Achieving fluency in a foreign language is challenging, often because of limited opportunities for speaking practice. Speaking in a different language can be difficult and sometimes embarrassing. This study introduces a tool designed to prioritize language-speaking practice by integrating the ”Taboo” game with speech recognition technology and a large language model (LLM). This game involves players conveying a target word to an AI-based agent without using specific ”Taboo” words. Our contribution focuses on proposing a meticulously designed taboo game for speaking practice that incorporates engaging interactions with the LLM. This methodology offers a novel and beneficial alternative for English as a Second Language (ESL) learners. Initial experimental trials were conducted, and user feedback highlighted a positive experience with reduced anxiety compared to conventional, human-based speaking practice. However, challenges have emerged, particularly in terms of pronunciation and the expression of certain words. In response to this valuable feedback, we presented a design proposal aimed at addressing these challenges to improve the overall learning experience and ensure effective language practice.

In conventional bilingual dictionary creation by using crowdsourcing, the main method is to ask multiple workers to translate the same words or sentences and take a majority vote. However, when this method is applied to the creation of bilingual dictionaries for low-resource languages with few speakers, many low-quality workers are expected to participate in the majority voting, which makes it difficult to maintain the quality of the evaluation by the majority voting. Therefore, we apply an effective aggregation method using a hyper question, which is a set of single questions, for quality control. Furthermore, to select high-quality workers, we design a task-allocation method based on the reliability of workers which is evaluated by their work results.

In order to find solutions to various international problems, Global Citizenship Education (GCED) for children must consider the diversity of societies beyond basic issues of language and culture. For GCED, machine translation can be used as a tool to allow children to collaborate without a common language. However, low-resource language speakers often cannot enter the conversation and have difficulties in participating in a collaboration, because existing machine translations have poor translation quality in the low-resource languages. Messages from facilitators play an important role in encouraging children’s responses and participation. We, therefore, have analyzed the role of the facilitator in a real-world intercultural children workshop. Specifically, we examine actual conversation log data that links the facilitator’s utterances with children’s utterances in adjacency pairing. We annotate the paired data with tags and then statistically analyze the tagged data. The analysis results show that some types of facilitator messages can significantly impact the responses of low-resource language speaking children. For example, ”request” type utterances tended to encourage responses.

Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still difficult to determine the execution order of the constraint-based approach to reducing the total cost. Plan optimization is crucial in composing the order of bilingual dictionaries creation with the consideration of the methods and their costs. We formalize the plan optimization for creating bilingual dictionaries by utilizing Markov Decision Process (MDP) with the goal to get a more accurate estimation of the most feasible optimal plan with the least total cost before fully implementing the constraint-based bilingual lexicon induction. We model a prior beta distribution of bilingual lexicon induction precision with language similarity and polysemy of the topology as and parameters. It is further used to model cost function and state transition probability. We estimated the cost of all investment plans as a baseline for evaluating the proposed MDP-based approach with total cost as an evaluation metric. After utilizing the posterior beta distribution in the first batch of experiments to construct the prior beta distribution in the second batch of experiments, the result shows 61.5% of cost reduction compared to the estimated all investment plans and 39.4% of cost reduction compared to the estimated MDP optimal plan. The MDP-based proposal outperformed the baseline on the total cost.

In dynamic, open, and service-oriented computing environments, e.g., e-commerce and crowdsourcing, service consumers must choose one of the services or items to complete their tasks. Due to the scale and dynamic characteristics of these environments, service consumers may have little or no experience with the available services. To this end, reputation systems are proposed and have played a crucial role in the success of online service-oriented transactions. In this paper, we study the current reputation systems used in commercial environments. In these rating-based reputation systems, we found they are not only resilient to the changes (time lag) but also vulnerable to unfair ratings. To address the problems in parallel, we propose an adaptive reputation model (ARM). ARM can dynamically adjust its model parameters to adapt the latest changes in a service. To tackle time lag, the proposed model generalizes the fixed sliding window, used in current commercial platforms, into a dynamic sliding window mechanism. Thus, the model can completely mitigate the influence of obsolete ratings. To detect unfair ratings, our model implements a statistical strategy based on hypothesis testing after transforming the ratings in the linear window into residuals. Experiments not only validate the effectiveness of the proposed model but also show that it outperforms the existing reputation system by 45% on average based on five test cases. The results also show that the proposed model can asymptotically converge to the underlying reputation value as ratings begin to accumulate.Note to Practitioners—The reputation models adopted by current commercial platforms, such as Amazon, eBay, and Taobao, not only suffer heavily from unfair rating but also resilient to the changes in services. To address the problems simultaneously, we design and implement a hybrid model that continuously monitors received ratings and aggregates the reputation value in a self-adaptive way. Our model first fits received fair ratings using the Bayesian linear regression approach and captures the distribution of fair ratings; it then filters out unfair ratings leveraging hypothesis testing. Finally, to sensitively respond the dynamic service changes, the dynamic sliding window algorithm in our model shifts the rating collection window into a new one and discards outdated ratings, reputation value is aggregated in the new window to mitigate the influence of obsolete ratings. Extensive experiments are conducted on widely used scenarios to demonstrate the efficiency and the effectiveness of our proposed model.

Self-organization has been proposed to be implemented in complex systems which require the automation capabilities to govern itself and to adapt upon changes. Self-organizing systems can be modeled as multi-agent systems (MAS) since they share common characteristics in that they consist of multiple autonomous systems. However, most existing MAS engineering methodologies do not fully support self-organizing systems design since they require predefined goals and agent behaviors, which is not the case in self-organizing systems. Another feature that is currently not supported for designing self-organizing MAS is the separation between the design of agent behaviors and behavior adaptation, i.e. how agents adapt their behaviors to respond upon changes. To tackle these issues, this paper proposes a role modeling method, in which agent behaviors are represented as roles, to design how agents perform behavior adaptation at runtime by switching between roles. The applicability of the proposed role modeling method is evaluated in a case study of a self-organizing smart transportation system.

The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction a difficult task for low-resource languages. The pivot language and cognate recognition approaches have been proven useful for inducing bilingual lexicons for such languages. We propose constraint-based bilingual lexicon induction for closely related languages by extending constraints from the recent pivot-based induction technique and further enabling multiple symmetry assumption cycle to reach many more cognates in the transgraph. We further identify cognate synonyms to obtain many-to-many translation pairs. This article utilizes four datasets: one Austronesian low-resource language and three Indo-European high-resource languages. We use three constraint-based methods from our previous work, the Inverse Consultation method and translation pairs generated from Cartesian product of input dictionaries as baselines. We evaluate our result using the metrics of precision, recall, and F-score. Our customizable approach allows the user to conduct cross validation to predict the optimal hyperparameters (cognate threshold and cognate synonym threshold) with various combination of heuristics and number of symmetry assumption cycles to gain the highest F-score. Our proposed methods have statistically significant improvement of precision and F-score compared to our previous constraint-based methods. The results show that our method demonstrates the potential to complement other bilingual dictionary creation methods like word alignment models using parallel corpora for high-resource languages while well handling low-resource languages.

With the increasing volume of data to be analysed, one of the challenges in Service Oriented Architecture (SOA) is to make web services efficient in processing large-scale data. Parallel execution and cloud technologies are the keys to speed-up the service invocation. In SOA, service providers typically employ policies to limit parallel execution of the services based on arbitrary decisions. In order to attain optimal performance improvement, users need to adapt to the services policies. A composite service is a combination of several atomic services provided by various providers. To use parallel execution for greater composite service efficiency, the degree of parallelism (DOP) of the composite services need to be optimized by considering the policies of all atomic services. We propose a model that embeds service policies into formulae to calculate composite service performance. From the calculation, we predict the optimal DOP for the composite service, where it attains the best performance. Extensive experiments are conducted on real-world translation services. We use several measures such as mean prediction error (MPE), mean absolute deviation (MAD) and tracking signal (TS) to evaluate our model. The analysis results show that our proposed model has good prediction accuracy in identifying optimal DOPs for composite services.

The future Internet will be populated with a massive number of cooperating services due to the rapid growth of publicly available services and the adoption of service-oriented computing (SOC) into the Internet of Things. The adoption of SOC enables combining the functionalities of smart devices as combining services by means of service composition. These cooperating services form a large-scale service network where the nodes and the links represent services and the dependency between services, respectively. The dependency between services potentially causes cascading failure, where the failure of a service propagates to its dependent services. Due to the lack of research in this type of cascading failure, we analyzed cascading failure in service networks for different topology and different degree of service interdependency. We found that the number of cascading failure is somewhat linear to the average number of required services, and decays exponentially over the average number of alternate services. The latter suggests that cascading failure tolerance can be significantly improved by adding few alternate services to each required service if the average number of alternate services is currently low. In addition, we also found that scale-free topology provides better tolerance, subsequently followed by exponential and random topology.

The on-demand provisions of cloud services create a service market, where users can dynamically select services based on such attractive criteria as price and quality. An intuitive model of a service market is a reverse auction. In the first price auction, however, a service that is cheaper and provides better quality is not necessarily selected. This causes undesirable outcomes both for users and service providers. A possible solution is the Vickrey-Clarke-Groves (VCG) mechanism, where the dominant strategy for a service provider is to report the true cost of his service. In spite of this desirable property, implementing the VCG mechanism for service composition suffers from computational cost. The calculation of payments to service providers based on the VCG mechanism requires iterative service selection, even though each service selection can be NP-hard. Approximation algorithms cannot be applied because approximate solutions do not assure the desirable property of the VCG mechanism. Thus, we model VCG payments for service markets and propose a dynamic programming (DP)-based algorithm for service selection and VCG payment calculation. Our proposed algorithm solves service selection in quasi-polynomial time and gives an exact solution. Moreover, we extend it and focus on the iterative service selection process for VCG payment calculation to improve its performance. Our series of experiments show that our proposed algorithm solves practical scale service composition.

Pedestrian dead reckoning is a method of localization that uses inertial sensors to estimate the travelled distance from the previous position. This method can estimate the position when GPS or WiFi is not available such as indoor. However, pedestrian dead reckoning is subject to cumulative errors. In this paper, we exploit the fact that most of lights are located regularly in indoor environments. We calibrate the estimated position by pedestrian dead reckoning by the estimated distance between lights when we judge that lights are located regularly from the information obtained from illuminance sensor. In our proposed method, we do not need to know the position of lights. We judge whether lights are regularly located from the change of illuminance obtained by illuminance sensor in smartphone. When we judge that lights are regularly located, we calibrate the estimated position by the average estimated distance between lights. Our experimental results show that the accuracy of localization with our proposed method is higher than that of the conventional one that uses only inertial sensors.

A satisfactory solution for data-intensive web service is required to overcome the problems of web service performance and scalability. We address these problems and propose service atomization, a new approach in atomizing information analysis that tackles large scale information. It uses a measure to ease the creation and collaboration of services. We call this measure service enthalpy, which comprises enthalpy of data intensification, functionality simplification, analysis shall owing and data uniformization.

Social Network Services(SNSs), which are maintained by a community of people, are among the popular Web 2.0 tools. Multiple users freely post their comments to an SNS thread. It is difficult to understand the gist of the comments because the dialog in an SNS thread is complicated. In this paper, we propose a system that presents the gist of information at a glance and basic information about an SNS thread by using Wikipedia. We focus on the table of contents (TOC) of the relevant articles on Wikipedia. Our system compares the comments in a thread with the information in the TOC and identifies contents that are similar. We consider the similar contents in the TOC as the gist of the thread and paragraphs in Wikipedia similar to the comments in the thread as comprising basic information about the thread. Thus, a user can obtain the gist of an SNS thread by viewing a table with similar contents.

A composite Web service designed based on abstract Web services, which define only interfaces, allows an application developer to select services required for his application only by setting endpoints for the atomic Web services. In open environment, however, the composite Web service configured in this manner may fail due to unique behaviors of the selected services. It is difficult for the designer of the composite Web service to prevent the failure because he does not know which services are selected and how they behave. On the other hand, the application developer is not authorized to modify the composite Web service due to the need to protect intellectual rights. Our solution is Service Supervision, which monitors and controls execution of composite Web services. Service Supervision makes the followings possible. 1) An application developer can control the behavior of a composite Web service by changing the execution state, even if the he is not authorized to modify the composite Web services. 2) A control pattern for coordinating Web services can be applied to various composite Web services in order to reduce the load imposed by designing control processes. In order to realize Service Supervision, we introduce meta-level control of a composite Web service. Moreover we then use the choreography to define the interaction protocols for the controls. The proposed framework is based on existing standard languages, WS-BPEL and WS-CDL. Therefore we can exploit existing tools and expertise of SOA engineers.

SNSs and blogs, both of which are maintained by a community of people, have become popular in Web 2.0. We call these content as "Community-type content." This community is associated with the content, and those who use or contribute to community-type content are considered as members of the community. Occasionally, the members of a community do not understand the theme of the content from multiple viewpoints, hence, the amount of information is often insufficient. It is convenient to present the user missed information. In this way, when Web 2.0 became popular, the content on the Internet and type of users are changed. We believe that there is a need for next-generation search engines in Web 2.0. We require a search engine that can search for information users are unaware of; we call such information as "content holes." In this paper, we propose a method for searching content holes in community-type content. We attempt to extract and represent content holes from discussions on SNSs and blogs. Conventional Web search technique is generally based on similarities. On the other hand, our content-hole search is a different search. In this paper, we classify and represent a number of images for different searching methods; we define content holes and as the first step toward realizing our aim, we propose a content-hole search system using Wikipedia.

Customization of systems is costly, but it is necessary to better meet the needs of multi-various tasks and requirements in the fields such as medical care, education, and so on. A machine translation system, for example, can provide a certain level of multilingual communication support if a human translator is absent; the translation system may need to be customized, however, if it is applied to a new application area. To realize easy customization of multilingual communication systems, we have developed the Language Grid Playground, an online language Web service platform which enables easy customization of various multilingual communication tools. We have established a stepwise process of classifying language services into a layered architecture and developing and accumulating lightweight building blocks that constitute communication tool components. Through this stepwise process, we were able to identify useful components in multilingual communication tools which can be flexibly combined to create new Web services. By implementing several tools and actually accumulating building blocks, we identified useful language service components and developed these components as programs which can be deployed as Web services.

In community-type content such as blogs and SNSs, we call the user's unawareness of information as a "content hole" and the search for this information as a "content hole search." A content hole search differs from similarity searching and has a variety of types. In this paper, we propose different types of content holes and define each type. We also propose an analysis of dialogue related to community-type content and introduce content hole search by using Wikipedia as an example.

Public Web services are not designed to be used with specific other Web services in a composite Web service. This leads to the following requirements for the proper control of a composite Web service; 1) adaptation to changes in Web services, 2) coordinating the contexts of internal processing, 3) flexible execution of human tasks. These controls must be applied following the policies of stakeholders, such as service providers. Some previous works proposed a method that adds processes to a composite Web service. Unfortunately, they fail to implement the controls needed and do not consider the policies of stakeholders. In this paper, we propose a meta-level architecture named Service Supervision, which controls a composite Web service based on the policies of stakeholders in order to achieve the requirements. To show the effectiveness of our architecture, we mention applications that currently apply Service Supervision to composite Web services consisting of machine translator Web services and human tasks.

This paper reports on our experiences with combining Heart of Gold and Language Grid technology to provide more language resources available on Web. Heart of Gold is known as middleware architecture for integrating deep and shallow Natural Language Processing components. The Language Grid is an infrastructure built on top of the Internet to provide distributed language services. Having Heart of Gold available as Web services in the Language Grid environment would contribute to interoperability among language services.

A framework is proposed to improve Web service performance based on context-aware communication. Two key ideas are introduced to represent a client context: (1) available protocols that the client can handle, and (2) operation usage that shows how the client uses Web service operations. We call our context aware approach a situated Web service (SiWS). We implemented and evaluated the SiWS and found that the overall performance was improved if more than three Web services were executed between context changes