Search

In this paper we propose a novel personalized paper search system using the relevance among user's queried keywords and user's behaviors on a searched paper list. The proposed system builds user's individual relevance network from analyzing the appearance frequencies of keywords in the searched papers. The relevance network is personalized by providing weights to the appearance frequencies of keywords according to users' behaviors on the searched list, such as "downloading," "opening," and "no-action." In the experimental section, we demonstrate our method using 100 faculties' search information in the University of Suwon. [ABSTRACT FROM AUTHOR]

Dirty paper coding are relevant for wireless networks, multiuser channels, and digital watermarking. We show that the problem of dirty paper is essentially equivalent to some classes of constrained memories, and we explore the binary so-called nested codes, which are used for efficient coding and error-correction on such channels and memories. Keywords: dirty paper, constrained memory, nested codes, covering codes. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

One of the simplest protocols for clock synchronization in wireless ad hoc and sensor networks is the converge-to-max protocol, which has the simple logic of adjusting each node's clock to be at least as large as any neighbor's. This paper examines the converge-to-max protocol, showing it to be stabilizing even when node clocks have skew, bounded domains, and dynamic communication links. [ABSTRACT FROM AUTHOR]

We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81. [ABSTRACT FROM AUTHOR]

This paper presents way of generation color documents from elements extracted from an original document image. This scheme yields synthetic documents of similar visual information to the original one. This method has several advantages as it allows a far more efficient way of storing and transmitting the information. Its rationale is to decompose a color document into paper and ink texture parameters and textual information. The textual information may be either typed or handwritten and is stored as a compressed monochromatic image. [ABSTRACT FROM AUTHOR]

The techniques for organizing and retrieving the artifacts from digital libraries (DLs) semantically are discussed, which include letting the taxonomies and semantic relations work in tandem to index the artifacts in DLs; integrating the techniques used in natural language processing and taxonomies to help users to start their retrieval processes; and ranking scientific papers on similarity in terms of contents or ranking the relevant papers on multi-factors. These techniques are verified through the design and implementation of a prototype of DLs for scientific paper management. [ABSTRACT FROM AUTHOR]

The tenth year of the annual International Conference on Research in Computational Biology (RECOMB) provides an opportunity to reflect on its history. RECOMB has been held across the world, including 6 different countries spanning 3 continents (Table 1). Over its 10 year history, RECOMB has published 373 papers and 170 individuals have served on its various committees. While there are many new faces in RECOMB each year, a significant number of researchers have participated over many years forming the core of the RECOMB community. [ABSTRACT FROM AUTHOR]

Reputation systems play an important role in many Internet communities. They allow individuals to estimate other individual's behavior during interactions. However, a more privacy-friendly reputation system is desirable while maintaining its trustworthiness. This paper presents a fair anonymous submission and review system. The review process is reputation-based and provides better anonymity properties than existing reputation systems. Moreover, the system allows for accountability measures. Anonymous credentials are used as basic blocks. [ABSTRACT FROM AUTHOR]

Summary: This book constitutes the refereed proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015, held in Hyderabad, India, in December 2015. The 48 full papers and 8 short papers presented together with 4 doctoral consortium papers were carefully reviewed and selected from 185 submissions. The papers cover a wide range of topics including information retrieval, machine learning, pattern recognition, knowledge discovery, classification, clustering, image processing, network security, speech processing, natural language processing, language, cognition and computation, fuzzy sets, and business intelligence.

Automatic evaluation of visual print quality is addressed in this study. Due to many complex factors of perceived visual quality its evaluation is divided to separate parts which can be individually evaluated using standardized assessments. Most of the assessments however require active evaluation by trained experts. In this paper one quality assessment, missing dot detection from printed dot patterns, is addressed by defining sufficient hardware for image acquisition and method for detecting and counting missing dots from a test strip. The experimental results are evidence how the human assessment can be automated with the help of machine vision, thus making the test more repeatable and accurate. [ABSTRACT FROM AUTHOR]

Ukiyo-e is one famous traditional woodblock type Japanese drawing. Some pattern printed by special print techniques can only be seen from some special direction. This phenomenon relate to the reflection properties on the surface of Ukiyo-e. In this paper, we propose a method to measure these reflection properties of Ukiyo-e. Fitstly, the normal on the surface and the direction of the fiber in Japanese paper are computed from photos which are taken by a measuring machine named OGM. Then, fit the reflection model to the measured data and the reflection properties of Ukiyo-e can be obtained. Based on these parameters, the the appearance of Ukiyo-e can be rendered on real-time. [ABSTRACT FROM AUTHOR]

Most, if not all, conferences use an online system to handle paper submissions and reviews. Introduction of these systems has significantly facilitated the administration, submission and review process compared to traditional paper-based ones. However, it is crucial that these systems have strong resistance against Web attacks as they involve confidential data and privacy. Some submissions could be leading edge breakthroughs that authors do not wish to leak out and be subtly plagiarized. Also, security of the employed system will attract more submissions to conferences that use it and gives confidence of the quality that the conferences uphold. In this paper, we analyze the security of the Web-Submission-and-Review (WSaR) software - latest version 0.53 beta at the time of writing; developed by Shai Halevi from IBM Research. WSaR is currently in use by top cryptology and security-related conferences including Eurocrypt 2007 & 2008, Crypto 2007, and Asiacrypt 2007, annually sponsored by the International Association for Cryptologic Research (IACR). We present detailed analysis on WSaR's security features. In particular, we first discuss the desirable security features that are designed into WSaR and what attacks these features defend against. Then, we discuss how some untreated security issues may lead to problems, and we show how to enhance WSaR security features to take these issues into consideration. Our results are the first known careful analysis of WSaR, or any type of online submission system for that matter. [ABSTRACT FROM AUTHOR]

This paper presents a novel physically-based model for virtual Chinese brush. Compared with previous works, the main advantage of our method lies in the use of physically based modeling methods that describe the behavior of the real brush's deformation in terms of the interaction of the external and internal forces with the virtual writing paper. Instead of simulating the brush using bristles, we use points to simulate the whole brush bundle, which can drastically decrease the complexity inherent in the conventional bristle-level approach. A spring network is derived to calculate the physical deflection of brush according to the force exerted on it. With this model, we can get a more effective simulation of real brush painting. [ABSTRACT FROM AUTHOR]

Several features of today's grid are based on centralized or hierarchical services. However, as the grid size increasing, some of their functions especially resource discovery should be decentralized to avoid performance bottlenecks and guarantee scalability. A novel grid resource discovery method based on adaptive k-Nearest Neighbors clustering is presented in this paper. A class is formed by a collection of nodes with some similarities in their characteristics, each class is managed by a leader and consists of members that serve as workers. Resource requests are ideally forwarded to an appropriate class leader that would then direct it to one of its workers. This method can handle resource requests by searching a small subset out of a large number of nodes by resource clustering which can improve the resource query efficiency; on the other hand, it also achieves well scalability by managing grid resources with adaptive mechanism. It is shown from a series of experiments that the method presented in this paper achieves more scalability and efficient lookup performance than other existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of scheduling a set of n periodic preemptive independent hard real-time tasks on the minimum number of processors. We assume that the partitioning strategy is used to allocate the tasks to the processors and the EDF method is used to schedule the tasks on each processor. It is known that this scenario is NP-hard; thus, it is unlikely to find a polynomial time algorithm to schedule the tasks on the minimum number of processors. In this work, we derive a lower and an upper bound for the number of processors required to satisfy the constraints of our problem. We also compare a number of heuristic algorithms with each other and with the bounds derived in this paper. Numerical results demonstrate that our lower bound is very tight and it is very close to the optimal solution. [ABSTRACT FROM AUTHOR]

In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN's) and the SVM's with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN's, and the second pass is followed by using the SVM's to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN's and the SVM's while remedying their disadvantages. Compared with the HENN's and the SVM's, a significant improvement of recognition performance over them has been achieved by the new method. [ABSTRACT FROM AUTHOR]

Generally, it is difficult to find constant patterns on identifiers in a container image, since the identifiers are not normalized in color, size, and position, etc. and their shapes are damaged by external environmental factors. This paper distinguishes identifier areas from background noises and removes noises by using an ART2-based quantization method and general morphological information on the identifiers such as color, size, ratio of height to width, and a distance from other identifiers. Individual identifier is extracted by applying the 8-directional contour tracking method to each identifier area. This paper proposes a refined ART2-based RBF network and applies it to the recognition of identifiers. Through experiments with 300 container images, the proposed algorithm showed more improved accuracy of recognizing container identifiers than the others proposed previously, in spite of using shorter training time. [ABSTRACT FROM AUTHOR]

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is black or white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Noise areas are replaced with a mean pixel value of the whole image and areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which creates nodes of the hidden layer by applying ART2 between the input and the hidden layers and improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm between the hidden and the output layers. Experiments using many images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed. [ABSTRACT FROM AUTHOR]

Increasing the smooth degree of data series is key factor of grey model's precision. In this paper, the more general method is put forward on the basis of summarizing several kinds of ways to improve smooth degree of data series, and a new transformation is represented. The practical application shows the effectiveness and superiority of this method. [ABSTRACT FROM AUTHOR]

A method based on multi-feature and multi-stage verification is proposed in this paper. The direction and texture features obtained by Gabor filter are used for the first-stage verification. Then the sham dynamic features representing the writing force are used for the second-stage verification. Considering the number of samples for training is limited, the support vector machine (SVM) is selected as the classifier. The experimental results show that this method can not only increase the accuracy, but also satisfy the requirement of speed in real-time system as well. [ABSTRACT FROM AUTHOR]

The Software Engineering track received 63 papers from which 7 papers were selected after an intensive reviewing and selection process. Many good papers could not be selected due to lack of space in the program. The selected papers cover a diverse range of topics within software engineering: from software reliability prediction to middle-ware for component management to runtime validation and code generation. The paper by Roychoudhury, Negi and Mitra analyzes programs loops for estimating program execution time. They use constraint propagation techniques to detect infeasible paths followed by timing analysis that employ memoization techniques. The paper by Sengupta and Cleaveland presents the operational semantics of timed message sequence charts to help detect errors and inconsistencies in specifications. Tripathi and Mall present a method for making predictions about reliability of software during the software development process itself when the failure data from the field cannot be available. The paper by Wang presents a logic programming framework for integrating architecture description languages (ADLs) which allows tools developed for one ADL to be used even though the architectural specification is written in another ADL. In a similar vain, the paper by Stevenson, Fu and Dong presents a framework for automated and validated realization of software architecture designs. The paper by Bhattarcharjee and Shyamsundar presents a method for validated code generation for activity diagrams which are useful in model driven design of software. Finally, the paper by Mousavi et al presents techniques that exploit symmetry for tackling the state-space explosion problem that arises in model checking. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss the level of compliance for timetabling research to two important research qualities, namely measurability and reproducibility, analyzing what we believe are the most important contributions in the literature. Secondly, we discuss some practices that, in our opinion, could contribute to the improvement on the two aforementioned qualities for future papers in timetabling research. For the sake of brevity, we restrict our scope to university timetabling problems (exams, courses, or events), and thus we leave out other equally important timetabling problems, such as high-school, employee, and transportation timetabling. [ABSTRACT FROM AUTHOR]

Considerable progress has been made in recent years in complexity analysis of Nash equilibrium, so we restrict our attention to seek it from the empirical perspective in this paper. Based on a new description format of game - stimulate - response pair proposed in the paper, we put forward a constraints satisfaction-based algorithm on this data structure to compute pure Nash equilibrium of graphical game. And then, we discuss how to employ search strategies when finding the equilibrium in different types. To evaluate our algorithm, we use a comprehensive game generator - GAMUT, to produce a mass of data and take a famous tool - gambit as competitor. At last the experimental result is presented. [ABSTRACT FROM AUTHOR]

This paper extends a survey on Networked Control Systems (NCSs) published in previous papers. General frameworks and some fundamental issues for the study of NCSs are proposed. A significant emphasis has been on developing control methodologies to study the character of NCSs. This survey paper presents recent NCSs control methodologies, some promising research directions are also covered. [ABSTRACT FROM AUTHOR]

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

The split-radix FFT computes a size-n complex DFT, when n is a large power of 2, using just $4n\lg n-6n+8$ arithmetic operations on real numbers. This operation count was first announced in 1968, stood unchallenged for more than thirty years, and was widely believed to be best possible. Recently James Van Buskirk posted software demonstrating that the split-radix FFT is not optimal. Van Buskirk's software computes a size-n complex DFT using only $(34/9+o(1))n\lg n$ arithmetic operations on real numbers. There are now three papers attempting to explain the improvement from 4 to 34/9: Johnson and Frigo, IEEE Transactions on Signal Processing, 2007; Lundy and Van Buskirk, Computing, 2007; and this paper. This paper presents the "tangent FFT," a straightforward in-place cache-friendly DFT algorithm having exactly the same operation counts as Van Buskirk's algorithm. This paper expresses the tangent FFT as a sequence of standard polynomial operations, and pinpoints how the tangent FFT saves time compared to the split-radix FFT. This description is helpful not only for understanding and analyzing Van Buskirk's improvement but also for minimizing the memory-access costs of the FFT. [ABSTRACT FROM AUTHOR]

This paper surveys first-order probabilistic languages (FOPLs), which combine the expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data. [ABSTRACT FROM AUTHOR]

Let consider n autonomous mobile robots that can move in a two dimensional plane. The gathering problem is one of the most fundamental tasks of autonomous mobile robots. In short, given a set of robots with arbitrary initial locations, gathering must make all robots meet in finite time at a point that is not predefined. In this paper, we study about the feasibility of gathering by mobile robots that have φ-absolute error dynamic compasses. While the direction of each local coordinate system is fixed in usual systems, the dynamic compass model allows the angle difference between a local coordinate system and the global coordinate system to vary with time in the range of [0, φ]. This paper proposes a semi-synchronous gathering algorithm for n robots with (π/2 − ε)-absolute error dynamic compasses, where ε is an arbitrary small constant larger than zero. To the best of our knowledge, the proposed algorithm is the first one that considers both inaccurate compass models and more than two robots. We also show the optimality of our algorithm. It is proved that for any φ ≥ π/2, there is no algorithm to gather two robots with φ-absolute error dynamic compasses. [ABSTRACT FROM AUTHOR]

We consider some problems about the computation time of evolutionary algorithms in this paper. First, some exact analytic expressions of the mean first hitting times of general evolutionary algorithms in finite search spaces are obtained theoretically by using the properties of Markov chain associated with evolutionary algorithms considered here. Then, by introducing drift analysis and applying Dynkin's Formula, the general upper and lower bounds of the mean first hitting times of evolutionary algorithms are estimated rigorously under some mild conditions listed in the paper. Those results in this paper are commonly useful. Also, the analytic techniques adopted in the paper are widely instructive for analyzing the computation time of evolutionary algorithms in a given search space as long as some specific mathematical arts are introduced accordingly. [ABSTRACT FROM AUTHOR]

A software product-line is a set of products built from a core set of software components. Although software engineers develop software product-lines for various application types, they are most commonly used for embedded systems development, where the variability of hardware features requires variability in the supporting firmware. Feature models are used to represent the variability in these software product-lines. Various feature modeling approaches have been proposed, including feature diagrams, domain specific languages, constraint languages, and the semantic web language OWL. This paper explores a conceptual graph approach to feature modeling in an effort to produce feature models that have a more natural, and more easily expressed mapping to the problem domain. It demonstrates the approach using a standard Graph Product-line problem that has been discussed in various software product-line papers. A conceptual graph feature model is developed for the graph product-line and it is compared to other feature models for this product-line. [ABSTRACT FROM AUTHOR]

This paper introduces a new paradigm to realize various types of cryptographic primitives such as authenticated key exchange and key encapsulation in the standard model under three standard assumptions: the decisional Diffie-Hellman (DDH) assumption, target collision resistant (TCR) hash functions and pseudo-random functions (PRFs). We propose the first (PKI-based) two-pass authenticated key exchange (AKE) protocol that is comparably as efficient as the existing most efficient protocols like MQV and that is secure in the standard model (under these standard assumptions), while the existing efficient two-pass AKE protocols such as HMQV, NAXOS and CMQV are secure in the random oracle model. Our protocol is shown to be secure in the (currently) strongest security definition, the extended Canetti-Krawczyk (eCK) security definition introduced by LaMacchia, Lauter and Mityagin. This paper also proposes a CCA-secure key encapsulation mechanism (KEM) under these assumptions, which is almost as efficient as the Kurosawa-Desmedt KEM. This scheme is also secure in a stronger security notion, the chosen public-key and ciphertext attack (CPCA) security. The proposed schemes in this paper are redundancy-free (or validity-check-free) and the implication is that combining them with redundancy-free symmetric encryption (DEM) will yield redundancy-free (e.g., MAC-free) CCA-secure hybrid encryption. [ABSTRACT FROM AUTHOR]

It presents an approach to the automatic generation of pencil sketching with the effects of paper texture. First, proposes a texture mapping of strokes in the aspect of skeleton, the filter algorithm and the standard deviation algorithm for rendering image, the near distances recover algorithm for real-time browsing, and finally implement the Pen-and-Ink Style of pencil sketching Rendering System. In the static rendering, it needn't adjust the threshold for the convenient on rendering and the outlines are more precise and exact. Besides, attaching the graftal and sketching shadow makes the composition of an image more attractive. Through a series of clever image processes, the system finally presents excellent colored pencil style drawings. Because the proposed algorithm is not complicated, the rendering time is quite short compared to other past related studies. Compared with other research works and Photoshop on a set of benchmarks, the system demonstrates its strength in the aspects of full automation, stability of sketching quality and higher visual satisfaction, all achieved in a considerably shorter time. [ABSTRACT FROM AUTHOR]

The storage capacity of the conventional neural network is 0.14 times of the number of neurons (P=0.14N). Due to the huge difficulty in recognizing large number of images or patterns,researchers are looking for new methods at all times. Quantum Neural Network (QNN), which is a young and outlying science built upon the combination of classical neural network and quantum computing,is a candidate to solve this problem.This paper presents Quantum Probability Distribution Network (QPDN) whose elements of the storage matrix are distributed in a probabilistic way on the base of quantum linear superposition and applies QPDN on image recognition. Contrasting to the conventional neural network, the storage capacity of the QPDN is increased by a factor of 2N,where N is the number of neurons. Besides,the case analysis and simulation tests have been carried out for the recognition of images in this paper, and the result indicates that QPDN can recognize the images or patterns effectively and its working process accords with quantum evolvement process. [ABSTRACT FROM AUTHOR]

In this paper we propose a formal approach to the generative design of artefacts. The founding idea, bridging the gap between the domain of architectural artefacts and the field of ontologies, is to represent the notion of species as it exists in the context of generative design by the concept of class existing in the field of formal ontologies. In this paper we propose an artefact design methodology and a system based on it that assists a designer in the building process of artefact 3D models. The implemented system is structured in three conceptual levels: the Design Strategy, the Computational Model, and the 3D graphical rendering system. The Computational model has been defined and developed using the Frame Logic formalism. The proposed methodology is independent of specific modelling environments. In this paper we also present methodological directions to couple artefact representations with 3D rendering systems. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of global stability for a class of neural networks with time-varying delays. A new sufficient condition for global stability is proposed by using some slack matrix variables to express the relationship between the system matrices. The restriction on the derivative of the delay function to be less than unit is removed. A numerical example shows that the result obtained in this paper improves the upper bound of the delay over some existing ones. [ABSTRACT FROM AUTHOR]

The digital watermark is used for detection of digital image alteration. However, most of digital images are printed on the paper document for submitting. Once digital images are printed on the paper, it is hard to detect alteration of it. In this paper, the detection method of printed image alteration by using digital watermark is proposed. [ABSTRACT FROM AUTHOR]

Process models play important role in computer aided pro- cess engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor. [ABSTRACT FROM AUTHOR]

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects (in Java), freed from the intricacies of the PDF file. The second stage consists of discovering simple graphics entities which we call graphemes, e.g., a pair of primitive graphic objects satisfying certain geometric constraints. The third stage uses machine learning to classify figures using grapheme statistics as attributes. A boosting-based learner (LogitBoost in the Weka toolkit) was able to achieve 100% classification accuracy in hold-out-one training/testing using 16 grapheme types extracted from 36 figures from BioMed Central journal research papers. The approach can readily be adapted to raster graphics recognition. Keywords: Graphics Recognition, PDF, Graphemes, Vector Graphics, Machine Learning, Boosting. [ABSTRACT FROM AUTHOR]

Traditional indexing methods face the difficulty of"curse of dimensionality" at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval. [ABSTRACT FROM AUTHOR]

We investigate an automatic tuning method for an eigensolver of a dense symmetric matrix. The aim of this paper is to investigate how to select the unrolling depth. To do this, we evaluate the performance of various unrolled reduction loops of the eigensolver for every matrix size from 3000 to 4000 on the Hitachi SR8000/F1 and on the IBM RS/6000 SP3. We also analyze the trend between Byte/Flop and performance for various patterns of loop unrolling. The result shows that the performance is degraded with higher depth of unrolling in some matrix sizes, where it does not occur with lower depth of unrolling. The result also shows that selection of the unrolling depth should be examined in the case of several matrix sizes. [ABSTRACT FROM AUTHOR]

This paper addresses a capacitated production planning (CPP) problem with outsourcing. Specially, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production, inventory, outsourcing, and sale loss without postponement or backlog; (2) The goal of the solution to this problem is to minimize the total cost covering the production, setup, inventory holding, sale loss, and outsourcing activities; (3) different from previous studies which usually involved some special assumptions on the cost function for these activities, the cost function structure we consider in this paper are arbitrary and time-varying; and (4) we consider a general problem situation where the production, inventory, and outsourcing have a capacity limitation. It is likely that there is no solution in the model due to the consideration of such general cost functions and many constraints, hence we employ a Bellman/Ford algorithm for checking feasible solutions of the problem and resulting in a feasible solution region. After that, a proposed algorithm is applied for searching a global optimal solution with in the feasible solution region. [ABSTRACT FROM AUTHOR]

Raster map should be corrected after scanned because of the errors caused by paper map deformation. In the paper, the deficiency of the polynomial fitting method is analyzed. The paper introduces an ANN (Artificial Neural Network) correcting method that utilizes the advantage of its function approximation ability. In the paper, two types of ANNs, BP and GRNN, are designed for the correcting. The comparing experiment is done with the same data by the polynomial fitting and ANN methods, utilizing the MALAB. The experiment results show that the ANN methods, especially the GRNN method, performances far better than the polynomial fitting method does. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The system listens to the product sound by the multimedia technology and the sound features are extracted to detect and predict faults by the neural network. The paper analyzes the reasons of timer faults and the corresponding sound features. Experiments are made in the laboratory to demonstrate the proposed method. The technology is expected to apply in factories in coming years for realizing automatic product test and improving efficiency of product inspection. [ABSTRACT FROM AUTHOR]