/TAAI57707.2022.00014","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00014","url":null,"abstract":"In the past, a method called Two-Phase Optimization Algorithm (TPOA) was designed by Shan-Tai Chen to solve the game of Mastermind and the AB game efficiently. In this paper, we proposed a modified version called Entropy-Based TPOA (EBTPOA) for Wordle-like games. It is a combination of his algorithm and our previous work on Nerdle with greedy method. It focuses on not only effectiveness but also efficiency while finding optimal results. In Wordle-like games, EBTPOA performs better with fewer guess times on average than TPOA. In Wordle, EBTPOA hits the answer optimally within 3.42117 times on average, and 5 times in the worst case, and the best opening word is “SALET”. In Nerdle, EBTPOA hits the answer within 3.01947 times on average, and 4 times in the worst case, and the best opening equation is ’’ $52-34=18$ ’’. These are the best results up to date, and particularly none has achieved the result for Nerdle so far. As for efficiency, by expanding with fewer branches and limiting the depth of exploration, EBTPOA can obtain the optimal result with a lower time complexity compared to related works.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132564176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Maintenance of Frequent Episodes 频繁发作的维持

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00011

Yue-Shi Lee, Show-Jane Yen

引用次数: 0

2022 International Conference on Technologies and Applications of Artificial Intelligence 2022年国际人工智能技术与应用会议

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/taai57707.2022.00001

引用次数: 0

Gumbel MuZero for the Game of 2048 2048年游戏的Gumbel MuZero

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00017

Chih-yu Kao, Hung Guei, Ti-Rong Wu, I-Chen Wu

{"title":"Gumbel MuZero for the Game of 2048","authors":"Chih-yu Kao, Hung Guei, Ti-Rong Wu, I-Chen Wu","doi":"10.1109/TAAI57707.2022.00017","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00017","url":null,"abstract":"In recent years, AlphaZero and MuZero have achieved remarkable success in a broad range of applications. AlphaZero masters playing without human knowledge, while MuZero also learns the game rules and environment's dynamics without the access to a simulator during planning, which makes it applicable to complex environments. Both algorithms adopt Monte Carlo tree search (MCTS) during self-play, usually using hundreds of simulations for one move. For stochasticity, Stochastic MuZero was proposed to learn a stochastic model and uses the learned model to perform the tree search. Recently, Gumbel MuZero was proposed to ensure the policy improvement and can thus learn reliably with a small number of simulations. However, Gumbel MuZero used a deterministic model as in MuZero, limiting its performance in stochastic environments. In this paper, we propose to combine Gumbel MuZero and Stochastic MuZero, the first attempt to apply Gumbel MuZero to a stochastic environment. Our experiment on the stochastic puzzle game 2048 demonstrates that the combined algorithm can perform well and achieve an average score of 394,645 with only 3 simulations during training, greatly reducing the computational resource needed for training.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Graph Neural Network in Administrative Medical Dataset 探索图神经网络在行政医疗数据集中的应用

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00028

Wei-Chen Liu, Chih-Chieh Hung, Wen-Chih Peng

{"title":"Exploring Graph Neural Network in Administrative Medical Dataset","authors":"Wei-Chen Liu, Chih-Chieh Hung, Wen-Chih Peng","doi":"10.1109/TAAI57707.2022.00028","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00028","url":null,"abstract":"Administrative medical dataset contains medical records of patients. Using administrative medical dataset can extract disease association to help finding comorbidity. Previous methods only use statistics on administrative medical dataset such as computing probabilities of disease occurrence and are limited by the capability of statistics. To enhance hidden information usage of administrative medical dataset, we propose two different methods based on graph neural networks to exploit hidden information in administrative medical dataset. One is using graph neural networks to generate disease embeddings and pass through kNN algorithm to find similar diseases for suggestion to physicians. The other one is that we formulate sequence prediction problem and use gated graph neural network to model every disease sequence by forming session graphs. Different from previous methods for sequence prediction that only consider current sequence, we also consider all sequences in dataset at the same time. Besides, we use position-aware soft-attention mechanism to aggregate disease embeddings to session embeddings and predict the next disease of a patient. We conduct extensive experiments on two methods and show its ability to outperform several baselines.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116615019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0