2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)最新文献

{"title":"Extraction Method for Important Words as a Viewer’s Reaction Arousal Factor from YouTube - Transcription","authors":"Ryuichi Hirano, Ryotaro Okada, T. Nakanishi","doi":"10.1109/iiaiaai55812.2022.00129","DOIUrl":"https://doi.org/10.1109/iiaiaai55812.2022.00129","url":null,"abstract":"We present a novel extraction method for important words as a viewer’s reaction arousal factor from YouTube transcription. The proposed method analyses the content of social media posts. Further, it extracts important words that are likely or unlikely to evoke reactions from the viewers. In this study, we analyze the subtitles and the statistical data obtained from YouTube videos. Further, a database is created consisting of the extracted words that are likely or unlikely to evoke reactions. The method consists of obtaining the subtitle data and the statistical data from YouTube, building a database, and constructing a machine learning model to classify them. This is followed by the local interpretation of the model to extract the aforementioned words. The experimental results showed that the machine learning model was effective using the created database.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566788","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}

{"title":"Pseudo data acquisition using machine translation and simile identification","authors":"Jintaro Jimi, Kazutaka Shimada","doi":"10.1109/IIAIAAI55812.2022.00084","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00084","url":null,"abstract":"The simile is a kind of figurative language. It expresses the target of the figurative language by using some typical phrases such as \"like\". It is important to distinguish whether the sentence is a simile or a literal for understanding a sentence. However, a large amount of data is required to generate a classifier by machine learning. Moreover, creating the dataset is costly. In this paper, we propose a pseudo dataset acquisition method for simile identification. We first construct a dataset of simile and literal sentences using machine translation. This process automatically generates pseudo simile and literal instances from three types of corpora. Then, we apply some machine learning approaches to the simile identification task. We compare Support Vector Machine, Naive Bayes, and BERT in the experiment. The experimental result shows the validity of the pseudo dataset as compared with a simple baseline. For the fine-tuning of BERT, our large pseudo training data were more effective than a small manual training data.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124957883","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}

{"title":"The impact of zoning on congestion caused by workers with different walking speeds in order picking operations","authors":"Kirika Matsuda, A. Ishigaki","doi":"10.1109/IIAIAAI55812.2022.00103","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00103","url":null,"abstract":"In order to improve the efficiency of logistics warehouses, it is important to reduce travel time since most working hours in order picking operations are spent on travel. In actual distribution warehouses, delays due to the aging of workers and the congestion of multiple workers are also a problem. Research on evacuation guidance has shown that the congestion of pedestrians with different walking speeds affects travel time, so the congestion of workers with different walking speeds is expected to affect travel time and cause work delays at field sites. Therefore, in this study, we focused on elderly people who have different physical characteristics and considered a reduction in walking speed as one of those characteristics. Our objective was to design and allocate work areas to alleviate congestion among workers with different walking speeds in the order picking process using a multi-agent system.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121115052","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}

{"title":"Applying Reward Design Based on Payment Mechanism to Shaped-Reward DQN for Beer Game","authors":"Masaaki Hori, T. Matsui","doi":"10.1109/IIAIAAI55812.2022.00083","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00083","url":null,"abstract":"We focus on the application of multiagent reinforcement learning for supply chain management. The beer game is an example of a problem in supply chain management and has been studied as a cooperation problem in multiagent systems. In the previous study, a method SRDQN that is based on deep reinforcement learning and reward shaping has been applied as a solution to the beer game. In the previous study of SRDQN, a single agent in a game performs reinforcement learning considering other agents to reduce the global cost for inventories of beers. However, it is possible to employ other reward shaping techniques to improve learning stability. It can also be effective in the systems consisting of multiple agents that perform reinforcement learning. We apply a reward shaping technique based on mechanism design to SRDQN to improve the cooperative policies, and then we empirically evaluate the effectiveness of the proposed approach.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124034326","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}

{"title":"Multi-product Inventory Routing Problem Considering Demand Uncertainty","authors":"Taichi Kawamura, Tetsuya Sato, T. Shiina","doi":"10.1109/IIAIAAI55812.2022.00123","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00123","url":null,"abstract":"The inventory routing problem simultaneously considers both the inventory problem and the delivery problem; it determines the amount of delivery of inventory and the delivery route such that the total cost is minimized. In this study, we use stochastic programming to consider a multi-product inventory routing problem that considers the demand variation throughout multiple periods. Determining the delivery route for each period is difficult. Therefore, we propose a model that fixes the delivery route throughout the planning period and compare the calculation results to prove its practicality. In addition, the problem in this study is an integer programming problem, and solving a large-scale problem using the direct method would be time-consuming. Therefore, we apply the accelerated Benders decomposition method, which combines two cuts: the optimality cut with the solution of the linear relaxation problem, and the Pareto-optimal cut. We demonstrate its effectiveness through numerical experiments.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126863698","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}

{"title":"An Analysis of Educational Cloud Platforms using Multi-agent Learning","authors":"Asmita Kandel, Ihsan Ibrahim, Naoki Fukuta","doi":"10.1109/IIAIAAI55812.2022.00053","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00053","url":null,"abstract":"With the rapid increment of benefits in on-demand service, large network access, and availability, more and more industries move their focus into cloud platforms, and the field of education is no different. With the covid-19 pandemic situation, educational cloud platforms are getting more popularity and relevance among educational institutions such as open and distance universities and research institutes. This paper presents a multi-agent reinforcement learning-based approach for supporting better use of educational cloud platforms by trying to come up with a mechanism to recognize the best available option of educational cloud platforms for a specific user, identifying the adverse effects of using the selected options of platforms, if there's any and to come up with a mechanism to monitor plagiarism across platforms. In this paper, we explore multi-agent reinforcement learning techniques in finding adaptive solutions for this issue.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121446311","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}

{"title":"Modality estimation methods in internal training reflection texts using BERT.","authors":"M. Yamada, Tsunenori Mine","doi":"10.1109/IIAIAAI55812.2022.00032","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00032","url":null,"abstract":"In-house training includes opportunities for employees to reflect on the training through the writing of written reflections. Its purpose is to create opportunities to look back and think about \"how the content learned in the training could be utilised in subsequent work,\" and to allow employees to discover their own issues and set future action goals. In addition, it is possible to measure the effectiveness of the training, and the planners can also discover issues in the training itself, such as the appropriateness of the content, which is useful for improving future training programs. However, there has been little research on these employee reflection texts, especially text analysis using natural language processing (NLP), and there is a need for methods to find useful uses for the texts. By taking a NLP approach to the accumulated data of reflective statements, it is possible to estimate the degree of understanding of the training content and to grasp the degree of growth of employees over time. In addition, there is a potential for proposing training content suited to each employee and improving the efficiency of training as a further development of this approach. As a first step towards the realization of these possibilities, this study proposes automatic modality estimation methods focusing on sentence-final expressions in retrospective self-reflection sentences. Modality in Japanese includes the speaker’s subjective semantic content, which indicates the speaker’s judgement of facts and his/her attitude towards speech and communication to the listener. Therefore, we believe that the modality part of self-reflection sentences on training texts contains important information that leads to the understanding of each employee’s behavior and intentions. Experimental results show that the proposed methods enables modality estimation with higher accuracy than manually formulated rules based on sentence structure. The results also show the possibility of developing the proposed methods to be applied to a deeper analysis approach to reflection sentences.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"46 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127379586","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}

{"title":"Cross-Domain Event Participant Prediction with Public Event Description","authors":"Yihong Zhang, Masumi Shirakawa, Takahiro Hara","doi":"10.1109/IIAIAAI55812.2022.00017","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00017","url":null,"abstract":"Event participant prediction is an important problem when planning future events. Previous works have found that cold-start recommendation techniques can be used to solve the problem effectively. However, we consider the case of a newly started domain where training data for learning the recommendation model is limited. We propose to use a support domain data that has a similar user behavior model to help improve the training process. Our assumption does not involve linked users across domains, but uses public event description as the bridge. We show how such data can be combined with the target domain data under such assumptions. Experimental evaluation with real-world event participation datasets shows that adding a support domain data with our method does steadily improve prediction accuracy in the target domain.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546934","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}

{"title":"Enhanced Quantile Portfolio for Multifactor Model with Deep Learning","authors":"Masaya Abe, Kei Nakagawa","doi":"10.1109/IIAIAAI55812.2022.00066","DOIUrl":"https://doi.org/10.1109/IIAIAAI55812.2022.00066","url":null,"abstract":"Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Although machine learning methods are increasingly popular and effective in stock return prediction in the cross-section, still most of the previous studies rely on a simple quantile portfolio. In this paper, we apply deep learning for stock return prediction in the cross-section and propose a more sophisticated portfolio construction framework called Enhanced Quantile Portfolios. These portfolios are inspired by Pure Quantile Portfolio that overcome the main drawbacks of simple quantile portfolios based on a single sort. The formulation of Enhanced Quantile Portfolio is a quadratic programming problem that considers the trade-off between portfolio alpha and stock diversification, while maintaining the characteristics of a simple quantile portfolio. The experimental comparison shows that the proposed approach outperforms a simple quantile portfolio.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123949668","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}

{"title":"Analyzing the Structure of U.S. Patents Using Patent Families","authors":"Jun Nakamitsu, S. Fukuda, Hidetsugu Nanba","doi":"10.1109/iiaiaai55812.2022.00038","DOIUrl":"https://doi.org/10.1109/iiaiaai55812.2022.00038","url":null,"abstract":"Researchers and developers search for patents in fields related to their own research to obtain information on issues and effective technologies in those fields for use in their research. However, it is impossible to read through the full text of many patents, so a method that enables patent information to be grasped briefly is needed. In this study, we analyze the structure of U.S. patents with the aim of extracting important information. Using Japanese patents with structural tags such as \"field\", \"problem\", \"solution\", and \"effect\", and corresponding U.S. patents (patent families), we automatically created a dataset of 81,405 U.S. patents with structural tags. Furthermore, using this dataset, we conduct an experiment to assign structural tags to each sentence in the U. S. patents automatically. For the embedding layer, we use a language representation model, Bidirectional Encoder Representations from Transformer, pretrained on patent documents and construct a multi-label classifier that classifies a given sentence into one of four categories: \"field\", \"problem\", \"solution\", or \"effect\". Using a loss function that considers the unbalanced amount of data for each structural tag, we are able to classify sentences related to \"field\", \"problem\", \"solution\", and \"effect\" with precision of 0.6994, recall of 0.8291, and F-measure of 0.7426.","PeriodicalId":156230,"journal":{"name":"2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127733191","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}