ppxscal/embedding_network huggingface dataset:
Face Recognition from a Single Image per Person Using Common Subfaces Method
Authors: Jun-Bao Li, Jeng-Shyang Pan, Shu-Chuan Chu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, we propose a face recognition method from a single image per person, called the common subfaces, to solve the "one sample per person" problem. Firstly the single image per person is divided into multiple sub-images, which are regarded as the training samples for feature extraction. Then we propose a novel formulation of common vector analysis from the space isomorphic mapping view for feature extraction. In the procedure of recognition, the common vector of the subfaces from the test face image is derived with the similar procedure to the common vector, which is then compared with the common vector of each class to predict the class label of query face. The experimental results suggest that the proposed common subfaces approach provides a better representation of individual common feature and achieves a higher recognition rate in the face recognition from a single image per person compared with the traditional methods.
An Optimal On-Line Algorithm for Preemptive Scheduling on Two Uniform Machines in the lp Norm
Authors: Tianping Shuai, Donglei Du, Xiaoyue Jiang
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

One of the basic and fundamental problems in scheduling is to minimize the machine completion time vector in the ¿ p norm (a direct extension of the l ¿ norm: the makespan) on uniform parallel machines. We concentrate on the on-line and preemptive version of this problem where jobs arrive one by one over a list and are allowed to be preempted. We present a best possible deterministic on-line scheduling algorithm along with a matching lower bound when there are two machines, generalizing existing results for the identical machines scheduling problem in the literature. The main difficulty in the design of the algorithm and the analysis of the resultant competitive ratio as well as the proof of the lower bound is that the competitive ratio is only known to be the root of some equation systems, which admits no analytic solution--a distinct feature from most existing literature on competitive analysis. As a consequence, we develop some new ideas to tackle this difficulty. Specifically we need to exploit the properties of the equations system that defines the competitive ratio.
A Privacy-Aware Identity Design for Exploring Ubiquitous Collaborative Wisdom
Authors: Yuan-Chu Hwang, Soe-Tsyr Yuan
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Privacy and security has been considered as the top criterion for the acceptance of e-service adoption. In this paper, we proposed a privacy aware identity protection design that improves the flaw in cheap pseudonym design under the limitation of ambient e-service environment. This design is being applied to a collaborative iTrust platform for ambient e-service, a new scope of mobile ad-hoc e-service. The collaborative iTrust platform highlights the collaborative power from nearby user groups to eliminate potential risk and provide appropriate estimation for trust decision in the ubiquitous environment.
Second International Workshop on Model Size Metrics
Authors: Michel Chaudron, Christian F. Lange
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

This paper reports on the Model Size Metrics (MSM) workshop held as satellite event of the MODELS 2007 conference. The focus of the workshop is to develop metrics for use in model-centric software development. As a starting point for discussion we focused on size. The workshop aim was to bring together researchers in this area, share experiences and discuss future directions. This paper summarizes the presentations that were given and the highlights of the discussion that followed and summarizes open issues for future work.
Deformable Volumetric Simplex Meshes
Authors: Luis Cruz, Luis Gerardo Fraga
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

In this article, two possible ways to build volumetric meshes of simplexes are described. One structure uses the crystalline organization of carbon atoms in diamond and it is used to simulate the deformation under internal pressure of a container. The second structure is in layers and it is used to simulate human body tissues that could be cut. We also describe the cutting process for this layered structure.
Modeling of neural-network security tools for computer-aided systems: A formal model of adaptation and operation. I
Authors: G. F. Nesteruk, L. G. Nesteruk, F. G. Nesteruk
Venue: Automation and Remote Control (2008)

Basing on the biosystem analogy and model of adaptive security that uses intellectual mechanisms of neural networks and fuzzy logic, tools for security of information of computer-aided systems are organized in the form of hierarchy of security levels: immune (for operative reaction to the variation of threats) and receptor (for accumulated know-how in the form of information fields of neural networks). Tools for describing processes in information fields of neural networks by batch neural-network programs, i.e., a formal model of adaptation and operation of neural-network security tools.
A Study of System-Based Model for Evaluating EF (Exposure Factor) in Quantitative Security Risk Analysis
Authors: Young-Hwan Bang, Sang-Dong Lee
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Security risk analysis has been paid much attention due to its implication for information protection. For risk analysis, we should evaluate SV, ARO and EF. By statistical data, it is feasible to evaluate SV and ARO. However Evaluation EF is difficult to perform even though it is a critical item for the assessment. In this paper, we propose a system-based model for evaluating EF in quantitative risk analysis.
Improved Accuracy by Relearning and Combining Distance Functions
Authors: Naohiro Ishii, Takahiro Yamada, Yongguang Bao
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

The k-nearest neighbor(kNN) is improved by applying the distance functions with relearning and ensemble computations with the higher accuracy values. In this study, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on different distance functions with relearning and ensemble computations. The proposed combining algorithm shows higher generalization accuracy, compared to our previous studies and other conventional algorithms by artificial intelligence techniques. First, to improve classification accuracy, a relearning method with genetic algorithm is developed. Second, ensemble computations are followed by the relearning. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository.
Performance of Multi-service System with Retrials due to Blocking and Called-Party-Busy
Authors: S. N. Stepanov, O. A. Kokina, V. B. Iversen
Venue: Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking (2008)

In this paper we construct a model of a multi-service system with an arbitrary number of bandwidth flow demands, taking into account retrials due to both blocking along the route and to called-party-busy. An approximate algorithm for estimation of key performance measures is proposed, and the problem of dimensioning the system is considered.
Orthogonal Shrinkage Methods for Nonparametric Regression under Gaussian Noise
Authors: Katsuyuki Hagiwara
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In this article, for regression problems, we proposed shrinkage methods in training a machine in terms of a regularized cost function. The machine considered in this article is represented by a linear combination of fixed basis functions, in which the number of basis functions, or equivalently, the number of adjustable weights is identical to the number of training data. This setting can be viewed as a nonparametric regression method in statistics. In the regularized cost function employed in this article, the error function is defined by the sum of squared errors and the regularization term is defined by the quadratic form of the weight vector. By assuming i.i.d. Gaussian noise, we proposed three thresholding methods for the orthogonal components which are obtained by eigendecomposition of the Gram matrix of the vectors of basis function outputs. The final weight values are obtained by a linear transformation of the thresholded orthogonal components and are shrinkage estimators. The proposed methods are quite simple and automatic, in which the regularization parameter is enough to be fixed for a small constant value. Simple numerical experiments showed that, by comparing the leave-one-out cross validation method, the computational costs of the proposed methods are strictly low and the generalization capabilities of the trained machines are comparable when the number of data is relatively large.
The Embodiment of a DUAL/AMBR Based Cognitive Model in the RASCALLI Multi-agent Platform
Authors: Stefan Kostadinov, Maurice Grinberg
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

The current paper explores some of the cognitive abilities of an embodied agent based on the DUAL/AMBR architecture. The model has been extended with several capabilities like continuous operation and learning based on encoding of episodes and general knowledge. A new mechanism of analogical transfer has been implemented and demonstrated on a simulated interaction with a user. A focus of interest discussed throughout the paper is how a cognitive model can be embodied in a virtual environment and what are the benefits of combining 'soft' cognitive capabilites with hard AI based platform. The latter is based on a Mind-Body-Environment metaphor which positions the cognitive agent in a situation similar to the one of a robot in a real environment. In the paper, results from simulations of simple interactions with a hypothetical user are presented and the internal cognitive mechanisms are discussed.
Automatic Construction of Fuzzy Rules for Modelling and Prediction of the Central Nervous System
Authors: Fernando Vázquez, Pilar Gómez
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

The main goal of this work is to study the performance of CARFIR (Automatic Construction of Rules in Fuzzy Inductive Reasoning) methodology for the modelling and prediction of the human central nervous system (CNS). The CNS controls the hemodynamical system by generating the regulating signals for the blood vessels and the heart.CARFIR is able to automatically construct fuzzy rules starting from a set of pattern rules obtained by FIR. The methodology preserves as much as possible the knowledge of the pattern rules in a compact fuzzy rule base. The prediction results obtained by the fuzzy prediction process of CARFIR methodology are compared with those of other inductive methodologies, i.e. FIR, NARMAX and neural networks.
Towards Accessing Disparate Educational Data in a Single, Unified Manner
Authors: Erica Melis, Bruce M. Mclaren, Silvana Solomon
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Educational researchers need to exchange and compare their learner-interaction data in order to benefit the learning science community as a whole. In order to support this, we propose accessing data in different repositories via a mediator component that maps generic queries to the specific format of a target repository. This approach is supported by a common ontology, and we illustrate the beginnings of such an ontology. We are in the early stages of developing this concept but show its promise by discussing how it can be applied to repositories of disparate educational data, such as collaborative learning interactions and cognitive tutor data.
Global Tiling for Communication Minimal Parallelization on Distributed Memory Systems
Authors: Lei Liu, Li Chen, Chengyong Wu, Xiao-Bing Feng
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

Most previous studies on tiling focus on the division of iteration space. However, on distributed memory parallel systems, the decomposition of computation and the distribution of data must be handled at the same time, in order to attain load balancing and to minimize data migration. In this paper, we formulate a 0-1 integer linear programming for the problem of globally optimal tiling to minimize the total execution time. To simplify the selection of tiling parameters, we restrict the tile shape to semi-oblique shape, and present two effective approaches to decide the tile shape for multi-dimensional semi-oblique shaped tiling. Besides, we present a tile-to-processor mapping scheme based on hyperplanes, which can express diverse parallelism and gain better performance than traditional methods. The experimentations with NPB2.3-serial SP and LU on Qsnet connected cluster achieved the average parallel efficiency of 87% and 73% respectively.
Web services: the story so far - an academic and industrial account
Authors: Alain Leger
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

Recent advances in networks, information and computation grids, and the Web have resulted in the proliferation of a multitude of physically distributed and autonomously developed component Web services. The W3C Web Services Architecture defines "Web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine processible format (specifically WSDL) and other systems can interact with it in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards". So, web-services constitute a distributed computing infrastructure made up of many different systems trying to communicate over the Internet to virtually form a single logical system. Web-services are an effective means for linking loosely coupled systems together using a technology that does not bind to a particular component model, programming language or platform. Used according to the semantic web principles, semantic web services offer key integration capabilities in businesses as well as in scientific research over the Internet and corporate intranets, and so are applicable to a broad variety of applications including e-Enterprise, e-Business, e-Government, and e-Science. Correspondingly, the construction and deployment of composite services by combining and reusing independently developed component services is an important capability in the emerging Web-based computing infrastructure. In this presentation we will focus on a detailed analysis of existing discovery reasoning mechanisms and the current key solutions for composition of component services. We will conclude with current roadblocks and pilot applications.
Enhanced one-sided confidence regions for a multivariate location parameter
Authors: Michael Vock
Venue: Journal of Multivariate Analysis (2008)

If a one-sided test for a multivariate location parameter is inverted, the resulting confidence region may have an unpleasant shape. In particular, if the null and alternative hypothesis are both composite and complementary, the confidence region usually does not resemble the alternative parameter region in shape, but rather a reflected version of the null parameter region. We illustrate this effect and show one possibility of obtaining confidence regions for the location parameter that are smaller and have a more suitable shape for the type of problems investigated. This method is based on the closed testing principle applied to a family of nested hypotheses.
Stability Analysis of Generalized Nonautonomous Cellular Neural Networks with Time-Varying Delays
Authors: Xiaobing Nie, Jinde Cao, Min Xiao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, a class of generalized nonautonomous cellular neural networks with time-varying delays are studied. By means of Lyapunov functional method, improved Young inequality a m b n ≤ ma t ¿ n + nb t m (0 ≤ m ≤ 1, m + n = 1,t > 0) and the homeomorphism theory, several sufficient conditions are given guaranteeing the existence, uniqueness and global exponential stability of the equilibrium point. The proposed results generalize and improve previous works. An illustrative example is also given to demonstrate the effectiveness of the proposed results.
Towards Multi-Directional OCT for Speckle Noise Reduction
Authors: Lukas Ramrath, Guillermo Moreno, Heike Mueller, Tim Bonin, Gereon Huettmann, Achim Schweikard
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Multi-directional optical coherence tomography (MD-OCT) applies and extends the concept of angular compounding for speckle noise reduction to the area of OCT imaging. OCT images are acquired from a wide range of angles of view. Averaging of the rotated images therefore requires compensation of the parallax which is achieved by simple image registration for image reconstruction. Test measurements of a sample structure in a low and highly scattering environment show that the method improves the signal-to-noise ratio by a factor of 4 and hence reduces speckle noise significantly. Experimental results also show that the proposed averaging increases the performance of common edge-detection algorithms.
Sequential Fuzzy Diagnosis for Condition Monitoring of Rolling Bearing Based on Neural Network
Authors: Huaqing Wang, Peng Chen
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In the case of fault diagnosis of the plant machinery, diagnostic knowledge for distinguishing faults is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper propose a sequential fuzzy diagnosis method for condition monitoring of a rolling bearing used in a centrifugal blower by the possibility theory and a neural network. The possibility theory is used for solving the ambiguous problem of the fault diagnosis. The neural network is realized with a developed back propagation neural network. As input data for a neural network, the non-dimensional symptom parameters are also defined in time domain. Fault types of a rolling bearing can be effectively, sequentially distinguished on the basis of the possibilities of the normal state and abnormal states at early stage by the fuzzy diagnosis approach. Practical examples of diagnosis are shown in order to verify the efficiency of the method.
A Balanced Resource Allocation and Overload Control Infrastructure for the Service Grid Environment
Authors: Jun Wang, Yi Ren, Di Zheng, Quan-Yuan Wu
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

For the service based Grid applications, the applications may be integrated by using the Grid services across Internet, thus we should balance the load for the applications to enhance the resource's utility and increase the throughput. To overcome the problem, one effective way is to make use of load balancing. Kinds of load balancing middleware have already been applied successfully in distributed computing. However, they don't take the services types into consideration and for different services requested by clients the workload would be different out of sight. Furthermore, traditional load balancing middleware uses the fixed and static replica management and uses the load migration to relieve overload. Therefore, we put forward an autonomic replica management infrastructure to support fast response, hot-spot control and balanced resource allocation among services. Corresponding simulation tests are implemented and results indicated that this model and its supplementary mechanisms are suitable to service based Grid applications.
An Analytical Comparison of the Slow-but-Steady and Impatient Variants of TCP New Reno
Authors: Roman Dunaytsev, Konstantin Avrachenkov, Yevgeni Koucheryavy, Jarmo Harju
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

Current standard defines two variants of TCP NewReno: the Slow-but-Steady and Impatient. While the behavior of various TCP implementations has been extensively studied over the last years, little attention has been paid to performance analysis of different variants of TCP NewReno. In this paper, we first develop an analytical model of the Impatient variant, which being combined with the earlier proposed model of the Slow-but-Steady variant gives a comprehensive analytical model of TCP NewReno throughput. We then make an analytical comparison of the Impatient and Slow-but-Steady throughputs in the presence of correlated losses. We show that, although neither of the two variants is optimal, the Impatient variant provides the same throughput as the Slow-but-Steady one in a wide range of network conditions and significantly outperforms it in case of large windows and multiple packet drops. This could be of special interest for networks with large bandwidth and long delay.
Global Synchronization in an Array of Delayed Neural Networks with Nonlinear Coupling
Authors: Jinling Liang, Ping Li, Yongqing Yang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, synchronization is investigated for an array of nonlinearly coupled identical connected neural networks with delay. By employing the Lyapunov functional method and the Kronecker product technique, several sufficient conditions are derived. It is shown that global exponential synchronization of the coupled neural networks is guaranteed by a suitable design of the coupling matrix, the inner linking matrix and some free matrices representing the relationships between the system matrices. The conditions obtained in this paper are in the form of linear matrix inequalities, which can be easily computed and checked in practice. A typical example with chaotic nodes is finally given to illustrate the effectiveness of the proposed synchronization scheme.