ppxscal/embedding_network huggingface dataset:
RBFN Based Motion Blur Parameter Estimation
Authors: Ratnakar Dash, Pankaj Kumar Sa, Banshidhar Majhi
Venue: Proceedings of the 2009 International Conference on Advanced Computer Control (2009)

The image restoration techniques are oriented towards constructing the original image from a degraded observation. Motion blur in images is one of the most common degradation phenomena. Restoration of such images require correct estimation of motion blur parameters. In this paper we have proposed a novel technique for estimating the angle and the length of motion blur. Gabor filter has been used to find the angle of the blur and a simple neural network has been used to find the length of blur. The proposed method was tested on various standard images. The experimental results reveal that proposed method works better than the existing methods in terms of visual quality and PSNR.
Some new characterizations and properties of quasi-copulas
Authors: José Antonio Rodríguez-Lallena, Manuel Úbeda-Flores
Venue: Fuzzy Sets and Systems (2009)

In this paper we characterize the concept of quasi-copula in three different ways: as a special subclass of aggregation operators, in terms of its associated volume and-for the bivariate case-in terms of non-increasing tracks on [0,1]^2. We also provide sufficient conditions and new properties for quasi-copulas.
A Computational Scheme Based on Random Boolean Networks
Authors: Elena Dubrova, Maxim Teslenko, Hannu Tenhunen
Venue: Transactions on Computational Systems Biology X (2008)

For decades, the size of silicon CMOS transistors has decreased steadily while their performance has improved. As the devices approach their physical limits, the need for alternative materials, structures and computational schemes becomes evident. This paper considers a computational scheme based on an abstract model of the gene regulatory network called Random Boolean Network (RBN). On one hand, our interest in RBNs is due to their attractive fault-tolerant features. The parameters of an RBN can be tuned so that it exhibits a robust behavior in which minimal changes in network's connections, values of state variables, or associated functions, typically cause no variation in the network's dynamics. On the other hand, a computational scheme based on RBNs seems appealing for emerging technologies in which it is difficult to control the growth direction or precise alignment, e.g. carbon nanotubes.
Traffic Sign Detection and Pattern Recognition Using Support Vector Machine
Authors: Kiran C.G., Lekhesh V. Prabhu, Abdu Rahiman V., Rajeev K.
Venue: Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition (2009)

A vision based vehicle guidance system must be able to detect and recognize traffic signs. Traffic sign recognition systems collect information about road signs and helps the driver to make timely decisions, making driving safer and easier. This paper deals with the detection and recognition of traffic signs from image sequences using the colour information. Colour based segmentation techniques are employed for traffic sign detection. In order to improve the performance of segmentation, we used the product of enhanced hue and saturation components. To obtain better shape classification performance, we used linear support vector machine with the Distance to Border features of the segmented blobs. Recognition of traffic signs are implemented using multi-classifier non-linear support vector machine with edge related pixels of interest as the feature.
Finding anomalous periodic time series
Authors: Umaa Rebbapragada, Pavlos Protopapas, Carla E. Brodley, Charles Alcock
Venue: Machine Learning (2009)

Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD's reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.
Reduced dimensional HRTF processing for gaming environments
Authors: Arun Ravindran
Venue: Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (2008)

Processing of acoustic signals with head-related transfer functions (HRTFs) is a commonly used technique for simulating 3D audio in interactive virtual environments and gaming applications. It is computationally very demanding to process the high dimensional HRTFs and yet capturing the variability across listeners and this could be a limiting factor for real time 3D audio applications. In this paper we show how the dimensionality of the HRTFs can be reduced to their minimum phase components, without losing the localization information contained in them, and their performance is evaluated under a localization classification paradigm using decision trees and compared with PCA.
Single Carrier Frequency Domain Equalizer for Underwater Wireless Communication
Authors: Chengbing He, Jianguo Huang, Qunfei Zhang, Xiaohong Shen
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01 (2009)

Single carrier modulation with frequency domain equalizer is proposed for underwater acoustic channel. An unique training sequence, viewed as cyclic prefix, is inserted into transmitted sequences so that low complexity minimum mean square error (MMSE) frequency domain equalization (FDE) is used for combating the inter-symbol interference in the receivers. Maximum likelihood estimation for robust initial phase error and Doppler shift are derived by using training sequence. The receiver estimates the initial phase error, Doppler shift and underwater acoustic channelblock-by-block. Compare to zero padding or cyclic prefix of single carrier modulation, training sequence has the potential to provide more accurate estimation than decision-directed methods and alleviate the time-scale effect caused by significant Doppler shiftfor wideband acoustic signal. The proposed UWA communication system with single transmitter and single receiver has been tested in practical South China Sea on June 2007. At a distance of 30 km, the data rates of 450 bps and 900 bps are achieved with uncoded bit error rates of 4 out of 8333 (BPSK) and 103 out of 16666 (QPSK).
A two-stage modeling with genetic algorithms for the nurse scheduling problem
Authors: Chang-Chun Tsai, Sherman H. A. Li
Venue: Expert Systems with Applications: An International Journal (2009)

This study develops a two-stage mathematical modeling for a nurse scheduling system wherein hospital management requirements, government regulations, and nursing staffs' shift preferences are incorporated. In the first stage, the nurse work and vacation schedules are arranged and genetic algorithm (GA) is used to solve for the optimal schedules and to check for any violation of government regulations, hospital management requirements, and the scheduling fairness. In the second stage, the nurse roster schedule is arranged and GA is further adopted to solve the optimal schedule. An empirical case study is performed and the results show that GA can be an efficient tool for solving the nurse scheduling problem. In addition, it can also be easily modified to suit different cases encountered in hospitals.
On graph-based characteristics of optimal overlay topologies
Authors: Mina Youssef, Caterina Scoglio
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2009)

In this paper, we address the challenge of overlay topology design by considering which overlay topology best minimizes cost function, taking into account overlay link creation cost and routing cost. First, we formulate the problem as Integer Linear Programming (ILP) given a traffic matrix and assuming cooperative behavior of nodes. Then, we propose some heuristics to find near-optimal overlay topologies with a reduced complexity. The solutions to the ILP problem on real network topologies have been analyzed, showing that the traffic demands between the nodes affect the decision to create new overlay links. Next, the obtained optimal and near-optimal overlay topologies are thoroughly analyzed and the heuristics are compared through extensive numerical evaluations. Finally guidelines for the selection of the best heuristic as a function of the cost parameters are also provided.
Interface Design for MicroBiomanipulation and Teleoperation
Authors: Leonardo Mattos, Darwin Caldwell
Venue: Proceedings of the 2009 Second International Conferences on Advances in Computer-Human Interactions (2009)

Current challenges in biomanipulations for life-sciences research include extensive operator training, low success rates and low consistency of operations. These problems were tackled here through the use of teleoperation techniques and the development of a unified interface for simultaneous control of all devices used for standard biomanipulations. The developed system was created with high-end commercial biomanipulation equipment similar to those currently in use at many research laboratories, and also included game joysticks for teleoperated control. These were integrated into a single system through the design of modular component abstractions and the implementation of a central control structure. This structure enabled the creation of an open, flexible and user-friendly biomanipulation system for improved operation performance. This paper describes the design and implementation of such system.
Drawing Borders Efficiently
Authors: Kazuo Iwama, Eiji Miyano, Hirotaka Ono
Venue: Theory of Computing Systems (2009)

A spreadsheet, especially MS Excel, is probably one of the most popular software applications for personal-computer users and gives us convenient and user-friendly tools for drawing tables. Using spreadsheets, we often wish to draw several vertical and horizontal black lines on selective gridlines to enhance the readability of our spreadsheet. Such situations we frequently encounter are formulated as the Border Drawing Problem (BDP). Given a layout of black line segments, we study how to draw it efficiently from an algorithmic view point, by using a set of border styles and investigate its complexity. (i)&#x00a0;We first define a formal model based on MS Excel, under which the drawability and the efficiency of border styles are discussed, and then (ii)&#x00a0;show that unfortunately the problem is $\mathcal{NP}$-hard for the set of the Excel border styles and for any reasonable subset of the styles. Moreover, in order to provide potentially more efficient drawing, (iii)&#x00a0;we propose a new compact set of border styles and show a necessary and sufficient condition of its drawability.
Dynamic Handwritten Signature Verification Based on Statistical Quantization Mechanism
Authors: Thian Song Ong, Wee How Khoh, Andrew Beng Jin Teoh
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02 (2009)

Online handwritten signature has been widely used for identity verification. However, it suffers from large intra-class variation problem as individual’s signature may deviate from time to time due to variations in signing position, signature size, writing surface, and other factors. In addition, signatures are easier to forge than other biometrics and this leads to random and skilled forgeries issues. In this paper, we propose a novel Statistical Quantization Mechanism (SQM) to suppress the intra-class variation in signature features and thus discriminate the difference between genuine signature and its forgery. Experimental results show the proposed method is feasible in practice.
Shortest Path Tree Computation in Dynamic Graphs
Authors: Edward P. F. Chan, Yaya Yang
Venue: IEEE Transactions on Computers (2009)

Let G = (V, E, w) be a simple digraph, in which all edge weights are nonnegative real numbers. Let G^{\prime} be obtained from G by an application of a set of edge weight updates to G. Let s\in V and let T_{s} and T_{s}^{\prime} be Shortest Path Trees (SPTs) rooted at s in G and G^{\prime}, respectively. The Dynamic Shortest Path (DSP) problem is to compute T_{s}^{\prime} from T_{s}. Existing work on this problem focuses on either a single edge weight change or multiple edge weight changes in which some of them are incorrect or are not optimized. We correct and extend a few state-of-the-art dynamic SPT algorithms to handle multiple edge weight updates. We prove that these algorithms are correct. Dynamic algorithms may not outperform static algorithms all the time. To evaluate the proposed dynamic algorithms, we compare them with the well-known static Dijkstra algorithm. Extensive experiments are conducted with both real-life and artificial data sets. The experimental results suggest the most appropriate algorithms to be used under different circumstances.
Accomplishing reuse with a simulation conceptual model
Authors: Osman Balci, James D. Arthur, Richard E. Nance
Venue: Proceedings of the 40th Conference on Winter Simulation (2008)

Reuse has been very difficult or in some cases impossible in the Modeling and Simulation (M&S) discipline. This paper focuses on how reuse can be accomplished by using a conceptual model (CM) in a community of interest (COI). We address the issue of reuse in a multifaceted manner covering many areas (types) of M&S such as discrete, continuous, Monte Carlo, system dynamics, gaming-based, and agent-based. M&S is commonly employed and reuse is critically needed by many COIs such as air traffic control, automobile manufacturing, ballistic missile defense, business process reengineering, emergency response management, military training, network-centric operations and warfare, supply chain management, telecommunications, and transportation. We present how a CM developed for a COI can assist in reuse for the design of any type of large-scale complex M&S application in that COI. A CM becomes an asset for a COI and offers significant economic benefits through its effective reuse.
hybseek: Pathogen primer design tool for diagnostic multi-analyte assays
Authors: Christian Frech, Karin Breuer, Bernhard Ronacher, Thomas Kern, Christof Sohn, Gerhard Gebauer
Venue: Computer Methods and Programs in Biomedicine (2009)

Due to recent advances in genome sequencing, the detection of pathogens by DNA signatures, i.e. by oligonucleotide sequences that uniquely identify a specific genome, is becoming increasingly popular in modern clinical diagnostics. However, currently available screening methods, such as PCR and microarrays, lack multiplexing and sensitivity, respectively. Solid-phase amplification (SPA) is an emerging approach with the potential to overcome these limitations. SPA-based diagnostic assays require both pathogen-specific and compatible primer pairs for many, often closely related pathogens. Currently, none of the available tools supports an automated design of such primer sets, making it an iterative, labor-intensive, and often difficult procedure. Here we describe hybseek, a Web interface for efficient design of both pathogen-specific and compatible primer pairs for DNA-based diagnostic multi-analyte assays. hybseek achieves pathogen-specificity by selecting only candidates with unique 3' subsequence, and the degree of this uniqueness is quantitatively expressed by a specificity score. qPCR experimental data confirm the feasibility of our design strategy. The service is freely available at https://www.hybseek.com.
On existence of reporter strands in DNA-based graph structures
Authors: Nataa Jonoska, Nadrian C. Seeman, Gang Wu
Venue: Theoretical Computer Science (2009)

Through self-assembly of branched junction molecules many different DNA structures (graphs) can be assembled. We show that every multigraph can be assembled by DNA such that there is a single strand that traces each edge in the graph at least once. This strand corresponds to a boundary component of a two-dimensional orientable surface that has the given graph as a deformation retract. This boundary component traverses every edge at least once, and it defines a circular path in the graph that ''preserves the graph structure'' and traverses each edge.
An Observer-Based Neural Network Controller for Chaotic Lorenz System
Authors: Suwat Kuntanapreeda
Venue: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence (2008)

The paper proposes an alternative control design for the chaotic Lorenz system based on neural networks. The controller is a feedforward neural network trained by a model reference technique. Implementation of the control design requires system states for feedback, while in most of practical applications only the system output is available. To overcome this problem, a nonlinear observer is used to estimate the states of the system. Simulation results have illustrated the feasibility and effectiveness of the proposed observer-based neural network controller.
Low Power LDPC Code Decoder Architecture Based on Intermediate Message Compression Technique
Authors: Kazunori Shimizu, Nozomu Togawa, Takeshi Ikenaga, Satoshi Goto
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

Reducing the power dissipation for LDPC code decoder is a major challenging task to apply it to the practical digital communication systems. In this paper, we propose a low power LDPC code decoder architecture based on an intermediate message-compression technique which features as follows: (i) An intermediate message compression technique enables the decoder to reduce the required memory capacity and write power dissipation. (ii) A clock gated shift register based intermediate message memory architecture enables the decoder to decompress the compressed messages in a single clock cycle while reducing the read power dissipation. The combination of the above two techniques enables the decoder to reduce the power dissipation while keeping the decoding throughput. The simulation results show that the proposed architecture improves the power efficiency up to 52% and 18% compared to that of the decoder based on the overlapped schedule and the rapid convergence schedule without the proposed techniques respectively.
Covering graphs with few complete bipartite subgraphs
Authors: Herbert Fleischner, Egbert Mujuni, Daniël Paulusma, Stefan Szeider
Venue: Theoretical Computer Science (2009)

We consider computational problems on covering graphs with bicliques (complete bipartite subgraphs). Given a graph and an integer k, the biclique cover problem asks whether the edge-set of the graph can be covered with at most k bicliques; the biclique partition problem is defined similarly with the additional condition that the bicliques are required to be mutually edge-disjoint. The biclique vertex-cover problem asks whether the vertex-set of the given graph can be covered with at most k bicliques, the biclique vertex-partition problem is defined similarly with the additional condition that the bicliques are required to be mutually vertex-disjoint. All these four problems are known to be NP-complete even if the given graph is bipartite. In this paper, we investigate them in the framework of parameterized complexity: do the problems become easier if k is assumed to be small? We show that, considering k as the parameter, the first two problems are fixed-parameter tractable, while the latter two problems are not fixed-parameter tractable unless P=NP.
Comparing performance metrics in organic search with sponsored search advertising
Authors: Anindya Ghose, Sha Yang
Venue: Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising (2008)

With the rapid growth of search advertising, there has been an increased interest amongst both practitioners and academics in enhancing our understanding of how consumers respond to contextual and sponsored search advertising on the Internet. An emerging stream of work has begun to explore these issues. In this paper, we focus on a previously unexplored question: How does sponsored search advertising compare to organic listings with respect to predicting conversion rates, order values and profits from a keyword ad? We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. Our analysis suggests that on an average while the conversion rates, order values and profits from paid search advertisements were much higher than those from natural search, most of the keyword-level characteristics have a statistically significant and stronger impact on these three performance metrics for organic search than paid search. This could shed light on understanding what the most "attractive" keywords are from advertisers' perspective, and how advertisers should invest in search engine advertising campaigns relative to search engine optimization.
An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference
Authors: Jian-Da Wu, Chuang-Chin Hsu, Hui-Chu Chen
Venue: Expert Systems with Applications: An International Journal (2009)

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.
The Garmin Nuvi Pocket Guide, 1st edition
Authors: Jason D. O'Grady
Venue:  (2008)

Here is your essential companion to the nuvi. The Garmin nuvi Pocket Guide steers you through how to:Set up and quickly start using your nuvi.Personalize nuvi.Find your destinations and points of interest (POIs).Master multiple-point routing.Create proximity alerts for speed traps, safety cameras, and school zones.Receive traffic, weather, and news.Master hands-free and POI dialing via BluetoothTune in with the built-in FM transmitter.Use the nuvi's travel features: its currency and unit converters, World Clock, and the Language Guide.Listen to music and audiobooks, view photos, and play games.Keep your nuvi software and maps up to date.