ppxscal/embedding_network huggingface dataset:
Multivariate Option Pricing Using Quasi-interpolation Based on Radial Basis Functions
Authors: Liquan Mei, Peipei Cheng
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Radial basis functions are well-known successful tools for interpolation and quasi-interpolation of the equal distance or scattered data in high dimensions. Furthermore, their truly mesh-free nature motivated researchers to use them to deal with partial differential equations(PDEs). With more than twenty-year development, radial basis functions have become a powerful and popular method in solving ordinary and partial differential equations now. In this paper, based on the idea of quasi-interpolation and radial basis functions approximation, a fast and accurate numerical method is developed for multi-dimensions Black-Scholes equation for valuation of european options prices on three underlying assets. The advantage of this method is that it does not require solving a resultant full matrix, therefore as indicated in the the numerical computation, this method is effective for option pricing problem.
A Logic-Based Approach to Mining Inductive Databases
Authors: Hong-Cheu Liu, Jeffrey Xu Yu, John Zeleznikow, Ying Guan
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this paper, we discuss the main problems of inductive query languages and optimisation issues. We present a logic-based inductive query language and illustrate the use of aggregates and exploit a new join operator to model specific data mining tasks. We show how a fixpoint operator works for association rule mining and a clustering method. A preliminary experimental result shows that fixpoint operator outperforms SQL and Apriori methods. The results of our framework could be useful for inductive query language design in the development of inductive database systems.
The Decomposition and Representation Theorem of Rough Fuzzy Set
Authors: Zengtai Gong, Yabin Shao
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

In this paper, aimed at rough fuzzy sets, the concept of four kinds upper (lower) λ- cut sets and stronger λ-cut sets are introduced. Finally, the new operators and a kind of new nested sets are put forward. Using them, the decomposition and the representation theorems are obtained. With this change, relatively less information will be lost in the decision-making process. It gives us new theoretical basis for information process.
Embedding Legacy Keyword Search into Queries for the Ubiquitous ID Database
Authors: Tetsuo Kamina, Noboru Koshizuka, Ken Sakamura
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

Ubiquitous ID is a general purpose framework for implementing context-aware ubiquitous computing applications, where identifiers (called ucode numbers) and their relations are maintained in a large scale distributed database called UCRDB. Since we have to maintain a huge amount of data in UCRDB, it is sometimes desirable to delegate some subqueries to the "legacy" text-base search engines. In this paper, we propose a new query language construct for UCRDB (but can be applied to any other similar technologies such as RDF databases) that enables such a dynamic linking. Using this approach, context of the real world and legacy contents exist in the digital space can be seamlessly combined, and we can view UCRDB and legacy search engines as a single hybrid database so that no programming to "hard-wire" existing services is required. Furthermore, in our system, configurations of dynamic linking are described as a rule base stored in UCRDB itself, thus the resulting system is very simple but highly flexible and extensible.
A Bio-inspired Neural Model for Colour Image Segmentation
Authors: Francisco Javier Díaz-Pernas, Míriam Antón-Rodríguez, José Fernando Díez-Higuera, Mario Martínez-Zarzuela
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

This paper describes a multi-scale neural model to enhance regions and extract contours of colour-texture image taking into consideration the theory for visual information processing in the early stages of human visual system. It is composed of two main components: the Colour Opponent System (COS) and the Chromatic Segmentation System (CSS). The structure of the CSS architecture is based on BCS/FCS systems, so the proposed architecture maintains the essential qualities of the base model such as illusory contours extraction, perceptual grouping and discounting the illuminant. Experiments performed show the good visual results obtained and the robustness of the model when processing images presenting different levels of noise.
A Ubiquitous Interactive Museum Guide
Authors: Yo-Ping Huang, Tsun-Wei Chang, Frode Eika Sandnes
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

This paper proposes a PDA-based guide and recommendation system. The intelligent guide provides an alternative to the current audio-based guide tools. Techniques from both data mining and extension theory and RFID technology are deployed to provide better interaction between exhibitions and visitors.
Soft memberships for spectral clustering, with application to permeable language distinction
Authors: Richard Nock, Pascal Vaillant, Claudia Henry, Frank Nielsen
Venue: Pattern Recognition (2009)

Recently, a large amount of work has been devoted to the study of spectral clustering-a powerful unsupervised classification method. This paper brings contributions to both its foundations, and its applications to text classification. Departing from the mainstream, concerned with hard membership, we study the extension of spectral clustering to soft membership (probabilistic, EM style) assignments. One of its key features is to avoid the complexity gap of hard membership. We apply this theory to a challenging problem, text clustering for languages having permeable borders, via a novel construction of Markov chains from corpora. Experiments with a readily available code clearly display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.
Privacy Preserving Data Mining within Anonymous Credential Systems
Authors: Aggelos Kiayias, Shouhuai Xu, Moti Yung
Venue: Proceedings of the 6th international conference on Security and Cryptography for Networks (2008)

Regular (non-private) data mining can be applied to manage and utilize accumulated transaction data. For example, the accumulated relative service time per user per month can be calculated given individual transaction data from which the user compliance with a service agreement can be determined and possibly billing can be processed. Nevertheless, due to user privacy concerns, cryptographic research developed transactions based on unlinkable anonymous credentials. Given the nature of anonymous credentials the ease of managing accumulated data (e.g., per user) is lost. To restore the possibility of management and accumulation of data it seems that a suitable form of privacy preserving data mining is needed. Indeed, privacy preserving data mining methods have been suggested for various protocols and interactions where individual data can be contributed in an encrypted form, but not within the context of anonymous credentials. Given our motivation we suggest a new notion of performing "privacy preserving data mining within the context of anonymous cryptographic credential systems," so as to protect both the privacy of individually contributed data and the identity of their sources while revealing only what is needed. To instantiate our approach we focus on a primitive we call "data mining group signatures" (DMGS), where it is possible for a set of authorities to employ distributed quorum control for conducting privacy preserving data mining operations on a batch of transactions while preserving maximum possible anonymity. We define and model the new primitive and its security goals, we then present a construction and finally show its privacy and security properties. Along the way we build a methodology that safely combines multi-server protocols as sub-procedures in a more general setting.
Emergence of Topographic Cortical Maps in a Parameterless Local Competition Network
Authors: A. Ravishankar Rao, Guillermo Cecchi, Charles Peck, James Kozloski
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

A major research problem in the area of unsupervised learning is the understanding of neuronal selectivity, and its role in the formation of cortical maps. Kohonen devised a self-organizing map algorithm to investigate this problem, which achieved partial success in replicating biological observations. However, a problem in using Kohonen's approach is that it does not address the stability-plasticity dilemma, as the learning rate decreases monotonically.In this paper, we propose a solution to cortical map formation which tackles the stability-plasticity problem, where the map maintains stability while enabling plasticity in the presence of changing input statistics. We adapt the parameterless SOM (Berglund and Sitte 2006) and also modify Kohonen's original approach to allow local competition in a larger cortex, where multiple winners can exist.The learning rate and neighborhood size of the modified Kohonen's method are set automatically based on the error between the local winner's weight vector and its input. We used input images consisting of lines of random orientation to train the system in an unsupervised manner. Our model shows large scale topographic organization of orientation across the cortex, which compares favorably with cortical maps measured in visual area V1 in primates. Furthermore, we demonstrate the plasticity of this map by showing that the map reorganizes when the input statistics are chanaged.
Red-Black Half-Sweep Iterative Method Using Triangle Finite Element Approximation for 2D Poisson Equations
Authors: J. Sulaiman, M. Othman, M. K. Hasan
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper investigates the application of the Red-Black Half-Sweep Gauss-Seidel (HSGS-RB) method by using the half-sweep triangle finite element approximation equation based on the Galerkin scheme to solve two-dimensional Poisson equations. Formulations of the full-sweep and half-sweep triangle finite element approaches in using this scheme are also derived. Some numerical experiments are conducted to show that the HSGS-RB method is superior to the Full-Sweep method.
A class of qD continuous-time time-varying acausal systems
Authors: Valeriu Prepelita
Venue: Proceedings of the 7th WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems (2008)

Multidimensional (qD, q ≤ 2) time-varying acausal systems are presented. These systems represent the continuous-time counterparts of the Attasi's 2D discrete-time time-invariant models. The state space representation is given and the formula of the input-output map is obtained. An adjoint system is introduced and the relationship between the input-output maps of the acausal systems and of their adjoints is emphasized by means of two suitable inner products.
Neural networks approaches for discovering the learnable correlation between gene function and gene expression in mouse
Authors: Emad A. M. Andrews, Quaid Morris, Anthony J. Bonner
Venue: Neurocomputing (2008)

Identifying gene function has many useful applications. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. Recent studies have shown that there is a strong learnable correlation between gene function and gene expression. In previous work, we presented novel clustering and neural network (NN) approaches for predicting mouse gene functions from gene expression. In this paper, we build on that work to significantly improve the clustering distribution and the network prediction error by using a different clustering algorithm along with a new NN training technique. Our results show that NNs can be extremely useful in this area. We present the improved results along with comparisons.
Random weighting approximate of NA sample mean and application research
Authors: Yuren Li, Shesheng Gao, Haiwei Wang
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

Random weighting method is widely applied in estimation of sample mean. However, consider random variables are not the series of independent and identical distribution (i.i.d.), research results with this method have not been reported yet. In this paper, sample mean's approximation problem is researched by random weighting method, under the condition of random variables series being NA associated sample. Firstly, Negative associated sample is defined between strongly stationary and independent classes. Secondly, sample is divided into k classes and the weight 1/k are given to the k jackknifed virtual values Yi i=1...k of Xn, then the empirical distribution function can be obtained. Therefore, the distribution of √n(Xn - µ) is simulated by the conditional distribution of √n(Yk* - Xn), where independent sample Y1*,...,Yk* is sampled from empirical distribution function Fk* = 1/kΣj-1k xj. For the distribution of n1/2{Fn-1(g)- F-1(g)}, the consistency of the random weighting approximation is proved. Finally, simulation results show that random weighting estimation of NA sample mean has a better effect than with Bootstrap method and estimation precision is improved.
An Empirical Comparison of Exact Nearest Neighbour Algorithms
Authors: Ashraf M. Kibriya, Eibe Frank
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Nearest neighbour search (NNS) is an old problem that is of practical importance in a number of fields. It involves finding, for a given point q, called the query, one or more points from a given set of points that are nearest to the query q. Since the initial inception of the problem a great number of algorithms and techniques have been proposed for its solution. However, it remains the case that many of the proposed algorithms have not been compared against each other on a wide variety of datasets. This research attempts to fill this gap to some extent by presenting a detailed empirical comparison of three prominent data structures for exact NNS: KD-Trees, Metric Trees, and Cover Trees. Our results suggest that there is generally little gain in using Metric Trees or Cover Trees instead of KD-Trees for the standard NNS problem.
CallimachusDL: Using Semantics to Enhance Search and Retrieval in a Digital Library
Authors: Juan Miguel Gómez-Berbís, Ricardo Colomo-Palacios, Ángel García-Crespo
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

Among the challenges of classifying, locating and accessing knowledge in Digital Libraries tackling with the huge amount of resources the Web provides, improving Digital Libraries by means of different strategies, particularly, using semantics remains a promising and interesting approach. In this paper, we present CallimachusDL, a semantics-based Digital Library which provides faceted search, enhanced access possibilities and a proof-of-concept implementation.
Cleaning Up the Clutter: Why Web Standards Matter
Authors: Ryan A. MacMichael
Venue: IT Professional (2004)

When following Web standards, changing an entire site's appearance is as easy as designing a single new style sheet. 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
Application System of the Universal Sign Code - Development of the Portable Sign Presenter -
Authors: Tsutomu Kimura, Masanori Katoh, Atsushi Hayashi, Kazuyuki Kanda, Daisuke Hara, Kazunari Morimoto
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

We have developed an ubiquitous sign presentation system using the portable game machines and the mobile phones. An application for this system is supposed to be a guide at a shop or at a museum, and a movie dictionary for sign learning. The system reads the USC proposed by us through a bar-code reader or by a camera, and it indicates a signing movie corresponding. The movie is located in the memory in the system. If the corresponding movie does not exist in the memory of a mobile phone system, it is downloaded from the web server to play it.
An Enhanced Positioning Scheme Based on Optimal Diversity for Mobile Nodes in Ubiquitous Networks
Authors: Seokyong Yang, Sekchin Chang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

A lot of schemes have been proposed for the realization of u-city. Especially, most of the schemes are based on ubiquitous networks. For various u-city services to be available, mobile nodes can be employed in ubiquitous networks. However, an accurate positioning scheme is required for the practical usage of the mobile nodes in the ubiquitous networks. In this paper, an optimal diversity technique is proposed to improve the positioning performance. Simulation results indicate that using the optimal diversity scheme, the positioning performance can considerably be enhanced for mobile nodes in ubiquitous networks.
Perpetuality for Full and Safe Composition (in a Constructive Setting)
Authors: Delia Kesner
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

We study perpetuality in calculi with explicit substitutions having full composition. A simple perpetual strategy is used to define strongly normalising terms inductively. This gives a simple argument to show preservation of β-strong normalisation as well as strong normalisation for typed terms. Particularly, the strong normalisation proof is based on implicit substitution rather than explicit substitution, so that it turns out to be modular w.r.t. the well-known proofs for typed lambda-calculus. All the proofs we develop are constructive.
An Adaptive Energy-Efficient and Low-Latency MAC Protocol in Wireless Sensor Networks
Authors: Men Chaoguang, Lu Yongqian, Zhang Huajian, Li Guang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In wireless sensor networks, an efficient Medium Access Control (MAC) protocol is critical, especially in terms of energy consumption and message latency. This paper introduces a novel protocol that nodes periodically sleep to conserve energy. When the source node has data packets to send, it uses a wakeup message to awake the sleeping nodes. The protocol needn't take the synchronization which must be achieved in S-MAC. It especially suits to the wireless sensor networks with narrow bandwidth.
A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network
Authors: Yu-Mei Chai, Zhi-Wu Yang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Multiple-Instance Learning is increasingly becoming one of the most promiscuous research areas in machine learning. In this paper, a new algorithm named NRBF-MI is proposed for Multi-Instance Learning based on normalized radial basis function network. This algorithm defined Compact Neighborhood of bags on which a new method is designed for training the network structure of NRBF-MI. The behavior of kernel function radius and its influence is analyzed. Furthermore a new kernel function is also defined for dealing with the labeled bags. Experimental results show that the NRBF-MI is a high efficient algorithm for Multi-Instance Learning.
Price-based resource allocation for wireless ad hoc networks with multi-rate capability and energy constraints
Authors: Yu-Fen Kao, Jen-Hung Huang
Venue: Computer Communications (2008)

Wireless ad hoc networks have attracted a lot of attentions recently. Resource allocation in such networks needs to address both fairness and overall network performance. Pricing is a prospective direction to regulate behaviors of individual nodes while providing incentives for cooperation. In this work, we develop some pricing strategies for resource allocation by taking account of factors like multiple transmission rates and energy consumption of nodes, which have not been well studied in former works. Multi-rate transmission capability is commonly seen in most wireless products nowadays, while energy is one of the most important resources in portable devices. We propose a clique-based model which allows us to achieve optimal resource utilization and fairness among network flows when multi-rate transmission is considered. We also show how to extend the model to dynamically adjust prices based on energy consumptions of flows. In particular, our model takes into account energy consumptions in the transmitters' side, the receivers' side, and those that are non-transmitters and non-receivers but are interfered by these activities. So our model can more accurately reflect the real energy constraint in a wireless network. Simulation results are presented to show the convergence and other properties of these strategies.
Assessment personalization in the Semantic Web
Authors: Lilia Cheniti-Belcadhi, Nicola Henze, Rafik Braham
Venue: Journal of Computational Methods in Sciences and Engineering (2008)

The work described in this paper relates to the formal description, implementation and use of a web-based personalized assessment system. The assessment techniques are formalized with First Order Logic rules which are able to reason over resources annotated with Semantic Web metadata formats according to LOM and IMS/ QTI standards. They were tested using TRIPLE, a rule-based language for the Semantic Web. We also describe the service-based architecture, which was implemented as part of the Personal Reader Framework. Personalization functionalities are available as web services that communicate via Semantic Web technologies without the need for centralized control. Appropriate assessment resources are searched after checking the learner's last saved performance. We illustrate this system with an object oriented programming course taken by students in our university.
Efficient Bit-Rate Estimation for Mode Decision of H.264/AVC
Authors: Shuwei Sun, Shuming Chen
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

CAVLC is the entropy coding method adopted in H.264/AVC baseline profile. RDO is the mode decision method commended by JVT, which can improve the performance of the encoder markedly. However, during the process of RDO, entropy coding should be performed for every candidate coding mode, which increases the computation complexity of video encoding largely. To avoid the expensive computation of Lagrange costs, in this paper, we propose a transform-domain bit-rate estimation method for CAVLC based on the analyses of its adaptive characteristic and apply it in a fast mode decision algorithm, in which bit-rate estimation is adopted instead of actual entropy coding in RDO mode decision, thus reducing the computing time for mode decision. Simulation results show that the fast algorithm based on proposed bitrate estimation method for CAVLC saves much encoding time while the losses of compression performance are negligible.