ppxscal/embedding_network huggingface dataset:
A Neural Network-Based Segmentation Tool for Color Images
Authors: D. Goldman, M. Yang, N. Bourbakis
Venue: Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence (2002)

The research paper focuses on the development of an efficient and accurate tool for segmenting color images. The segmentation is a problem that has been widely studied since machine vision first evolved as a research area. However, the vast majority of this research has predominantly focused on segmentation as a means of preprocessing to enhance the performance of subsequent recognition mechanisms. Thus, the needs of applications such as OCR (optical character recognition) or handwriting recognition and as well as other recognitionapplications have largely shaped the requirements and targeted performance of segmentation mechanisms. Specifically, these recognition applications do not require single pixel or sub-pixel accuracy to function properly and since providing this accuracy is difficult and computationally expensive, most segmentation approaches do not provide it. In effect, for recognition applications it does not matter if one or two pixels have been improperly categorized as belonging to inappropriate segments within the image. Subsequent recognition processing is able to effectively compensate for this type of noise that may be introduced and hence still recognize a shape or set of shapes as intended. However, for the embroidery application proposed here, the artwork being scanned is often quite unique and does not usually conform to specific predefined recognizable objects. Additionally, since the ultimate goal is to produce an accurate reproduction of the scanned image, the aesthetic accuracy of the segmentation approach is very important. Similar requirements exist in the area of medical imaging where segmentation must provide the highest possible precision. Although in this area, still different goals are dominant. For example, there have been many neural network approaches to segmenting medical images such as x-rays or MRIs. However, here a more important goal has been to classify pixels into known predetermined categories to allow easier detection of anomalies indicating disease or other malignancies. Thus the area of study presented here stillremains open and largely un-addressed by previous research.
New Information on the Effectiveness of Different Reed-Muller Algebras on the Representation of Quaternary Functions
Authors: K. J. Adams, J. McGregor
Venue: Proceedings of the 33rd International Symposium on Multiple-Valued Logic (2003)

Efforts to get reliable data about the efficiency of aset of basis functions in representing a Reed-Mullercanonical form with the minimum number ofcoefficients are frustrated by the long computationtimes necessary for calculation. Using anintegrated suite of utility programs employing fasttransforms, we provide statistics on theperformance of six different algebras for two,three, four and five variables. Based onexperimental results we suggest that the choice ofbasis functions has only a marginal effect forrandom samples from the entire function space. Inaddition we provide evidence which suggests thatproviding additional polarities has a more andmore marginal effect upon efficiency.
An Incremental Methodology for Parallelizing Legacy Stencil Codes on Message-Passing Computers
Authors: N. S. Sundar, S. Jayanthi, P. Sadayappan, Miguel Visbal
Venue: Proceedings of the 1999 International Conference on Parallel Processing (1999)

There is considerable interest in converting existing production sequential/vector codes into scalable portable parallel message-passing programs. However the task is very tedious and error prone. In this paper, we describe a systematic incremental methodology that greatly eases parallelization of stencil codes, by operating in a single address space while addressing data partitioning issues and then transitioning to multiple address spaces to handle communication and synchronization issues. The approach has been successfully used in manually parallelizing a production CFD code. Since the steps in the methodology are formalized as algorithms, the approach is amenable to automation.
Sun ONE Studio Programming, 1 edition
Authors: Rashim Mogha, Ruchi Bhargava
Venue:  (2002)

:The Sun ONE Studio Integrated development environment helps you create full-featured Java applications -- and dramatically slash development time. This book shows you how to make the most of all three Sun ONE Studio editions -- Community, Enterprise, and Mobile. Starting with basics such as debugging and GUI creation, the authors go on to examine a full range of advanced Sun ONE Studio tools and techniques, covering everything from Web, database, and distributed programming to Web services, Sun ONE Application Server integration, and mobile applications. Brimming with crystal-clear explanations, real-world examples, and eleven whole chapters on customizing Sun ONE Studio for the way you work, this is the only book you need to master the premier Java IDE -- and take your productivity to a new level.
Finding Test-and-Treatment Procedures Using Parallel Computation
Authors: D L Duval, A R Wagner, Yijie Han, W D Loveland
Venue:  (1986)

A parallel algorithm for the NP-hard problem test-and-treatment is presented for a machine whose number of connections is $\frac {3p} {2}$, where $p$ is the number of processing elements (PEs), and where the PEs are simple enough such that a machine with $2 ^ {20}$ PEs is currently implementable and $2 ^ {30}$ PE machine is feasible. The speedup of $O( \frac {p} {logp} )$ is realizable because we are able to transform the dynamic programming solution into the ASCEND/DESCEND scheme with considerable attention to the communication problem. This algorithm is realized on the Boolean Vector Machine, a fully designed cube-connected-cycle system where processor allocation and other control issues have been faced. The particular NP-hard problem is of independent interest; it generalizes the binary testing problem by introducing treatments on an equal basis with tests. Applications of this test-and-treatment problem are found in medical diagnosis, systematic biology, machine fault location, laboratory analysis and many other fields.
Is One NP Question as Powerful as Two?
Authors: Jim Kadin
Venue:  (1987)

For any integer $k$, $P^{SAT[k]}$ is the class of languages accepted by deterministic polynomial time oracle machines that make at most $k$ queries to an oracle for SAT. It is easy to see that $P^{SAT[1]} \subseteq D^{P} \subseteq P^{SAT[2]}$. We use a technique called oracle replacement to show that if $D^{P}$ = co-$D^{P}$, then there is a sparse set $S$ such that $\overline{SAT} \in NP^{S}$ , there exist ``small'''' NP machines that recognize initial segments of $\overline{SAT}$, and the polynomial time hierarchy (PH) is contained in $\Delta^{3}_{P}$. Thus for deterministic polynomial time oracle machines, if one NP question is as powerful as two, then the PH collapses to $\Delta^{3}_{P}$. As another application of oracle replacement, we show that if there exists a sparse set $S \in NP^{SAT}$ such that $\overline{SAT} \in NP^{S}$, then $PH \subseteq \Delta^{3}_{P}$. We also discuss how oracle replacement is a unifying principle for many of the results concerning sparse oracles for NP. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A Comparison of Three Prolog Extensions
Authors: W D Reed, W D Loveland
Venue:  (1989)

We present three extensions of Prolog, discussing their similarities and differences. The systems - {\bf near-Horn Prolog} (Loveland), {\bf Simplified Problem Reduction Format} (Plaisted), and {\bf N-Prolog} (Gabby \& Reyle) - differ in their approach to the extension of Prolog, yet each utilizes case analysis as the mechanism for non-Horn reasoning. The fact that these systems with quite different origins, purposes, and presentation forms utilize the case analysis method in a strikingly similar fashion suggests that their underlying reasoning is general and intuitive. This paper describes the three systems and outlines the close relationship between them. While they are closely related, the systems also appear to have essential differences, properties of one system that cannot be incorporated in another system without serious distortion of the unique properties of the receiving system. Highlighting those tradeoffs aids our understanding of the systems involved.
Two-stage support vector machines for protein secondary structure prediction
Authors: Minh Ngoc Nguyen, Jagath C. Rajapakse
Venue: Neural, Parallel Scientific Computations (2003)

Neural network approaches using Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs) for protein secondary structure prediction are presented. A two-stage SVM approach is proposed to capture the contextual relationship of secondary structure elements. The proposed technique yielded higher accuracy than the PHD (Profile network from HeiDelberg) method that cascades two MLPs. We also demonstrate that it is feasible to improve current single-stage approaches to protein secondary structure prediction by adding a second-stage prediction scheme to capture the contextual information among secondary structural elements and thereby improving the accuracy of prediction. Two-stage SVM approach achieved prediction accuracies of 72.4% and 76.7% on two databases of 126 and 513 nonhomologous globular proteins, respectively.
Quasi-Newton Methods, Motivation and Theory
Authors: John E. Dennis,  Jr., Jorge J. More
Venue:  (1974)

This paper is an attempt to motivate and justify quasi-Newton methods as useful modifications of Newton''s method for general and gradient nonlinear systems of equations. References are given to ample numerical justification; here we give an overview of many of the important theoretical results and each is accompanied by sufficient discussion to make the results and hence the methods plausible. Key Words and Phrases: unconstrained minimization, nonlinear simultaneous equations, update methods, quasi-Newton methods.
The Synthesis of Multiple-Valued Logic Circuits Using Local-Excitation-Type Neuron Models
Authors: Masayuki Matsumoto, Yoshinori Ueda, Isami Nomoto
Venue: Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logic (2000)

We propose a new neuron model to which local excitation phenomena in the organism are applied. We report some of the learning characteristics of the local-excitation-type neuron model in this paper. We have verified that the characteristics of the local-excitation-type neuron were changed by the learning method. Concerning the synthesis method of the 4-valued logic circuit, we note that the local-excitation-type neurons were used as an application example.
Systematic Composition of Objects in Distributed Internet Applications: Processes and Sessions
Authors: K. M Chandy, Adam Rifkin
Venue:  (1996)

We consider a system with the infrastructure for the creation and interconnection of large numbers of distributed persistent objects. This system is exemplified by the Internet: potentially, every appliance and document on the Internet has both persistent state and the ability to interact with large numbers of other appliances and documents on the Internet. This paper elucidates the characteristics of such a system, and proposes the compositional requirements of its corresponding infrastructure. We explore the problems of specifying, composing, reasoning about, and implementing applications in such a system. A specific concern of our research is developing the infrastructure to support structuring distributed applications by using sequential, choice, and parallel composition, in the anarchic environment where application compositions may be unforeseeable, and interactions may be unknown prior to actually occurring. The structuring concepts discussed are relevant to a wide range of distributed applications; our implementation is illustrated with collaborative Java processes interacting over the Internet, but the methodology provided can be applied independent of specific platforms.
Ring reconfiguration in presence of close fault cuts
Venue: Proceedings of the 1996 International Symposium on Parallel Architectures, Algorithms and Networks (1996)

The problem of subring formation in the presence of faulty nodes in redundant ring networks was studied in Masuyama and Ichimori (1989). An efficient reconfiguration technique was given for d-link redundant network (with d/spl ges/2) for certain topological condition of the network partition. In this paper, we extend the previous results and propose suitable and efficient reconfiguration algorithms to deal with the cases not considered in Masuyama and Ichimori. We also bound the number of "misses" (i.e., non-faulty nodes that are not included as part of the subring) which serves as a measure of the size of the subring formed.
A Theory for Memory-Based Learning
Authors: Jhy Lin, Scott J Vitter
Venue:  (1993)

A memory-based learning system is an extended memory management system that decomposes the input space either statically or dynamically into subregions for the purpose of storing and retrieving functional information. The main generalization techniques employed by memory-based learning systems are the nearest-neighbor search, space decomposition techniques, and clustering. Research on memory-based learning is still in its early stage. In particular, there are very few rigorous theoretical results regarding memory requirement, sample size, expected performance, and computational complexity. In this paper, we propose a model for memory-based learning and use it to analyze several methods--$\epsilon$-covering, hashing, clustering, tree-structured clustering, and receptive-fields--for learning smooth functions. The sample size and system complexity are derived for each method. Our model is built upon the generalized PAC learning model of Haussler [1] and is closely related to the method of vector quantization in data compression. Our main result is that we can build memory-based learning systems using new clustering algorithms (Lin and Vitter [2]) to PAC-learn in polynomial time using only polynomial storage in typical situations. {\bf Keywords:} Memory-based learning, PAC learning, clustering, approximation, linear programming, relaxation, covering, hashing.
ARCA Detailed Design Document
Authors: Alberto Catoni, Mario Loffredo, Oreste Signore
Venue:  (1996)

This report presents the Detailed Design of the ARCA Target. Using the Object Modelling Technique approach, it refines the specification introduced in the architectural design phase.
The CORE User Interface
Authors: James E. Archer,  Jr., Richard W. Conway, Andrew I. Shore, Leonard S. Silver
Venue:  (1980)

The distinctive characteristic of the CORE program development environment is its ttolerant user interface. This paper describes that interface. In addition to its unusual response to incomplete and incorrect constructions entered by the user, the interface is ``mode-free'''', unusually frugal in the size of the command set, and unusually consistent in its treatment of statements, immediate statements and commands.
Inside Intuit: How the Makers of Quicken Beat Microsoft and Revolutionized an Entire Industry
Authors: Suzanne Taylor, Kathy Schroeder
Venue:  (2003)

: The Exclusive Story behind Intuit's Hard-Won Success It's a modern-day David and Goliath story for the business world: a company dreamed up at a kitchen table, built on explosive PC growth, and forced to battle a giant in the race to revolutionize an industry. This is the story of Intuit, creator of renowned software products like Quicken, QuickBooks, and TurboTax-the company that beat mighty Microsoft and changed the way 25 million people manage their finances. Written by Intuit veteran Suzanne Taylor and seasoned business manager Kathy Schroeder-who were granted exclusive interviews with founder Scott Cook and other key figures- Inside Intuit tells this company's original and fascinating tale for the first time. The book vividly recounts each dramatic stage of Intuit's development: from initial conception to "bet the company" investments; from strokes of marketing genius to disastrous product launches; and from battles for survival to successive victories against arch-rival Microsoft-the company no one else could beat. Evident throughout this account is the power of Intuit's relentless customer focus, which guided the company from tiny start-up to a 6,000-employee, $1.4 billion business. Instructive and inspiring, Inside Intuit chronicles an enduring company's extraordinary success against overwhelming odds. About the Author: Suzanne Taylor is a marketing consultant who worked at Intuit for eight years. Kathy Schroeder was a marketing executive at the Ford Motor Company for seven years. Both have Stanford M.B.A.'s.
Neural Networks: Binary Monotonic and Multiple-Valued
Authors: Jacek M. Zurada
Venue: Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logic (2000)

Neural networks are parallel computing architectures consisting of densely interconnected linear multiplier elements (weights) and nonlinear processing units (neurons). Dynamic components in connections and processing units are also allowed, enhancing the capabilities of neural networks to model continuous and discrete-time processes. A particularly appealing feature of neural networks is their inherent ability to acquire knowledge by learning from data [1].Compared to biological neural networks, artificial neural networks are ¿neural¿ in the sense that they have been inspired by neuroscience, but not necessarily, because they are faithful models of biological or cognitive phenomena. In fact, typical neural network architectures are more related to mathematical and/or statistical models, such as non-parametric pattern classifiers, clustering algorithms, nonlinear filters, and statistical regression models, rather than to neurobiological models [2-3].Rooted in mathematics, computational physics and statistics, neural networks have emerged over the last decade as a special class of computing paradigm which features versatile modeling capabilities. Thanks to the ability to learn from data, they offer substantial advantage over traditional engineering approaches to systems modeling. The traditional modeling in engineering has mostly relied on rigorous and formal analytical techniques. Typically, a specialized algorithm has been needed to solve each specialized task. As a result, many dedicated approaches have been developed for pattern classification and recognition, system identification and control, forecasting, image compression and for building expert systems [4-8].In contrast to algorithmic approaches usually tailored to specific tasks, neural networks offer a wide variety of learning techniques, which are more versatile and are applicable to a large class of problems. Here, learning in data-rich environments leads to models of specific tasks. Although neural networks-based models emerging because of learning are rather specialized and can only be applied to the case for which they have been designed, their development fits into a general learning framework. Through learning from specific data with rather general neural network architectures, neurocomputing techniques can yield problem-specific solutions.After a dozen years of spectacular growth, neural network technology has not only been recognized as a quickly maturing research field. Of equal or greater importance today may be the fact that this technology offers powerful solutions to a variety of scientific and engineering tasks. In addition to a number of books, numerous special issues of technical journals have recently featured various aspects of implementation and applications of neural networks [9-18].
Risks and challenges of component-based software development
Authors: Padmal Vitharana
Venue: Communications of the ACM (2003)

Component developers, application assemblers, and customers must all know CBSD advantages and disadvantages before developing components and component-based applications.
The structure of the information visualization design space
Authors: S. K. Card, J. Mackinlay
Venue: Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97) (1997)

Research on information visualization has reached the point where a number of successful point designs have been proposed and a variety of techniques have been discovered. It is now appropriate to describe and analyze portions of the design space so as to understand the differences among designs and to suggest new possibilities. This paper proposes an organization of the information visualization literature and illustrates it with a series of examples. The result is a framework for designing new visualizations and augmenting existing designs.
Teach Yourself VISUALLY Digital Video, 1 edition
Authors: Jinjer L. Simon, Richard J. Simon
Venue:  (2002)

:Covers the essentials of getting into and getting started in digital video including choosing and buying a camera, shooting top-notch video, dumping video to a PC, editing with both basic and advanced editing tools, and outputting your finished product to various media.Teach Yourself VISUALLY features over 500 annotated, full-color screenshots, quick and to-the-point introductions, and useful tips.
A Prototype System for Cooperative Architecture Design
Venue: Proceedings of the 1999 International Conference on Information Visualisation (1999)

The paper presents a prototype system for cooperative architecture design. The system includes a multi-site cooperative 3D editor and a cooperative and communication support platform. By using the prototype system, the users are able to load a design scene, modify its properties and save the result cooperatively. The on-line basic cooperative, interactive editing functionality on the designed objects via the communication network has been achieved.
An Integrated Resource Sharing Policy for Multimedia Storage Servers Based on Network-Attached Disks
Authors: Nabil J. Sarhan, Chita R. Das
Venue: Proceedings of the 23rd International Conference on Distributed Computing Systems (2003)

In this paper, we propose using the network-attacheddisk (NAD) architecture to design highly scalable andcost-effective multimedia-on-demand (MOD) servers. Inorder to ensure enhanced performance, we propose twoschemes, called distributed interval caching (DIC) andmulti-objective scheduling (MOS). The DIC scheme utilizesthe on-disk buffers for caching intervals between successivestreams, while the MOS scheme improves resource sharingby scheduling requests for service intelligently based onfour predefined criteria. We then integrate the two schemesand study the overall performance benefits through extensivesimulation. We also study the effectiveness of the proposedDIC scheme by developing an analytical model thatestimates the performance limit of DIC. The results demonstratethat the integrated policy works very well in increasingthe number of customers that can be serviced concurrentlywhile decreasing their waiting times, and that theperformance improvements scale with the number of disksin the server.
Demystifying Venture Capital Investing
Authors: Mei Chuah
Venue: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02) (2002)

Since the crash of the dot.coms, investors have gotten a lot more careful with where they place their money [3]. Now more than ever it becomes really important for venture capitalists (VCs) to monitor the state of the startups market and continually update their investment strategy to suit the rapidly changing market conditions. This paper presents three new visualization metaphors (Spiral Map, TimeTicker, and Double Histogram) for monitoring the startups market. While we are focusing on the VC domain, the visual metaphors developed are general and can be easily applied to other domains.