The security notion of indifferentiability was proposed by Maurer, Renner, and Holenstein in 2004. In 2005, Coron, Dodis, Malinaud, and Puniya discussed the indifferentiability of hash functions. They have shown that the Merkle-Damgård construction is not secure in the sense of indifferentiability. In this paper, we analyze the security of single-block-length and rate-1 compression functions in the sense of indifferentiability. We formally show that all single-block-length and rate-1 compression functions, which include the Davies-Meyer compression function, are insecure. Furthermore, we show how to construct a secure single-block-length and rate-1 compression function in the sense of indifferentiability. This does not contradict our result above.

We propose in this paper a SOM-like algorithm that accepts online, as inputs, starts and ends of viewing of a multimedia content by many users; a one-dimensional map is then self-organized, providing an approximation of density distribution showing how many users see a part of a multimedia content. In this way "viewing behavior of crowds" information is accumulated as experience accumulates, summarized into one SOM-like network as knowledge is extracted, and is presented to new users as the knowledge is transmitted. Accumulation of multimedia contents on the Internet increases the need for time-efficient viewing of the contents and the possibility of compiling information on many users' viewing experiences. In the circumstances, a system has been proposed that presents, in the Internet environment, a kind of summary of viewing records of many viewers of a multimedia content. The summary is expected to show that some part is seen by many users but some part is rarely seen. The function is similar to websites utilizing "wisdom of crowds" and is facilitated by our proposed algorithm.

A Verilog-AMS model of a fractional-N frequency synthesizer is presented that is capable of predicting spurious tones as well as noise and jitter performance. The model is based on a voltage-domain behavioral simulation. Simulation efficiency is improved by merging the voltage controlled oscillator (VCO) and the frequency divider. Due to the benefits of Verilog-AMS, the ΔΣ modulator which is incorporated in the synthesizer is modeled in a fully digital way. This makes it accurate enough to evaluate how the performance of the frequency synthesizer is affected by cyclic behavior in the ΔΣ modulator. The spur-minimizing effect of an odd initial condition on the first accumulator of the ΔΣ modulator is verified. Sequence length control and its effect on the fractional-N frequency synthesizer are also discussed. The simulated results are in agreement with prior published data on fractional-N synthesizers and with new measurement results.

This paper describes logic and analog test schemes that improve the testability of a pixel-parallel fingerprint identification circuit. The pixel contains a processing circuit and a capacitive fingerprint sensor circuit. For the logic test, we propose a test method using a pseudo scan circuit to check the processing circuits of all pixels simultaneously. In the analog test, the sensor circuit employs dummy capacitance to mimic the state of a finger touching the chip. This enables an evaluation of the sensitivity of all sensor circuits on logical LSI tester without touching the chip with a finger. To check the effectiveness of the schemes, we applied them to a pixel array in a fingerprint identification LSI. The pseudo scan circuit achieved a 100% failure-detection rate for the processing circuit. The analog test determines that the sensitivities of the sensor circuit in all pixels are in the proper range. The results of the tests confirmed that the proposed schemes can completely detect defects in the circuits. Thus, the schemes will pave the way to logic and analog tests of chips integrating highly functional devices stacked on a LSI.

The support vector machines (SVMs) are one of the most effective classification techniques in several knowledge discovery and data mining applications. However, a SVM requires the user to set the form of its kernel function and parameters in the function, both of which directly affect to the performance of the classifier. This paper proposes a novel method, named a kernel-tree, the function of which is composed of multiple kernels in the form of a tree structure. The optimal kernel tree structure and its parameters is determined by genetic programming (GP). To perform a fine setting of kernel parameters, the gradient descent method is used. To evaluate the proposed method, benchmark datasets from UCI and dataset of text classification are applied. The result indicates that the method can find a better optimal solution than the grid search and the gradient search.

This study introduces the fuzzy Lyapunov function to the fuzzy PID control systems, modified fuzzy systems, with an optimized robust tracking performance. We propose a compound search strategy called conditional linear matrix inequality (CLMI) approach which was composed of the proposed improved random optimal algorithm (IROA) concatenated with the simplex method to solve the linear matrix inequality (LMI) problem. If solutions of a specific system exist, the scheme finds more than one solutions at a time, and these fixed potential solutions and variable PID gains are ready for tracking performance optimization. The effectiveness of the proposed control scheme is demonstrated by the numerical example of a cart-pole system.

This paper proposes a soft-error model for accurately estimating reliability of a computer system at the architectural level within reasonable computation time. The architectural-level soft-error model identifies which part of memory modules are utilized temporally and spatially and which single event upsets (SEUs) are critical to the program execution of the computer system at the cycle accurate instruction set simulation (ISS) level. The soft-error model is capable of estimating reliability of a computer system that has several memory hierarchies with it and finding which memory module is vulnerable in the computer system. Reliability estimation helps system designers apply reliable design techniques to vulnerable part of their design. The experimental results have shown that the usage of the soft-error model achieved more accurate reliability estimation than conventional approaches. The experimental results demonstrate that reliability of computer systems depends on not only soft error rates (SERs) of memories but also the behavior of software running in computer systems.

A chaotic UWB communication system based on IEEE 802.15.4a is proposed for wireless connectivity applications. A compact and simple architecture is implemented by using a chaotic UWB signal and a non-coherent detection scheme. The chaotic UWB signal has noise-like characteristics in time and frequency domains and naturally wide spectrum within a limited bandwidth. The chaotic UWB signal generator is designed on two methods with the bandwidth of 3.1 to 5.1 GHz, and a baseband process is realized on an FPGA including an adaptive decision and a channel code for non-source coded data stream. The system performance is evaluated by transmitting MP3 audio/voice with 32-byte length PSDUs and measuring PERs for assessing the system sensitivity and the interferer compatibility. The proposed system can be an excellent candidate for short-range connectivity services, as well as an inexpensive system with good capability for narrow-band interferences.

In this paper, we present a novel force-directed method for automatically drawing intersecting compound mixed graphs (ICMGs) that can express complicated relations among elements such as adjacency, inclusion, and intersection. For this purpose, we take a strategy called unified simplification that can transform layout problem for an ICMG into that for an undirected graph. This method is useful for various information visualizations. We describe definitions, aesthetics, force model, algorithm, evaluation, and applications.

In this paper, a computationally efficient stack-based iterative detection algorithm is proposed for V-BLAST systems. To minimize the receiver's efforts as much as possible, the proposed scheme employs iterative tree search for complexity reduction and storage saving. After an M-ary tree structure by QR decomposition of channel matrix is constructed, the full tree depth is divided into the first depth and the remaining ones. At tree depth of one, the proposed algorithm finds M candidate symbols. Based on these symbols, it iteratively searches the remaining symbols at second-to-last depth, until finding an optimal symbol sequence. Simulation results demonstrate that the proposed algorithm yields the performance close to that of sphere detection (SD) with significant saving in complexity and storage.

Named entities play an important role in many Natural Language Processing applications. Currently, most named entity recognition systems rely on a small set of general named entity (NE) types. Though some efforts have been proposed to expand the hierarchy of NE types, there are still a fixed number of NE types. In real applications, such as question answering or semantic search systems, users may be interested in more diverse specific NE types. This paper proposes a method to extract categories of person named entities from text documents. Based on Dual Iterative Pattern Relation Extraction method, we develop a more suitable model for solving our problem, and explore the generation of different pattern types. A method for validating whether a category is valid or not is proposed to improve the performance, and experiments on Wall Street Journal corpus give promising results.

In a community which consists of a large number of people interacting with each other, social dilemma is an important problem. This problem occurs when the payoff of each person conflicts with the total payoff of the community which he/she belongs to. Evolutionary game theory has been used as a powerful mathematical framework to analyze such a social problem. Recently, the authors have proposed replicator dynamics with reallocation of payoffs. In this model, the government is willing to lead the population to a desirable goal state by using collections and reallocations of payoffs. In this paper, we investigate this model, and show conditions for the goal state to be a locally or a globally asymptotically stable equilibrium point, respectively. We also propose a government's strategy depends on population states which can stabilize the goal state globally.

Since we can accumulate a large amount of data including useless information in recent years, it is important to investigate various extraction method of clusters from data including much noises. The Self-Organizing Map (SOM) has attracted attention for clustering nowadays. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction. TSOM consists of two kinds of SOM whose features are different, namely, one self-organizes the area where input data are concentrated, and the other self-organizes the whole of the input space. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. We apply TSOM to data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that we do not know the number of clusters in advance.

In this letter, we propose an enhanced method for inter-layer motion prediction in scalable video coding (SVC). For inter-layer motion prediction, the use of refined motion data in the Fine Granular Scalability (FGS) layer is proposed instead of the conventional use of motion data in the base quality layer to reduce the inter-layer redundancy efficiently. Experimental results show that the proposed method enhances coding efficiency without increasing the computational complexity of the decoder.

In the acquisition of projection data of X-ray CT, logarithm operation is indispensable. But noise distribution is nonlinearly projected by the logarithm operation, and this deteriorates the precision of CT number. This influence becomes particularly remarkable when only a few photons are caught with a detector. It generates a strong streak artifact (SA) in a reconstructed image. Previously we have clarified the influence of the nonlinearity by statistical analysis and proposed a correction method for such nonlinearity. However, there is a problem that the compensation for clamp processing cannot be performed and that the suppression of SA is not enough in photon shortage state. In this paper, we propose a new technique for correcting the nonlinearity due to logarithm operation for noisy data by combining the previously presented method and an adaptive filtering method. The technique performs an adaptive filtering only when the number of captured photons is very few. Moreover we quantitatively evaluate the influence of noise on the reconstructed image in the proposed method by the experiment using numerical phantoms. The experimental results show that there is less influence on spatial resolution despite suppressing SA effectively and that CT number are hardly dependent on the number of the incident photons.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

Expansion of imagination is crucial for lively creativity. However, such expansion is sometimes rather difficult and an environment which supports creativity is required. Because people can attain higher creativity by using words with a thematic relation rather than words with a taxonomical relation, we tried to extract word lists having thematic relations among words. We first extracted word lists from domain specific documents by utilizing inclusive relations between words based on a modifiee/modifier relationship in documents. Next, from the extracted word lists, we removed the word lists having taxonomical relations so as to obtain only word lists having thematic relations. Finally, based on the assumption what kind of knowledge a person can associate when he/she looks at a set of words correlates with how the word set is effective in creativity support, we examined whether the word lists direct us to informative pages on the Web for verifying the availability of our extracted word lists.

Many researchers have attempted to solve the combinatorial optimization problems, that are NP-hard or NP-complete problems, by using neural networks. Though the method used in a neural network has some advantages, the local minimum problem is not solved yet. It has been shown that the Inverse Function Delayed (ID) model, which is a neuron model with a negative resistance on its dynamics and can destabilize an intended region, can be used as the powerful tool to avoid the local minima. In our previous paper, we have shown that the ID network can separate local minimum states from global minimum states in case that the energy function of the embed problem is zero. It can achieve 100% success rate in the N-Queen problem with the certain parameter region. However, for a wider parameter region, the ID network cannot reach a global minimum state while all of local minimum states are unstable. In this paper, we show that the ID network falls into a particular permanent oscillating state in this situation. Several neurons in the network keep spiking in the particular permanent oscillating state, and hence the state transition never proceed for global minima. However, we can also clarify that the oscillating state is controlled by the parameter α which affects the negative resistance region and the hysteresis property of the ID model. In consequence, there is a parameter region where combinatorial optimization problems are solved at the 100% success rate.

A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.