Although the unsupervised discovery and localization of common objects from within a set of images has received considerable attention, the difficulty of this task means that current methods are not sufficiently accurate. This paper describes an unsupervised method that more accurately discovers and localizes common particular objects within a set of images. First, our method constructs a match graph that precisely represents the relationship among particular object proposals extracted from images. The match graph necessarily contains communities, i.e., subgraphs where object proposals with identical subjects are densely connected by edges, indicating matches between local image features in the proposals. Our method determines the common particular objects by scoring the object proposals using a novel node centrality measure for graphs with a community structure, which we call the restrained random-walk centrality. Experiments demonstrate that our method is considerably more accurate than previous approaches on a particular object image set.

In this paper, we propose a restrained random-walk similarity method for detecting the community structures of graphs. The basic premise of our method is that the starting vertices of finite-length random walks are judged to be in the same community if the walkers pass similar sets of vertices. This idea is based on our consideration that a random walker tends to move in the community including the walker's starting vertex for some time after starting the walk. Therefore, the sets of vertices passed by random walkers starting from vertices in the same community must be similar. The idea is reinforced with two conditions. First, we exclude abnormal random walks. Random walks that depart from each vertex are executed many times, and vertices that are rarely passed by the walkers are excluded from the set of vertices that the walkers may pass. Second, we forcibly restrain random walks to an appropriate length. In our method, a random walk is terminated when the walker repeatedly visits vertices that they have already passed. Experiments on real-world networks demonstrate that our method outperforms previous techniques in terms of accuracy.

The technology used to detect community structures in graphs, or graph clustering technology, is important in a wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community detection methods have relied on the optimization of a quantity referred to as modularity, which is a quality index for the partition of a graph into communities. However, such methods suffer from a key drawback, namely, the inability to identify relatively small communities. To overcome this drawback, we propose a novel community detection method that can detect small communities. This is based on the property that a random walker will not readily leave a community even if it is small. The work presented in this paper demonstrates that our method detects both small and large communities in the practical application of clustering tourist attraction images obtained from Flickr.

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture→exacerbate desertification from the web using se-mantic relation (between nouns), context, and association features. Experiments show that our method outperforms base-lines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture→exacerbate desertification→increase Asian dust (from China)→asthma gets worse. Experi-ments show that we can generate 50,000 scenarios with 68 % precision. We also generated a scenario deforestation con-tinues→global warming worsens→sea temperatures rise→vibrio parahaemolyti-cus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted ” the future event sequence in a sense. 1

We represent the inconsistencies of the past research on the connections among such heterogeneous fields as Linked Data, Semantic Web, Bridge Ontology, and Schema Mapping as well as our own past researches. Graph structures are commonly represented as links in relationships. For the same domain, the relationships agree with each other in the domain, because the transitive and order relations are defined. However, in most heterogeneous domains, we have to define the new order relation to link heterogeneous sets. This limit exists when we consider the relation among heterogeneous fields in set theory. Three inconsistencies of linking heterogeneous resources exist: 1) the inconsistency that shows that the relation does not guarantee the future; 2) the inconsistency where no transitive relation is true, when anyone connects links for heterogeneous fields; and 3) the inconsistency where no relation in heterogeneous fields can be discovered in set theory. Closed assumption systems have already reached their limit. In the big data era, we must consider a new framework for the Three Opened Assumption's Evil. As one solution, we present a map transformation method from set theory to the Cartesian system of coordinates to interconnect these heterogeneous sets and the Three Opened Assumption's Evil by two easy mathematical proofs of transitive and order relations to interconnect the heterogeneous resources. In addition, we define a new functional predicate as an example of a map transformation from set theory to a Cartesian system of coordinates to interconnect the heterogeneous resources for our solution. We also define a “dependOn” function as an example of this framework.

On behalf of the rapidly and widely disseminated smartphone technology into the public, lots of social network sites and location-based social applications are accumulating a huge volume of massive crowd's daily experiences and thoughts in an unprecedented scale. We can regard them as novel data sources for accomplishing various social analytics, which have usually required lots of efforts to collect crowds' opinion and behavioral data. Thus, we can take advantages of abundant social datasets by integrating them appropriately. However, when we integrate disparate sources to derive a comprehensive view for a survey, it is necessary to know intrinsic exclusive values of each data source compared to others in an intuitive and succinct way. In fact, lots of efforts and time are wasted to overview various datasets consequently to confidently choose a dataset to be integrated in a final result. In this paper, we propose a complementarity index, which can estimate the exclusive usefulness of data sources in terms of spatial and topical coverage when selecting data sources for social analytics purposes. We conducted an experiment about complementarity measurement with two real social datasets from Twitter and VoiceTra; the latter is a speech-to-speech translation app, with which we can additionally obtain crowds' verbal translation logs. With the proposed complementarity index, we can measure the capability of a dataset comparing to others before integrating datasets, thus enabling analysts to examine much more datasets from as many related data sources as possible by focusing on exclusive coverage and relative strength of relevant topics.

Network-based mobile services, such as speech-to-speech translation and voice search, enable the construction of large-scale log database including speech. We have developed a smartphone application called VoiceTra for speech-to-speech translation and have collected 10,000,000 utterances so far. This huge corpus is unique in size and spatio-temporal information; it contains information on anonymized user locations. This spatiotemporal corpus can be used for improving the accuracy of its speech recognition and machine translation, and it will open the door for the study of the location dependency of vocabulary and new applications for location-based services. This paper first analyzes the corpus and then presents a novel method for classifying utterances using linguistic and non-linguistic information. L2-regularized Logistic Regression is used for utterance classification. Our experiments performed on the VoiceTra log corpus revealed that our proposed method outperformed baseline methods in terms of F measure.

We propose a structural approach to service composition using a relational model. We focus on structural aspects of service composition and apply the relational model. Services in our model are defined and organized by relations, and service manipulations such as service invoke and service composition, are achieved by relational operations to the corresponding relations. Since conventional relational algebra is insufficient for service management, we extend relational algebra and introduce a new θ-operator to deal with services.

In this paper, we present a Web spam detection algorithm that relies on link analysis. The method consists of three steps: (1) decomposition of web graphs in densely connected sub graphs and calculation of the features for each sub graph, (2) use of SVM classifiers to identify sub graphs composed of Web spam, and (3) propagation of predictions over web graphs by a biased Page Rank algorithm to expand the scope of identification. We performed experiments on a public benchmark. An empirical study of the core structure of web graphs suggests that highly ranked non-spam hosts can be identified by viewing the coreness of the web graph elements.

Abstract Recently, the number of users who use search engines for not only retrieving Web pages but also understanding or learning an arbitrary concept has been increasing. However, it is difficult to understand and learn an arbitrary concept by using the existing search engines. This paper presents a method for the interconnection of heterogeneous knowledge bases on Knowledge Grid for knowledge sharing and provision. In this paper, for realizing the proposed method, we introduce two types of services—intra‐operation services and inter‐operation services—on Knowledge Grid. The system dynamically creates a semantic associative network that connects the user's interests and concepts, and provides correlation views that represent various relationships for connecting related resources. In addition, we employ a link‐free browsing system by this method called correlation browsing, which represents the semantic association utilizing collective knowledge by connecting various knowledge bases. The current web browsing has two actions—browsing and jumping by static hyperlinks. It is difficult to understand the relationship between each page that is connected by static hyperlink. The system navigates the related contents while repeatedly switching between a content browsing mode and a correlation browsing mode. Therefore, a user can access various contents while understanding the relationships. Copyright © 2010 John Wiley & Sons, Ltd.

Geosocial networking services allow users to create,use, and share information and to communicate with other people regarding geographical locations and time. Geosocial networking services generate large amounts of spatiotemporal contents that mainly include information about personal interests, activities, or real-life experiences. This paper discusses a new geovisualization tool named StickViz that helps retrieve k-neighbors by considering the spatial, temporal, and thematic interests of users over geosocial networking services.In particular, we define phenomena of interest for combining user interests with respect to location, time, and topic and propose a phenomenon-based k-neighbor query to find people having similar interests in spatial, temporal, and thematical dimensions. Moreover, we use a three-dimensional (two spatial dimensions and one temporal dimension) space so that the queries and spatiotemporal contents can be represented in the same space. Finally, we present a prototype of StickViz to navigate spatiotemporal contents and search k-neighbors on the basis of the predefined phenomena of interest through three-dimensional visual interfaces.

With the advance of ubiquitous computing and mobile environments, we have begun to continuously monitor changes in real-world condition and environment through wireless sensor networks. Opportunities also exist for people to create information related to the world around them by using mobile phones equipped with sensing devices, and share that information online with others. In this paper, we propose a novel approach for the interconnection of earth observation data and spatiotemporal web contents on the basis of spatiotemporal and thematic relationships. In particular, we use the concept of moving phenomena of interests to link between measurement sensing data and people-centric contents on the basis of spatiotemporal proximity and thematic relevance. This paper also shows a simple application that automatically generates semantic tags with respect to natural geographic phenomena, such as typhoons, climate changes, and air pollution, on the basis of our interconnection approach. We are able to easily understand qualitative meanings with respect to a certain phenomenon expressed by quantitative numeric conditions.

Various knowledge resources are spread to a world-wide scope. Unfortunately, most of them are community-based and never thought to be used among different communities. That makes it difficult to gain “connection merits” in a web-scale information space. This paper presents a three-layered system architecture for computing dynamic associations of events to related knowledge resources. The important feature of our system is to realize dynamic interconnection among heterogeneous knowledge resources by event-driven and event-centric computing with resolvers for uncertainties existing among those resources. This system navigates various associated data including heterogeneous data-types and fields depending on user's purpose and standpoint. It also leads to effective use for the sensor data because the sensor data can be interconnected with those knowledge resources. This paper also represents application to the space weather sensor data.

The internet and web applications have changed business and human life. Nowadays everybody is used to obtain data through the internet. Most applications are still Web 1.0 applications. Web 2.0 community collaboration and annotated data on the basis of Web 3.0 technologies supports new businesses and applications. The quality dimension of the web is however one of the main challenges. Knowledge systems target at high-quality data on safe grounds, with a good reference to established science and technology and with data adaptation to user's needs and demands. Knowledge system can be build based on existing and novel technologies. This paper discusses the challenges, two solutions and the fundamentals of knowledge system environments.

This paper presents an interconnection method for heterogeneous knowledge bases depending on user's interests as a context. Various knowledge bases have been created in each field by using collaborative working environments such as Wiki. One of the important issues is how to interconnect these knowledge bases and represent the relationships between various concepts in heterogeneous fields. An event affects various aspects of an area, field, or community. In order to understand an arbitrary event or concept, it is necessary to find various relationships over heterogeneous fields. Generally, the relationships over heterogeneous fields strongly depend on contexts and situations. It is important to realize the dynamic interconnection of knowledge bases depending on contexts and situations. Therefore, we design an interrelation management function (IMF) that defines the operator for interconnection data. In this paper, we propose a framework for a context-dependent dynamic interconnection method by using the interrelation management function.

With the advance of the Web technologies, people can more easily access the geographic information and generate various types of user contents including geo-information on the Web. Consequently, geo-web and geo-communities have been infrastructures to share and connect information on the Web for many years, and people start to describe a specific phenomenon on places by own representation methods such as text, images, videos, etc. In this demonstration, we introduce a new type of location-based services, called Sticker, which can search and aggregate the relevant user generated contents/media to moving phenomena such as hurricanes, flooding, and global warming. In particular, the Sticker navigates user-generated contents with three dimensional view of space-time(2D+1D) and allows users to retrieve related information with the moving phenomena in a spatiotemporal domain as well as interesting keywords.

Utilization of global information resources as a part of risk management is insufficient. The authorities are maintaining information systems mainly for their own purposes, without access to high quality public information sources in Internet and without interoperability between systems of different authorities. Beneficial use of all available information resources would provide an opportunity to create knowledge based on different pieces of information. However, powerful distributed knowledge management, mining of the information items, analysing the quality of them, is needed to create new information to be utilized. The distributed operations needs support of complex network architectures, models supporting mutual understanding over the cultures and language borders, and ability to recognize the context and adapt the results to the new context. This paper opens discussion from different viewpoints to the topic of global risk management. Architectural solutions supporting interoperability, quality of data in wide networks, ubiquity and mobility as well as time dimension of the information space are covered.

This paper described our development dialog system on Kyoto tourist information assistance. Dialog part of our system helped user to make an appropriate query. Information analysis part would be assisted for user to select the retrieved information. Nowadays we can get most information through the Internet. However, we have a trouble to pick up expected information from the huge results with conventional search engines. Especially in mobile terminal, we are confronted with great difficulties for two factors. One is that most of users cannot make an appropriate query because their request is vague with theirselves. The other is that the retrieved information has huge variation and mobile terminal has small area for displaying them. Therefore, we aim to develop technologies for the users to input their requests by familiar way and clarify what they want to know with displaying the retrieved information with suitable method.

This paper proposes a general framework of for a system with a semantic browsing and visualization interface called Knowledge Communication, Collaboration and Creation Browser (KC3 Browser) integrates multimedia contests and web services on the grid networks, and makes a semantic mash-up called knowledge workspace (k-workspace) with various visual gadgets according to user's contexts (e.g. their interests, purpose and computational environments).

We developed a mechanism for photographing people and annotating their behavior along with nearby elements using multiple embedded cameras. In addition, we developed a method of dynamically integrating and presenting the recorded content. As conventional cameras are used to photograph objects selected by users, taking pictures that include users while holding the camera is difficult. Security camera systems take pictures that include people and nearby elements, but such systems cannot show the intentions of the people being photographed. After detecting the intentions and behaviors of subjects with radio frequency identification (RFID) tags, our system selects the best camera from cameras located in an area, and then the camera photographs the subject and the surrounding area. In addition, these photos can be annotated with information about the context and movement history of the subject. We created a prototype of our system and determined its effectiveness experimentally.

In next-generation networking environments, ubiquitous networks will be available both indoors and outdoors. Various devices will be ubiquitously embedded in our homes and cityscape. Digital content will be stored not only by servers on the Internet, but also in embedded devices belonging to ubiquitous networks. In this paper, we propose a content-processing mechanism for use in environment-enabling collaborative acquisition of embedded digital content in real-world situations. We have developed a network management device that makes it possible to acquire embedded content using coordinated ubiquitous devices. This management device actively configures networks that include content-providing devices and browsing devices to enable sharing of a range of digital content. To demonstrate our system, we built a practical prototype called the "Virtual Insect Catching System", which is simple enough for children to use. In a test that 48 children took part in we demonstrated that the system can be used to find embedded devices, build peer-topeer networks, acquire embedded digital content, retrieve related content from the Internet, and then create new web content.

We describe a browser for the past web. It can retrieve data from multiple past web resources and features a passive browsing style based on change detection and presentation. The browser shows past pages one by one along a time line. The parts that were changed between consecutive page versions are animated to reflect their deletion or insertion, thereby drawing the user's attention to them. The browser enables automatic skipping of changeless periods and filtered browsing based on user specified query.

This paper proposes to establish a collaborative environment to achieve enhanced information access on the small-form-factor devices. We designed a distributed user interface that crosses devices to cooperatively present information adaptable to small displays. We applied our system to the document and webpage that are ubiquitously available media content on mobile devices.

We propose a novel concept for displaying and browsing Web content in public spaces via a ubiquitous network. This is a new form of the device cooperative content which we previously proposed (Kidawara, Y. et al., PACRIM, 2003). The original idea for this content was based on temporal synchronization. The new type of content is semantically synchronized and based on the concept of device cooperative browsing. This concept is an interactive method of browsing for content related to the content currently being viewed by a user. The operating mechanism is based on multiple devices connected via a peer-to-peer network. The mechanism operates cooperatively in order to find each user's particular interests and maintain information about them. Multiple devices sharing common information might thus have common topics of interest on various user devices. When new devices are connected in this environment, they detect the required information through filtering, and obtain it from other devices in the local ubiquitous network. In addition to describing this concept, we also discuss WebBoard, which is a successful implementation of our novel approach.

The main issue regarding cross-media information retrieval is the determination of correlations between different types of media objects. The conventional approach derives the correlations based on common properties extracted from media contents or synchronous presentation of multiple media pre-authored in a scheduled scenario. We propose a novel approach for determining the cross-media correlation derived from the referential contexts of media objects in the Web. A Web page links to the media objects distributed over the Web so that it aggregates them with respect to the page content. Our approach extracts the referential context by analyzing the logical structure of the Web and discovers the aspect of a media object, which means the latent semantics of the referential context. The aspect-based correlation reveals the relation between media objects regarding their reputations on the Web. In this paper, we propose an approach for discovering aspect-based correlations with an experimental implementation

Soon all people will have mobile terminals and they will collect information based on their interests and purposes in usual situations. We propose the method of information delivery for communications in temporary groups which are built in mobile environments in this paper. In our proposed method, profiles of a temporary group are automatically generated from contents which are stored into mobile terminals, and then, information is delivered to a temporary group based on profiles. It is possible to support communication in mobile environments by, using our proposed method.

Recently, much attention has been focused on potential Internet technology for delivering medical care. Many researchers are trying to adapt network technologies to telemedicine. Network technology, which makes possible instant communication over great distances, could control devices in remote places in real time. In particular, the broadband Internet achieves transmission of high quality video image data and other huge amounts of data to distant places. Therefore, transmitting high-quality video images that medical doctors request should not be so difficult. The IPv6 network technology that will be applied to the Next-Generation Internet will provide a large increase of global IP addresses. Many people anticipate that various medical devices will be connected to networks with ease. We report research conducted to examine the effectiveness of device cooperation for medical purposes. To this end, we developed a remote-control microscope that we are proposing as next-generation remote-control medical equipment. We developed this microscope by using a technology called "Network Accessible Device on the Internet Architecture" (NADIA). In an effort to create a remote medical system for use with the next-generation Internet, we developed and confirmed the effectiveness of the ProcessStorageMicroscopeNAD, a machine that combines three devices.

A method of compliant and collision avoidance control is presented. The collision avoidance motion is first generated in off-line routine and its data are stored as as a sequence of desired joint angles. Next, virtual impedance model-following control is employed as force feedback control. Redundancy is used for minimization of the variance from the desired joint angle during compliant motion. In particular, the computation time for the inverse kinematics at each sampling time was reduced by using approximate values of Jacobian matrix. The stability of the control system is analyzed and the effectiveness of the proposed method is discussed.>