ppxscal/embedding_network huggingface dataset:
Research on the Traffic Matrix Based on Sampling Model
Authors: Fengjun Shang
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Traffic matrix information is very important to networks. In this paper, a traffic matrix model is proposed based on passive measurement that can be used to high-speed IP network. The core of model has three parts as follows: 1) measuring traffic at the edge node of network. The passive measurement method is introduced to measure the node traffic based on software measurement. Because the software is based on flow measurement, the flow matching, that is, packet classification is a key problem. In packet classification, the dual hash algorithm is proposed. The algorithm is introduced based on the non-collision hash and XOR hash. 2) introducing non-intrusive measurement method to acquire path information and then the sampling method is introduced. In this method, the path information is writen in the flag field. 3) deducing sampling probability so that the point of optimization is selected. Simulation results prove the effectiveness of this algorithm.
Design with Intent: Persuasive Technology in a Wider Context
Authors: Dan Lockton, David Harrison, Neville Stanton
Venue: Proceedings of the 3rd international conference on Persuasive Technology (2008)

Persuasive technology can be considered part of a wider field of `Design with Intent' (DwI) - design intended to result in certain user behaviour. This paper gives a very brief review of approaches to DwI from different disciplines, and looks at how persuasive technology sits within this space.
Operating Systems Principles for Data Flow Networks
Authors: P. J. Denning
Venue: Computer (1978)

A deep look at contemporary operating systems finds data flow principles of value to computer networks large and small.
Theory of input spike auto-and cross-correlations and their effect on the response of spiking neurons
Authors: Rubén Moreno-Bote, Alfonso Renart, Néstor Parga
Venue: Neural Computation (2008)

Spike correlations between neurons are ubiquitous in the cortex, but their role is not understood. Here we describe the firing response of a leaky integrate-and-fire neuron (LIF) when it receives a temporarily correlated input generated by presynaptic correlated neuronal populations. Input correlations are characterized in terms of the firing rates, Fano factors, correlation coefficients, and correlation timescale of the neurons driving the target neuron. We show that the sum of the presynaptic spike trains cannot be well described by a Poisson process. In fact, the total input current has a nontrivial two-point correlation function described by two main parameters: the correlation timescale (how precise the input correlations are in time) and the correlation magnitude (how strong they are). Therefore, the total current generated by the input spike trains is not well described by a white noise gaussian process. Instead, we model the total current as a colored gaussian process with the same mean and two-point correlation function, leading to the formulation of the problem in terms of a Fokker-Planck equation. Solutions of the output firing rate are found in the limit of short and long correlation timescales. The solutions described here expand and improve on our previous results (Moreno, de la Rocha, Renart, & Parga, 2002) by presenting new analytical expressions for the output firing rate for general IF neurons, extending the validity of the results for arbitrarily large correlation magnitude, and by describing the differential effect of correlations on the mean-driven or noise-dominated firing regimes. Also the details of this novel formalism are given here for the first time. We employ numerical simulations to confirm the analytical solutions and study the firing response to sudden changes in the input correlations. We expect this formalism to be useful for the study of correlations in neuronal networks and their role in neural processing and information transmission.
Parallel Data Flow Analysis for OpenMP Programs
Authors: Lei Huang, Girija Sethuraman, Barbara Chapman
Venue: Proceedings of the 3rd international workshop on OpenMP: A Practical Programming Model for the Multi-Core Era (2007)

The paper presents a compiler framework for analyzing and optimizing OpenMP programs. The framework includes Parallel Control Flow Graph and Parallel Data Flow equations based on the OpenMP relaxed memory consistency model. It enables traditional compiler analyses as well as specific optimizations for OpenMP. Based on the framework, we describe dead code elimination and barrier elimination algorithms. An OpenMP code example is showed in the paper to illustrate the optimizations. The framework guarantees that the traditional optimizations can be performed safely to OpenMP programs, and it further increases the opportunities for more aggressive optimizations.
Patch Relational Neural Gas --- Clustering of Huge Dissimilarity Datasets
Authors: Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural gas or the self-organizing map offer an intuitive and fast variant which represents data by means of typical representatives, thereby running in linear time. Recently, an extension of these methods towards relational clustering has been proposed which can handle general non-vectorial data characterized by dissimilarities only, such as alignment or general kernels. This extension, relational neural gas, is directly applicable in important domains such as bioinformatics or text clustering. However, it is quadratic in m both in memory and in time (m being the number of data points). Hence, it is infeasible for huge data sets. In this contribution we introduce an approximate patch version of relational neural gas which relies on the same cost function but it dramatically reduces time and memory requirements. It offers a single pass clustering algorithm for huge data sets, running in constant space and linear time only.
Designing a DRM System
Authors: Franco Frattolillo, Federica Landolfi
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

The Digital Rights Management (DRM) systems aim at protecting and enforcing the legal rights associated with the use of digital content distributed on the Internet. Most of such systems use watermarking techniques to implement the content protection. Although the effectiveness of the implemented protection strictly depends on the adopted watermarking techniques, an important role is also played by watermarking protocols. They define the scheme of the interactions that have to take place among the entities involved in the processes of content protection and web-based distribution governed by the DRM systems. This paper presents a DRM system developed as a web software platform to implement the copyright protection of multimedia digital content distributed on the Internet. The DRM system is based on a watermarking protocol previously developed by the authors, and has been designed as a service oriented architecture composed of a federation of coordinated web entities that play distinct roles and interact within a trusted environment.
Peer to peer model for virtual knowledge communities
Authors: Pierre Maret, Julien Subercaze, Jacques Calmet
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

Knowledge management and communication scheme are keystones of virtual organizations or virtual communities. Both concepts can be based upon the peer-to-peer approach. This point of view is highly suitable to address virtual organization issues because it emphasizes both autonomy and communication. A Virtual Knowledge Community is a dynamic set of peers or agents gathered around a common topic of interest. We propose in this paper the design of successive packages that implement peers' abilities in acting within communities. Our packages are fully generic and help implementing any types of communities. It is based upon a paradigm which considers a peer as an agent, and composed of knowledge and of a decision mechanism.
Scientific Workflow Systems for 21st Century, New Bottle or New Wine?
Authors: Yong Zhao, Ioan Raicu, Ian Foster
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

With the advances in e-Sciences and the growing complexity of scientific analyses, more and more scientists and researchers are relying on workflow systems for process coordination, derivation automation, provenance tracking, and bookkeeping. While workflow systems have been in use for decades, it is unclear whether scientific workflows can or even should build on existing workflow technologies, or they require fundamentally new approaches. In this paper, we analyze the status and challenges of scientific workflows, investigate both existing technologies and emerging languages, platforms and systems, and identify the key challenges that must be addressed by workflow systems for e-science in the 21st century.
Implementation of OpenMP Work-Sharing on the Cell Broadband Engine Architecture
Authors: Jun Sung Park, Jung-Gyu Park, Hyo-Jung Song
Venue: Proceedings of the 3rd international workshop on OpenMP: A Practical Programming Model for the Multi-Core Era (2007)

The Cell Broadband Engine (CBE) is a single-chip multiprocessor composed of one Power Processor Element (PPE) core and multiple Synergistic Processor Element (SPE) cores. Due to the heterogeneous processor type of the CBE, multi-core programming for the CBE is very difficult which can be alleviated by OpenMP. In this paper, we introduce some issues in implementing OpenMP work-sharing constructs on the CBE. To address overheads in creating and managing work-sharing control blocks across different processor types, we have come up with a method for aggressively reusing the work-sharing control block by precisely checking the reusability of it. This scheme allows us better performance over naive implementation of work-sharing constructs, when evaluated with modified EPCC benchmark.
Hierarchical Approach to Evolutionary Multi-Objective Optimization
Authors: Eryk Ciepiela, Joanna Kocot, Leszek Siwik, Rafał Dreżewski
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

In this paper a new "hierarchical" evolutionary approach to solving multi-objective optimization problems is introduced. The results of experiments with standard multi-objective test problems, which were aimed at comparing "hierarchical" and "classical" versions of multi-objective evolutionary algorithms, show that the proposed approach is a very promising technique.
Scenario Description Language for Multi-agent Systems
Authors: Armin Stranjak, Igor Čavrak, Mario Žagar
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Increasing complexity of interactions among agents presents a serious challenge in designing, building and maintaining of such systems. This paper analyses existing interaction description languages and methods, and proposes a novel language based on descriptions of interaction scenarios. Program code generated from scenario descriptions, in synergy with related platform components, simplifies design, implementation and maintenance phases of multi-agent systems characterized by complex and interwoven communication.
The Integrated Methodology of KPCA and Wavelet Support Vector Machine for Predicting Financial Distress
Authors: Jian-Guo Zhou, Tao Bai, Ji-Ming Tian
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

In this paper, a hybrid intelligent system, combining kernel principal component analysis (KPCA) and wavelet support vector machine (WSVM), is applied to the study of predicting financial distress. KPCA method is used as a preprocessor of classifier to extract the nonlinear features of input variables. Then a method that generates wavelet kernel function of the SVM is proposed based on the theory of wavelet frame and the condition of the SVM kernel function. The Mexican Hat wavelet is selected to construct the SVM kernel function and form the wavelet support vector machine (WSVM). The effectiveness of the proposed model is verified by experiments through the contrast of the results of SVMs with different kernel functions and other models.
Neural Network Based High Accuracy Frequency Harmonic Analysis in Power System
Authors: Xiaohua Wang, Yigang He, Ying Long
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A back-propagation neural network method is proposed for accurate frequencies, amplitudes and phases estimation from periodic signals in power systems, and the convergence theorem shows that the proposed algorithm can be convergent asymptotically to its global minimum. The method is aimed at the system in which the sampling frequency cannot be locked on the actual fundamental frequency. Some simulating examples are given and the results show that the accuracy of the estimates provided by the proposed approach in the asynchronous case is relatively better than that of the estimates obtained with the conventional harmonic analysis methods.
A Continuous Internal-State Controller for Partially Observable Markov Decision Processes
Authors: Yuki Taniguchi, Takeshi Mori, Shin Ishii
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In this study, in order to control partially observable Markov decision processes, we propose a novel framework called continuous state controller (CSC). The CSC incorporates an auxiliary "continuous" state variable, called an internal state, whose stochastic process is Markov. The parameters of the transition probability of the internal state are adjusted properly by a policy gradient-based reinforcement learning, by which the dynamics of the underlying unknown system can be extracted. Computer simulations show that good control of partially observable linear dynamical systems is achieved by our CSC.
Measuring Graph Similarity Using Spectral Geometry
Authors: Hewayda Elghawalby, Edwin R. Hancock
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper we study the manifold embedding of graphs resulting from the Young-Householder decomposition of the heat kernel [19]. We aim to explore how the sectional curvature associated with the embedding can be used as feature for the purposes of gauging the similarity of graphs, and hence clustering them. To gauging the similarity of pairs of graphs, we require a means of comparing sets of such features without explicit correspondences between the nodes of the graphs being considered. To this end, the Hausdorff distance, and a robust modified variant of the Hausdorff distance are used. we experiment on sets of graphs representing the proximity image features in different views of different objects. By applying multidimensional scaling to the Hausdorff distances between the different object views, we demonstrate that our sectional curvature representation is capable of clustering the different views of the same object together.
A Measurement Study of Cache Rejection in P2P Live Streaming System
Authors: Yishuai Chen, Changjia Chen, Chunxi Li
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

In P2P system, each peer caches the received data for accessing of other peers. P2P live streaming system audience’s TV-like watching pattern brings unique characteristics for its cache management algorithm. By measuring and studying the cache rejection algorithm used in PPLive with our PPLive crawler, we found a simple but interesting cache rejection algorithm used in PPLive, i.e. the cache rejection of peers is synchronized with the chunk upload of media server on chunk offset. We modeled this algorithm as a fixed-duration virtual buffer which includes the P2P network and peer’s local buffer and then verified it in our trace. We finally gave some analysis and explanations for this algorithm. What we found is benefit to the future design and modeling of P2P live streaming system.
Conditional Computational Entropy, or Toward Separating Pseudoentropy from Compressibility
Authors: Chun-Yuan Hsiao, Chi-Jen Lu, Leonid Reyzin
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

We study conditional computational entropy: the amount of randomness a distribution appears to have to a computationally bounded observer who is given some correlated information. By considering conditional versions of HILL entropy (based on indistinguishability from truly random distributions) and Yao entropy (based on incompressibility), we obtain: a separation between conditional HILL and Yao entropies (which can be viewed as a separation between the traditional HILL and Yao entropies in the shared random string model, improving on Wee's 2004 separation in the random oracle model); the first demonstration of a distribution from which extraction techniques based on Yao entropy produce more pseudorandom bits than appears possible by the traditional HILL-entropy-based techniques; a new, natural notion of unpredictability entropy, which implies conditional Yao entropy and thus allows for known extraction and hardcore bit results to be stated and used more generally.
Detection Algorithms for the Nano Nose
Authors: J. M. Karthikeya Udayagiri V. R., Taleb Moazzeni, Yingtao Jiang, Biswajit Das
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The Nano nose is an instrument with an array ofnano sized optical sensors that produces digitalpatterns when exposed to radiation passingthrough a gaseous mixture. This paper outlines analgorithm using a combination of neural networksand partial least squares (PLS) regression,Kalman filter capable of processing these digitalpatterns and generate an output. This outputwould not only show the detection of the individualconstituents in the gaseous mixture but also theprediction of their concentrations. The developedalgorithm in the experiments conducted, hasperformed detection and prediction of quite lowconcentrations of constituent gases successfullywith a prediction error of less than 10% in thepresence of noise.
High Resolution Parabolic Radon Transform by Frequency Domain Method for Eliminating Multiple
Authors: Junhua Zhang, Cun Yang, Hui Xu, Haicheng Yu, Lianyu Shan
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Parabolic Radon transform (PRT) is one of the most effective methods to eliminate multiple. Based on model study and programming, the article shows the physical signification of FPRT firstly, then presents the theories and implementation processes about the forward and inverse parabolic Radon transform infrequency domain. It also discusses the aliasing in radon domain, the stability of radon transform, the surgical mute of multiple, the spatial truncation, the adaptive filtering and the precision of FPRT processing. The theoretical and practical data processing shows the method has following features: high resolution, fast speed, adaptive filtering, surgical muting and friendly interface.
CAD dependent estimation of optimal k-value in FSM onto k-LUT FPGA mappings, based on standard benchmark networks
Authors: Dokouzyannis Stavros, Arzoumanidis Efsevios
Venue: Proceedings of the 7th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications (2008)

The problem of k value is a crucial one for k-LUT based FPGA architectures. A digital circuit quality mapping onto a k- LUT FPGA, is basically CAD dependable, but to a great degree k value dominates over all of the mapping parameters. Namely area and level of a mapped circuit (network) are strongly dependent on k value. As k increases, the total area of a mapped circuit also increases, while the relative level decreases, leading to a faster circuit. The question is which is the optimum k value, namely the value beyond which the area tradeoff of a mapped circuit (network) is unnecessarily increased for some level gain. This problem has been extensively studied through years and resulted in architectures implemented by most FPGAs manufacturers. For years, optimum values of k are considered between 3 and 5, with k = 4 accepted as being the best choice. Our study is an experimental evaluation of above results in present times, utilizing SIS, MVSIS and ABC, academic CAD systems, from California Berkeley University (UCB). These CAD systems, especially MVSIS and the under development ABC, incorporate state of the art optimization and mapping algorithms, among many other features. We optimized and mapped 20 medium and large scale sequential benchmark networks, for k values from 2 to 6, using all three mentioned CAD systems. In all cases our outcomes verify that k should be again in the range of 3 to 5 for best results, with k=4 being the optimum value.
Scalable Software Model Checking Using Design for Verification
Authors: Tevfik Bultan, Aysu Betin-Can
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

There has been significant progress in automated verification techniques based on model checking. However, scalable software model checking remains a challenging problem. We believe that this problem can be addressed using a design for verification approach based on design patterns that facilitate scalable automated verification. We have been investigating a design for verification approach based on the following principles: 1) use of stateful, behavioral interfaces which isolate the behavior and enable modular verification, 2) an assume-guarantee style verification strategy which separates verification of the behavior from the verification of the conformance to the interface specifications, 3) a general model checking technique for interface verification, and 4) domain specific and specialized verification techniques for behavior verification. So far we have applied this approach to verification of synchronization operations in concurrent programs and to verification of interactions among multiple peers in composite web services. The case studies we conducted indicate that scalable software verification is achievable in these application domains using our design for verification approach.
Policy Learning for Motor Skills
Authors: Jan Peters, Stefan Schaal
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution.