ppxscal/embedding_network huggingface dataset:
Decomposition approach to nonlinear diffraction problems of nanoelectromagnetics and nanophotonics using autonomous blocks with Floquet channels
Authors: G. S. Makeeva, O. A. Golovanov, M. Pardavi-Horvath, G. A. Kouzaev
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

A new approach at electrodynamic accuracy level for mathematical modeling of microwave and photonic nonlinear nanostructures and 3D nanodevices was developed using a decomposition on nonlinear autonomous blocks with virtual Floquet channels (NABFs). To determine the NAFBs descriptors an iterative computational process for solving the nonlinear Maxwell's equations with the non-asymptotic radiation boundary conditions was performed using the projection method. The iteration method of recomposition of NABFs was developed using the linearization of NABFs descriptors. Using NABFs method the accurate electromagnetic modeling of propagation, diffraction and nonlinear interactions of electromagnetic waves in nano-sized array ferrite spheres was performed.
Using Web Services for Distributed Medical Visualisation
Authors: Xia Zhao, Enjie Liu, Gordon Clapworthy, Paolo Quadrani, Debora Testi, Marco Viceconti
Venue: Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics (2008)

Collaboration on the storage, processing and visualisation of medical data via the Internet is a considerable challenge and one that is growing in importance due to the escalating interest in the Virtual Physiological Human. Investigations into possible approaches for distributed, realtime visualisation are important in this context. This paper presents a Web Services approach to distributed visualisation that has been developed within the LHDL project. The processing and visualisation Web Services that have been created are based on existing MAF applications; this has formed a useful testbed for transforming legacy software into Web Services. On the client side, a Rich Internet Application concept was adopted, while a web-based, user friendly Flex client has been implemented.
Robust Complex Salient Regions
Authors: Sergio Escalera, Oriol Pujol, Petia Radeva
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

The challenge of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. In this paper, we present a novel set of saliency features that takes into account the region inhomogeneity in terms of intensity and shape. The region complexity is estimated at real-time by means of the entropy of the grey-level information. On the other hand, shape information is obtained by measuring the entropy of normalized orientations. The normalization step is a key point in this process. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The new set of interest points shows robustness to a wide set of transformations and high repeatability. Besides, we show the temporal robustness of the novel salient regions in two real video sequences.
Data Mining Algorithms on the Cell Broadband Engine
Authors: Rubing Duan, Alfred Strey
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

The Cell Broadband Engine (CBE) is a new heterogeneous multi-core processor from IBM, Sony and Toshiba, and provides the potential to achieve an impressive level of performance for data mining algorithms. In this paper, we describe our implementation of three important classes of data mining algorithms: clustering (k-Means), classification (RBF network), and association rule mining (Apriori) on the CBE. We explain our parallelization methodology and describe the exploitation of thread- and data-level parallelism in each of the three algorithms. Finally we present experimental results on the Cell hardware, where we could achieve a high performance of up to 10 GFLOP/s and a speedup of up to 40.
On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport
Authors: Jason A. Atkin, Edmund K. Burke, John S. Greenwood, Dale Reeson
Venue: Journal of Scheduling (2008)

This paper addresses the challenge of building an automated decision support methodology to tackle the complex problem faced every day by runway controllers at London Heathrow Airport. Aircraft taxi from stands to holding areas at the end of the take-off runway where they wait in queues for permission to take off. A runway controller attempts to find the best order for aircraft to take off. Sequence-dependent separation rules that depend upon aircraft size, departure route and speed group ensure that this is not a simple problem to solve. Take-off time slots on some aircraft and the need to avoid excessive delay for any aircraft make this an even more complicated problem. Making this decision at the holding area helps to avoid the problems of unpredictable push-back and taxi times, but introduces a number of complex spatial constraints that would not otherwise exist. The holding area allows some flexibility for interchange of aircraft between queues, but this is limited by its physical layout. These physical constraints are not usually included in academic models of the departure problem. However, any decision support system to support the take-off runway controller must include them. We show, in this paper, that a decision support system could help the controllers to significantly improve the departure sequence at busy times of the day, by considering the taxiing aircraft in addition to those already at the holding area. However, undertaking this re-introduces the issue of taxi time uncertainty, the effect of which we explicitly measure in these experiments. Empirical results are presented for experiments using real data from different times of the day, showing how the performance of the system varies depending upon the volume of traffic and the accuracy of the provided taxi time estimations. We conclude that the development of a good taxi time prediction system is key to maximising the benefits, although benefits can be observed even without this.
Regulating Exceptions in Healthcare Using Policy Spaces
Authors: Claudio Agostino Ardagna, Sabrina Capitani Di Vimercati, Tyrone Grandison, Sushil Jajodia, Pierangela Samarati
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

One truth holds for the healthcare industry - nothing should interfere with the delivery of care. Given this fact, the access control mechanisms used in healthcare to regulate and restrict the disclosure of data are often bypassed. This "break the glass" phenomenon is an established pattern in healthcare organizations and, though quite useful and mandatory in emergency situations, it represents a serious system weakness.In this paper, we propose an access control solution aimed at a better management of exceptions that occur in healthcare. Our solution is based on the definition of different policy spaces regulating access to patient data and used to balance the rigorous nature of traditional access control systems with the prioritization of care delivery.
Discrete Phase-Space Structures and Mutually Unbiased Bases
Authors: A. B. Klimov, J. L. Romero, G. Björk, L. L. Sánchez-Soto
Venue: Proceedings of the 1st international workshop on Arithmetic of Finite Fields (2007)

We propose a unifying phase-space approach to the construction of mutually unbiased bases for an n-qubit system. It is based on an explicit classification of the geometrical structures compatible with the notion of unbiasedness. These consist of bundles of discrete curves intersecting only at the origin and satisfying certain additional conditions. The effect of local transformations is also studied.
Language and program design for functional dependencies
Authors: Mark P. Jones, Iavor S. Diatchki
Venue: ACM SIGPLAN Notices (2009)

Eight years ago, functional dependencies, a concept from the theory of relational databases, were proposed as a mechanism for avoiding common problems with multiple parameter type classes in Haskell. In this context, functional dependencies give programmers a means to specify the semantics of a type class more precisely, and to obtain more accurate inferred types as a result. As time passed, however, several issues were uncovered - both in the design of a language to support functional dependencies, and in the ways that programmers use them - that led some to search for new, better alternatives. This paper focusses on two related aspects of design for functional dependencies: (i) the design of language/type system extensions that implement them; and (ii) the design of programs that use them. Our goal is to clarify the issues of what functional dependencies are, how they should be used, and how the problems encountered with initial proposals and implementations can be addressed.
Secure communication in microcomputer bus systems for embedded devices
Authors: Sascha Mühlbach, Sebastian Wallner
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

The protection of the microcomputer bus system in embedded devices is essential to prevent eavesdropping and the growing number of todays hardware hacking attacks. This contribution presents a hardware solution to ensure microcomputer bus systems via the Tree Parity Machine Rekeying Architecture (TPMRA). For this purpose a scalable TPMRA IP-core is designed and implemented in order to meet adaptability, low cost terms and variable bus performance requirements. It allows the authentication of different bus participants as well as the encryption of chip-to-chip buses from a single primitive. The solution is transparent and easy applicable to an arbitrary microcomputer bus system for embedded devices on the market. A proof of concept implementation shows the applicability of the TPMRA in the standardized Advanced Microprocessor Bus Architecture (AMBA) by implementing the IP-core extension into the peripheral AMBA bus-to-bus interface. It will be shown that the proposed solution is latency free and can be easy implemented into the AMBA bus interface bridge in order to protect the ARM bus system with a low hardware overhead considering all AMBA bus features.
On the Timing of Discrete Events in Event-Driven Control Systems
Authors: Manel Velasco, Pau Martí, Camilo Lozoya
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

This paper presents an analysis method to determine offline at what intervals have to be taken the samples for various types of event-driven control systems.
Bayesian inference with mixtures of logistic regression: functional approximation, statistical consistency and algorithmic convergence
Authors: Wenxin Jiang
Venue:  (2008)

One of the most commonly used techniques for classification problem is logistic regression. For example, logistic regression for a binary response assumes that the odds Pr(y = 1|x)/Pr( y = 0|x) = e(&alpha;+&beta;x) . However, in reality, the pattern of the data can be so complicated that logistic regression model often fails, as such a model is not very flexible. The 'mixtures of experts' (ME) approach enriches the model by mixing m (m &ge; 2) 'experts', with each expert being one such logistic regression. This thesis first studies the approximation rate of the proposed ME models where binary/multinomial logistic regression models are mixed. We show that the combined models can be used to approximate a class of 'smooth' probability models for binary or multiclass responses. With bounded second derivatives of log-odds, the approximation rate is O(m -2/s) in Hellinger distance or O(m-4/s) in Kullback-Leibler divergence. Here s = dim(x) is the dimension of the input space (or the number of predictors). Bayesian inference conditional on the observed data can then be used for regression and classification. In the thesis, we first consider the binary classification problem. Conditions are established on choosing the number of experts (i.e., number of mixing components) k, or choosing a prior distribution for K, so that Bayesian inference is 'consistent', in the sense of 'often approximating' the underlying true relationship between y and x. The resulting classification rule is also 'consistent', in the sense of having near-optimal performance in classification. We show these desirable consistency properties with a nonstochastic k growing slowly with the sample size n of the observed data, or with a random K that takes large values with nonzero but small probabilities. Then for the multiclass problem, we also show that 'consistency' in regression and classification can be achieved, simultaneously for all classes, when posterior based inference is performed in a Bayesian framework. The Metropolis-Hastings algorithm is one of the important Markov Chain Monte Carlo (MCMC) methods available for sampling from a posterior distribution. The thesis outlines the methods and strategies to construct the simple Metropolis-Hastings chain for ME with nonstochastic number of experts, and the hybrid Metropolis-Hastings chain for ME with random number of experts as well. In addition, recent results in Markov chain theory are applied to both simple and hybrid algorithms and conditions are established for the algorithms to converge.
A Kolmogorov-Smirnov Correlation-Based Filter for Microarray Data
Authors: Jacek Biesiada, Włodzisław Duch
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

A filter algorithm using F-measure has been used with feature redundancy removal based on the Kolmogorov-Smirnov (KS) test for rough equality of statistical distributions. As a result computationally efficient K-S Correlation-Based Selection algorithm has been developed and tested on three high-dimensional microarray datasets using four types of classifiers. Results are quite encouraging and several improvements are suggested.
A Quickly Searching Algorithm for Optimization Problems Based on Hysteretic Transiently Chaotic Neural Network
Authors: Xiuhong Wang, Qingli Qiao
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper presents a fast algorithm based on the hysteretic transiently chaotic neural network (HTCNN) model for solving optimization problems. By using hysteretic activation function, HTCNN has higher ability of overcoming drawbacks that suffer from the local minimum. Meanwhile, in order to avoid oscillation and offer a considerable acceleration of converging to the optimal solution, a fast speed strategy is involved in HTCNN. Numerical simulation of a combinatorial optimization problem-assignment problem shows that HTCNN with fast speed strategy (FHTCNN) can overcome drawbacks that suffer from the local minimum and find the global optimal solutions quickly.
Novelty as a form of contextual re-ranking: efficient KLD models and mixture models
Authors: Ronald T. Fernández, David E. Losada
Venue: Proceedings of the second international symposium on Information interaction in context (2008)

Current Information Retrieval systems are often based on topicality. They estimate relevance by comparing the similarity between the user query and each document. These systems do not take into account important contextual information. More specifically, they do not often apply mechanisms to filter out redundant information. We interpret context here as the set of chunks of text from the ranked set of documents that the user has already seen. This is a valuable contextual information to guide the retrieval processes in a way that avoids redundancy. It is desirable that the ranking of results is composed by relevant but also novel material. This means that each document must provide to the user unseen information which is related to his need. In this work we study different novelty detection approaches that make good use of this contextual information. We show that these techniques can be applied effectively and efficiently at the sentence level.
Software Development Globalization from the Baltic Perspective
Authors: Guntis Urtāns
Venue: Proceedings of the 8th international conference on Product-Focused Software Process Improvement (2007)

Future predictions say that together with its neighbors Estonia and Lithuania, Latvia will be a major outsourcing center for Northern / continental Europe. The Baltic region is known for its well-educated and multinational workforce, one of the most efficient tax systems in Europe, a liberal economy, great affinity with Nordics, and identical legislation. So, where have we been 15 years ago and where are we now? 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
Compressive Sampling of Binary Images
Authors: Vladimir Stankovic, Lina Stankovic, Samuel Cheng
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of binary images. A system is proposed whereby the image is split into non-overlapping blocks of equal size and compressive sampling is performed on selected blocks only using the orthogonal matching pursuit technique. The remaining blocks are sampled fully. This way, the complexity and the required sampling time is reduced since the orthogonal matching pursuit operates on a smaller number of samples, and at the same time local sparsity within an image is exploited. Our simulation results show more than 20% saving in acquisition for several binary images.
Robust Color-Based Skin Detection for an Interactive Robot
Authors: Alvise Lastra, Alberto Pretto, Stefano Tonello, Emanuele Menegatti
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Detection of human skin in an arbitrary image is generally hard. Most color-based skin detection algorithms are based on a static color model of the skin. However, a static model cannot cope with the huge variability of scenes, illuminants and skin types. This is not suitable for an interacting robot that has to find people in different rooms with its camera and without any a priori knowledge about the environment nor of the lighting.In this paper we present a new color-based algorithm called VR filter. The core of the algorithm is based on a statistical model of the colors of the pixels that generates a dynamic boundary for the skin pixels in the color space. The motivation beyond the development of the algorithm was to be able to correctly classify skin pixels in low definition images with moving objects, as the images grabbed by the omnidirectional camera mounted on the robot. However, our algorithm was designed to correctly recognizes skin pixels with any type of camera and without exploiting any information on the camera.In the paper we present the advantages and the limitations of our algorithm and we compare its performances with the principal existing skin detection algorithms on standard perspective images.
Power-Directed Software Prefetching Algorithm with Dynamic Voltage Scaling
Authors: Juan Chen, Yong Dong, Huizhan Yi, Xuejun Yang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

We first demonstrate software prefetching provides an average 66.28% performance enhancement with much higher average power on six memory-intensive benchmarks. Then we propose a power-directed software prefetching algorithm with dynamic voltage scaling (PDP-DVS) that monitors a system's power and adapts the voltage level accordingly to guarantee no power increase while maintaining good performance boost. Our PDP-DVS algorithm achieves a 35.75% performance gain with only 1.19% power increase.
CLBCRA-Approach for Combination of Content-Based and Link-Based Ranking in Web Search
Authors: Hao-Ming Wang, Ye Guo
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

There were two kinds of methods in information retrieval, based on content and based on hyper-link. The quantity of computation in systems based on content was very large and the precision in systems based on hyper-link only was not ideal. It was necessary to develop a technique combining the advantages of two systems. In this paper, we drew up a framework by using the two methods. We set up the transition probability matrix, which composed of link information and the relevant value of pages with the given query. The relevant value was denoted by TFIDF. We got the CLBCRA by solving the equation with the coefficient of transition probability matrix. Experimental results showed that more pages, which were important both in content and hyper-link, were selected.
A Parameter-Free Associative Classification Method
Authors: Loïc Cerf, Dominique Gay, Nazha Selmaoui, Jean-François Boulicaut
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association rules) in such difficult contexts. Actually, most of the CBA-like methods are one-vs-all approaches (OVA), i.e., selected rules characterize a class with what is relevant for this class and irrelevant for the union of the other classes. Instead, our method considers that a rule has to be relevant for one class and irrelevant for every other class taken separately. Furthermore, a constrained hill climbing strategy spares users tuning parameters and/or spending time in tedious post-processing phases. Our approach is empirically validated on various benchmark data sets.
Lighting-by-Example with Wavelets
Authors: Hai Nam Ha, Patrick Olivier
Venue: Proceedings of the 8th international symposium on Smart Graphics (2007)

Automatic lighting design aims to provide users with semi-automated approaches, and easy-to-use tools, to configure lighting for 3D scenes. We present LightOpex the first fully automatic example-based local illumination lighting design system. Utilizing a wavelet-based lighting design framework, by which image quality is modeled using a cognitively inspired objective function, this approach to lighting design both: (1) allows the declarative specification of lighting; and (2) facilitates intuitive and natural specification of scene lighting. LightOpex enables users to select the desired lighting for a scene using exemplar 2D images and uses the spatial distribution of the luminance in these images as the target values of an optimization step. We demonstrate the utility of LightOpex through a range of examples, and conduct a preliminary investigation of the performance of a number of different of optimization schemes.
FearNot! Involving Children in the Design of a Virtual Learning Environment
Authors: Lynne Hall, Sarah Woods, Ruth Aylett
Venue: International Journal of Artificial Intelligence in Education (2006)

This paper presents FearNot, a virtual learning environment populated by synthetic characters interacting in bullying scenarios, aimed at 8-12 year old children. FearNot was designed within the VICTEC project where a key aim was to ensure that children participated in the design process. A range of techniques were used to gain the children's input. This paper discusses the various techniques used within VICTEC and highlights some key examples of the results gained by using such techniques, challenges encountered, and the design implications.