ppxscal/embedding_network huggingface dataset:
The Whirlwind I computer
Authors: R. R. Everett
Venue: Papers and discussions presented at the Dec. 10-12, 1951, joint AIEE-IRE computer conference: Review of electronic digital computers (1951)

Project Whirlwind is a high-speed computer activity sponsored at the Digital Computer Laboratory, formerly a part of the Servomechanisms Laboratory, of the Massachusetts Institute of Technology (M.I.T.) by the Office of Naval Research (O.N.R.) and the United States Air Force. The project began in 1945 with the assignment of building a high-quality real-time aircraft simulator. Historically, the project has always been primarily interested in the fields of real-time simulation and control; but since about the beginning of 1947 most of its efforts have been devoted to the design and construction of the digital computer known as Whirlwind I (WWI). This computer has been in operation for about 1 year and an increasing proportion of project effort now is going into application studies.
Two Approaches to a Computer Model for Quadric Surfaces
Authors: Ronald Goldman
Venue: IEEE Computer Graphics and Applications (1983)

In this detailed analysis of the two basic approaches to computer models for quadric surfaces, internal consistency emerges as the primary virtue of the superior model.
A comparative study of MPPT techniques for PV systems
Authors: Roberto Faranda, Sonia Leva
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

The output characteristic of a photovoltaic array is nonlinear and changes with solar irradiation and the cell's temperature. Therefore, a Maximum Power Point Tracking (MPPT) technique is needed to draw peak power from the solar array to maximize the produced energy. In order to understand witch MPPT technique has the best performance, this paper presents a comparative study of ten widely-adopted MPPT algorithms; their performance is evaluated using the simulation tool Simulink®. In particular, this study compares the behaviors of each technique in the presence of solar irradiance variations.
Data mining with cellular automata
Authors: Tom Fawcett
Venue: ACM SIGKDD Explorations Newsletter (2008)

A cellular automaton is a discrete, dynamical system composed of very simple, uniformly interconnected cells. Cellular automata may be seen as an extreme form of simple, localized, distributed machines. Many researchers are familiar with cellular automata through Conway's Game of Life. Researchers have long been interested in the theoretical aspects of cellular automata. This article explores the use of cellular automata for data mining, specifically for classification tasks. We demonstrate that reasonable generalization behavior can be achieved as an emergent property of these simple automata.
Essays on software market: security, liability, and pricing
Authors: Tridas Mukhopadhyay
Venue:  (2007)

Software has become an indispensable part of our daily lives. Not only large-scale operating systems in businesses but also home electric appliances such as television sets and telephones are powered by thousands of program instructions. In the new era of information technology, understanding economic issues becomes a key success factor for the software industry, along with maintaining technological superiority. Despite the economic nature and the increasing attention from the stakeholders in the software industry, many problems of the software market have not been examined by the economists and hence still remain unanswered especially in the emerging areas such as software security and open source software. My dissertation consists of three essays which examine two issues through an economic lens: (i) software security and liability and (ii) open source software pricing. I aim to make both academic and practical contributions by applying economic tools to analyze problems that have been intensely discussed by computer scientists, software engineers, policy makers and jurists, and by justifying ongoing argument on the two issues with academic rigor. Essay I proposes a risk-sharing mechanism between software vendors and customers as a possible way to incentivize the software vendors to improve software security. Essay II examines a policy-oriented approach of software liability and identifies conditions under which the legal liability mechanism is effective in terms of improving software security. In Essay III, I study the pricing mechanisms newly introduced to the software market as a way to make money from open source software. Comparison between commercial pricing model with open source software pricing models such as dual-licensing model and support model leads to the conditions under which each pricing model is viable. Factors that affect the success of the open source software pricing models are identified.
Hough Transform in Music Tunes Recognition Systems
Authors: Maciej Hrebień, Józef Korbicz
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

This paper presents a method of music tunes recognition based on adopted Hough transform. One can also find here experimental results showing the effectiveness of the presented solution. Perspectives of further work and quality improvements are also stated as a base for subsequent research.
A Mobile web application architecture for generating destination-oriented LRT Route
Authors: A. A. Azlianor, S. M. Suhaizan, K. Massila, A. M. Haziq Lim
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

Integrated Light Rail Transit (LRT) System in the capital of Malaysia connects some key districts where historical places, interesting places, business areas and shopping malls are concentrated. The train services are running independently but have points where they have interchanges. This may leads the traveler facing difficulty when they are choosing incorrect destination station especially on different LRT lines which contribute to time consuming and high costing. Thus, Dynamic Route Map (DRM) is proposed where the destination-oriented routes need to be dynamically generated by determining the nearest station according to the specific places. In this paper, we present an architecture in developing mobile application that runs on a Personal Digital Assistant (PDA) to provide the solution for traveler to reach the desired destination.
DIWeDa - Detecting Intrusions in Web Databases
Authors: Alex Roichman, Ehud Gudes
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

There are many Intrusion Detection Systems (IDS) for networks and operating systems and there are few for Databases- despite the fact that the most valuable resources of every organization are in its databases. The number of database attacks has grown, especially since most databases are accessible from the web and satisfactory solutions to these kinds of attacks are still lacking.We present DIWeDa - a practical solution for detecting intrusions to web databases. Contrary to any existing database intrusion detection method, our method works at the session level and not at the SQL statement or transaction level. We use a novel SQL Session Content Anomaly intrusion classifier and this enables us to detect not only most known attacks such as SQL Injections, but also more complex kinds of attacks such as Business Logic Violations. Our experiments implemented the proposed intrusion detection system prototype and showed its feasibility and effectiveness.
Hierarchical Shape Statistical Model for Segmentation of Lung Fields in Chest Radiographs
Authors: Yonghong Shi, Dinggang Shen
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

The standard Active Shape Model (ASM) generally uses a whole population to train a single PCA-based shape model for segmentation of all testing samples. Since some testing samples can be similar to only sub-population of training samples, it will be more effective if particular shape statistics extracted from the respective sub-population can be used for guiding image segmentation. Accordingly, we design a set of hierarchical shape statistical models, including a whole-population shape model and a series of sub-population models. The whole-population shape model is used to guide the initial segmentation of the testing sample, and the initial segmentation result is then used to select a suitable sub-population shape model according to the shape similarity between the testing sample and each sub-population. By using the selected sub-population shape model, the segmentation result can be further refined. To achieve this segmentation process, several particular steps are designed next. First, all linearly aligned samples in the whole population are used to generate a whole-population shape model. Second, an affinity propagation method is used to cluster all linearly aligned samples into several clusters, to determine the samples belonging to the same sub-populations. Third, the original samples of each sub-population are linearly aligned to their own mean shape, and the respective sub-population shape model is built using the newly aligned samples in this sub-population. By using all these three steps, we can generate hierarchical shape statistical models to guide image segmentation. Experimental results show that the proposed method can significantly improve the segmentation performance, compared to conventional ASM.
A non-equilibrium analysis and control framework for active queue management
Authors: Tansu Alpcan, Paul Wang, Prashant G. Mehta, Tamer Başar
Venue: Automatica (Journal of IFAC) (2008)

We present a non-equilibrium analysis and control approach for the Active Queue Management (AQM) problem in communication networks. Using simplified fluid models, we carry out a bifurcation study of the complex dynamic queue behavior to show that non-equilibrium methods are essential for analysis and optimization in the AQM problem. We investigate an ergodic theoretic framework for stochastic modeling of the non-equilibrium behavior in deterministic models and use it to identify parameters of a fluid model from packet level simulations. For computational tractability, we use set-oriented numerical methods to construct finite-dimensional Markov models, including control Markov chains and hidden Markov models. Subsequently, we develop and analyze an example AQM algorithm using a Markov Decision Process (MDP) based control framework. The control scheme developed is optimal with respect to a reward function, defined over the queue size and aggregate flow rate. We implement and simulate our illustrative AQM algorithm in the ns-2 network simulator. The results obtained confirm the theoretical analysis and exhibit promising performance when compared with well-known alternative schemes under persistent non-equilibrium queue behavior.
Robust Clustering by Aggregation and Intersection Methods
Authors: Ida Bifulco, Carmine Fedullo, Francesco Napolitano, Giancarlo Raiconi, Roberto Tagliaferri
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

When dealing with multiple clustering solutions, the problem of extrapolating a small number of good different solutions becomes crucial. This problem is faced by the so called Meta Clustering [12], that produces clusters of clustering solutions. Often such groups, called meta-clusters, represent alternative ways of grouping the original data. The next step is to construct a clustering which represents a chosen meta-cluster. In this work, starting from a population of solutions, we build meta-clusters by hierarchical agglomerative approach with respect to an entropy-based similarity measure. The selection of the threshold value is controlled by the user through interactive visualizations. When the meta-cluster is selected, the representative clustering is constructed following two different consensus approaches. The process is illustrated through a synthetic dataset.
Short-term streamflow forecasting: ARIMA vs neural networks
Authors: Juan Frausto-Solis, Esmeralda Pita, Javier Lagunas
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network.
SRASS - A Semantic Relevance Axiom Selection System
Authors: Geoff Sutcliffe, Yury Puzis
Venue: Proceedings of the 21st international conference on Automated Deduction: Automated Deduction (2007)

This paper describes the design, implementation, and testing of a system for selecting necessary axioms from a large set also containing superfluous axioms, to obtain a proof of a conjecture. The selection is determined by semantics of the axioms and conjecture, ordered heuristically by a syntactic relevance measure. The system is able to solve many problems that cannot be solved alone by the underlying conventional automated reasoning system.
A Distributed Architecture for IP Traffic Analysis
Authors: Cristian Morariu, Burkhard Stiller
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

Current high-speed links become a challenge to traditional real-time analysis of IP traffic. Major research was done in finding sampling methods for IP packets and IP flows in order to reduce the amount of data that needs to be processed while keeping a high level of result accuracy. Although sampling proves to be a promising approach, there may be application sce-narios foreseen, in which decisions may not be based on sampled data, e.g., usage based charging or intrusion detection systems. This paper proposes a distributed architecture for collecting, analysing and storing of IP traffic data. This approach aims to provide a high level of automation, self-configuration, and self-healing so that new nodes may be easily added or removed to/from the analysis network. The proposed solution makes use of unused processing power existing in the network (such as customer's PCs of an ISP) to achieve real-time analysis of IP traffic for high-speed network links.
Towards SMT Model Checking of Array-Based Systems
Authors: Silvio Ghilardi, Enrica Nicolini, Silvio Ranise, Daniele Zucchelli
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

We introduce the notion of array-based system as a suitable abstraction of infinite state systems such as broadcast protocols or sorting programs. By using a class of quantified-first order formulae to symbolically represent array-based systems, we propose methods to check safety (invariance) and liveness (recurrence) properties on top of Satisfiability Modulo Theories solvers. We find hypotheses under which the verification procedures for such properties can be fully mechanized.
Investigating Generic Methods to Solve Hopf Bifurcation Problems in Algebraic Biology
Authors: Thomas Sturm, Andreas Weber
Venue: Proceedings of the 3rd international conference on Algebraic Biology (2008)

Symbolic methods for investigating Hopf bifurcation problems of vector fields arising in the context of algebraic biology have recently obtained renewed attention. However, the symbolic investigations have not been fully algorithmic but required a sequence of symbolic computations intervened with ad hoc insights and decisions made by a human. In this paper we discuss the use of generic methods to reduce questions on the existence of Hopf bifurcations in parameterized polynomial vector fields to quantifier elimination problems over the reals combined with simplification techniques available in REDLOG. We can reconstruct most of the results given in the literature within a few seconds of computation time. As no tedious hand computations are involved we presume that the use of these generic methods will be a useful tool for investigating other examples.
Detection Performance of MIMO Radar for Coherent Pulses
Authors: Jin You Qu, Jian Yun Zhang, Xiu Wang, Chun Sheng Liu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

The detection performance of spatial diversity multiple-input multiple-output (MIMO) radar for Swerling I and Swerling II target is analyzed based on coherent pulses. The detection method provides an unified frame for conventional radar and the MIMO radar. Simulation results demonstrate that MIMO radar can get better detection performance when more coherent pulses is used. In contrast with conventional radar, the detection performance of MIMO radar in Swerling I target outperforms Swerling II target.
An empirical investigation of tree ensembles in biometrics and bioinformatics research
Authors: Bojan Cukic, E. James Harner
Venue:  (2007)

This thesis study is intended to explore the usability and efficiency of tree ensemble learning when applied to the following research areas: multi-modal biometrics information fusion, software defect prediction and microarray data analysis. The data sets from these three areas have various structures in terms of the sample size, number of features, and the class labels distribution. For example, microarray experiments produce high-dimensional data with at least thousands of variables (genes), while the data sets from biometrics and software engineering studies are large-sized with hundreds to thousands of observations, majority of which are of the same class label. Different data structure has certain requirements on the learning algorithms. No matter what requirements are posed on the learning techniques, the ultimate goal is to achieve a high prediction accuracy as much as possible. This thesis centers on making the best use of tree ensemble learning methodologies in biometrics, software engineering and microarray research. Tree ensembles can also be used in analyzing time-course microarrays. When the toxicological microarray experiments involve both treatment and time, although it is of interest to find genes whose expression differ significantly among toxins (or over time), the principal interest is in the toxin-based interactions of gene response patterns over time. It is possible to decompose the toxin levels, time points and toxin-time interactions into contrasts and apply tree ensembles as a supervised learning technique to the groups defined by each contrast. The variables that are important in distinguishing between groups specified by the contrast on treatment and time interaction are consistent with the genes showing significant interaction effect. The procedure is most interpretable when the factors have two levels, that is, a 2 k factorial design. This approach is tested on a simulated data set based on a data obtained from a propanil study.
Dynamic Frequency and Voltage Scaling for a Multiple-Clock-Domain Microprocessor
Authors: Grigorios Magklis, Greg Semeraro, David H. Albonesi, Steven G. Dropsho, Sandhya Dwarkadas, Michael L. Scott
Venue: IEEE Micro (2003)

Multiple clock domains is one solution to the increasing problem of propagating the clock signal across increasingly larger and faster chips. The ability to independently scale frequency and voltage in each domain creates a powerful means of reducing power dissipation.
Decentralized Coordination of Distributed Interdependent Services
Authors: Asa MacWilliams, Thomas Reicher, Bernd Bruegge
Venue: IEEE Distributed Systems Online (2003)

Current middleware technology for ubiquitous computing connects mobile computers to services in the environment. This is often insufficient because it doesn't address the issue of services depending on other services. The DWARF framework offers a decentralized approach to configuring and coordinating interdependent services.
Determination of Storage Time of Rice Seed Using ANN Based on NIRS
Authors: Zhu Dengsheng, Li Xiaoli
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A simple, fast and nondestructive approach was put forward to classify rice seed of different storage time. This discrimination was conducted by integrated with wavelet transform (WT), principal component analysis (PCA) and artificial neural networks (ANN) based on near infrared reflectance spectroscopy (NIRS). Four classes' samples from four different storage times were used for Vis/NIR spectroscopy on 325-1075 nm using a field spectroradiometer. WT and PCA were used to reduce spectral data dimension and extract diagnostic information from spectra data. The first eight PCs, which accounted for 99.94% of the raw spectral variables, were used as input of the ANN model. The ANN model yielded high discrimination accuracy. The discrimination accuracy was 97.5% for rice seed samples of four different storage years.
Encryption Modes with Almost Free Message Integrity
Authors: Charanjit S. Jutla
Venue: Journal of Cryptology (2008)

We define a new mode of operation for block ciphers which, in addition to providing confidentiality, also ensures message integrity. In contrast, previously for message integrity a separate pass was required to compute a cryptographic message authentication code (MAC). The new mode of operation, called Integrity Aware Parallelizable Mode (IAPM), requires a total of m+1 block cipher evaluations on a plain-text of length m blocks. For comparison, the well-known CBC (cipher block chaining) encryption mode requires m block cipher evaluations, and the second pass of computing the CBC-MAC essentially requires additional m+1 block cipher evaluations. As the name suggests, the new mode is also highly parallelizable.
Easing the Formalization of Clinical Guidelines with a User-tailored, Extensible Agile Model Driven Development (AMDD)
Authors: Patrick Martini, Katherina Kaiser, Silvia Miksch
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Transforming a text-based clinical guideline in a computer-interpretable form is a time-consuming and demanding task due to the various users involved, who have different technical and medical background. In the past, different guideline representation languages and supporting tools have been developed, however, these approaches seldom address the various users' demands and needs in the different steps of the guidelines' life cycle. Our approach is oriented on the guideline life-cycle and takes the requirements and the interactions of the various actors into account to formalize guidelines in a computer-interpretable guideline representation by using a semi-automatic way based on NLP techniques. We analyzed the guideline life-cycle and the roles of the actors to build such a model. This model is prototypical implemented and showed the usefulness and utility for the various users.