ppxscal/embedding_network huggingface dataset:
A New Algorithm for On-Line Multivariate ARMA Identification Using Multimodel Partitioning Theory
Authors: Stylianos Sp. Pappas, Nicholas Harkiolakis, Panagiotis Karampelas, Lambros Ekonomou, Sokratis K. Katsikas
Venue: Proceedings of the 2008 Panhellenic Conference on Informatics (2008)

In this study an adaptive algorithm for Multivariate (MV) ARMA model order identification and parameter estimation is presented based on the Multi-model Partitioning Theory (MMPT). The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters, each fitting a different order model. The first step will be to select the order of the MV ARMA model using the MPPT, for general (not necessarily Gaussian) data pdf's. Τhe assumption made is that the true model order is \theta (\lambda, \lambda) where \lambda = max (p, q), p is the order of the AR component and q the order of the MA component. The second step will be to estimate the AR and MA coefficients and the actual values of p and q.
The Similarity Computing of Documents Based on VSM
Authors: Qinglin Guo
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

The precision and efficiency of the similarity computing of documents is the foundation and key of other documents processing. In this paper, the DF and TF-IDF algorithms are improved. First, DF's time complexity is linear which suits mass documents processing, but it has the fault that exceptional useful features may be deleted, so we make up that by adding the count of the words at the important places. Second, we rectify the weight of feature by the result of feature selection phase. In this way, we improve the precision of documents similarity without adding much time and space complexity.
Interval-valued probability in the analysis of problems containing a mixture of possibilistic, probabilistic, and interval uncertainty
Authors: Weldon A. Lodwick, K. David Jamison
Venue: Fuzzy Sets and Systems (2008)

A simple definition of interval-valued probability measure (IVPM) is used and its implications are examined for problems in mathematical analysis. In particular, IVPMs are constructed and then used to develop the extension of these measures in such a way that probability, possibility, clouds, and intervals fit within the context of IVP. With the extension principle, integration and product measures that are derived below, mathematical analysis applied to this new structures is enabled. Optimization will be the mathematical analysis used to illustrate the approaches that are developed.
CDGMiner: A New Tool for the Identification of Disease Genes by Text Mining and Functional Similarity Analysis
Authors: Fang Yuan, Yanhong Zhou
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In the post-genomic era, the identification of genes involved in human disease is one of the most important tasks. Disease phenotypes provide a window into the gene function. Several approaches to identify disease related genes based on function annotations have been presented in recent years. Most of them, starting from the function annotations of known genes associated with diseases, however, can not be used to identify genes for diseases without any known pathogenic genes or related function annotations. We have built a new system, CDGMiner, to predict genes associated with these diseases which lack detailed function annotations. CDGMiner is implemented mainly by two phases, text mining and functional similarity analysis. The performance of CDGMiner was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the OMIM database. Our results show that, on average, the target gene was in the top 13.60%, and the target gene was in the top 5% with a 40.70% chance. CDGMiner shows promising performance compared to other existing tools.
An Excel spreadsheet to recast analyses of garnet into end-member components, and a synopsis of the crystal chemistry of natural silicate garnets
Authors: Andrew J. Locock
Venue: Computers Geosciences (2008)

A Microsoft Excel spreadsheet has been programmed that allows users to calculate with ease the molar proportions of garnet end-members from chemical analyses. Recent advances in the understanding of the crystal chemistry of natural garnets, especially of the Ti-bearing garnets, are used to evaluate 29 end-members (15 species and 14 hypothetical end-members) for each analysis. The amounts of Fe^2^+ and Fe^3^+ (and Mn^3^+, if necessary) are calculated by stoichiometric constraints if these quantities have not been measured. The input data can include: SiO"2, TiO"2, ZrO"2, SnO"2, Y"2O"3, Al"2O"3, Sc"2O"3, Cr"2O"3, V"2O"3, FeO, Fe"2O"3, MnO, MgO, CaO, Na"2O, H"2O+ and F. The spreadsheet can be used with large data sets (up to 100 analyses at a time), and is accompanied by results calculated for 470 garnet analyses taken from the literature. The spreadsheet employs a simple scoring algorithm to measure the quality of a garnet analysis. The propagation of error from the input chemical data to the calculation of end-member proportions is also discussed briefly.
Finite Model Reasoning on UML Class Diagrams Via Constraint Programming
Authors: Marco Cadoli, Diego Calvanese, Giuseppe Giacomo, Toni Mancini
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Finite model reasoning in UML class diagrams is an important task for assessing the quality of the analysis phase in the development of software applications in which it is assumed that the number of objects of the domain is finite. In this paper, we show how to encode finite model reasoning in UML class diagrams as a constraint satisfaction problem (CSP), exploiting techniques developed in description logics. In doing so we set up and solve an intermediate CSP problem to deal with the explosion of "class combinations" arising in the encoding. To solve the resulting CSP problems we rely on the use of off-the-shelf tools for constraint modeling and programming. As a result, we obtain, to the best of our knowledge, the first implemented system that performs finite model reasoning on UML class diagrams.
Fusion of Palmprint and Iris for Personal Authentication
Authors: Xiangqian Wu, David Zhang, Kuanquan Wang, Ning Qi
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Traditional personal authentication methods have many instinctive defects. Biometrics is an effective technology to overcome these defects. The unimodal biometric systems, which use a single trait for authentication, can result in some problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. These problems can be addressed by using multi-biometric features in the system. This paper investigates the fusion of palmprint and iris for personal authentication. The features of the palmprint and the iris are first extracted and matched respectively. Then these matching distances are normalized. Finally, the normalized distances are fused to authenticate the identity. The experimental results show that combining palmprint and iris can dramatically improve the accuracy of the system.
The Input Pattern Order Problem: Evolution of Combinatorial and Sequential Circuits in Hardware
Authors: Martin Trefzer, Tüze Kuyucu, Andrew Greensted, Julian F. Miller, Andy M. Tyrrell
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

Evolution is particularly good at finding specific solutions, which are only valid for exactly the input and environment that are presented during evolution. In most evolution experiments the input pattern order problem is not considered, even though the ability to provide a correct result for any input pattern is a prerequisite for valid circuits. Therefore, the importance of including randomness in the input pattern applied during evolution is addressed in this paper. This is shown to be mandatory--particularly in the case of unconstrained intrinsic evolution of digital circuits--in order to find valid solutions. The different ways in which unconstrained evolution and constrained evolution exploit resources of a hardware substrate are compared. It is also shown that evolution benefits from versatile input configurations. Furthermore, hierarchical fitness functions, previously introduced to improve the evolution of combinatorial circuits, are applied to the evolution of sequential circuits.
Service-Oriented Enterprise Architectures: Evolution of Concepts and Methods
Authors: Gregor Engels, Martin Assmann
Venue: Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference (2008)

This paper depicts the evolution of enterprise architectures to their today often used service-oriented form and presents a state-of-the-art development process for this kind of architecture. The development process covers both the development of business architecture as well as the appropriate software architecture. While showing up a possible form of further evolution of enterprise architectures, we identify the major challenges for future development methods of enterprise architectures.
Graph coloring algorithms for tdma scheduling in wireless sensor networks
Authors: Timothy Henry
Venue:  (2007)

Wireless Sensor Networks (WSN) as an emerging technique has received increasing attention in both research and application field. Since these Micro-Electro-Mechanical systems (MEMS) are constrained by power, physical size, and wireless communication range, efficient use of the technique leaves many challenges for researchers. One of the challenges comes from the medium access control (MAC), which determines the physical and network layer communication model within the network. In this research, we focus on MAC protocol, investigate into the mathematical model of the MAC protocols in graph theory, and develop the following algorithms that would improve the performance of data delivery applications in wireless sensor networks. First, algorithms for TDMA (Time-Division Multiple Access) based conflict-free MAC protocols in wireless sensor networks are developed. These algorithms are based on a novel graph coloring technique called the Color Constraint Heuristic (CCH). The algorithms create a near optimal TDMA frame size for the system, which in turn produces a near-optimal periodic data throughput. Both a centralized TDMA slot assignment (CSA) algorithm that uses CCH and assumes knowledge of the entire network topology and a distributed version of the algorithm, Distributed Slot Assignment (DSA) are developed and presented in this work. Second, another TDMA coloring scheme for data aggregation applications in wireless sensor networks is presented. With careful investigation on data aggregation applications in sensor networks, a theoretical lower bound for any TDMA based protocol was found. By an in-depth comparison of node based coloring and edge based coloring, a hybrid coloring algorithm called RAC-CT (Routing Aware Coloring - Conflict Tolerant) is developed. Thirdly, we present a mathematical approach to show that the total number of colors TDMA would use is bounded by a 13 approximation of &omega;(G) and also it converges to 2&Delta;(G) in a grid random in any type of sensor networks deployment. Analytical and experimental results show that our algorithms are not only affordable in implementation, but also, yield near optimal performance for data query applications in wireless sensor networks.
New Results on Combination Methods for Boosting Ensembles
Authors: Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

The design of an ensemble of neural networks is a procedure that can be decomposed into two steps. The first one consists in generating the ensemble, i.e., training the networks with significant differences. The second one consists in combining properly the information provided by the networks. Adaptive Boosting, one of the best performing ensemble methods, has been studied and improved by some authors including us. Moreover, Adaboost and its variants use a specific combiner based on the error of the networks. Unfortunately, any deep study on combining this kind of ensembles has not been done yet. In this paper, we study the performance of some important ensemble combiners on ensembles previously trained with Adaboost and Aveboost. The results show that an extra increase of performance can be provided by applying the appropriate combiner.
Producing Short Counterexamples Using "Crucial Events"
Authors: Sujatha Kashyap, Vijay K. Garg
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

Ideally, a model checking tool should successfully tackle state space explosion for complete system validation, while providing short counterexamples when an error exists. Techniques such as partial order (p.o.) reduction [1,2] are very effective at tackling state space explosion, but do not produce short counterexamples. On the other hand, directed model checking [3,4] techniques find short counterexamples, but are prone to state space explosion in the absence of errors. To the best of our knowledge, there is currently no single technique that meets both requirements. We present such a technique in this paper.For a subset of CTL, which we call CETL (Crucial Event Temporal Logic), we show that there exists a unique minimum set of events in each program trace whose execution is both necessary and sufficient to lead to an error state. These events are called "crucial events". We show how crucial events can be used to produce short counterexamples, while also providing state space reduction.We have implemented the techniques presented here as an extension to the model checker SPIN, called SPICED (Simple PROMELA Interpreter with Crucial Event Detection). Experimental results are presented.
Estimating parameters of the synchronous twofold-stochastic flow of events
Authors: I. V. Bushlanov, A. M. Gortsev, L. A. Nezhel'Skaya
Venue: Automation and Remote Control (2008)

Consideration was given to estimation of the parameters of the synchronous twofold-stochastic flow of events which makes up a mathematical model of the information flow of demands circulating in the queuing systems and networks. Two variants were studied. For the first variant, the problem of optimal estimation of the parameters of a synchronous flow of events with a finite arbitrary number of states was solved. The second variant differs in that the event flow operates in the environment where part of events is lost during the so-called dead time. Consideration was given to a synchronous flow with two states. The problem of estimating the length of the dead time in the conditions of continued dead time was solved. The results of numerical estimation based on the simulation model of the synchronous flow were presented for both variants.
Simulation-Based Optimization Approach for Software Cost Model with Rejuvenation
Authors: Hiroyuki Eto, Tadashi Dohi, Jianhua Ma
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

Software rejuvenation is a preventive and proactive maintenance solution that is particularly useful for counteracting the phenomenon of software aging. In this paper we consider an operational software system with multiple degradations and derive the optimal software rejuvenation policy minimizing the expected operation cost per unit time in the steady state, via the dynamic programing approach. Especially, we develop a reinforcement learning algorithm to estimate the optimal rejuvenation schedule adaptively and examine its asymptotic properties through a simulation experiment.
An SVM-Based Algorithm for Classifying Promoter-Associated CpG Islands in the Human and Mouse Genomes
Authors: Leng Han, Ruolin Yang, Bing Su, Zhongming Zhao
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

CpG islands (CGIs) are clusters of CpG dinucleotides in GC-rich regions and represent an important gene feature of mammalian genomes. Several algorithms have been developed to identify CGIs. Here we applied Support Vector Machine (SVM), a machine learning approach, to classify CGIs that are associated with the promoter regions of genes. We demonstrated that our SVM-based algorithm had much higher sensitivity and specificity in classifying promoter-associated CGIs than other algorithms, and had high reliability. The advantages of SVM in our method and future improvements were discussed.
The Portfolio Selection Model of Oil/Gas Projects Based on Real Option Theory
Authors: Zefu Lin, Jianyue Ji
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Portfolio selection of oil/ gas projects is a fundamental subject of capital budgeting in the energy sector. In industry, strategic planning requires the selection of a portfolio of projects to advance the corporate goals. The term portfolio selection is more devoted to a set of financial assets that are easily traded and divisible. The principles and fundaments of portfolio selection can be used to compare strategies of investments, so that firms get maximum returns. In this paper, we discuss the oil/gas projects valuation methods first. Considering the characteristic of the oil/gas project, Real options method is more suitable for the oil/gas project than NPV method, and then a new model extended from the Black-Scholes model is built up. Finally, this paper put forward the portfolio selection model and gives a Computational case.
Maximal-Robustness-Minimal-Fragility Controller: A Compromise between Robustness and Fragility of Biochemical Networks
Authors: Ming-Guang Shi, Michael R. Lyu, Tat-Ming Lok
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Establishing a trade-off between robustness and fragility has been an active research topic in constructing biochemical networks. In this paper, we formulate a compromise between robustness and fragility as a cooperative game, based on which a dynamical incomplete information game is constructed. In addition, three channels are chosen as players, and eight pure control strategies are created. Algorithms in seeking the perfect Bayesian-Nash equilibrium are consequently constructed. Based on the perfect Bayesian-Nash equilibrium, Maximal-Robustness-Minimal-Fragility controller (MRMFc) is derived, and MRMFc is effectively applied to biochemical networks. And computer simulations demonstrate that the biochemical network achieves a good balance between robust stability and dynamical performance. Consequently, an attractive solution in attacking the problem of the trade-offs between robustness and fragility is therefore laid out in this paper.
Real-Life Emotion Recognition in Speech
Authors: Laurence Devillers, Laurence Vidrascu
Venue: Speaker Classification II: Selected Projects (2007)

This article is dedicated to Real-life emotion detection using a corpus of real agent-client spoken dialogs from a medical emergency call center. Emotion annotations have been done by two experts with twenty verbal classes organized in eight macro-classes. Two studies are reported in this paper with the four macro classes: Relief, Anger, Fear and Sadness: the first investigates automatic emotion detection using linguistic information whith a detection score of about 78% and a very good detection of Relief, whereas the second investigates emotion detection with paralinguistic cues with 60% of good detection, Fear being best detected.
Non-linear dynamics in multiagent reinforcement learning algorithms
Authors: Sherief Abdallah, Victor Lesser
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions according to a probability distribution). Weighted Policy Learner (WPL) is a MARL algorithm that belongs to this subset and was shown, experimentally in previous work, to converge and outperform previous MARL algorithms belonging to the same subset. The main contribution of this paper is analyzing the dynamics of WPL and showing the effect of its non-linear nature, as opposed to previous MARL algorithms that had linear dynamics. First, we represent the WPL algorithm as a set of differential equations. We then solve the equations and show that it is consistent with experimental results reported in previous work. We finally compare the dynamics of WPL with earlier MARL algorithms and discuss the interesting differences and similarities we have discovered.
Ti and Ti/Sb ohmic contacts on n-type 6H-SiC
Authors: Bohumil Barda, Petr Macháč, Marie Hubičková
Venue: Microelectronic Engineering (2008)

Ohmic contacts consisting of Ti and Ti/Sb were prepared on silicon and carbon face of 6H-SiC with different doping densities. Ni contacts were used as reference. All structures were gradually annealed at different temperatures. Specific contact resistance was measured and morphology was monitored after each annealing. It was found that the same metallization has different properties on different 6H-SiC polar faces. Sb addition to Ti contact helped to reach lower values of specific contact resistance on Si-faces of substrates. On C-faces of substrates, pure titanium contacts were comparable or better than Ti/Sb. Annealing at 960^oC and 1065^oC caused contact morphology deterioration and surface spreading of Ti and Ti/Sb contacts. Ni contacts kept good morphology at all annealing temperatures and had the best values of specific contact resistance after annealing at 960^oC and 1065^oC. Using XPS profiling only small amount of free carbon was found in Ti-based contacts.
Extending the geometric build-up algorithm for the molecular distance geometry problem
Authors: Ricardo dos Santos Carvalho, Carlile Lavor, Fábio Protti
Venue: Information Processing Letters (2008)

One of the most important problems in computational biology is the determination of the three-dimensional structure of a protein using the amino acid sequence that generates it. This task can be experimentally performed by using NMR techniques. However, NMR data usually provide only a sparse set of distances between atoms of a molecule. In this case, the aim is to determine its three-dimensional structure using a set of distances for only some pairs of atoms. This problem is known as the Molecular Distance Geometry Problem (MDGP). This work extends the Geometric Build-up Algorithm (GBA) by Dong and Wu, proposed to solve instances of the MDGP. Computational results show that the presented approach, the Extended Geometric Build-up Algorithm (EGBA), is capable of dealing with instances not previously solved by the GBA.