Services Computing

We have been working on technologies related to service computing. Our work-in-progress are listed as follows:

Quality Control for Crowdsourced Bilingual Dictionary Creation

In conventional crowdsourcing-based bilingual dictionary creation, multiple workers freely translate words and evaluate correctness by majority vote. However, when this method is used to create bilingual dictionaries for languages with few speakers, such as low-resource languages, many workers who are not familiar with the language participate in the majority vote, and the reliability of the majority vote evaluation is significantly reduced. Therefore, we aim to improve the quality of the generated bilingual data by using a method that assigns tasks to workers who are familiar with the corresponding language pairs based on confidence values and a method of integrating responses using a super-problem in which majority voting is performed on evaluation sequences for multiple evaluation tasks.

[地田大樹, 村上陽平. 対訳辞書作成のための信頼に基づくクラウドソーシングの評価, サービスコンピューティング(SC), 電子情報通信学会技術研究報告, Vol. 119, No. 482, SC2019-49, pp. 91-96, 2020.-In Japanese]

Presentation: 山本涼太郎, 村上陽平. Deep Knowledge Tracingを用いた対話作成タスクの予測, 情報処理通信学会総合大会, 2024

Decentralized Crowdsourcing Platform Based On Blockchain

In environments where it is not possible to assume a trusted crowdsourcing platform, using a distributed, permission-based blockchain built by multiple organizations would enables the operation of a stable crowdsourcing infrastructure that robustly maintains the work history of workers. Furthermore, by implementing smart contracts that enable direct micropayments between users and workers based on the use of deliverables, we aim to make crowdsourcing sustainable even in environments with few users and workers, such as low-resource languages.

[張 禹王, 村上陽平. 低資源言語ためのブロックチェーンに基づく非中央集権型辞書システム, サービスコンピューティング(SC), 電子情報通信学会技術研究報告, Vol. 120, No. 434, SC2020-37, pp. 25-30, 2021.-In Japanese]

Service Clustering Using Graph Embedding

A new service created by combining multiple services is called a composite service. Composite services have the advantage of providing extended functionality over component services or reusing existing services.For example, by combining a map information service and a weather information service, a composite service can be created that displays the weather for each region on a map. In such a composite service, when some of the combined services become unavailable, the entire composite service is affected in a chain reaction. To solve this problem, this research aims to discover services that are functionally similar to the unavailable service by clustering composite services based on their service dependency networks.

[大久保弘基, 村上陽平. グラフ埋め込みを用いた代替サービスの推薦, サービスコンピューティング(SC), 電子情報通信学会技術研究報告, Vol. 119, No. 482, SC2019-48, pp. 85-90, 2020.-In Japanese]

Presentation: 松本賢司, 村上陽平. サービス連携関係に基づくソフトクラスタリング, 情報処理通信学会総合大会, 2024