ppxscal/embedding_network huggingface dataset:
Simple object oriented designed computer algebra system
Authors: D. M. Akbar, Shaiq A. Haq, Zafar Ullah Khan, Zaki Ahmed
Venue: Journal of Computational Methods in Sciences and Engineering (2008)

Computer Algebra System (CAS) is a software program that facilitates symbolic mathematics. The core functionality of a typical CAS is manipulation of mathematical expressions in symbolic forms. The expressions manipulated by CAS normally include polynomials in multiple variables, standard trigonometric and exponential functions, various special functions for example gamma, zeta, Bessel, etc. and also arbitrary functions like derivatives, integrals, sums, and products of expressions. Our implementation of a CAS tool provides an object oriented design framework. The system is portable to other platforms and highly scalable. The other key features include a very simple and interactive user GUI support for a formula editor, making it a self contained system, additionally the formula editor provides a real-time syntax checking for expressions.
Visualization of Cooperative Options for Small Maritime Companies
Authors: Sylvia Encheva, Sharil Tumin
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

This paper discuses graphical representation of joint transport cargo operations of small maritime companies. Hasse diagrams facilitate a summary of various cooperation options and their relationships. Such information is beneficial for both companies and their customers.
Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm
Authors: Kwang-Baek Kim, Sungshin Kim, Gwang-Ha Kim
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the nucleus region is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy c-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.
Resource Bounded Frequency Computations with Three Errors
Authors: Ulrich Hertrampf, Christoph Minnameier
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

We deal with frequency computations in polynomial time, or more generally with resource bounded frequency computations. We investigate the first non-trivial case of the Hinrichs-Wechsung conjecture, which states that as soon as we have at least 2 d + d inputs to be queried, it does not become harder to get an answer with at most d errors, if we increase the number of inputs to be queried. This conjecture can easily be seen to hold for cases d < 3, and it seems very hard to prove in general. We solve the problem affirmatively in the case d = 3 by a combination of theoretical reasoning with a highly optimized computer search.
Control Charts of Workflows
Authors: Calin Ciufudean, Constantin Filote, Dumitru Amarandei
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

This paper focuses on the control of the performance characteristics of workflows modeled with stochastic Petri nets (SPN's). This goal is achieved by focusing on a new model for Artificial Social Systems (ASS's) behaviors, and by introducing equivalent transfer functions for SPN's. ASS's exist in practically every multi-agent system, and play a major role in the performance and effectiveness of the agents. This is the reason why we introduce a more suggestive model for ASS's. To model these systems, a class of Petri nets is adopted, and briefly introduced in the paper. This class allows representing the flow of physical resources and control information data of the ASS's components. In the analysis of SPN we use simulations in respect to timing parameters in a generalized semi-Markov process (GSMP). By using existing results on perturbation (e.g., delays in supply with raw materials, derangements of equipments, etc.) analysis and by extending them to new physical interpretations we address unbiased sensitivity estimators correlated with practical solutions in order to attenuate the perturbations.
Enhancing NetLogo to Simulate BDI Communicating Agents
Authors: Ilias Sakellariou, Petros Kefalas, Ioanna Stamatopoulou
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

The implementation process of complex agent and multi-agent systems (AMAS) can benefit significantly from a simulation platform that would allow rapid prototyping and testing of initial design ideas and choices. Such a platform, should ideally have a small learning curve, easy implementation and visualisation of the AMAS under development, while preserving agent oriented programming characteristics that would allow to easily port the design choices to a fully-fledged agent development environment. However, these requirements make such a simulation platform an ideal learning tool as well. We argue that NetLogo meets most of the requirements that suit our criteria. In addition, we describe two extra NetLogo libraries, one for BDI-like agents and one for ACL-like communication that allow effortless development of goal-oriented agents, that communicate using FIPA-ACL messages. We present one simulation scenario that employs these libraries to provide an implementation in which agents cooperate under a Contract Net protocol.
Error Entropy and Mean Square Error Minimization Algorithms for Neural Identification of Supercritical Extraction Process
Authors: Rosana Paula de Oliveira Soares, Adriana Rosa Garcez Castro, Roberto Célio Limão de Oliveira, Vladimiro Miranda
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

In this paper, Artificial Neural Networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology – IBET in Oeiras – Lisbon – Portugal. A strategy is used to complement the experimental data collected in laboratory during extraction procedures of useful compositions for the pharmaceutical industry using Black Agglomerate Residues (BAR) originating from of the cork production as raw material. The strategy involves fitting of data obtained during an operation of extraction. Two neural models are presented: the neural model trained using a Mean Square Error (MSE) minimization algorithm and the neural model which the learning was based on the error entropy minimization. A comparison of the performance of the two models is presented.
Amore Address Mapping with Overlapped Rotating Entries
Authors: G. J. Dekker, A. j. van de Goor
Venue: IEEE Micro (1987)

A memory management unit that supports demand paging is implemented with standard logic and fast-access RAM chips, resulting in much faster address translation that that provided by the standard Motorola MC68451 MMU.
Study of surface acoustic waves under periodic grating structures
Authors: Cheng-Liang Hsu, Chi-Yen Shen, Ming-Yau Su, Rume-Tze Tsai
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

The propagation characteristics of the surface acoustic waves (SAW) propagating under the periodic metal grating on ST-cut quartz substrates were theoretically investigated in order to achieve more accurate design of the devices. The theoretical method used here is based on Floquet's theorem using the space harmonics method (SHM) as an orthogonal function set. The boundary conditions are satisfied by solving the real boundary integral equations, which are derived from the method of weighted residuals for a period of each region, i.e., substrate, metal, and free space. The boundary integral equations are solved by using the Galerkin procedure. The periodic strip gratings are investigated, considering the convergence of the numerical computation for the number of the space harmonics. The dispersion properties for shorted and open gratings are derived from SHM. Finally, we can calculate the parameters of Coupling-of-Modes (COM) and design a resonator to prove the accuracy of propagation analysis.
Detecting Humans in 2D Thermal Images by Generating 3D Models
Authors: Stefan Markov, Andreas Birk
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

There are two significant challenges to standard approaches to detect humans through computer vision. First, scenarios when the poses and postures of the humans are completely unpredictable. Second, situations when there are many occlusions, i.e., only parts of the body are visible. Here a novel approach to perception is presented where a complete 3D scene model is learned on the fly to represent a 2D snapshot. In doing so, an evolutionary algorithm generates pieces of 3D code that are rendered and the resulting images are compared to the current camera picture via an image similarity function. Based on the feedback of this fitness function, a crude but very fast online evolution generates an approximate 3D model of the environment where non-human objects are represented by boxes. The key point is that 3D models of humans are available as code sniplets to the EA, which can use them to represent human shapes or portions of them if they are in the image. Results from experiments with real world data from a search and rescue application using a thermal camera are presented.
Efficient Tracking with AdaBoost and Particle Filter under Complicated Background
Authors: Yuji Iwahori, Naoki Enda, Shinji Fukui, Haruki Kawanaka, Robert J. Woodham, Yoshinori Adachi
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Particle filter, which is the probability technique, can be used for the robust tracking to the noise and the occlusion. However, when many objects are tracked simultaneously, the real-time tracking becomes difficult as the computational cost increases. While, the AdaBoost has an ability that it has the remarkable efficiency as a statistical technique in pattern recognition. AdaBoost can be used to detect an object region for the efficient tracking with a particle filter. However, it is difficult to detect the moving object under the complicated background by AdaBoost. This paper proposes an improvement of efficiency of particle filter by introducing further distinction features using AdaBoost for the complicated background.
Deterministic regression model and visual basic code for optimal forecasting of financial time series
Authors: Alejandro Balbás, Beatriz Balbás, Inna Galperin, Efim Galperin
Venue: Computers Mathematics with Applications (2008)

A new, non-statistical method is presented for analysis of the past history and current evolution of economic and financial processes. The method is based on the sliding model approach using linear differential or difference equations applied to discrete information in the form of known chronological data (time series) about the process. An algorithm is proposed that allows one to project the current evolution of the process onto some period of its future development. Computer code in visual basic is developed that has been validated in application to American stock index S&P 500, with predicted values within 5% of real data over long periods of the recent past history. The algorithm and the code can be applied to practical problems in finance and economy in time of its normal evolution without catastrophic events.
Learning to Predict One or More Ranks in Ordinal Regression Tasks
Authors: Jaime Alonso, Juan José Coz, Jorge Díez, Oscar Luaces, Antonio Bahamonde
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

We present nondeterministic hypotheses learned from an ordinal regression task. They try to predict the true rank for an entry, but when the classification is uncertain the hypotheses predict a set of consecutive ranks (an interval). The aim is to keep the set of ranks as small as possible, while still containing the true rank. The justification for learning such a hypothesis is based on a real world problem arisen in breeding beef cattle. After defining a family of loss functions inspired in Information Retrieval, we derive an algorithm for minimizing them. The algorithm is based on posterior probabilities of ranks given an entry. A couple of implementations are compared: one based on a multiclass SVM and other based on Gaussian processes designed to minimize the linear loss in ordinal regression tasks.
Online Hardware Task Scheduling and Placement Algorithm on Partially Reconfigurable Devices
Authors: Thomas Marconi, Yi Lu, Koen Bertels, Georgi Gaydadjiev
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

In this paper, we propose an online hardware task scheduling and placement algorithm and evaluate it performance. Experimental results on large random task set show that our algorithm outperforms the existing algorithms in terms of reduced total wasted area up to 89.7%, has 1.5 % shorter schedule time and 31.3% faster response time.
Lie series application to the identification of a multibody mechanical system
Authors: S. D'Ambrosio, C. Guarnaccia, D. Guida, T. L. L. Lenza, J. Quartieri
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

A direct method of system identification and parameters monitoring is introduced for a general class of non-linear systems. The only requirement is that the system characteristics must be modeled by analytic or sufficiently smooth functions of the state variables, including the time parameter. The approach is based on the Lie operator representations and the corresponding Lie series solutions. This kind of solutions can be obtained for a general class of non-linear systems in the form of analytical power series including the system parameters. Despite of the local characterization of such solutions, the corresponding information carried by the solutions appears to be sufficiently complete and provides precise estimates of the system parameters. In this paper a simple application is proposed. It is mainly addressed to identify the parameters of a mechanical system composed of a pendulum jointed to a sliding mass. In this application a set of numerical results obtained by integrating the motion equations has been used. Then, a numerical procedure has been developed in order to minimize the difference between numerical and approximating solution, at last obtained by using Lie series. Recently this methodology has been extended to differential systems with an unlimited number of equations and this allows one to treat non-linear partial differential equations arising from evolutionary or delayed problems [2,3].
Matching the Message to the Medium
Authors: Casey A. Klofstad, Shelley Boulianne, Danna Basson
Venue: Social Science Computer Review (2008)

This article reports the results of an experiment that varies whether respondents are informed that they will receive a reminder email if they fail to complete an Internet questionnaire. The findings show that informing respondents about impending reminder emails effectively increases Internet survey response rates. Moreover, this method increases the representativeness of the study without negatively affecting data quality.
Novel signal processing techniques for Doppler radar cardiopulmonary sensing
Authors: Dennis R. Morgan, Michael G. Zierdt
Venue: Signal Processing (2009)

In this paper, we develop new signal processing techniques for Doppler radar cardiopulmonary sensing. These techniques enable independent recovery of respiration and heartbeat signals from measurements of chest-wall dynamic motion, which are subsequently used for independent estimation of respiration and heart rate. In particular, three novel elements are introduced: the concept of representing the composite demodulated signal in the complex plane as a vector sum of various components, combining dc coupling with block mean removal, and adaptive cancellation of respiration harmonics. From this, algorithms are derived for arc-length demodulation and cardio/pulmonary separation. A test signal generator is developed to simulate actual signals. Also, an experimental setup is presented and several sets of real data are analyzed using the new signal processing techniques.
The ? Project: An Experience with a Multimicroprocessor System
Authors: P. Civera, G. Conte, D. Corso, F. Gregoretti, E. Pasero
Venue: IEEE Micro (1982)

Which processor interconnection method makes a modular, reconfigurable system perform best in a given application? Certain tools and strategies help provide the answer.
Turán Graphs, Stability Number, and Fibonacci Index
Authors: Véronique Bruyère, Hadrien Mélot
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

The Fibonacci index of a graph is the number of its stable sets. This parameter is widely studied and has applications in chemical graph theory. In this paper, we establish tight upper bounds for the Fibonacci index in terms of the stability number and the order of general graphs and connected graphs. Turán graphs frequently appear in extremal graph theory. We show that Turán graphs and a connected variant of them are also extremal for these particular problems.
iCat: an affective game buddy based on anticipatory mechanisms
Authors: Iolanda Leite, Carlos Martinho, André Pereira, Ana Paiva
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

In this paper, we study the role of emotions and expressive behaviour in socially interactive characters employed in educational games. More specifically, on how we can use such emotional behaviour to help users to better understand the game state. An emotion model for these characters, which is mainly influenced by the current state of the game and is based on the emotivector anticipatory mechanism, was developed. We implemented the model in a social robot named iCat, using chess as the game scenario. The results of a preliminary evaluation suggested that the emotional behaviour embedded in the character indeed helped the users to have a better perception of the game.
Mapping Natural Language into SQL in a NLIDB
Authors: Alessandra Giordani
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

Since the 1970's, there has been growing interest in understanding and answering human language questions. Despite this, little progress has been made in developing an interface that any untrained user can use to query very large databases using natural language. In this research, the design of a novel system is discussed. Tree-like structures are built for every question and each query, and a tree kernel, which represents trees in terms of their substructures, is used to define feature spaces. A machine learning algorithm is proposed that takes pairs of trees as training input and derives the unknown final SQL query by matching propositional and relational substructures.
Modelling with Relational Calculus of Object and Component Systems - rCOS
Authors: Zhenbang Chen, Abdel Hakim Hannousse, Dang Hung, Istvan Knoll, Xiaoshan Li, Zhiming Liu, Yang Liu, Qu Nan, Joseph C. Okika, Anders P. Ravn, Volker Stolz, Lu Yang, Naijun Zhan
Venue: The Common Component Modeling Example: Comparing Software Component Models (2007)

This chapter presents a formalization of functional and behavioural requirements, and a refinement of requirements to a design for CoCoME using the Relational Calculus of Object and Component Systems (rCOS). We give a model of requirements based on an abstraction of the use cases described in Chapter 3.2. Then the refinement calculus of rCOS is used to derive design models corresponding to the top level designs of Chapter 3.4. We demonstrate how rCOS supports modelling different views and their relationships of the system and the separation of concerns in the development.
RFID and Its Vulnerability to Faults
Authors: Michael Hutter, Jörn-Marc Schmidt, Thomas Plos
Venue: Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems (2008)

Radio Frequency Identification (RFID) is a rapidly upcoming technology that has become more and more important also in security-related applications. In this article, we discuss the impact of faults on this kind of devices. We have analyzed conventional passive RFID tags from different vendors operating in the High Frequency (HF) and Ultra-High Frequency (UHF) band. First, we consider faults that have been enforced globally affecting the entire RFID chip. We have induced faults caused by temporarily antenna tearing, electromagnetic interferences, and optical inductions. Second, we consider faults that have been caused locally using a focused laser beam. Our experiments have led us to the result that RFID tags are exceedingly vulnerable to faults during the writing of data that is stored into the internal memory. We show that it is possible to prevent the writing of this data as well as to allow the writing of faulty values. In both cases, tags confirm the operation to be successful. We conclude that fault analysis poses a serious threat in this context and has to be considered if cryptographic primitives are embedded into low-cost RFID tags.
Haplotype Inferring Via Galled-Tree Networks Is NP-Complete
Authors: Arvind Gupta, Ján Maňuch, Ladislav Stacho, Xiaohong Zhao
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

The problem of determining haplotypes from genotypes has gained considerable prominence in the research community since the beginning of the HapMap project. Here the focus is on determining the sets of SNP values of individual chromosomes (haplotypes), since such information better captures the genetic causes of diseases. One of the main algorithmic tools for haplotyping is based on the assumption that the evolutionary history for the original haplotypes satisfies perfect phylogeny. The algorithm can be applied only on individual blocks of chromosomes, in which it is assumed that recombinations either do not happen or happen with small frequencies. However, exact determination of blocks is usually not possible. It would be desirable to develop a method for haplotyping which can account for recombinations, and thus can be applied on multiblock sections of chromosomes. A natural candidate for such a method is haplotyping via phylogenetic networks or their simplified version: galled-tree networks, which were introduced by Wang, Zhang, Zhang ([25]) to model recombinations. However, even haplotyping via galled-tree networks appears hard, as the algorithms exist only for very special cases: the galled-tree network has either a single gall ([23]) or only small galls with two mutations each ([8]). Building on our previous results ([6]) we show that, in general, haplotyping via galled-tree networks is NP-complete, and thus indeed hard.