We present a visual traceability interface that enables users to verify AI-generated answers by navigating from natural-language outputs to original meeting audio. Even with textual evidence, RAG users still struggle to verify whether answers come from specific utterances or from transcription or summarization errors. Our system introduces a four-level interaction model connecting (1) AI-generated answers, (2) contributing meetings, (3) summarized meeting chunks, and (4) time-aligned utterance-level audio. The interface lets users traverse this evidence chain and directly play the corresponding audio segments. We demonstrate how audio-grounded traceability supports answer verification, error detection, and trust calibration in AI-assisted meeting analysis.

In recent years, Japan has grappled with a rapidly aging population, leading to pressing issues in emergency medical care and an uptick in ambulance services. Our research team, collaborating with the Kobe City Fire Department, aims to address this by devising a predictive model for strategic deployment of medical services. By analyzing extensive EMS data and population demographics, we aim to forecast long-term emergency transport demand accurately, bypassing machine learning algorithms. The results of the forecast revealed regional disparities in future emergency demand, providing indicators for optimal deployment of emergency resources and strategic planning for emergency services.

In pursuit of realizing Society 5.0, this study explores efficient development methods for services tailored to individual user needs. The rapid evolution of digital devices and the diversification of user demographics have led to swiftly changing service needs, necessitating increased personalization. This research focuses on developing technologies that enable service development based on a deep understanding of specific user needs. By utilizing a virtual agent (VA)-based interactive need extraction system developed in previous research, and leveraging the Sharing Up cycling Cases with Context and Evaluation for Efficient Software Development System (SUCCEED System), we aim to automate the extraction of user needs and provide insights to developers. We propose an interactive need extraction method for novel, undeveloped services and a method for obtaining development cases based on these needs, thereby contributing to the efficiency of personalized service development approaches.

The increase in single-person households and the onset of “COVID-19 frailty” due to COVID-19 have become problems. Our research group focuses on technology support and develops virtual agents using speech recognition technology. In this service, we examined how to promote self-care by having the agent listen to the older person and increase conversation opportunities. However, this service has the problem that it cannot increase opportunities for dialogue with family and friends, which leads to mutual support. In this study, we aim to propose and implement a service called “Easy Video Chat Service” that increases the opportunities for conversation with others through a virtual agent listening service for the elderly person at home.

Japan is facing the challenge of an aging society with a significant increase in the number of dementia patients, making it an urgent social issue. Ac-cording to the Ministry of Health, Labour and Welfare, it is estimated that by 2040, the population of elderly individuals aged 65 and above will reach 35.3 % of the total population, with over 7 million of them being dementia patients. Against this backdrop, measures for dementia prevention and support for elderly dementia patients are deemed necessary. Memory impairment is one of the core symptoms of dementia, and current strategies include recording and presenting information by caregivers and using memory aid tools such as notepads for rehabilitation. However, these methods have their limitations, prompting the need for new memory aid techniques that reduce caregiver burden and are sustainable for dementia patients. Therefore, recording daily activities and preserving them as docu-ments is proposed as a method for memory assistance. This study aims to verify the effectiveness of this technique by setting evaluation criteria, conducting self-recordings of daily activities, creating explanatory texts by multiple individuals, and subjectively eval-uating texts generated by recognition AI. Based on the results, the study examines the extent to which the texts generated by recognition AI are useful for memory assistance and provides insights accordingly.

In this study, we describe the design and implementation of a smart service for automated management of hybrid meeting spaces. Hybrid formats, where participants interact both online and offline, are becoming common in modern meeting rooms. To meet this new need, we have developed a remote task execution service using VNC and WebDriver. This service automates the launching of online services and website operations during meeting preparations, greatly reducing the time and effort required by the user. Specifically, the service provides functions to automatically execute multiple remote control tasks, such as launching Zoom and a web site for meeting minutes. In this paper, we confirm the actual operation of the system through a prototype implementation in our laboratory.

With the popularization of smartphones, SNS, which allows easy message exchange, has become popular. On the other hand, a metaverse has attracted attention recently. In the metaverse, participants interact with each other through avatars in a virtual space. Therefore, if we can show participants the existing SNS space as the metaverse, we can provide participants with more intuitive use cases. This manner is beneficial for elderly people who are unfamiliar with using smartphones. In this paper, we propose LINE metaverse. LINE is the most popular messaging application in Japan. The LINE metaverse is characterized by replacing the bot mechanism used for marketing using SNS with a metaverse agent. In the LINE metaverse, elderly people can exchange messages via avatars on the metaverse.

The number of elderly people living at home (requiring nursing care or living alone) has been increasing due to the aging of society in recent years. As the elderly stay at home for long periods of time, their connection with the local community becomes less and less, and loneliness becomes a serious problem. In our research group, we are developing a system that allows elderly people to interact with the virtual agent. The purpose of this paper is to develop a method for generating personalized dialogue in order to realize continuous and sentimental dialogue. As the approach, morphological analysis and sentiment analysis are performed on the dialogue content (text) obtained from speech recognition. Then, we present a new method for generating personalized dialogues based on related conversation log summarization. In this way, it is promising to realize smarter personalized dialogues that do not rely on manual creation.

In this study, we aim to realize an assistive technology that can present necessary information to elderly people with cognitive concerns or dementia in a way that is adaptable to their home life. To achieve this goal, we propose ALPS (Assisted Living by Personalized Speaker), a system that presents appropriate information according to various locations and times in the home. We installs IoT speakers with motion sensors at key locations in the home, and by linking them to ECA(Event-Condition-Action) rules in the cloud, ALPS provides information based on location and time in voice. We implemented a prototype of the proposed ALPS and conducted a case study of two elderly people. As a result, it was found that by defining ECA rules for each problem, the system can present information according to the individual’s lifestyle.

In this study, we conducted a multifaceted analysis of heatstroke cases using the emergency transported big data in Kobe City, and discovered the characteristics of heatstroke incidents in Kobe City in 2020 that differed from previous years. As a result of the analysis, it was found that the peak period of WBGT in 2020 was later than usual, and it was found that the peak period of WBGT is later than usual in 2020, and the occurrences of heatstroke in 2020 is characterized by an increase in the occurrences of heatstroke in people over 65 years old and outdoors, and a decrease in the occurrences of heatstroke in people under 65 years old and indoors.

In advanced smart systems, heterogeneous distributed services are integrated dynamically, based on various contexts in cyber/physical worlds. Currently, the logic of such service integration is implemented specifically in each application. Thus, as the way of integration becomes sophisticated, the complexity and development effort of the application become quite expensive. In this research, we propose a service, called Uni-messe (Unified Rule-based Message Delivery Service), which provides context-aware service integration in an application-neutral manner. Based on ECR (Event-Condition-Routing) rules, Uni-messe routes an event message from an application to a designated application based on a specified condition. We first present the architecture of Uni-messe with publish/subscribe messaging platform. We then propose the data model of the ECR rules based on 6W1H (Who/Whom/When/Where/What/Why/How) event model. Finally, we define the condition evaluation and routing behaviors. Using the implementation of Uni-messe, we demonstrate automatic curtain control and daily routine automation in a smart home. Since Uni-messe de-couples the rule-based service integration from individual applications, it allows developers and users to implement and reuse flexible integration efficiently.

The personal adaptation of services, in which a system provides services according to the preferences and needs of individual users, is a key to the realization of the emerging Society 5.0. The personal adaptation of systems has been implemented through personal settings by users. However, it is very difficult for users who are not familiar with ICT to manually define the settings that meet their needs. In this paper, we therefore propose a new notion of smart system: Smart System with Interactive Personalization (SSIP). In SSIP, the system and the user have continuous and interactive conversations during the system operation. In the dialogue, the user tells his or her needs and the system introduces its functions. In this way, the user and the system understand each other and dynamically co-create personal settings. In this paper, in order to define SSIP, we present three functional requirements that the system must meet. We also characterize SSIP from the quality viewpoint by quality in use of the international standard SQuaRE. Finally, as a case study, we take the Mind Monitoring Service being developed by our group, and apply the proposed SSIP framework to individual adaptation of the incentive provision feature.

Japan is currently facing super-aging society and the assistive technology for self-help and mutual aid of the elderly is becoming urgent. The purpose of this paper is to build a system that can execute various services through dialogue with agents, in order to support elderly people who cannot use Internet services due to lack of access to devices. To achieve the goal, we discuss a framework for executing microservices using dialogue agents. More specifically, the framework consists of the next two essential elements: (1) Managing user information for the various microservices centrally. (2) Configuring the behavior of the agents when executing the services correctly. In the proposed method, we first discuss each element in detail. Then, we demonstrate the effectiveness of the framework by applying it to the actual integration of a dialogue agent and several microservices.

Currently, Japan is entering a super-aged society. It is difficult for the elderly at home to deal with their problems in their lives by self aid and mutual aid. So this study aims to propose and implement Compass4SL, a problem and solution sharing service for the elderly at home. We design and implement the system according to the following steps. First, we find the use cases and entities. Then, we design the overall system structure with a layered architecture. Finally, we implement the system as a Web application based on the obtained design using Java and Spring Boot framework.

We have been developing "Facial expression sensing service" for emotional analysis and quantitative evaluation of care based on subtle facial movements and conducted a preliminary experiment about its practicality. In this research, focusing both obtaining facial expression data and searching for an efficient care method, to elderly people can activate themselves and relieve their stress, we have developed "Video player service" that can easily play videos and automatically collect facial expression data. After developing the service, we have asked people who engage in elderly care to try it and obtained feedback. As a result, we received favorable comments for the usefulness of the service, and we were able to get facial expression data for four people.

To support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based monitoring, the daily recording and externalization of mental states. In the previous research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, a chatbot asks a user specific questions to acquire his/her mental state. Based on the answers, the service assesses the mental state. In this research, we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice to improve the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmed that the weekly feedback encouraged self-care consciousness and increased motivation of subjects.

To support in-home long-term care, we are studying techniques of Mind Sensing, which externalizes internal states of elderly people as words through conversations with agents or robots. We have previously developed a prototype system of Mind Sensing, integrated with an activity recognition system and an LINE chatbot. However, the system was tightly coupled with the fixed systems, it was difficult to add or change the setting of questions from the chatbot to individual elderly people.

In order to support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, as well as to encourage their ability of self-care. However, the technical challenges include the limitations on human interventions and sensor-based monitoring, as well as daily recording and externalization of mental states. In this research, we propose Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, an agent asks a user specific questions to acquire his/her mental state. Based on the answers, the service then assesses the mental state and sends feedback. We implement a prototype service, and evaluate the feasibility of the service through a preliminary experiment. The results show that data characterizing mental states of individual subjects was obtained successfully, and that some subjects externalized their minds by feedback from the service.

Worldwide, there has been an increase in the number of individuals that live alone in one-person households (OPHs). Compared to those living with family, people in OPHs easily lose control of life rhythm. Given that the disturbance of life rhythm leads to chronic disease, they have a higher risk of illness. As such, there is an urgent demand for assistive technology that allows people in OPHs to enjoy healthy, high-quality lives. For decades, there has been significant research and development of smart systems to assist people at home. However, there are still limitations on the practical use of these systems in actual OPHs. More specifically, they are often too intrusive to the lifestyle of users or home objects. In addition, they are often expensive to deploy and maintain. Furthermore, these systems are unable to evaluate the quality of life rhythm. As a result, it is difficult for individual users to determine what their healthy life rhythms should be, and how to improve their current situation.The goal of research is to develop a new smart system for OPHs that can minimize intrusiveness and cost, while also facilitating the assessment of life rhythms of individual users. The new system collects user position and environmental data inside the house in a non-intrusive way, using affordable IoT devices. From this data, the system then recognizes the daily activities of the user. Based on these activities, eventually, the system can quantitatively evaluate the users life rhythms and provide practical advice for maintaining a healthy life.

The emerging deep learning technology is a promising means for context recognition with multimedia data. We are interested in using the deep learning with images for context recognition in smart homes. In the home context recognition, the room layout, the environment, and the contexts to be recognized are different from one household to another. Therefore, a unique recognition model is required for every different household. For this, if we take a naive approach that uses the deep learning directly, a huge amount of labeled images are required, which is practically impossible for general households. The goal of this research is to develop an image-based context recognition method that is affordable at home. In the proposed method, we exploit a cognitive API which performs general image recognition, and retrieve the information within the image as text. By using the text as features, we classify the context with ordinal supervised machine learning. Compared with the expensive approach with deep learning, the proposed method uses generic image recognition of the cognitive API, and light-weight machine learning. As a result, the context recognition customized for every household can be achieved with much less effort.

Social coding platforms (SCPs) have realized spontaneous software evolution, where new source code and ideas are spontaneously proposed by altruistic developers. Although there are many projects operated by active communities performing spontaneous evolution, it is yet unclear that how such successful projects and communities have been formed and governed. In this paper, we propose a method that can investigate the history of every project in the SCP. Introducing the concept of project as a city, we consider every project in the SCP as a city, where a government and citizens develop a city through collaborative activities. We then identify essential attributes that characterize a state of a city. For each attribute, we develop metrics that quantity the state S(p, t) of a project p at time t. An experimental evaluation investigating GitHub projects of famous code editors shows that the proposed metrics well visualize the history of the projects from essential perspectives of a city.

It is known that the disturbance of daily life rhythm leads to chronic disease. Hence, it is important for everyone to keep a healthy rhythm. Owing to emerging technologies of smart phones and IoT, many studies and products recognizing personal daily activities exist. However, there is few research to evaluate if the life rhythm, characterized by the daily activities, is good or bad for the person. As a result, it is hard for individuals to understand what to be their own healthy life rhythms, and how to improve the current situation. To cope with the problem, we are developing a system that quantitatively assesses user's life rhythm based on the daily activity log and the self-assessment of QoL (Quality of Life). In this paper, we present a method of measuring user's life rhythm by analyzing sleep and eating log data. We then construct a personalized quantitative assessment model of life rhythm using regression analysis. We conduct a preliminary experiment in an actual apartment. Based on the derived model, we find personalized advice on daily activities to maintain healthy life rhythm of the resident.

In our previous research, we proposed a security information service, called PRISM, which personalizes the incident information based on living area of individual users. PRISM computes the severity of a given incidents based on distance, time, and type. It then visualizes the incident with the severity on a heat map. In this paper, we extend the functionality of PRISM, in order to analyze street crimes around living area in more details. More specifically, we add three new features to PRISM: showing a past heat map, showing a heat map focused on specified type of incidents, and showing statistics of incidents for every type. Using the extended features, we visualize the dynamic transition of street crimes in a specific area and the whole region. The visualization also reveals the ecology of wild boars. Finally, we also show that PRISM can be used to compare different districts by statistics of street crimes.

Recently, many studies about Activities of Daily Living (ADLs) recognition have been conducted, which can be applied to many real-life, human-centric problems such as eldercare and healthcare. In our previous work, we proposed an ADLs recognition system based on non-intrusive environment sensing for people in One-person Household (OPH). However, the proposed recognition system did not perform well, the micro-averaged and macro-averaged precision of most of the recognition models was only around 60%. In order to improve the quality of the system, in this article, we propose a new ADLs recognition system by integrating environment sensing and Bluetooth Low Energy (BLE) beacon technology and evaluate the new version of the ADLs recognition model by comparing the experimental data collected from a real resident in OPH.

Location information is expressed by various formats that depend on services. Location information is divided into two categories: absolute location information (such as latitude/longitude and address), and relative location information (such as distance and direction). Each service that utilizes location information defines location information system individually. Therefore, sharing location informations between the services is difficult. Consequently, reusability of location information decreases. Then, we consider new common location information system, which can be converted from location information of various systems and expresses location more flexible. In this study, we propose probabilistic location information, which expressed as a combination of area and existence probability. Moreover, we propose the algorithm which calculates probabilistic location information based on geographic coordinate information and pass-by information (PLPA-GP).

Local governments in Japan recently provide security information services for residents, which deliver regional incident information using Email or Web. However, since the conventional services usually provide “one-for-all” information. users tend to miss important incidents within the flood of information. In this paper, we propose a new security information service, called PRISM (Personalized Real-time Information with Security Map). For given incident information and user's living area, PRISM first computes severity of the incident, based on distance, time, and type of the incident. It then visualizes the incidents with the severity on a heat map. Thus, PRISM provides real-time personalized information adapted to individual situation of users. To illustlate the feasibility, we implement PRISM as a Web application using Hyogo Bouhan Net, and Kobe city facility open data.

Pervasive sensing technologies are promising for increasing one-person households (OPH), where the sensors monitor and assist the resident to maintain healthy life rhythm. Towards the practical use, the recognition of activities of daily living (ADL) is an important step. Many studies of the ADL recognition have been conducted so far, for real-life and human-centric applications such as eldercare and healthcare. However, most existing methods have limitations in deployment cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this paper presents a new indoor ADL recognition system especially for OPH. To minimize the deployment cost as well as the intrusions to user and house, we exploit an IoT-based environment-sensing device, called Autonomous Sensor Box (SensorBox) which can autonomously measure 7 kinds of environment attributes. We apply machine-learning techniques to the collected data, and predicts 7 kinds of ADLs. We conduct an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADL recognition with more than 88%, by carefully developing the features of environment attributes.

A Diagnosis explains why a program fails. Making a correct diagnosis is necessary for debugging a practical program, but it requires many efforts to comprehend the program. Supporting program comprehension for making a diagnosis is thus very important Maintainers usually try to comprehend their programs by interacting with debugging tools that permit access to runtime information. These tools should be evaluated based on how they help maintainers' comprehension during the interactions with them. However, most tools are evaluated based only on the final results of maintenance tasks. We are currently developing a method to evaluate the contribution of a supporting tool to a maintainer's internal comprehension process based on the relevance to finding the chain of infection. The relevance is calculated using the trace analysis technology we have developed. In this paper we explain the overview of our evaluation method, and shows its validation by an exploratory experiment.

To realize indoor environmental sensing, which is a key technology of providing smart services in smart city, with low cost, our research group has proposed a small IoT device named sensor box. In the previous sensor box, however, it is difficult to deploy for the smart city with some problems. In this paper, we propose an indoor environment sensing service using autonomous sensor box to adapt the previous sensor box for the smart city. To confirm the effectiveness of proposed service, we deploy autonomous sensor boxes on practical indoor environments.

To fix various dependencies of application development using pass-by detection by a mobile device, we propose Pass-by Framework that handles data with standardization. In this study, we evaluate effects of performance of pass-by detection by differences in methods of implementation the sonar of Pass-by Framework. Therefore, we develop pass-by application using Bluetooth Low Energy as a first effort. We then conduct evaluation experiments for confirmation of change pass-by detection behavior depends on the difference of parameters.

In recently years, many care robots have received a lot of attention to help elderly people. However existing care robots have difficult to adopt personalization. For instance, some programmers have to customize robot program to meet needs of each elderly. Even if a care robot which has a feature of machine learning, it takes a long time to learn a preference for each elderly. In this paper, our goal is to deploy a smart care service integration agent that provides a personalization and integration for each elderly people. Our proposed service consists of three essential components, Virtual Care Giver (VCG), Virtual Care Personalizer (VCP) and Care Template. VCG is a robot agent, where executes care tasks in each home. The VCG is offered care tasks based on care template which Virtual Care Personalizer (VCP) generates. Virtual Care Personalizer (VCP) manages and generates personalization of care tasks the on cloud. Moreover, we deploy Care Template on the cloud which enables to provide the basic care tasks. To demonstrate the feasibility, we consider three kinds of usecase scenarios for two persona people.

As typically seen in Smart City, emerging technologies enable large-scale environment sensing using IoT devices deployed in a wide area. From the viewpoint of cost and efficiency, infrastructure of the large-scale environment sensing should be shared by multiple applications, with dynamically adapting the sensing behavior for different purposes. To achieve this, the infrastructure must implement a clever method that can command and control a lot of IoT devices in good order. To implement such multi-purpose large-scale environment sensing, we introduce an analogy of military system. Specifically, we propose a mission-oriented sensing with army hierarchy, where individual IoT devices and their dynamic purposes are regarded as soldiers and missions, respectively.

A number of reminder systems have been developed to help elderly people with dementia. However, the existing reminder systems lack the awareness of human context, the sympathetic human-machine interaction, and the flexibility of personal adaptation. To cope with the limitations, we are currently studying a new reminder service for people with dementia. Specifically, we exploit a BLE-based indoor positioning system to capture the current location and context of the patient. We then use a virtual agent system for rich interactions. Finally, we develop a schedule management system for personalized reminders. To integrate these heterogeneous systems, we re-design and deploy the systems as three services with Web-API: Location Service, Agent Service, and Schedule Service. These services are loosely integrated by Coordinator Service, based on the service-oriented architecture, In this paper, we first present the system architecture, and then discuss the key idea to implement the services. We also demonstrate “reminder at the entrance” as a practical scenario of the proposed services. In order to evaluate the Agent Service, which is a key component of proposed service, we have conducted the a preliminary experiment with 17 people with dementia.

The emerging indoor positioning systems (IPS) enable indoor location-aware applications (InL-App) within indoor space where GPS cannot reach. In most conventional systems, however, IPS and InL-App are tightly coupled, where one system cannot reuse location data or operation of other systems. This fact yields expensive development cost and effort of InL-App. To cope with the problem, this paper propose a cloud-based integration framework, called CIF4InL. With a common data model, CIF4InL integrates indoor location data obtained from heterogeneous IPS. It then provides application-neutral API for various InL-Apps. To evaluate the practical feasibility, we integrate two different IPS (RedPin and BluePin) using CIF4InL, where the applications transparently access the indoor locations gathered by two different IPS. Since CIF4InL allows the loose coupling between IPS and InL-Apps, it significantly improves reusability of indoor location information and operation.

This paper proposes a unified locating service, KULOCS, which horizontally integrates the heterogeneous locating services. Focusing on technology-independent elements [when], [where] and [who] in location queries, KULOCS integrates data and operations of the existing locating services. In the data integration, we propose a method where the time representation, the locations, the namespace are consolidated by Unix time, the location labels and the alias table, respectively. Based on possible combinations of the three elements, we then derive API for the operation integration.

Managing a home server within individual house is a major obstacle to practical use of home network system (HNS). Delegating the home server to a cloud is a promising approach. However, the conventional multitenant SaaS-based solution has the following risks among different households: security/privacy violation, failure propagation and insufficient resource demand. In this paper, we propose a novel cloud-based architecture for the home network system that achieves security isolation, fault isolation and resource isolation. Specifically, we first create a virtual home server for every household using IaaS. On top of every virtual home server, we then create additional virtual machines, each of which contains a single service or application. Finally, using dynamic scaling, we allocate resources needed for individual virtual home servers. Based on the idea, we construct the proposed architecture by three layers: HNS Lite, House Cloud and Service Provider.

The goal of this paper is to find an answer that how remote monitoring sensor should be accurate. To achieve the goal, we propose three methods, generalization by three-actor model, design the algorithm of the three-actor and development of RMS simulator. With the three-actor model, we can generalize RMS by interactions among three actors. As the second step, we design the algorithms that how to work the actor in RMS. So we could express how often the elderly become ill. Moreover, using the developed simulator, we could simulate with many patterns of conditions. The result of simulations shows that if the accuracy of the sensor is greater than 0.9990, then the RMS has much more detectionPower.

Modern cloud services and machine-to-machine (M2M) systems provide various kinds of data via various Web services. Implementing context-aware services integrating such global data are promising in various applications. However, it has been challenging to manage heterogeneous contexts and services defined in various Web services. To cope with this, we design a framework, called RuCAS, which systematically manages every context-aware service in form of ECA (Event-Condition-Action) rule. We also develop RuCAS platform, which publishes API of RuCAS as Web service. Using the RuCAS platform, users can define their own contexts with various Web services (e.g., information service, sensor services, networked appliances, etc.). Based on the defined contexts, they can create ECA rules to define custom context-aware services. To support users, We also implement a GUI front-end of RuCAS platform, called RuCAS.me. RuCAS.me supports users even if the users are non-expert. A case study in a real home network system demonstrates practical feasibility of RuCAS platform and RuCAS.me. The contribution of this paper is to provide design and implementation details of RuCAS, by which one can fully understand systematic management of context-aware services with Web services.

In order to achieve intuitive and easy operations for home network system (HNS), we have previously proposed user interface with virtual agent (called HNS virtual agent user interface, HNS-VAUI). The HNS-VAUI was implemented with MMDAgent toolkit. A user can operate appliances and services interactively through dialog with a virtual agent in a screen. However, the previous prototype heavily depends on MMDAgent, which causes a tight coupling between HNS operations and agent behaviors, and poor capability of using external information. To cope with the problem, this paper proposes a service-oriented framework that allows the HNS-VAUI to provide richer interaction. Specifically, we decompose the tightly-coupled system into two separate services: MMC Service and MSM service. The MMC service concentrates on controlling detailed behaviors of a virtual agent, whereas the MSM service defines logic of HNS operations and dialog with the agent with richer state machines. The two services are loosely coupled to enable more flexible and sophisticated dialog in the HNS-VAUI. The proposed framework is implemented in a real HNS environment. We also conduct a case study with practical service scenarios, to demonstrate effectiveness of the proposed framework.

Recently many frameworks are used in software development without proper documentation, and are misused by application developers in calling framework APIs. Debugging a failure caused by a wrong API call is difficult and requires a proper supporting technique. In our preceding study we developed a dynamic analysis technique to detect possibly unexpected side effects that cause failures. In this paper, we introduce a case study to identify a wrong API call using this technique.

A home network system (HNS) provides value-added services for home users by networking house-hold appliances and sensors. In the conventional architecture, the HNS appliances and services are tightly coupled. It is therefore difficult for users to freely choose their favorite appliances and services. In this paper, we propose a new HNS architecture that accommodates multi-vendor services by extensively using cloud technologies. The new architecture manages individual HNS operations and data as standard services within the cloud. The vendor services must go through the cloud to access the HNS. Thus, loose coupling among the HNS and services can be achieved. As a result, the proposed architecture realizes more flexible HNS beneficial for both users and vendors.

With the spread of Machine-to-Machine (M2M) systems and cloud services, various kinds of data are available through Web services. A context-aware service recognizes a real-world context from such data and behaves autonomously based on the context. However, it has been challenging to manage contexts and services defined on the heterogeneous and distributed Web services. In this paper, we propose a framework, called RuCAS, which systematically creates and manages context-aware service using various Web services (e.g. information services, sensor services, networked appliances, etc.). The framework describes every context-aware service by an ECA (Event-Condition-Action) rule. For this, an event is a context triggering the service, a condition is a set of contexts to be satisfied for execution, and the action is a set of Web services to be executed by the service. Thus, every context-aware service is simply managed in a uniform manner. Since the RuCAS is published as a Web service, it is easy for various applications to reuse and integrate created contexts and services. As a case study, RuCAS is applied to creating context-aware services in a real home network system.

Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents but also to improving traffic flow. Adaptive cruise control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to cooperative ACC (CACC). This paper presents the design, development, implementation, and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

Smart city provides various value-added services by collecting large-scale data from houses and infrastructures within a city. To use such large-scale raw data, individual applications usually take expensive computation effort and large processing time. To reduce the effort and time, we propose Materialized View as a Service (MVaaS). Using the MVaaS, each application can easily and dynamically construct its own materialized view, in which the raw data is cached in an appropriate format for the application. Once the view is constructed, the application can quickly access necessary data. In this paper, we design a framework of MVaaS specifically for large-scale house log, managed in our smart-city data platform Scallop4SC. In the framework, each application first specifies how the raw data should be filtered, grouped and aggregated. For a given data specification, MVaaS dynamically constructs a MapReduce batch program that converts the raw data into a desired view. The batch is then executed on Hadoop, and the resultant view is stored in HBase. We conduct an experimental evaluation to compare the response time between cases with and without the proposed MVaaS.

To support mashup of heterogeneous lifelog services, we have previously implemented the lifelog common data model (LLCDM). The previous LLCDM was implemented with MySQL, where various types of application-specific data (e.g., numeric values, text, JSON or XML) were all stored in a column in a schemaless text format. Any query with application-specific data had to be managed by individual applications. It had also a scalability issue as the data size grew.

Smart city provides various value-added services by collecting large-scale data from houses and infrastructures within a city. However, it takes a long time for individual applications to use and process the large-scale raw data directly. To reduce the response time, we use the concept of materialized view of database. For a given requirement of an application, the proposed method constructs a materialized view for caching the application-specific data. In this paper, we especially develop a method that uses MapReduce for large-scale power consumption data stored in HBase KVS. We conduct an experimental evaluation to compare the response time between cases with and without the materialized view. As a result, the proposed method with materialized view is effective especially when application repeatedly access the same data, or when the application-specific data is derived from a large set of raw data.

Learning how to use application frameworks effectively becomes important in their widespread use in software development. Learning frameworks is often difficult because of lack of their documentation and their complexity. In order to help framework learning, we propose a behavioral model, called feature component model, which abstracts internal behaviors of framework applications in terms of their behavioral characteristics such as inversion of controls. We apply our behavioral model to an example misuse of a practical framework developed by a third party in order to show its practical usefulness.

We have proposed a framework named SO-MSR: service-oriented mining software repository, which applied service oriented architecture to MSR. Following the SO-MSR, we have developed a web service, named MetricsWebAPI, for metrics calculation from a variety of software repositories and a variety source codes. In this paper, we develop and propose Metrics Viewer, which is client of Metrics Viewer and is a web application to support personal process improvement. Metrics Viewer provides an interactive user interface for repository file exploring. Moreover the Metrics Viewer visualizes change of source code metrics to support overhead view of personal process. End user can improve their development activities based on software repository data without MSR specific knowledge by using Metrics Viewer. We have conducted a pilot study to evaluate the effect of proposed system for personal process improvement.

In past research, we proposed an authentication method that combines actions with spatiotemporal information such as location, time, and distance. With the method, a user succeeds in authentication when he/she performs preset actions such as pushing button n times on preset intervals defined by spatiotemporal information. In this paper, we improve the authentication method using a partial matching method. We propose two kinds of partial matching methods for pushing button and interval. A type I method assumes the number of pushing button is sometimes less than preset count, but the number never exceeds it, and a user never pushes the button out of preset areas. A type II method assumes the number of pushing button is less or more than preset count occasionally, and a user pushes the button out of preset areas. We showed how to calculate FAR when the type I or II is applied. In the experiment, we compared the type I and II methods with a conventional method to evaluate their security. As a result, the type I method improved false acceptance rate (FAR) from 0.097% to 0.053%. The type II method improved FAR from 0.097% to 0.035%.

Visualizing energy consumption is a useful method that prompts people to perform energy-saving behaviors. Many systems for the energy visualization come onto the market. Since purposes and attitudes to energy saving vary among individuals, it is difficult to satisfy all users by static ready-made visualization. To cope with the problem, this paper presents Personalized Energy Visualization Service (PEVS), which dynamically generates appropriate visualization for individuals based on preferences. PEVS extracts user's requirement with a questionnaire wizard. We design the questionnaire in a goal-oriented fashion, from viewpoints of term, unit and scope. We then consider how to select an appropriate graph from the answer of the questionnaire. An experimental evaluation shows that every subject was able to create a unique personalized visualization easily.

Electricity peak shaving is a promising approach to energy saving at home. The conventional peak shaving has been conducted in an automatic and system-centric approach, using the emerging home energy management systems (HEMS). However, for more sustainable energy-saving, it is also important to develop a human-centric approach, which encourages residents to perform energy-saving behaviors by themselves. In this paper, we present a smartphone application, named Peak Cutter, which assists home users to perform spontaneous peak shaving. Cooperating with a home network system, Peak Cutter monitors electricity demand in a house, and notifies the user via a smartphone when the demand exceeds a threshold. The user then checks the current energy consumption of each home appliance, and can remotely turn off some appliances to shave the peak. We have also conducted an experiment, where five subjects tried to save energy of our laboratory using Peak Cutter. It was shown that a certain amount of energy was reduced within the one-week experiment, and that Peak Cutter promoted energy-saving attitudes of most of the subjects.

Application frameworks are widely used in order to increase efficiency and reliability in object-oriented software development. In this paper we put a focus on side effects caused by misuse of frameworks. A processes of such a side effect often includes cross-border method invocations between an application and its framework, and is difficult to resolve. This paper proposes an approach to visualizing such a side effect as a feature interaction between a framework and its application. This paper shows a case study to apply our approach to a practical framework application, and discuss its practical usefulness.

We have been developing a hands-free voice controller for a home network system (HNS) by using microphone arrays. In our current implementation, however, all human-HNS interactions are performed by voice only. Hence, the interactions tend to be mechanical, dreary and uninformative. To achieve richer interactions, we try to introduce the virtual agent technology as a feedback interface of the HNS. In this paper, we implement the virtual agent as a Web service, by using MMDAgent Toolkit extensively. The agent is then integrated with the HNS and microphone arrays in a service-oriented fashion. Finally, we conduct a user experiment with three versions of virtual agents. In the experiment, we evaluate how the virtual agent can enrich the interactions.

Smart city is a next-generation city planning. In the smart city, some value-added services such as energy saving and optimization of traffic are provided using wide variety of logs collected from various appliances and sensors. We are currently developing a smart city platform, called Scallop4SC, which supports collecting and processing the extremely large-scale log data. This system stores variety of logs on the HBase key-value store, and supports powerful data processing by Hadoop MapReduce. The goal of this paper is to propose service API of Scallop4SC. The API should be implemented as a Web service, which allows heterogeneous clients to access large-scale log (of energy, device and environment), as well as house configuration data of the smart city. In order to cover queries from a variety of applications, we design possible combinations of service type, data type, access type and queries, based on practical service scenarios. As a case study, we present concrete smart city services for energy saving and quality of life improvement. Then, we evaluate how the proposed API can implement these practical services.

In the smart city environment, a wide variety of data are collected from sensors and devices to achieve value-added services. In this paper, we especially focus on data taken from smart houses in the smart city, and propose a platform, called Scallop4SC, that stores and processes the large-scale house data. The house data is classified into log data or configuration data. Since the amount of the log is extremely large, we introduce the Hadoop/MapReduce with a multi-node cluster. On top of this, we use HBase key-value store to manage heterogeneous log data in a schemaless manner. On the other hand, to manage the configuration data, we choose MySQL to process various queries to the house data efficiently. We propose practical data models of the log data and the configuration data on HBase and MySQL, respectively. We then show how Scallop4SC works as a efficient data platform for smart city services. We implement a prototype with 12 Linux servers. We conduct an experimental evaluation to calculate device-wise energy consumption, using actual house log recorded for one year in our smart house. Based on the result, we discuss the applicability of Scallop4SC to city-scale data processing.

The human interface devices for the home network system (HNS) should be flexible enough to reflect individual preferences and lifestyles of home users. To fill the requirement, this paper presents a novel framework that enables Personal Home Controllers (PHC) on smartphones. The proposed framework is designed so that a user can freely design screen layouts with buttons and pictures, and can define favorite HNS operations on the layouts. The first part of our contribution is PHC Framework, which dynamically implements a PHC based on a given user definition. The second part is PHC Creater, a GUI application which supports the user to create the user definition. The proposed framework is implemented for Android mobile devices. We conduct an experimental evaluation, where subjects create PHCs for operating an actual HNS. The experimental result shows that every subject creates a unique PHC with his favorite motif, and that most subjects find usefulness and pleasure in creating their own PHCs.

In this paper, we try to empirically characterize user's habituation effect of the voice control in the Home Network System (HNS). We propose three kinds of metrics that capture the user's habituation quantitatively: (M1) the time of system speech, (M2) the number of support commands and (M3) the number of mistakes. The experimental results show that the metrics M1 and M2 are reasonable to capture the habituation of the user.

In the future ubiquitous network, it is expected to realize an adaptive service platform, which dynamically integrates various service resources to provide adaptive and context-aware services. This paper presents a service registry, called UBI-REGI, in order to support efficient and dynamic discovery of service resources in the ubiquitous network.

In order to support efficient integration of heterogeneous lifelog services, we have previously proposed a lifelog mashup platform consisting of the lifelog common data model (LL-CDM) and the lifelog mashup API (LLAPI) to access the standardized data. However, it had the performance bottleneck, and was poor in the portability. To cope with these problems, we re-engineer the LLCDM and the LLAPI with the relational database MySQL and the Web services, respectively. Furthermore, we evaluate the practical feasibility through an actual development project.

In this paper, we design and implement a consumer-oriented service platform called ReceiptLogService to store and use their receipt log by consumers. ReceiptLogService assumes receipt data as a lifelog. The receipt lifelog may provide gaining awareness of economic for consumers. We also develop a social network Web application called ReciLog that extensively uses the ReceiptLogService platform. ReciLog extensively supports every end user to review daily life from financial and living aspects. We then conducted an experimental evaluation of the ReciLog with actual subjects. The experimental results showed that it was interesting and useful for end users to review and share the receipt log.

Mining software repository is one of important topic in empirical software engineering. A wide variety of mining tools are published on the Web and we can easily apply individual mining approaches. However, there is no supporting system for sharing the mining techniques, procedures, knowledge and know-how. This sharing problem also poses great difficulties for independent validation and experimental replication from mining researchers. The goal of this paper is to provide a framework that supports sharing the repository mining techniques for reducing mining effort and external validation of analysis results. This paper proposes Service Oriented Framework for Mining Software Repository (SO-MSR) which applied Service Oriented Architecture (SOA) to the repository mining. Following the SO-MSR, we also develop Metrics Web API which is a prototype system for metrics measurement. Metrics Web API can measure a variety of source code metrics without relying on any types of repositories and programming languages. The proposed system is designed and implemented as a Web service and demonstrated using actual software repository.

This paper presents a framework that integrates different kinds of lifelog services. For efficient data mashup, we first propose the lifelog common data model (LLCDM), which normalizes data structures and formats of heterogeneous lifelog records. We derive application-neutral data items by an interrogative analysis of what, why, when, who, where and how. We then implement the lifelog mashup API (LLAPI) to achieve standardized access to heterogeneous lifelogs. A case study of integrating practical lifelog services (Twitter, Flickr and GARMIN Connect) demonstrates the effectiveness of the proposed framework. It was shown that the development effort with the proposed APIs was reduced to 11.9%, compared to the conventional mashup development with the proprietary APIs.

To support legacy migration to service-oriented architecture (SOA), this paper presents a method that derives candidates of SOA services from procedural programs. In SOA, every service is supposed to be a process (procedure) with (1) open interface, (2) self-containedness, (3) coarse granularity for business. We identify such services from the source code and its data flow diagram (DFD), by analyzing data and control dependency among processes. Specifically, we first obtain the DFD with reverse-engineering techniques. For each layer of the DFD we classify every data flow into three categories. Using the data category and control among procedures, we characterize four types of dependency. We finally apply six rules that aggregate mutually dependent procedures and extract them as a service. A case study with a liquor shop inventory control system extracts service candidates with various granularity.

The technology of a home network system (HNS) allows integration of several kinds of home appliances to provide a user with value-added integrated services. Development of the integrated HNS services requires implementation of the appliance components (with APIs) and the services, according to each home-network environment. There are various implementation standards such as DLNA, ECHONET, OSGi and Jinifor the HNS applications. Therefore, even if a developer can choose the optimal one, it's very difficult to develop the integrated services by composing a new HNS implementation. In this paper, we propose a model-driven development of integrated HNS service applications. In our former manuscript, platform-independent design language for verifying HNS service scenarios was proposed. Our model-driven development method uses this design language as a meta-model of integrated HNS services. By model transformation to concrete implementation together with verification by SMV (symbolic model checking), productivity and quality of this kind of HNS applications are improved.

This paper proposes to use eye movements to characterize the performance of individuals in reviewing source code of computer programs. We first present an integrated environment to measure and record the eye movements of the code reviewers. Based on the fixation data, the environment computes the line number of the source code that the reviewer is currently looking at. The environment can also record and play back how the eyes moved during the review process. We conducted an experiment to analyze 30 review processes (6 programs, 5 subjects) using the environment. As a result, we have identified a particular pattern, called scan, in the subjects' eye movements. Quantitative analysis showed that reviewers who did not spend enough time for the scan tend to take more time for finding defects.

This paper presents a framework that adapts the conventional home electric appliances with the infrared remote controls (legacy appliances) to the emerging home network system (HNS). The proposed method extensively uses the concept of service-oriented architecture to improve programmable interoperability among multi-vendor appliances. We first prepare APIs that assist a PC to send infrared signals to the appliances. We then aggregate the APIs within self-contained service components, so that each of the component achieves a logical feature independent of device(or vendor)-specific operations. The service components are finally exported to the HNS as Web services. Thus, the legacy appliances can be used as distributed components with open interfaces. To demonstrate the effectiveness, we also implement an actual HNS and integrated services with multi-vendor legacy appliances

This paper presents a framework to specify and verify integrated services of a home network system (HNS). We first develop a modeling language to describe the HNS and the integrated services. Complementing our previous work, the language captures each appliance as an object consisting of properties and methods, encapsulating the underlying protocols and platforms. We then present a method that verifies the integrated services with symbolic model checking, by translating the proposed language into the SMV (symbolic model verifier) language. Thus, it is possible to validate if the integrated service is specified as intended, automatically and exhaustively. Using the proposed framework, service developers can effectively detect design flaws in a single integrated service, as well as feature interactions among multiple services, in early stages of service development.

This paper presents a method to implement integrated services of networked home electric appliances, which provide more convenient and comfortable living for home users. The conventional methods generally employ a home server to achieve the integrated services. The server controls all the networked appliances in a centralized manner. However, as the number of sophisticated appliances increases, the centralized server suffers from the concentration of load, as well as a decline in the reliability and interoperability. To cope with this problem, we adopt the service-oriented architecture (SOA) for the implementation of the integrated services. In the proposed framework, the appliances export own features as services, and autonomously execute the exported services one another. Thus, the appliances are loosely coupled via the exported services without any centralized home server, which enables more flexible, balanced and reliable integrated services. We first present a framework to design and implement the integrated services based on SOA, and then illustrate a prototype system developed with Web services. We also define three kinds of metrics (i.e., reliability, workload, and coupling), and conduct a comparative evaluation between the proposed and the previous systems.

The programmable service for Internet telephony (VoIP) allows end-users or third parties to define their own customized services. However, it imposes a serious drawback that service description created by end-users is likely to contain problems that are semantically ambiguous or inconsistent. To cope with this problem, we have so far proposed semantic warnings, which are the guidelines to guarantee the semantic correctness for the CPL (call processing language) programmable service environment. We evaluate the proposed semantic warnings with practical VoIP system, VOCAL (Vovida open communication application library). In the experiment, the proposed warnings revealed a semantic redundancy in a ready-made feature of VOCAL. It is also shown that customized features containing the semantic warnings often led VOCAL to problematic situations. Thus, the proposed warnings can help feature provisioning system to detect semantic flaws in programmable service environment.

In this paper, we present a new method to protect software against illegal acts of hacking. The key idea is to add a mechanism of self-modifying codes to the original program, so that the original program becomes hard to be analyzed. In the binary program obtained by the proposed method, the original code fragments we want to protect are camouflaged by dummy instructions. Then, the binary program autonomously restores the original code fragments within a certain period of execution, by replacing the dummy instructions with the original ones. Since the dummy instructions are completely different from the original ones, code hacking fails if the dummy instructions are read as they are. Moreover, the dummy instructions are scattered over the program, therefore, they are hard to be identified. As a result, the proposed method helps to construct highly invulnerable software without special hardware.

Feature interaction is a kind of inconsistent conflict between multiple communication services and considered an obstacle to developing reliable telephony systems. In this paper we present an automatic method for detecting feature interactions in service specifications. This method uses bounded model checking, a SAT-based automatic verification technique.

Design of practical routing protocols is complex and difficult due to complicated requirements of fault tolerance and flexibility. The protocol is defined to be fault-tolerant if messages can be rerouted via another path when the communication channel fails. In this paper, we propose a new synthesis method for generating a fault-tolerant routing protocol for a given service specification and a network topology. The routing protocol thus obtained adopts a multipath routing augmented with sets, where each set stores the next nodes for routing, and updates the sets according to network topology changes. Additionally, the routing protocol can attain flexibility by the multipath routing mechanism in the sense that only a small amount of changes is needed for addition or deletion of nodes. Finally, we show the effectiveness of the proposed method through an application to a typical routing service of message delivery from a source node to a destination node.

The non-deterministic behavior in telecommunication services are well-known as one of the most typical feature interactions, and they should be detected and eliminated from the telecommunication service specifications. The conventional analysis method of this non-determinism is based on reachability analysis. Since the method must exhaustively enumerate all reachable global states, it cannot be applied to the complex communication services which include many users. We propose an alternative method based on a Petri-net model. The method constructs a logically equivalent Petri-net for a given service specification, and obtains a set of states which cause the non-deterministic behavior using rules in the service specification. Then, the method identifies states in the set which are not reachable from the initial state using the P-invariant of the Petri-net, and deletes them from the set. As the P-invariant is utilized as the necessary condition, we must finally apply reachability analysis to states in the resultant set. Since the number of states in the resultant set may be reduced to a relatively small value the new method enables us to analyze the more complex services.

The trend toward the enrichment of communication services in ISDN and IN has greatly increased the size and complexity of communication protocols which realize the services. A large and complex protocol is constructed by integrating components, each of which corresponds to a subfunction specified in a service specification. The conventional approach to this construction is to integrate components on the protocol level using the existing protocol integration methods. In this approach, the reachability analysis of protocol components is required in the integration stage. So if the size of components becomes large, the integration stage would be a bottleneck because of the state explosion problem of the reachability analysis. Therefore, we propose a new approach to construct the target protocol which, at first integrates components on the service specification level and then transforms an integrated service specification into the target protocol by the protocol synthesis technique. As a result, the construction of the target protocol from the component service specifications can be efficiently effected in a small state space without paying special attention to the timing of protocol messages.