ppxscal/embedding_network huggingface dataset:
Investigating organizational learning in eGovernment projects: A multi-theoretic approach
Authors: Chee Wei Phang, Atreyi Kankanhalli, Caizuan Ang
Venue: The Journal of Strategic Information Systems (2008)

Organizational learning is essential during eGovernment implementation to leverage the benefits of such efforts. However, there is a lack of holistic understanding of organizational learning in IT-related change in public organizations. Motivated by such concerns, we construct a structurational framework that inter-relates elements pertinent to IT-related change from four theoretical perspectives: organizational politics, organizational culture, institutional theory, and organizational learning itself. Inter-relationships between elements of these perspectives are explicated through the notions of structures, modalities, and actions from structuration theory. Guided by the framework, we conducted a case study to examine the organizational learning occurring during a successful eGovernment project. The analysis indicates the inextricable interactions between elements from the different perspectives during the project implementation. The proposed framework offers both theoretical and practical benefits for understanding and facilitating organizational learning in eGovernment projects.
Effectiveness of using quantified intermarket influence for predicting trading signals of stock markets
Authors: Chandima D. Tilakaratne, Musa A. Mammadov, Sidney A. Morris
Venue: Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70 (2007)

This paper investigates the use of influence from foreign stock markets (intermarket influence) to predict the trading signals, buy, hold and sell, of the of a given stock market. Australian All Ordinary Index was selected as the stock market whose trading signals to be predicted. Influence is taken into account as a set of input variables for prediction. Two types of input variables were considered: quantified (weighted) input variables and their un-quantified counterparts. Two criteria was applied to determine the trading signals: one is based on the relative returns while the other uses the conditional probability that a given relative return is greater than or equals zero. The prediction of trading signals was done by Feedforward neural networks, Probabilistic neural networks and so called probabilistic approach which was proposed in past studies. Results suggested that using quantified intermarket influence as input variables to predict trading signals, is more effective than using their un-quantified counterparts.
Extensions of edge-coloured digraphs
Authors: Hortensia Galeana-Sánchez, Rocío Rojas-Monroy
Venue: Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS (2006)

A digraph D is said to be an m-coloured digraph, if its arcs are coloured with m colours. A directed path (or a directed cycle) is called monochromatic if all of its arcs are coloured alike. A set N ⊆ V (D) of vertices of D is said to be a kernel by monochromatic paths of the m-coloured digraph D, if it satisfies the two following properties : (1) N is independent by monochromatic paths; i.e. for any two different vertices x, y ∈ N, there is no monochromatic directed path between them, and (2) N is absorbent by monochromatic paths; i.e. for each vertex u ∈ V (D) - N, there exists a uv-monochromatic directed path, for some v ∈ N. In this paper we present a method to construct a large variety of m-coloured digraphs with (resp. without a kernel) kernel by monochromatic paths; starting with a given m-coloured digraph D0. A previous result is generalized.
Evolvable hardware a new approach for control design
Authors: Tatiane Jesus De Campos, José Raimundo De Oliveira, Mario Jungbeck
Venue: Proceedings of the 7th WSEAS International Conference on Evolutionary Computing (2006)

Evolvable hardware (EHW) is a technique introduced to automatically design circuits where the circuit configuration is carried out by evolutionary algorithms. This paper shows that the evolutionary approach can discover the concept and learning behavior of the control systems. The system tested is a nonlinear pendulum. Experiments with different processes indicate that the gains obtained with evolvable hardware may provide better responses than those obtained by the classical PID controller whose gains are set by Genetic Algorithms. In this paper the Integral of the Square Error (ISE) criteria was used as performance index to be minimized through applying of Genetic Algorithms and Evolvable Hardware controllers. Analysis and simulation of nonlinear pendulum is provided to demonstrate that the proposed evolvable controller has excellent results.
Decentralized swarm aggregation with static communication links
Authors: Dimos V. Dimarogonas, Kostas J. Kyriakopoulos
Venue: Proceedings of the 1st international conference on Robot communication and coordination (2007)

The objective of this paper is decentralized swarm aggregation with collision avoidance. Specifically, each agent is assigned with a control law which is the sum of two elements: a repulsive potential field, which is responsible for the collision avoidance objective, and an attractive potential field, that forces the agents to converge to a configuration where they are close to each other. It is shown that under the proposed control law agents converge to a configuration where each agent is located at a bounded distance from each of its neighbors. Connectivity of the communication graph is then shown to be a sufficient condition for swarm aggregation. When a global objective is imposed, namely aggregation of the robotic swarm close to a desired location we show that only one agent, which plays the role of the leader of the swarm, has to be aware of this objective. The leader is shown to be able to drag the swarm around the desired location. We also show that these results are applied to both cases of robots satisfying single integrator kinematics and kinematic unicycle-type robots.
Texture Characteristic Extraction for Dominant Directions in Content-based Medical Image Retrieval
Authors: Gang Zhang, Z. M. Ma, Ying He, Tie-Nan Zhao
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02 (2008)

The paper presents a texture characteristic extraction method for dominant directions. Enhanced Fourier spectrum is computed for an image in a medical image database. Direction histogram is computed from the enhanced Fourier spectrum. The set of dominant directions is computed according to the histogram. Gray co-occurrence matrix extracts texture characteristics based on the set. Euclidean distance is used to measure similarity between the images. Experiments show that the method in the paper show good performance for directional texture images and the same performance as currently widely used methods for non-directional texture images.
Spatial sketching with the auditory reflection for the plausible virtual conceptual design
Authors: Sang-Hun Nam, Sang-Min Choi, Jun Choi, Young-Ho Chai
Venue: Proceedings of the 5th symposium on Applied perception in graphics and visualization (2008)

Most industrial designers work with a pencil and some plane papers. In spite of the paper-based plane modeling data by these designers, the majority of the industrial modeling data for manufacturing are based on the spatial concept. While converting the designer's 2D drawing data to the digital form of 3D industrial modeling, designer's intention is often misunderstood or misinterpreted. If the designer can use the spatial modeling tool in their conventional way, it is easy to express their intention and there is no need to transform the 2D drawings to 3D model.
A lower bound for matrix multiplication
Authors: N. H. Bshouty
Venue: Proceedings of the 29th Annual Symposium on Foundations of Computer Science (1988)

It is proved that computing the product of two n*n matrices over the binary field requires at least 2.5n/sup 2/-O(n/sup 2/) multiplications.
Mitigating attacks against virtual coordinate based routing in wireless sensor networks
Authors: Jing Dong, Kurt E. Ackermann, Brett Bavar, Cristina Nita-Rotaru
Venue: Proceedings of the first ACM conference on Wireless network security (2008)

Virtual coordinate system (VCS) based routing provides a practical, efficient and scalable means for point-to-point routing in wireless sensor networks. Several VCS-based routing protocols have been proposed in the last few years, all assuming that nodes behave correctly. However, many applications require deploying sensor networks in adversarial environments, making VCS-based routing protocols vulnerable to numerous attacks. In this paper, we study the security of VCS-based routing protocols. We first identify novel attacks targeting the underlying virtual coordinate system. The attacks can be mounted with little resource, yet are epidemic in nature and highly destructive to system performance. We then propose lightweight defense mechanisms against each of the identified attacks. Finally, we evaluate experimentally the impact of the attacks and the effectiveness of our defense mechanisms using a well-known VCS-based routing protocol, BVR.
An Embedded Tree Matching Algorithm based on Metaphorical Dependency Structure
Authors: Jianfeng Li, Yun Yang, Changle Zhou
Venue: Proceedings of the 2007 International Conference on Convergence Information Technology (2007)

Knowledge extraction plays an active role in intelligent systems. This paper proposes an embedded tree matching algorithm oriented to knowledge extraction and natural language processing in intelligent systems to extract semantic structures from discourses. The algorithm is designed and tested on Chinese metaphoric expressions to find in a given sentence all the dependency semantic relations that most probably occur in metaphors. The main process is top-down searching and bottom-up amending. Experiment results show the algorithm can expectantly find out accurate dependency relations. This work proposes a preliminary work for discovering potential metaphorical structures or other semantic structures in Chinese sentences which would be useful for intelligent systems capable of natural language understanding.
The Statistical Failure Analysis for the Design of Robust SRAM in Nano-Scale Era
Authors: Young-Gu Kim, Soo-Hwan Kim, Hoon Lim, Sanghoon Lee, Keun-Ho Lee, Young-Kwan Park, Moon-Hyun Yoo
Venue: Proceedings of the 9th international symposium on Quality Electronic Design (2008)

Increase of the process variability with aggressive technology scaling causes many productivity issues in VLSI manufacturing. Analysis about the relationship between process variability and failure has been performed to specify guidelines in both technology and design aspects for yield optimization. By applying the proposed methodology, the core scheme and the operating voltage of the 200MHz SRAM were determined to secure the immunity to operational failures. In DFM point of view, the statistical circuit analysis for failure characteristics is indispensable to guarantee an optimal yield in manufacturing.
Where Do You Want to Go in Your SOA Adoption Journey?
Authors: Shrikant Mulik, Sushil Ajgaonkar, Kavindra Sharma
Venue: IT Professional (2008)

Not every company has the same needs in adopting a service-oriented architecture (SOA). The authors compare four options for increasing system flexibility.
Hierarchical clustering of subpopulations with a dissimilarity based on the likelihood ratio statistic: application to clustering massive data sets
Authors: Antonio Ciampi, Yves Lechevallier, Manuel Castej&#x00f3;n Limas, Ana Gonz&#x00e1;lez Marcos
Venue: Pattern Analysis Applications (2008)

The problem of clustering subpopulations on the basis of samples is considered within a statistical framework: a distribution for the variables is assumed for each subpopulation and the dissimilarity between any two populations is defined as the likelihood ratio statistic which compares the hypothesis that the two subpopulations differ in the parameter of their distributions to the hypothesis that they do not. A general algorithm for the construction of a hierarchical classification is described which has the important property of not having inversions in the dendrogram. The essential elements of the algorithm are specified for the case of well-known distributions (normal, multinomial and Poisson) and an outline of the general parametric case is also discussed. Several applications are discussed, the main one being a novel approach to dealing with massive data in the context of a two-step approach. After clustering the data in a reasonable number of &#x2018;bins&#x2019; by a fast algorithm such as k-Means, we apply a version of our algorithm to the resulting bins. Multivariate normality for the means calculated on each bin is assumed: this is justified by the central limit theorem and the assumption that each bin contains a large number of units, an assumption generally justified when dealing with truly massive data such as currently found in modern data analysis. However, no assumption is made about the data generating distribution.
Dependable design technique for system-on-chip
Authors: Pavel Kubalík, Hana Kubátová
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

A technique for highly reliable digital design for two FPGAs under a processor control is presented. Two FPGAs are used in a duplex configuration system design, but better dependability parameters are obtained by the combination of totally self-checking blocks based on a parity predictor. Each FPGA can be reconfigured when a SEU fault is detected. This reconfiguration is controlled by a control unit implemented in a processor. Combinational circuit benchmarks have been considered in all our experiments and computations. All our experimental results are obtained from a XILINX FPGA implementation using EDA tools. The dependability model and dependability calculations are presented to document the improved reliability parameters.
Securing Ad Hoc Storage through Probabilistic Cooperation Assessment
Authors: Nouha Oualha, Yves Roudier
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2008)

The trend towards self-organization of systems like peer-to-peer or ad hoc networks generates increasing needs for designing distributed storage schemes that should themselves be self-organized and cooperative. Unfortunately, in such systems, the data stored are exposed to new disruption attacks either because of the selfishness of participating nodes with respect to their resources or even because self-organization that leaves such systems much more subject to maliciousness. This setting, combined with the high churnout of nodes and with the high chances of network partition, particularly in mobile ad hoc systems, makes it quite uneasy to ensure the long-term availability of data stored in such fashion. This paper discusses a verification framework based on the probabilistic assessment of small cryptographic verifications in order to assess storage and to prevent data destruction. A protocol for determining the availability of data stored in an ad hoc fashion is detailed. The design of a reputation system based on this protocol and inciting nodes to cooperate towards storage is then discussed.
Robust detection of outliers for projection-based face recognition methods
Authors: Sid-Ahmed Berrani, Christophe Garcia
Venue: Multimedia Tools and Applications (2008)

In this paper, the impact of outliers on the performance of high-dimensional data analysis methods is studied in the context of face recognition. Most of the existing face recognition methods are based on PCA-like methods: faces are projected into a lower dimensional space in which similarity between faces is supposed to be more easily evaluated. These methods are, however, very sensitive to the quality of the face images used in the training and in the recognition phases. Their performance significantly drops when face images are not well centered or taken under variable illumination conditions. In this paper, we study this phenomenon for two face recognition methods, namely PCA and LDA2D, and we propose a filtering process that allows the automatic selection of noisy face images which are responsible for the performance degradation. This process uses two techniques. The first one is based on the recently proposed robust high-dimensional data analysis method called RobPCA. It is specific to the case of recognition from video sequences. The second technique is based on a novel and effective face classification technique. It allows isolating still face images that are not very precisely cropped, not well-centered or in a non-frontal pose. Experiments show that this filtering process significantly improves recognition rates by 10 to 30%.
Arc reversal in nonhamiltonian circulant oriented graphs
Authors: Jozef Jirásek
Venue: Journal of Graph Theory (2005)

Locke and Witte described infinite families of nonhamiltonian circulant oriented graphs. We show that for infinitely many of them the reversal of any arc produces a hamiltonian cycle. This solves an open problem stated by Thomassen in 1987. We also use these graphs to construct counterexamples to &Aacute;d&aacute;m's conjecture on arc reversal. One of them is a counterexample with the smallest known number of vertices. &copy; 2005 Wiley Periodicals, Inc. J Graph Theory 49: 59&ndash;68, 2005
A new key exchange scheme based on extended Chebyshev polynomials
Authors: Wang Dahu, Wei Xueye
Venue: Proceedings of the 4th WSEAS International Conference on Applied Mathematics and Computer Science (2005)

Kocarev gave the first cryptosystems based on the semi-group property of Chebyshev polynomials, which seemed excellent but actually insecure. Due to the inherent periodicity of trigonometric function, an attack can easily get plaintext given ciphertext. In this paper, we extend Chebyshev polynomials from real number to finite fields to avoid the attack and present the corresponding key exchange scheme, which is secure and practical.
Reliability-centric gate sizing with simultaneous optimization of soft error rate, delay and power
Authors: Koustav Bhattacharya, Nagarajan Ranganathan
Venue: Proceeding of the 13th international symposium on Low power electronics and design (2008)

The reliability against transient faults poses a significant challenge due to technology scaling trends. Several circuit optimization techniques have been proposed in the literature for preventing soft errors in logic circuits. However, most approaches do not incorporate the effects of other design metrics like delay and power while optimizing the circuit for soft error protection. In this work, we develop a first order model of the soft error phenomenon in logic circuits and incorporate power and delay metrics to formulate a convex programming based reliability-centric gate sizing technique. The proposed algorithm has been implemented and validated on the ISCAS`85 benchmarks. Experimental results indicate that our multi-objective optimization technique can achieve significant reductions in soft error rate with simultaneous optimization of delay and power.
Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping
Authors: Fernando V. Paulovich, Luis G. Nonato, Rosane Minghim, Haim Levkowitz
Venue: IEEE Transactions on Visualization and Computer Graphics (2008)

The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections (LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in $mD$. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in $2D$. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high quality methods, particularly where it was mostly tested, that is, for mapping text sets.
AN ACCELERATED ALGORITHM FOR DENSITY ESTIMATION IN LARGE DATABASES USING GAUSSIAN MIXTURES
Authors: Alvaro Soto, Felipe Zavala, Anita Araneda
Venue: Cybernetics and Systems (2007)

Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian mixture models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of expectation maximization (EM). This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept of data condensation which, in addition to substantially diminishing the computational load, provides sound starting values that allow the algorithm to reach convergence faster. We also focus on the model selection problem. We test our algorithm using synthetic and real databases, and find several advantages, when compared to other standard existing procedures.
Multi-Character Processor Array for Pattern Matching in Network Intrusion Detection System
Authors: Yeim-Kuan Chang, Ming-Li Tsai, Yu-Ru Chung
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications (2008)

Network Intrusion Detection System (NIDS) is a system developed for identifying attacks by using a set of rules. NIDS is an efficient way to provide the security protection for today’s internet. Pattern match algorithm plays an important role in NIDS that performs searches against multiple patterns for a string match. Pattern matching is a computationally expensive task. Traditional software-based NIDS solutions usually can not achieve a high-speed required for ever growing Internet attacks. In order to satisfy high-speed packet content inspection, hardware-implementable pattern match algorithm is required. In this paper, we propose a hardware-based pattern match architecture that employs a multi-character processor array. The proposed multi-character processor array is a parallel and pipelined architecture which can process multiple characters of the input stream per cycle. The proposed architecture can reduce a lot of unnecessary computations and thus it is power efficient. We use Snort pattern sets and DEFCON packet traces to perform our simulations. Our experiment results show that, with a 3-character processor array, we can reduce 83% of the computations compared with the brute force approach.