ppxscal/embedding_network huggingface dataset:
Achieving Self-management in a Distributed System of Autonomic BUT Social Entities
Authors: N. Samaan
Venue: Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments (2008)

This paper presents a novel self-management model for resource allocation in an autonomic system (AS) comprised of individual, but social, autonomic entities (AEs). Each AE is associated with an interdependent utility function that, not only models its utility over its resource allocations, but also depends on other AEs allocations and, hence, the global AS welfare. Pervious utility-based approaches are limited to representing the AS as a set of independent AEs that aim at self-optimizing their performance unaware of other AEs' behavior. In contrast to these dominant approaches, the proposed scheme efficiently models various social behaviors, such as cooperation, selfishness and competition, among those AEs to dynamically change the overall resource allocations in different scenarios such as in the case of anomalies or varying service demands. These behavior patterns are incorporated into the utility function of each AE which is composed of two components, local and global utilities. The former reflects the AE's utility of its resource consumption while the latter is dependent on the other AEs' consumptions. By controlling these utilities, AEs create a social community where they lend/borrow resources and reward/punish other well/mal- behaving AEs. Experimental results demonstrate that creating such a social AS is more efficient than simplified systems of independent utilities.
KCMAC: A Novel Fuzzy Cerebellar Model for Medical Decision Support
Authors: S. D. Teddy
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Most of the current advanced clinical decision support systems rely on some form of computational intelligence methodologies. As the machine intelligence paradigm shifted towards brain-inspired computing approach, it is interesting to investigate the performance of such a computing methodology in clinical data analysis. The human cerebellum constitutes a vital part of the brain system that possesses the capability to accurately model highly nonlinear physical dynamics. This paper presents a novel brain-inspired computational model of the human cerebellum named the kernel density-based CMAC (KCMAC) model for clinical decision support. The structure of the KCMAC model is inspired by the neurophysiological aspects of cerebellar learning and development process. The proposed KCMAC model is then applied to two medical case studies; namely, breast cancer diagnosis and the modeling of the human glucose metabolic process. The experimental results are encouraging.
X-SRQ - Improving Scalability and Performance of Multi-core InfiniBand Clusters
Authors: Galen M. Shipman, Stephen Poole, Pavel Shamis, Ishai Rabinovitz
Venue: Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2008)

To improve the scalability of InfiniBand on large scale clusters Open MPI introduced a protocol known as B-SRQ [2]. This protocol was shown to provide much better memory utilization of send and receive buffers for a wide variety of benchmarks and real-world applications.Unfortunately B-SRQ increases the number of connections between communicating peers. While addressing one scalability problem of InfiniBand the protocol introduced another. To alleviate the connection scalability problem of the B-SRQ protocol a small enhancement to the reliable connection transport was requested which would allow multiple shared receive queues to be attached to a single reliable connection. This modified reliable connection transport is now known as the extended reliable connection transport.X-SRQ is a new transport protocol in Open MPI based on B-SRQ which takes advantage of this improvement in connection scalability. This paper introduces the X-SRQ protocol and details the significantly improved scalability of the protocol over B-SRQ and its reduction of the memory footprint of connection state by as much as 2 orders of magnitude on large scale multi-core systems. In addition to improving scalability, performance of latency-sensitive collective operations are improved by up to 38% while significantly decreasing the variability of results. A detailed analysis of the improved memory scalability as well as the improved performance are discussed.
Interactive Dynamic Information Extraction
Authors: Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, Norbert Reithinger
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

The IDEX system is a prototype of an interactive dynamic Information Extraction (IE) system. A user of the system expresses an information request for a topic description which is used for an initial search in order to retrieve a relevant set of documents. On basis of this set of documents unsupervised relation extraction and clustering is done by the system. In contrast to most of the current IE systems the IDEX system is domain-independent and tightly integrates a GUI for interactive exploration of the extraction space.
A Generic Methodology for Classification of Complex Data Structures in Automotive Industry
Authors: Dymitr Ruta
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Driving a vehicle is a complex mixture of real-time processes of cognition and control. Recent advances in pattern recognition and machine learning brought automotive industry to the verge of direct AI applications in vehicles. They would continuously sense the external environment, monitor vehicle's internal systems and track drivers actions in an attempt to support driver's decisions, making them better informed or even take over decision process if the reliability and confidence of perceived safety critical situations outperform human performance. This work intends to contribute to the automated real-time classification methodology suitable for applications in the realistic circumstances of driving a vehicle. It proposes a coherent strategy to a fast extraction of simple but robust features out of complex data structures like images, continuous and discrete signals etc. It advocates the use genetic algorithm for feature selection paired with simple classifiers suitable for further combination at the decision level. The presented generic methodology is open for various data transformations, classifiers, features and scales well with the data size. It has been tested in two independent competitions: NISIS Competition 2007 concerned with automated classification of pedestrian images and Ford Classification Challenge 2008 dedicated to symptoms detection from high-frequency signal patterns. The model was announced the winner of the NISIS'2007 Competition achieving pedestrian recognition rate exceeding 95% and is now under evaluation for the second challenge.
Constructing a User Preference Ontology for Anti-spam Mail Systems
Authors: Jongwan Kim, Dejing Dou, Haishan Liu, Donghwi Kwak
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

The judgment that whether an email is spam or non-spam may vary from person to person. Different individuals can have totally different responses to the same email based on their preferences. This paper presents an innovative approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based approaches. We build a user preference ontology to formally represent the important concepts and rules derived from a data mining process. Then we use an inference engine that utilizes the knowledge to predict the user's action on new incoming emails. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules. Experimental results showed that our user preference based architecture achieved good performance and the rules derived from the architecture and the optimization method have better quality in terms of comprehensibility.
Smart Content Delivery on the Internet
Authors: Hong Shen
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

As the ever-growing Internet applications in the emerging e-society are centered in sharing of various types of digital contents, content delivery on the Internet as a hot topic has been attracting most attentions. Web caching is an important technology for improving the efficiency of content delivery. As an emerging technology en-route caching computes locations among caches on access paths to store copies of an object such that specified objectives (such as satisfying future access demands to the object) are achieved. In this talk, I will first give an overview on recent developments in efficient content delivery. I will then introduce our recent work in tackling this problem by applying the technique of en-route caching, and present efficient solutions to the problem in systems containing single server and multiple servers respectively. While the problem is NP-hard in the general case, our solutions apply dynamic programming technique and run in polynomial-time in the worst case for both unconstrained and constrained cases in the network topologies of trees (for broadcast delivery) and linear arrays (for point-to-point delivery) respectively. They are shown theoretically either optimal or convergent to optimal. Finally, I will show some possible extensions of our solutions to other system settings.
Plenary lecture IV: Malliavin calculus of Bismut type in semi-group theory
Authors: Remi Leandre
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

The talk is dovided in 5 parts. In the first part, we translate Malliavin Calculus of Bismut type in semi group theory, giving a sufficient condition in order a diffusion semi-group has a smooth density. In the second part, we translate Varadhan estimates for hypoelliptic diffusions in semi-group theory, estimates we got a long time ago by using the Brownian motion. In the third part, we translate in semi-group theory the Ben Arous and us theorem giving a sufficient condition in order an heat-kernel is strictly positive, where the Bismutian semi-distance plays an important role. In the fourth part, we translate one of the result we got a long time ago by probability by using a stochastic analog of the Rothschild-Stein inhomogeneous division method. In the fifth part, we translate in semigroup Bismut's ways of the Malliavin Calculus for Poisson process where the generator is an integro-differential operator.
Modeling Input Space for Testing Scientific Computational Software: A Case Study
Authors: Sergiy A. Vilkomir, W. Thomas Swain, Jesse H. Poore, Kevin T. Clarno
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

An application of a method of test case generation for scientific computational software is presented. NEWTRNX, neutron transport software being developed at Oak Ridge National Laboratory, is treated as a case study. A model of dependencies between input parameters of NEWTRNX is created. Results of NEWTRNX model analysis and test case generation are evaluated.
The Five-Minute Rule 20 Years Later: and How Flash Memory Changes the Rules
Authors: Goetz Graefe
Venue: Queue (2008)

The old rule continues to evolve, while flash memory adds two new rules. In 1987, Jim Gray and Gianfranco Putzolu published their now-famous five-minute rule for trading off memory and I/O capacity. Their calculation compares the cost of holding a record (or page) permanently in memory with the cost of performing disk I/O each time the record (or page) is accessed, using appropriate fractions of prices for RAM chips and disk drives. The name of their rule refers to the break-even interval between accesses. If a record (or page) is accessed more often, it should be kept in memory; otherwise, it should remain on disk and read when needed.
Structuring Natural Language Data by Learning Rewriting Rules
Authors: Guillaume Cleuziou, Lionel Martin, Christel Vrain
Venue: Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (2007)

The discovery of relationships between concepts is a crucial point in ontology learning (OL). In most cases, OL is achieved from a collection of domain-specific texts, describing the concepts of the domain and their relationships. A natural way to represent the description associated to a particular text is to use a structured term (or tree). We present a method for learning transformation rules, rewriting natural language texts into trees, where the input examples are couples (text, tree). The learning process produces an ordered set of rules such that, applying these rules to a text gives the corresponding tree.
General Particle Swarm Optimization Based on Simulated Annealing for Multi-specification One-Dimensional Cutting Stock Problem
Authors: Xianjun Shen, Yuanxiang Li, Bojin Zheng, Zhifeng Dai
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

In this paper a general particle swarm optimization based on simulated annealing algorithm (SA-GPSO) for the solution to multi-specification one-dimensional cutting stock problem is proposed. Due to the limitation of its velocity-displacement search model, particle swarm optimization (PSO) has less application on discrete and combinatorial optimization problems effectively. SA-GPSO is still based on PSO mechanism, but the new updating operator is developed from crossover operator and mutation operator of genetic algorithm. In order to repair invalid particle and reduce the searching space, best fit decrease (BFD) is introduced into repairing algorithm of SA-GPSO. According to the experimental results, it is observed that the proposed algorithm is feasible to solve both sufficient one-dimensional cutting problem and insufficient one-dimensional cutting problem.
A New Approach for Next Day Load Forecasting Integrating Artificial Neural Network Model with Weighted Frequency Bin Blocks
Authors: M. Kurban, U. Basaran Filik
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

In this study, a new method is developed for the next day load forecasting integrating Artificial Neural Network(ANN) model with Weighted Frequency Bin Blocks (WFBB). After the WFBB is applied to all data, the results obtained from this analysis are used as the inputs in the ANN structure. However, the conventional ANN structure is also used for the next day load forecasting. The forecasting results obtained from ANN structure and the hybrid model are compared in the sense of root mean square error (RMSE). It is observed that the performance and the RMSE values for the hybrid model,the ANN model with WFBB, are smaller than the values for the conventional ANN structure. Furthermore, the new hybrid model forecasts better than the conventional ANN structure. The suitability of the proposed approach is illustrated through an application to actual load data taken from the Turkish Electric Power Company in 2002.
Simulation, Orchestration and Logical Clocks
Authors: David Kitchen, Evan Powell, Jayadev Misra
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

A language in which discrete event simulations can be coded needs to support the features (1) to describe behavior of a single physical process, (2) to describe concurrent ctivities of multiple physical processes, including communication, synchronization and interruption, (3) to account for passage of time, and (4) to record system state at appropriate points and create statistical summaries. Orc, a recent language for orchestration of distributed services, combines these features so that complex simulations can be expressed very succinctly. This talk describes the relevant features of Orc for simulation and illustrates them using a number of realistic examples. Additionally, we show that certain combinatorial problems, such as shortest paths in graphs and many problems in computational geometry, can be cast as simulation problems, and solved very simply in Orc.
The Nordsieck computer
Authors: Arnold Nordsieck
Venue: Proceedings of the February 4-6, 1953, western computer conference (1953)

After some experience with a mechanical differential analyser of the Bush type, the author became convinced that there was a need for a smaller, cheaper instrument of this type with a faster and more convenient setup procedure. The large instruments are so large and expensive mainly because of the torque amplifiers they contain and are somewhat inconvenient to use because, aside from considerations of accessibility to the individual researcher, they have rather long and complicated setup procedures.
A Study on Fast Preparation of Electron Microscope Biological Specimens
Authors: Guo Huashan, Yao Hongjun, Wu Yang, Yang Tian, Zhang Wei, Guo Jianjun
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

The electron microscope is widely used in chemistry, physics and biology fields, especially in biomedicine ultrastructure research. Using the ultrasonic waves, the time that we spend in preparing the biological specimen will be decreased from 5~10 days to 3~5 hours. A few investigation and discussion of fast preparation was proposed in the paper. This work is very significant to biomedicine research and clinic pathology diagnoses aspects. It is better to choose amplitude with low frequency for the purpose of fast preparation.
Implementation of a TCG-Based Trusted Computing in Mobile Device
Authors: Sugil Choi, Jinhee Han, Jeongwoo Lee, Jongpil Kim, Sungik Jun
Venue: Proceedings of the 5th international conference on Trust, Privacy and Security in Digital Business (2008)

Our implementation is aimed at estimating the possibility of employing TCG-based trusted computing mechanisms, such as verifying the code-integrity of executables and libraries at load-time and remote attestation, in mobile devices. Considering the restrained resource in mobile device, the experimentation shows promising results, thereby enabling these mechanisms to be used as a basic building block for a more secured mobile service. To this end, we add a new feature of integrity measurement and verification to Wombat Linux kernel and Iguana embedded OS. We also implement attestation agents, Privacy CA, and TCG Software Stack.
Using the Whittle Estimator for the Selection of an Autocorrelation Function Family
Authors: Maria-Estrella Sousa-Vieira, Andrés Suárez-González
Venue: Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications (2008)

With the increasing popularity of multimedia applications, video data represents a large portion of the traffic in modern networks. Consequently, adequate models of video traffic, characterized by a high burstiness and a strong positive correlation, are very important for the performance evaluation of network architectures and protocols. This paper presents a method that uses the Whittle estimator to choose, between several models for VBR video traffic based on the M/G/&infin; process, the one that gives rise to a better adjustment of the spectral density, and therefore of the correlation structure, of the traffic to model.
Classification of Pathological and Normal Voice Based on Linear Discriminant Analysis
Authors: Ji-Yeoun Lee, Sangbae Jeong, Minsoo Hahn
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

This paper suggests a new method to improve the performance of the pathological/normal voice classification. The effectiveness of the mel frequency-based filter bank energies using the fisher discriminant ratio (FDR) is analyzed. Also, mel frequency cepstrum coefficients (MFCCs) and the feature vectors through the linear discriminant analysis (LDA) transformation of the filter bank energies (FBE) are implemented. In addition, we emphasize the relation between the pathological voice detection and the feature vectors through the FBE-LDA transformation. This paper shows that the FBE LDA-based GMM is a sufficiently distinct method for the pathological/normal voice classification. The proposed method shows better performance than the MFCC-based GMM with noticeable improvement.
Level set methods for optimization problems involving geometry and constraints II. Optimization over a fixed surface
Authors: Emmanuel Maitre, Fadil Santosa
Venue: Journal of Computational Physics (2008)

In this work, we consider an optimization problem described on a surface. The approach is illustrated on the problem of finding a closed curve whose arclength is as small as possible while the area enclosed by the curve is fixed. This problem exemplifies a class of optimization and inverse problems that arise in diverse applications. In our approach, we assume that the surface is given parametrically. A level set formulation for the curve is developed in the surface parameter space. We show how to obtain a formal gradient for the optimization objective, and derive a gradient-type algorithm which minimizes the objective while respecting the constraint. The algorithm is a projection method which has a PDE interpretation. We demonstrate and verify the method in numerical examples.
An Audiovisual Talking Head for Augmented Speech Generation: Models and Animations Based on a Real Speaker's Articulatory Data
Authors: Pierre Badin, Frédéric Elisei, Gérard Bailly, Yuliya Tarabalka
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

We present a methodology developed to derive three-dimensional models of speech articulators from volume MRI and multiple view video images acquired on one speaker. Linear component analysis is used to model these highly deformable articulators as the weighted sum of a small number of basic shapes corresponding to the articulators' degrees of freedom for speech. These models are assembled into an audiovisual talking head that can produce augmented audiovisual speech, i.e. can display usually non visible articulators such as tongue or velum. The talking head is then animated by recovering its control parameters by inversion from the coordinates of a small number of points of the articulators of the same speaker tracked by Electro-Magnetic Articulography. The augmented speech produced points the way to promising applications in the domain of speech therapy for speech retarded children, perception and production rehabilitation of hearing impaired children, and pronunciation training for second language learners.
A Strategy for Drawing a Conceptual Neighborhood Diagram Schematically
Authors: Yohei Kurata
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Conceptual neighborhood diagram is a network diagram that schematizes a set of spatial/temporal relations. This paper proposes a strategy for arranging the relations in a diagrammatic space such that the graph highlights the symmetric structure of the relation set.