Password-protected secret sharing (PPSS, for short) schemes were proposed by Bagherzandi, Jarecki, Saxena and Lu. In this paper, we consider another attack for PPSS schemes which is based on public parameters and documents. We show that the protocol proposed by Bagherzandi et al. is broken with the attack. We then propose an enhanced protocol which is secure against the attack.

A method to synthesize facial caricatures with non-planar expression is proposed. Several methods have been already proposed to synthesize facial caricatures automatically, but they mainly synthesize plane facial caricatures which look somewhat monotonous. In order to generate expressive facial caricature, the image should be expressed in non-planar style, expressing the depth of the face by shading and highlighting. In this paper, a new method to express such non-planar effect in facial caricatures is proposed by blending the grayscale information of the real face image into the plane caricature. Some methods also have been proposed to generate non-planar facial caricature, but the proposed method can adjust the degree of non-planar expression by interactive evolutionary computing, so that the obtained expression is satisfied by the user based on his/her subjective criteria. Since the color of the face looks changed, when the grayscale information of the natural face image is mixed, the color information of the skin area are also set by interactive evolutionary computing. Experimental results show the high performance of the proposed method.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

In this paper, a hybrid parallel implementation of inverse matrix computation using SMW formula is proposed. By aggregating the memory bandwidth in the hybrid parallel implementation, the bottleneck due to the memory bandwidth limitation in the authors previous multicore implementation has been dissolved. More than 8 times of speed up is also achieved with dual-core 8-nodes implementation which leads more than 20 simulation steps per second, or near real-time performance.

In this paper, we propose an automatic parameter adjustment method for audio equalizers using an interactive genetic algorithm (IGA). It is very difficult for ordinary users who are not familiar with audio devices to appropriately adjust the parameters of audio equalizers. We therefore propose a system that can automatically adjust the parameters of audio equalizers on the basis of user's evaluation of the reproduced sound. The proposed system utilizes an IGA to adjust the gains and Q values of the peaking filters included in audio equalizers. Listening test results demonstrate that the proposed system can appropriately adjust the parameters on the basis of the user's evaluation.

An anonymous credential system enables individuals to selectively prove their attributes while all other knowledge remains hidden. We considered the applicability of such a system to large scale infrastructure systems and perceived that revocations are still a problem. Then we contrived a scenario to lessen the number of revocations by using more attributes. In this scenario, each individual needs to handle a huge number of attributes, which is not practical with conventional systems. In particular, each individual needs to prove small amounts of attributes among a huge number of attributes and the manager of the system needs to certify a huge number of attributes of individuals periodically. These processes consume extremely large resources. This paper proposes an anonymous credential system in which both a user's proving attributes set, which is included in a huge attribute set, and manager's certifying attributes are very efficient. Conclusion Our proposal enables an anonymous credential system to be deployed as a large scale infrastructure system.

This paper presents a new non-interactive multi-trapdoor commitment scheme from the standard RSA assumption. Multi-trapdoor commitment is a stronger variant of trapdoor commitment. Its notion was introduced by Gennaro at CRYPTO 2004. Multi-trapdoor commitment schemes are very useful because we can convert a non-interactive multi-trapdoor commitment scheme into a non-interactive and reusable non-malleable commitment scheme by using one-time signature and transform any proof of knowledge into a concurrently non-malleable one (this can be used as concurrently secure identification). Gennaro gave concrete constructions of multi-trapdoor commitment, but its security relies on stronger assumptions, such as the strong RSA assumption and the q-strong Diffie-Hellman assumption as opposed to our construction based on the standard RSA assumption. As a corollary of our results, we constructed a non-interactive and reusable non-malleable commitment scheme from the standard RSA assumption. Our scheme is based on the Hohenberger-Waters (weak) signature scheme presented at CRYPTO 2009. Several non-interactive and reusable non-malleable commitment schemes (in the common reference string model) have been proposed, but they all rely on stronger assumptions (such as the strong RSA assumption). Thus, we give the first construction of a non-interactive and reusable non-malleable commitment scheme from the standard RSA assumption.

We revisit the problem with generic object recognition from the point of view of human-computer interaction. While many existing algorithms for generic object recognition first try to detect target objects before features are extracted and classified in processing, our work is motivated by the belief that solving the task of detection by computer is not always necessary in many practical situations, such as those involving mobile recognition systems with touch displays and cameras. It is natural for these systems to ask users to input the segmentation data for targets through their touch displays. Speaking from the perspective of usability, such systems should involve rough segmentation to reduce the user workload. In this situation, different people would provide different segmentation data. Here, an interesting question arises – if multiple training samples are generated from a single image by using various segmentation data created by different people, what would happen to the accuracy of classification? We created “20 wild bird datasets” that had a large number of rough segmentation datasets made by 383 people in an attempt to answer this question. Our experiments revealed two interesting facts: (i) generating multiple training samples from a single image had positive effects on classification accuracies, especially when image features including spatial information were used and (ii) augmenting training samples with artificial segmentation data synthesized with a morphing technique also had slightly positive effects on classification accuracies.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

This paper introduces an interactive expression editing system that allows users to design facial expressions easily. Currently, popular example-based methods construct face models based on the examples of target face. The shortcoming of these methods is that they cannot create expressions for novel faces: target faces not previously recorded in the database. We propose a solution to overcome this limitation. We present an interactive facial-geometric-feature animation system for generating expressions of novel faces. Our system is easy to use. By click-dragging control points on the target face, on the computer screen display, unique expressions are generated automatically. To guarantee natural animation results, our animation model employs prior knowledge based on various individuals' expressions. One model prior is learned from motion vector fields to guarantee effective facial motions. Another, different, model prior is learned from facial shape space to ensure the result has a real facial shape. Interactive animation problem is formulated in a maximum a posterior (MAP) framework to search for optimal results by combining the priors with user-defined constraints. We give an extension of the Motion Propagation (MP) algorithm to infer facial motions for novel target faces from a subset of the control points. Experimental results on different facial animations demonstrate the effectiveness of the proposed method. Moreover, one application of our system is exhibited in this paper, where users create expressions for facial sketches interactively.

In our previous project, an XML-based authoring tool was provided for teachers to script multimedia teaching material with animated agents, and a stand-alone learning system was designed for students to display the material and interact with animated agents. We also provided evidence that the authoring tool and learning system in computer-assisted learning systems successfully enhances learning performance. The aim of this study is to continue the previous project, to develop a Web-based multimedia learning system that presents materials and an animated agent on a Web browser. The Web-based multimedia learning system can provide an opportunity for students to engage in independent learning or review of school course work. In order to demonstrate the efficiency of this learning system, it was applied to one elementary school. An experimental material, `Road Traffic Safety', was presented in two learning systems: a Web-based PowerPoint learning system and a Web-based multimedia learning system. The experiment was carried out in two classes that had a total of thirty-one 3rd-grade students. The results suggest that using our authoring tool in a Web-based learning system can improve learning, and in particular, enhance learners' problem-solving ability. Students with higher achievement on the post-test showed better comprehension in problem-solving questions. Furthermore, the feedback from the questionnaire surveys show students' learning interest can be fostered when an animated agent is integrated into multimedia teaching materials, and that students prefer to adopt the Web-based multimedia learning system for independent learning after school.

An interactive support system for image quality enhancement to adjust display equipments according to the user's own subjectivity is developed. Interactive support system for image quality enhancement enable the parameters based on user's preference to be derived by only selecting user's preference images without adjusting image quality parameters directly. In the interactive support system for image quality enhancement, the more the number of parameters is, the more effective this system is. In this paper, lightness, color and sharpness are used as the image quality parameters and the images are enhanced by increasing the number of parameters. Shape of tone curve is controlled by two image quality adjustment parameters for lightness enhancement. Images are enhanced using two image quality adjustment parameters for color enhancement. The two parameters are controlled in L* a* b* color space. Degree and coarseness of image sharpness enhancement are adjusted by controlling a radius of mask of smoothing filter and weight of adding. To confirm the effectiveness of the proposed method, the image quality and derivation time of the proposed method are compared with a manual adjustment method.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

The recent growth in available network bandwidth envisions the wide-spread use of broadband applications such as uncompressed HD-SDI (High-definition serial digital interface) over IP. These cutting-edge applications are also driving the development of a media-oriented infrastructure for networked collaboration. This paper introduces imCast, a high-quality digital media platform dealing with uncompressed HD-SDI over IP, and discusses its internal architecture in depth. imCast mainly provides cost-effective hardware-based approaches for high-quality media acquisition and presentation; flexible software-based approaches for presentation; and allows for economical network transmission. Experimental results (taken over best-effort IP networks) will demonstrate the functional feasibility and performance of imCast.

This paper proposes an adaptive transmission control method for massive and intensive telecommunication traffic generated by communication-broadcasting integrated services. The proposed method adaptively controls data transmissions from viewers depending on the congestion states, so that severe congestion can be effectively avoided. Furthermore, it utilizes the broadcasting channel which is not only scalable, but also reliable for controlling the responses from vast numbers of viewers. The performance of the proposed method is evaluated through experiments on a test bed where approximately one million viewers are emulated. The obtained results quantitatively demonstrate the performance of the proposed method and its effectiveness under massive and intensive traffic conditions.

In 2006, Tanaka has proposed an efficient variant of Maurer-Yacobi's identity-based non-interactive key sharing scheme. In Tanaka's scheme, the computational complexity to generate each user's secret information is much smaller than that of Maurer-Yacobi's scheme. Tanaka's original key sharing scheme does not provide completeness, and so Tanaka has corrected the original scheme to provide completeness. In this paper, we show that Tanaka's corrected key sharing scheme is not secure against collusion attacks. That is, two users can collaborate to factorize a system modulus with their secret information and thus break the key sharing scheme.

This letter proposes a prototyping tool for Web-based Multiuser Online Role-Playing Game (MORPG). The design goal is to make this tool simple and powerful. The tool is comprised of a GUI editor, a translator and a runtime environment. The GUI editor is used to edit state-transition diagrams, each of which defines the behavior of the fictional characters. The state-transition diagrams are translated into C program codes, which plays the role of a game engine in RPG system. The runtime environment includes PHP, JavaScript with Ajax and HTML. So the prototype system can be played on the usual Web browser, such as Firefox, Safari and IE. On a click or key press by a player, the Web browser sends it to the Web server to reflect its consequence on the screens which other players are looking at. Prospected users of this tool include programming novices and schoolchildren. The knowledge or skill of any specific programming languages is not required to create state-transition diagrams. Its structure is not only suitable for the definition of a character behavior but also intuitive to help novices understand. Therefore, the users can easily create Web-based MORPG system with the tool.

Hwang-Lo-Lin proposed a user identification scheme [3] based on the Maurer-Yacobi scheme [6] that is suitable for application to the mobile environment. Hwang-Lo-Lin argued that their scheme is secure against any attack. Against the Hwang-Lo-Lin argument, Liu-Horng-Liu showed that the Hwang-Lo-Lin scheme is insecure against a Liu-Horng-Liu attack mounted by an eavesdrop attacker. However, Liu-Horng-Liu did not propose any improved version of the original identification scheme which is still secure against the Liu-Horng-Liu attack. In this paper, we propose an identification scheme that can solve this problem and a non-interactive public key distribution scheme also.

This paper presents a new interactive learning method for spoken word acquisition through human-machine audio-visual interfaces. During the course of learning, the machine makes a decision about whether an orally input word is a word in the lexicon the machine has learned, using both speech and visual cues. Learning is carried out on-line, incrementally, based on a combination of active and unsupervised learning principles. If the machine judges with a high degree of confidence that its decision is correct, it learns the statistical models of the word and a corresponding image category as its meaning in an unsupervised way. Otherwise, it asks the user a question in an active way. The function used to estimate the degree of confidence is also learned adaptively on-line. Experimental results show that the combination of active and unsupervised learning principles enables the machine and the user to adapt to each other, which makes the learning process more efficient.