ppxscal/embedding_network huggingface dataset:
Adaptive Hypermedia Techniques for 3D Educational Virtual Environments
Authors: Luca Chittaro, Roberto Ranon
Venue: IEEE Intelligent Systems (2007)

Highly interactive learning environments, such as 3D Educational Virtual Environments (EVEs), can help people learn through direct experience by visualizing concepts and letting them perform tasks in real-world simulations or completely fictional environments that are suited to the learning task. For such applications, adaptivity can play an important role in increasing both learning process effectiveness and interface usability. The authors propose a system that delivers personalized learning content in 3D EVEs by exploiting techniques used in adaptive hypermedia by the well-known AHA! system. They discuss their design and present a case study of an adaptive 3D application that teaches students how to build interactive 3D graphics content using the Extensible 3D language. This article is part of a special issue on intelligent educational systems.
A New Algorithm for Discrete Cosine Transform of Arbitrary Number of Points
Authors: M. D. Wagh
Venue: IEEE Transactions on Computers (1980)

An alternate algorithm to compute the discrete cosine transform (DCT) of sequences of arbitrary number of points is proposed. The algorithm consists of partitioning the DCT kernel into submatrices which by proper row and column shuffling and negations can be made equivalent to the group tables (or parts of them) of appropriate Abelian groups. The computations pertaining to the submatrices can be carried out using multidimensional cyclic convolutions. Algorithms are also developed to perform the computations associated with the submatrices that are parts of larger group tables. The new algorithms are more versatile and generally better in terms of the computational complexity in comparison with the existing algorithms.
Evaluation of a mobile information system in context
Authors: Ayşe Göker, Hans Myrhaug
Venue: Information Processing and Management: an International Journal (2008)

The motivation for this work has been to provide relevant information to the right situation for mobile phone users. We have iteratively developed applications for travellers and tourists to provide a general travel guide with local tourist information. This used a combination of wireless tag technology and information from content service providers. The paper presents our evaluation methodology and how we used it to develop the applications. The methodology is user centred, iterative, and progressive. It combines information retrieval (IR) evaluation methods with human-computer interaction (HCI) development techniques. Two groups of three user studies and a large survey were conducted in context with the studies scaling up to 238 participants and 438 user survey responses. The participants tested the applications in context, including the IR functions. They also filled in a pre and post questionnaire immediately before and after the system use. Some participants were additionally interviewed.
Transmission Theory of the Risk Neural Network
Authors: Cunbin Li, Kecheng Wang
Venue: Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops (2007)

On the basis of general project risk element transmission theory, in order to consider an effective solution of portfolio selection problem. This research proposed a new neural network model: Risk Neural Network model. Based on artificial neural network model analysis, we introduced three computational rules: analysis, weighted and polymerization in Risk Neural Network. By solving the hidden layer of random variables characteristic function, a series of definitions were built in Risk Neural Network, including feed-forward risk neural network model and muti-hidden layer risk neural network model. According to the series of definitions, we divided risk elements into discrete model and continuous model to be discussed separately, eventually the analytical model of Risk Neural Network was built. The interactive approach was illustrated with a practical example.
A Purpose-Based Access Control Model
Authors: Naikuo Yang, Howard Barringer, Ning Zhang
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

Achieving privacy preservation in a data-sharing com- puting environment is a challenging problem. The require- ments for a privacy preserving data access policy should be formally specified in order to be able to establish consis- tency between the privacy policy and its purported imple- mentation in practice. Previous work has shown that when specifying a privacy policy, the notion of purpose should be used as the basis for access control. A privacy policy should ensure that data can only be used for its intended purpose, and the access purpose should be compliant with the data's intended purpose. This paper presents a mech- anism to specify privacy policy using VDM. The entities in the purpose-based access control model are specified, the invariants corresponding to the privacy requirements in pri- vacy policy are specified, and the operations in the model and their proof obligations are defined and investigated.
Generalized fuzzy c-means algorithms
Authors: Nicolaos B. Karayiannis
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2000)

This paper presents the development, testing and evaluation of generalized fuzzy c-means (FCM) algorithms. The proposed algorithms are developed by relaxing the constraints imposed on the membership functions by the axiomatic requirements associated with fuzzy c-partitions. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership functions. This minimization problem results in a broad family of Generalized FCM algorithms, which include the FCM algorithm as a special case. The Minimum FCM and Geometric FCM algorithms are also obtained as limiting cases of Generalized FCM algorithms. The proposed formulation assigns to each feature vector a parameter that can be used to measure the certainty of its assignment to various clusters. These parameters can be used to identify outliers in the feature set. The Generalized FCM algorithms are evaluated and tested by experiments involving the IRIS data set and a two-dimensional vowel data set.
Analysis of Group Key Management Protocols for Secure Multicasting in Vehicular Software Distribution Network
Authors: Irina Hossain, Syed Masud Mahmud
Venue: Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (2007)

designing key management architecture for secure multicasting in remote software distribution to advance vehicles. We consider the Vehicular Software Distribution Network (VSDN) as wireless network where vehicles are connected to the software distributors through roadside Base Stations (BSs). The large VSDN is divided into small regions and a Regional Group Manager (RGM) manages a region. Depending on the level of trust in the supporting BSs, we propose two Group Key Management (GKM) protocols for the Multicast Groups (MGs) formed for software distribution. We categorize the VSDN as fully- trusted and semi-trusted systems. In a fully-trusted system the BSs have access to the multicast data and take part in GKM whereas in a semi-trusted system the BSs are not allowed to understand the multicast data, rather they act as proxies for the vehicles by honestly relaying the encrypted multicast data from the RGM to the vehicles. We compare the performance of these two protocols in terms of computation and communication overhead incurred on each entity involved during multicast session initialization, re-keying and handoff, storage overhead, security and scalability. Our analysis shows that the GKM protocol efficiency can be traded-off with the level of trust on the BSs.
Choquet integral for criteria aggregation in the flexible job-shop scheduling problems
Authors: Ihsen Saad, Slim Hammadi, Mohamed Benrejeb, Pierre Borne
Venue: Mathematics and Computers in Simulation (2008)

Most complex scheduling problems are combinatorial problems and difficult to solve. That is why, several methods focus on the optimization according to a single criterion such as makespan, workloads of machines, waiting times, etc. In this paper, the Choquet integral is introduced as a general tool for dealing with multiple criteria decision making and used in optimization flexible job-shop scheduling problems. The considered optimization problem is based of the Genetic Algorithm (GA) used as objective function the Choquet integral for criteria aggregation. Then lower bounds are defined for each criterion. Presented examples illustrate theoretical considerations and show the efficiency of the proposed approach.
Introduction to the Special Issue on Optical Computing
Authors: D. Casasent, S. Horvitz
Venue: IEEE Transactions on Computers (1975)

IN introducing what some may consider an unusual special issue, it is appropriate to recall that at the 1967 Fall Joint Computer Conference, the name of this journal was changed from IEEE TRANSACTIONS ON ELECTRONIC COMPUTERS to IEEE TRANSACTIONS ON COMPUTERS. This Special Issue on Optical Computing aptly reflects the broad aspects of computing for which this journal was intended.
A combinatorial proof of Gotzmann's persistence theorem for monomial ideals
Authors: Satoshi Murai
Venue: European Journal of Combinatorics (2008)

Gotzmann proved the persistence for minimal growth of Hilbert functions of homogeneous ideals. His theorem is called Gotzmann's persistence theorem. In this paper, based on the combinatorics of binomial coefficients, a simple combinatorial proof of Gotzmann's persistence theorem in the special case of monomial ideals is given.
Grey Neural Network Method for Disaster Rank Prediction of Typhoon Storm Surge
Authors: Zheng Sun, Qimin Feng, Yan Sun
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

Although the method of multi-index disaster rank prediction of typhoon storm surge can avoid missing too much disaster-causing information and is in favor of improving prediction precision, it faces the problem how to identify the key disaster-causing factors and develop a prediction model. To solve this problem, a multi-index grey neural networks prediction model is presented, which combines the identification method of the key disaster-causing factors by grey relational analysis with artificial neural networks (ANN) BP algorithm owning a better learning and training ability. In the end, case study based on the typhoon storm surge data in Qingdao area validates the feasibility and effectiveness of the proposed method.
Cast Shadow and Reflection Image Suppressing of Dynamic Object Segmentation with Edge Features Kernel Density Estimation
Authors: Zheng Li, Hong Ma, Jian Yang, DongMing Tang
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

In image analysis or vision understanding systems, the accuracy of segmentation results affects the quality of the systems seriously. The dynamic cast shadows and reflection images are ever-present fake objects in dynamic object segmentation, and they deteriorate the segmentation quality seriously. This paper presents a technique for cast shadow and reflection image suppressing of dynamic objects segmentation in videos. This technique is based on dynamic edge features and kernel density estimation. Unlike the classic kernel density estimation model which can only suppress cast shadows in color videos, this model can also suppress them in intensity videos, and with normal conditions it can suppress reflection images effectively. Although this technique introduces a little drawback of true object segmentation quality, its ability of fake objects suppressing is remarkable. Several experimental results with real videos are presented to demonstrate the effectiveness of this model.
Two-Phase Computation and Data Scheduling Algorithms for Workflows in the Grid
Authors: Fangpeng Dong, Selim G. Akl
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

In this paper, a workflow scheduling approach, which consists of two algorithms, is proposed. A submitted workflow is first partitioned into subgraphs on the global Grid level by the graph partitioning algorithm according to features of the workflow itself and the status of selected available resource clusters. Then, at the resource cluster level, metatasks in each subgraph are allocated to computational resources by the metatask mapping algorithm. To reduce the total makespan of a workflow, the schedule of raw input data preloading are considered by the two algorithms. This two-phase approach does not require detailed resource information or control privilege on every Grid resource for Grid schedulers at the global Grid level, so that the dependence on Grid information services is reduced and the higher priority of local resource management policies is respected.
A clustering framework based on subjective and objective validity criteria
Authors: M. Halkidi, D. Gunopulos, M. Vazirgiannis, N. Kumar, C. Domeniconi
Venue: ACM Transactions on Knowledge Discovery from Data (TKDD) (2008)

Clustering, as an unsupervised learning process is a challenging problem, especially in cases of high-dimensional datasets. Clustering result quality can benefit from user constraints and objective validity assessment. In this article, we propose a semisupervised framework for learning the weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on: (i) user constraints; and (ii) the quality of intermediate clustering results in terms of their structural properties. The proposed framework uses the clustering algorithm and the validity measure as its parameters. We develop and discuss algorithms for learning and tuning the weights of contributing dimensions and defining the &ldquo;best&rdquo; clustering obtained by satisfying user constraints. Experimental results on benchmark datasets demonstrate the superiority of the proposed approach in terms of improved clustering accuracy.
A New Method for Concurrency in B-Trees
Authors: Yat-Sang Kwong
Venue: IEEE Transactions on Software Engineering (1982)

In this paper we study the problem of supporting concurrent operations in B-trees and their variants. A survey of previous work is given and two basic types of solutions to this problem are identified. A new solution with a greater degree of concurrency is proposed. As solutions are surveyed or presented we identify useful techniques which have wider applicability. In particular, we introduce the technique of side-branching in our new solution.
Learning process sharing on the web: approach to course material generation
Authors: Hiroyuki Mitsuhara, Kazuhide Kanenishi, Yoneo Yano
Venue: Proceedings of the sixth conference on IASTED International Conference Web-Based Education - Volume 2 (2007)

This paper describes how to generate course material for learners in the Internet age. Our fundamental idea is that the course material can be generated from learners' learning processes on the web. We focus on "learning process sharing" as a method for course material generation. In the learning process sharing, a learner can not only reflect on his/her learning process but also review other learners' learning processes. In other words, the learner can (1) reconstruct his/her constructed knowledge by reflecting on his/her learning process and (2) construct knowledge by reviewing other learners' learning processes. The learning process produced by such review becomes the target of reflection. The learning process produced by such reflection can work as course material from learners' points of view.
A People-Counting System Based on BP Neural Network
Authors: Na-Na Li, Jie Song, Rui-Ying Zhou, Jun-Hua Gu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

A people-counting system based on a back propagation (BP) neural network is proposed in this paper. The proposed system uses cheap photoelectric sensor to collect data and introduces BP neural network for counting and recognition, and it is effective and flexible for the purpose of performing people counting. In this paper, new methods for segmentation and feature extraction are developed to enhance the classification performance. Promising results were obtained and the analysis indicates that the proposed system based on BP neural network provides good results with low false rate and it is effective for people-counting.
Wavelet Image Compression Based on Context Predicting With Past Information in Rows (Columns)
Authors: Xiaoyuan Yang, Hui Ren, Bo Li, Zhengzhang Chen
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

In order to exploit correlation between bits of wavelet coefficients as much as possible, this paper presents a new algorithm called Wavelet Image Compression Based on Context Predicting with Past Information in Rows (Columns) on the thought of context-based coding. In the selection of high-order context, this paper presents Seven Points in Three Rows (Columns) Context using mutual information as a theoretical basis. In the prediction and quantization of high-order context, we make use of mathematical tools, such as Fisher discriminant, dynamic programming, etc., to obtain a good conditional probability estimate of significance and sign bits of wavelet coefficients. The experiments show that reasonable context modeling of the presented algorithm leads to satisfying results. This paper makes some valuable contributions into finding theoretical bases for wavelet image compression.
High-Speed M-Sequence Generators
Authors: W. W. ,  Jr. Warlick, J. E. Hershey
Venue: IEEE Transactions on Computers (1980)

The generation of high-speed maximal length "M"-sequences is at the heart of many diverse techniques, notably code-division multiple access communications systems, or spectrum spreading, and precise ranging disciplines. This paper develops a rich family of M-sequences whose upper speed bound is set by the fastest shift register stage available in the technology under consideration and not by logic gate delays.
Proposed Microcomputer System 796 Bus Standard
Authors: R. W. Boberg
Venue: Computer (1980)

Offered herefor public comment before submission to the IEEE Standards Board, this proposed standard allows manufacturers to produce varied but compatible microcomputer modules. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Measuring saliency of features extracted by model-based methods from internal carotid arterial Doppler signals using signal-to-noise ratios
Authors: Elif Derya Übeyli
Venue: Digital Signal Processing (2008)

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. The noise in a classification model can be reduced by identifying a set of salient features and then more accurate classification can be obtained. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of internal carotid arterial Doppler signals (ICADS). In order to extract features representing the ICADS, model-based methods were used. The PNNs used in the ICADS classification were trained for the SNR screening method. The application results of the SNR screening method to the ICADS demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and nonsalient input features.
Persistent Software Errors
Authors: R. L. Glass
Venue: IEEE Transactions on Software Engineering (1981)

Persistent software errors-those which are not discovered until late in development, such as when the software becomes operational-are by far the most expensive kind of error. Via analysis of software problem reports, it is discovered that the predominant number of persistent errors in large-scale software efforts are errors of omitted logic..., that is, the code is not as complex as required by the problem to be solved. Peer design and code review, desk checking, and ultrarigorous testing may be the most helpful of the currently available technologies in attacking this problem. New and better methodologies are needed.
Image Denoising Based on MORF and Minimization Total Variation
Authors: Chengwu Lu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

A new tight frame called monoscale orthonormal ridgelet frame (MORF) is presented in this paper. The localization principle and the orthonormal ridgelet constructed by Donoho are applied to construct the MORF. And then, It is deployed for image denoising, where a thresholding process for MORF coefficients of the noisy image is carried out firstly. To remove the artifact such as pseudo-Gibbs and to restore sharp discontinuities, while the other structures are preserved, a minimization total variation approach is applied to restore the denoised results. The experiments show that the method has more improvement both in terms of PSNR and visual effect than traditional thresholding method for image denoising.
Microprogramming: A Tutorial and Survey of Recent Developments
Authors: T. G. Rauscher
Venue: IEEE Transactions on Computers (1980)

The present status of microprogramming is developed from Wilkes's initial concept of microprogramming to the more recent bit-slice microprogrammable computer components. This tutorial defines basic concepts and gives a simple example of machine language interpretation. Describing the characteristics of microprogrammed machines provides the background for an examination of microprogrammable bit slice microprocessors, the most significant commercial development since the previous survey paper [3]. The descriptions of recent applications of microprogramming indicate the potential for microprogram use in several areas. A substantial bibliography directs the interested reader to details in many aspects of microprogramming.
A Robust 130 nm-CMOS Built-In Current Sensor Dedicated to RF Applications
Authors: M. Cimino, H. Lapuyade, M. Matos, T. Taris, Y. Deval, J. B. Bégueret
Venue: Journal of Electronic Testing: Theory and Applications (2007)

In this paper we present a design methodology that allows a dramatic reduction of the dependency on process variation, yielding to a new version of this BICS. Taking advantage of a 130 nm VLSI CMOS technology, the proposed BICS has a peak-to-peak dispersion lower than 10% of its output full-scale range. It makes it more suitable to implement the test functionality while maintaining the initial BICS intrinsic performances. The built-in self-test methodology is illustrated by monitoring the supply current of Low-Noise Amplifiers (LNAs). Measurements confirm the BICS's transparency relative to the circuit-under-test (CUT) and its accuracy.