ppxscal/embedding_network huggingface dataset:
Understanding how people design trading agents over time
Authors: Efrat Manisterski, Raz Lin, Sarit Kraus
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

As computerized agents are becoming more and more common, e-commerce becomes a major candidate for incorporation of automated agents. Thus, it is vital to understand how people design agents for online markets and how their design changes over time. This, in turn, will enable better design of agents for these environments. We focus on the design of trading agents for bilateral negotiations with unenforceable agreements. In order to simulate this environment we conducted an experiment with human subjects who were asked to design agents for a resource allocation game. The subjects' agents participated in several tournaments against each other and were given the opportunity to improve their agents based on their performance in previous tournaments. Our results show that, indeed, most subjects modified their agents' strategic behavior with the prospect of improving the performance of their agents, yet their average score significantly decreased throughout the tournaments and became closer to the equilibrium agents' score. In particular, the subjects modified their agents to break more agreements throughout the tournaments. In addition, the subjects increased their means of protection against deceiving agents.
Learning Recursive Patterns for Biomedical Information Extraction
Authors: Margherita Berardi, Donato Malerba
Venue: Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (2007)

Information in text form remains a greatly unexploited source of biological information. Information Extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In biomedical IE tasks, extracting patterns that model implicit relations among entities is particularly important since biological systems intrinsically involve interactions among several entities. In this paper, we resort to an Inductive Logic Programming (ILP) approach for the discovery of mutual recursive patterns from text. Mutual recursion allows dependencies among entities to be explored in data and extraction models to be applied in a context-sensitive mode. In particular, IE models are discovered in form of classification rules encoding the conditions to fill a pre-defined information template. An application to a real-world dataset composed by publications selected to support biologists in the task of automatic annotation of a genomic database is reported.
Controlling load distribution in the queuing systems
Authors: Yu. I. Mitrofanov, E. S. Rogachko
Venue: Automation and Remote Control (2008)

A method to control load distribution in the closed exponential queuing networks with one class of customers was proposed. It is based on simultaneous use of routing control and control of servicing intensities. Consideration was given to an evolution model of the queuing network with control of load distribution and an approximate method of analysis of the queuing networks of this type. A technique to calculate the stationary distribution and formulas to calculate other stationary characteristics of the queuing networks with control of load distribution were described. Examples of analysis of the queuing networks of this type were presented.
A Study on Sunspot Number Time Series Prediction Using Quantum Neural Networks
Authors: Xin Li, Chun-Tian Cheng, Wen-Chuan Wang, Feng-Ying Yang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Sunspot number time series, as a multivariable, strong coupling and nonlinear time series, has encountered troubles to describe its changes rules with modeling method owing to great complexity of sunspot number change. The main aim of this study is to develop a novel prediction method, based on the Quantum Neural Networks, which is composed of some quantum neurons and traditional neurons based on certain topology structure and connection rules. 308 years (1700-2007) actual Sunspot Number data are employed for developing prediction model, in which 258 years (1700-1957) are used for training Quantum Neural Networks (QNN) whilst 50 years (1958-2007) are used for testing the predictive ability of the model proposed. Through the comparison of its performance with the Common BP neural networks (CBPNN), it is demonstrated that the QNN model is a more effective method to predict the Sunspot Number time series.
Automatic Detection of Calcified Coronary Plaques in Computed Tomography Data Sets
Authors: Stefan C. Saur, Hatem Alkadhi, Lotus Desbiolles, Gábor Székely, Philippe C. Cattin
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

The detection of calcified plaques is an essential step in the assessment of coronary heart diseases. However, manual plaque segmentation is subjected to intra- and inter-observer variability. We present a novel framework for the automatic detection of calcified coronary plaques in Computed Tomography images. In contrast to the state-of-the-art, both the native and the angio data sets are included to gain additional information about each plaque for its detection and subsequent assessment. The framework was successfully tested on 127 patients where 85.5% of the calcified and 96% of the obstructive plaques have been detected.
Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks
Authors: Zhuo Song, Ning Zhang, Zhuo Yang, Tao Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In prediction of secondary structure of proteins there are always some suspected segments. These suspected segments confuse people and lower the accuracy of prediction methods. To deal with this problem, a set of neural networks (NNs) are built based on helix, strand and coil segments selected from PDB. The test performance of these NNs on training data is perfect without surprise. However the prediction on test data is not good enough because the training data are lake of great representativeness. The results support the fact that closer neighbor vectors have the similar outputs of NNs. One can improve representativeness of training data without enlarging data scale as long as select less data from dense region and more from sparse region on condition that distribution of sample data has been known.
Comparative Evaluation of Rank Correlation Based DPA on an AES Prototype Chip
Authors: Lejla Batina, Benedikt Gierlichs, Kerstin Lemke-Rust
Venue: Proceedings of the 11th international conference on Information Security (2008)

We propose a new class of distinguishers for differential side-channel analysis based on nonparametric statistics. As an example we use Spearman's rank correlation coefficient. We present a comparative study of several statistical methods applied to real power measurements from an AES prototype chip to demonstrate the effectiveness of the proposed method. Our study shows that Spearman's rank coefficient outperforms all other univariate tests under consideration. In particular we note that Pearson's correlation coefficient requires about three times more samples for reliable key recovery than the method we propose. Further, multivariate methods with a profiling step which are commonly assumed to be the most powerful attacks are not significantly more efficient at key extraction than the attack we propose. Our results indicate that power models which are linear in the transition count are not optimal for the attacked prototype chip.
Some New Stability Conditions of Delayed Neural Networks with Saturation Activation Functions
Authors: Wudai Liao, Dongyun Wang, Jianguo Xu, Xiaoxin Liao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Locally and globally asymptotical stability on equilibria of delayed neural networks with saturation activation functions are studied by the Razumikhin-type theorems, which are the main approaches to study the stability of functional differential equations, and some new stability conditions are obtained, which are constructed by the networks' parameters. In the case of local stability conditions, the attracted fields of equilibria are also estimated. All results obtained in this paper need only to compute the eigenvalues of some matrices or to verify some inequalities to be holden.
A New GHSOM Model Applied to Network Security
Authors: Esteban J. Palomo, Enrique Domínguez, Rafael Marcos Luque, José Muñoz
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

The self-organizing map (SOM) have shown to be successful for the analysis of high-dimensional input data as in data mining applications such as network security. However, the static architecture and the lack of representation of hierarchical relations are its main drawbacks. The growing hierarchical SOM (GHSOM) address these limitations of the SOM. The GHSOM is an artificial neural network model with hierarchical architecture composed of independent growing SOMs. One limitation of these neural networks is that they just take into account numerical data, even though symbolic data can be present in many real life problems. In this paper a new GHSOM model with a new metric incorporing both numerical and symbolic data is proposed. This new GHSOM model is proposed for detecting network intrusions. An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statical models using metrics derived from observation of the user's actions. Randomly selected subsets that contains both attacks and normal records from the KDD Cup 1999 benchmark are used for training the proposed GHSOM. Experimental results are provided and compared to other hierarchical neural networks.
No Science without Semantics
Authors: Charles Petrie
Venue: IEEE Internet Computing (2007)

Computer science often faces the criticism that it isn't actual science. Doing science means using the scientific method, which includes reporting results so that others can repeat and verify them. The problem with the computer science community is the lack of shared terms with precise semantics. When a researcher reports phenomenal results that can't be replicated, the author might be viewed as a brilliant alchemist, but such works undermine claims of being "science."
Computer-Based Training System for Simulating Wrist Arthroscopy
Authors: Fadi Yaacoub, Yskandar Hamam, Antoine Abche
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

The minimally invasive approach of arthroscopy means less pain and faster recovery time for patients compared to open surgery. However, it implies a high difficulty of performance. In this paper, a functional prototype of a computer-based training simulator for wrist arthroscopy is introduced. A 3-D virtual representation of the bones constituting the wrist of a patient is shown. Objects are modeled using the convex hull approaches and an algorithm to simulate real time collision detection during the training on the operation is presented. In addition, a force feedback device is used as a haptic interface with the computer simulation system. This leads in the development of a low cost system that is used by trainees with the same benefits as professional devices. In this regard, the wrist arthroscopy can be simulated and medical students can learn the basic skills required with safety, flexibility and less cost.
On-Line Classification of Human Activities
Authors: J. C. Nascimento, M. A. Figueiredo, J. S. Marques
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

In this paper we address the problem of on-line recognition of human activities taking place in a public area such as a shopping center. We consider standard activities; namely, entering, exiting, passing or browsing. The problem is motivated by surveillance applications, for which large numbers of cameras have been deployed in recent years. Such systems should be able to detect and recognize human activities, with as little human intervention as possible.In this work, we model the displacement of a person in consecutive frames using a bank of switched dynamical systems, each of which tailored to the specific motion regimes that each trajectory may contain.Our experimental results are based on nearly 20,000 images concerning four atomic activities and several complex ones, and demonstrate the effectiveness of the proposed approach.
Introduction to ITRON the Industry-oriented Operating System
Authors: Hiroshi Monden
Venue: IEEE Micro (1987)

Consistent naming rules, reconfigurability options, and fast response times explain the rapid acceptance of this industrial real-time system designed to TRON specifications. 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
Log-Domain Binary SVM Image Classifier
Authors: Robert Groza, Lelia Festila, Sorin Hintea, Mihaela Cirlugea
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

We propose in this paper a binary log-domain Support Vector Machine classifier based on a polynomial decision function. To implement such a classifier log-domain multipliers proposed by the authors are used. For the parallel-serial implementation a log-domain summing amplifier and a current mode comparator are also needed. Current mode log-domain design is used for its low voltage, low power and high frequency characteristics. The resulted classifier is simulated taking into account real parameters of transistors in BiCMOS technology.
Evolution of Broadband Network Management System Using an AOP
Authors: Eunyoung Cho, Ho-Jin Choi, Jongmoon Baik, In-Young Ko, Kwangjoon Kim
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

In broadband networks, dynamic service change affects the management system because of the variability in time to market, transmission quality and transport system. Therefore, reusability and maintainability become the important quality factors in Network Management System (NMS). An aspect-oriented software development method is a feasible solution for the evolvability of NMS. In this paper, we propose a method to generate aspects in a standard management information model using Aspect Conversion and Metric Calculation (ACMC) algorithm. We applied it ITU-T M.3100 and evaluated it via the ratio of reduced redundancy in point of crosscutting concerns.
Classification and Diagnosis of Mechanical Faults Using the RBF Network Based on the Local Bispectra
Authors: Shuhua Xu, Benxiong Huang, Yuchun Huang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper introduces an efficient technique to design a classifier for classifying and diagnosing mechanical faults. The bispectra features of vibration signals resulting from mechanical faults are extracted and then evaluated using the Fisher's class-separability discriminant measure. The local bispectra with the most discriminant power and sensitivity are selected as the classification feature vector that can effectively represent the fault class of concern over a broad range of sample data. A RBF neural network is implemented to realize identification and diagnosis for different mechanical faults. The suggested technique is demonstrated to design a classifier for fault signals of rolling bearings that is verified to be highly accurate and robust even in the presence of excessive noise.
Trikonic Inter-Enterprise Architectonic
Authors: Gary Richmond
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

There is a need for information, application, and other enterprise architectures which are robust and flexible enough to meet the challenges of today's heterogeneous, rapidly changing, digitally networked environment. Developing advanced architectures may prove essential for achieving emerging research, business, and social goals. Indeed, the profoundly changed landscape suggests that a new paradigm may be needed, an inter-enterprise architectonic (I-EA) informing architectures capable of integrating all key components and processes in an increasingly interconnected environment. To meet this challenge, a systems architectonicis outlined that is based on the trichotomic category theory of Charles S. Peirce. Trikonic Inter-Enterprise Architectonic involves a pragmatic approach to the observation andmanipulationof diagrams as models of enterprise and inter-enterprise processes.
Maps and Legends: Designing FPS-Based Interfaces for Multi-user Composition, Improvisation and Immersive Performance
Authors: Robert Hamilton
Venue: Computer Music Modeling and Retrieval. Sense of Sounds: 4th International Symposium, CMMR 2007, Copenhagen, Denmark, August 27-31, 2007. Revised Papers (2008)

This paper describes an interactive multi-channel multi-user networked system for real-time composition and immersive performance built using a modified version of the Quake III gaming engine. By tracking users' positional and action data within a virtual space, and by streaming that data over a network using OSC messages formatted as UDP packets to a multi-channel Pure Data patch, actions in virtual space are correlated to sonic output in a physical space. Virtual environments designed as abstract compositional maps or representative models of the users' actual physical space are investigated as means to guide and shape compositional and performance choices. This paper analyzes both the technological concerns for building and realizing the system as well as the compositional and perceptual issues inherent in the project itself. 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
Efficient Hierarchical Reasoning about Functions over Numerical Domains
Authors: Viorica Sofronie-Stokkermans
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

We show that many properties studied in mathematical analysis (monotonicity, boundedness, inverse, Lipschitz properties possibly combined with continuity, derivability) are expressible by formulae in a class for which sound and complete hierarchical proof methods for testing satisfiability of sets of ground clauses exist. The results are useful for automated reasoning in analysis and in the verification of hybrid systems.
Poisson-Based Self-Organizing Neural Networks for Pattern Discovery
Authors: Haiying Wang, Huiru Zheng
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Based on unsupervised learning paradigm, self-organizing neural networks have achieved great success in applications of automatic pattern discovery. However, the development of self-organizing neural networks is traditionally based on the assumption that data are governed by a normal distribution. Application of self-organizing neural networks in the areas where data are better modelled by other statistical distributions such as a Poisson distribution has received less attention. Based on the incorporation of the statistical nature of data with a Poisson distribution into a Self-Organizing Map, this paper presents a Poisson-based self-organizing neural network. The proposed network has been tested on two datasets including a real biological example. The results indicate that, in comparison to traditional self-organizing maps, the proposed model offers substantial improvements in pattern discovery in data governed by a Poisson distribution.
ISAR Imaging for Multiple Targets Based on Randomized Hough Transform
Authors: Xiongjun Fu, Meiguo Gao
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Multiple targets imaging using inverse synthetic aperture technique is a key problem on radar imaging field. This paper proposes a new approach to perform the motion compensation for multiple targets using randomized Hough transform (RHT). The targets' amplitude distribution, i.e., corrugation image is transformed into frequency domain, then The RHT processing is operated on the frequency image to estimate each target's Doppler frequencies respectively, and the multi-target ISAR images will be obtained after range alignment and phase compensation processing for each object respectively.
Biquadratic and Quadratic Springs for Modeling St Venant Kirchhoff Materials
Authors: Hervé Delingette
Venue: Proceedings of the 4th international symposium on Biomedical Simulation (2008)

This paper provides a formal connexion between springs and continuum mechanics in the context of two-dimensional and three dimensional hyperelasticity. First, we establish the equivalence between surface and volumetric St Venant-Kirchhoff materials defined on linear triangles and tetrahedra with tensile, bending and volumetric biquadratics springs. Those springs depend on the variation of square edge length while traditional or quadratic springs depend on the change in edge length. However, we establish that for small deformations, biquadratic springs can be approximated with quadratic springs with different stiffnesses. This work leads to an efficient implementation of St Venant-Kirchhoff materials that can cope with compressible strains. It also provides expressions to compute spring stiffnesses on triangular and tetrahedral meshes.
One-Round ID-Based Blind Signature Scheme without ROS Assumption
Authors: Wei Gao, Guilin Wang, Xueli Wang, Fei Li
Venue: Proceedings of the 2nd international conference on Pairing-Based Cryptography (2008)

In this paper, we propose the first one-round identity-based blind signature (IDBS) scheme without ROS assumption, which supposes that it is infeasible to find an overdetermined, solvable system of linear equations modulo q with random inhomogenities [25]. Our construction has the following features. First, it achieves the optimal bound of round complexity for blind signatures, i.e., each signature can be generated with one round (or two moves) of message exchanges between the signer and signature requesting user. Second, the proposed IDBS scheme is provably secure against generic parallel attack without relying on the ROS assumption. This means our scheme can guarantee the same security level with smaller security parameter, in contrast to some IDBS schemes with ROS assumptions, such as the IDBS deduced from the blind Schnorr signature. Third, our construction is based on bilinear pairings from scratch (i.e. without using existing identity-based signature schemes, and without using existing computational assumptions). Finally, the security of our IDBS is based on a new formalized assumption, called one-more bilinear Diffie-Hellman inversion (1m-BDHI) assumption.