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.

Interactive dialogue agents like smart speakers have become more and more popular in recent years. These agents are being developed on machine learning technologies that use huge amounts of language resources. However, many entities in specialized fields are struggling to develop their own interactive agents due to a lack of language resources such as dialogue corpora, especially when the end users need interactive agents that offer multilingual support. Therefore, we aim at providing a general design framework for multilingual interactive agents in specialized domains that, it is assumed, have small or non-existent dialogue corpora. To achieve our goal, we first integrate and customize external language services for supporting multilingual functions of interactive agents. Then, we realize context-aware dialogue generation under the situation of small corpora. Third, we develop a gradual design process for acquiring dialogue corpora and improving the interactive agents. We implement a multilingual interactive agent in the field of healthcare and conduct experiments to illustrate the effectiveness of the implemented agent.

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.

I am very happy to report that TSC has gained an Impact Factor (IF) of 3.520 and the 5-year IF of 4.245, both of which represent significant increases from the previous years. This further speaks to the global reputation of the journal and the amazing work done by the past EICs, all the current and past EB members, and reviewers - all of whom have volunteered their precious time despite their very busy schedule to support and contribute to the growth of this journal. I hope to count on your continued engagement for the future growth of this journal. Over this past year, several esteemed EB members have completed their terms of service to TSC after serving for several years. On behalf of the Services Computing community and the TSC EAB, I would like to thank the following Associate Editors who retired from TSC EB in 2017 for their invaluable service and contributions to the journal. Overall, I am very proud of the success that TSC has achieved in 2017. This would not have been possible without the continued support of the authors, readers, reviewers, TSC EAB, TSC EB, and the staff of IEEE and IEEE Computer Society. I look forward to exploring ways to further enhance the reputation and impact of our journal. I would love to hear your suggestions and comments, and I hope to have your continued support.

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 advantages and disadvantages of different crowdsourcing workflow structures have been analyzed. Existing studies on crowdsourcing workflow mainly focused on the quality control of the tasks using iterative and parallel processes. On the other hand, the characteristics of workflow considering the various task and crowdsourcing environments are not yet fully analyzed. Therefore, we face two difficulties in making use of workflow: the workflow optimization and the prior quality estimation. This research proposes the crowdsourcing workflow model for the set of improvement tasks, considering the ability distribution of crowdsourcing workers, improvement difficulty of the task, and the preference of the requester. In addition, we show the optimal workflow can be found by the search algorithm on the proposed model. The result of optimization can be used for both constructing the best workflow and estimating the quality of task performance. Experimental results under various conditions indicate that the degree of parallelism of the optimal workflow increases with the variance of worker ability. Also, iterative processes should be used when the average ability of the workers trends away from the middle level. These results include the existing research and therefore the model presented is useful in understanding crowdsourcing workflows.

Real-time transcripts generated by automatic speech recognition (ASR) technologies hold potential to facilitate non-native speakers’ (NNSs) listening comprehension. While introducing another modality (i.e., ASR transcripts) to NNSs provides supplemental information to understand speech, it also runs the risk of overwhelming them with excessive information. The aim of this paper is to understand the advantages and disadvantages of presenting ASR transcripts to NNSs and to study how such transcripts affect listening experiences. To explore these issues, we conducted a laboratory experiment with 20 NNSs who engaged in two listening tasks in different conditions: audio only and audio+ASR transcripts. In each condition, the participants described the comprehension problems they encountered while listening. From the analysis, we found that ASR transcripts helped NNSs solve certain problems (e.g., “do not recognize words they know”), but imperfect ASR transcripts (e.g., errors and no punctuation) sometimes confused them and even generated new problems. Furthermore, post-task interviews and gaze analysis of the participants revealed that NNSs did not have enough time to fully exploit the transcripts. For example, NNSs had difficulty shifting between multimodal contents. Based on our findings, we discuss the implications for designing better multimodal interfaces for NNSs.

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.

High-quality bilingual dictionaries are very useful, but such resources are rarely available for lower-density language pairs, especially for those that are closely related. Using a third language to link two other languages is a well-known solution and usually requires only two input bilingual dictionaries A-B and B-C to automatically induce the new one, A-C . This approach, however, has never been demonstrated to utilize the complete structures of the input bilingual dictionaries, and this is a key failing because the dropped meanings negatively influence the result. This article proposes a constraint approach to pivot-based dictionary induction where language A and C are closely related. We create constraints from language similarity and model the structures of the input dictionaries as a Boolean optimization problem, which is then formulated within the Weighted Partial Max-SAT framework, an extension of Boolean Satisfiability (SAT). All of the encoded CNF (Conjunctive Normal Form), the predominant input language of modern SAT/MAX-SAT solvers, formulas are evaluated by a solver to produce the target (output) bilingual dictionary. Moreover, we discuss alternative formalizations as a comparison study. We designed a tool that uses the Sat4j library as the default solver to implement our method and conducted an experiment in which the output bilingual dictionary achieved better quality than the baseline method.

Previous research has shown that audio communication is particularly difficult for non-native speakers (NNS) during multilingual collaborations. Especially when audio signals become distorted, NNS are overburdened by not only having to communicate with imperfect language skills, but also compensating for the deteriorations. Under these faulty audio conditions, NNS need to pay extra time and effort to understand the conversation. In order to give NNS more time to process conversations, we tested the insertion of silent gaps (from 0.2 to 0.4 seconds) between conversational turns. First, gaps were inserted into a previously taped conversation, resulting in a significant improvement of NNS's understanding of the conversation. Second, gaps were inserted during a real-time audio conference by adding artificial delay between native speakers. The results show that the added delays have a combination of beneficial and detrimental effects for both native and non-native speakers. The findings have implications towards how audio conferencing can be improved for NNS.

Machine translation is increasingly used to support multilingual communication. Because of unavoidable translation errors, multilingual communication cannot accurately transfer information. We propose to shift from the transparent channel metaphor to the human-interpreter (agent) metaphor. Instead of viewing machine translation mediated communication as a transparent channel, the interpreter (agent) encourages the dialog participants to collaborate, as their interactivity will be helpful in reducing the number of translation errors, the noise of the channel. We examine the translation issues raised by multilingual communication, and analyze the impact of interactivity on the elimination of translation errors. We propose an implementation of the agent metaphor, which promotes interactivity between dialog participants and the machine translator. We design the architecture of our agent, analyze the interaction process, describe decision support and autonomous behavior, and provide an example of repair strategy preparation. We conduct an English-Chinese communication task experiment on tangram arrangement. The experiment shows that, compared to the transparent-channel metaphor, our agent metaphor reduced human communication effort by 21.6%.

With the development of service-oriented computing environments, more and more Web services have become available. Service providers have to consider how to design composite services to meet requirements from various users. However, QoS of composite service is difficult to evaluate and predict due to the uncertainty and multiple metrics of QoS, which makes it necessary for user-centered design of composite services. Moreover, combining human services and Web services is becoming an important issue in service composition in cases that Web services cannot meet the requirements from users. Therefore, it is necessary to test the service composition environments for human-computer interaction and study human behaviors. In this paper, we address the above issues by proposing a participatory service design approach based on user-centered QoS considering a real-world case of field-based multi-language communication service design. To achieve this goal, we first describe the QoS model for service design considering users' requirements. Then, we propose the participatory service design process, consisting of service model refinement, participatory simulation, QoS evaluation and QoS data update. Finally, we use the field study of multi-language service design for Vietnamese agricultural knowledge communication to illustrate our proposed design methodology.

QoS computation plays an important role in Web service selection. It involves property value preprocessing aspect, user satisfaction calculation aspect, and aggregation of multiple QoS properties aspect. However, little attention has been paid to users participating in QoS computation. In this paper, we examine QoS computation from the angle of experienced users and novice users. An experienced user is able to be more active in providing configuration information such as expected boundary of a QoS property, distribution function of user satisfaction, and the aggregation weight of each QoS property. While a novice user has limited experience to do this. Based on the study of user-centered factors in QoS computation, we propose a user-centered QoS computation, which provides a new choice of normalization in property value preprocessing aspect, an approach of approximation in user satisfaction calculation aspect, and a weight suggestion way in aggregation of multiple properties aspect. A case study in translation service selection shows that the proposed user-centered QoS calculation is more efficient for novice users than random configuration, and much more efficient for experience users.

We present the research and work carried out towards analyzing patterns in creating teaching materials from the Web and how this leads to the model of the multilingual learning resource system using Web service technologies for today's educators, to efficiently share, reuse and translate existing teaching materials into minority languages and make new ones.

In this demonstration video, we introduce the Language Grid Toolbox, an open source multilingual communication tool, and two community sites based on the Language Grid Toolbox. The G30 Community Site aims to create a multilingual and multicultural community to accommodate the needs of Japanese and international students. The Pangaea Community Site is used by facilitators of NPO Pangaea located in different countries around the world to communicate in their native languages through machine translation supported multilingual BBS.

Consistent word selection in machine translation is currently realized by resolving word sense ambiguity through the context of a single sentence or neighboring sentences. However, consistent word selection over the whole article has yet to be achieved. Consistency over the whole article is extremely important when applying machine translation to collectively developed documents like Wikipedia. In this paper, we propose to consider constraints between words in the whole article based on their semantic relatedness and contextual distance. The proposed method is successfully implemented in both statistical and rule-based translators. We evaluate those systems by translating 100 articles in the EnglishWikipedia into Japanese. The results show that the ratio of appropriate word selection for common nouns increased to around 75% with our method, while it was around 55% without our method.

Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct truly useful MABS is agent modeling for reproducing realistic behaviors. In order to make agent models realistic, it seems natural to learn from human behavior in the real world. The challenge presented in this paper is to obtain an individual behavior model by using participatory modeling in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on the set of prior knowledge. In the real world, human drivers often perform unintentional actions, and occasionally, they have no logical reason for their actions. In these cases, we cannot rely on prior knowledge to explain them. We are forced to construct a behavior model with an insufficient amount of knowledge to reproduce the driving behavior. To construct such individual driving behavior model, we take the approach of using knowledge from others to complement the lack of knowledge from the target. To clarify that the behavior model including prior knowledge from others offers individuality in driving behavior, we experimentally confirm that the driving behaviors reproduced by the hybrid model correlate reasonably well with human behavior.

Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.

Content providers want to make recommendations across multiple interrelated domains such as music and movies. However, existing collaborative filtering methods fail to accurately identify items that may be interesting to the user but that lie in domains that the user has not accessed before. This is mainly because of the paucity of user transactions across multiple item domains. Our method is based on the observation that users who share similar items or who share social connections, can provide recommendation chains (sequences of transitively associated edges) to items in other domains. It first builds domain-specific-usergraphs (DSUGs) whose nodes, users, are linked by weighted edges that reflect user similarity. It then connects the DSUGs via the users who rated items in several domains or via the users who share social connections, to create a cross-domain-user graph (CDUG). It performs Random Walk with Restarts on the CDUG to extract user nodes that are related to the starting user node on the CDUG even though they are not present in the DSUG of the starting user node. It then adds items possessed by those users to the recommendations of the starting node user. Furthermore, to extract many more user nodes, we employ a taxonomy-based similarity measure that states that users are similar if they share the same items and/or same classes. Thus we can set many suitable routes from the starting user node to other user nodes in the CDUG. An evaluation using rating datasets in two interrelated domains and social connection histories of users as extracted from a blog portal, indicates that our method identifies potentially interesting items in other domains with higher accuracy than is possible with existing CF methods.

When people communicate in their native languages using machine translation, they face various problems in constructing common ground. This study investigates the difficulties of constructing common ground when multiparty groups (consisting of more than two language communities) communicate using machine translation. We compose triads whose members come from three different language communities--China, Korea, and Japan--and compare their referential communication under two conditions: in their shared second language (English) and in their native languages using machine translation. Consequently, our study suggests the importance of not only grounding between speaker and addressee but also grounding between addressees in constructing effective machine-translation-mediated communication. Furthermore, to successfully build common ground between addressees, it seems important for them to be able to monitor what is going on between a speaker and other addressees.

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.

In this paper, we present the concept for collaborative translation, where two non-bilingual people who use different languages collaborate to perform the task of translation using machine translation (MT) services, whose quality is imperfect in many cases. The key idea of this model is that one person, who handles the source language (source lan-guage side) and another person, who handles the target language (target language side), play different roles: the target language side modifies the translated sentence to improve its fluency, and the source language side evaluates its adequacy. We demonstrated the effectiveness and the practicality of this model in a tangible way.

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.

Real-time interactive virtual classroom with teleeducation experience is an important approach in distance learning. However, most current systems fail to meet new challenges in extensibility and scalability, which mainly lie with three issues. First, an open system architecture is required to better support the integration of increasing human-computer interfaces and personal mobile devices in the classroom. Second, the learning system should facilitate opening its interfaces, which will help easy deployment that copes with different circumstances and allows other learning systems to talk to each other. Third, problems emerge on binding existing systems of classrooms together in different places or even different countries such as tackling systems intercommunication and distant intercultural learning in different languages. To address these issues, we build a prototype application called Open Smart Classroom built on our software infrastructure based on the multiagent system architecture using Web Service technology in Smart Space. Besides the evaluation of the extensibility and scalability of the system, an experiment connecting two Open Smart Classrooms deployed in different countries is also undertaken, which demonstrates the influence of these new features on the educational effect. Interesting and optimistic results obtained show a significant research prospect for developing future distant learning systems.

Multilingual expression services (MESs) are available in many multicultural fields, such as education, medical care, and disaster prevention. In Japan, for example, local governments and NPOs create parallel-texts and provide expression services in such fields. However, these services present a common difficulty: the lack of parallel-texts. Service-computing technology has the potential to allow them to collaborate. In this paper, we show how Service Oriented Architecture (SOA) can be applied to multicultural activities. As a first step, we propose an architecture, in which language resources are available as web services, and MESs can obtain parallel-texts from the language web services. To achieve this, RDF metadata are attached to parallel-texts and stored in a separate RDF repository, with which users can describe what they need in detail. This arrangement allows MESs to select only the contents they need from the shared resources and show them to users. Our implementation confirms that the proposed architecture works well with two real services in use.

Globalization and advanced information technologies cause the increasing development of collaborative business. This paper proposes an interorganizational workflow modeling approach for collaborative business based on local process views. In the proposed model, instead of sharing a pre-defined global workflow view among organizations, each organization has its own understanding and preference of the whole collaboration and can manage its local workflow process distributively and autonomously. Further, we provide a methodology for interorganizational workflow collaboration by two phases: the bottom-up coordination phase and the top-down implementation phase. We illustrate the effectiveness of the proposed model and collaboration approach by a case of collaborative software development process.

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.

To increase the accessibility and usability of online language services, this paper explains the Language Grid, a service oriented collective intelligence initiative. The Language Grid is an infrastructure that allows end-users to create new language services for their intercultural / multilingual collaboration activities. To this end, language resources including data and programs are wrapped as Web services so that users can easily share and combine these Web services to create Web service workflows that support their own collaboration tools.

Even though multilingual communities that use machine translation to overcome language barriers are increasing, we still lack a complete understanding of how machine translation affects communication. In this study, eight pairs from three different language communities--China, Korea, and Japan--worked on referential tasks in their shared second language (English) and in their native languages using a machine translation embedded chat system. Drawing upon prior research, we predicted differences in conversational efficiency and content, and in the shortening of referring expressions over trials. Quantitative results combined with interview data show that lexical entrainment was disrupted in machine translation-mediated communication because echoing is disrupted by asymmetries in machine translations. In addition, the process of shortening referring expressions is also disrupted because the translations do not translate the same terms consistently throughout the conversation. To support natural referring behavior in machine translation-mediated communication, we need to resolve asymmetries and inconsistencies caused by machine translations.

In this paper, we describe new methodologies for reinforcing the social consensus building of the multicultural coexistence assistance using participatory simulation in civil societies. In this methodology, both human and intelligent software agents participate in a participatory gaming simulation. This simulation, which has mutual interaction between humans and agents, can achieve the gathering and analyzing of beneficial information to solve problems in multicultural civil societies. This type of simulation will help to provide knowledge for the formulation of social consensus, and to create new opportunities for discussion with people with different cultures and social backgrounds

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

Multilingual communities using machine translation to overcome language barriers are showing up with increasing frequency. However, when a large number of translation errors get mixed into conversations, users have difficulty completely understanding each other. In this paper, we focus on misconceptions found in high volume in actual online conversations using machine translation. We first examine the response patterns in machine translation-mediated communication and associate them with misconceptions. Analysis results indicate that response messages to include misconceptions posted via machine translation tend to be incoherent, often focusing on short phrases of the original message. Next, based on the analysis results, we propose a method that automatically predicts the occurrence of misconceptions in each dialogue. The proposed method assesses the tendency of each dialogue including misconceptions by calculating the gaps between the regular discussion thread (syntactic thread) and the discussion thread based on lexical cohesion (semantic thread). Verification results show significant positive correlation between actual misconception frequency and gaps between syntactic and semantic threads, which indicate the validity of the method.

Multilingual communities using machine translation to overcome language barriers are showing up with increasing frequency. However, when a large number of translation errors get mixed into conversation, it becomes difficult for users to fully understand each other. In this paper, we focus on misconceptions found in high volume in actual online conversations using machine translation. By comparing responses via machine translation and responses without machine translation, we extract two response patterns, which may be strongly related to the occurrence of misconceptions in machine translation-mediated communication. The two response patterns are that users tend to respond to short phrases of the original message and tend to trip on the wording of the original message when responding via machine translation.

We describe a method for automatically rating the machine translatability of a sentence for various machine translation (MT) systems. The method requires that the MT system can bidirectionally translate sentences in both source and target languages. However, it does not require reference translations, as is usual for automatic MT evaluation. By applying this method to every component of a sentence in a given source language, we can automatically identify the machine-translatable and non-machinetranslatable parts of a sentence for a particular MT system. We show that the parts of a sentence that are automatically identified as nonmachine-translatable provide useful information for paraphrasing or revising the sentence in the source language, thus improving the quality of the final translation.

Many studies have been conducted on supporting communication in home and office spaces, but relatively few studies have explored supporting communication in large-scale public spaces, despite the importance of such environments in our daily lives. We propose a transcendent means of communication as an emerging style in this pervasive computing era: a system that allows administrative staff to effectively help visitors in large-scale public spaces. The visitors' context is used to provide a bird's-eye view of a simulated public space for the staff to grasp the situation and point at a particular location within the view to indicate the visitors they intend to address. The results of an experiment showed synergic effects between the bird's-eye view and the first-person one in determining the spatial movements of people. In indoor and outdoor large-scale public spaces, a central railway station and a park, we installed our prototypes and learned the implications of its use.

We have integrated technologies related to virtual social interaction, e.g. virtual environments, visual simulations, and lifelike characters. In our previous efforts to integrate them, the asymmetry between agents and avatars made the systems too complex to be used widely. Another crucial problem we faced is that it took a long time to construct agents that play various roles, since each role needs its specific behavioral repertory. To eliminate these problems, we developed a general-use platform, FreeWalk/Q, in which agents and avatars can share the same interaction model and scenario. We created a control mechanism to reduce the behavioral differences between agents and avatars, and a description method to design the external role rather than the internal mechanism. In the development, we found that it was necessary to prepare several topologies of control mechanism and several granular levels of description method.

Domain-specific Web search engines are effective tools for reducing the difficulty experienced when acquiring information from the Web. Existing methods for building domain-specific Web search engines require human expertise or specific facilities. However, we can build a domain-specific search engine simply by adding domain-specific keywords, called "keyword spices," to the user's input query and forwarding it to a general-purpose Web search engine. Keyword spices can be effectively discovered from Web documents using machine learning technologies. The paper describes domain-specific Web search engines that use keyword spices for locating recipes, restaurants, and used cars.

For realizing a robot working in human society, interaction with humans is the key issue. We have developed a robot that interacts with humans based on visual recognition. This robot has two vision systems: an omnidirectional vision system for acquiring necessary visual information and a binocular stereo vision system. The binocular vision system indicates what the robot is looking at and is not used for locomotion. Gaze control plays an important role in human-robot interaction. This paper reports how the robot's gaze influences subjects' impressions of the robot. With a statistically significant number of robot observers, we employed psychological methods, the semantic differential method (SD), and factor analysis. This careful psychological analysis of robot impressions is the first trial in robotics. Through the experiment, we have found that the impressions mainly consist of four factors: familiarity, enjoyment, activity, and performance. The computer skills of subjects affect their impressions of the robot.

A digital city is a social information infrastructure for urban life (including shopping, business, transportation, education, welfare and so on). We started a project to develop a digital city for Kyoto based on the newest technologies including cooperative information agents. This paper presents an architecture for digital cities and shows the roles of agent interfaces in it. We propose two types of cooperative information agents as follows: (a) the front-end agents determine and refine users' uncertain goals, (b) the back-end agents extract and organize relevant information from the Internet, (c) Both types of agents opportunistically cooperate through a blackboard. We also show the research guidelines towards social agents in digital cities; the agent will foster social interaction among people who are living in/visiting the city.

In robot navigation, one of the important and fundamental issues is to find positions of landmarks or vision sensors located around the robot. This paper proposes a method for reconstructing qualitative positions of multiple vision sensors from qualitative information observed by the vision sensors, i.e., motion directions of moving objects. In order to directly acquire the qualitative positions of points, the method proposed in this paper iterates the following steps: 1) observing motion directions (left or right) of moving objects with the vision sensors, 2) classifying the vision sensors into spatially classified pairs based on the motion directions, 3) acquiring three point constraints, and 4) propagating the constraints. Compared with the previous methods, which reconstruct the environment structure from quantitative measurements and acquire qualitative representations by abstracting it, this paper focuses on how to acquire qualitative positions of landmarks from low-level, simple, and reliable information (that is, "qualitative"). The method has been evaluated with simulations and also verified with observation errors.

This paper introduces a new application area for agents in the computer interface: the support of human-human interaction. We discuss an interface agent prototype that is designed to support human-human communication in virtual environments. The prototype interacts with users strategically during conversation, spending most of its time listening. The prototype mimics a party host, trying to find a safe common topic for guests whose conversation has lagged. We performed an experimental evaluation of the prototype's ability to assist in cross-cultural conversations. We designed the prototype to introduce safe or unsafe topics to conversation pairs, through a series of questions and suggestions. The agent made positive contributions to participants' experience of the conversation, influenced their perception of each other and of each others' national group, and even seemed to effect their style of behavior. We discuss the implications of our research for the design of social agents to support human-human interaction.

Open-source software, usually created by volunteer programmers dispersed worldwide, now competes with that developed by software firms. This achievement is particularly impressive as open-source programmers rarely meet. They rely heavily on electronic media, which preclude the benefits of face-to-face contact that programmers enjoy within firms. In this paper, we describe findings that address this paradox based on observation, interviews and quantitative analyses of two open-source projects. The findings suggest that spontaneous work coordinated afterward is effective, rational organizational culture helps achieve agreement among members and communications media moderately support spontaneous work. These findings can imply a new model of dispersed collaboration.

This paper discusses the implementation using mobile agent and the performance of the market computing to allocate network quality of service most efficiently, based on users' preference. Though the protection of users' private preference and the efficient calculation are both important, these two requirements often contradict each other. By implementing QoS Market, a market-based network resource allocation system, using mobile agents, the communication time between agents and an auctioneer, which is the largest overhead in market computing, is reduced dramatically without leeking users' privacy information. The result of experiments show that a mobile agent approach is more efficient than others, and the overhead of one market computing cycle in this system is about 450 msec, which means that the system achieves sufficient efficiency.

Proposes a robot architecture that enables us to progressively develop a robot. The architecture consisting of situated modules has merits of both the traditional function-based and behavior-based architectures in addition to the merit in the development. We have developed a robot based on the architecture. By reporting the development process, the paper discusses advantages of the proposed architecture.

We develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in distributed artificial intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems.

Article FreeWalk: supporting casual meetings in a network Share on Authors: Hideyuki Nakanishi Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 Japan Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 JapanView Profile , Chikara Yoshida Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 Japan Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 JapanView Profile , Toshikazu Nishimura Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 Japan Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 JapanView Profile , Toru Ishida Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 Japan Department of Information Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-01 JapanView Profile Authors Info & Claims CSCW '96: Proceedings of the 1996 ACM conference on Computer supported cooperative workNovember 1996 Pages 308–314https://doi.org/10.1145/240080.240314Published:16 November 1996 82citation564DownloadsMetricsTotal Citations82Total Downloads564Last 12 Months19Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The state space of a sensor-based robot in the most previous works has been determined based on human intuitions, however the state space constructed from human viewpoints is not always appropriate for the robot. The robot has a different body, sensors, and tasks, therefore, we consider the robot should have an original internal state space determined based on actions, sensors, and tasks. This paper proposes an approach to construct such a robot oriented state space by statistically analyzing the actions, sensor patterns, and rewards given, as results of task executions. In the state space construction, the robot creates sensor pattern classifiers called empirically obtained perceivers (EOPs) the combinations of which represents internal states of the robot. We have confirmed that the robot can construct original state spaces through its vision sensor and achieve navigation tasks with the obtained state spaces in a complicated simulated world.

This paper investigates real-time bidirectional search (RTBS) algorithms, where two problem solvers, starting from the initial and goal states, physically move toward each other. To evaluate the RTBS performance, two kinds of algorithms are proposed and are compared to real-time unidirectional search. One is called centralized RTBS where a supervisor always selects the best action from all possible moves of the two problem solvers. The other is called decoupled RTBS where no supervisor exists and the two problem solvers independently select their next moves. Experiments on mazes and n-puzzles show that: 1) in clear situations decoupled RTBS performs better, while in uncertain situations, centralized RTBS becomes more efficient; and 2) RTBS is more efficient than real-time unidirectional search for 15-and 24-puzzles but not for randomly generated mazes. It is shown that the selection of the problem solving organization is the selection of the problem space, which determines the baseline of the organizational efficiency; once a difficult problem space is selected, the local coordination among problem solvers hardly overcome the deficit.

Presents the basic concepts behind AgenTalk, a language used for describing coordination protocols in multiagent systems. Many coordination protocols, such as the contract net protocol, have been proposed, and many application-specific coordination protocols are expected to be required as soon as the building of more software agents begins. Thus, a language for defining and implementing such coordination protocols plays a crucial role in the development of multiagent systems. By incorporating an inheritance mechanism, AgenTalk allows coordination protocols to be incrementally defined and easily customized to suit various application domains. Its capability is demonstrated by describing the contract net and multistage negotiation protocols in AgenTalk using its inheritance mechanism.

Considers the case of heuristic search where the goal may change during the course of the search. For example, the goal may be a target that actively avoids the problem solver. The authors present a moving-target search algorithm (MTS) to solve this problem. The authors prove that if the average speed of the target is slower than that of the problem solver, then the problem solver is guaranteed to eventually reach the target in a connected problem space. The original MTS algorithm was constructed with the minimum operations necessary to guarantee its completeness, and hence is not very efficient. To improve its efficiency, the authors introduce ideas from the area of resource-bounded planning into MTS, including 1) commitment to goals, and 2) deliberation for selecting plans. Experimental results demonstrate that the improved MTS is 10 to 20 times more efficient than the original MTS in uncertain situations.>

Recent production system applications have been experiencing exceedingly difficult software maintenance problems. This is because the control of rule firings has been buried in the production rules themselves. To cope with this problem, we propose a meta-level control architecture for production systems, where procedural programming languages, such as Lisp and C, are employed to explicitly describe the control plans of production systems. The key idea of the architecture is to view production systems as a collection of independent rule processes, each of which monitors the global database and performs actions when its conditions are satisfied by the database. Procedural Control Macros (PCMs), which are based on C.A.R. Hoare's (1978) CSP, are then introduced into procedural programming languages to establish communication with the collection of rule processes. Although the PCMs are simple and easy to implement, the readability and maintainability of production system applications are greatly enhanced. Together with the original facilities of procedural languages, the PCMs enable users to efficiently specify the control plans for production systems. Furthermore, since control information is gathered into control plans, production rules can be declarative and thereby application-independent. This new feature makes it possible to develop large-scale shared rule bases.>

As the scale of rule-based expert systems increases, the efficiency of production systems becomes a pressing concern. Recently developed production systems thus enable users to specify an appropriate ordering or clustering of join operations. Various efficiency heuristics have been introduced to optimize production rules manually. However, since the heuristics often conflict With each other, users have to proceed by trial and error. The problem addressed in this paper is how to automatically determine efficient join structures for production system programs. Our algorithm does not directly apply efficiency heuristics to programs, but rather enumerates possible join structures under various constraints and selects the best one. For this purpose, the cost model for production systems is introduced to estimate the run-time cost of join operations. Evaluation results demonstrate that the proposed algorithm can generate programs that are as efficient as those obtained by manual optimization, and thus can reduce the burden of manual optimization.>

The authors demonstrate the enhancement in maintainability made possible by introducing meta-level control architectures to production systems. They assess the amount of effort expended in upgrading a rule base through the range referred to by maintenance staff for rule base upgrading. The rule base of an intelligent CAI system currently in operation that employs the meta-level control architecture and an identical one without the architecture have been upgraded so that they satisfy the same upgraded specifications. The measured range confirms that the meta-level control architecture curtails the effort expended in upgrading the rule base by 19%. In particular, the effort expended in upgrading control flow is reduced by 41%.

Viewing cooperative distributed problem solving (CDPS) as distributed constraint satisfaction provides a useful formalism for characterizing CDPS techniques. This formalism and algorithms for solving distributed constraint satisfaction problems (DCSPs) are compared. A technique called asynchronous backtracking that allows agents to act asynchronously and concurrently, in contrast to the traditional sequential backtracking techniques used in constraint satisfaction problems, is presented. Experimental results show that solving DCSPs in a distributed fashion is worthwhile when the problems solved by individual agents are loosely coupled.>

The authors introduce two reorganization primitives, composition and decomposition, which change the population of agents and the distribution of knowledge in an organization. To create these primitives, they formalize organizational knowledge, which represents knowledge of potential and necessary interactions among agents in an organization. The authors develop computational organizational self-design (OSD) techniques for agents with architectures based on production systems to take advantage of the well-understood body of theory and practice. They first extend parallel production systems, where global control exists, into distributed production systems, where problems are solved by a society of agents using distributed control. Then they introduce OSD into distributed production systems to provide adaptive work allocation. Simulation results demonstrate the effectiveness of the approach in adapting to changing environmental demands. The approach affects production system design and improves the ability of build production systems that can adapt to changing real-time constraints.>

To improve the performance of forward chaining production systems, a new parallel execution model in which multiple production rules are fired in parallel in each production cycle is proposed. Solutions for three major problems in parallel rule firing are provided. The first solution, interference analysis, detects cases where a parallel firing result is different from the result of any sequential firing. Based on a data dependency graph of production systems, general techniques applicable to both compile- and run-time interference analyses are proposed. The second solution, parallel firing algorithms, achieve the parallel firing on multiple processor architectures. An efficient selection algorithm is provided to select multiple rules to be fired in parallel by combining the compile- and run-time interference analysis techniques. The parallel programming environment, the third solution, provides language facilities to enable programmers to make full use of potential parallelism without considering the internal parallel mechanisms. A parallel firing simulator is also implemented to estimate the effectiveness of parallel firings of production system programs. The effectiveness of parallel rule firings on several production system applications is evaluated. Results show that the degree of concurrency can be increased by a factor of 2-9. The sources of parallelism are investigated based on the evaluation results.>

This paper describes a global compaction algorithm which can produce efficient microprograms with respect to both space and time. The algorithm depends upon a generalized data dependency graph (GDDG), which can integratedly express the concurrency of microorders and their mobility past the boundaries of basic blocks, as well as the control flow for a microprogram. In the algorithm an initial GDDG is first built up from a source microprogram consisting of a sequence of microorders, and this is transformed by repeated application of a set of microscopic transformation rules to the graph's edges until all of them have been examined. Then microorders are packed into microinstruction fields so that the more frequently executed basic blocks may contain as few microinstructions as possible. The intrinsic nature of the control flow's joins and forks allows determination of which basic block should accommodate those microorders that can be placed in more than one basic block.