ppxscal/embedding_network huggingface dataset:
Vogue: a novel variable order-gap state machine for modeling sequences
Authors: Christopher Carothers, Mohammed Zaki, Boloslaw Szymanski
Venue:  (2006)

In this thesis we present VOGUE, a new state machine that combines two separate techniques for modeling complex patterns in sequential data: data mining and data modeling. VOGUE relies on a novel Variable-Gap Sequence miner (VGS), to mine frequent patterns with different lengths and gaps between elements. It then uses these mined sequences to build the state machine. Moreover, we propose two variations of VOGUE: C-VOGUE that tends to decrease even further the state space complexity of VOGUE by pruning frequent sequences that are artifacts of other primary frequent sequences; and K-VOGUE that allows for sequences to form the same frequent pattern even if they do not have an exact match of elements in all the positions. However, the different elements have to share similar characteristics. We apply VOGUE to the task of protein sequence classification on real data from the PROSITE and SCOP protein families. We show that VOGUEs classification sensitivity outperforms that of higher-order Hidden Markov Models and of HMMER, a state-of-the-art method for protein classification, by decreasing the state space complexity and improving the accuracy and coverage.
These Are Not Your Father's Widgets
Authors: George Lawton
Venue: Computer (2007)

The Internet advertising community is trying something new with an old technology: widgets—portable, lightweight Web applications that can be embedded in an HTML-based page.
A Black Hen Lays White Eggs
Authors: Masayuki Yoshino, Katsuyuki Okeya, Camille Vuillaume
Venue: Proceedings of the 8th IFIP WG 8.8/11.2 international conference on Smart Card Research and Advanced Applications (2008)

This paper proposes novel algorithms for computing double-size modular multiplications with few modulus-dependent precomputations. Low-end devices such as smartcards are usually equipped with hardware Montgomery multipliers. However, due to progresses of mathematical attacks, security institutions such as NIST have steadily demanded longer bit-lengths for public-key cryptography, making the multipliers quickly obsolete. In an attempt to extend the lifespan of such multipliers, double-size techniques compute modular multiplications with twice the bit-length of the multipliers. Techniques are known for extending the bit-length of classical Euclidean multipliers, of Montgomery multipliers and the combination thereof, namely bipartite multipliers. However, unlike classical and bipartite multiplications, Montgomery multiplications involve modulus-dependent precomputations, which amount to a large part of an RSA encryption or signature verification. The proposed double-size technique simulates double-size multiplications based on single-size Montgomery multipliers, and yet precomputations are essentially free: in an 2048-bit RSA encryption or signature verification with public exponent e = 216 + 1, the proposal with a 1024-bit Montgomery multiplier is 1.4 times faster than the best previous technique.
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Detection Classifier
Authors: Yong Feng, Zhong-Fu Wu, Jiang Zhong, Chun-Xiao Ye, Kai-Gui Wu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, training a cosine RBFNN base on gradient descent learning process. Also, the new method is applied for intrusion detection. Experimental results show that the average DR and FPR of our ESIC-based RBFNN detection classifier maintained a better performance than BP, SVM and OLS RBF.
Non-planar shock waves in a magnetic field
Authors: Rajan Arora
Venue: Computers Mathematics with Applications (2008)

The method of multiple time scale is used to obtain the asymptotic solution to the spherically and cylindrically symmetric flow into a perfectly conducting gas permeated by a transverse magnetic field. The transport equations for the amplitudes of resonantly interacting high frequency waves are also found. The evolutionary behavior of non-resonant wave modes culminating into shock waves is studied.
Biological Clustering Method for Logistic Place Decision Making
Authors: Rohani Binti Abu Bakar, Junzo Watada
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

One of the main tasks in supply chain network is to identify the determination of logistic location. The main factors could influence the selections are costs and profits for the company itself. Most appropriate place is urgently essentials in today business world to ensure the company could be more competitive then other competitors in the industry. A lot of considerations should be taken during selecting a location to build a logistic place to serve other retailers city effectively. Currently, there are so many algorithms based on different approaches are proposed by other researchers. Thus, this paper intends to propose DNA computing approach to solve the problem. In this study, a cluster-based approach is employ when all cities are grouped before we choose a right city as distribution center. A case study is presented at the end of this paper to illustrate how the proposed technique works.
On the Effects of Learning Set Corruption in Anomaly-Based Detection of Web Defacements
Authors: Eric Medvet, Alberto Bartoli
Venue: Proceedings of the 4th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2007)

Anomaly detection is a commonly used approach for constructing intrusion detection systems. A key requirement is that the data used for building the resource profile are indeed attack-free, but this issue is often skipped or taken for granted. In this work we consider the problem of corruption in the learning data, with respect to a specific detection system, i.e., a web site integrity checker. We used corrupted learning sets and observed their impact on performance (in terms of false positives and false negatives). This analysis enabled us to gain important insights into this rather unexplored issue. Based on this analysis we also present a procedure for detecting whether a learning set is corrupted. We evaluated the performance of our proposal and obtained very good results up to a corruption rate close to 50%. Our experiments are based on collections of real data and consider three different flavors of anomaly detection.
From Reaction Models to Influence Graphs and Back: A Theorem
Authors: François Fages, Sylvain Soliman
Venue: Proceedings of the 1st international workshop on Formal Methods in Systems Biology (2008)

Biologists use diagrams to represent interactions between molecular species, and on the computer, diagrammatic notations are also more and more employed in interactive maps. These diagrams are fundamentally of two types: reaction graphs and activation/inhibition graphs. In this paper, we study the formal relationship between these graphs. We consider systems of biochemical reactions with kinetic expressions, as written in the Systems Biology Markup Language SBML, and interpreted by a system of Ordinary Differential Equations over molecular concentrations. We show that under a general condition of increasing monotonicity of the kinetic expressions, and in absence of both activation and inhibition effects between a pair of molecules, the influence graph inferred from the stoichiometric coefficients of the reactions is equal to the one defined by the signs of the coefficients of the Jacobian matrix. Under these conditions, satisfied by mass action law, Michaelis-Menten and Hill kinetics, the influence graph is thus independent of the precise kinetic expressions, and is computable in linear time in the number of reactions. We apply these results to Kohn's map of the mammalian cell cycle and to the MAPK signalling cascade. Then we propose a syntax for denoting antagonists in reaction rules and generalize our results to this setting.
From λ-Calculus to Universal Algebra and Back
Authors: Giulio Manzonetto, Antonino Salibra
Venue: Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science (2008)

We generalize to universal algebra concepts originating from &lambda;-calculus and programming to prove a new result on the lattice &lambda;T of &lambda;-theories, and a general theorem of pure universal algebra which can be seen as a meta version of the Stone Representation Theorem. In this paper we introduce the class of Church algebras (which includes all Boolean algebras, combinatory algebras, rings with unit and the term algebras of all &lambda;-theories) to model the "if-then-else" instruction of programming. The interest of Church algebras is that each has a Boolean algebra of central elements, which play the role of the idempotent elements in rings. Central elements are the key tool to represent any Church algebra as a weak Boolean product of indecomposable Church algebras and to prove the meta representation theorem mentioned above. We generalize the notion of easy &lambda;-term of lambda calculus to prove that any Church algebra with an "easy set" of cardinality n admits (at the top) a lattice interval of congruences isomorphic to the free Boolean algebra with n generators. This theorem has the following consequence for the lattice of &lambda;-theories: for every recursively enumerable &lambda;-theory &phi; and each n, there is a &lambda;-theory &phi; n &iquest; &phi; such that {&psi;: &psi; &iquest; &phi; n } "is" the Boolean lattice with 2 n elements.
Towards a Perception-Based Evaluation Model for Spoken Dialogue Systems
Authors: Sebastian Möller, Klaus-Peter Engelbrecht
Venue: Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems (2008)

In order to facilitate the evaluation of advanced spoken dialogue systems (SDSs), we present the architecture for a new quality prediction model. The architecture follows the perception, judgment and action processes which are assumed to take place in a user interacting with a dialogue system. It is pointed out which components of the model are already available, and how they may be improved in the future.
Collaborative Attack Detection in High-Speed Networks
Authors: Martin Rehák, Michal Pěchouček, Pavel Čeleda, Vojtěch Krmíček, Pavel Minařík, David Medvigy
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

We present a multi-agent system designed to detect malicious traffic in high-speed networks. In order to match the performance requirements related to the traffic volume, the network traffic data is acquired by hardware accelerated probes in NetFlow format and preprocessed before processing by the detection agent. The proposed detection algorithm is based on extension of trust modeling techniques with representation of uncertain identities, context representation and implicit assumption that significant traffic anomalies are a result of potentially malicious action. In order to model the traffic, each of the cooperating agents uses an existing anomaly detection method, that are then correlated using a reputation mechanism. The output of the detection layer is presented to operator by a dedicated analyst interface agent, which retrieves additional information to facilitate incident analysis. Our performance results illustrate the potential of the combination of high-speed hardware with cooperative detection algorithms and advanced analyst interface.
The Linear Programming Polytope of Binary Constraint Problems with Bounded Tree-Width
Authors: Meinolf Sellmann, Luc Mercier, Daniel H. Leventhal
Venue: Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2007)

We show how to efficiently model binary constraint problems (BCP) as integer programs. After considering tree-structured BCPs first, we show that a Sherali-Adams-like procedure results in a polynomial-size linear programming description of the convex hull of all integer feasible solutions when the BCP that is given has bounded tree-width.
Joint Segmentation of Thalamic Nuclei from a Population of Diffusion Tensor MR Images
Authors: Ulas Ziyan, Carl-Fredrik Westin
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Several recent studies explored the use of unsupervised segmentation methods for segmenting thalamic nuclei from diffusion tensor images. These methods provide a plausible segmentation on individual subjects; however, they do not address the problem of consistently identifying the same functional areas in a population. The lack of correspondence between the segmented nuclei make it more difficult to use the results from the unsupervised segmentation tools for morphometry. In this paper we present a novel segmentation algorithm to automatically segment the gray matter nuclei while ensuring consistency between subjects in a population. This new algorithm, referred to as Consistency Clustering, finds correspondence between the nuclei as the segmentation is achieved through a single model for the whole population, similar to the brain atlases experts use to identify thalamic nuclei.
Synchronization of Impulsive Fuzzy Cellular Neural Networks with Parameter Mismatches
Authors: Tingwen Huang, Chuandong Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, we study the effect of parameter mismatches on the fuzzy neural networks with impulses. Since it is impossible to make two non-identical neural networks complete synchronized, we study the synchronization of two neural networks in terms of quasi-synchronization. Using Lyapunov method and linear matrix inequality method, we obtain a sufficient condition for a global synchronization error bound of the two neural networks.
A Genetic Algorithm for Solving a Special Class of Nonlinear Bilevel Programming Problems
Authors: Hecheng Li, Yuping Wang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

A special nonlinear bilevel programming problem (BLPP), whose follower-level problem is a convex programming with a linear objective function in y, is transformed into an equivalent single-level programming by using Karush-Kuhn-Tucker (K-K-T) conditions. To solve the equivalent problem effectively, a new genetic algorithm is proposed. First, a linear programming (LP) is constructed to decrease the dimensions of the transformed problem. Then based on a constraint-handling scheme, a second-phase evolving process is designed for some offspring of crossover and mutation, in which the linear property of follower's function is used to generate high quality potential offspring.
Towards a Neural Model of Mental Simulation
Authors: Matthew Hartley, John Taylor
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

We develop a general neural network-based architecture for the process of mental simulation, initially treated at a somewhat abstract level. To develop the theory further it is shown how the theory can handle observational learning as a specific form of mental simulation: simulations are presented of simple paradigms and results obtained on children undergoing tests on observational learning. Questions of learning and other aspects are treated in a discussion section.
A Discriminative Representation for Symbolic Image Similarity Evaluation
Authors: Guanglin Huang, Wan Zhang, Liu Wenyin
Venue: Graphics Recognition. Recent Advances and New Opportunities: 7th International Workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007. Selected Papers (2008)

Visual similarity evaluation plays an important role in intelligent graphics system. A basic problem of it is how to extract the content information of an image and how to describe the information with an intermediate representation, namely, image representation, because the image representation has great influence on the efficiency and performance of the similarity evaluation. In this paper, we focus on the domain of symbolic image recognition and introduce the Directional Division Tree representation, which is the image representation used in our algorithm. The conducted experiment shows that similarity evaluation algorithm based on this representation can yield satisfactory efficiency and performance.
Multilabel classification via calibrated label ranking
Authors: Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker
Venue: Machine Learning (2008)

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.
An Ontology-Based Reconfiguration Agent for Intelligent Mechatronic Systems
Authors: Yazen Al-Safi, Valeriy Vyatkin
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

This paper discusses an ontology-based reconfiguration agent that uses ontological knowledge of the manufacturing environment for the purpose of reconfiguration without human intervention. The current mass customization era requires increased flexibility and agility in the manufacturing systems to adapt changes in manufacturing requirements and environments. Our configuration agent minimizes the overheads of the current reconfiguration process by automating it. It infers facts about the manufacturing environment from the ontological knowledge model and then decides whether the current environment can support the given manufacturing requirements.
Satisficing Approximation Response Model Based on Neural Network in Multidisciplinary Collaborative Optimization
Authors: Ye Tao, Hong-Zhong Huang, Bao-Gui Wu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) approaches, is a two-level optimization method for large-scale and distributed-analysis engineering design problem. In practical application, CO exists some known weaknesses, such as slow convergence, complex numerical computation, which result in further difficulties when modeling the satisfaction degree in CO. This paper proposes the use of approximation response model in place of discipline-level optimization in order to relieve the aforementioned difficulties. In addition, a satisficing back propagation neural network based on multiple-quality and multiple-satisfaction mapping criterion is applied to the design of the satisfaction degree approximation for disciplinary objective. An example of electronic packaging problem is provided to demonstrate the feasibility of the proposed method.
Standards Confusion and Harmonization
Authors: Jeffrey M. Voas, Phillip A. Laplante
Venue: Computer (2007)

Standards blending offers an alternative to absolute harmonization.
Structure Automatic Change in Neural Network
Authors: Han Honggui, Qiao Junfei, Li Xinyuan
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

In this paper, we propose a novel structure automatic change algorithm for neural-network. It can solve the problem that most neural-networks can not change the structure online. This algorithm consists of two main steps: 1) The computation of the neural-network ability to judge whether need to add nodes to the hidden layer or pruning, we use the improved support vector machine (SVM) to decide when and where to change the structure of neural-network hidden layer in this step; 2) Adjusting the parameter of the neural-network, this learning rule for the neural-network is a novel approach based on the modified back-propagation (BP). On the basis of the former methods, we propose a structure automatic changed neural network (SACNN). Finally, the SACNN is applied to track the nonlinear functions, the simulation results show that the results by this neural network perform better than the former growing cell structure (GCS) neural-network.
Text Classification on Embedded Manifolds
Authors: Catarina Silva, Bernardete Ribeiro
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

The problem of overfitting arises frequently in text mining due to high dimensional feature spaces, making the task of the learning algorithms difficult. Moreover, in such spaces visualization is not feasible. We focus on supervised text classification by presenting an approach that uses prior information about training labels, manifold learning and Support Vector Machines (SVM). Manifold learning is herein used as a pre-processing step, which performs nonlinear dimension reduction in order to tackle the curse of dimensionality that occurs. We use Isomap (Isometric Mapping) which allows text to be embedded in a low dimensional space, while enhancing the geometric characteristics of data by preserving the geodesic distance within the manifold. Finally, kernel-based machines can be used with benefits for final text classification in this reduced space. Results on a real-world benchmark corpus from Reuters demonstrate the visualization capabilities of the method in the severely reduced space. Furthermore we show the method yields performances comparable to those obtained with single kernel-based machines.