Neuromorphic computing achieves highly energy-efficient computations while adapting to environmental changes. Processing time series data by spiking neural networks can further reduce the power consumption of neuromorphic computing devices because most of the energy in the network is consumed only when the neuron generates and transmits a spike. In this study, we designed fully analog two-variable spiking neuron and spiking neural network circuits, taking advantage of the physical properties of transistors as analog devices. The energy consumption of the circuit for generating a spike was 22.7 fJ/spike when the MOS transistors were operating in the subthreshold region. The proposed circuits were implemented on an analog very large-scale integrated (VLSI) circuit chip in a$0.18~\mu $m CMOS process. The circuits exhibit complex spike dynamics even under subthreshold operation according to chip measurements. We demonstrated that the spike sequence generated by the spiking neural network circuit was successfully applied to spoken digit recognition tasks via a reservoir computing framework with 14.4 fJ/SOP efficiency. These results provide important insights into edge AI applications of SNN-based neuromorphic hardware.

Research has shown that deep neural networks (DNNs) have vulnerabilities that can lead to the misrecognition of Adversarial Examples (AEs) with specifically designed pertur-bations. Various adversarial attack methods have been proposed to detect vulnerabilities under hard-label black box (HL-BB) conditions in the absence of loss gradients and confidence scores. However, these methods fall into local solutions because they search only local regions of the search space. Therefore, this study proposes an adversarial attack method named EvolBA to generate AEs using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) under the HL-BB condition, where only a class label predicted by the target DNN model is available. Inspired by formula-driven supervised learning, the proposed method introduces domain-independent operators for the initial-ization process and a jump that enhances search exploration. Experimental results confirmed that the proposed method could determine AEs with smaller perturbations than previous methods in images where the previous methods have difficulty.

This paper presents an interactive method for designing robot facial expressions and motions of an intelligent transformable phone robot BaBi. Designing an objective function to evaluate facial expressions and motions suitable for BaBi with an original structure is quite hard although users can easily imagine favorite motions. Therefore, the proposed method employs Interactive Evolutionary Computation (IEC) in addition to general optimization. The method allows users to add candidates to a case base when finding satisfying candidates and evaluates candidates' scores according to the case base. It applies a Different Evolution (DE) method to generate new candidates. Users may alternately push the buttons to generate face and body motion without making any selection of candidates with a selection interface. The method will make selection with the scores when users do not make any selection of candidates. Users can select or not select. It is an IEC process when users make selections, while it is a non-Interactive Evolutionary Computation (non-IEC) process when users do not make any selection, resulting in being a fusion of IEC and non-IEC. Adding more candidates to the case base can speed up generating the desired motion and promote the non-IEC progress and reduce the burden on users. Experimental results have shown that the proposed method produces the robot motions effectively.

This paper proposes an adversarial attack method to deep neural networks (DNNs) for monocular depth estimation, i.e., estimating the depth from a single image. Single image depth estimation has improved drastically in recent years due to the development of DNNs. However, vulnerabilities of DNNs for image classification have been revealed by adversarial attacks, and DNNs for monocular depth estimation could contain similar vulnerabilities. Therefore, research on vulnerabilities of DNNs for monocular depth estimation has spread rapidly; however, many of them assume white-box conditions where inside information of DNNs is available or are transferability-based black-box attacks that require a substitute DNN model and a training dataset. Utilizing Evolutionary Multi-objective Optimization, the proposed method in this paper analyzes DNNs under the black-box condition where only output depth maps are available. In addition, the proposed method does not require a substitute DNN that has a similar architecture to the target DNN nor any knowledge about training data used to train the target model. Experimental results showed that the proposed method succeeded in attacking two DNN-based methods trained with indoor and outdoor scenes, respectively.

2D codes are widely used as a shortcut from analog media to online information, and it has been used for authentication of boarding passes of airplanes and trains. Especially in recent years, 2D codes have become popular as a means of cashless payment. However, they are subject to distortion when printed on non-rigid materials, such as papers and clothes. Although general 2D code decoders correct uniform distortion such as perspective distortion, it is difficult to correct non-uniform and irregular distortion of the 2D code itself. This paper proposes a method that can decode 2D codes if they are distorted irregularly and non-uniformly. The proposed method formulates a problem of recognizing the relative position of edges constituting 2D code modules as a combinatorial optimization problem, and locates all modules to reconstruct 2D codes by solving the problem. Experimental results showed that the proposed method decodes the irregularly distorted 2D codes without the help of auxiliary patterns embedded in 2D codes.

This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels to produce AEs. Thanks to EMO's property of population based-search, the proposed method produces various types of AEs involving ones locating between AEs generated by the previous two approaches, which helps to know the characteristics of a target model or to know unknown attack patterns. Experimental results showed the potential of the proposed method, e.g., it can generate robust AEs and, with the aid of DCT-based perturbation pattern generation, AEs for high resolution images.

This study proposes a novel distance metric learning method called evolutionary distance metric learning (EDML) to improve clustering quality that simultaneously evaluates inter- and intra-clusters. While we also provide an extension which integrates kernelization technique to the proposed method n amely kernelized evolutionary distance metric learning (K-EDML). Hence, the non-linear transformation of distance metric can be performed while maintaining all properties of EDML. The proposed methods are able to handle either class label or pairwise constraints and directly improve any clustering index as an objective function. Both can be viewed as utilization of cluster-level soft constraints, unlike other instance-level hard constraints which sometimes collapse the clustering. Also, maintaining neighbor relation of clusters can lead to better visualization of the clustering result. For multimodality problem of the objective function, an evolutionary algorithm (EA), differential evolution with self-adapting control parameters and generalized opposition-based learning (GOjDE), is employed to optimize a metric transform matrix based on the Mahalanobis distance. We empirically demonstrate the drawback of EDML in non-linearly separable input space and illustrate the benefit of kernel function to extension K-EDML method by showing its superior result benefits to other clustering algorithms in the semi-supervised clustering on various real-world datasets.

Many research studies on distance metric learning (DML) reiterate that the definition of distance between two data points substantially affects clustering tasks. Recently, variety of DML methods have been proposed to improve the accuracy of clustering by learning a distance metric; however, most of them only perform a linear transformation, which yields insignificant to non-linear separable data. This study proposes a DML method which provides an integration of kernelization technique with Mahalanobis-based DML. Thus, non-linear transformation of the distance metric can be performed. Moreover, a cluster validity index is optimized by an evolutionary algorithm. The empirical results on semi-supervised clustering suggest the promising result on both synthetic and real-world data set.

One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. In this paper, we impose multiple projection patterns into each single captured image to realize temporal super resolution of the depth image sequences. With our method, multiple patterns are projected onto the object with higher fps than possible with a camera. In this case, the observed pattern varies depending on the depth and motion of the object, so we can extract temporal information of the scene from each single image. The decoding process is realized using a learning-based approach where no geometric calibration is needed. Experiments confirm the effectiveness of our method where sequential shapes are reconstructed from a single image. Both quantitative evaluations and comparisons with recent techniques were also conducted.

Surrogate-Assisted Evolutionary Computation (SAEC) has widely applied to approximate an objective function. However, SAEC may potentially also reduce the processing time of inexpensive optimization problems wherein solutions are evaluated within a few seconds or minutes. To achieve this, the approximation model of a fitness function should be iterated as few times as possible during optimization. This paper proposes an adaptive SAEC algorithm using the rank correlations between the actually evaluated and approximately evaluated values of the objective function. These correlations are then used to adaptively switch the approximation and actual evaluation phases, reducing the number of runs required to learn the approximation model. It was confirmed experimentally that the proposed method could successfully reduce the processing time in some benchmark functions even under inexpensive scenario.

QR code, a kind of two-dimensional barcode, is used as shortcuts from physical media to online information in addition to object recognition purpose. However, QR code has become visually unpleasant with the global popularization of QR code. This paper proposes a method to decorate QR code by arranging module (black and white cell) patterns to be similar to a target image. The proposed method takes a hierarchical approach; upper layer solver using Cooperative Evolution by User and System (CEUS) arranges input images, i.e., location, pose, and scale to form the target image, and lower layer solver based on greedy search controls module pattern of QR code according to the target. A QR code designed by the proposed method completely satisfies QR code specifications and has visual meaning for human. Unlike the previous work controlling module patterns by codewords, the proposed method controls module patterns by modules without being influenced codeword region shape. In addition, CEUS allows users to obtain the most desirable solution with less manual operation.

In the ocean around the world, depth series ocean data of temperature and salinity have being measured. However, it is difficult to discriminate the errors from the normal data since the variation of ocean areas are different. In this research, using hierarchical clustering, we partitioned the ocean into some areas so that the ocean data have the same variation in each area. Then, transforming the ocean data into sets of sliding windows in consideration of depth series, we applied some anomaly detection methodologies. Finally, we succeeded in assigning high anomaly scores on errors that seemed to be normal.

Active one-shot scanning techniques have been widely used for various applications. Stereo-based active one-shot scanning embeds a positional information regarding the image plane of a projector onto a projected pattern to retrieve correspondences entirely from a captured image. Many combinations of patterns and decoding algorithms for active one-shot scanning have been proposed. If the capturing environment lacks the assumed conditions, such as the absence of strong external lights, then reconstruction using those methods is degraded, because the pattern decoding fails. In this paper, we propose a general reconstruction algorithm that can be used for any kind of patterns without strict assumptions. The technique is based on an efficient feature extraction function that can drastically reduce redundant information from the raw pixel values of patches of captured images. Shapes are reconstructed by efficiently finding correspondences between a captured image and the pattern using low-dimensional feature vectors. Such a function is created automatically by a convolutional neural network using a large database of pattern images that are efficiently synthesized by using GPU with wide variation of depth and surface orientation. Experimental results show that our technique can be used for several existing patterns without any ad hoc algorithm or information regarding the scene or the sensor.

In data mining and machine learning, the definition of the distance between two data points substantially affects clustering and classification tasks. We propose a distance metric learning (DML) method for multi-label clustering, that uses evolutionary multi-objective optimization and a cluster validity measure with a neighbor relation that simultaneously evaluates inter- and intra-clusters. The proposed method produces clustering results considering multiple class labels and allows the induction of knowledge regarding relations between class labels in multi-label clustering or between objective functions and elements in transform matrix. Experimental results have shown that the proposed DML method produces better transform matrices than single-objective optimization and is helpful in finding the attributes that affect the trade-off relationship among objective functions.

This paper proposes a suggestion-based interactive evolution method for video summarization, which attempts to enhance users' creative thought process without disturbing user edit operation. Although solutions of video summarization are time-varying, a few solutions can be evaluated in parallel. Therefore, the proposed method optimizes summarized video in background asynchronously with user operation. The background optimization is modeled as bi-objective optimization of user preference estimated by user operations and solution novelty. In this, Pareto solutions are stored to an archive and solutions are selected to suggest to the user based on α-domination. Experimental results using an eye-tracking device have revealed that the proposed method enhances convergent thinking rather than divergent thinking.

Globally-covered ocean monitoring system Argo with more than 3,600 small and light-weight drifting buoys is always working for oceanic temperature and salinity measurement. The accumulated big ocean observation data helps many studies such as investigation into climate change mechanism. Although human experts visually confirm and revise quality control (QC) labels, it is difficult to regularize the quality of the ocean observation data of all over the world. Therefore, this paper proposes a method for error detection in Argo observation data to realize an automatic QC with high accuracy equal to human experts. The target dataset is imbalanced data and requires consideration of sequence of both features and quality labels for accurate labeling in each depth. The proposed method utilizes Conditional Random Field (CRF) to assign quality labels for observed temperature and salinity values, and adopts Support Vector Machine (SVM) to design a feature function for numerical attributes. Experimental results have shown that the proposed method showed better accuracy of QC label assignments than those of point-wise prediction method using SVM and the actually operated system in Argo project.

The central projection model commonly used to model cameras as well as projectors, results in similar advantages and disadvantages in both types of system. Considering the case of active stereo systems using a projector and camera setup, a central projection model creates several problems, among them, narrow depth range and necessity of wide baseline are crucial. In the paper, we solve the problems by introducing a light field projector, which can project a depth-dependent pattern. The light field projector is realized by attaching a coded aperture with a high frequency mask in front of the lens of the video projector, which also projects a high frequency pattern. Because the light field projector cannot be approximated by a thin lens model and a precise calibration method is not established yet, an image-based approach is proposed to apply a stereo technique to the system. Although image-based techniques usually require a large database and often imply heavy computational costs, we propose a hierarchical approach and a feature-based search for solution. In the experiments, it is confirmed that our method can accurately recover the dense shape of curved and textured objects for a wide range of depths from a single captured image.

This paper proposes a decoding method of 2D code robust against non-uniform, complicated distortion and occlusion. 2D codes printed on paper and cloth cause non-uniform distortions because they are not rigid. The proposed method uses 2D code involving auxiliary lines which allow recognition of distortion and occluded areas, and identify the lines using Belief Propagation (BP). In addition, the proposed method works with more than one camera without complicated processes such as calibration and registration, resulting in enhancing robustness against occlusion. Experimental results show that the proposed method can decode the 2D code with non-uniform, non-smooth distortions and occlusion.

This paper proposes a 2D code and its decoding method robust against non-uniform, complicated distortions, assuming an application to automatic recognition of a plastic garbage bag. Printing a 2D code on a garbage bag is a promising approach for automatic bag recognition from the perspective of information content and cost. However, a 2D code printed on the bag causes non-uniform distortions because the bag is not rigid and does not hold a fixed shape. The proposed 2D code is based on Quick Response (QR) code and has auxiliary lines which allow recognition of distortion and occlusion areas, and the proposed decoding method localizes the lines by reliability calculation and iterated DP matching. Experimental results show that the proposed method in conjunction with the error correction function of QR code could decode the 2D code with non-uniform, non-smooth distortions and occluded areas.

This paper proposes a method for designing a coded aperture, which is installed in a projector for active depth measurement. The aperture design is achieved by genetic algorithm. In this paper, a system for depth measurement using projector and camera system is introduced. The system involves a prototype projector with a coded aperture which is used to reconstruct a shape using Depth from Defocus (DfD) technique. Then, we propose a method to create a coded aperture to achieve the best performance on depth measurement. We also propose an efficient calibration technique on defocus parameters and a cost function for DfD to improve the accuracy and the robustness. Experimental results show that the proposed method can create an aperture pattern using simulation considering noise for fitness calculation. Evaluations are conducted to confirm that the proposed pattern is better than other patterns with our depth measurement algorithm.

This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance.

In this paper, we propose an active 3D measurement method using structured light system, which can measure wide range of depth. General structured light system requires correspondence between projection patterns and camera observed patterns. Hence, both the projected pattern and the camera image should be in focus on the target. This condition makes a severe limitation on depth range of 3D measurement. Our technique resolves the range limitation by using coded aperture (CA) on projector. It can be understood as a structured light system using Depth from Defocus (DfD) technique, in which defocus of projected light pattern is utilized. By allowing blurry pattern of projection, the measurement range is extended compared to common structured light system. Moreover, CA efficiently improves its accuracy.

This paper provides an interactive method for decorating 2-dimensional barcode with some illustrations inside the code without detracting machine-readability and stored information. The proposed method allows a user to watch the search progress of non-Interactive Evolutionary Computation (non-IEC) and to evaluate solution candidates intuitively such as Interactive Evolutionary Computation (IEC). The proposed method therefore produces decorated barcodes in which illustrations are positioned in order to satisfy user preferences and to minimize deficit of embedded information which can be revised by Reed-Solomon error correction code. Experimental results have shown that the proposed method allows users to find desirable solutions effectively, and is suited for tasks in which users can easily imagine favorite solutions but hardly verify their feasibility.

Floorplacement" has attracted attention, as a placement formula- tion for designs with thousands or millions of soft macro blocks. In this paper, we investigate the "standard block" approach, where soft blocks are shaped to have uniform height, rather than a wide range of different sizes. This allows many macro blocks can be treated as standard cells, simplifying the problem to one of ordinary mixed size placement. We obtain high quality results for a suite of recent benchmarks, and also present novel legalization algorithms that are more robust than the widely-used mixed-size tetris approach.

This paper proposes an algorithm using multi-objective Particle Swarm Optimization (MOPSO) for finding robust solutions against small perturbations of design variables. If an optimal solution is sensitive to small perturbations of variables, it may be inappropriate or risky for practical use. Robust optimization finds solutions which are moderately good in terms of optimality and also good in terms of robustness against small perturbations of variables. The proposed algorithm formulates robust optimization as a bi-objective optimization problem, and finds robust solutions by searching for Pareto solutions of the bi-objective problem. This paper also proposes a hybridization of MOPSO and quasi-Newton method as an attempt to design effective memetic algorithm for robust optimization. Experimental results have shown that the proposed algorithms could find robust solutions effectively. The advantage and drawback of the hybridization were also clarified by the experiments, helping design an effective memetic algorithm for robust optimization.

This paper proposes a system for decorating 2-dimensional barcode with some illustrations inside the code without detracting machine-readability and stored information. The proposed system formulates the task of finding appropriate positions, scales, and angles of illustrations, and solves the task by using real-coded genetic algorithm. The proposed system also uses multiple barcode decoder with the aim of improving decode feasibility of the decorated barcode. Experiments have shown that the proposed system can decorate barcodes with three illustrations, and that using more than one decoder can improve decoding feasibility of the decorated barcodes.

Rising interconnect delay and power consumption have motivated the investigation of alternative integrated circuit routing architectures. In particular, the X architecture, which features preferred routing in diagonal directions, has gained a measure of industry support, and has even been validated at 65nm. While there has been extensive study of Manhattan design methods, there are markedly fewer published results for non-Manhattan design. To help fill this gap, we study a patented placement method for the X architecture; to our knowledge, there have been no prior published results for the method. Surprisingly, we find that the patented method in fact performs worse than traditional Manhattan methods - for both Manhattan and X routing metrics. We also present a theoretic formulation which explains why solution quality is degraded. Many groups in industry are evaluating the merits of non-Manhattan routing architectures. By providing concrete experimental results, we hope to improve the accuracy of these evaluations.

In recent years, many evolutionary computation methods have been proposed and applied to real-world problems; however, gradient methods are regarded as promising for their capacity to solve problems involving real-coded parameters. Addressing real-world problems should not only involve the search for a single optimal solution, but also a set of several quasi-optimal solutions. Although some methods aiming the search for multiple solutions have been proposed (e.g. Genetic Algorithm with Sharing and Immune Algorithm), these could not render highly optimized solutions to real-coded problems. This paper proposes hybrid algorithms combining real-coded evolutionary computation algorithms and gradient search methods for multiple-solution search in multimodal optimization problems. Furthermore, a new evaluation function of solution candidates with gradient is presented and discussed in order to find quasi-optimal solutions. Two hybrid algorithms are proposed - a hybridization between Immune Algorithm and Quasi-Newton method (IA+QN) and a hybridization between Genetic Algorithm with Sharing and Quasi-Newton method (GAS+QN). Experimental results have shown that the pro posed methods can find optimal and quasi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal benchmark functions. The results have also shown that GAS+QN has better performance and higher robustness in terms of parameter configuration than IA+QN.

Regular structures are present in many types of circuits. If this structure can be identified and utilized, performance can be improved dramatically. In this paper, we present a novel placement approach that successfully identifies regularity, and obtains placements that are superior to other "general purpose" methods. This method has been integrated into our Feng Shui 2.6 bisection-based placement tool.On experiments with the PEKO benchmarks, our results are within 32% of optimal for both the large and small suites. The largest example, with 2.1 million cells, can be completed in sixteen hours. The majority of our run time is during detail placement--global placement takes under three hours. The success of our method shows that it can find structure, even when the structure was not expected or intended.As part of this work, we have made a number of observations related to the nature of suboptimality in placement. These observations have shown that some neglected research areas have great potential, while problems that receive considerable attention are essentially adequately solved.

Circuit placement has a large impact on all aspects of performance; speed, power consumption, reliability, and cost are all affected by the physical locations of interconnected transistors. The placement problem is NP-Complete for even simple metrics.In this paper, we apply techniques developed by the Operations Research (OR) community to the placement problem. Using an Integer Programming (IP) formulation and by applying a "branch-and-price" approach, we are able to optimally solve placement problems that are an order of magnitude larger than those addressed by traditional methods. Our results show that suboptimality is rampant on the small scale, and that there is merit in increasing the size of optimization windows used in detail placement.

In this paper, we summarize circuit placement techniques and algorithms developed by the BLAC CAD research group; these have been integrated into our recursive bisection based placement tool feng shui. We also briefly describe current research interests.

This paper proposes JSGrid, as a software framework to build up computer clusters running on various operating systems. JSGrid makes good use of idle computational resources without installing any software. JSGrid therefore enables various kinds of computers to be a part of a cluster, for instance, educational terminals at schools and universities, computers for development, office computers at companies and laboratories, and information retrieval terminals at libraries. An experimental result has shown that JSGrid could manage various operating systems such as Windows, Linux, and Mac OS X, and keep high throughput almost equal to theoretical prediction even under the circumstance in which dynamic join and termination of computers occurred.

Recursive bisection is a popular approach for large scale circuit placement problems, combining a high degree of scalability with good results. In this paper, we present a bisection-based approach for both standard cell and mixed block placement; in contrast to prior work, our horizontal cut lines are not restricted to row boundaries. This technique, which we refer to as a fractional cut, simplifies mixed block placement and also avoids a narrow region problem encountered in standard cell placement. Our implementation of these techniques in the placement tool Feng Shui 2.6 retains the speed and simplicity for which bisection is known, while making it competitive with leading methods on standard cell designs. On mixed block placement problems, we obtain substantial improvements over recently published work. Half perimeter wire lengths are reduced by 29% on average, compared to a flow based on Capo and Parquet; compared to mPG-ms, wire lengths are reduced by 26% on average.

Many current designs contain a large number of standard cells intermixed with larger macro blocks. The range of size in these “mixed block ” designs complicates the placement process considerably; traditional methods produce results that are far from satisfactory. In this paper we extend the traditional recursive bisection standard cell placement tool Feng Shui to directly consider mixed block designs. On a set of recent benchmarks, the new version obtains placements with wire lengths substantially lower than other current tools. Compared to Feng Shui 2.4, the placements of a Capo-based approach have 29 % higher wire lengths, while the placements of mPG are 26 % higher. Run times of our tool are also lower, and the general approach is scalable.

In this paper, we present improvements to recursive bisection based placement.In contrast to prior work, our horizontal cut lines are not restricted to row boundaries; this avoids a "narrow region" problem.To support these new cut line positions, a dynamic programming based legalization algorithm has been developed.The combination of these has improved the stability and lowered the wire lengths produced by our Feng Shui placement tool.On benchmarks derived from industry partitioning examples, our results are close to those of the annealing based tool Dragon, while taking only a fraction of the run time.On synthetic benchmarks, our wire lengths are nearly 23% better than those of Dragon.For both benchmark suites, our results are substantially better than those of the recursive bisection based tool Capo and the analytic placement tool Kraftwerk.

This paper describes a problem for rearranging floor layouts and proposes a system which generates a plan and schedule synchronously. Rearrangement of floor layout (RAFL) is a search problem, which consists of pre- and post-change layouts of objects, workers, and a work hour limit. The goal in solving RAFL is to find a pair of rearrangement plan and its schedule. RAFL deals with planning and scheduling simultaneously, whereas a schedule is usually determined after a plan is found. In RAFL, even if an optimal plan is obtained in advance, a schedule consisting of its elements may not be necessarily feasible. The proposed system uses constraints to represent the required knowledge and tree search algorithm. The system is interactive and informs the users of their search progress. The users may interrupt a search at any time to correct the system if the search is too slow or reaches an insufficient solution. Experimental results show that the system interacted with the user can find a valid solution in scores of minutes, where the time to solve is considerably reduced.

A tournament-type match table has been widely employed to decide the winner of games. In the case of a balanced table, participants can win after the same number of games, or one time less, depending on the number of participants. Spectators want to watch good exciting games, especially in semi-finals and the final, so powerful participants are given seeded rights in the table. Powerful participants may expect to win after one time less or even less. Therefore, there may be two kinds of seeded rights: nominal and substantial. The nominally seeded players have to play the same number of games or one time less and the substantially seeded do one time less or even less. We succeeded in generating a good table under the strict 2-3-4 trees structure by unbalanced partitions.

We propose a method to control the view divergence of replicated data when copies of sites in a replicated database are asynchronously updated. The view divergence of the replicated data is the difference in the lateness of the updates reflected in the data acquired by clients. Our method accesses multiple sites and provides a client with data that reflects all the updates received by the sites. We first define the probabilistic lateness of updates reflected in acquired data as read data freshness (RDF). The degrees of RDF of data acquired by clients is the range of the view divergence. Second, we propose a way to select sites in a replicated database by using the probability distribution of the update delays so that the data acquired by a client satisfies its required RDF. This way calculates the minimum number of sites in order to reduce the overhead of read transactions. Our method continues to adaptively and reliably provide data that meet the client's requirements in an environment where the delay of update propagation varies and applications' requirements change depending on situations. Finally, we evaluated the view divergence we can feasibly control using our method. The evaluation is done by means of simulations. The evaluation shows that our method can feasibly control the view divergence to about 1/4 that of a normal read transaction.

With the growing of computer networks, continuous media communication and processing are expected in distributed systems. Continuous media communication and processing require both time and frequency synchronization between computer clocks. The authors propose a statistical time synchronization method for computer clocks that have precisely frequency-synchronized oscillators. This method not only improves time synchronization accuracy but also prevents degradation of the frequency stability of the precise oscillators when errors in the measured time offset between computer clocks caused by network traffic possess a Gaussian distribution. The improvement of time synchronization accuracy is achieved by estimating the confidence interval of the measured time offsets between the computer clocks. The prevention of frequency stability degradation is achieved by testing the difference between the latest mean and the mean at the last time adjustment for deciding the need for time correction. Evaluation by simulation of changes in the time offset in an ISDN clock synchronization system showed that this method achieves accurate time and stable frequency synchronization.