ppxscal/embedding_network huggingface dataset:
Estimation of Market Share by Using Discretization Technology: An Application in China Mobile
Authors: Xiaohang Zhang, Jun Wu, Xuecheng Yang, Tingjie Lu
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

The mobile market is becoming more competitive. Mobile operators having been focusing on the market share of high quality customers. In this paper, we propose a new method to help mobile operator to estimate the share in high quality customers market based on the available data, inter-network calling detail records. The core of our method is a discretization algorithm which adopts the Gini criterion as discretization measure and is supervised, global and static. In order to evaluate the model, we use the real life data come from one mobile operator in China mainland. The results prove that our method is effective. And also our method is simple and easy to be incorporated into operation support system to predict periodically
Intelligent Voice Navigation of Spreadsheets
Authors: Derek Flood, Kevin Mc Daid, Fergal Mc Caffery, Brian Bishop
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

Interaction with software systems has become second nature to most computer users, however when voice recognition is introduced, this simple procedure becomes quite complex. To reduce this complexity for spreadsheet users, the authors have developed an intelligent voice navigation system called iVoice. This paper outlines the iVoice system and details an experiment that was conducted to determine the efficiency of iVoice when compared to a leading voice recognition technology.
Constant-Space Localized Byzantine Consensus
Authors: Danny Dolev, Ezra N. Hoch
Venue: Proceedings of the 22nd international symposium on Distributed Computing (2008)

Adding Byzantin tolerance to large scale distributed systems is considered non-practical. The time, message and space requirements are very high. Recently, researches have investigated the broadcast problem in the presence of a f l -local Byzantin adversary. The local adversary cannot control more than f l neighbors of any given node. This paper proves sufficient conditions as to when the synchronous Byzantin consensus problem can be solved in the presence of a f l -local adversary.Moreover, we show that for a family of graphs, the Byzantin consensus problem can be solved using a relatively small number of messages, and with time complexity proportional to the diameter of the network. Specifically, for a family of bounded-degree graphs with logarithmic diameter, O(logn) time and O(n logn) messages. Furthermore, our proposed solution requires constant memory space at each node.
A Virtual Agent's Behavior Selection by Using Actions for Focusing of Attention
Authors: Haris Supic
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

In this paper we present an approach for focusing of IVA's attention in order to select appropriate behavior in continuously changing virtual environment. An action for focusing of attention represents the act of moving the attention to the currently relevant attributes of the local virtual environment.
A Hierarchical Approach for Multi-task Logistic Regression
Authors: Àgata Lapedriza, David Masip, Jordi Vitrià
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

In the statistical pattern recognition field the number of samples to train a classifier is usually insufficient. Nevertheless, it has been shown that some learning domains can be divided in a set of related tasks, that can be simultaneously trained sharing information among the different tasks. This methodology is known as the multi-task learning paradigm. In this paper we propose a multi-task probabilistic logistic regression model and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performed in two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-task learning approach with respect to the single task approach when using the same probabilistic model.
Effects of synaptic synchrony on the neuronal input-output relationship
Authors: Xiaoshen Li, Giorgio A. Ascoli
Venue: Neural Computation (2008)

The firing rate of individual neurons depends on the firing frequency of their distributed synaptic inputs, with linear and nonlinear relations subserving different computational functions. This letter explores the relationship between the degree of synchrony among excitatory synapses and the linearity of the response using detailed compartmental models of cortical pyramidal cells. Synchronous input resulted in a linear input-output relationship, while asynchronous stimulation yielded sub-and supraproportional outputs at low and high frequencies, respectively. The dependence of input-output linearity on synchrony was sigmoidal and considerably robust with respect to dendritic location, stimulus irregularity, and alteration of active and synaptic properties. Moreover, synchrony affected firing rate differently at lower and higher input frequencies. A reduced integrate-and-fire model suggested a mechanism explaining these results based on spatiotemporal integration, with fundamental implications relating synchrony to memory encoding.
Feature Selection through Dynamic Mesh Optimization
Authors: Rafael Bello, Amilkar Puris, Rafael Falcón, Yudel Gómez
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

This paper introduces the Dynamic Mesh Optimization meta-heuristic, which falls under the evolutionary computation techniques. Moreover, we outline its application to the feature selection problem. A set of nodes representing subsets of features makes up a mesh which dynamically grows and moves across the search space. The novel methodology is compared with other existing meta-heuristic approaches, thus leading to encouraging empirical results.
MMFI: An Effective Algorithm for Mining Maximal Frequent Itemsets
Authors: Shiguang Ju, Chen Chen
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Existing algorithms for mining maximal frequent itemsets have to do superset checking, and some of them using FP-tree have to construct conditional frequent pattern trees recursively. We present a novel algorithm for mining maximal frequent itemsets from a transactional database. In the algorithm, the FP-Tree data structure is used and adapted, and a new strategy called “NBN” (Node By Node) is used for traversing the adapted FP-Tree. Neither superset checking nor constructing conditional frequent pattern trees is needed in the algorithm. We analyze the performance of the algorithm and compare our method with existing algorithms. Our technique works better for mining maximal frequent itemsets. It is also proved by experimental comparison that our algorithm is more fast and efficient.
Pattern-Based Reasoning System Using Self-incremental Neural Network for Propositional Logic
Authors: Akihito Sudo, Manabu Tsuboyama, Chenli Zhang, Akihiro Sato, Osamu Hasegawa
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

We propose an architecture for reasoning with pattern-based if-then rules that is effective for intelligent systems like robots solving varying tasks autonomously in a real environment. The proposed system can store pattern-based if-then rules of propositional logic, including conjunctions, disjunctions, negations, and implications. The naive pattern-based reasoning can store pattern-based if-then rules and make inferences using them. However, it remains insufficient for intelligent systems operating in a real environment. The proposed system uses an algorithm that is inspired by self-incremental neural networks such as SONIN and SOINN-AM in order to achieve incremental learning, generalization, avoidance of duplicate results, and robustness to noise, which are important properties for intelligent systems
An Efficient Statistical Approach for Automatic Organic Chemistry Summarization
Authors: Florian Boudin, Juan-Manuel Torres-Moreno, Patricia Velázquez-Morales
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

In this paper, we propose an efficient strategy for summarizing scientific documents in Organic Chemistry that concentrates on numerical treatments. We present its implementation named yachs (Yet Another Chemistry Summarizer) that combines a specific document pre-processing with a sentence scoring method relying on the statistical properties of documents. We show that yachs achieves the best results among several other summarizers on a corpus made of Organic Chemistry articles.
Implementing Synchronous Models on Loosely Time Triggered Architectures
Authors: Stavros Tripakis, Claudio Pinello, Albert Benveniste, Alberto Sangiovanni-Vincent, Paul Caspi, Marco Di Natale
Venue: IEEE Transactions on Computers (2008)

Synchronous systems offer a clean semantics and an easy verification path at the expense of often inefficient implementations. Capturing design specifications as synchronous models and then implementing the specifications in a less restrictive platform allow to address a much larger design space. The key issue in this approach is maintaining semantic equivalence between the synchronous model and its implementation. We address this problem by showing how to map a synchronous model onto a loosely time-triggered architecture that is fairly straightforward to implement as it does not require global synchronization or blocking communication. We show how to maintain semantic equivalence between specification and implementation using an intermediate model (similar to a Kahn process network but with finite queues) that helps in defining the transformation. Performance of the semantic preserving implementation is studied for the general case as well as for a few special cases.
Clustering by Chaotic Neural Networks with Mean Field Calculated Via Delaunay Triangulation
Authors: Elena N. Benderskaya, Sofya V. Zhukova
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

High quality clustering is impossible without a priori information about clustering criteria. The paper describes the development of new clustering technique based on chaotic neural networks that overcomes the indeterminacy about number and topology of clusters. Proposed method of weights computation via Delaunay triangulation allows to cut down computing complexity of chaotic neural network clustering.
Flip Algorithm for Segment Triangulations
Authors: Mathieu Brévilliers, Nicolas Chevallier, Dominique Schmitt
Venue: Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science (2008)

Given a set S of disjoint line segments in the plane, which we call sites, a segment triangulation of S is a partition of the convex hull of S into sites, edges, and faces. The set of faces is a maximal set of disjoint triangles such that the vertices of each triangle are on three distinct sites. The segment Delaunay triangulation of S is the segment triangulation of S whose faces are inscribable in circles whose interiors do not intersect S. It is dual to the segment Voronoi diagram. The aim of this paper is to show that any given segment triangulation can be transformed by a finite sequence of local improvements in a segment triangulation that has the same topology as the segment Delaunay triangulation. The main difference with the classical flip algorithm for point set triangulations is that local improvements have to be computed on non convex regions. We overcome this difficulty by using locally convex functions.
Off-Line Arabic Handwritten Characters Recognition Based on a Hidden Markov Models
Authors: M. Amrouch, M. Elyassa, A. Rachidi, D. Mammass
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

We present a system based on Hidden Markov Models (HMMs) for off-line isolated Arabic handwritten characters recognition. The Hough accumulator of the character image is partitioned into equal horizontal bands that will be used to extract directional information. This information is translated into sequences of observations that are used to train the model for each character during the learning step.
Related-Key Chosen IV Attacks on Grain-v1 and Grain-128
Authors: Yuseop Lee, Kitae Jeong, Jaechul Sung, Seokhie Hong
Venue: Proceedings of the 13th Australasian conference on Information Security and Privacy (2008)

The slide resynchronization attack on Grain was proposed in [6]. This attack finds related keys and initialization vectors of Grain that generate the 1-bit shifted keystream sequence. In this paper, we extend the attack proposed in [6] and propose related-key chosen IV attacks on Grain-v1 and Grain-128. The attack on Grain-v1 recovers the secret key with 222.59 chosen IVs, 226.29-bit keystream sequences and 222.90 computational complexity. To recover the secret key of Grain-128, our attack requires 226.59 chosen IVs, 231.39-bit keystream sequences and 227.01 computational complexity. These works are the first known key recovery attacks on Grain-v1 and Grain-128.
Intrusion Detection as Passive Testing: Linguistic Support with TTCN-3 (Extended Abstract)
Authors: Krzysztof M. Brzezinski
Venue: Proceedings of the 4th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2007)

We explore the idea of using the internationally standardized test language TTCN-3 (Testing and Test Control Notation) as a platform for Intrusion Detection (ID) systems. Intrusion detection is treated as an application of verification by passive testing. It is argued that TTCN contains many features embodied in various "detection languages", and is relevant for ID. As a case study, we discuss a TTCN-based IDS for detecting the Smurf attack.
Kipnis-Shamir Attack on HFE Revisited
Authors: Xin Jiang, Jintai Ding, Lei Hu
Venue: Information Security and Cryptology: Third SKLOIS Conference, Inscrypt 2007, Xining, China, August 31 - September 5, 2007, Revised Selected Papers (2007)

In this paper, we show the claims in the original Kipnis-Shamir attack on the HFE cryptosystems and the improved attack by Courtois that the complexity of the attacks is polynomial in terms of the number of variables are invalid. We present computer experiments and a theoretical argument using basic algebraic geometry to explain why it is so. Furthermore we show that even with the help of the powerful new Gröbner basis algorithm like F 4, the Kipnis-Shamir attack still should be exponential but not polynomial. This again is supported by our theoretical argument.
Semantic Fusion for Query Processing in Grid Environment
Authors: Jinguang Gu
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

To enable accessing web information at semantic level, this paper develops a semantic fusion mechanism in Mediator-Wrapper based grid environment to support ontology-based query planning with GAV style querying request. It employs semantic communication language to build mediator site for different virtual organizations (VO), and creates global semantic information for VOs. The procedure of ontology based semantic fusion is discussed in detail. 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
Interface connecting the INET simulation framework with the real world
Authors: Michael Tüxen, Irene Rüngeler, Erwin P. Rathgeb
Venue: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (2008)

The INET framework for the widely used OMNeT++ simulation environment supports discrete event simulation for IP-based networks. During the development of a simulation model for the new IETF transport protocol SCTP (Stream Control Transmission Protocol), INET was extended to also support external interfaces. These interfaces allow to set up hybrid scenarios where simulated nodes communicate with real external IP-based nodes. This paper will first give a short introduction to the OM-NeT++ simulation environment and the INET framework. Then the requirements for the external interfaces will be discussed and some implementation aspects will be described. Hybrid scenarios offer a whole range of potential applications which will also be presented briefly. The performance of this technique is crucial for its applicability. Therefore, several test setups are evaluated to verify the feasibility of this approach.
Selective generation of training examples in active meta-learning
Authors: Ricardo B. C. Prudêncio, Teresa B. Ludermir
Venue: International Journal of Hybrid Intelligent Systems (2008)

Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) is related to a learning problem and stores the experience obtained in the empirical evaluation of a set of candidate algorithms when applied to the problem. The generation of a good set of meta-examples can be a costly process depending for instance on the number of available learning problems and the complexity of the candidate algorithms. In this work, we proposed the Active Meta-Learning, in which Active Learning techniques are used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In an implemented prototype, we evaluated the use of two different Active Learning techniques applied in two different Meta-Learning tasks. The performed experiments revealed a significant gain in the Meta-Learning performance when the active techniques were used to support the meta-example generation.
Global Exponential Stability of Fuzzy Cohen-Grossberg Neural Networks with Variable Delays
Authors: Jiye Zhang, Keyue Zhang, Dianbo Ren
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, we extend the Cohen---Grossberg neural networks from classical to fuzzy sets, and propose the fuzzy Cohen---Grossberg neural networks (FCGNN). The global exponential stability of FCGNN with time-varying delays is studied. Without assuming the boundedness and differentiability of the activation functions, based on the properties of M-matrix, by constructing vector Liapunov functions and applying differential inequalities, the sufficient conditions ensuring existence, uniqueness, and global exponential stability of the equilibrium point of fuzzy Cohen---Grossberg neural networks with variable delays are obtained.
Generating Dual-Directed Recommendation Information from Point-of-Sales Data of a Supermarket
Authors: Masakazu Takahashi, Toshiyuki Nakao, Kazuhiko Tsuda, Takao Terano
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Even at the supermarket in Japan, it is commonly used the reward card. However, it is used for only sales expansion objectives with the twice or triple points so far. This paper proposes the methods extracting customer preference information and the characteristics of the commodity from the Point of Sales (POS) data with the reward card. One of the challenges in this paper is how to grasp not only the customer preferences but the trends of the preferences. In the conventional methods, customer preference and market information are managed with two-dimensional vectors of customer and preference category axes. In this proposed method, we add time axis to make it threedimensional vectors in order to figure out the time-series changes. With this preferences extracting algorithm, we have set up the dual-recommendation site at daikoc.net to browse the trend for both items and customers. Furthermore, we have found trend leaders among the customers, that which confirm that there is a possibility to make appropriate recommendations to the other group member based on the transitions of the trend leaders' preferences.
Model-based linear manifold clustering
Authors: Robert Haralick
Venue:  (2008)

A new paradigm of clustering called &ldquo;Linear Manifold Clustering&rdquo; which is based on linear manifolds is designed, analyzed, and evaluated throughout this thesis. A Linear manifold is a translated subspace. Linear manifold clustering seeks to identify groups of points that are embedded in lower dimensional linear manifolds. The &ldquo;birth&rdquo; of this paradigm of clustering is a consequence of what we believe is a need for an important yet overlooked cluster model, and as a result of what we identify as an acute need to sufficiently address certain clustering requirements that current state of art methods are unable to address.In many problem domains it assumed that linear models are sufficient enough to describe and capture the data's inherent structure. Yet very few remote attempts have been made to devise clustering methods able to identify or learn mixtures of linear manifolds. None of these attempts posed the problem in a model-based statistical setting intended to model and understand the underlying &ldquo;process&rdquo; responsible for generating sets of points that lie on lower dimensional linear manifolds. In this thesis we introduce a formal stochastic linear manifold cluster model. Based on this model we present a series of results and techniques demonstrating the applicability of the linear manifold clustering paradigm to a wide range of applications. We show that this model is a generalization of other more common and somewhat limited cluster models. This generalization allows for less assumptions to be imposed on the data, which typically yield biased results, and more freedom for the data to &ldquo;speak for itself&rdquo;. An emphasis is put on the paradigms of pattern and correlation clustering and on the application of DNA microarray analysis, where we show that pattern clusters or correlations manifest themselves as linear manifolds in the data space. Based on the linear manifold cluster model we present two clustering algorithms: one for clustering or learning mixtures of linear manifolds, and the other tailored to the application of correlation clustering and DNA micro array analysis. The efficacy of these techniques is demonstrated by a series of experiments on synthetic and real data sets. Most clustering methods focus only on the grouping aspects of clustering and lack the ability to provide any scientific content. In this thesis, we also present two linear manifold based modeling techniques that deliver scientific content and with which data can be described. One based on a probabilistic density estimation model with which statistical inference such as predictions can be based upon. The other, a model which describes the linear dependencies in the data in the form of a set of linear equations.
Exploiting Cluster-Structure to Predict the Labeling of a Graph
Authors: Mark Herbster
Venue: Proceedings of the 19th international conference on Algorithmic Learning Theory (2008)

The nearest neighbor and the perceptron algorithms are intuitively motivated by the aims to exploit the "cluster" and "linear separation" structure of the data to be classified, respectively. We develop a new online perceptron-like algorithm, Pounce, to exploit both types of structure. We refine the usual margin-based analysis of a perceptron-like algorithm to now additionally reflect the cluster-structure of the input space. We apply our methods to study the problem of predicting the labeling of a graph. We find that when both the quantity and extent of the clusters are small we may improve arbitrarily over a purely margin-based analysis.