ppxscal/embedding_network huggingface dataset:
Bifurcation Angles in Ant Foraging Networks: A Trade-Off between Exploration and Exploitation?
Authors: Luc Berthouze, Alexander Lorenzi
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

The distribution of bifurcation angles found in ant foraging networks has been shown to give polarity to the networks so that nest-bound ants reaching a bifurcation can choose the appropriate direction. In this paper, we use an individual-based model to test the hypothesis that this distribution is an emergent property of a population of foraging ants optimising the trade-off between exploitation of the existing network to maximise food intake and exploration of the environment to maximise the population's ability to rapidly adapt to novel or changing environments. We identify a parameter regulating an ant's drives to forage existing trails and explore uncovered areas of the environment as a collective variable controlling the distribution of bifurcation angles in the foraging network and we show that when the exploration-exploitation trade-off is realised, the resulting distribution exhibits the same informational characteristics as that found in the original study.
A Density Based Clustering for Node Management in Wireless Sensor Network
Authors: Md. Obaidur Rahman, Byung Goo Choi, Md. Mostafa Monowar, Choong Seon Hong
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

This paper represents a new clustering approach for wireless sensor network. It is a decentralized algorithm having the topology control information in each sensor node. A post leader selection algorithm is acted upon each of the clusters just after their formation. Experimental validation shows that the proposed scheme is an efficient approach for sensor node management.
The Verification of the On-Chip COMA Cache Coherence Protocol
Authors: Thuy Duong Vu, Li Zhang, Chris Jesshope
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

This paper gives a correctness proof for the on-chip COMA cache coherence protocol that supports the Microgrid of microthreaded architecture, a multi-core architecture capable of integrating hundreds to hundreds of thousands of processors on single silicon chip. We use the Abstract State Machine (ASM) as a theoretical framework for the specification of the on-chip COMA cache coherence protocol. We show that the protocol obeys the Location Consistency model proposed by Gao and Sakar.
On Efficiency Group Multicasting Algorithm with Multiple Minimum Steiner Trees
Authors: Moonseong Kim, Minseok Kang, Hyunseung Choo, Jong S. Yang, Young-Cheol Bang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In this paper, we study the problem of constructing minimum cost group multicast trees with bandwidth reservations. Our algorithm uses multiple candidate paths to select a path from source to each destination member in each multicast tree construction. We show that our proposed algorithm performs better in terms of total tree cost for real life networks over well-known algorithm GKMB. The enhancement is up to about 10% ~ 25% in terms of normalized surcharge for the GKMB tree cost.
Evolutionary Bi-objective Learning with Lowest Complexity in Neural Networks: Empirical Comparisons
Authors: Yamina Mohamed Ben Ali
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

This paper introduces a new study in evolutionary computation technique in order to learn optimal configuration of a multilayer neural network. Inspired from thermodynamic perception, the used evolutionary framework undertakes the optimal configuration problem as a Bi-objective optimization problem. The first objective aims to learn optimal layer topology by considering optimal nodes and optimal connections by nodes. Second objective aims to learn optimal weights setting. The evaluation function of both concurrent objectives is founded on an entropy function which leads the global system to optimal generalization point. Thus, the evolutionary framework shows salient improvements in both modeling and results. The performance of the required algorithms was compared to estimations distribution algorithms in addition to the Backpropagation training algorithm. 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
Parsimony-based genetic algorithm for haplotype resolution and block partitioning
Authors: E. James Harner, Elaine M. Eschen
Venue:  (2007)

This dissertation proposes a new algorithm for performing simultaneous haplotype resolution and block partitioning. The algorithm is based on genetic algorithm approach and the parsimonious principle. The multiloculs LD measure (Normalized Entropy Difference) is used as a block identification criterion. The proposed algorithm incorporates missing data is a part of the model and allows blocks of arbitrary length. In addition, the algorithm provides scores for the block boundaries which represent measures of strength of the boundaries at specific positions. The performance of the proposed algorithm was validated by running it on several publicly available data sets including the HapMap data and comparing results to those of the existing state-of-the-art algorithms. The results show that the proposed genetic algorithm provides the accuracy of haplotype decomposition within the range of the same indicators shown by the other algorithms. The block structure output by our algorithm in general agrees with the block structure for the same data provided by the other algorithms. Thus, the proposed algorithm can be successfully used for block partitioning and haplotype phasing while providing some new valuable features like scores for block boundaries and fully incorporated treatment of missing data. In addition, the proposed algorithm for haplotyping and block partitioning is used in development of the new clustering algorithm for two-population mixed genotype samples. The proposed clustering algorithm extracts from the given genotype sample two clusters with substantially different block structures and finds haplotype resolution and block partitioning for each cluster.
MTM2: Scalable Memory Management for Multi-tasking Managed Runtime Environments
Authors: Sunil Soman, Chandra Krintz, Laurent Daynès
Venue: Proceedings of the 22nd European conference on Object-Oriented Programming (2008)

Multi-tasking, managed runtime environments (MREs) for modern type-safe, object-oriented programming languages enable isolated, concurrent execution of multiple applications within a single operating system process. Multi-tasking MREs can potentially extract high-performance on desktop and hand-held systems through aggressive sharing of classes and compiled code, and by exploiting high-level dynamic program information.We investigate the performance of a state-of-the-art multi-taking MRE for concurrent program execution. We find that due to limited support for multi-tasking and performance isolation in the memory management subsystem, multi-tasking performs poorly compared to a production-quality, single-tasking MRE. We present MTM 2: a comprehensive memory management system for concurrent multi-tasking. MTM 2 facilitates performance isolation and efficient heap space usage through on-demand allocation of application-private regions. MTM 2 mitigates fragmentation using a novel hybrid garbage collector that combines mark-sweep with opportunistic copying. Our evaluation shows that MTM 2 improves overall performance, scalability, and footprint for concurrent workloads over state-of-the-art, multi- and single-tasking MREs.
Unbiased Linear Neural-Based Fusion with Normalized Weighted Average Algorithm for Regression
Authors: Yunfeng Wu, S. C. Ng
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Regression is a very important data mining problem. In this paper, we present a new unbiased linear fusion method that combines component predictors so as to solve regression problems. The fusion weighted coefficients assigned are normalized, and updated by estimating the prediction errors between the component predictors and the desired regression values. The empirical results of our regression experiments on five synthetic and four benchmark data sets show that the proposed fusion method improves prediction accuracy in terms of mean-squared error, and also provides the regression curves with better fidelity with respect to normalized correlation coefficients, compared with the popular simple average and weighted average fusion rules.
Scheduler-Assisted Prefetching: Efficient Demand Paging for Embedded Systems
Authors: Stanislav A. Belogolov, Jiyong Park, Jungkeun Park, Seongsoo Hong
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

Embedded systems tend to use demand paging in order to provide more memory to applications in a cost-effective manner. However, demand paging drastically degrades the performance when the page fault rate is high. Prefetching has been known as a common remedy for page fault overhead. Although many prefetching mechanisms have been proposed, they are either effective only for specific page access patterns or too straight-forward to decrease a page fault rate to an acceptable level. We propose a scheduler-assisted prefetching mechanism which does not have such fundamental defects. As a proof of concept, our mechanism was completely implemented in Linux. We have also conducted a series of experiments to show its effectiveness. The experimental results showed a significant improvement: the number of the major page faults and the scheduling latency decreased by 30% and 51%, respectively.
MSD Recombination for Statistical Machine Translation into Highly-Inflected Languages
Authors: Jerneja Žganec-Gros, Stanislav Gruden
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

Freely available tools and language resources were used to build the VoiceTRAN statistical machine translation (SMT) system. Various configuration variations of the system are presented and evaluated. The VoiceTRAN SMT system outperformed the baseline conventional rule-based MT system in both English-Slovenian in-domain test setups. To further increase the generalization capability of the translation model for lower-coverage out-of-domain test sentences, an "MSD-recombination" approach was proposed. This approach not only allows a better exploitation of conventional translation models, but also performs well in the more demanding translation direction; that is, into a highly inflectional language. Using this approach in the out-of-domain setup of the English-Slovenian JRC-ACQUIS task, we have achieved significant improvements in translation quality.
Forest-RK: A New Random Forest Induction Method
Authors: Simon Bernard, Laurent Heutte, Sébastien Adam
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper we present our work on the parametrization of Random Forests (RF), and more particularly on the number K of features randomly selected at each node during the tree induction process. It has been shown that this hyperparameter can play a significant role on performance. However, the choice of the value of K is usually made either by a greedy search that tests every possible value to choose the optimal one, either by choosing a priori one of the three arbitrary values commonly used in the literature. With this work we show that none of those three values is always better than the others. We thus propose an alternative to those arbitrary choices of K with a new "push-button" RF induction method, called Forest-RK, for which K is not an hyperparameter anymore. Our experimentations show that this new method is at least as statistically accurate as the original RF method with a default K setting.
The Use of High-Level 3-D Graphical Types in the Mira Animation System
Authors: Nadia Magnenat-Thalmann, Daniel Thalmann
Venue: IEEE Computer Graphics and Applications (1983)

A graphical extension of Pascal enables programmers to build complex 3-D objects and to describe their motions.
Detection of Microcalcifications Using a Nonuniform Noise Model
Authors: Guido Schie, Nico Karssemeijer
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

For the detection of microcalcifications in mammograms, accurate noise estimation is of crucial importance. In this paper we present a method that makes a robust estimate of the signal dependent image noise, by taking into account quantum noise and detector inhomogeneity. In digital mammograms, high frequency image noise is dominated by quantum noise, which in raw images can be described by a square root model where the noise is proportional to the pixel value. However, due to detector inhomogeneity, the anode heel effect and other sources of variation, noise properties vary across an image. We developed a method that deals with these effects in a general way, by making a nonuniform noise model that is pixel value and location dependent. This is established by subdividing the image into tiles. In each tile a square root model of the noise is estimated, that by interpolation gives a model of the noise as a function of pixel value and location. Results indicate some improvement in microcalcification detection, when this model is used to estimate noise in images acquired with an inhomogeneous detector.
Towards an Ontology to Conceptualize Solution Analysis Tasks in CSCL Environments
Authors: Rafael Duque, Crescencio Bravo, Manuel Ortega
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

New technologies based on meta-models and ontology engineering allow the formalization and conceptualization of the components that take part in the process of collaboration and interaction analysis in CSCL (Computer-Supported Collaborative Learning) environments. In this article, a proposal to characterize the process of analysis of solutions in CSCL environments is made by means of an ontology. These solutions are built by the learners through a process of collaboration following a problem solving approach.
Harvesting Relational and Structured Knowledge for Ontology Building in the WPro Architecture
Authors: Daniele Bagni, Marco Cappella, Maria Teresa Pazienza, Marco Pennacchiotti, Armando Stellato
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

We present two algorithms for supporting semi-automatic ontology building, integrated in WPro, a new architecturefor ontology learning from Web documents. The first algorithm automatically extracts ontological entities from tables, by using specific heuristics and WordNet-based analysis. The second algorithm harvests semantic relations from unstructured texts using Natural Language Processing techniques. The integration in WPro allows a friendly interaction with the user for validating and modifying the extracted knowledge, and for uploading it into an existing ontology. Both algorithms show promising performance in the extraction process, and offer a practical means to speed-up the overall ontology building process.
The Condition of Kernelizing an Algorithm and an Equivalence Between Kernel Methods
Authors: Wenan Chen, Hongbin Zhang
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

For a learning algorithm, especially a linear algorithm, it can usually be extended to its kernel version endowed with the power of extracting non-linear features. In this paper, we explore two key questions in the kernelization of an algorithm. The first is the existence of the kernel version of an algorithm. We propose a new method to determine whether an algorithm can be kernelized. It has the advantage that it is not limited by the specific form of the algorithm and shows an insight view of kernelization. The second question is how to kernelize an algorithm. We prove a kind of equivalence between two kernelization processes. Related details are also discussed.
The Self-synchronizing Stream Cipher Moustique
Authors: Joan Daemen, Paris Kitsos
Venue: New Stream Cipher Designs: The eSTREAM Finalists (2008)

We present a design approach for hardware-oriented self-synchronizing stream ciphers and illustrate it with a concrete design called Moustique. The latter is intended as a research cipher: it proves that the design approach can lead to concrete results and will serve as a target for cryptanalysis where new attacks may lead to improvements in the design approach such as new criteria for the cipher building blocks.
Cluster Selection Based on Coupling for Gaussian Mean Fields
Authors: Yarui Chen, Shizhong Liao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

Gaussian mean field is an important paradigm of cluster-based variational inference, and its cluster selection is critical to the tradeoff between the variational accuracy and the computational complexity of cluster-based variational inference. In this paper, we explore a coupling based cluster selection method for Gaussian mean fields. First, we propose the model coupling and the quasi-coupling concepts on Gaussian Markov random field, and prove the coupling-accuracy theorem for Gaussian mean fields, which regards the quasi-coupling as a cluster selection criterion. Then we design a normalized cluster selection algorithm based on the criterion for Gaussian mean fields. Finally, we design numerical experiments to demonstrate the validity and efficiency of the cluster selection method and algorithm.
Machine learning for host-based anomaly detection
Authors: Philip K. Chan
Venue:  (2008)

Anomaly detection techniques complement, signature based methods for intrusion detection. Machine learning approaches are applied to anomaly detection for automated learning and detection. Traditional host-based anomaly detectors model system call sequences to detect novel attacks. This dissertation makes four key contributions to detect host anomalies. First, we present an unsupervised approach to clean training data using novel representations for system call sequences. Second, supervised learning with system call arguments and other attributes is proposed for enriched modeling. Third, techniques to increase model coverage for improved accuracy are presented. Fourth, we propose spatio-temporal modeling to detect suspicious behavior for mobile hosts. Experimental results on various data sets indicate that our techniques are more effective than traditional methods in capturing attack-based host anomalies. Additionally, our supervised methods create succint models and the computational overhead incurred is reasonable for an online anomaly detection system.
Cooperative search for optimizing pipeline operations
Authors: T. Mora, A. B. Sesay, J. Denzinger, H. Golshan, G. Poissant, C. Konecnik
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track (2008)

We present an application of a multi-agent cooperative search approach to the problem of optimizing gas pipeline operations, i.e. finding control parameters for a gas transmission network that result in a low usage of energy to make the required gas deliveries. Our cooperative search approach improves on the pure competition of search agents by having them exchange good solutions from time to time that both are integrated into the search state of the agents and used to improve the search control of the agents. Our experimental evaluation with real problem instances from TransCanada show that our system meets TransCanada's time requirements and reliably outperforms the interactive method that is the current state-of-the-art by creating solutions that require more than 10 percent less energy.
Automatic Synthesis of Decision Procedures: A Case Study of Ground and Linear Arithmetic
Authors: Predrag Janičić, Alan Bundy
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

We address the problem of automatic synthesis of decision procedures. Our synthesis mechanism consists of several stages and sub-mechanisms and is well-suited to the proof-planning paradigm. The system (adeptus), that we present in this paper, synthesised a decision procedure for ground arithmetic completely automatically and it used some specific method generators in generating a decision procedure for linear arithmetic, in only a few seconds of cpu time. We believe that this approach can lead to automated assistance in constructing decision procedures and to more reliable implementations of decision procedures.
Exploring Social Networks in Request for Proposal Dynamic Coalition Formation Problems
Authors: Carlos Merida-Campos, Steven Willmott
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

In small scale multi-agent environments, every agent is aware of all of the others. This allows agents to evaluate the potential outcomes of their interaction for each of their possible interaction partners. However, this farsighted knowledge becomes an issue in large scale systems, leading to a combinatorial explosion in evaluation and is unrealistic in communication terms. Limited awareness of other agents is therefore the only plausible scenario in many large-scale environments. This limited awareness can be modeled as a sparse social network in which agents only interact with a limited subset of agents known to them. In this paper, we explore a model of dynamic multi-agent coalition formation in which agents are connected via fixed underlying social networks that exhibit different well known structures such as Small World, Random and Scale Free topologies. Agents follow different exploratory policies and are distributed in the network according to a variety of metrics. The primary results of the paper are to demonstrate different positive and negative properties of each topology for the coalition formation problem. In particular we show that despite positive properties for many problems, Small World topologies introduce blocking factors which hinder the emergence of good coalition solutions in many configurations.
Fuzzy Real Option Analysis for IT Investment in Nuclear Power Station
Authors: Chen Tao, Zhang Jinlong, Liu Shan, Yu Benhai
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

This paper develops a comprehensive but simple methodology to valuate information technology investment in a nuclear power station based on fuzzy risk analysis and real option approach. By linking the variability of expected payoffs to specific sources of risk factors, this method could help decision makers achieve a more reliable valuation of the target investment under multiple sources of risk, and avoid complex estimation tasks at the same time. The proposed approach can be easily adopted to value any irreversible investment subject to a high degree of uncertainty that is common in the field of energy economics.