Updated on 2024/06/24

写真a

 
BAO, Siya
 
Affiliation
Faculty of Science and Engineering, School of Fundamental Science and Engineering
Job title
Assistant Professor(without tenure)

Research Experience

  • 2020.04
    -
    Now

    Waseda University   Faculty of Science and Engineering   Assistant Professor

Education Background

  • 2017.04
    -
    2020.03

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2015.09
    -
    2017.03

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2011.09
    -
    2015.09

    Waseda University   School of Fundamental Science and Engineering  

Committee Memberships

  • 2023.06
    -
    Now

    情報処理学会  論文誌ジャーナル/JIP編集委員会委員

  • 2022.04
    -
    Now

    情報処理学会  高度交通システムとスマートコミュニティ研究会

Professional Memberships

  •  
     
     

    情報処理学会

  •  
     
     

    電子情報通信学会

  •  
     
     

    IEEE

Research Areas

  • High performance computing / Database

Research Interests

  • 量子計算

  • 地理空間情報処理

  • テキストマイニング

Awards

  • 優秀論文賞

    2023.11   DICOMO 2023   ACOによる時間変化に対応した旅行計画最適化手法

    Winner: 佐伯 越志, 鮑 思雅, 高山 敏典, 戸川 望

  • Best Student Opponent

    2020.01  

    Winner: IEEE ICSC 2020

  • 海外渡航旅費援助

    2017.09   電気通信普及財団  

 

Papers

  • BERT-Based Prediction Model of Management Sales Forecast Error Using Japanese Firms' Earnings Meeting Transcripts

    Siya Bao, Yiqun Jin

    2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)     1066 - 1067  2024.01  [Refereed]

     View Summary

    Earnings meeting transcripts contain valuable information relevant to investment decision-making, and reflect firm's future performance such as sales revenue forecasts. In this paper, we proposed a BERT-based model to predict whether the actual sales beat the forecast using long-text Japanese EM transcripts. According to the experiment results, our proposed method outperforms the five conventional methods with an improvement ≥ 4% in accuracy.

    DOI

  • Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API.

    Etsushi Saeki, Siya Bao, Toshinori Takayama, Nozomu Togawa

    ICCE     1 - 2  2024  [Refereed]

     View Summary

    In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction.

    DOI

    Scopus

  • Carrying-Mode-Free Stair Ascent and Descent Estimation using Smartphones.

    Dai Kajimoto, Etsushi Saeki, Siya Bao, Nozomu Togawa

    ICCE     1 - 6  2024  [Refereed]

     View Summary

    This paper proposes a smartphone-based pedestrian dead reckoning to track stair ascent and descent. By using accelerometer and barometer information, pedestrians' gait patterns are collected under different carrying modes to detect the stair ascent and descent motions. Also, weather influences are considered to eliminate the barometer measurement errors. We evaluate the proposed method in a seven-floor environment, and we compare our method with a conventional method without multiple carrying modes and weather considerations. The minimal error rate of the proposed method is only 4.2% when ascending or descending seven floors, and at most 14.4% error are reduced compared with the conventional method.

    DOI

    Scopus

  • Ising-Machine-Based Solver for Constrained Graph Coloring Problems.

    Soma Kawakami, Yosuke Mukasa, Siya Bao, Dema Ba, Junya Arai, Satoshi Yagi, Junji Teramoto, Nozomu Togawa

    IEICE Trans. Fundam. Electron. Commun. Comput. Sci.   107 ( 1 ) 38 - 51  2024.01  [Refereed]

     View Summary

    Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. The graph coloring problem, which is one of the difficult combinatorial optimization problems, is to assign a color to each vertex of a graph such that no two vertices connected by an edge have the same color. Although methods to map the graph coloring problem onto the Ising model or quadratic unconstrained binary optimization (QUBO) model are proposed, none of them considers minimizing the number of colors. In addition, there is no Ising-machine-based method considering additional constraints in order to apply to practical problems. In this paper, we propose a mapping method of the graph coloring problem including minimizing the number of colors and additional constraints to the QUBO model. As well as the constraint terms for the graph coloring problem, we firstly propose an objective function term that can minimize the number of colors so that the number of used spins cannot increase exponentially. Secondly, we propose two additional constraint terms: One is that specific vertices have to be colored with specified colors; The other is that specific colors cannot be used more than the number of times given in advance. We theoretically prove that, if the energy of the proposed QUBO mapping is minimized, all the constraints are satisfied and the objective function is minimized. The result of the experiment using an Ising machine showed that the proposed method reduces the number of used colors by up to 75.1% on average compared to the existing baseline method when additional constraints are not considered. Considering the additional constraints, the proposed method can effectively find feasible solutions satisfying all the constraints.

    DOI

  • Multi-Day Intermodal Travel Planning for Urban Cities Using Ising Machines

    Siya Bao, Nozomu Togawa

    IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC     54 - 60  2023  [Refereed]

     View Summary

    The multi-day intermodal travel planning problem (MITPP) is an optimization problem (OP) and it generates the optimal sequences of point-of-interests (POIs) and hotels while searching for the most suitable transport modes between POIs and hotels. Conventional methods and solvers using von Neumann computers provide good approximate solutions to the OPs, but the computation time grows exponentially dealing with large-scale or complex OPs. Meanwhile, Ising machines or quantum annealing machines are non-von Neumann computers that are designed to solve complex OPs. In this paper, we focus on solving the MITPP by a two-phase Ising-based method. The first POI clustering phase aims at generating POIs clusters for sightseeing days and the second POI routing phase generates travel routes for each day with the optimal transport modes. Practical factors such as POI satisfaction, POI duration, hotel fee, and transportation fee are included in the MITPP. We map these elements onto quadratic unconstrained binary optimization (QUBO) models. For evaluation, we use a real-world dataset in Sapporo, Japan. Empirical results confirm that the proposed method can effectively solve the MITPP both in terms of solution quality and execution time and outperforms a conventional solver, a conventional method, and the latest Ising-based method.

    DOI

    Scopus

  • An Ising-Machine-Based Solver of Vehicle Routing Problem With Balanced Pick-Up

    Siya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa

    IEEE Transactions on Consumer Electronics   70 ( 1 ) 445 - 459  2023  [Refereed]

    Authorship:Corresponding author

     View Summary

    Vehicle routing applications are ubiquitous in the field of pick-up and delivery service. We focus on the vehicle routing problem with balanced pick-up called VRPBP which originates from the package pick-up service. The aim of the problem is not only to efficiently explore the shortest travel route but also to balance loads between depots and vehicles. These problems can be regarded as optimization problems, and recent developments in Ising machines, including quantum annealing machines, bring us a new opportunity to solve complex real-world optimization problems. In this paper, a two-phase method and a three-phase method using Ising machines are proposed for solving the VRPBP. As the applicability of current Ising machines is limited due to the small size of Ising spins and connectivities, we partition the complex problem into two or three sub-problems, and the key elements of each sub-problem are mapped onto quadratic unconstrained binary optimization (QUBO) models to fit in the structure of the Ising machines. We first compared the performances of the Ising machine on the standard TSP and CVRP datasets with a conventional state-of-the-art solver and three conventional methods. Then, we evaluated the performances of the proposed methods compared with five conventional method for solving the VRPBP. The results confirm the effectiveness of the two proposed methods in solving vehicle-routing-related optimization problems.

    DOI

    Scopus

  • Trip Planning Based on subQUBO Annealing.

    Tatsuya Noguchi, Keisuke Fukada, Siya Bao, Nozomu Togawa

    IEEE Access   11   100383 - 100395  2023  [Refereed]

     View Summary

    The trip planning problem (TPP) can be formulated as a combinatorial optimization problem that searches for the best route to visit a series of landmarks and hotels. Meanwhile, Ising machines have attracted attention due to their efficiency in solving combinatorial optimization problems. The Ising machines solve the combinatorial optimization problems by transforming the problems into quadratic unconstrained binary optimization (QUBO) models. However, the possible input QUBO size of current Ising machines is quite limited. Thus, it is hard to directly embed a large-scale TPP onto the current Ising machines. In this paper, we propose a novel subQUBO annealing method based on the combined variable selection method to solve the TPP. The proposed method finds a quasi-optimal solution to a large problem by repeatedly partitioning the original QUBO model into small subQUBOs that can be embedded onto the Ising machine. Specifically, to construct a subQUBO, we select variables from the original QUBO model, which have small deviation values. Further, we select variables randomly from the original QUBO model, so as not to fall into the local optimum. We have conducted an evaluation experiment using Ising machines on TPP and confirmed that the proposed method outperforms the state-of-the-art methods in terms of POI satisfaction and POI cost.

    DOI

    Scopus

  • A Constrained Graph Coloring Solver Based on Ising Machines.

    Soma Kawakami, Yosuke Mukasa, Siya Bao, Dema Ba, Junya Arai, Satoshi Yagi, Junji Teramoto, Nozomu Togawa

    ICCE   2023-January   1 - 6  2023  [Refereed]

     View Summary

    Optimum or quasi-optimum solutions of combinatorial optimization problems can be efficiently found by using Ising machines. The graph coloring problem (GCP) is known as a difficult combinatorial problem. Given a graph, each vertex should be assigned a color and any two vertices connected by an edge must not be colored the same. Although methods to map the GCP onto the Ising model are proposed, none of them considers minimizing the number of colors. In this paper, we propose a mapping method of the GCP including minimizing the number of colors and additional constraints to the QUBO (Quadratic Unconstrained Binary Optimization) model. As well as the constraint terms for the GCP, we firstly propose an objective function term that can minimize the number of colors so that the number of used spins cannot increase exponentially. Secondly, we propose two more additional terms so that our proposed method can be applicable to practical consumer applications. The experimental evaluations on an Ising machine showed that the proposed method reduces the number of used colors by up to 75.1% on average compared with the existing baseline method. Considering the additional constraints, the proposed method can effectively find feasible solutions satisfying all the constraints.

    DOI

    Scopus

  • ML-Based Trading Strategy for Short-Term Price Reactions on Earnings Announcement Reports

    Yiqun Jin, Siya Bao

    Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022     6682 - 6683  2022  [Refereed]

     View Summary

    An earnings announcement report (EAR) contains the latest information about a company's financial situation and operating performance. Short-term stock price reacts strongly to such information. In this paper, to gain investment return from the short-term price reaction to EARs, we use 28 important variables from EARs and propose an ML-based trading strategy (MLTS) with random forest (RF). Results show that our strategy achieves the highest final investment return of 178.1%.

    DOI

    Scopus

  • Multi-Objective Trip Planning Based on Ant Colony Optimization Utilizing Trip Records.

    Etsushi Saeki, Siya Bao, Toshinori Takayama, Nozomu Togawa

    IEEE Access   10   127825 - 127844  2022  [Refereed]

     View Summary

    Trip planning services have been developed along with tourism promotion and information technology evolutions, where we must construct trip routes that simultaneously optimize multi-objective functions such as trip expenses and user satisfaction. Moreover, utilization of past-trip records is essential, because similarities to past-trip records well reflect users' general preferences and tendencies during trip planning. In this paper, we propose a multi-objective trip planning method using ant colony optimization (ACO). By effectively using the pheromones in ACO, we can construct trip routes similar to trip records stored before and the constructed route can reflect users' general preferences. In addition, we vary ants' behaviors in ACO corresponding to various objective functions and hence we can obtain multi-objective trip routes naturally. Experimental results demonstrated that our method outperforms the baseline methods in terms of point-of-interest (POI) satisfaction, POI cost, and past-trip similarity. We also conducted a user study, which clearly indicates that our method obtains high scores through various user questionnaires.

    DOI

    Scopus

    5
    Citation
    (Scopus)
  • An Approach to the Vehicle Routing Problem with Balanced Pick-up Using Ising Machines

    Siya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa

    2021 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)    2021.04  [Refereed]

     View Summary

    Vehicle routing problems (VRPs) can be solved as optimization problems. Practical applications of the VRPs are involved in various areas including manufacturing, supply chain, and tourism. Conventional approaches using von Neumann computers obtain good approximate solutions to the optimization problems, but conventional approaches show disadvantages of computation costs in large-scale or complex problems due to the combinatorial explosion. Oppositely, Ising machines or quantum annealing machines are non-von Neumann computers that are designed to solve complex optimization problems. In this paper, we propose an Ising-machine based approach for the vehicle routing problem with balanced pick-up (VRPBP). The development of the VRPBP is motivated by postal items pick-up services in the real-world. Our approach includes various features of VRP variants. We propose a 2-phase approach to solve the VRPBP and key elements in each phase are mapped onto quadratic unconstrained binary optimization (QUBO) forms. Specifically, the first phase belongs to the clustering phase which is an extension to the knapsack problem with additional distance and load balancing concerns. The second phase is mapped to the traveling salesman problem. Experimental results of our approach are evaluated in terms of solution quality and computation time compared with conventional approaches.

    DOI

  • Multi-day Travel Planning Using Ising Machines for Real-world Applications.

    Siya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa

    24th IEEE International Intelligent Transportation Systems Conference(ITSC)   2021-September   3704 - 3709  2021  [Refereed]

     View Summary

    The multi-day travel planning assists users with realistic travel itineraries by searching for the optimal travel routes through a set of candidate hotels and point-of-interests (POIs). The multi-day travel planning problem (MTPP) can be solved as an optimization problem. Although conventional methods using von Neumann computers obtain good approximate solutions to the optimization problems, large computation costs are required to solve large-scale or complex problems due to the combinatorial explosion. On the other hand, Ising machines or quantum annealing machines are non-von Neumann computers, and those machines are developed to deal with complex optimization problems. In this paper, we propose an Ising-machine-based method for the MTPP. Practical factors of the MTPP include the POI satisfaction, travel expenses, and time limits. Those factors are mapped onto quadratic unconstrained binary optimization (QUBO) forms. We evaluate the proposed method using two real-world datasets including Sapporo and Tokyo, Japan. Experimental results show that the MTPP can be effectively solved using Ising machines compared with the conventional methods in terms of the solution quality and the execution time. To the best of our knowledge, this study is the first solution of the MTPP using Ising machines.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • Document-level sentiment classification in japanese by stem-based segmentation with category and data-source information

    Siya Bao, Nozomu Togawa

    Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020     311 - 314  2020.02  [Refereed]

     View Summary

    © 2020 IEEE. Existing studies focus on text information while ignoring category and data source information, both of which are verified to be important in interpreting sentiments in travel comments in this paper. Furthermore, the unique linguistic characteristics of Japanese cause difficulty in applying the conventional token-based word segmentation methods to Japanese comments directly. In this paper, we propose a method of stem-based segmentation based on Japanese linguistic characteristics and incorporate it with category and data source information into a hierarchical network model for document-level sentiment classification. Empirical results of our proposed model outperform existing models on a real-world dataset.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A travel decision support algorithm: Landmark activity extraction from japanese travel comments

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Studies in Computational Intelligence   849   109 - 123  2020  [Refereed]

     View Summary

    © Springer Nature Switzerland AG 2020. To help people smoothly and efficiently make travel decisions, we utilize the advantages of travel comments posted by thousands of other travelers. In this paper, we analyze the feasibility of exploring landmark activity queries and representative examples from Japanese travel comments. Contributions in this paper include a framework for extracting activity concerned keywords and queries, quantifying the relationship between landmark activities and comment contents. An evaluation of activity-example extraction is conducted in two case studies through 18,939 travel comments.

    DOI

    Scopus

  • Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018     3628 - 3637  2019.01  [Refereed]

     View Summary

    © 2018 IEEE. Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.

    DOI

    Scopus

  • Personalized Landmark Recommendation for Language-Specific Users by Open Data Mining

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Studies in Computational Intelligence   791   107 - 121  2019  [Refereed]

     View Summary

    © 2019, Springer Nature Switzerland AG. This paper proposes a personalized landmark recommendation algorithm aiming at exploring new sights into the determinants of landmark satisfaction prediction. We gather 1,219,048 user-generated comments in Tokyo, Shanghai and New York from four travel websites. We find that users have diverse satisfaction on landmarks those findings, we propose an effective algorithm for personalize landmark satisfaction prediction. Our algorithm provides the top-6 landmarks with the highest satisfaction to users for a one-day trip plan our proposed algorithm has better performances than previous studies from the viewpoints of landmark recommendation and landmark satisfaction prediction.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • Personalized landmark recommendation algorithm based on language-specific satisfaction prediction using heterogeneous open data sources

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Proceedings - 2018 10th International Conference on Computational Intelligence and Communication Networks, CICN 2018     70 - 76  2018.08  [Refereed]

     View Summary

    © 2018 IEEE. This paper proposes a personalized landmark recommendation algorithm based on the prediction of users' satisfaction on landmarks. We have accumulated 270,239 user-generated comments from travel websites of Ctrip, Jaran and TripAdvisor for 196 landmarks in Tokyo, Japan. We find that users do have different satisfaction on landmarks depending on their commonly used languages and travel websites. Then we establish a database for landmarks with abundant and accurate landmark type and landmark satisfaction information. Finally, we propose an effective personalized landmark satisfaction prediction algorithm based on users' landmark type, language and travel website preferences. After that, landmarks with the top-6 highest satisfaction are provided to the user for a one-day visit plan in Tokyo. Experimental results demonstrate that the proposed algorithm can recommend landmarks that fit the user's preferences and our algorithm also successfully predicts the user's landmark satisfaction with a low error rate less than 7%, which is superior to other previous studies.

    DOI

    Scopus

  • Road-illuminance level inference across road networks based on Bayesian analysis

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    2018 IEEE International Conference on Consumer Electronics, ICCE 2018   2018-January   1 - 6  2018.03  [Refereed]

     View Summary

    © 2018 IEEE. This paper proposes a road-illuminance level inference method based on the naive Bayesian analysis. We investigate quantities and types of road lights and landmarks with a large set of roads in real environments and reorganize them into two safety classes, safe or unsafe, with seven road attributes. Then we carry out data learning using three types of datasets according to different groups of the road attributes. Experimental results demonstrate that the proposed method successfully classifies a set of roads with seven attributes into safe ones and unsafe ones with the accuracy of more than 85%, which is superior to other machine-learning based methods and a manual-based method.

    DOI

    Scopus

  • Personalized one-day travel with multi-nearby-landmark recommendation

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin   2017-September   239 - 242  2017.12  [Refereed]

     View Summary

    © 2017 IEEE. Travel route recommendation can strongly influence users' satisfaction and the success of touristic businesses. This paper proposes a personalized travel recommendation algorithm with time planning. We use landmark categorization and region clustering to obtain effective elements. Then we build a travel map to generate all possible travel routes. Our proposed algorithm has higher precision in landmark recommendation and time planning than thoes in previous algorithms.

    DOI

    Scopus

    5
    Citation
    (Scopus)
  • A safe and comprehensive route finding algorithm for pedestrians based on lighting and landmark conditions

    Siya Bao, Tomoyuki Nitta, Masao Yanagisawa, Nozomu Togawa

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E100A ( 11 ) 2439 - 2450  2017.11  [Refereed]

     View Summary

    Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. In this paper, we propose a safe and comprehensive route finding algorithm for pedestrians based on lighting and landmark conditions. Safety and comprehensiveness can be predicted by the five possible indicators: (1) lighting conditions, (2) landmark visibility, (3) landmark effectiveness, (4) turning counts along a route, and (5) road widths. We first investigate impacts of these five indicators on pedestrians' perceptions on safety and comprehensiveness during route findings. After that, a route finding algorithm is proposed for pedestrians. In the algorithm, we design the score based on the indicators (1), (2), (3), and (5) above and also introduce a turning count reduction strategy for the indicator (4). Thus we find out a safe and comprehensive route through them. In particular, we design daytime score and nighttime score differently and find out an appropriate route depending on the time periods. Experimental simulation results demonstrate that the proposed algorithm obtains higher scores compared to several existing algorithms. We also demonstrate that the proposed algorithm is able to find out safe and comprehensive routes for pedestrians in real environments in accordance with questionnaire results.

    DOI

    Scopus

    5
    Citation
    (Scopus)
  • A safe and comprehensive route finding method for pedestrian based on lighting and landmark

    Siya Bao, Tomoyuki Nitta, Kazuaki Ishikawa, Masao Yanagisawa, Nozomu Togawa

    2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016    2016.12  [Refereed]

     View Summary

    © 2016 IEEE. This paper proposes a safe and comprehensive route finding method for pedestrians. We evaluate five factors that do relieve pedestrians' fear of darkness. Based upon the evaluation, we propose a comprehensive route finding method by taking road width and reduction on turning points into consideration. The experimental results on real outdoor environments under different lighting situations confirm that the proposed method can obtain safety and comprehensive routes for pedestrians.

    DOI

    Scopus

    8
    Citation
    (Scopus)
  • A landmark-based route recommendation method for pedestrian walking strategies

    Siya Bao, Tomoyuki Nitta, Daisuke Shindou, Masao Yanagisawa, Nozomu Togawa

    2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015     672 - 673  2016.02  [Refereed]

     View Summary

    © 2015 IEEE. This paper proposes a landmark-based route recommendation method for enjoyable walking atmosphere strategies by accumulating and analyzing geographical information. We utilize landmark categorization and region clustering to obtain effective elements. Experimental results demonstrate that our proposed method improves walking environment quality and confirm that it is applicable in both urban and rural areas.

    DOI

    Scopus

    5
    Citation
    (Scopus)

▼display all

Books and Other Publications

  • Machine Learning for Indoor Localization and Navigation

    Siya Bao, Nozomu Togawa( Part: Contributor, Smart Device-Based PDR Methods for Indoor Localization)

    Springer  2023.06 ISBN: 9783031267116

Presentations

  • 量子計算でより便利&スマートな暮らしへ

    鮑思雅

    IPSJ-ONE 2024 

    Presentation date: 2024.03

  • イジングマシンによる複数日にまたがる旅程最適化

    鮑 思雅  [Invited]

    第92回高度交通システムとスマートコミュニティ研究発表会 

    Presentation date: 2023.03

  • A quantum computing-based optimization method for multi-day travel recommendation

    Siya Bao  [Invited]

    An International Network on Quantum Annealing Seminar 

    Presentation date: 2022.11

Research Projects

  • Adaptive Ising-machine-based Solvers for Large-scale Real-world Geospatial Optimization Problems

    日本学術振興会  科学研究費助成事業

    Project Year :

    2024.04
    -
    2027.03
     

    鮑 思雅

  • Applications of Large-scale Real-world Geospatial Optimization Problems Using Ising Machines

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists

    Project Year :

    2021.04
    -
    2024.03
     

Misc

  • 部分QUBOアニーリングを用いたインターモーダル旅程最適化

    野口竜弥, 深田佳佑, 鮑思雅, 戸川望

    情報処理学会研究報告(Web)   2024 ( ITS-96 )  2024

    J-GLOBAL

  • ACOによる時間変化に対応した旅行計画最適化手法

    佐伯, 越志, 鮑, 思雅, 高山, 敏典, 戸川, 望

    マルチメディア,分散,協調とモバイルシンポジウム2023論文集   2023   490 - 503  2023.06

     View Summary

    観光産業の振興と情報科学技術の発展によって,ユーザの旅行計画を補助する技術の開発が進んでいる.旅行計画では,人気度や費用など複数の目的関数を同時に最適化することで,ユーザが満足する経路を生成する必要がある.さらに,ユーザに旅行の詳細な情報を与え,ユーザが行動しやすい旅行経路を生成するには,時間依存で変化する移動時間や観光地の価値を考慮するべきである.例えば,移動に公共交通機関を利用する場合,時刻表や移動経路によって出発時刻に依存して移動時間が変化する.観光地の価値についても,夜景が綺麗な観光地や,イベントを開催する観光地,営業時間の存在など,訪問時間によって価値が変化する.本稿では,旅行計画における時間変化する価値を考慮し,複数の目的関数を最適化できる,時間依存多目的旅行計画問題最適化手法を提案する.提案手法は,蟻コロニー最適化において複数の目的関数を異なる重みで考慮する蟻を設定し,フェロモンに時間属性を付加することで時間依存多目的旅行計画問題を解法する.特に,タイムスタンプ付きの過去のユーザの旅行履歴を利用することで時間依存の観光地の価値に対応し,詳細経路 API を利用して時間変動する移動時間に対応する.その上で,詳細経路 API 利用時の応答時間の増加を想定し,API 呼出回数を削減する工夫を導入する.評価実験により,提案手法は既存手法に対し,より時間変化する価値を最適化した旅行経路を生成した.

  • 歩行特性を利用したスマートフォン階段昇降推定

    梶本, 大, 佐伯, 越志, 鮑, 思雅, 戸川, 望

    マルチメディア,分散,協調とモバイルシンポジウム2023論文集   2023   329 - 335  2023.06

     View Summary

    GPS (Global Positioning System) をはじめとして,我々は日常的に自己位置を推定している.しかし,GPS を利用できない環境の場合,携帯端末のセンサを用いた PDR (Pedestrian Dead Reckoning) 等の相対的測位手法が必要となる.特に複雑な屋内空間において,歩行者は水平方向に移動するだけでなく垂直方向にも移動する.このとき,エレベータやエスカレータのように歩行者の揺れや振動が少ない移動手段だけではなく,階段のような歩行者に不規則に揺れや振動が生じる場合にも,正確に垂直方向の移動を推定する必要がある.本稿では,スマートフォンを利用した階段昇降推定手法を提案する.提案手法は,歩行者の歩行特性を利用してフロアの水平部分を検出し気圧センサの誤差を解消することで,高い精度で階段中のフロアを推定する.さらに,気圧センサの値がスマートフォンの姿勢に左右されない特性を利用することで,スマートフォンの姿勢によらない階段昇降推定を実現する.評価実験の結果,提案手法は既存手法と比較して,推定誤差を低減し階段昇降を推定できた.

  • 補正処理を導入した部分QUBOアニーリングによる複数日旅程最適化

    野口竜弥, 深田佳佑, 鮑思雅, 戸川望

    電子情報通信学会技術研究報告(Web)   123 ( 258(VLD2023 30-79) )  2023

    J-GLOBAL

  • 部分QUBOアニーリングによる複数日旅程最適化問題の解法

    野口竜弥, 深田佳佑, 鮑思雅, 戸川望

    情報処理学会研究報告(Web)   2023 ( ITS-092 )  2023

    J-GLOBAL

  • ACOによる多目的要求に対応した旅行計画最適化手法

    佐伯, 越志, 鮑, 思雅, 高山, 敏典, 戸川, 望

    マルチメディア,分散,協調とモバイルシンポジウム2022論文集   2022   1556 - 1569  2022.07

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    観光産業の振興と情報科学技術の発展によって,旅行計画サービスの開発が進んでいる.旅行計画サービスが対象とする旅行計画では,満足度や費用など複数の目的関数を同時に最適化することで,ユーザが満足する経路を生成する必要がある.とりわけ,過去に多くのユーザが同様な旅程を計画している,あるいは部分的に同様な旅程を計画していることから,いかに過去のユーザの旅行経路を再利用するかが旅行計画の大きな鍵となる.本稿では,旅行計画に対するユーザの要求を満足するため,多目的オリエンテーリング問題をベースに過去のユーザの旅行経路を陽に利用した旅行計画最適化手法を提案する.提案手法は,蟻コロニー最適化を利用することで,過去のユーザの旅行経路を陽に反映した旅行計画を可能とする.その上で,蟻コロニー最適化において蟻の行動を多様な目的関数に対応して変化させることで,多目的オリエンテーリング問題を解法する.評価実験により,既存手法に対し,過去の旅行者の旅行経路に近く,よりユーザの要求を満足する旅行経路を生成した.

  • イジングマシンを用いた複数日にまたがる観光地選出手法

    鮑思雅, 戸川望

    電子情報通信学会大会講演論文集(CD-ROM)   2022  2022

    J-GLOBAL

  • An Evaluation Method of Road Illuminance Levels Using Road Lights and Landmarks

    BAO Siya, YANAGISAWA Masao, TOGAWA Nozomu

    電子情報通信学会大会講演論文集(CD-ROM)   2017  2017

    J-GLOBAL

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Industrial Property Rights

  • 組合せ最適化装置、組合せ最適化方法、およびプログラム

    巴 徳瑪, 新井 淳也, 八木 哲志, 寺本 純司, 川上 蒼馬, 武笠 陽介, 鮑 思雅, 戸川 望

    Patent

 

Syllabus

Teaching Experience

  • Laboratory for Science and. Engineering 2A/2B

    Waseda University  

    2020.09
    -
    Now
     

  • Introduction to C programming

    Waseda University  

    2020.04
    -
    Now
     

 

Internal Special Research Projects

  • イジングマシンを用いた旅程最適化

    2023  

     View Summary

     本研究では,複数日にまたがる観光地選出問題に注目し,実イジングマシンによる二段階解法を提案する.POIの満足度,POIの滞在時間,ホテルの料金,交通費などの実要素を考慮したうえで,複数日にまたがる観光地選出問題をイジングモデルと等価なQuadratic Unconstrained Binary Optimization (QUBO) に変換し,実イジングマシンによる解法する.1番目のPOIクラスタリングでは,観光日のPOIクラスターを生成し,2番目のPOIルーティングでは,日ごとに最適な交通手段を選択し旅行経路を生成する.提案手法を評価するために,札幌並びに東京周辺を対象に評価実験を行い,制約条件を満たす解が得られたことを確認した.

  • Document-level Sentiment Classification in Japanese Travel Comments of Heterogeneous Sources

    2020  

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     A user's travel satisfaction is directly and explicitly reflected in their comments compared with the other types of travelogues such as GPS trajectory and check-in data. In this advantage of user comments, it is aiming at shedding lights on determinants of travel satisfaction to serve personalized travel route recommendation. To obtain a large dataset, 479,799 user comments are collected in Tokyo, Kyoto, and Sapporo from three travel websites including TripAdvisor, Jaran, and Ctrip. Prior works have been elaborated on data-source-specific and language-specific analysis. It is found that landmark coverages vary among different websites and users have diverse satisfaction on landmarks depending on their frequently used languages and travel websites. With those findings, a personalized travel route recommendation algorithm is proposed that (1) recommends top-6 personalized landmarks and (2) generates a realistic travel route for a one-day visit. Experimental results confirm the advantages of the proposed algorithm beyond previous studies from the viewpoints of landmark recommendation precision and travel time optimization.