In this paper we describe a multi-camera traffic monitoring system relying on the concept of probability fusion maps (PFM) to detect vehicles in a traffic scene. In the PFM, traffic images from multiple cameras are inverse perspective-mapped and registered onto a common reference frame, combining the multiple camera information to reduce the impact of occlusions. Although the unconstrained perspective projection is non-invertible, imposing the condition that the image points be co-planar allows the relaxation of this constraint. In images of road scenes, the road surface is locally planar, and as such can be inversely mapped. We show that computing the perspective undistortion by finding 4 matching points between a camera image and an ideal non-perspectively distorted image, a system of linear equations can be solved that corresponds to applying the rotation, translation, and re-projection onto the common reference plane without needing calibrated cameras. We show that this method yields good results in the detection of vehicles for subsequent tracking and monitoring.

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Quality of Service (QoS) routing in Mobile Ad hoc NETworks (MANETs) is hard to achieve because the network topology tends to change constantly in a dynamic network. In this paper, we propose an effective QoS routing scheme to satisfy the required service demand and adapt to the dynamic changes in network resources. This novel approach of Application-Level QoS Routing Scheme (ALRS) comprises three significant features: (1) Estimation of consumed bandwidth on each node by using 2-hop neighbors' traffic information, (2) Route construction combined with admission control on each application session with (1), and (3) Route maintenance based on (1) and (2). By using our proposed ALRS scheme the availability of bandwidth is greatly improved for high-load communications, such as multimedia applications. By computer simulation we confirmed that ALRS increases the delivery ratio by up to 50% and decreases delay by less than one-fourth of the existing traditional method.

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In this paper, we propose a new solution for route optimization that can be applied to heterogeneous mobile IP networks that support client-based and network-based IP mobility management. The proposed solution aims to satisfy important requirements such as an efficient network resources utilization, an improvement of the end-to-end quality of service, and the capability to monitor user traffic for policy control and charging. In the proposed solution, the management of the route optimization context is performed by the mobility anchor, while the IP packet processing for route optimization is performed by the nodes located around the edge of the networks. We provide a comparative evaluation of the proposed solution and existing solutions in terms of: 1) the ability to maintain route optimization when the mobile node moves across a boundary between network domains where different types of IP mobility management are employed, 2) the range of application, 3) the latency required to continue route optimization upon handover, and 4) the amount of signaling. The evaluation results show that our proposed solution best satisfies the route optimization requirements for the heterogeneous mobile IP networks that we consider in this paper.

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The mobile Internet has become increasingly visible in everyday life. As mobile Internet penetration leverages content business opportunities, it is crucial to identify methodologies to fit mobile-specific demands. Regularity is one of the important measures to capture easy-come, easy-go mobile users. It is known that users with multiple visits per day with a long interval in between have a higher likelihood of revisiting in the following month than other users. The author proposes a 3+1bit method to incorporate this empirical law in order to cope with the two major mobile restrictions: distributed server environments and large data streams. The proposed method can be performed in a one-path manner with 32-bit word boundary-awareness for memory compaction. Experimental results show that the method is promising for identifying revisiting users under mobile-specific constraints.

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In the present paper, we propose an automatic snapping method that aligns fuzzy objects in a multi-resolution grid system in order to improve the efficiency of sketch-based CAD systems. The sketch-based CAD system that we have previously realized successfully identifies sketch drawings as primitive geometrical curve objects by treating the sketches as fuzzy objects, the fuzziness of which is associated with the roughness of the drawing manner. However, when the system aligns the identified objects with a grid system, difficulties in the grid resolution setting arise because the identified objects often consist of both fine and coarse portions and thus require different grid resolution settings for proper alignment. Meanwhile, the resolution problem with respect to cursor point snapping has been solved by multi-resolution fuzzy grid snapping (MFGS), which realizes automatic selection of the snapping resolution by treating the cursor as a fuzzy point, the fuzziness of which is associated with the roughness of the pointing manner of the user. The present paper proposes a method to apply MFGS to fuzzy objects in order to resolve the difficulties involved in the setting of the snapping resolution of the sketch-based CAD system. Experimental results show that users can align identified objects to an appropriate resolution through MFGS by controlling the roughness of the drawing manner.

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Recently, P2P networks have been evolving rapidly. Efficient authentication of P2P network nodes remains a difficult task. As described herein, we propose an authentication method called Hash-based Distributed Authentication Method (HDAM), which realizes a decentralized efficient mutual authentication mechanism for each pair of nodes in a P2P network. It performs distributed management of public keys using Web of Trust and a Distributed Hash Table. The scheme markedly reduces both the memory size requirement and the overhead of communication data sent by the nodes. Simulation results show that HDAM can reduce the required memory size by up to 95%. Furthermore, the results show that HDAM is more scalable than the conventional method: the communication overhead of HDAM is O(log p).

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This paper proposes a discriminative named entity recognition (NER) method from automatic speech recognition (ASR) results. The proposed method uses the confidence of the ASR result as a feature that represents whether each word has been correctly recognized. Consequently, it provides robust NER for the noisy input caused by ASR errors. The NER model is trained using ASR results and reference transcriptions with named entity (NE) annotation. Experimental results using support vector machines (SVMs) and speech data from Japanese newspaper articles show that the proposed method outperformed a simple application of text-based NER to the ASR results, especially in terms of improving precision.

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The lecture is one of the most valuable genres of audiovisual data. Though spoken document processing is a promising technology for utilizing the lecture in various ways, it is difficult to evaluate because the evaluation require a subjective judgment and/or the verification of large quantities of evaluation data. In this paper, a test collection for the evaluation of spoken lecture retrieval is reported. The test collection consists of the target spoken documents of about 2, 700 lectures (604 hours) taken from the Corpus of Spontaneous Japanese (CSJ), 39 retrieval queries, the relevant passages in the target documents for each query, and the automatic transcription of the target speech data. This paper also reports the retrieval performance targeting the constructed test collection by applying a standard spoken document retrieval (SDR) method, which serves as a baseline for the forthcoming SDR studies using the test collection.

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This paper describes a query-by-humming (QbH) music information retrieval (MIR) system based on a novel tonal feature and statistical modeling. Most QbH-MIR systems use a pitch extraction method in order to obtain tonal features of an input humming. In these systems, pitch extraction errors inevitably occur and degrade the performance of the system. In the proposed system, a cross-correlation function between two logarithmic frequency spectra is calculated as a tonal feature instead of a difference of two successive pitch frequencies, and probabilistic models are prepared for all tone intervals existing in the database. The similarity scores between an input humming and musical pieces in a database are calculated using the probabilistic models. The advantages of this system are that it can obtain more appropriate tonal features than the pitch-based method, and it is also robust against inaccurate humming by the user thanks to its statistical approach. From experimental results, the top-1 retrieval accuracy given by the proposed method was 86.8%, which was more than 10 points higher than the conventional single pitch method. Moreover, several integration methods were applied to the proposed method with several conditions. The majority decision method showed the highest accuracy, and 5% reduction of retrieval error was obtained.

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NGN (Next Generation Network) refers to a network developed to be suitable for an environment in which there is a convergence between wired, wireless communications and broadcasting. Most telecommunication providers have a plan or are conducting a migration of their network to NGN. One of the most important issues for constructing NGN is to provide end-to-end QoS (Quality of Service). This paper aims to propose carriers, who begin to evolve their network to NGN, a construction strategy in the interests of QoS by introducing an example of nationwide NGN in Korea. In viewing the strategy, we develop converged services, and define services provided through NGN first. Next, we define our own standard of service performance metrics, network performance metrics, and quality of service policy from the transport stratum view point. Then we design and construct NGN. Finally, we verify end-to-end performance objectives comparing predefined metrics with collected measurement data on the NGN and derive improvements. This paper deals with voice and video telephony data to analyze the traffic characteristics statistically. It should be noted that voice and video are real time data and reflect the absolute need for QoS in the network.

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A large number of embedded computers, such as network appliances and sensors, have rapidly spread out to home and office environments in the last few years. These embedded computers have enough CPU power to execute the software components that can control hardware. Managing distributed components together can enhance human activity and change the real world into a “Smart Space.” We name such collaboration of components “federated service” or “application.” In this paper, we have developed and evaluated a novel middleware named uBlocks which enables users to build and manage applications. uBlocks, unlike other distributed application-building middleware, is distinguished by two major features. The first is a flexible communication mechanism named RT/Dragon. RT/Dragon enables the connection of heterogeneous components. The second is the universal modeling of various distributed components to support building applications by multiple users in parallel. Additionally, to enable building applications in a simple way, we provide various user interfaces (UI) for multi-modal visualization: 2D/3D User Interface, and a web interface. These features lead to reduce the cost of building and managing distributed applications by the user. This research proves that the idea of building applications by users is practical and effective.

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Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common approach to compensating for the bias caused by covariate shift is to reweight the loss function according to the importance, which is the ratio of test and training densities. We propose a novel method that allows us to directly estimate the importance from samples without going through the hard task of density estimation. An advantage of the proposed method is that the computation time is nearly independent of the number of test input samples, which is highly beneficial in recent applications with large numbers of unlabeled samples. We demonstrate through experiments that the proposed method is computationally more efficient than existing approaches with comparable accuracy. We also describe a promising result for large-scale covariate shift adaptation in a natural language processing task.

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Information Retrieval (IR) test collections are growing larger, and relevance data constructed through pooling are suspected of becoming more and more incomplete and biased. Several studies have used IR evaluation metrics specifically designed to handle this problem, but most of them have only examined the metrics under incomplete but unbiased conditions, using random samples of the original relevance data. This paper examines nine metrics in more realistic settings, by reducing the number of pooled systems and the number of pooled documents. Even though previous studies have shown that metrics based on a condensed list, obtained by removing all unjudged documents from the original ranked list, are effective for handling very incomplete but unbiased relevance data, we show that these results do not hold when the relevance data are biased towards particular systems or towards the top of the pools. More specifically, we show that the condensed-list versions of Average Precision, Q-measure and normalised Discounted Cumulative Gain, which we denote as AP', Q' and nDCG', are not necessarily superior to the original metrics for handling biases. Nevertheless, AP' and Q' are generally superior to bpref, Rank-Biased Precision and its condensed-list version even in the presence of biases.

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Scientific researchers need support for their information gathering from the Web, because the growth of Internet accessibility raises the problem of Internet information overload. Social Bookmarking Service (SBS) is a promising technology to solve the problem by the benefit of collaborative information gathering. The paper describes the design and evaluation of a novel function of SBS to foster collaborative information gathering by providing mutual awareness information about browsing behaviors of SBS users. This information increases the probabilities of discovering the useful information by recommending the potential collaborators to the user. A case study on an experimental SBS was performed to evaluate the feasibility of the mutual awareness information for individuals and research communities. In order to verify the validity of the design quantitatively, an experiment was conducted using agent-based simulation based on an extension of the SIR model for epidemics. The results, either from the case study or the agent-based simulation, argue the effectiveness of the proposed function to provide mutual awareness information for fostering collaborative information gathering in SBS.

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Lighting simulation is very important in realistic image synthesis, and the simulation of subsurface scattering has recently attracted much attention. Although the dipole/multipole model has succeeded in creating realistic images, it is still difficult to deal with volumetric features in subsurface scattering, which is important when rendering optically thin objects. This paper proposes a novel rendering method that utilizes the plane-parallel solution and the ray-marching method. The ray-marching method has been used to calculate single scattering solutions, and the plane-parallel solution has been adopted to calculate BRDFs. By combining these techniques, the proposed method efficiently captures volumetric features in multiple subsurface scattering events. In our experiments, the proposed method demonstrated a performance superior to that of previous methods in terms of accuracy.

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We describe an improved way of estimating parameters for an integrated weighted-mixture model consisting of both harmonic and inharmonic tone models. Our final goal is to build an instrument equalizer (music remixer) that enables a user to change the volume of parts of polyphonic sound mixtures. To realize the instrument equalizer, musical signals must be separated into each musical instrument part. We have developed a score-informed sound source separation method using the integrated model. A remaining but critical problem is to find a way to deal with timbre varieties caused by various performance styles and instrument bodies because our method used template sounds to represent their timbre. Template sounds are generated from a MIDI tone generator based on an aligned score. Difference of instrument bodies between mixed signals and template sounds causes timbre difference and decreases separation performance. To solve this problem, we train probabilistic distributions of timbre features using various sounds to reduce template dependency. By adding a new constraint of maximizing the likelihood of timbre features extracted from each tone model, we can estimate model parameters that express the timbre more accurately. Experimental results show that separation performance improved from 4.89 to 8.48dB.

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In ACNS'06, Cliff, et al. proposed the password-based server aided key exchange (PSAKE) as one of password-based authenticated key exchanges in the three-party setting (3-party PAKE) in which two clients with different passwords exchange a session key with the help of their corresponding server. Though they also studied a strong security definition of the 3-party PAKE, their security model is not strong enough because there are desirable security properties which cannot be captured. In this paper, we define a new formal security model of the 3-party PAKE which is stronger than the previous model. Our model captures all known desirable security requirements of the 3-party PAKE, like the resistance to key-compromise impersonation, to the leakage of ephemeral private keys of servers and to the undetectable on-line dictionary attack. Also, we propose a new scheme as an improvement of PSAKE with the optimal number of rounds for a client, which is secure in the sense of our model.

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Recently, cryptographic schemes based on the user's attributes have been proposed. An Attribute-Based Group Signature (ABGS) scheme is a kind of group signature scheme, where a user with a set of attributes can prove anonymously whether she has these attributes or not. An access tree is applied to express the relationships among some attributes. However, previous schemes did not provide a way to change an access tree. In this paper, we propose a dynamic ABGS scheme that can change an access tree. Our ABGS is efficient in that re-issuing of the attribute certificate previously issued for each user is not necessary. The number of calculations in a pairing does not depend on the number of attributes in both signing and verifying. Finally, we discuss how our ABGS can be applied to an anonymous survey for collection of attribute statistics.

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Recently, source IP spoofing attacks are critical issues for the Internet. These attacks are considered to be sent from bot infected hosts. There has been active research on IP traceback technologies. However, the traceback from an end victim host to an end spoofing host has not yet been achieved, due to the lack of traceback probes installed on each routing path. Alternative probes should be employed in order to reduce the installation cost. In this research, we propose an IP traceback scheme against bots using DNS logs of existing servers. Many types of bots retrieve IP addresses of victim hosts from fully qualified domain names (FQDNs) at the beginning of an attack. The proposed scheme checks from the destination to the source DNS logs, in order to extract the actual IP addresses of bot infected hosts. Also, we propose a scheme to ascertain the reliability of traceback results, and a method to distinguish spoofing from non-spoofing attacks. We collect bot communication patterns to confirm that the DNS log can be used for reasonable probes and for achieving a high traceback success rate.

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In general, standard benchmark suites are critically important for researchers to quantitatively evaluate their new ideas and algorithms. This paper proposes CHStone, a suite of benchmark programs for C-based high-level synthesis. CHStone consists of a dozen of large, easy-to-use programs written in C, which are selected from various application domains. This paper also analyzes the characteristics of the CHStone benchmark programs, which will be valuable for researchers to use CHStone for the evaluation of their new techniques. In addition, we present future challenges to be solved towards the practical high-level synthesis.

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The sensor nodes of wireless sensor networks are placed in observation areas and transmit data to the observer by using multi-hop communication between nodes. Because these nodes are small and have a limited power supply, they must save power in order to prolong the network's lifetime. We propose HGAF (Hierarchical Geographic Adaptive Fidelity) to give a layered structure to GAF (Geographic Adaptive Fidelity), a power saving technique using location information in sensor networks. Simulation results reveal that HGAF outperforms GAF in terms of the number of survived nodes and packet delivery ratio when the node density is high. The lifetime of dense and randomly distributed sensor networks with HGAF is about 200% as long as ones with GAF.

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This paper presents a modified QIM-JPEG2000 steganography which improves the previous JPEG2000 steganography using quantization index modulation (QIM). It does not increase the post-embedding file size, producing less degraded stego images. Steganalysis experiments show that the modified QIM-JPEG2000 is more secure than the previous QIM-JPEG2000 and is the most secure among major steganographic methods for JPEG2000 ever proposed.

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In this paper, we present a backup technique for Peer to Peer applications, such as a distributed asynchronous Web-Based Training system that we have previously proposed. In order to improve the scalability and robustness of this system, all contents and functions are realized on mobile agents. These agents are distributed to computers, and using a Peer to Peer network that modified Content-Addressable Network they can obtain. In the proposed system, although entire services do not become impossible even if some computers break down, the problem that contents disappear occurs with an agent's disappearance. As a solution for this problem, backups of agents are distributed to computers. If failures of computers are detected, other computers will continue service using backups of the agents belonging to the computer. The developed algorithms are examined by experiments.

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In this study, the authors propose and implement a particle display system (PDS) that consists of hundreds of randomly distributed pixels. The wireless capability of this system enables each node to move freely without distant limitation of the use of wire cables. The authors also propose effective visual presentation techniques for a display system with randomly distributed pixels. One of the optimization techniques involves the extension of a well-known phenomenon where humans can perceive two-dimensional static or moving images from a set of high-frequency flashing one-dimensional pixel arrays, such as LED arrays, as a characteristic of a human's vision system. While this technique can only extend the virtual resolution of a display in a direction perpendicular to the aligned pixels, our technique enables the display of multi-directional scrolling of two-dimensional images with randomly distributed pixels. In addition, the advantages of presenting information on a display with nonuniform pixel distribution and virtual pixels with fast flash of pixels are discussed. The proposed techniques help in reducing the cost of installing a large-scale display and the time taken for the initial preparation of the setup, which involves carrying large pixel arrays and determining the precise size and shape of the display.

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This paper describes Musicream, a novel music-listening interface that lets a user unexpectedly come across various musical pieces similar to those liked by the user. Most existing “query-by-example” interfaces are based on similarity-based searching, so they return the same results for the same query, meaning that a user of those systems always receives the same list of musical pieces sorted by similarity. Therefore, most existing systems do not provide a user an opportunity to encounter various unfamiliar musical pieces by chance. Musicream facilitates active, flexible, and unexpected encounters with musical pieces by providing four functions: the music-disc streaming functionwhich creates a flow of many musical-piece entities (discs) from a large music collection, the similarity-based sticking functionwhich allows a user to easily pick out and listen to similar pieces from the flow, the meta-playlist functionwhich can generate a playlist of playlists (ordered lists of pieces), and the time-machine functionwhich automatically records all Musicream activities and allows a user to visit and retrieve a past state as if using a time machine. In our experiments, these functions were used seamlessly to achieve active and creative querying and browsing of music collections, confirming the effectiveness of Musicream.

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The development of wireless technologies, cellular networks, WiFi, and Bluetooth has created heterogeneous network environments. In these environments, users can access anything at anytime, anywhere using these wireless technologies; however, in order to make full use of such wireless networks, users need to discover whether or not wireless networks exist in their vicinity and select the most appropriate one. Then, users have to obtain and input parameter settings for the wireless networks to begin communication. In this paper, we propose a network composition framework that enables automatic connections to a wireless network suitable for the user's context with minimal interaction. Based on this framework, we introduce network composition procedures which realize network discovery, network selection, configuration information notification, and device configuration with the support of a cellular network connection. We implement the proposed framework and procedures in a real environment comprised of cellular phones and laptop PCs. We examine implemented functions and their performance using this experimental implementation and present several attractive examples of actual use.

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This paper presents a matrix-based algorithm for integrating inheritance relations of access rights for generating integrated access control policies which unify management of various access control systems. Inheritance relations of access rights are found in subject, resource, and action categories. Our algorithm first integrates inheritance relations in each category, and next, integrates inheritance relations of all categories. It is shown that these operations can be carried out by basic matrix operations. This enables us to implement the integration algorithm very easily.

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