ppxscal/embedding_network huggingface dataset:
A Hybrid Evolutionary Multiobjective Approach for the Component Selection Problem
Authors: Andreea Vescan, Crina Grosan
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

Component selection is a crucial problem in Component Based Software Engineering (CBSE). CBSE is concerned with the assembly of pre-existing software components that leads to a software system that responds to client-specific requirements. We formulate the problem as multiobjective, involving 3 objectives. The approach used in this paper is an evolutionary computation technique combined with a greedy algorithm which is used to fine tune the solutions after the application of the genetic operators. The experiments and comparisons with Greedy technique show the effectiveness of the proposed approach.
Application of the Fuzzy Min-Max Neural Networks to Medical Diagnosis
Authors: Anas Quteishat, Chee Peng Lim
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used for tackling medical diagnostic problems. The original FMM network establishes hyperboxes with fuzzy sets in its structure for classifying input patterns into different output categories. While the first modified FMM model uses the membership function and the Euclidian distance to classify the input patterns, the second modified FMM model employs only the Euclidian distance for the same process. Unlike the original FMM network, the two modified FMM models undergo a pruning process, after network training, to remove hyperboxes with low confidence factors. To assess the effectiveness of the three FMM networks in medical diagnosis, a set of real medical records from suspected Acute Coronary Syndrome (ACS) patients is collected and used for experimentation. The bootstrap method is used to analyze the results statistically. Implications of the experimental outcomes are discussed, and the potential of using the FMM networks a decision support tool for medical prognostic and diagnostic problems is demonstrated.
Search of Weighted Subgraphs on Complex Networks with Maximum Likelihood Methods
Authors: Marija Mitrović, Bosiljka Tadić
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

Real-data networks often appear to have strong modularity, or network-of-networks structure, in which subgraphs of various size and consistency occur. Finding the respective subgraph structure is of great importance, in particular for understanding the dynamics on these networks. Here we study modular networks using generalized method of maximum likelihood. We first demonstrate how the method works on computer-generated networks with the subgraphs of controlled connection strengths and clustering. We then implement the algorithm which is based on weights of links and show it's efficiency in finding weighted subgraphs on fully connected graph and on real-data network of yeast.
Mechanical construction of a new efficient FLATTEN
Authors: Yves Kodratoff, Jean-Pierre Jouannaud
Venue: Issue 3 (December 1979) (1979)

An efficient function FLATTEN is "well-known" and due to Mc CARTHY[5]. We give below a version of it, using the predicates NULL and ATOM.
Service-Oriented Operating System: A Key Element in Improving Service Availability
Authors: Nikola Milanovic, Miroslaw Malek
Venue: Proceedings of the 4th international symposium on Service Availability (2007)

The operating system's role is often neglected in the availability analysis of modern, service-oriented applications. The usual argumentation is that the underlying OS seems to be irrelevant in the world of today's web-centric applications. We propose a framework for construction of "service-oriented operating system" and examine the role it plays in physical and user-perceived service availability by investigating potential abstractions and integration points between service-oriented applications and OS architecture, such as treating OS as a set of collaborating services, introducing standard middleware services as parts of an OS and including support for server consolidation through virtualization. We demonstrate how to address the following dependability attributes at the OS level: service availability (readiness for correct service), service reliability (continuity of correct service), integrity (absence of improper system alterations) and maintainability (ability to undergo modifications and repair). We further argue that availability at the OS level plays the key role in the availability of service-oriented applications and propose an orthogonal OS design methodology suited for that purpose.
Optimizing the high dynamic range imaging pipeline
Authors: Erik Reinhard
Venue:  (2007)

High dynamic range (HDR) imaging is a rapidly growing field in computer graphics and image processing. It allows capture, storage, processing, and display of photographic information within a scene-referred framework. The HDR imaging pipeline consists of the major steps an HDR image is expected to go through from capture to display. It involves various techniques to create HDR images, pixel encodings and file formats for storage, tone mapping for display on conventional display devices and direct display on HDR capable screens. Each of these stages have important open problems, which need to be addressed for a smoother transition to an HDR imaging pipeline. We addressed some of these important problems such as noise reduction in HDR imagery, preservation of color appearance, validation of tone mapping operators, and image display on HDR monitors. The aim of this thesis is thus, to present our findings and describe the research we have conducted within the framework of optimizing the HDR imaging pipeline.
An Introduction to the Lambda Calculus
Authors: Zoltán Csörnyei, Gergely Dévai
Venue: Central European Functional Programming School: Second Summer School, CEFP 2007, Cluj-Napoca, Romania, June 23-30, 2007, Revised Selected Lectures (2008)

Lambda calculus (¿-calculus) is one of the most well-known formal models of computer science. It is the basis for functional programming like Turing machines are the foundation of imperative programming. These two systems are equivalent and both can be used to formulate and investigate fundamental questions about solvability and computability.First, we introduce the reader to the basics of ¿-calculus: its syntax and transformation rules. We discuss the most important properties of the system related to normal forms of ¿-expressions. We present the recursive version of ¿-calculus and finally give the classical results that establish the link between ¿-calculus, partial recursive functions and Turing machines.
Multimodal Hand-Palm Biometrics
Authors: Ryszard S. Choraś, Michał Choraś
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Hand geometry based biometric verification has proven to be the most suitable and acceptable biometrics trait for medium and low security applications. Hereby a new approach for the personal identification using hand images is presented. Two kinds of biometric indicators are extracted from the low-resolution hand images; (i) palmprint features, which are composed of principal lines, wrinkles, minutiae, delta points, etc., and (ii) hand geometry features which include area/size of palm, length and width of fingers. In the article we focus on feature extraction methods applied to one-sensor multimodal hand-palm biometrics system.
Simulation of the Electron Tunneling Paths in Networks of Nano-particle Films
Authors: Milovan Šuvakov, Bosiljka Tadić
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

Thin films of nano-particles deposited on substrates are important for new technological applications. Their physical properties depend crucially on particle size and structural inhomogeneity of the deposition. To systematically investigate these dependencies, we apply graph theory and simulations of voltage-driven tunneling conduction. We implement a network model of nano-particle films, based on data of particle positions on a two-dimensional plane. Assuming that connected particles on the network are within the tunneling distance, we then implement a continuous-time electron tunneling on the network and show how the current---voltage characteristics depend on the graph structure. We visualize the conduction paths through the network which correspond to the measured currents both in random and spatially inhomogeneous films.
Future Data Traffic Volume
Authors: R. W. Hough
Venue: Computer (1970)

In the last several years it has become commonplace for representatives of the computer industry and others to speak very convincingly of the enormous amount of data that will soon be transmitted via communication lines throughout the U.S., quoting such authoritative sources as AT&T itself for their information. So forceful have been these remarks that we have generally been lulled into believing them wholeheartedly, even to the point of listening almost without question to predictions of the complete breakdown of the telephone system, because of the amount of data that it will have to handle in the future. 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
Feature Extraction of Speech Signal by Genetic Algorithms-Simulated Annealing and Comparison with Linear Predictive Coding Based Methods
Authors: Melih İnal
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

This paper presents Genetic Algorithms and Simulated Annealing (GASA) based on feature extraction of speech signal and comparison with traditional Linear Predictive Coding (LPC) methods. The performance of each method is analyzed for ten speakers with independent text speaker verification database from Center for Spoken Language Understanding (CSLU) which was developed by Oregon Graduate Institute (OGI). The GASA algorithm is also analyzed with constant population size for different generation numbers, crossover and mutation probabilities. When compared with the Mean Squared Error (MSE) of the each speech signal for each method, all simulation results of the GASA algorithm are more effective than LPC methods. 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
Calculation of EEG correlation dimension: Large massifs of experimental data
Authors: Alexey Mekler
Venue: Computer Methods and Programs in Biomedicine (2008)

Correlation dimension of reconstructed attractor (D"2) is one of the specific values for human electroencephalogram (EEG). It makes it possible to evaluate variability of human brain functioning. There are some requirements, made for time series to be analyzed by Grassberger-Procaccia method, and EEG does not meet them perfectly. Also, realization of this algorithm uses subjective evaluation procedure and needs setting of additional parameters for calculation, such as lag and embedding dimension. Method of calculation, suggested in the article, meets specifics of EEG and allows calculating without using subjectivity. This makes it possible to process large amounts of experimental data. The value, calculated by this method, is not D"2 in strict sense, but it is useful for evaluated variability of brain functioning.
A Solid Modeler with a 4 x 4 Determinant Processor
Authors: Fujio Yamaguchi, Toshiya Tokieda
Venue: IEEE Computer Graphics and Applications (1985)

Simple and efficient, this algorithm for Boolean shape operations makes use of the continuity of a shape. It is fastest when special hardware performs the repetitious geometric computations.
A linear goal programming priority method for fuzzy analytic hierarchy process and its applications in new product screening
Authors: Ying-Ming Wang, Kwai-Sang Chin
Venue: International Journal of Approximate Reasoning (2008)

Fuzzy analytic hierarchy process (AHP) has been widely used for a variety of applications such as supplier selection, customer requirements assessment and the like. The vast majority of the applications, however, were found avoiding the use of sophisticated approaches for fuzzy AHP such as fuzzy least squares method while using a simple extent analysis for the sake of simplicity. The extent analysis proves to be incorrect and may lead to a wrong decision being made. This paper proposes a sound yet simple priority method for fuzzy AHP which utilizes a linear goal programming (LGP) model to derive normalized fuzzy weights for fuzzy pairwise comparison matrices. The proposed LGP priority method is tested with three numerical examples including an application of fuzzy AHP to new product development (NDP) project screening decision making.
The ludic aspect of interaction during a pervasive game activity
Authors: Charalampos Rizopoulos, Katerina Diamantaki, Dimitris Charitos
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This paper describes a location-based game activity that took place in Athens as part of a related research project and briefly examines the ludic aspects of such an activity.
Enabling novel premium service classes in DiffServ over MPLS-enabled network
Authors: Hamada Alshaer, Jaafar M. H. Elmirghani
Venue: International Journal of Network Management (2008)

Network resources dimensioning and traffic engineering influence the quality in provisioned services required by the Expedited Forwarding (EF) traffic in production networks established through DiffServ over MPLS-enabled network. By modeling EF traffic flows and the excess of network resources reserved for it, we derive the range of delay values which are required to support these flows at DiffServ nodes. This enables us to develop an end-to-end (e2e) delay budget-partitioning mechanism and traffic-engineering techniques within a framework for supporting new premium QoS levels, which are differentiated based on e2e delay, jitter and loss. This framework enables ingress routers to control EF traffic flow admission and select appropriate routing paths, with the goal of EF traffic balancing, avoiding traffic congestion and getting the most use out of the available network resources through traffic engineering. As a result, this framework should enable Internet service providers to provide three performance levels of EF service class to their customers provided that their network is DiffServ MPLS TE aware.
Stochastic Cellular Neural Network for CDMA Multiuser Detection
Authors: Zhilu Wu, Nan Zhao, Yaqin Zhao, Guanghui Ren
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A novel method for the multiuser detection in CDMA communication systems based on a stochastic cellular neural network (SCNN) is proposed in this paper. The cellular neural network (CNN) can be used in multiuser detection, but it may get stuck in a local minimum resulting in a bad steady state. The annealing CNN detector has been proposed to avoid local minima; however, the near-far effect resistant performance of it is poor. So, the SCNN detector is proposed here through adding a stochastic term in a CNN. The performance of the proposed SCNN detector is evaluated via computer simulations and compared to that of the conventional detector, the stochastic Hopfield network detector, and the Annealing CNN detector. It is shown that the SCNN detector can avoid local minima and has a much better performance in reducing the near-far effect than these detectors, as well as a superior performance in bit-error rate.
Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformation
Authors: Wenbo Cao, Robert Haralick
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Dimension reduction methods are often applied in machine learning and data mining problems. Linear subspace methods are the commonly used ones, such as principal component analysis (PCA), Fisher's linear discriminant analysis (FDA), et al. In this paper, we describe a novel feature extraction method for binary classification problems. Instead of finding linear subspaces, our method finds lower- dimensional affine subspaces for data observations. Our method can be understood as a generalization of the Fukunaga-Koontz Transformation. We show that the proposed method has a closed-form solution and thus can be solved very efficiently. Also we investigate the information-theoretical properties of the new method and study the relationship of our method with other methods. The experimental results show that our method, as PCA and FDA, can be used as another preliminary data-exploring tool to help solve machine learning and data mining problems.
Electromagnetism-Like Mechanism Based Algorithm for Neural Network Training
Authors: Xiao-Juan Wang, Liang Gao, Chao-Yong Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Due to the complex nature of training neural network (NN), this problem has gained popularity in the nonlinear optimization field. In order to avoid falling into local minimum because of inappropriate initial weights, a number of global search techniques are developed. This paper applies a novel global algorithm, which is electromagnetism-like mechanism (EM) algorithm, to train NN and the EM based algorithm for neural network training is presented. The performance of the proposed algorithm is evaluated in classification problems and the comparison with BP and GA algorithms shows its effectiveness.
Managing Sensors and Information Sources Using Semantic Matchmaking and Argumentation
Authors: Alun Preece
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

Effective deployment and utilisation of limited and constrained intelligence resources -- including sensors and other sources -- is seen as a key issue in modern multinational coalition operations. In this talk, I will examine the application of semantic matchmaking and argumentation technologies to the management of these resources. I will show how ontologies and reasoning can be used to assign sensors and sources to meet the needs of missions, and show how argumentation can support the process of gathering and reasoning about uncertain evidence obtained from sensor probes.
IEEE 1473: An Open-Source Communications Protocol For Railway Vehicles
Authors: Phillip A. Laplante, Frederick C. Woolsey
Venue: IT Professional (2003)

The open Standard IEEE-1473-1999 embodies the most popular and widely deployed vehicle and train network standards in the world.
RNN with a Recurrent Output Layer for Learning of Naturalness
Authors: Ján Dolinský, Hideyuki Takagi
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

The behavior of recurrent neural networks with a recurrent output layer (ROL) is described mathematically and it is shown that using ROL is not only advantageous, but is in fact crucial to obtaining satisfactory performance for the proposed naturalness learning. Conventional belief holds that employing ROL often substantially decreases the performance of a network or renders the network unstable, and ROL is consequently rarely used. The objective of this paper is to demonstrate that there are cases where it is necessary to use ROL. The concrete example shown models naturalness in handwritten letters.
A Sophisticated Privacy-Enhanced Yet Accountable Security Framework for Metropolitan Wireless Mesh Networks
Authors: Kui Ren, Wenjing Lou
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Recently, multi-hop wireless mesh networks (WMNs) have attracted increasing attention and deployment as a low-cost approach to provide broadband Internet access at metropolitan scale. Security and privacy issues are of most concern in pushing the success of WMNs for their wide deployment and for supporting service-oriented applications. Despite the necessity, limited security research has been conducted towards privacy preservation in WMNs. This motivates us to develop PEACE, a soPhisticated privacy-Enhanced yet Accountable seCurity framEwork, tailored for WMNs. At the one hand, PEACE enforces strictuser access control to cope with both free riders and malicious users. On the other hand, PEACE offers sophisticated user privacy protection against both adversaries and various other network entities. PEACE is presented as a suite of authentication and key agreement protocols built upon our proposed short group signature variation. Our analysis shows that PEACE is resilient to a number of security and privacy related attacks.
Introduction: Anticipation in Natural and Artificial Cognition
Authors: Giovanni Pezzulo, Martin V. Butz, Cristiano Castelfranchi, Rino Falcone
Venue: The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems (2008)

What will artificial cognitive systems of the future look like? If we are asked to imagine robots, or intelligent software agents, several features come to our mind such as the capability to adapt to their environments and to satisfy their goals with only limited human intervention, to plan sequences of actions for realizing long-term objectives, to act collectively in view of complex objectives, to interact and cooperate with us, with and without natural language, to take decisions (also in our place), etc.Currently these capabilities are far beyond the possibilities of robots and other artificial systems. In the next years a huge effort will be required for scaling up the potentialities of the artificial systems that we are able to build nowadays. One way to overcome these limitations is to take inspiration form the functioning of living organisms. A large body of evidence, which we review in this chapter, indicates that natural cognitive systems are not reactive but essentially anticipatory systems. We do not think that this is a mere coincidence. On the contrary, we claim that anticipation is a crucial--and foundational--phenomenon in natural cognition. Individual behavior is guided by anticipatory mechanisms that are used for behavioral control, perceptual processing, goal-directed behavior, and learning. And also effective social behavior relies on the anticipation of the behavior of other agents. We argue that anticipation is a key ingredient for the design of autonomous, artificial cognitive agents of the future: Only cognitive systems with anticipation mechanisms can be credible, adaptive, and successful in interaction with both the environment and other autonomous systems and humans. This is the challenge that we anticipate for the future of cognitive systems research: the passage from reactive to anticipatory cognitive embodied systems.