Driven by increasing urbanization and serious economic and environmental challenges, what is called smart grid and smart cities, the transformation to smart public infrastructure system will require technological progress. This paper presents an emerging trend of the systems development and a framework of systems technologies to achieve the smart public infrastructure of the future.

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This paper reports recent management technologies of IT (Information Technology) risks that are potential losses caused by problems on confidential, integrity and availability in information systems. Focusing on dramatical changes in information systems: varieties of users, system structures and application areas, we analyze issues on managing the IT risks. Through surveying the recent management technologies of the IT risks, we outline the appropriate management technologies for the issues, and discuss the road map.

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Recent years, maintenance of corporate soundness and compliance with the law and corporate ethics are getting more significant in the insurance industry, regardless of life insurance. In the other hand, division of production and distribution is increasing. Therefore the problem of compliance with an agency is getting more significant. We propose a contract procedure in an agency separated products explanation and applied process for protection of the personal information. Our proposed procedure protects the personal information of the contractor and supports the compliance observance for contracts with the background texture watermarks and the redactable signature. We have developed a prototype system of the solution to check its feasibility.

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Development and spread of internet technology contributes service firms to obtaining the high capability of brand information transmission as well as relative customer feedback data collection. In this paper, we propose a new evaluation method for service branding using firms and consumers data on the internet. Based on service marketing 7Ps (Product, Price, Place, Promotion, People, Physical evidence, Process) which are the key viewpoints for branding, we develop a brand evaluation system including coding methods for Word-of-Mouth (WoM) and corporate introductory information on the internet to identify both customer's service value recognition vector and firm's service value proposition vector. Our system quantitatively clarify both customer's service value recognition of the firm and firm's strength in service value proposition, thereby analyzing service brand communication gaps between firm and consumers. We applied this system to Japanese Ryokan hotel industry. Using six ryokan-hotels' data on Jyaran-net and Rakuten travel, we made totally 983 codes from WoM information and analyzed their service brand value according to three price based categories. As a result, we found that the characteristics of customers' service value recognition vector differ according to the price categories. In addition, the system clarified that there is a firm that has a different service value proposition vector from customers' recognition vector. This helps to analyze corporate service brand strategy and has a significance as a system technology supporting service management.

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Data Envelopment Analysis (DEA) is a method for evaluating the management efficiency of Decision Making Units (DMUs). In DEA, there are three discussion issues in order to evaluate from multi-view points — 1. number of input and output items, 2. uniqueness of solution, and 3. complexity to understand features of DMUs from results — In order to overcome these issues, we propose a hierarchical structure DEA model and a visualization method. Then the utility of the proposed method is examined for Japanese universities data.

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This paper addresses a high-speed generation of goal-oriented scenarios on the qualitative and quantitative hybrid model. The goal-oriented scenarios can be generated efficiently by the bidirectional combination of the forward propagation and the backward propagation, if differences between values related to each scenario are small on a certain time step. So, the method combines the scenarios when each value can be regarded as the same from qualitative value viewpoints. Furthermore, the proposed method decides the number of both propagations to generate the required number of goal-oriented scenarios with decreasing the number of time-consuming backward propagations. Experimental results show that the computational time for generating the required number of the goal-oriented scenarios is decreased by 98% compared to the solely forward propagation.

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In a sales promotion task, wholesaler prepares and presents the display proposal for commodities in order to negotiate with retailer's buyers what commodities they should sell. For automating the sales promotion tasks, the proposal has to be constructed according to the target retailer's buyer. However, it is difficult to construct the proposal suitable for the target retail store because of too much combination of commodities. This paper proposes a construction method by Genetic algorithm (GA). The proposed method represents initial display proposals for commodities with genes, improve ones with the evaluation value by GA, and rearrange one with the highest evaluation value according to the classification of commodity. Through practical experiment, we can confirm that display proposal by the proposed method is similar with the one constructed by a wholesaler.

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This paper addresses identifying a related group of threads for a recent bug thread posted by a developer in the forum of open source software. A typical approach is to identify as the group of threads that is much similar to the recent bug thread. However, most of recent bug threads that are not related to any groups of threads are wrongly identified. Focusing on the peak characteristics that similarities to related group of threads are much higher than similarities to others, the proposed method identifies whether recent bug threads are related to a group of threads by Support Vector Machine with the peak characteristics of similarities. Experimental results show that the proposed method can keep the recall rate of 76% and improve the precision rate by 49% compared to the method using only thresholds for similarities.

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While abundant information can be referred to the Internet Cloud, it is becoming difficult for a user to acquire desired information pertinently. Therefore, it is necessary to support an unfamiliar user to IT to offer the information suitable for user's preference. This research proposes a service integration supporting system equipped with recommendation function based on a user's preference. The research regards bookmarks on Web browser and tags given to Web services as information showing a user's preference, and a user profile is created using them. A service integration model is built and the proposed system recommends some appropriate example of integrated services. As compared with conventional supporting systems, the proposed system requires less amount of work for creation or execution of service integration.

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Traditionally, a Management Information System (MIS) has been developed without using formal methods. By the informal methods, the MIS is developed on its lifecycle without having any models. It causes many problems such as lack of the reliability of system design specifications. In order to overcome these problems, a model theory approach was proposed. The approach is based on an idea that a system can be modeled by automata and set theory. However, it is very difficult to generate automata of the system to be developed right from the start. On the other hand, there is a model-driven development method that can flexibly correspond to changes of business logics or implementing technologies. In the model-driven development, a system is modeled using a modeling language such as UML. This paper proposes a new development method for management information systems applying the model-driven development method to a component of the model theory approach. The experiment has shown that a reduced amount of efforts is more than 30% of all the efforts.

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While thin-client systems are diffusing as an effective security method in enterprises and organizations, there is a new approach called pseudo thin-client system. In this system, local disks of clients are write-protected and user data is forced to save on the central file server to realize the same security effect of conventional thin-client systems. Since it takes purely the software-based simple approach, it does not require the hardware enhancement of network and servers to reduce the installation cost. However there are several problems such as no write control to external media, memory depletion possibility, and lower security because of the exceptional write permission to the system processes. In this paper, we propose WriteShield, a pseudo thin-client system which solves these issues. In this system, the local disks are write-protected with volume filter driver and it has a virtual cache mechanism to extend the memory cache size for the write protection. This paper presents design and implementation details of WriteShield. Besides we describe the security analysis and simulation evaluation of paging algorithms for virtual cache mechanism and measure the disk I/O performance to verify its feasibility in the actual environment.

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We propose a method for solving the crew rescheduling problem (CRP) and the vehicle rescheduling problem (VRP) based on the Lagrangian relaxation method. The CRP/VRP is formulated as an integer programming problem on the basis of a network flow modeling approach from which a Lagrangian relaxation problem is constructed by relaxing the constraint that links multiple resources. Using two procedures that generate the upper and lower bounds of the primal problem, both of which utilize an efficient shortest path algorithm for the directed acyclic graph (DAG), the proposed method gradually improves the gap between the upper and lower bounds while updating Lagrangian multipliers. Experimental results of real-world vehicle rescheduling data from Japanese railway lines indicated that the proposed method generated feasible solutions that were confirmed to be fairly close to the optimal solutions according to the gap between the upper and lower bounds, and also clarified the quality of the other method's solution by using the gap, which could lead to streamlining and sophisticating real-world rescheduling related activities.

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This paper addresses a classification method of open-ended questionnaire using category-based dictionary from categorized samples. Different from generally-used document classification methods, our proposed method here introduces a new category-based dictionary which is generated from a small set of categorized samples, because the purpose of analysis is to have meaningful results along with a user's viewpoints in a sense. This category-based dictionary is used to judge a category as to a questionnaire with tf-idf (term frequency inverted document frequency) and co_tf-idf (co-occurrence tf-idf). We show the effectiveness in applying our proposed method to questionnaires about an university lecture.

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In order to improve the efficiency of traffic systems in the global perspective, a traffic control strategy for the dynamic traffic management in the road network has been proposed in this paper. The main idea of the proposed traffic control strategy is based on Q value-based Dynamic Programming with Boltzmann Distribution, and the temperature parameters in Boltzmann Distribution are adjusted by the proposed temperature parameter control strategies, which are Network Method and Intersection Method, depending on the time-varying traffic situations. In the simulation, it is supposed that the route guidance is given to each vehicle and all the vehicles in the traffic system follow the guidance. The simulation results show that temperature parameter control in Q value-based Dynamic Programming with Boltzmann Distribution could improve the performance of the traffic system.

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In this research, it is assumed that the degraded images, which are represented as convolution of the original images with the PSFs (Point Spread Functions), are defocused images by the lens system or the limit of resolution. It has been proposed to estimate both of the PSF and the original image using a Bayesian-based iterative method. In the Bayesian-based iterative method of image restoration, we need to set suitably an initial value of the PSF and the original image. The initial value of the original image is a blurred image, since there is only a little difference between blurred and original images. On the other hand, as there is insufficient information on the PSFs, it is too difficult to set the initial values of the PSFs. If blurred images are represented as convolution of the original images with the PSFs, the band-limit of the blurred image will approach to a band-limit of the PSF. Therefore, we determine the initial value of the PSF by estimating the band-limit of the PSF from logarithmic amplitude spectrum of the blurred image. Finally, we confirmed the effectiveness of the proposed method by simulations and experiments with out-of-focus pictures.

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This paper proposes a new smoothing technique for range images taken by a pulsed Time-of-Flight range finder. A Time-of-Flight range finder gives a range value from the sensor to the target by measuring the time until the emitted light is reflected on the target's surface and returned to the sensor. At the same time, the power of the reflected light is also measured as a side product of the range value for the most of range finders. The basic idea of the proposed technique is that the reflectance and range images are utilized for smoothing the range image corrupted by noise so that the detailed shape in the range image is preserved by applying the trilateral filter. Simulations and experiments using a laser range finder are successfully carried out for verifying the performance of the proposed technique.

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Recently, we can easily have huge database with the development of computer network. Accordingly, it becomes difficult for users to extract knowledge from the database. In this paper, we focus on data mining, especially classification. In the real-world data mining, missing value problem is happened, for example, speech containing noises, facial occlusions, and so on. When the test sample have missing values, classification systems can not classify that. In previous studies, various imputation methods have been developed. Previous imputation methods were developed to solve the missing value problem with lots of explanatory variable, even if some explanatory variables are ineffective for imputation. It has been said that using lots of variable deteriorates in learning efficiency, thus we believe that imputation methods should be developed considering relations among explanatory variables. Moreover, it is effective considering not only relations among explanatory variables but also between the test sample and each of the training sample. Therefore we propose the imputation method by using Bayesian network with weighted learning. Through the experiments, we could confirm that the proposed method imputed missing values with approximate values, and a classification system successfully classified the test sample, in which missing values were imputed by the proposed method, in comparison with some conventional methods.

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A Project Assessment Indicator (PAI) Model has recently been applied to evaluate monthly project performance based on 15 project elements derived from the project management (PM) knowledge areas. While the PAI Model comprehensively evaluates project performance, it lacks objectivity and universality. It lacks objectivity because experts assign model weights intuitively based on their PM skills and experience. It lacks universality because the allocation of ceiling scores to project elements is done ad hoc based on the empirical rule without taking into account the interactions between the project elements. This study overcomes these limitations by applying a DBN approach where the model automatically assigns weights and allocates ceiling scores to the project elements based on the DBN weights which capture the interaction between the project elements. We train our DBN on 5 IT projects of 12 months duration and test it on 8 IT projects with less than 12 months duration. We completely eliminate the manual assigning of weights and compute ceiling scores of project elements based on DBN weights. Our trained DBN evaluates monthly project performance of the 8 test projects based on the 15 project elements to within a monthly relative error margin of between ±1.03 and ±3.30%.

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Social Bookmark services are web services which enable users to share their personal bookmark data with other users. In our research, we propose a web clustering method in a social bookmark service using bookmark data modified with user network data. Here, we define bookmark data as a pair of a user and a webpage he bookmarks, and user network data as links between users and their favorite users. In more detail, we use Probabilistic Latent Semantic Indexing (PLSI) which enables to project social bookmark data on latent semantic space and classify them by their semantic attributes. Using PLSI, we can classify webpages according to their topics, and regarding user network, we can append all bookmark data of user, who are reachable for a user, to his bookmark data. To confirm an effectiveness of our proposed clustering method, we have evaluated a clustering examination using real Social Bookmark Data. And finally we have confirmed that our proposed method could have classified webpages in the same topic.

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Equipment service increases in which venders have equipments in place of users and receive charge corresponding to effect of use. In this service business, income of venders change by performance that how users operate motors. To promote the business safely, a method of selecting plants where equipments are introduced is necessary, not only increasing expected earnings but also decreasing width of the earnings change. In addition, venders should negotiate interactively with users, so a method of selecting plant combination in a short time is necessary. In this paper, we propose the method for selecting plants combination considering the correlation between plants and using branch and bound approach in short time.

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A key aspect of the optimal support of students with special needs is co-ordination and co-operation between school, home and specialized agencies. Communication between these entities is of prime importance and can be facilitated through the use of a support system implementing ICF guidelines as outlined. This communication system can be considered to be a preventative rather than allopathic support.

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MIMO is a mighty method to increase data rate of wireless LAN systems. It is necessary to keep a suitable distance between each antenna to work MIMO function effectively, especially in the poor multipath environment. Then the radio over fiber technique is proposed to extend antenna distances outdoors. In this paper, we explain the influence of the delay and propagation loss cased by using of radio over fiber to the throughput performance experimentally.

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A map matching method with fuzzy inference using vehicle existence factors of each segmented road link is presented. The vehicle existence factors are defined by the cumulative distance and angle, which are calculated with GPS data. From the simulation results with actual data, a map matching error reduction of the proposed map matching algorithm is confirmed.

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